Valuation of Owner-Occupied Housing in the Netherlands

VALUATION OF OWNER-OCCUPIED

HOUSING IN THE NETHERLANDS Capturing Locational Information while Maintaining Simplicity

A master thesis presented by W.P.A. van Sprundel (758845) to Tilburg School of Economics and Management in partial fulﬁllment of the requirements for the degree of Master of Science in the subject of Finance

Supervisors: Prof. Dr. B. Melenberg (Tilburg University), Ing. L. Liplijn (CEO DataQuote), MSc. P.R.F. van Loon (Data Scientist DataQuote)

Tilburg University Tilburg, the Netherlands October 2013

Prof. Dr. B. Melenberg W.P.A. van Sprundel Ing. L. Liplĳn MSc. P.R.F. van Loon

VALUATION OF OWNER-OCCUPIED HOUSES IN THE NETHERLANDS A New Approach to Capture Locational Information while Maintaining Simplicity

ABSTRACT

There are difficulties in appraising owner-occupied houses in the Netherlands because of market imperfections. Other related issues, such as the lack of a coherent definition of market value, contribute to this intransparency. The market value definition of the International Valua- tion Standards Council has been adopted. Although this definition refers to the expected selling price, it is unclear whether the listing or transaction price of owner-occupied houses should be used. The use of transaction prices has been advocated because it is the result of actual market behavior. The Hedonic Pricing Method has been used to decompose market value into its characteristics. Similar studies yield insight in relevant characteristics and a dataset of 154,509 owner-occupied houses in the Netherlands has been used to construct five models. The first four models are used to test whether locational and regional information has value. By including province, COROP number, and municipality dummies, in relation to a country-wide model, it is clear that the goodness-of-fit improves when adding locational and regional information. To keep the simplicity of the country-wide and province model, while recognizing (very) locational information, a fifth model has been constructed as an Automated Valuation Model. This model performs a non-parametric weighted local regression on the K most nearby houses of a target

(K=30 by default). By doing so, an accuracy of 94.00% has been achieved.

TABLE OF CONTENTS ABSTRACT TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES PREFACE 1 INTRODUCTION AND PROBLEM DEFINITION ...... 1 1.1 Research Objectives and Problem Definition ...... 5 1.2 Relevance ...... 6 1.2.1 Theoretical and Scientific Relevance ...... 6 1.2.2 Practical Relevance ...... 6 1.3 Procedure: literature study and research method ...... 7 1.4 DataQuote ...... 9 2 CONTEXT OF THE DUTCH HOUSING MARKET ...... 10 2.1 Real Estate Markets Versus Other Markets ...... 11 2.2 Housing Markets Versus Other Real Estate Markets ...... 13 2.3 Dutch Housing Market ...... 15 2.3.1 History of Housing in the Netherlands after WWII ...... 16 2.3.2 Dutch Owner-Occupied Housing Sector ...... 17 2.3.3 Dutch Rental Sector ...... 21 2.4 Neoclassical Theory Applied to the Dutch Housing Market ...... 22 2.4.1 Relevant Factors That Partly Explain Demand ...... 24 2.4.2 Housing Supply ...... 27 2.5 Sub-conclusion ...... 28 3 DEFINING AND RECOGNIZING VALUE ...... 29 3.1 Value and its Interpretation ...... 31 3.1.1 Factual and Value Judgment ...... 31 3.1.2 Values as Objective Versus Values as Subjective ...... 31 3.2 Defining the Dependent Variable: Economic Value of Houses in the Netherlands ...... 33 3.2.1 Individualistic Economic Values ...... 34 3.2.2 Collective Economic Values ...... 35 3.2.3 Quantifying the Difference between Listing and Transaction Prices ...... 39

3.3 Relevant Values of Independent Variables under Subjectivism ...... 44 3.3.1 Environmental Value ...... 45 3.3.2 Aesthetic Value ...... 47 3.3.3 Cultural Heritage Value ...... 50 3.4 Sub-conclusion ...... 52 4 MEASURING VALUE AND COMPARABLE STUDIES ...... 56 4.1 Measurement Techniques ...... 58 4.1.1 Revealed versus Stated Preferences ...... 58 4.1.2 Travel Cost and Averting Behavior Method ...... 59 4.1.3 Hedonic Pricing Method ...... 61 4.2 Comparable country-wide models ...... 63 4.3 Comparable district models ...... 68 4.4 Comparable city models ...... 73 4.5 Sub-conclusion ...... 81 5 DATASET ANALYSIS AND OPTIMIZATION ...... 83 5.1 Dataset Cleaning ...... 84 5.2 Operationalization and Deﬁnition of Variables ...... 86 5.3 Estimating Housing Type ...... 89 5.4 Summary Statistics and Sample Selection Bias ...... 93 5.4.1 Summary Statistics of Interval and Ratio Variables ...... 94 5.4.2 Summary Statistics of Nominal and Ordinal Variables ...... 95 5.5 Sub-conclusion ...... 97 6 MODEL DEVELOPMENT AND RESULTS ...... 100 6.1 Transforming Variables and Conceptual Model ...... 101 6.2 Multivariate regression ...... 102 6.3 Results before Optimization ...... 104 6.3.1 Relation Between Continuous Variables and the Dependent Variable ...... 105 6.3.2 Correlations ...... 107 6.3.3 Principal Component Analysis ...... 108 6.3.4 Cross-Validation with Training, Validation and Test Sets ...... 111 6.3.5 Model 1: Excluding Locational Indicators ...... 112 6.3.6 Model 2: Including Province as Locational Indicator ...... 116

6.3.7 Model 3: Including COROP as Locational Indicator ...... 119 6.3.8 Model 4: Including Municipality as Locational Indicator ...... 124 6.4 Results after Optimization ...... 126 6.4.1 Model 5: Combining Strengths of Simplicity and Locality ...... 127 6.4.2 Results Model 5 ...... 129 6.4.3 Adjusting Model 5 and Results after Adjustments ...... 132 6.5 Sub-conclusion ...... 135 7 DISCUSSION ...... 138 7.1 Practical Usefulness ...... 138 7.2 Boom and Bust Cycles ...... 140 7.3 Macro-economic Trends ...... 141 8 SUMMARY, CONCLUSION AND RECOMMENDATION ...... 143 8.1 Summary ...... 143 8.2 Conclusion ...... 144 8.3 Recommendations for Further Research ...... 148 9 REFERENCES ...... 150

APPENDICES ...... I Appendix A: Comparing Amount of Owner-Occupied Houses per Province in the Sample with the Amount of Owner-Occupied Houses per Province in the Population ...... II Appendix B: Model 4 Regression Results ...... III Appendix C: Model 5 Regression Results ...... XVI Appendix D: Counting Cases by Listing Date per Province ...... XXVIII

LIST OF TABLES

1 Descriptive information and calculation of difference in listing and transaction price 40 2 Difference in listing and transaction price initially and after update 42 3 Descriptive information and difference in listing and transaction price per category 43 4 Definition and operationalization of variables 86 5 First stage random forest categorization in number of cases and percentages 91 6 Second state random forest categorization: specification of “other” 91 7 Summary statistics of variables on a continuous scale of the cleaned dataset 94 8 Bivariate correlations between parametric variables 108 9 Summary statistics of ratio variables from the training, validation and test set 111 10 Results of the training and validation set for model 1 113 11 Results of the training and validation set for model 2 117 12 Results of the training and validation set for model 3 120 13 Results of model 5 before and after implementing the three adjustments 132

LIST OF FIGURES

1 Hypothetical conceptual model 8 2 Chapter overview 9 3 Relevant country-wide, district and city models in other countries 57 4 Variable importance of stage 1 (left) and 2 (right) of random forest classification 92 5 Distribution of cases across the Netherlands (left) and heat map (right) 96 6 Conceptual model (with the exclusion of “Location” ) 102 7 Scatterplot of the dependent variable versus “Log of surface” 106 8 Scatterplot of the dependent variable versus “Log of volume” 106 9 Scree plot of the eigenvalues versus the number of components 110 10 Scatterplot of standardized residuals versus transformations of “Size” and histogram 114 11 Scatterplot of fitted versus actual updated listing prices model 1 116 12 Scatterplot of standardized residuals versus transformations of “Size” and histogram 118 13 Scatterplot of fitted versus actual updated listing prices model 2 119 14 Scatterplot of standardized residuals versus transformations of “Size” and histogram 122 15 Scatterplot of fitted versus actual updated listing prices model 3 123 16 Scatterplot of standardized residuals versus transformations of “Size” and histogram 124 17 Scatterplot of fitted versus actual updated listing prices model 4 125 18 Scatterplot of the actual versus the fitted listing prices model 5 129 19 Scatterplot of accuracy per municipality versus number of houses in a municipality 131

PREFACE

This master thesis is the result of a challenging “journey” of collecting data, merging several datasets and writing scripts to estimate values that are unnecessary and/or expensive to buy from third parties. In this master thesis, one potential product has been developed, which is especially relevant in the current economic environment.

Regarding this master thesis, the intellectual property rights of the theoretical chapters of this master thesis rest with the author. The developed (ﬁnal) model is in full ownership of

DataQuote, which is negotiated in exchange for a dataset with listing prices and characteristics of owner-occupied houses in the Netherlands and the possibility to write a master thesis on this topic.

I am grateful to all the persons and organizations that have supported me in writing this master thesis. In particular, I would like to thank my supervisor Prof. dr. B. Melenberg for critically reviewing all chapters of this master thesis and for giving feedback on the models. In addition, I want to thank Ing. L. Liplijn and MSc. P. R. F. van Loon for giving their opinions and for their brainstorm sessions to transform concepts into commercial products.

To all readers: I hope that you will enjoy reading this master thesis. Continuous eﬀorts will be done to update both the theoretical as well as the practical aspects. If you have suggestions or remarks, please let me know.

W.P.A. van Sprundel

Mail: [email protected]

Tilburg, October 2013

INTRODUCTION AND PROBLEM DEFINITION

Valuations are needed for many reasons, including fairness opinions, investment analysis, capital budgeting and determination of taxes. They should give a clear and realistic understanding of value. However, with regard to the valuation of the Dutch housing market, which is a collection of all individual houses in the Netherlands, opinions diﬀer. According to Xu-Doeve

(2010) 1, the market is vastly overvalued, which leads to an unhealthy and unstable market. The

International Monetary Fund (2010), on the other hand, views the Dutch housing market as fundamentally healthy, stable, and sustainable. From these opinions, it is clear that there is a diﬀerence in opinions with respect to the current Dutch housing market and future prospects.

Several reasons may (partly) explain this disagreement.

First, there are diﬀerent interpretations of “value” . Lotze (in: Brown, 1984) developed a gener-

1 Senior Partner of ANRC Consulting, elected member: International Statistical Institute, International Union for the Scientific Study of Population, Irving Fisher Committee on Central Bank Statistics, International Association for Official Statistics and founding member of the International Global Research Association Moscow

al theory of value 2 in the 19th century, in which he described the essence of value in a wide range of settings where evaluation takes place. In every setting, the word “value” has another meaning because value can incorporate many (related) fields, including: economics, politics and social sciences. Because individuals or organizations may assign a different value or worthiness to things, valuation is highly subjective. With regard to the Dutch housing market, different estimates for “value” are used, such as WOZ-value 3 (for tax purposes), market value (what the market would pay), willingness-to-pay (per individual), liquidation value, investment value, and several non-economic proxies such as aesthetic and historical value.

Second, there are diﬀerent metrics that all aim to estimate the same type of value in an objective manner. Several indices of the Dutch housing market derive a value of an individual house, based on transactions or appraisals of houses in the Netherlands. The Prĳs Bestaande

Koopwoningen (PBK) index 4 is derived by Centraal Bureau voor de Statistiek (CBS) 5 in collabo-

ration with Kadaster 6 (van der Molen, 2012). The PBK index estimates a house price by correcting the historical transaction price with annual percentages, which reﬂect growing, or declining trends in the transaction prices of all comparable houses in the Netherlands (nation-wide), a region (North, East, South or West) or the Province, depending on the chosen geographical

2 A theory that includes all types of human values in a systematic and scientific way. For more information, see: Hart (1971) and Brown (1984).

3 The annual determined value of a property by the municipality. It forms the basis for calculations of municipal taxes such as property and income tax.

4 Index based on the prices of houses sold in a given year compared to other years.

5 In the Netherlands, the CBS gathers, revises and publishes reliable statistical information for governments, science and organizations.

6 The Dutch Land Registry, which holds a public, available register of real properties and the underlying rights per property (e.g. ownership and mortgage).

boundaries. The Nederlandse Vereniging van Makelaars (NVM) 7 uses a median price index to

derive a value for every housing type in the Netherlands. This means that every year all transac-

tions are ranked by categories and the middle ﬁgure represents the median transaction price.

The growth or decline between median prices of housing types can be calculated over a desired

time span, followed by updating the historical transaction price of a housing type to current

practices. CALCASA 8 progressed on the indices of CBS, Kadaster, and NVM by developing an

Automated Valuation Model (AVM) at the end of the 1990s (CALCASA, 2012). Because the model is automated, it can be used to quickly derive the market value of individual houses and portfolios of houses, which led to the increasing popularity of the model amongst banks and investors. The AVM compares owner-occupied houses, based on spatial and object characteristics (CALCASA, 2012). Then it derives a market value.

Third, stemming from the fact that there are different interpretations as well as different metrics to estimate value, definitions of proxies for value are unclear. The discussed indices derive a “market value” based on the monetary worthiness of a house. The historical transaction price or a historical appraisal is often used as a proxy for worthiness. What contributes to “market value” and what not is inconsistent in current definitions, resulting in different outcomes.

Fourth, even when definitions are clear and all metrics measured the same type of value, giving insight into relevant attributes that drive prices (economic dimension) of individual houses in the Netherlands seems to be difficult because information is available but scattered or held privately among various organizations. An example is the AVM of CALCASA, where it remains unobservable how influential the spatial and object characteristics are for price. Some foreign

7 The Dutch Association of Real Estate Brokers and Real Estate Experts, with a core focus on housing, business (commercial real estate) and agricultural & rural premises.

8 CALCASA is Spanish for “calculation of the house” . The company is specialized in data collection, quantitative research, software development and building statistical models. It has a mission to give insight into the value and characteristics of all houses in the Netherlands so that risks can be managed more effectively.

indices do show this transparency, in their country, by including attributes that partly explain price. An example is the Halifax House Price Index in the United Kingdom that uses purchase price, location, housing type, age, tenure (freehold or leasehold), number of rooms, number of separate toilets, type of central heating, number of garages, presence of garden, land area and road charge liability as indicators for the current market price (Halifax, 2012). Another example is the house price index of Malaysia that also takes into account national trends such as the unemployment rate, total loans to housing, and per capita income (Keng, 1997).

Fifth, besides the current setting in which valuation takes place, there are macro-economic trends that aﬀect several markets, which may include the housing market in the Netherlands.

According to the Organisation for Economic Co-operation and Development (OECD, 2005), the annual increase or decrease in the average house price in the Netherlands, compared to the previous year, moves in a synchronized pattern compared to other European countries. From this, it could be concluded that international trends, at least partly, explain price movements.

These trends aﬀect the whole housing market and are, therefore, on a bigger scale than trends that aﬀect submarkets such as a decline in the number of large detached houses due to aging of the population in a country. Internationalization and globalization increase the importance of macro-economic trends, and as a result, they should be acknowledged by appraisers of houses.

Currently, relevant macro-economic trends are not considered by appraisers due to the fact that they are hard to predict, but the ﬁnancial crisis of 2008 that has led to a 2008-2012 global reces- sion with declining consumer wealth, a downturn in economic activity and a contribution to the

European sovereign-debt crisis has proved its importance for housing prices. Parties such as banks have, compared to appraisers, a more general market-view and do use macro-economic predictors and this leads to an inconsistency amongst those interested in valuation of houses.

Finally, it is diﬃcult to predict the value of individual houses in the Netherlands because of several local, regional, and national uncertainties. These uncertainties add risk to predictions 4

and lead to a range of possible values instead of a point estimate. The further into the future predictions are made, the wider the range of possible values. From these ranges, diﬀerent conclusions regarding the Dutch housing market might be reached.

1.1 Research Objectives and Problem Deﬁnition

Diﬃculties in appraising houses in the Netherlands lead to uncertainties regarding economic,

ﬁnancial, and social stability 9 in the Netherlands (Xu-Doeve, 2010). To address these vulnerabil- ities, this master thesis aims to develop an Automated Valuation Model (AVM) that can be used to accurately estimate “value” of owner-occupied houses in the Netherlands. The following problem deﬁnition and sub-questions assist in reaching these objectives:

How can an AVM be developed to accurately appraise the value of owner-occupied houses in the Netherlands?

What is value of owner-occupied house in the Netherlands?

Which values with respect to owner-occupied housing are used in practice?

What method(s) could be used to determine value of owner-occupied houses?

Which housing and spatial characteristics are potentially relevant for explaining market

values of owner-occupied houses in the Netherlands?

Which housing and spatial characteristics of the Dutch owner-occupied housing market

are statistically and economically signiﬁcant in explaining value?

How can an AVM be developed for the Dutch owner-occupied housing market?

What is the accuracy of the AVM?

Which macro-economic trends may aﬀect the valuation of houses in the Netherlands?

9 Xu-Doeve (2010) mentions social stability because for households, a home is an economic necessity, an expensive durable consumer good, and a store of accumulated wealth, where uncertainties have an impact on a household life-cycle perspective.

1.2 Relevance

Relevance can be subdivided into theoretical (see subsection 1.2.1) and practical (see subsection 1.2.2) relevance.

1.2.1 Theoretical and Scientiﬁc Relevance

The results of this master thesis will yield insight into the relevant factors of the economic dimension of value of owner-occupied houses in the Netherlands, which will be used to develop an AVM. Supportive to these results is a clear deﬁnition of the type of value with regard to housing that will be analyzed, an explanation how this value will be measured and the role that macro-economic dimensions may have on value. By doing so, this master thesis is special in terms that, such a value decomposition has probably not been done before with a sample of houses from the Dutch owner-occupied housing market. In addition, AVMs follow from the development of computing packages and the increasing transparency of housing markets. They are relatively new and their importance in the Netherlands has only been recognized and stressed in 2012. Giving insight into the development of an AVM and the accuracy of such a model contributes to its scarce theory.

1.2.2 Practical Relevance

In practice, values of houses are relevant for diﬀerent stakeholders. Buyers and sellers base their negotiations on the market value. For them, housing property constitutes nearly all of their non-pension assets (Venti and Wise, 1991). Also, lenders base their mortgage loan amounts on the market value, local governments raise revenues through taxing based on market value, insur- ance companies settle claims based on a derivative of the market value and market values are often required in legal proceedings (e.g. dividing property as a result of a divorce). Investors can

use it to ﬁnd undervalued houses, which they can then buy and reposition on the market to exploit an undervaluation (e.g. foreclosures). Dutch housing associations can use market values to price their rental dwellings when the law draft “Kooprecht voor huurder corporatiewoning” 10 might be approved by the Council of State. Also, all stakeholders are interested in future values of houses, which can be estimated from past market values. As a result, the transparency of the

Dutch housing market could be increased with a model that can accurately estimate “value” of

owner-occupied housing.

1.3 Procedure: literature study and research method

A literature review will be done to answer several sub-questions. An overview of the Dutch housing market in relation to other markets brings all chapters into perspective (chapter 2).

From this chapter, it follows that the Dutch housing market cannot be characterized by perfect competition. There are numerous trades at the non-equilibrium price, which makes valuation of houses diﬃcult. Then, a clear deﬁnition of value is needed that can be used for valuation models of houses in the Netherlands (chapter 3). Collective economic values are preferred over individualistic economic values, resulting in choosing “market value” as the type of value to measure.

Market value has many dimensions and may be hard to measure. Therefore, a description of value measurement techniques results in choosing the hedonic pricing method. In addition, similar studies that apply the hedonic pricing method will be analyzed internationally (chapter

4). Based on the results from this analysis, data will be collected and selected in accordance.

The deﬁnition, reliability, and validity of data is crucial. Therefore, a clear description of the dataset in terms of reliability and validity is needed in which variables are operationalized

10 In this draft, tenants should get the opportunity to buy their rental premise from the housing association against the current market value (Rijksoverheid, 2011).

(chapter 5). It is, hypothetically, assumed that housing and spatial attributes directly inﬂuence value and that there may be interaction variables, such as the season in which a house is listed

(see figure 1). From the operationalized dataset, meta-analysis provides an answer to the hypothesis, which states that locational information has no value. This hypothesis is rejected from results of four models (chapter 6), which are a national, provincial, COROP, and municipal model, respectively. Decreasing the scale improves the performance, both in an increasing goodness-of-fit as well as a decrease in the median difference between actual and fitted prices. In addition, a fifth model is developed that is based on local regressions, which improves the performance even further.

Housing attributes

Value Spatial attributes

Interaction variables

Figure 1: Hypothetical conceptual model

Besides object and spatial characteristics, there may be macro-economic variables that aﬀect the valuation. These trends are growing in importance. Therefore, to correctly value houses, it is essential that an appraiser is aware of these trends. In a discussion, the practical usefulness of the AVM and a discussion of macro-economic trends will be central (chapter 7). Finally, there will be an overall summary and conclusion, before recommendations for further research will be given (chapter 8). Overall, this master thesis aims to give an appropriate overview to understand valuation of houses in the Dutch owner-occupied housing market. Readers that are not interest-

ed in the underlying theory, but only in the results, can break the coherent structure by reading chapter 6, 7 and 8 (see ﬁgure 2).

CH1: Introduction and Problem Definition

CH2: Context of the Dutch Housing Market

CH3: Defining and Recognizing Value

CH4 : Measuring Value and Comparable Studies

CH5: Dataset Analysis and Optimalization

CH6: Model Development and Results

CH7: Discussion

CH8: Summary, Conclusion and Recom mendations

Figure 2: Chapter overview

1.4 DataQuote

This master thesis will be written in collaboration with DataQuote. DataQuote was founded in 2009 by Ing. Luke Liplijn. Its mission is to be an international supplier of quality real estate data and research and to make real estate markets more transparent. Strategies to achieve this mission are collecting real estate data from several sources, structuring and linking data, quality and verification procedures and expanding to other European markets. Clients are banks, investors, brokers, developers, real estate managers, architects, governments, lawyers, and universities. As per August 1, 2013, DataQuote has been fully acquired by Sandd. Sandd has a strategic rationale to improve its marketing business, an area in which DataQuote developed itself since its foundation. 9

CONTEXT OF THE DUTCH HOUSING MARKET

Several deﬁnitions of “market” can be found in studies of the earliest economists (e.g.

Cournot, 1838 and Marshall, 1920). A common conception of the term market relates to a place where exchange of goods and services takes place. This is congruent with the Latin word “merca- tus” , meaning trade or gathering for the purpose of commerce. More recent definitions also include terms relating to the outcome of the trade (i.e. prices or quantities). One such a recent definition stems from Louwes (2012), in which market is described as “the place where supply and demand meet and where, through the mechanism of free competition between market operatives, there emerges a balanced price, which allows the individual planning of consumption and production budgets to be brought into line with each other and permits the economic process to be car- ried on in an orderly way ”. With regard to the Dutch housing market, the market is bounded geographically by national borders and houses are the “products” offered in this market. There are various factors that influence the demand for housing, which ideally explain total supply. 10

However, this does not have to mean that there will always emerge a balanced price because overly simplistic assumptions may not hold.

The remainder of this chapter restricts the scope of this master thesis, by going from general real estate markets to the Dutch owner-occupied housing market. This section is organized as follows: section 2.1 discusses why real estate markets are diﬀerent from other markets. Section

2.2 discusses diﬀerences between housing markets and other real estate markets. Section 2.3 describes the institutional setting of the Dutch housing market in relation to foreign housing markets. Section 2.4 explains why neoclassical theory of supply and demand fails when applying its concepts to the Dutch housing market. The section then acknowledges relevant trends that partially inﬂuence demand, which in theory, (partially) determine supply. Section 2.5 concludes by linking all other sections and explaining the Dutch housing market as a search market 11 .

2.1 Real Estate Markets Versus Other Markets

Following several deﬁnitions of a market (e.g. Louwes, 2012); many markets are analyzed by the extent to which they are characterized by perfect and free competition (Lipczynski et al.,

2009). If a market meets all the criteria of perfect competition, then supply and demand analysis can fully show and predict the optimal quantity oﬀered and the price level (Prüfer, 2011 and

Lipczynski et al., 2009). On the other hand, if the market does not meet all of the criteria, then it is characterized by imperfect competition and supply and demand functions are not the only predictors of the optimal output and price. The criteria to meet perfect competition are: infinite buyers and sellers, perfect information, homogenous products, profit maximization, zero entry and exit barriers and persons (or firms) that can sell as much as they want at the current mar-

11 De Wit et al. (2010) defines a search market as ‘’a market where prices fail to clear the market at every instant and fluctuations in the number of sales primarily reflect changes in time-on-the-market rather than in underlying fundamentals of supply and demand theory’’ .

ket price (Lipczynski et al., 2009). Real estate markets do not meet the criteria. First, there may be limited transactions in a certain area, so buyers and sellers may have a fairly large impact on the average market price in that area. On a more aggregate scale (e.g. national), a fairer market price may be derived, based on more transactions. Second, according to Kosić (2008), perfect and complete information of real estate markets is hard to obtain because transactions are conﬁdential, decentralized and diﬃcult to examine. As a result, market participants contact real estate professionals and experts to get more information before making a decision (Kosić, 2008 and Lusht, 2001). Third, every real estate object is unique in terms of location, building and

financing (Hitchcock, 1978 and Fallis, 1985) which leads to heterogeneity in the stock of real estate. This heterogeneity makes it harder to compare real estate objects. Fourth, firms and persons do not necessarily have to act independently of each other and they also do not have to maximize profits. An example that supports this view includes Dutch housing associations that have a strategy to provide affordable housing with a price that is dependent on the income of a household. Fifth, there are several entry and exit barriers that exist, such as regulations on a national and local level and monetary and fiscal policies (Kosić, 2008). Also, a direct investment in real estate demands a large amount of money in comparison with other investments. As a result, investors cannot diversify easily within real estate, which leads to significant entry barriers. At last, investments in real estate are illiquid, meaning that a property cannot be sold instantaneously at a fair market price. According to Hitchcock (1978), real estate markets are subject to long time delays, which affect both supply and demand.

To make the importance of real estate in countries in relation to their economies visible, a basis for comparison is needed. The components of the Gross Domestic Product (GDP) can give valuable insight into the economy’s driving forces. The European Commission (2011) describes

GDP as: “the sum of total consumption, investment, government spending and net exports ”. The

U.S. Department of Commerce, Bureau of Economic Analysis (2011) gives a breakdown of the 12

United States GDP by industry. The percentage real estate, rental and leasing of the total Unit- ed States’ GDP remained remarkably stable in the past 8 years and was 12.2% during 2010.

Statistics from the Organisation for Economic Co-operation and Development (OECD) conﬁrm this ﬁgure. Furthermore, 2010 data from OECD (2011) concludes that 9.63% of total GDP in the European Union (12.36% in the Netherlands), 3.13% in Japan, and 2.25% in India should be contributed to real estate, rental, and leasing. Little information is known from China. There are only rough estimations, with many missing values. However, the National Bureau of Statistics in

China (2012) announced the nation’s total GDP and contributions per sector. Using this information, the percentage of GDP that should be contributed to real estate, rental, and leasing is calculated to be 13.09%.

To summarize, there are considerable inefficiencies in real estate markets. This view of imperfect real estate markets is adopted by many researchers (e.g. Cowles and Jones, 1937, Beechey et al., 2000, Burton, 1996, Fama, 1965 and 1970, Holbrook, 1960, Khan and Arshad, 1986, Samuel- son, 1965). These imperfections come from, amongst others, heterogeneous objects, information asymmetries, and infrequent trading. This in turn, could lead to difficulties in estimating market values, which leads to inefficient and unexplainable transactions. This makes deriving a fair value challenging, which is an issue because, when analyzing real estate as a part of GDP, it is clear that real estate is the third largest asset class besides stocks and bonds so inefficiencies could lead to instability.

2.2 Housing Markets Versus Other Real Estate Markets

According to Walker (1998) and Kahn (2000), the “products” in market deﬁnitions can be best described for housing markets as “buildings or structures that have the ability to be occupied

for habitation ”. Housing markets can be local, national or global; depending on the research (e.g.

speciﬁc versus more aggregate). The housing market is globally the biggest real estate submarket in terms of market share, size, and number of annual transactions (Lindqvist, 2011). Part of these results can be backed by analyzing the fraction of GDP that is explained by residential real estate. Results of the World Factbook (CIA, 2011) are that Asia Paciﬁc has the largest

GDP with $20.51 trillion. The largest contributor is China ($11.3 trillion), followed by India

($4.46 trillion), and Japan ($4.39 trillion). The NAFTA region is ranked secondly with a GDP of

$18.09 trillion. There is one main contributor, which is the United States ($15.04 trillion). Coun- tries like Mexico and Canada only have a negligible role. At last, the European Union is ranked third with a GDP of $15.39 trillion. The three main contributors, following the results of the

Central Intelligence Agency (2011), are Germany ($3.09 trillion), the United Kingdom ($2.25 trillion), and France ($2.21 trillion). Compared with the NAFTA region, the contribution to

GDP in the European Union is much more scattered among the member countries. After Italy,

Spain and Poland, The Netherlands follows with a GDP of $0.71 trillion. Following the breakdown of GDP, the monetary amount of the triad’s GDP invested in real estate totals $5.03 trillion (The Netherlands: $87 billion). However, it is unclear which fraction of this $5.03 trillion comes from residential real estate. One study by Dobbs et al. (2010) conclude that the desired global investment in residential real estate should be $2.4 trillion, which makes the residential real estate submarket the largest contributor to total real estate with a share of 48%.

Because of the relatively large number of annual transactions, the housing market is the most transparent real estate market (Brounen, 2011). According to Cannon et al. (2006), the transparency and the large number of annual transactions lead to a reduction in total risk. This reduction in risk also leads to a lower return for investors in houses, making the housing market the least risky but also the submarket with the lowest expected return. Contrary to other real estate submarkets, houses do not necessarily have to be investment objects. They have the function to provide space for living and, as a result, serve as consumption goods (Lindqvist, 14

2011). For most typical households, purchasing a home is the most prominent investment decision they make in their lives (Verbruggen et al., 2005).

Another major diﬀerence between housing markets and other real estate markets is that the demand for housing is directly determined by demographic forces, such as an increase or decrease, age. and composition of the population (Smith et al., 1984), which (indirectly) aﬀects

GDP.

2.3 Dutch Housing Market

Lindqvist (2011) argues that housing markets undergo rapid changes. Tiwari and White

(2010) observe a significant global economic integration over the past 60 years and the emergence of an international housing market during the last two decades. They explain that globalization in the transaction market for houses, involves internationalization in investments as well as internationalization of real estate companies and services. However, Lindqvist (2011) also mentions that there are large differences between housing markets with an increasing or decreasing population. Currently, several differences exist between the Dutch housing market and foreign housing markets. In the context of housing markets, a distinction should been made between the owner-occupied and rental sector. A short history of the Dutch housing market after the Second World War (because data from before is limited) is inevitable, before describing how the owner-occupier sector has been shaped by government policies (notably taxation), and how the rental sector has been shaped by institutional factors such as spatial planning and social housing. Their interdependencies will become clear throughout this section. Within this master thesis, a comprehensive and in-depth comparison between different national housing markets

surpasses its scope 12 . Section 7.2 and 7.3 compare the macro-economic dimensions that aﬀect housing markets in diﬀerent countries.

2.3.1 History of Housing in the Netherlands after WWII

Right after the Second World War, the spatial planning policy was set to building dwellings as rapidly as possible (Haffner et al., 2009 and Dieleman and Hooimeijer, 2005). Especially social rented dwellings were built, partially financed by bricks-and-mortar subsidies from the central government (Haffner et al., 2009). According to Xu-Doeve (2010), the policy in the 1960s changed from reconstruction and high density expansion to clustered de-concentration, where residential and industrial functions were separated. Compared to the period right after the

Second World War, this was a move towards more sustainable neighborhoods with a higher quality of living. The period between 1945 and 1970 is characterized by central government dominance in the provision of housing (Dieleman and Hooimeijer, 2005). In addition, this period also had an increasing process of decentralization (Flynn, 1986). Lower level governments and parties, such as municipalities and housing associations, became increasingly responsible for the regional and local housing task. According to Haﬀner et al. (2009), this has led to the cuts in subsidies at the end of the 1970s. Following the economic decline because of the 1973 oil crisis, the owner-occupied market collapsed in 1978 (Hellema et al., 1998 and Haﬀner et al., 2009).

This has led to a substantial production of rental dwellings, but during the second half of the

1980s, economic recovery led to an increasing development of owner-occupied dwellings (Haffner et al., 2009). In 1989, efficiency and deregulation were topics addressed by the Minister of Hous- ing Mr. E. Heerma, where he pleaded for a greater influence of market forces and a more effective housing market during the 1990s (VROM, 1989). In 1992, deregulation and decentralization

12 This involves comparison of demographic, economic, social, technological, ecological and political factors. A comparison between European countries and North America is given by Holmans, 1994 and by Musso et al., 2011.

started with replacing existing subsidies by the Besluit Woninggebonden Subsidies 13 (Haﬀner et

al., 2009). In 1993, municipalities were granted more responsibility in assessing the performance

of housing associations through the Besluit Beheer Sociale Huursector 14 (Gruis and Nieboer,

2006). According to Boelhouwer (2003) and Haﬀner et al. (2009), the Grossing and Balancing

Act of 1995 led to the ending of the ﬁnancial tie between the central government and housing associations because the central government paid oﬀ its long-standing subsidy commitments, while simultaneously housing associations paid back on their outstanding loans (balancing).

Through decentralization and deregulation, the Dutch housing market should be more influenced by the market mechanism, making it more transparent and efficient. Haffner et al. (2009) and

Aedes (2007) mention that since the Balancing and Grossing operation, operational risks are contributed to housing associations rather than the government. During 1995, the Dutch central government entered building agreements to add 650,000 dwellings between 1995 and 2004

(Haﬀner et al., 2009). These agreements should reduce the shortage, which still remained after the Second World War, and they have led to a maximum of 30% of this shortage in the form of social rental dwellings. In 2005, Ms. Dekker announced to further limit the shortage to 1.5% before the year 2009, meaning a development of 420,000 new dwellings (Haﬀner et al., 2009).

Annual production targets were not reached according to Haffner et al. (2009). A cause of this is the financial crisis, starting in 2007, making the economy as a whole unstable and riskier. The impact of this crisis still lasts and Haffner et al. (2009) expects that housing policy for the near future will mainly focus on the quality of existing dwellings instead of developing new dwellings.

2.3.2 Dutch Owner-Occupied Housing Sector

13 A decree in which the central government pooled the brick-and-mortar subsidies and left it to the municipalities to decide which dwellings would receive part of these subsidies.

14 A decree that described the relationship between local governments and housing associations.

In contrast to most foreign owner-occupied housing sectors, Dutch homeowners do not have to make a minimum down payment when buying a house (Dröes, 2011). In his research, Dröes

(2011) mentions that the average ratio of mortgage loan to value, in the Netherlands, is 90% at the time of purchase, and can be as high as 115% in extreme cases. This average ﬁgure is the highest in the world when compared to foreign owner-occupied housing sectors (Dröes, 2011).

Compared to the United States, the average loan-to-value is 75%, with a maximum of 95%

(Green and Wachter, 2005). This high average loan-to-value ratio in the Netherlands means that down-payment constraints do not have to be binding for homeowners. This is indeed the case because mortgage qualiﬁcation is mainly based on the household income (Dröes, 2011). The

Gedragscode Hypothecaire Financieringen (GHF) 15 further supports this argument by indicating that, since the 1 st of January 2007, the mortgage should not exceed 4.5 times the gross annual income of the owner of the house, and the monthly mortgage payments should not be higher than 35% of gross income (NVB, 2007). As of August 2011, the GHF has been modiﬁed as a result of the ﬁnancial crisis (NVB, 2011). Changes include that the maximum loan is 104% of the value of a house, a maximum deferral of amortization of 50% of the value, and increased monitoring by independent organizations to observe whether banks perform in accordance with the GHF (NVB, 2011).

Another diﬀerence between the Dutch owner-occupied sector and other owner-occupied sectors is that transaction costs can be ﬁnanced by the mortgage, and that mortgage interest payments are tax deductible (Dröes, 2011). Dröes (2011) states that, since 2004, mortgage rents are fully tax deductible if the net housing equity after selling the house is used to buy a new property. He mentions that the Dutch government stimulates the demand for owner-occupied housing through deductibility of mortgage rents and that this has led to over-consumption of owner-

15 A code of good conduct that applies to each mortgage loan that is provided in the Netherlands.

occupied housing. However, the Dutch government regulates the use of land, which results in a scarcer supply of owner-occupied housing. According to Rouwendal and Van der Straaten

(2008), these zoning regulations indicate the purpose of the Dutch government to preserve open space. Furthermore, tax deductibility of mortgage rents has been subject of governmental debate since 1893, when minister of Finance N. Pierson passed the law “Wet op de Ver-

mogensbelasting”16 through the House of Representatives (Kromhout and Oving, 2008). Before,

taxes were levied through excise duties on products and services. These excise duties were the

same for all and were considered unfair for poorer people. Several political parties demanded a

tax duty on the basis of income 17 . Owner-occupied housing was part of income because every

owner had the right to rent (parts of) his house to make money. Kromhout and Oving (2008)

state that, in the case of full habitation by the owner, the owner “rented” from him/herself. So

this “rent” was added to taxable income. However, part of the reformation was also that certain

costs could be subtracted from taxable incomes, including expenses of owning a dwelling: costs

of maintenance, devaluation, and rent on mortgage loans (Kromhout and Oving, 2008) 18 . These subtractions led to missing of taxes for the Dutch central government, but the total share of owner-occupied housing as a percentage of total residential real estate was low (Kromhout and

Oving, 2008). Since 1893, several tax laws have been dropped, added and/or changed, and after the Second World War, owner-occupied housing was stimulated by the Dutch government (i.e.

Nationale Hypotheek Garantie in 1956 19 and Wet Bevordering Eigenwoningbezit in 2011 20 ). The

16 A law that states that a direct tax should be levied on capital, independent of how the capital is earned.

17 This was one of the demands (“dringende eisch der recthvaardigheid”) of the Queen-Regent Emma in 1891.

18 According to Kromhout and Oving (2008), this was a compensation for people with a higher income who all of a sudden had to pay more taxes than before.

19 Guarantee in which the Dutch central government backs the amortization of a mortgage when owners could not pay due to unforeseen circumstances.

Dutch government believed that home ownership led to increased livability in the direct vicinity and that it could act as an example for those who rented (Kromhout and Oving, 2008). With stimulating home ownership, the Dutch central government missed more and more money due to mortgage interest deductibility. Research showed that the richest Dutch people profited the most from this fiscal advantage, which is why the second Cabinet-Kok discarded the mortgage interest deductibility from taxes for a second home and limited mortgage interest deductibility to 30 years in 2000 (Kromhout and Oving, 2008). Current debates led to stricter rules for mortgage interest deductibility, effective on the 1 st of January 2013. Resulting is a limiting in mortgage

interest deductibility with 0.5% per annum, starting in 2014, to a maximum deductibility of

38%, compared with 52% in 2013 and 2014 (ABN AMRO, 2013). Current debates limit mort-

gage interest deductibility, which is in contrast with the view as it was intended. Since 2000,

there is an increasingly weakened stimulation of home ownership by the Dutch central govern-

ment. Also, by setting limits on mortgage interest deductibility, home ownership becomes less

attractive, which may lead to a ﬂight to the rental-sector. According to Vandevyvere and

Zenthöfer (2012), the mortgage interest deductibility artiﬁcially raises house prices and dispro-

portionately favors high-income taxpayers. The standpoint of the central government is contra-

dictory to stimulating home ownership, which it did for more than 50 years.

Dröes (2011) also mentions that relatively high transaction costs (e.g. transfer taxation)

prevent people to move. This relationship is demonstrated by Van Ommeren and Van Leu-

vensteijn (2005), which concluded that a 1% reduction in transaction costs increased the number

of house moves by 8%. The prevention of people to move could lead to inappropriate housing for

certain households, based on their lifestyle and income.

20 A law that states that people with less income and who are willing to buy a house, are entitled to the possibility to get a subsidy.

Finally, several types of mortgages are used throughout the world. One of the most common is the no pay-off mortgage, which is an interest-only mortgage (Dröes, 2011). Interest has to be paid on specific time intervals, but the initial amount (principal) is repaid entirely at maturity of the loan. This is also the case in the Netherlands where most of the mortgages have a fixed interest rate (De Vries, 2010). In addition, sub-prime mortgages 21 are rare in the Netherlands, particularly because the GHF restricts discriminating between persons (NVB, 2011 and Dröes,

2011).

2.3.3 Dutch Rental Sector

In the Netherlands, more than 41% of the total housing stock belongs to the rental sector

(Haffner et al., 2011). This is a record share within the European Union. This is the outcome of repeated intervention of the government since the 1901 Housing Act. Haffner et al. (2009) states that rental dwellings can belong to two categories: commercial and non-profit rental dwellings, also called the market and social sector. According to Elsinga et al. (2007), both sectors have dwellings with regulated and deregulated rents. Haffner et al. (2009) states that about 95% of the social and market sector stock of housing is regulated. According to Vandevyvere and

Zenthöfer (2012), this is 93%, which translates into 2,7 million dwellings. Rents are regulated because the Dutch government has a policy to provide aﬀordable housing to households (Dröes,

2011). According to Vandevyvere and Zenthöfer (2012), rent regulation is based on three elements: maximum rent level (depending on quality of dwelling and surrounding area), annual rent adjustments that are capped by the government (linked to inﬂation) and tenant protection

(subsidies). The result is that regulated rents are often lower than market rents (Romijn and

Besseling, 2008). Rent regulation has become increasingly a topic of debate because many be-

21 Type of mortgage for borrowers with low credit ratings and thus high risk of default. The interest rate is often higher to compensate the issuer of the loan for the added risk.

lieve that this is an essential condition to reform the housing market as it is now (Vandevyvere and Zenthöfer, 2012). This can be done through various policy spectrums, including eliminating the point system. In total, 15% of social and private tenants receive subsidies for rents, which is the third highest amongst all OECD 22 countries. Another result, and a potential disadvantage for housing associations, is that many households rent or are able to rent a house for a price that is often too low in relation to their income (Dröes, 2011). Together with no transaction costs, renters move houses more easily than owners, which lead to long waiting lists in this sector. Haﬀner et al. (2009) provides evidence that the mobility in the social rental sector versus the owner-occupied sector is 7% versus 4.5%.

In relation to other countries, housing associations in the Netherlands play a crucial role because they own substantial shares in the supply of social housing. This is because of decentralization, deregulation and the Balancing and Grossing act. However, current policy proposals to liberalize rents are linked to reformation of social housing associations because they would collect more equity as rents move towards market levels (Vandevyvere and Zenthöfer, 2012). This

(extra) equity then cannot be used to lower rents to increase aﬀordability because this is in contradiction to liberalizing rents. This extra equity could lead to mismanagement of housing associations as has been seen in recent and multiple cases. Therefore, some argue for collecting housing association their equity in a separate fund, which is under government supervision so that it is used for the public interest (Vandevyvere and Zenthöfer, 2012).

2.4 Neoclassical Theory Applied to the Dutch Housing Market

22 Organisation for Economic Co-operation and Development with member countries: Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and the United States

Neoclassical theory of demand and supply and derivations from this theory, to make it applicable to real estate markets (e.g. DiPasqualle and Wheaton’s four quadrant model), cannot be used to fully explain every real estate transaction. According to Buchanan (2008), reality shows that there are “false trades” in each market. He deﬁnes a false trade as a transaction at the non-equilibrium price 23 . There are many false trades within the Dutch housing market. Lusht

(2001), states that appraisals of houses are just opinions. Transactions on the basis of opinions

of appraisers will, most certainly, lead to deviations from the equilibrium price which results in

many false trades and an ineﬃcient market. According to Mills (1998), these false trades are the

direct result of the following housing characteristics: necessity (basic human need to shelter),

importance (housing is the single most important item of consumption), durability (housing is

the most durable commodity), spatial ﬁxity (ﬁxed in place), indivisibility (a house cannot be

divided), complexity and multi-dimensional heterogeneity (all the attributes of housing), infor-

mational asymmetry (not everyone has perfect information), and the importance of transaction

costs (search costs, moving costs and transaction fees). Then, besides false trades, there are

boom and bust cycles in developed housing markets (Tomura, 2013), which are fueled by macro-

economic variables (see section 7.2 and 7.3). These boom and bust cycles directly aﬀect house

prices in the short term and may indirectly aﬀect demand and supply in the long run (e.g.: a

crisis directly decreases the prices of all houses and indirectly may lead to an increased demand

for cheaper houses because the crisis may have led to unemployment, and because of that, less

income to spend on housing). The result of boom and bust cycles on supply and demand is

lagged and this introduces a time eﬀect.

An extension of neoclassical theory of supply and demand is search theory. With regard to

search theory, Lazear (1986) mentions that the price of a heterogeneous good depends on infor-

23 The price on the point where the demand function intersects the supply function

mation that the seller has about interested buyers. This price can then be adjusted in time based on new information, which he refers to as a learning eﬀect. Although his conclusion is based on the retail market, housing is also heterogeneous, albeit less compared to retail. Herrin et al. (2004) mentions that search theory assumes that the learning period is there to match sellers and buyers. Herrin et al. (2004), Glower et al. (1998) and Knight (2002) also refer to housing markets as “excellent” examples of cases where search theory holds. A “fair” price devel-

ops throughout time, once the seller learns what the house is really worth in the market.

Assuming search theory as an extension or modiﬁcation of neoclassical theory of demand and

supply, which is applicable to housing, insight into demand and supply functions of housing in

the Netherlands is needed. It is hard to derive the supply and demand functions for housing in

the Netherlands, because it involves several (interrelated) factors, including demographic, eco-

nomic, social, technological, ecological, and political variables. A comprehensive and in-depth

analysis of all of these variables surpasses the scope of this master thesis 24 because demand and

supply theory cannot be used to fully explain transactions in real estate (there are false trades).

However, this does not mean that it is completely useless, which is why some demographic and

economic factors will be discussed because they have a direct inﬂuence on demand and price as

opposed to the other factors. Supply of housing is often determined on the basis of demand

(Lipczynski, 2009), but it is also regulated by central, regional and local governments in the

Netherlands.

2.4.1 Relevant Factors That Partly Explain Demand

Considering the demand curve, several factors could inﬂuence its steepness. According to

Mills (1990), the type of housing that a household chooses is sensitive to demographic and eco-

24 For more information, see Vermeulen and Ommeren (2009), who analyze housing supply, internal migration and employment growth in the Netherlands and Grant (2010), who explains methods for industry analysis.

nomic characteristics. Relevant demographic forces include age of the population, growth or decline in the population and household characteristics. CBS data reveals an aging population in the Netherlands .The average age of people in the Netherlands has increased from 1950 and onwards. The CBS has made forecasts and predicts that this average age will increase until 2040

(CBS, 2013a). Ceteris paribus, an ageing population could change the demand for certain housing types (e.g. bungalows or apartments) in comparison to others (e.g. villas), making them more attractive amongst elderly. This could lead to an increase in the price of these types of housing, especially when (regional) supply reacts slowly. However, housing developers will quickly respond to this scarcity because it becomes more proﬁtable for them if market prices are higher while costs stay more or less constant. When supply of these houses increases, they will become less scarce, forcing the price to go (slowly) down again.

Aging has a direct result on the number of inhabitants in the Netherlands. The total number of people in the Netherlands compared to the previous year is based on annual births, deceases, immigration, and emigration. Predictions of the CBS Statline (2013a) assume that the annual number of deceases wil increase because of the aging population in the Netherlands. In addition, the annual number of births will remain “fairly” stable between 176,000 and 190,000 births per year for the years 2014 to 2060. Both eﬀects together will lead to a scenario until 2040, in which there is a small growth because births overstate deceases and the migration saldo is positive.

Around 2040, it assumes a slight decrease and stabilization in the population at around 17.7 million inhabitants. The Dutch government analyzes these expectations, which may lead to restrictions in annual supply of houses to protect the housing market against oversupply and declining house prices. Under ceteris paribus conditions, demand for houses stabilizes in the long-run. Besides an ageing population, and a historical growth in the number of people but stabilization in the more distant future, households will change in size and composition. The

sum of single25 and multiple family 26 households is deﬁned as total private households. According to CBS (2013b), private households will continue to rise until stabilization in 2040. In 2040, there will be around 8.5 million households. Single family households will increase more and longer than multiple family households (CBS, 2013b). Multiple family households of 2, 3, 4, and

5 or more persons will remain stable (CBS, 2013b). Van Oorschot (2006) concludes that the trend of individualization and the ageing population in the Netherlands are the reason for the increase in single family households and decrease in multiple family households. Under ceteris paribus conditions, changes in household composition will change the demand for certain housing types, making dwellings for single family households more attractive as opposed to multiple family households. Supply (restrictions) could be adjusted to this trend to protect the Dutch housing market against a potential devaluation of multiple family households.

Relevant economic forces include purchasing power development, unemployment, and income.

CBS (2013c) states that purchasing power development 27 ranged from 5% (in 2000/2001) to -

0.5% (2009/2010) in comparison to the previous year, over the last 10 years. There are no predictions of purchasing power development in the future because it is a highly uncertain variable that is inﬂuenced by many other variables. Mills (2006) shows that the higher the purchasing power, the more a household will typically invest in housing. Under ceteris paribus conditions, an increasing purchasing power will then lead to a greater attractiveness of more expensive dwellings, which could make them more expensive if supply does not change accordingly. There is also uncertainty on income characteristics. It is likely that income will increase with at least the level of inﬂation, which is what the CBS assumes (CBS, 2013d). When income rises to

25 Private households consisting of a single person (CBS, 2013).

26 Private households consisting of more than one person (CBS, 2013).

27 The median of the purchasing power mutation of all people

compromise for overall higher prices, but when house prices stay equal, a higher income can be used to purchase (or rent) a more expensive house. Assuming the statement of Mills (2006), and the fact that a higher income leads to greater purchasing power, more money will be invested in housing, which makes more expensive housing attractive. Finally, CBS (2013e) states that the unemployment rate was fairly stable before 1980 at around 100,000 people. After 1980, the unemployment rate became volatile with registered unemployment between 120,000 and 650,000

(CBS, 2013e). Large periods of unemployment may lead to less income, a move to cheaper housing or a change from an owner-occupied home to a rental home.

2.4.2 Housing Supply

Mills (2006), states that the supply function cannot be determined for the Dutch housing market because of government regulations. These regulations are pervasive throughout history and lead to ineﬃciency and an increase in false trades when compared to the price that stems from neoclassical supply and demand theory. According to Lipczynski et al. (2009), price elasticity of supply measures the sensitivity of quantity supplied to market price. It follows that an increase in price should be associated with a decrease in quantity demanded for the price elasticity of supply to stay constant. Vermeulen and Van Ommeren (2009) state that there is a negative impact of land use regulations on the price elasticity of housing supply. Vermeulen and

Rouwendal (2007) back this result by concluding that housing supply in the Netherlands is almost fully inelastic; meaning that the price elasticity of supply is less than 1 (quantity supplied is almost not responsive to changes in price). They explain this price inelasticity in terms of restrictive planning by the government and rule out alternative explanations. In their research of the Dutch housing market, Vermeulen and Van Ommeren (2009) conclude that supply is hardly responsive to population or employment growth.

2.5 Sub-conclusion

In relation to other markets, the Dutch housing market is uniquely positioned. It has an imperfect structure, leading to an increased chance of ineﬃcient and unexplainable transactions

(false trades). These false trades stem from four reasons. First, there may be limited transactions in a given area, perfect and complete information is hard to obtain, every house is unique in terms of location, building and financing, firms and individuals do not have to act independently and maximize profits, there are several entry and exit barriers, and houses are illiquid investments. Second, there has always been a strong intervention of the central government through spatial planning policy and subsidies since the Housing Act in 1901, preventing the free market mechanism. Through decentralization, deregulation, and the Grossing and Balancing

Act, central government intervention was limited in time, but regional and local governments still have considerable power by setting-out policy objectives and regulations. Third, false trades emerge because of necessity, importance, durability, spatial ﬁxity, indivisibility, complexity and multi-dimension heterogeneity, information asymmetry, and the importance of transaction costs of houses. Fourth, appraisals of houses are merely opinions, which could be highly subjective.

Search theory provides a basis for understanding the emergence of housing prices. However, it is very hard to derive supply and demand curves for the entire Dutch housing market. This is because the curves involve many interrelated factors in which demographic, economic, and global forces play a crucial role.

All in all, it can be concluded that the Dutch housing market comes closer to a search market: “a market where prices fail to clear the market at every instant and ﬂuctuations in the number of sales primarily reﬂect changes in time-on-the-market rather than underlying fundamentals of supply and demand theory” (De Wit et al., 2010).

DEFINING AND RECOGNIZING VALUE

Philosophers from Plato and onward have put various issues related to value under headings such as the good, the right, the ultimate end, obligation, virtue, morality, truth, aesthetics, etc.

(Frankena, 1967 in: More et al., 1996). Plato believed that all of these came from the same root, but over time they were separated and the term “value” was referred to as the worth of a thing

(More et al., 1996 and Hutter and Throsby, 2008). In the 19 th century, philosophers such as

Lotze and Nietzsche argued for a return to the Platonic tradition, which led to the development

of a general theory of value (More et al., 1996). Urban (1909) mentioned that: “There has sel-

dom been a time in the history of thought, when the problem of “value” has so occupied the centre

of attention as at present. Fundamental changes in the actual values of mankind…have brought

with them what may, without exaggeration, be described as a gradual shifting of the philosophical

centre of gravity from the problem of knowledge to the problem of values. The problem of

knowledge has itself become, in some quarters wholly, in others partially, a problem of value.

More and more the conviction gains ground that a general theory of value, which shall compre-

hend in a systematic and scientiﬁc way all types of human values, is an absolute necessity”. 29

According to Frankena (1967), this general theory of value matured during the nineties, but all disciplines (e.g. economics, psychology, and humanities) still interpreted value within their own structures. Findlay (1970) argued that axiology (the study of value) is in its essential problemat- ic and a ﬁeld for everlasting philosophizing. As a result, each discipline maintained its own measurement techniques and methodologies to estimate value. This leads to confusion in the case where value is discussed in an interdisciplinary context (More et al., 1996, Hutter and

Throsby, 2008 and Vigaray and Hota, 2008), such as with the valuation of houses. When people value a house, they (mostly) do not only take into account object characteristics, but also information about the direct vicinity, such as social inﬂuences (e.g. safety, livability). This is in line with research of Krause and Bitter (2012), who describe the growing importance of spatial econometrics and the acknowledgement of value premiums in the aftermath of the recent boom and bust of US real estate.

The remainder of this chapter is organized as follows: section 3.1 distinguishes between facts and values, and values as subjective or objective. Section 3.2 discusses economic value; the value that is estimated most frequently, which tends more towards objectivism. Yet, this value might be dependent on other types of value, so section 3.3 describes proxies for value under subjectivism that may be relevant for the Dutch housing market. Finally, section 3.4 concludes this chapter by acknowledging the relevance of quantitative and more subjective data and by choosing the appropriate deﬁnition for the dependent variable ( “value” of a house). By decomposing the

general concept of “value” and choosing the value to answer the problem deﬁnition, measurement of this value is the next step (chapter 4).

3.1 Value and its Interpretation

Several definitions of value refer to facts. Although a fact is different from a value, both are taken into account when making a trade-off (e.g. buy or not buy a house). A clear distinction between the two is needed to interpret value (subsection 3.1.1). In addition, when facts are separated from values, values can be either objective or subjective, which can blur the initial distinction between facts and values (subsection 3.1.2).

3.1.1 Factual and Value Judgment

Both, facts and values are important when choosing among houses. According to Nolan

(1995), factual judgments are “descriptive statements of empirical qualities or relations” . On the other hand, he describes value judgments as more evaluative: “appraisals of the worth of objects, acts, feelings, and so on” . Hutcheson, Hume and Smith (in: More et al., 1996 and Hutter and

Throsby, 2008) pointed out that it is impossible to derive ‘’ought’’ statements (values) from “ is ” statements (facts). That means that facts can never fully explain what somebody ought to do.

On the other hand, Nolan (1995) describes that we cannot separate facts and values completely because there is an interaction. If facts change, evaluations often also change, making value judgments fact-dependent. This interaction is responsible for the distinction between values as objective versus values as subjective.

3.1.2 Values as Objective Versus Values as Subjective

Value judgments can be seen as expressions of feelings and desires, which give them subjective meaning. According to Nolan (1995) and More et al. (1996), objectivism is the most extreme position, which restricts the scope of meaning to the statements that involve reference to data that can be veriﬁed by sense experience (position that makes nearly anything a fact).

Objectivism concludes that value judgments either mean something (refer to facts) or nothing at all. Thereby it distinguishes between the act of judging and the thing or situation that is judged

(Nolan, 1995). To say that an object has value is declaring that it is in some sense good. Howev- er, Nolan (1995) concluded that what is felt to have value need not be judged to have value when the object is evaluated. An evaluation takes place by decomposing the object in sensory experiences such as shape, size, color, temperature, and smell. This combination makes value judgments fact-dependent. This objectivistic way of thinking is conﬁrmed by many philosophies, including that of Plato and Aristotle, medieval realism, neo-Thomism, modern realism, and idealism (Nolan, 1995).

With regard to economics, neoclassical theory of supply and demand (see section 2.4) was constructed conforming an objectivist point of view. Nelson (2008a, 2008b) argues that economists in the 1960s and 1970s doubted the adequacy of neoclassical theory of supply and demand.

These theories were insufficient to capture connections with others, traditions and relations of power, making them not objective enough (Nelson 1996). Objectivists define economic models as an affair that is characterized by a drive for distance and detachment, and where economists are autonomous, rational, and interested in “hard” knowledge. According to Keller (1985), objectivism is a belief in the possibility of connection-free knowledge from a viewpoint that is distant to nature, but in reality, scientists are embedded in nature and cannot attain perfect and objective information. Nelson (2008a, 2008b) describes that, as a result, subjectivism disputes objectivism by acknowledging human interdependence, community, embodiment, emotion, “soft” and qualitative aspects, uncertainty, and value judgments. According to the view of values as subjective, value statements only express sentiments or emotions of liking or disliking (Nolan, 1995 and

More et al., 1996). Something is valuable if it evokes pleasurable feelings and maximizes one’s own utility.

With respect to valuation of houses, objectivism only takes into account characteristics of the target that are objective. Under subjectivism, the target is part of a neighborhood and district, whereby qualitative spatial characteristics are implicitly assumed to have an eﬀect on value.

Value judgments, such as “safe” and “beautiful” then inﬂuence value.

3.2 Deﬁning the Dependent Variable: Economic Value of Houses in the

Netherlands

The dependent variable of interest is the value of a house in its economic dimension. In this dimension, value takes the form of a price as monetary worth. While value is a theoretical and subjective concept ex-ante, price is a directly observable, ex-post and stated fact in the housing market. To derive a fair price, all relevant values (e.g. environmental and aesthetic value) and facts should be considered and transferred to the same unit of measurement: currency (price).

Because the Dutch housing market is a search market, prices cannot be derived on the basis of neoclassical theory of supply and demand (see chapter 2). According to Lusht (2001), imperfect markets are unlikely to produce perfect estimates of value, which leads to the conclusion that there is no general universal agreement on how diﬀerent economic values must be calculated.

Many proxies for the value of houses as a monetary worth exist, which are required by diﬀerent persons and organizations. These values are divided into individual values: investment value, use value, and insurable value (subsection 3.2.1), but also collective values: market value, liquidation value, and assessed value (subsection 3.2.2). Proxies such as book value, intrinsic value, going concern value and equilibrium value are considered irrelevant for the Dutch housing market as they relate to commercial real estate, real estate investment trusts 28 or to concepts that have

28 Securities that can be bought and sold as stocks on major indices that directly invest in real estate, making real estate more liquid for investors

proven not to be veriﬁable (i.e. equilibrium model). After a description of these types of values, quantiﬁcation is needed between listing and transaction prices (subsection 3.2.3). Listing prices may be individualistic in nature (set by one or a few persons) but they may be based on expectations of the market. Transaction prices may be more collective because they are the result of negotiations with one or more market participants.

3.2.1 Individualistic Economic Values

Individualistic economic values, which are relevant for the Dutch owner-occupied housing market, are use value, investment value, and insurable value.

Schram (2006) and Munizzo and Musial (2009a, 2009b) argue that use value is the monetary worth for an individual when the property is used for a particular purpose. As an example: houses in a rural area with large parcels have a higher use value for a farmer than for an individual who wants to live in a city with a high density. Schram (2006) states that governments sometimes grant property tax reliefs when a property is placed under a certain use (e.g. agricul- ture) and that there may be no competitive markets (e.g. schools and public buildings). Munizzo and Musial (2009a) refer to this as special use property and limited market property. When houses are placed under a special use, or when there is a lack of comparable properties, use value should be calculated instead of the highest and best value.

Schram (2006) and Munizzo and Musial (2009a) argue that investment value is the monetary worth of a property to a particular investor depending on individual investment criteria. Indi- vidual investment values can be measured by the willingness-to-pay of an investor, which is estimated from the investor’s earnings, interest, and return characteristics (Hicks, 1946 and

Fetter, 1937 in: Magni, 2009). However, earnings, interest, and return characteristics may be prone to interpretation. From an accountant’s perspective, earnings are increases in assets after

distributions of dividends to shareholders (if applicable), while from a financial point of view, interest is used to compensate the lender (Promislow, 2006 in: Magni, 2009). In capital budgeting, return is used, representing the difference between the end-of-period payoff and the initial investment (Magni, 2009). Magni (2009) concludes that all of these proxies for investment value are different and that differences stem from its use in different fields such as economics, finance, accounting and actuarial and financial mathematics.

Lusht (2001) and Schram (2006) mention that insurable value is set by the terms of an insur- ance policy and refers to the value of a property under reimbursement. They provide evidence that insurable value is often based on the depreciated replacement or reproduction costs of a building that is subject to loss from hazards. In the USA, there are precise formulas, constructed by the (local) government that should be used to calculate insurable value (Lusht, 2001).

Individualistic economic values are very dependent on a particular person. For a particular buyer, these values can be estimated to provide that person’s willingness-to-pay. However, this value does not give the seller information whether this price is fair. Analyzing this measure amongst many buyers provides a range and gives the seller information about what is high and low. This leads to collective measures of economic value.

3.2.2 Collective Economic Values

Most of the literature points out that market value is most often estimated in real estate valuations (e.g. Schram, 2006; Karagöl, 2007; Munizzo and Musial, 2009). However, diﬀerent deﬁnitions of market value exist. According to Munizzo and Musial (2009), the mechanics of a market and the development of a collective measure of value became clear when Adam Smith wrote his book “ An Inquiry into the nature and Causes of the Wealth of Nations” in 1776. Smith

distinguishes between value in use and value in exchange, of which the latter became known as

market value. Karvel and Unger (1991) state that, from an economic point of view, market value is the power of commanding commodities in exchange. Brown (1984) and Peterson (1993) also deﬁne market value broadly as a form of assigned value that helps facilitate choice by establish- ing a benchmark of one object in relation to others. For a particular object, market value refers to an average value of one object in relation to others, as assigned by all market participants.

This makes the concept a “same-for-all” number. Cannon (2002) defines market value as the present worth of future benefits that are realized from ownership. Dixon et al. (2005) defines market value as: “The estimated amount for which a property should exchange on the date of the

valuation between a willing buyer and a willing seller in an arm’s length transaction after prop-

erty marketing wherein the parties have each acted knowledgeably, prudently and without compul-

sion” . According to Schram (2006), the transaction is an arm’s length transaction “when buyer and seller are motivated, well informed as to the conditions of the market, and the subject property, and acting reasonably in their own self-interest, and without undue duress 29 ”. In addition,

the property must be exposed to the market for a reasonable time period. According to Rockwell

Publishing (2006), the Federal Housing Administration deﬁnes market value as “The price which

typical buyers would be warranted in paying for the property for long-term use or investment, if

they were well informed and acted intelligently, voluntarily and without necessity” . More recently,

market value is deﬁned by federal agencies such as Fannie Mae and Freddie Mac and by the

Uniform Standards of Professional Appraisal Practice (USPAP) as: “the most probable price

which a property should bring in a competitive and open market under all conditions requisite to

a fair sale, the buyer and seller each acting prudently and knowledgeably, and assuming the price

is not aﬀected by undue stimulus. Implicit in this deﬁnition is the consummation of a sale as of

a speciﬁed date and the passing of title from seller to buyer under conditions whereby: (1) the

29 Pressure that a person is put under to make him or her do something that he or she does not want to do (Black’s Law Dictionary, 2013)

buyer and seller are typically motivated; (2) both parties are well informed or well advised, and acting in what they consider their best interests; (3) a reasonable time is allowed for exposure in the open market; (4) payment is made in terms of cash in United States dollars or in terms of

financial arrangements comparable thereto; and (5) the price represents the normal consideration for the property sold unaffected by special or creative financing or sales concessions granted by anyone associated with the sale”. The most probable price is not the highest price and the probabilistic nature of a deal is acknowledged by stating that market value is the most probable price the property should bring (not “will bring” ). According to Lusht (2001), the most probable price can be calculated as the mode (single most likely outcome) or the expected price (average).

With respect to a competitive and open market, competitive are those markets that are in (or close to) equilibrium, which are characterized by perfect competition. The Dutch housing market is not a perfect market (see chapter 2), making it more difficult to derive an appropriate market value. Schram (2006) mentions that there are state laws in the US that define market value differently, often including a direct link to tax assessment (assessed value). Munizzo and Musial

(2009) mention that appraisers must use the definition of market value as is recognized by juris- diction in which the appraiser provides its services. In the absence of a definition of market value by governing law and jurisdictional rules, the definition of market value given by USPAP is expected to be implemented (Cannon, 2002). However, if there is no competitive and open market, the definition of USPAP does not hold, which leads to the hypothesis that market value cannot be estimated or calculated for houses in the Netherlands. Lusht (2001) mentions that the definition implies that appraisers must do everything in their effort to be wiser than the market, which is present in the part of the definition that both parties are well informed and advised. As a result, he states that the crash of some real estate markets in the 1980s should have been foreseen by appraisers. This continued with a discussion in the 1990s, which was focused on the difference between short- and long-run values and the stability of both values. As a result, a new 37

deﬁnition was formed by the International Valuation Standards Council (2000 in: Lusht, 2001):

‘’market value is the expected selling price of the speciﬁed real property rights in an arm’s-length transaction, as of the date of the appraisal, and assuming a reasonable exposure to the market’’ .

This deﬁnition explicitly recognizes the probabilistic nature of price setting in housing markets,

and refers to market value as the weighted average (the expected selling price) of a distribution

of possible prices (Lusht, 2001). Expected price is preferred to most probable price because it

suggests a single, most likely outcome rather than the average outcome (Lusht, 2001). Reference

to “the speciﬁed real property rights” separates the real property that is valued from a personal

property, such as favorable ﬁnancing, which may aﬀect the total transaction price. Reference to

an “arm’s-length transaction” excludes transactions under, for example, forced sales (liquidation

value). It reminds the appraiser that market value is a product of market transactions. Finally,

the deﬁnition imposes no idealistic assumptions about market equilibrium and perfect markets.

It also does not acknowledge that the appraiser is on average wiser than the market. Assuming

this deﬁnition, market values for houses in the Netherlands can be estimated or calculated.

Other collective measures of economic value, such as liquidation and assessed values are both

calculated on the basis of market value. Scharm (2006) argues that liquidation value is a form of

market value, with the restriction that the property must be sold in a limited period of time (i.e.

foreclosure). Munizzo and Musial (2009) refer to this value as associated with a quick transfer of

ownership rights. This is in contradiction with the “reasonable exposure to the market” character-

istic of market value. Because of this time constraint, the property is often sold below market

value. Schram (2006) states that assessed (or WOZ) value is set by state taxing authorities to

assess ad valorem property taxes. Schram (2006) and Munizzo and Musial (2009) deﬁne assessed

value as a market value multiplied with an assessment ratio. Schram (2006) explains that the

assessment ratio is speciﬁed via regulations in the USA. Carr et al. (2003) also acknowledges this

link between assessed value and market value. In the Netherlands, WOZ-value of houses is calculated on transactions of comparable houses in the direct vicinity.

All in all, market value is the value, determined by the open market under several conditions.

From the definitions of market value, a “most probable price” or “expected selling price” is estimated. The USPAP requires that appraisers accurately describe which definition of value they use, together with the sources that define that particular use (Munizzo and Musial, 2009). The market value definition is still broad, because it can refer to the use of listing or transaction prices of comparable houses. An analysis (see subsection 3.2.3) should distinguish whether there is an economic difference between listing and transaction prices and whether this difference is economic significant.

3.2.3 Quantifying the Difference between Listing and Transaction Prices

A dataset with initial listing prices, listing dates, transaction prices, transaction dates, and zip-codes of 97,413 owner-occupied houses in the Netherlands between July 2010 and December

2012 is provided by DataQuote. The difference between listing and transaction date is the conciliation period; the amount of time that passes from listing to transaction. Descriptive information and a calculation of the differences in listing and transaction prices per quarter ( , t=2010Q3,…, 2012Q3) 30 are derived from this dataset (see table 1). Houses are registered in the year and quarter in which they were first listed. The outcomes of this calculation yield insight into how much the listing price is decreased (or increased) to arrive at the transaction price. The

Ministry of Justice in Israel (2003 in: Eshet et al., 2007) mentions that the diﬀerence between listing and transaction price is between 7% and 10% in Israel.

30 = ∗ 100% 39

Table 1: Descriptive information and calculation of the difference in listing and transaction price Date #Objects Price #Days in Diﬀerence listing and

conciliation transaction price ( )

Average Median Average Median Average ( ) Median ( ) 2010Q 21,436 260,052 220,000 428 385 -11.1% -10.1% 2010Q 6,031 250,501 215,000 273 224 -9.7% -8.5% 2011Q 25,125 240,292 210,000 243 203 -8.8% -7.7% 2011Q 6,111 225,280 200,000 250 218 -7.6% -6.5% 2011Q 6,476 217,984 194,000 224 202 -7.2% -6.1% 2011Q 6,059 222,161 194,500 191 180 -7.1% -6.3% 2012Q 7,162 216,120 190,000 151 136 -6.8% -6.2% 2012Q 13,457 223,916 194,000 116 106 -7.0% -6.3% 2012Q 4,493 209,619 184,115 98 95 -6.2% -5.4% 2012Q 1,063 198,300 182,500 56 59 -4.8% -4.1% Total 97,413 226,423 198,412 203 181 -7.6% -6.7%

This is also the case for the Netherlands, when calculating the average of the quarterly averages. Based on the dataset, the average ( ) and median ( ) diﬀerence decreases over time,

which might lead to the conclusion that appraisers have improved their valuations and/or that

price negotiations have become less eﬀective. Yet, there are three issues related to this conclu-

sion, which stem from the dataset.

First, the average and median conciliation period is also decreasing over time, which might contribute to the decrease in → and →. For example: a house that is listed in the third quarter of 2010 will sell, on average, 428 days later. Within these 428 days, several local, regional, national, and international trends could influence these houses, forcing its price up and/or downwards. To the contrary, a house that is listed in the fourth quarter of 2011 will sell, on average, 191 days later. This shorter conciliation period makes the initial listing price more up-to-date. The issue of different conciliation periods between cases can be addressed by filtering out market trends within these periods. The listing price is “updated” to the year and quarter in which the transaction took place. Because zip-codes are known, prov- 40

ince and COROP 31 number can be added from CBS data. In addition, CBS publishes quarterly price developments per province, per housing type per province and per COROP number. Hous- ing type per COROP number is preferred because it is the smallest scale. A script has been written which uses the price developments per COROP number and the price developments per province to derive by how much price developments per COROP are more or less than those of the province in which the COROP number is. As an example: houses in the Southeast of Noord-

Brabant (COROP number 36) have an index of 104.6 during 2011Q1, while the province Noord-

Brabant has an index of 103.1 during the same quarter. The script divides the index of the

COROP number over the index of the province, which is 1.0145, and multiplies this with the price developments per housing type per province to derive the quarterly price development per housing type for COROP number 36. At the time, housing type is unknown for all houses in the dataset, so attached (or row) housing is assumed because it occurs most frequently. However, continuous eﬀorts are made to add housing type. Once this is known, more accurate price developments can be derived. From the analysis (see table 2), it follows that, after the adjustment

( , t=2010Q3,…, 2012Q4), → and =20103→20124is lower than

→ and →, respectively. In addition, values of have a narrower range than and do not show the same decreasing trend over time. The median diﬀerence between listing and transaction price is preferred to reduce the eﬀect of outliers, which have a minimum and maximum , and of -30% and +20%, respectively. The

average of all , given by , is -4.6%, so extreme outliers may have a large impact on the average, resulting in the use of and not in this master thesis.

Has a standard deviation of 1.5% across the quarters. This yields a 95% conﬁ-

31 Regional classification of areas in the Netherlands that are bigger than municipalities and smaller than provinces, for the purpose of analytical and statistical research (CBS, 2012).

dence interval of -7.7% to -1.6%. Every value of =20103→20124 falls within the 95% conﬁ- dence interval.

Table 2: Difference in listing and transaction price initially and after update Date #Objects Diﬀerence listing and transaction Diﬀerence listing and transaction

price initially ( ) price after update ( )

Average ( ) Median ( ) Average ( ) Median ( ) 2010Q 21,436 -11.1% -10.1% -7.6% -7.2% 2010Q 6,031 -9.7% -8.5% -7.8% -7.0% 2011Q 25,125 -8.8% -7.7% -6.4% -5.7% 2011Q 6,111 -7.6% -6.5% -4.7% -4.0% 2011Q 6,476 -7.2% -6.1% -3.8% -3.1% 2011Q 6,059 -7.1% -6.3% -4.1% -3.6% 2012Q 7,162 -6.8% -6.2% -3.7% -3.1% 2012Q 13,457 -7.0% -6.3% -4.0% -3.4% 2012Q 4,493 -6.2% -5.4% -6.0% -5.3% 2012Q 1,063 -4.8% -4.1% -4.8% -4.1% Total 97,413 -7.6% -6.7% -5.3% -4.6%

Second, the price of a house might influence . Assuming that more expensive houses are more unique, appraisers might have a more difficult task to find comparable listing or transaction prices to base their valuations on. This could then lead to more uncertainty and, ultimately, a transaction price that more than average deviates from the listing price. To address the second issue, per price category can be analyzed. Three hypothetical transaction price classes have been constructed: low, middle, and high. Houses in the lowest class are sold for a price below €150,000. The middle class ranges from €150,000 to €350,000 and the high class constitutes transaction prices above €350,000. Results indicate that middle class houses constitute the

majority in the sample and that is the highest in the highest class, while fairly

comparable in the middle and lowest class (see table 3). Of the lowest and middle class are close to those of the total sample (-4.6%, see table 2). 42

Table 3: Descriptive information and calculation of the difference in listing and transaction price per category Date #Objects Price #Days in Diﬀerence listing and transaction

conciliation price after update ( )

Average Median Average Median Average ( ) Median ( ) 2010Q 3,509 124,761 128,500 447 409 -8.5% -8.1% 2010Q 953 126,978 132,000 285 238 -8.9% -7.7% 2011Q 4,693 125,915 130,000 247 204 -7.1% -6.1% 2011Q 1,382 124,070 127,500 237 196 -5.7% -4.7% 2011Q 1,609 124,027 126,500 215 185 -4.3% -3.2% 2011Q 1,471 125,574 130,000 183 170 -4.5% -3.5% Low Low 2012Q 1,807 124,186 127,500 147 134 -3.7% -2.8% 2012Q 3,409 124,416 127,500 109 100 -4.3% -3.2% 2012Q 1,291 123,410 125,000 91 89 -6.2% -5.3% 2012Q 317 123,251 125,500 54 56 -5.4% -4.0% Total 20,441 124,659 128,000 202 178 -5.9% -4.9% 2010Q 14,260 227979 220,000 425 383 -7.2% -6.8% 2010Q 4,222 224,586 215,000 270 221 -7.3% -6.5% 2011Q 17,299 223,877 215,000 242 201 -6.0% -5.4% 2011Q 4,137 220,531 212,000 252 222 -4.3% -3.7% 2011Q 4,302 219,598 210,000 226 203 -3.5% -2.9% 2011Q 4,025 217,958 210,000 192 182 -3.8% -3.5%

Middle Middle 2012Q 4,738 215,160 205,000 153 135 -3.5% -3.0% 2012Q 8,696 218,804 210,000 118 108 -3.7% -3.3% 2012Q 2,868 214,680 205,000 100 98 -5.8% -5.1% 2012Q 691 214,038 205,000 57 59 -4.5% -4.1% Total 65,238 219,721 210,700 204 181 -5.0% -4.4% 2010Q 3,667 514,238 450,000 419 383 -8.5% -8.1% 2010Q 856 515,843 455,000 274 230 -9.2% -8.5% 2011Q 3,133 502,256 445,000 243 208 -7.4% -6.7% 2011Q 592 494,735 445,000 261 233 -5.2% -4.5% 2011Q 565 473,267 426,000 236 217 -4.9% -3.9% 2011Q 563 504,571 442,500 199 187 -5.4% -4.9% High High 2012Q 617 492,737 425,000 155 142 -5.0% -4.3% 2012Q 1,352 507,686 440,250 122 115 -5.1% -4.7% 2012Q 334 499,374 435,000 101 99 -7.2% -6.4% 2012Q 55 433,132 400,000 60 62 -5.6% -4.9% Total 11,734 493,784 436,375 207 188 -6.4% -5.7%

This might be evidence for the proposition that houses in the highest class are harder to appraise or that price negotiation is more effective in this class. In addition, the low, middle, and high classes have standard deviations of 1.9%, 1.4% and 1.6% and 95% confidence levels of -1.1% to -8.6%, -0.7% to -8.1% and -2.5% to -8.9%, respectively. All values of =20103→20124 are within their 95% confidence interval.

Third, the conciliation period for houses in more recent quarters is biased because the dataset is based on recorded transactions. For example: houses in the fourth quarter of 2012 have an average conciliation period of 56 days. Because the dataset has only recorded those listings in the fourth quarter of 2012 that have led to actual transactions, these results should be interpreted with care. There are several other listings in the fourth quarter of 2012, which have not yet led to transactions. Therefore, regressing the diﬀerence between listing and transaction price on the number of days in conciliation cannot be done.

3.3 Relevant Values of Independent Variables under Subjectivism

Literature points out the divergence of values in diﬀerent disciplines (Vigaray and Hota,

2008). According to More et al. (1996), when trade-offs involve difficult choices, public policy presupposes that the trade-offs are made in a principled fashion. Objectivism has been the main principle because it relies on facts, which are, most often, easy to measure in a quantitative way.

However, economists have recognized that aspects related to subjectivism are also important

(Lusht, 2001 and Nolan, 1995). With regard to housing, values under subjectivism refer to qualitative aspects. More et al. (1996) mentions that two non-market economic values have been developed since the late 1960s: use value as the beneﬁts of a resource for those who use it, and non-use values that produces beneﬁts for those that do not use it. Examples of use value are environmental (or intrinsic) value (subsection 3.3.1), aesthetic appreciation (subsection 3.3.2),

and cultural heritage value (subsection 3.3.3). They subdivide (non-)use values into existence value; the value one receives from knowing a resource exists, altruism; vale derived from others using a resource, and bequest value; preserving a resource for future generations. They also acknowledge that these concepts are often not properly deﬁned. They are formed in our biologi- cal, psychological, and social systems, which are responsible for whom we are and our relationship to the world, knowledge structures acquired through past experience, and held values (More et al., 1996). Together, these systems are responsible for assigning a subjective value (More et al., 1996). Some of these values can be captured by independent variables in meta-analysis, which explains their relevance for discussing them with respect to the Dutch housing market.

3.3.1 Environmental Value

Environmental value has become inﬂuential in economical and ethical concepts. According to

Kennedy (1993), these ideas come forth out of a sense of limitation in a world population that is expanding. So, in the opinion of Kennedy (1993), environmental values function to preserve the status quo. Environmental value is a use-value, which in the most extreme case refers to the idea that nature (as a synonym for environment) has an inherent worth that makes it valuable in and on itself, irrespective of human beneﬁts or the cost that nature may create (More et al., 1996).

Callicott (1992) mentions that “What is worth arguing…is the proposition that non-human natural entities and nature as a whole may be valued not only for what they do for us, but…also for their own sakes as well” . Philosophers as Stone (1974) and Nash (1989) conclude that all natural resource are of equal environmental value. This raises doubts whether environmental value exists. Nagel (1986) argues: “I do not know how to establish whether there are any such values…The problem is to account for external values in a way which avoids the implausible conse- quence that they retain their practical importance even if no one will ever be able to respond to them” . Randall and Stoll (1983) give a solution by deﬁning Q-altruism as the satisfaction that 45

one derives from knowing that the resource itself benefits from being preserved. Satisfaction is a vague term in the definition and it is also difficult to measure. More et al. (1996) and Attfield

(1998), mention that satisfaction refers to pleasure, which is an emotional state that comes with the fulﬁllment of some function or goal. According to More et al. (1996), if nature has value and moral worth of its own, then it is wrong to let go something out of existence.

Several approximations of environmental value can be assessed as a monetary amount by analyzing its relation with house prices. Because of the diﬃculties of measuring environmental quality (and Q-altruism), several approximations of environmental value have been developed.

Brasington and Hite (2005) use a dataset with characteristics of 44,255 houses in six metropolitan areas in Ohio, US. They enrich the data with neighborhood information and environmental hazards, where hazards are defined as waste management contamination sites. They find that proximity to environmental hazard has a small significant (at the 5% level or less) relation with average house price; increasing the distance of a house from the nearest environmental hazard by

10%, increases the average house price by 3%. So, proximity to contamination sites has a disvalue, and the monetary amount can be derived from its effect on housing prices. Other studies in the US report relationships between house prices and air pollution (e.g. Freeman, 2003; Beron et al., 2001 and Chattopadhyay, 1999) and water pollution (Hoehn et al., 1987). Hui et al. (2007) use several proxies for environmental quality in Hong Kong and find that sea view and air quality is statistically significant at the 5% or less. Greenbelt policies 32 are not statistically significant

and the noise level is positively correlated with house price, reﬂecting the dense living environ-

ment in Hong Kong. To the contrary, Espey and Lopez (2000) ﬁnd a negative correlation be-

tween noise and house prices with their study in Nevada, US. Tyrväinen (1997) acknowledges

that urban forests may represent use values, where pleasure is derived from the landscape, clean

32 Policies to protect nature surrounding urban areas and to improve air quality

air, peacefulness, screening, and the potential for recreational activities. By using meta-analysis on sales data of 1,006 apartments in Joensuu, Finland, in combination with environmental quality variables, results indicate that proximity to watercourses and wooded recreation and the proportion of forested area has a significant (at the 5% level or less) positive influence on apartment price, while the effect of small forest parks was not statistically significant. To the contrary, Jim and Chen (2010) find that the presence of small neighborhood parks is the third most important attribute of an apartment in Hong Kong, after gross area and the respective floor level.

All of these researchers point out the diversity of proxies that can be used for environmental quality. Overlapping variables, which are statistically significant at the 5% level or less, refer to air and water quality, pollution, view, and distance to nature. However, environmental quality variables could reflect several other variables, such as the impact of mining activities on property values (see Neelawala et al., 2013). There are differences between cities in different countries, which are attributable to a variety of factors. Together with the fact that there is a lack of comparable studies in the Netherlands, this makes it difficult to imply which environmental quality variables should be used in meta-analysis.

3.3.2 Aesthetic Value

Nolan (1995) mentions that three of the most widely accepted criteria for determining whether an object has aesthetic or artistic value are: the object (could be a house) is intended to be a work of art by the maker, the maker is an artist and important or recognized “experts” label the object as having aesthetic value. Examples of aesthetic value include beauty, elegance, harmony, simplicity, clarity, and familiarity (Schummer et al., 2009). Hutter and Shusterman (2006) distinguish between ten kinds of value that seem to be nested in artistic value: moral or religious

vision, expressiveness, communicative power, social and political value, cognitive value, experien- tial value, aesthetic values, art-technical value, art-historical value, and artistic cult value. A description of these values surpasses the purposes of this thesis, because all of these values are vague in terms of deﬁnition and measurement.

With regard to housing, people have different opinions on what they think possesses aesthetic value. This calls for uniformity because, if most people think differently, estimating aesthetic value is impossible. It is assumed that aesthetic value judgments can be deducted by analyzing trends in architecture. Architects design houses that are considered beautiful, elegant, simple, familiar, etc. by most people. Otherwise, nobody will buy the designed houses by the architect, which forces him or her out of business. Schummer et al. (2009) mention that aesthetic values in the field of architecture and urban planning have been largely shaped by the modern movement in architecture and planning in the late 19 th and 20 th centuries. The pioneers of the movement got their inspiration from purely functional forms and built forth on the early work from Ruskin

(Giedion, 1941). Ruskin (1849) deﬁned architecture as the aesthetic aspect of a building. Since aesthetic content is unnecessary for the main purpose of housing (provide functional space for living), Ruskin concluded that also architecture is not needed: “Let us conﬁne the name to that art which, taking up and admitting the necessities and common uses of the building, impresses on its form certain characters venerable and beautiful, but otherwise unnecessary, that is Archi- tecture” . Modernists challenged the view that purely functional buildings and forms could not be works of architectural art and on the other hand that architecture requires ornamentation and

“style” to be architecture (Schummer et al., 2009). As a result, early modernists such as Sullivan

(1896) suggested that the form of buildings should follow their function. With regard to aesthetic ornaments he wrote: “I should say that it would be greatly for our aesthetic good if we should refrain entirely from the use of ornament for a period of years, in order that our thought might concentrate upon the production of buildings well-formed and comely in the nude” (Sullivan, 1892 48

in: Schummer et al., 2009). Around the 1930s, many architects were so particular about aesthetics of modern architecture, that the “International Style” emerged in Europe and North America, making modern architecture instantly recognizable (Schummer et al., 2009). Architectural forms were “pure” geometrical, which Le Corbusier (1927 in Schummer et al., 2009) deﬁned as primary forms: “Primary forms are beautiful forms because they can be clearly appreciated. Architecture is

the masterly, correct and magniﬁcent play of masses brought together in light. Cubes, cones,

cylinders or pyramids are the great primary forms which light reveals to advantage. It is for this

reason that these are beautiful forms, the most beautiful forms” . From the 1960s and onwards,

there was vigorous critique against the modernistic style in architecture and planning (Jencks,

1980 and Schummer et al., 2009), leading to alternative aesthetic values. Great schemes of “slum

clearance” , “comprehensive redevelopment” , social housing and developing new cities were regard-

ed as having failed to create the functionally eﬃcient and aesthetically beautiful utopias that the

pioneers of modernism had dreamed of when designing them after the Second World War. Ra-

vetz (1980) referred to this as the modernist “clean sweep” because many buildings that were constructed according to this modernism view were either transformed or destroyed (devaluation). Schummer et al. (2009), mention that repetition of the same primary forms across large tracks of towns was seen as monotonous and alienating. Venturi (1977 in Schummer et al., 2009) reacted to the International Style as: “I like complexity and contradiction in architecture. Every- where, except in architecture, complexity and contradiction have been acknowledged. Architects can no longer aﬀord to be intimidated by the puritanically moral language of orthodox modern architecture. I like elements that are hybrid rather than ‘pure’, compromising rather than ‘clean’, distorted rather than ‘straightforward’, ambiguous rather than ‘articulated’. I am for messy vitality over obvious unity. I am for richness of meaning rather than clarity of meanin” . Ven- turi’s contradiction became the inspiration for post-modern architecture (Schummer et al.,

2009). 49

In short, modern architecture assigns aesthetic value to pure forms and undecorated surfaces

(minimalistic) while post-modern architecture assigns aesthetic value to a mix of building styles, some more progressive whilst others more conservative. Schummer et al. (2009), mention that aesthetic quality of buildings is important, but that aesthetic judgment is a matter of subjective personal taste. However, a style in architecture (e.g. International Style) could explain aesthetic value in a more objective way by referring to facts (e.g. forms, colors, functions). When the

“rules” and norms of such a style are widely accepted, a way of judging aesthetic value in a principal way is developed. Failures to do so give rise to contradictory styles that challenge the assumptions of the current style, which may ultimately lead to a devaluation of houses in the current style. This makes aesthetic value judgments diﬃcult to account for, as there is no rule for what objects are considered to have more aesthetic value than others (Kaplan, 1966). Hutter and Shusterman (2006) mention that economists excluded aesthetic value judgments from consideration until the second half of the 19 th century when Jevons proposed a way to measure utility in objects. He positioned aesthetic experiences under “sympathy” ; a state where the conditions of commercial society do not hold. Under this state, the mind is removed from its ordinary course of duties, making aesthetic experiences either pleasurable or painful (Hutter and Shus- terman, 2006). Hutter and Shusterman (2006) then argue that Jevons and other major authors of the following generations did not give aesthetic value a special role as use value.

3.3.3 Cultural Heritage Value

Tweed and Sutherland (2007) mention that National governments and European ﬁrms increasingly recognize the value of cultural heritage (e.g. Council of Europe Framework Conven- tion on the Value of Cultural Heritage for Society). According to Nuryanti (1996), heritage is anything associated with the word inheritance, which is a thing that is transferred from one

generation to another. Within this deﬁnition, bequest value is acknowledged to be part of cultural heritage value. Howard and Pinder (2003 in: Al-hagla, 2010) argue that heritage includes architectural and historical values, and that these values makes them worthy of conservation.

Tweed and Sutherland (2007) mention that such a deﬁnition, based on architectural and historical value is too narrow because it focusses solely on the object itself and not on the cultural values in the community. They argue that the identity of built cultural heritage should be con- served because globalization leads to increasingly multi-cultural societies, which leads to extreme homogenizing pressures. By acknowledging the interdependence with surroundings, cultural heritage value can be strengthened or weakened. With regard to the built environment, Tweed and Sutherland (2007) mention that heritage is crucial to community and its cultural identity.

Lunch (1960 in Tweed and Sutherland, 2007) mentions a community’s perception of its surroundings is relevant: “ Every citizen has had long associations with some part of his city, and his image is soaked in memories and meanings ”. Rapoport (1982) breaks down meanings into functional meanings, those that communicate power, identity, wealth, and status, versus those that are related to fundamental beliefs.

Tweed and Sutherland (2007) gain insight into the value of cultural heritage by doing 464 interviews in Belfast (United Kingdom), Liège (Belgium), Copenhagen (Denmark) and Prague and Telč (Czech Republic). Survey results indicate that perceived quality, as measured by visual activity (e.g. architectural and built elements) and ambient concepts (e.g. sunshine, quietness, light or colours), are found to be the most important for cultural heritage value. They ﬁnd that architectural value and historic values explain cultural heritage value, but so does the community via ambient concepts.

Ruijgrok (2006) concludes that, for cultural housing heritage in Tieler and Culembor- gerwaard, the Netherlands, conservation surpasses the costs. To reach this conclusion, cultural heritage value is broken down into housing comfort value, recreation value, and bequest value. 51

In short, cultural heritage value has several dimensions. Research agrees that historical and architectural values are part of cultural heritage, but there are diﬀerent opinions with respect to bequest value and the value that is assigned by the community. Vecco (2010) mentions that the selection criteria of cultural heritage have changed. Initially, in the 1980s, historic and aesthetic values were the only proxies for cultural heritage value. Since then, cultural value and value of identity and the capacity of the object to interact with the memory of people were added, making it more subjective (Vecco, 2010). This was characterized by recognizing the intangible parts of cultural heritage value. In addition, it is hard to account for each of the dimensions because of information unavailability and diﬃculties in measurement. This view is shared by Throsby

(2003), who mentions that cultural value is hard to measure because it is multi-dimensional, unstable, contested, lacks a common unit of account and contains elements that cannot be ex- pressed.

3.4 Sub-conclusion

This chapter answers the sub-questions “what is value?” and “which values are used in practice?” with respect to the Dutch owner-occupied housing market. Judgments can be either factu-

al (descriptive statements) or evaluative (appraisals and feelings). Value judgments are subjec-

tive by nature, but can be backed by facts, blurring the initial distinction. Two of the most

extreme subdivisions of value judgments are objectivism and subjectivism. Objectivism restricts

the meaning to those statements that involve reference to veriﬁable data. If data cannot be

veriﬁed, it is worthless by deﬁnition. As such, an object is decomposed in sensory experiences.

According to subjectivism, value statements only express sentiments or emotions. Something is

valuable if it evokes pleasurable feelings and maximizes a person’s utility. This view concludes

that every valuation is subjective by nature. With regard to the Dutch owner-occupied housing

market, this is relevant because judgments can refer to facts (e.g. surface, building year, and distance to nearby shops), but also to subjective values (e.g. safety of the direct vicinity). Econ- omists have recognized that subjective values are also important.

Literature refers to the use of different types of values in different disciplines. Also, each discipline has its own definitions and measurement techniques of values. This becomes problem- atic in an interdisciplinary context, such as housing. Amongst others, economic, social, and political disciplines interact in this context, making valuation difficult. This is exactly what is confirmed by literature on housing. Value is a broad concept, which needs to be decomposed. A decomposition between economic values and non-economic values is possible. Economic values relate to the dependent variable, whereas non-economic values relate to independent variables in the hypothetical conceptual model. Economic values can be divided into individualistic and collective economic values. Relevant individualistic economic values include use value, investment value, and insurable value. Individuals use these values to estimate what their willingness- to-pay is for a particular property. An advantage is that such an estimate directly gives a point- estimate of the worth (price) of a particular object to a buyer. However, this point-estimate does not give the seller any indication whether this price is fair, high or low. To overcome this problem, the willingness-to-pay of a collection of buyers should be analyzed. This leads to the collective economic values: market value, liquidation value, and assessed (or WOZ) value. Liquidation and assessed value can be derived from market value. The definition of USPAP is used by most appraisers, unless (local) governing law and jurisdictional rules impose their definition. However,

USPAP’s deﬁnition assumes a competitive and open market, whereas the Dutch owner-occupied housing market is a search market. This assumption has been released in the deﬁnition of the

International Valuation Standards Council. According to this deﬁnition: “market value is the

expected selling price of the speciﬁed real property rights in arm’s-length transaction, as of the

date of the appraisal, and assuming a reasonable exposure to the market” . Arm’s-length transac- 53

tion presumes that the buyer and seller are motivated, well informed, and act reasonably in their own self-interest without undue duress. While individualistic economic values are highly dependent on a particular person and thus highly subjective, market value is a same-for-all number. It is superior to individualistic economic values and clear in its deﬁnition. It is also the most used value by appraisers. Market value of houses is the value that will be analyzed for the dependent variable in this master thesis. Yet, it is unclear whether “expected selling price” refers to listing

or transaction price. Analyzing listing and transaction data quantiﬁes the link between the two.

The main conclusion is that appraisers have not improved their valuations and/or price negotia-

tions have not necessarily become less eﬀective throughout the time period 2010Q3 until

2012Q4. This result becomes clear when adjusting the listing price for market trends in the

conciliation period. is -4.6%, with a standard deviation of 1.5%. This average

ﬁgure can be further broken down for houses in diﬀerent transaction price classes. Those in the low (L) and middle (M) class, more or less, resemble the average of -4.6%, but houses in the

highest (H) class have a higher discount: = -5.7%. This could be evidence that

houses with transaction prices above €350,000 are harder to appraise than those in the low and

middle classes and/or that price negotiations are more eﬀective for houses in the highest class.

A problem within the listing and transaction analysis is that market trends are ﬁltered out to

make listing and transaction price truly comparable. This adjustment is based on the concilia-

tion period, which is unknown when a house is listed. As a result, given listing prices cannot be

translated into point estimates for transaction prices with 100% certainty. Average or median

conciliation periods of previous quarters can be used to calculate a range of possible transaction

prices, given a listing price. However, only completed transactions are visible in the dataset, so

houses that are listed but have not yet been sold do not matter. These unrecorded houses in-

crease the average and median conciliation period, which imposes especially a problem for more

recent quarters. From these arguments, it can be concluded that transaction prices are preferred over listing prices because they are a direct observable result, involving interaction, of participants in the housing market. Transaction prices are actual observations for willingness to pay, while listing prices are appraisals that estimate a value on which to base negotiations. Apprais- als can deviate in value from the transaction price, which might be attributable to the knowledge and experience of appraisers, but also to the inﬂuence of price negotiations.

Non-economic values, which could be present in independent variables, such as environmental, aesthetic, and cultural heritage value are hard to estimate. Literature points out the diversity in deﬁnitions and variables that can be used to measure these values. Because these values are highly subjective, some studies contradict other studies. In addition, research on the Dutch owner-occupied housing market is limited. To overcome the subjectivity, empirical research measures variables such as livability of a neighborhood, with objective variables such as distance to shops and local density of the population. An advantage is that these objective variables still capture (part of) the subjective variable.

MEASURING VALUE AND COMPARABLE STUDIES

Market value can be captured by diﬀerent methods. Bateman et al. (2002) states that total economic value can be subdivided into use and non-use values and that each of these values has a variety of techniques to capture monetary worth. Considering use values, total economic value can be captured through revealed preferences, while non-use values can be captured through stated preferences. By analyzing the advantages and disadvantages of revealed and stated preferences, methods can be selected in accordance. The scope of this master thesis is restricted to analyzing models, based on the measurement technique that will be chosen, that reveal the relevant attributes of market value of houses (see chapter 3) in a country. Relevant attributes may serve as a basis to construct a pricing model for owner-occupied housing in the Nether- lands. Because relevant studies involve foreign housing markets, results of these studies should be interpreted with care due to a potential lack of comparability with the Dutch housing market. Therefore, this chapter serves merely as a guideline for identifying, collecting, and opera- tionalizing data for housing in the Netherlands (see chapter 5). Comparable pricing studies on housing have been found in various European and non-European areas (see ﬁgure 3). Most of 56

them are interested in the coefficient and statistical significance of one variable. However, to derive this coefficient, the model has to be as complete as possible (i.e. including all other relevant variables that drive market value).

Because few studies are on a country-wide basis, studies that are more local will also be analyzed. Zip codes, or their equivalents in other countries, are often used in local studies as variables to capture all neighborhood information. This makes local studies more accurate than country-wide studies because of data unavailability on several neighborhood attributes in country-wide models. However, local studies cannot be used to derive national trends.

Figure 3: Relevant country-wide, district and city models in other countries

The remainder of this chapter is organized as follows: ﬁrst, measurement techniques will be discussed and a method is chosen that will be used for constructing a model in section 4.1.

Then, relevant studies that use the same method and have a country focus will be discussed in section 4.2. In accordance, section 4.3, and 4.4 analyze relevant studies in districts and cities.

This section concludes with a discussion on the chosen method and relevant attributes in section

4.5, which serves as a potential input (based on data availability) for a model for owner-occupied housing in the Netherlands (chapter 5).

4.1 Measurement Techniques

There are two trades-oﬀs to be made. First, a distinction and choice between revealed and stated preferences is needed (subsection 4.1.1). The choice determines the measurement techniques that are available, which will be discussed in accordance (subsection 4.1.2 and 4.1.3).

From these measurement techniques, a method will be chosen, so that comparable models can be analyzed.

4.1.1 Revealed versus Stated Preferences

The ﬁrst trade-oﬀ is between using revealed preference (RP) methods or stated preference

(SP) methods. The difference is that RPs involve actual behavior, whereas SPs constitutes hypothetical behavior. SP methods have the advantage that they can measure both use and non-use values, while RP methods can only measure use values. In this regard, SP models provide more complete information. An additional advantage of SP methods is the ability to present interviewees with scenario’s and tradeoffs that do not currently exist, forcing them to state their willingness to pay. Disadvantages of SP methods are that hypothetical tradeoffs are different from actual behavior. For example: interviewees can state an extreme, but otherwise unreal- istic, willingness to pay to drive the results of surveys in certain directions. In addition, stated preference methods rely on surveys. The research question of this master thesis is on a national level, making usage of surveys difficult. In addition, the research question concerns actual behavior in a real market (Dutch-owner occupied housing market), rather than hypothetical behavior

and data provided by DataQuote stems from actual behavior. In more local studies, the combination of RP methods and SP methods would provide the best alternative, but in this master thesis; RP methods have been chosen over SP methods. The second trade-oﬀ is between diﬀerent

RP methods. There are three main RP methods: the Travel Cost Method (TCM), Averting

Behavior Method and the Hedonic Pricing Method (HPM).

4.1.2 Travel Cost and Averting Behavior Method

The TCM is one of the earliest forms to value goods that are not tradable in a market(Banzhaf, 2010). It assumes that persons are willing to pay transport costs, entry fees and other expenditures to visit a site (Damigos, 2006; Jim and Chen, 2010; Hanley and Spash, 1993;

Kong et al., 2007; Fleming and Cook, 2008). Higher-quality sites attract more visitors and visitors from a greater distance (Banzhaf, 2010). Damigos (2006) attributes the money spent to recreational value of the site, and states that, in case of several observations, recreational value results from an estimated demand curve. Clawson and Knetschm (1966) refer to this demand curve as the relationship between the number of visits and the price of admission. According to

Fleming and Cook (2008), the demand curve is weak complementarity, meaning that if expenditures fall to zero, the marginal utility 33 of visitation is also zero.

With regard to the ABM, research is limited. Cropper and Oates (1992 in: Whitehead et al.,

1998) mention that the ABM assumes that people make choices to maximize their well-being.

According to Aravossis and Karydis (2004), the ABM assumes that marketed goods can be substitutes for environmental goods and that in times of a decline in environmental quality, expenditures can be made to mitigate these results. This reveals the link between expenditures to oﬀset environmental impacts. Whitehead et al. (1988) mention that willingness to pay is

33 The additional satisfaction a person gains from consuming or visiting goods or sites. A positive marginal utility means that the consumer gains from consuming or visiting an additional good or site.

measured by averting expenditures (i.e. self-protection measures), which are needed to counteract harmful eﬀects.

All in all, the TCM and the ABM can be used to assess the monetary worth of environmental

(or recreational) value. Whereas the TCM assumes that visiting a site evokes pleasurable feelings to visitors, which are worthy the money spent (Riera, 2000), the ABM assumes that people increase their well-being by making expenditures to oﬀset environmental impacts (Cropper and

Oates, 1992 in: Whitehead et al., 1998). Using one or both approaches for the Dutch owner- occupied housing market, environmental and recreational amenities can be valued. Then, the eﬀects of environmental and recreational values could be taken into account when appraising market values of houses. However, the connection between travel cost and averting behavior, via environmental and recreational value towards a percentage of market value of owner-occupied houses in the Netherlands is indirect and several issues arise. One is the treatment of travel time in the TCM. Garrod and Willis (1992) acknowledge that decisions of recreation are not solely based on travel costs but also on time availability. Whether this should be a ﬁxed percentage of wages is debatable (Damigos, 2006). In addition, there are issues with respect to allocation of travel costs in multipurpose trips, “zero cost” visitors (e.g. children and overseas visitors) and changes to environmental quality (Damigos, 2006). Randall (1994), states that the travel cost is inherently unobservable and subjective as it may vary across individuals for driving cost (e.g. vehicle size and fuel) and the cost of travel time. Englin and Shonkwiler (1995) and Cesario

(1976) propose to take into account distance and travel time measured by wage per hour as indicators for travel cost. However, Bockstael et al. (1987 in: Ovaskainen et al., 2012) concludes that the wage-based rule leads to disequilibrium in the labour market (e.g. people who do not work). Whether or not travel time should be approximated by wage rate, and whether this variable should be taken into account, remains a topic of debate (Ovaskainen, 2012; Fleming and

Cook, 2008). Also, the TCM has been used in urban situations (e.g. Dwyer et al., 1983), but 60

often does not work for neighborhood recreation resources because of limited variations in travel cost (Jim and Chen, 2010; Kong et al., 2007). Finally, the TCM value is dependent on the interviewees (Voke et al., 2013). Local residents have very low travel costs and these values might not be a correct representation of their valuation of the site. Voke et al. (2013), mention that the reverse might be true, especially when several generations have lived in the direct vicinity of a site. This contradicts the assumption that value generated by visits to the site is a function of expenditures. Regarding the ABM, a disadvantage is that marketed goods that serve as a proxy for a decrease in environmental quality diﬀer per person. This makes it hard to reach at a point

ﬁgure of monetary worth for a decrease in environmental quality.

4.1.3 Hedonic Pricing Method

According to Lancaster (1966), utility is generated by attributes of goods and not by the goods themselves per se. According to Jim and Chen (2010), this has led to the development of the HPM 34 , which was further developed by Rosen (1974) and Freeman (1974, 1979 and 1985).

Utility maximizing, pleasure, delight, feelings, and experiences, can be traced back to the view of value as subjective (subjectivism, see subsection 3.1.2), which states that something is valuable if it evokes pleasurable feelings and maximizes one’s own utility. Assuming the HPM, an object can be decomposed by its attributes and market value of houses can be approached by analyzing the role of these attributes: = (, , … , ). For example: if two houses are identical, but one house is located in the direct vicinity of a recreational site while the other is not, the house

34 According to the Stanford Encyclopedia of Philosophy (2004), the word “ hedonic” comes from the Greek word hēdonismos , meaning “delight” or “pleasure” . It states that only pleasure or pain motivates people because pleasure contains value while pain contains disvalue. It further describes pleasure broadly, including all pleasant feelings or experiences (e.g. ecstasy, delight, and enjoyment) and pain as all unpleasant feelings or experiences (e.g. aches, irritations, anxiety, discomfort, depression, guilt, and remorse).

in the vicinity of the recreational site may be valued higher, assuming that this vicinity is related to a higher livability, which leads to a higher utility. By using the HPM, people implicitly reveal their willingness to pay for vicinity to a recreational site. Jim and Chen (2010) and

Damigos (2006) mention that the implicit price of each characteristic can be derived by taking the partial derivatives of the market value with respect to the variables.

According to Cassel and Mendelsohn (1985 in: Jim and Chen, 2010), Malpezzi (2002 in: Jim and Chen, 2010) and McConnell and Walls (2005 in: Jim and Chen, 2010), several forms have been developed to measure the relation between market value and an object’s attributes, including linear, semi-logarithmic, double logarithmic, quadratic, and reciprocal forms. Jim and Chen

(2010) and Damigos (2006) conclude that, in their sample, the log-linear form: ln = +

+ + + , where S, N, and L are structural, neighborhood, and locational attributes,

has a better statistical ﬁt than other forms. By using the HPM, values of these attributes can be

estimated from market values of related objects.

An issue related to the HPM (and all other revealed preference methods) is the exclusion of

non-use values (Garrod and Willis, 1999; Banzhaf, 2010). In addition, extensive data is needed,

which may be diﬃcult to collect due to data constraints (Damigos, 2006). The hedonic model is

often dependent on many variables, which should be collected for each observation. Too few

independent variables will lead to inaccurate models. Oppositely, too many variables impose

problems with multicollinearity 35 (Tyrväinen and Miettinen, 2000).

The HPM is often applied to the housing market to identify attributes of housing value (e.g.

Jim and Chen, 2010; McLeod, 1984; Geoghegan et al., 1997; Tyrväinen and Miettinen, 2000;

35 Montgomery and Runger (2007) define multicollinearity as: a condition that can occur in multiple regression when some of the independent variables are nearly linearly dependent (perfect correlated). This can lead to instability in the estimates of the regression model.

Damigos, 2006). Brasington and Hite (2005), mention that the HPM is still underutilized in urban and environmental economics.

4.2 Comparable country-wide models

Similar and related country-wide models have been found for the Netherlands, Turkey, Israel, and Japan. With regard to the housing market in the Netherlands, Brounen and Kok (2011) estimated a hedonic pricing model to test the market adoption and economic implications of energy performance certiﬁcates. Data from the Dutch Association of Realtors (NVM) on 177,318 houses, which were sold between January 2008 and August 2009, has been used. This data has been merged with neighborhood data from the CBS. The model they estimate is: Pr() =

+ + + + + , in which is a binary variable that is 1 if dwelling i has an energy performance certiﬁcate and 0 otherwise (a logit model). is a vector of physical

characteristics of the dwelling: dwelling type (apartment, duplex, semi-detached, and detached),

log of dwelling size, age (1931-1944, 1945-1960, 1960-1970, 1971-1980, 1981-1990, 1991-2000, and

>2000), and dummy variables for monuments, central heating, insulation quality and exterior

maintenance. is a vector of characteristics of the neighborhood n on the zip code level: log of

housing density, log of average time on market and log of average monthly household income. is deﬁned as the fraction of votes for “green” parties in city c, during the 2006 national elections and is a dummy variable with a value of 1 if the dwelling belongs to province p and 0 other-

wise. Is the intercept and , , , and are regression coeﬃcients. Brounen and Kok (2011)

ﬁnd that, in their model with the best ﬁt (highest R²), all variables, except the duplex and semi-

detached dwelling type, monument, central heating, insulation quality, and exterior mainte-

nance, are statistical signiﬁcant at the 5% level or less. They progress by estimating the eﬀect of

an energy performance certiﬁcate on the transaction price per square meter (ln ). They use

data on 31,993 dwellings after 2000 that may be likely to have such a certiﬁcate and they esti-

mate: ln = + + + + + + . In this model, is a dummy varia-

36 ble for the energy label (ranging from A to G) and is the inverse Mills ratio , which is estimated by Heckman’s two-stage estimation procedure 37 . In this model, age has been excluded because all houses are post-2000 (last age category). Within , the number of rooms (as a

continuous variable) is included. In this model, all variables, also including the statistically

insignificant variables from the first model, are statistical significant at the 5% level or less and

the R² is 52.7%.

Regarding the Turkish housing market, Selim (2009) aims to examine the determinants of

house prices, by using data from the 2004 Household Budget Survey Data 38 . His model has 5,741 cases, of which 3,868 are located in urban areas, while 1,873 in rural areas. A disadvantage of the study is that it does not account for sample selection bias, so the hedonic model might not be representative for the entire housing market in Turkey. As an advantage, 46 variables or dummies are known for the sample: locational characteristic dummy (urban or rural), type of housing (detached, semi-detached, basement, apartment, shanty house or duplex), age (0-5, 5-10,

10-15, 15-20 or 20+), building type, type of saloon floor, type of living room floor, type of bathroom floor, type of heating system, number of rooms, size categories (square meters), and other structural characteristics 39 . Selim (2009) states that environmental characteristics could not be considered, but does not provide a reason. The model that is estimated is: ln = + ,

36 A variable to account for sample selection bias, which stems from a non-random sample of the population.

37 See Heckman (1979).

38 National survey that focusses on a household’s living conditions by analyzing its expenditures on goods and services. Data is collected by interviews and diaries of the households on a daily basis (European Commission, 2013).

39 Includes dummies for the presence of a sauna-jacuzzi, toilet, garbage grinder, water system, hot water, cable television, elevator, garage, pool and natural gas.

40 where P is the price of house i , a vector of independent variables and is the error term.

Results indicate that the locational characteristic, type of housing, age category 5-10 years, building type (excluding brick), saloon floor type (excluding floor tile, and carpet, mosaic or marble), living room floor type (excluding board), bathroom floor type, type of heating system, number of rooms, size, and all other structural characteristics are statistically significant at the

5% level or less. Selim (2009) also calculated the percentage eﬀects of independent variables on house prices and found that several other structural characteristics (toilet, garbage grinder, water system, cable television, and a pool), housing type, the presence of 4 or more rooms, and a size of more than 150 m² together have an absolute impact of more than 30% on housing price.

Considering the housing market in Israel, Eshet et al. (2007) do research on local perceived discomfort that is experienced by residents that live close to transfer stations 41 . By using the

HPM, the impact of these disamenities on property values can be modeled. They mapped all active transfer stations in Israel and found 10 sites that were close to residential areas. For 4 cities (Bet-Shemesh, Rehovot, Rishon-Leziyyon and Petah-Tiqwa), satisfactory data was available (Eshet et al., 2007). Bet-Shemesh is in the direct vicinity of Jerusalem and the other three cities surround Tel-Aviv. Based on this topography, the authors generalize results to the whole country. However, sample selection bias may still be present. Data is obtained via interviews, from the Ministry of the Environment, the CBS of Israel and a commercial real estate database.

This has resulted in 9,505 houses for sale in the 4 cities, between 2001 and 2004. The listing price of these houses serves as the dependent variable. The model, which Eshet et al. (2007)

40 Presumably the (expected or most probable) listing or transaction price because data comes from a household survey.

41 Eshet et al. (2007) define a transfer station as a facility that receives and holds waste from collection vehicles until it can be moved to other facilities.

found to have the best ﬁt (R² of 72.1%) is: ln () = + + ² + + + +

+ + + , where the independent variables refer to the distance (in km) to a trans-

fer station, squared difference to a transfer station, floor size (in m²), cluster number 42 , housing type (dummy for apartment or single family-home), garage dummy, elevator dummy, and the age of house i (dummy for before 1990, and after 1990), respectively. These variables are statistically significant at the 5% level or less. Housing type, the presence of an elevator, and age are the variables with the highest coefficient (apart from the constant term). According to Eshet et al. (2007), variables that were statistically insignificant are the number of rooms (collinear with size) and the distance to the central business district (because traditional CBDs have lost their major importance for individuals).

With regard to the Japanese housing market, Naoi et al. (2009) research whether earthquake prone areas lead to price discounts of nearby houses. They use data from the Keio Household

Panel Survey from 2004 to 2007, which involves answers to questionnaires from 4,005 respondents annually. This resulted in 7,039 unique cases. Respondents either live in a rental or an owner-occupied house. The dependent variable is the actual monthly rent paid or the self- assessed value 43 (Naoi et al., 2009). The hedonic regression model that they estimate is: ln ( ) = + + + + + + , where the dependent variable is the natural logarithm of the actual monthly rent or self-assessed housing value of household i in time t, is the earthquake risk variable, is a dummy that indicates whether there was a massive earthquake in the previous year, includes housing characteristics and characteristics of the respondent of the survey: number of rooms, ﬂoor space, garden space, building year, distance to nearest station and bus stop, construction type, presence of barrier-free equipment

42 Measures the socio-economic level of the neighborhood on a scale from 1 to 20 (where 20 is the highest rank) and includes: age, size of household, housing density, education and employment characteristics, and income.

43 Derived via the question: “How much do you think this house would sell for on today’s market?”

for elderly, city size, age of respondent, age of respondent squared, sex, marital status, educa-

44 tion, employment status and annual earnings, compromises for individual heterogeneity , is a time trend, and is the error term for household i on time t. Results indicate that, for

owner-occupied housing, the time-distance to the nearest railway station/bus stop is statistically

signiﬁcant at the 10% level, the interaction term is statistically signiﬁcant on the 5% level and

the age of the building, the number of rooms and the presence of barrier-free equipment for

elderly are statistically signiﬁcant at the 1% level (Naoi et al., 2009). This is with the inclusion

of respondent ﬁxed eﬀects, respondent characteristics, dwelling type dummies, city-size dum-

mies, and year dummies. The R² for owner-occupied housing is 14.7% (Naoi et al., 2009).

All in all, there is no consensus on the dependent variable. From the four comparable country-

wide studies, the dependent variable can refer to the transaction price per square meter (Brou-

nen and Kok, 2011), the most probable transaction or listing price according to homeowners

(Selim, 2009; Naoi et al., 2009) or the listing price (Eshet et al., 2007). This issue of an improper

deﬁnition of the dependent variable, which is meant to measure “market value” , leads to inade-

quacies between studies. Also, data constraints may lead to forced exclusions of certain variables

that may be statistically signiﬁcant in other studies. The comparable studies all ﬁnd that hous-

ing type, age, and size are statistically signiﬁcant at the 5% level or less. Also, all studies involve

locational characteristics, which might refer to housing density, average time on market, and

monthly household income in a zip code (Brounen and Kok, 2011), a dummy for province

(Brounen and Kok, 2011), a dummy for a urban or rural house (Selim, 2009), a cluster rank

(Eshet et al., 2007) or the ﬂoor on which a room is located (Naoi et al., 2009). In addition, all

studies include other structural characteristics that are measured with dummies, such as: mon-

umental status, living room ﬂoor type, elevator presence, garden presence or pool presence.

44 The variation in study outcomes between different studies.

4.3 Comparable district models

Similar and related district models have been found for Midden-Delﬂand 45 (Netherlands),

Auckland (New Zealand), Connecticut (USA), the “Leafy Eastern Suburbs” of the eastern Ade- laide plains (Australia), Singapore (Republic of Singapore), and southern Taiwan (Taiwan).

Regarding Midden-Delﬂand, Cotteleer and Peerlings (2011) do research on the impact of spatial planning procedures (i.e. policy plans and decisions on the construction of the highway A4) on housing prices in the period 1996 to 2006. The database of the Dutch Association of Real Estate

Agents has been used. After excluding transactions of houseboats, mobile homes, recreational properties, large rural estates, houses that were bought as investment, multiple transactions of the same house, and transaction of over €9,075,150 and below €11,345, 58,839 cases remained.

Cotteleer and Peerlings (2011) acknowledge spatial lag 46 and error dependence 47 in their model:

=++, where P is a vector of house prices, X is a matrix of housing attributes, W

is the spatial lag matrix and is the error term that is corrected for spatial error dependence by:

= + , where M is the spatial weighting matrix and u is the remaining error term. Varia-

bles that were found statistically signiﬁcant at the 5% level or less are: log of distance to nearest

highway , dummies for the log of distance to planned A4 highway in 2000, 2001, 2005, and 2006,

log of distance to nearest highway exit, log of distance to nearest railway, log of distance to

nearest social/cultural organization (including schools, hospitals, and universities), log of dis-

tance to nearest industrial area, dummy for the presence near a busy road, dummy for location

45 An area in the Netherlands that incorporates the cities of Rotterdam, Schiedam, Vlaardingen and Delft (Cotteleer and Peerlings, 2011).

46 The direct spatial relationship between house prices that are located near one another (Cotteleer and Peerlings, 2011).

47 The spatial relationship in the error term due to spatially related omitted variables (Cotteleer and Peerlings, 2011).

in the countryside, dummy for presence of garden, dummy for adjacency to forest, water, park and open space, dummies for construction periods (excluding ﬁrst category, which is between

1906 and 1930), dummy for ground-floor apartment or upstairs apartment, dummies housing type, number of balconies, number of roof terraces, number of kitchens, number of sculleries, dummy for attic for storage, dummy if property has a free standing practice, dummy for presence of carport, dummy for single car garage or multi car garage, dummies for maintenance of the house, dummy if the land is not part of the property, dummy for permanent residence, dummy for partly rented, population density within the neighborhood, percentage of non- western immigrants in the neighborhood, average real disposable income per inhabitant per year within the neighborhood, elevation level dummy, and dummies indicating when the property was sold (excluding the first years: 1997 and 1998). In addition, the spatial lag and error dependence coefficients are statistically significant at the 5% level or less (Cotteleer and Peerlings,

2011). The model with statistical signiﬁcant variables has an R² of 81%. Statistically insigniﬁ- cant variables are log of distance to planned A4 highway dummies in 1996, 1997, 1998, 1999,

2002, 2003, and 2004, log of distance to nearest railway station, log of distance to nearest shop or restaurant, dummy for location in the city center, dummy for period of construction between

1906 and 1930, dummy for newly developed houses, dummy if apartment is located on the ground ﬂoor or another ﬂoor, number of dormers, dummy if property has designated area for practice, number of insulation materials used, and dummies for sale in 1997 and 1998.

Considering Auckland, Bourassa et al. (2003) test whether out-of-sample hedonic value predictions can be improved when the housing market is divided into submarkets with their geographical area. By accessing data on detached and attached housing from the oﬃcial database of all real estate transaction in New Zealand in 1996, 8,421 transactions are acknowledged (5,716 for detached and 2,705 for attached houses). 80% of the transactions are used to perform hedonic regressions and the remaining observations are retained for out-of-sample testing (Bourassa et 69

al., 2003). Statistically significant variables at the 5% level or less, for the pooled data, are: log of floor area, log of land area, dummy for cross-leasing (land is owned collectively by owners of dwelling on a site), dummy for detached dwelling, age of dwelling, age of dwelling squared, dummy for walls in good condition, dummy for walls in average condition, dummies for roof type, dummies for wall type, dummies for superior and average quality of the principal structure, log of distance to CBD, and dummies for the quarter of the year in which the property is sold (quarter 3 is statistically insignificant). The model has an R² of 80.6%.

With regard to 53 towns in Connecticut, Clapp et al. (2012) estimate the value of the option to redevelop or reconﬁgure housing attributes. They collect data on 162,454 single-family houses that are sold between 1994 and 2007 from the Warren Group and publishers of Bankers &

Tradesman. As hedonic attributes, they use age, age squared, dummies for two or three bathrooms (Bath2or3), more than three bathrooms (Bath3p), interior square footage (Ftg), footage squared, size of the lot in square feet (Lotsf), size of lot squared, and year dummies. In addition, they control for location by using land value indicators ( ) that interact with interior footage

and squared footage to arrive at their standard hedonic model: ln = + +

+ + + + + + + + ℎ3 +

ℎ23 + + (Clapp et al., 2012). equals 1when the residuals from regressing

the assessed value of the lot on the log of the lot size is in the qth quartile and 0 otherwise

(Clapp et al., 2012). They estimate this model separately for each town and ﬁnd an average R²

of 85.2%. Dye and McMillen (2007 in: Clapp et al., 2012) mention that house price should be

decreasing in age but increasing in Age², so that the rate of depreciation declines with housing

age. House price should also be increasing in lot size, but at a decreasing rate ( positive and

negative). In addition, Bath2p is a variable to indicate large, luxurious houses (Clapp et al.,

2012).The independent variables are statistically signiﬁcant in almost all towns and the sign of the variables is as expected (Clapp et al., 2012).

Regarding to the “Leafy Eastern Suburbs” of the eastern Adelaide plains, MacDonald et al.

(2010) estimate the eﬀect of water restrictions on private properties. By using sale prices and attributes from RP Data on 4,751 houses between January 2005 and November 2007, a general hedonic model is constructed: ln () = + + + ƞ + +

ѱ + . In this model, , , and are j structural, k lot and l neighborhood

attributes, indicates the quarter in which a house was sold and are series of dummy

variables that indicate n local suburbs in the study area to control for unobserved neighborhood

characteristics (MacDonald et al., 2010). Their model has an R² of 85% and statistically signiﬁ-

cant variables at the 5% level or less are: building size, building size squared, number of bath-

rooms, number of rooms, house age, house age squared, cubic house age, land area, land area

squared, private green space ratio, private green space ratio squared, dummies for garage, double

garage, double carport and pool, house condition dummy, distance to CBD, distance to main

roads, distance to watercourses, distance to historical residential zones, distance to golf courses,

distance to public schools, distance to playground, and quarter and year dummies. Variables

that are statistically insigniﬁcant are distance to commercial zones, distance to cemeteries,

distance to private schools, distance to hiking and sport, dummy for carport, and the dummy for

April-June 2005 (oldest category).

Considering Singapore, Sue and Wong (2010) estimate the value of publicly provided local

goods and services in the constituencies of the ruling party relative to those of the opposition

parties. They restrict themselves to ﬂats near electoral boundaries. By doing so, all ﬂats are

located near each other and the problem of omitted variable bias due to data constraints is

limited. The model they estimate is: ln ( ) = + + + + + , where

is a column vector of physical attributes of flat i that includes age, area, floor level, flat type and a dummy whether the flat has been selected to undergo an upgrade program (only in the constituencies of the ruling party), is a column vector that includes locational characteristics of

flat i, including: proximity to good primary schools, industrial estates, expressways, public bus interchanges and subway (MRT) stations, is a dummy variable that measures whether flat i is located in a People’s Action Party constituency (PAP is the ruling political party since 1959) and is a column vector of boundary dummies to indicate the closest electoral boundary to flat i to control for all locational and neighborhood attributes that are shared between flats in boundaries (Sue and Wong, 2010). They collect data from the Housing & Development Board

(HBD, part of Singapore Government) on 10,510 ﬂats under the ruling constituency and 9,079

ﬂats under the opposition constituency which are transacted between February 2001 and April

2006 (Sue and Wong, 2010). All transactions have been deﬂated to 2001 dollars by using the

HDB quarterly Resale Price Index (Sue and Wong, 2010). Considering the flats under the ruling constituency, the model estimates that statistically significant variables, at the 5% level or less, are: dummy for flats in PAP areas, age, age squared, floor area, floor area squared, dummies for number of rooms (except 4-room flat), dummy for executive flat, dummy indicating the undergo- ing of the upgrade program, dummies with categories of levels on which flat is located, distance to good performance school dummies, and a dummy for bus interchange within 300m. With the inclusion of time trends and quarter dummies, this model has an R² of 93% (Sue and Wong,

2010). Statistical insignificant variables are: dummy for 4-room flat, dummy for presence of industrial estates within 400m, distance to nearest MRT station, distance to nearest MRT station squared, and dummies for distance to good progress schools. Considering the opposition constituency, variables that are statistically significant, at the 5% level or less, are all the statistically significant and insignificant variables as under the ruling constituency with the exception of a dummy for the presence of a bus interchange within 300m. This model has an R² of 95%. 72

With regard to southern Taiwan, Andersson et al. (2010) estimate the implicit price of a new high-speed railway line (HSR) by taking into account HSR accessibility in combination with other hedonic price attributes for the housing market. Transaction prices and structural attributes of 1,550 houses in 2007 are derived from the Department of Land Administration of the central government (Andersson et al., 2010). To these cases, they added socio-economic neighborhood characteristics from 2004 data of the Ministry of Finance. Statistically signiﬁcant variables, at the 5% level or less, are: log of ﬂoor area, log of lot size, log of age of house, dummy for shop or dwelling use, dummy for street frontage, log of road width, dummy for commercial zone, dummy for residential zone, log of share of district with residents over 15 years in age with junior college or more, log of distance to CBD, log of distance to HSR station and log of distance to Tainan Science-based Industrial Park. The model that they estimate has an R² of

80.4%. Variables that were excluded by Andersson et al. (2010) are the number of stories (highly correlated with ﬂoor area and age), mean income of households in a neighborhood (highly correlated with percentage of residents over 15 years in age with at least junior college education) and the distance to nearest freeway interchange (negligible t-value).

All in all, the district models have in common that they measure the same dependent variable (transaction price of houses). With regard to independent variables, most district models include size, age, lot size or value, housing type, distance to CBD, year and/or quarter dummies for the time period in which the house was sold, and the distance to local amenities such as roads, schools and railway stations.

4.4 Comparable city models

Comparable hedonic models have been found in Amsterdam (Netherlands), Dublin (Ireland),

Prague (Czech Republic), Athens (Greece), Cape Town (South Africa), Knoxville (Tennessee,

USA), Los Angeles (California, USA), Bogota (Colombia), Hong Kong (China), Guangzhou

(China), and Seoul (South Korea). Regarding Amsterdam, Dekkers and Van der Straaten (2009) aim to estimate the monetary value of aircraft noise disturbance with a hedonic housing model:

=++++, where P is a vector of house price transactions, S a matrix of transaction related attributes, L a matrix of structural attributes, and G a matrix of spatial attributes.

They use data from the Dutch Association of Real Estate Agents on 66,636 transactions in the period 1999-2003. Then, they have added neighborhood information from the CBS. Data on aircraft noise comes from the Netherlands Institute for Health and the Environment (RIVM).

Statistically signiﬁcant variables, at the 5% level or less, are: dummies indicating the year in which the transaction took place, dummy indicating the transaction was free of transfer tax, dummy for leased land, dummies with categories for building age (except dummy with age category between 1906 and 1930), log of surface area, log of number of rooms, dummy for 2 or more types of isolation, dummies for presence of garage, carport and garden, dummies for housing type (except dummy for simple terraced house), dummies for aircraft, railway and road noise above certain levels, dummies for the municipality, and dummies for the inside maintenance.

This model has an R² of 83%. Statistically insigniﬁcant variables at the 5% level or less are a dummy for the building age category 1906-1930, and a dummy for the simple terraced housing type. Municipal dummies were added to correct for municipal diﬀerences (Dekkers and Van der

Straaten, 2009).

With regard to Dublin, Stevenson et al. (2010) examine whether there are differences in the valuation process for auctioned and private housing transactions. They collect data from six brokers to arrive at a sample of 2,657 transaction prices (2,110 auctioned houses and 547 privately sold houses) for the Greater Dublin metropolitan between the first quarter of 1997 and the third quarter of 2004. Statistically significant variables, at the 5% level or less, are: number of bathrooms, number of bedrooms, dummy for the year in which the transaction took place, 74

dummies for apartment or detached housing types, dummy for central city location, dummy for south of city location, dummy for south county location, dummy for periphery location, and dummies for broker names 4, 5 and 6. Their model has an R² of 64.6%. Statistically insigniﬁcant variables are: dummies for sale mechanism (auction or private transaction), dummy for parking, dummies for semi-detached and terrace/mews housing types, and dummies for broker names 1, 2 and 3.

Considering Prague, Melichar and Kaprová (2013) examine the value of urban greenery amenities on housing by using a hedonic price model. They use a dataset from a company (reality.cz), which contains 8,568 apartment transaction prices from 2005 to 2008. Transaction prices were verified with data from Czech Statistical Office. In addition, they added neighborhood and environmental variables from City Development Authority Prague and Czech Statistical Office.

The model they estimate is: ln = + + + + , where , , and are structural, neighborhood, and environmental attributes, respectively. Statistically signiﬁcant variables, at the 5% level or less, are: log of the area, a dummy for a bad state (reconstruction necessary before living), log of surface of total building, inhabitants in the total building, log of distance to city center, a dummy whether apartment is located in apartment house, a dummy whether building material is brick or stone, distance to the nearest specially protected area, an interaction term of the distance to the nearest specially protected area with the log of the area of the nearest specially protected area distance to the nearest urban forest, an interaction term of the distance to the nearest urban forest and the log of the area of the nearest urban forest, and an index of air quality. Insigniﬁcant variables at the 5% level or less are: dummies for type of heating, dummy for presence of sewage system, distance to nearest tram station, log of area of the nearest specially protected area, distance to the nearest agricultural land, log of area of the nearest agricultural land, and an interaction term of distance to nearest agricultural land and the log of the area of the nearest agricultural land. Their model has an R² of 75.4%. 75

With regard to Athens, Efthymiou and Antoniou (2013) estimate hedonic models to analyze the effects of transportation infrastructure on 8,066 house prices. They developed software to capture additional data (listing or renting price, housing type, surface area, number of bedrooms, floor, year of construction, availability of independent heating, presence of air conditioning, presence of garden, presence of fire place, parking availability, type of parking, type of view, orientation, and geo-location) from publicly available real estate websites within the period of

September 2011 to January 2012. In their model, statistically signiﬁcant housing variables, at the 5% level or less, are: log of surface area, all building year category dummies except 1971-

1980, location (floor) in apartment building, and dummies for the presence of a basement, parking, open parking, storage place, fireplace, auto heat, air conditioning, single family house, sea view and a front orientation of the building. Transport attributes that they found statistically significant at the 5% level or less are: log of distance from port (Pireaus/Rafina), and dummies for presence in the inner ring, distance from bus station of less than 50m, distance from metro station of less than 500m, distance from ISAP station of less than 500m, distance from rail station of less than 500m, distance from railway of less than 100m, distance from suburban rail station of less than 2,000m, distance from tram station of less than 500m, distance to airport of less than 7,000m, distance from marina of less than 1,500m, distance from ring-road between 0m

-250m, distance from ring-road between 250m -1,500m and distance from ring-road larger than

1,500m. Other structural attributes that they found statistically signiﬁcant at the 5% level or less are: distance from CBD, log of distance to archaeological sites, distance from the coastline, log of percentage of population with a university degree or higher, and dummies for indication of northern suburbs, university area and population density of less than 500 people/km². Their model has an R² of 86.1%.

House prices in Cape Town have been modeled by Nahman (2011). He studies the eﬀect of environmental and social costs of landﬁlling on housing prices. By dividing Cape Town into two 76

areas (Bellville South and Coastal Park), he finds that lot size, house size, wall type, and the number of garages is statistically significant at the 5% level or less in both areas. The distance from a landfill site, age, condition of a house, number of floors, number of bedrooms, roof cover- ing, number of common walls with another property, wall type, and the presence of a swimming pool, security and a second dwelling on top of the property are found statistically insignificant at the 5% level or less or they differed in expected sign. The adjusted R² for both areas are

88.3% (Bellville South) and 90.6% (Coastal Park). This analysis is based on 101 recent sales for the Bellville South area and 104 for the Coastal Park. It is questionable whether these results can be generalized for Cape Town.

With regard to Knoxville, Cho et al. (2008) determine the monetary worth of quantity and quality of green open space as a part of housing prices. The dependent variable is the transaction price of 9,571 houses, which were adjusted with an index to 2000. Variables that they found to be statistically significant at the 5% level or less are: log of area size, building age, log of parcel size, number of stories, population density, log of income of household, travel time to work, log of distance to nearest deciduous forest patch, log of distance to mixed forest patch, forest patch density, forest edge density, mean forest patch size, and dummies for the use of brick in the façade, pool, fireplace, condition, overall quality, several school districts and season sold. Insignificant variables at the 5% level or less are: number of bedrooms, vacancy rate in neighborhood, unemployment rate in neighborhood, prime rate (interest rate minus inflation rate), log of distance to nearest evergreen forest patch, and dummies for several school districts.

The model has an R² of 78.0%.

Regarding Los Angeles, Saphorus and Li (2012) set up a hedonic pricing model for 20,660 transactions of single family detached houses. All of these houses were sold in 2003 or 2004

(Saphorus and Li, 2012). They are interested in the value of urban trees and (non-)irrigated grass areas. Variables that are statistically signiﬁcant at the 5% level or less are: log of parcel 77

area, log of structure area, log of number of bathrooms, square of log of house age, presence of a pool, presence of a highway within 0.25km, presence of railroad tracks within 0.25km, log of distance to nearest natural park, log of distance to nearest golf course, log of distance to nearest river, parcel irrigated grass cover, square of parcel irrigated grass cover, spatial lag coeﬃcient for

errors, spatial lag coeﬃcient for price, property crime, violent crime in neighborhood, and per-

centage Hispanics in the neighborhood. They do not indicate the R² of this model for unknown

reasons.

For Bogota, Rodríguez and Mojica (2009) estimate the value of a bus rapid transit (BRT)

station in the direct vicinity by setting up a hedonic pricing model with the log of the listing

price of 3,976 houses as dependent variable. These houses were listed between January 2001 and

December 2006. To control for the time eﬀect, they have added ﬁve dummy variables for every

year (2001 as reference category). Statistically signiﬁcant variables at the 5% level or less are:

dummies indicating expansions in BRT stations in 2002, 2003, 2004, 2005 and 2006, ﬂoor num-

ber, age category 1-10 years old, number of bedrooms, number of bathrooms, number of garage

spaces, ﬂoor area, neighborhood socio-economic stratum, percentage of neighborhood area in

industrial uses in 1998, percentage of neighborhood area of parks and open spaces in 1998,

distance between property and Calle 13 (district) station, and dummies for apartment, location

within 500m of Cra 7a (district), location within 500m of Calle 72 (district) and location within

500m of Av Boyaca. Insigniﬁcant variables at the 5% level or less are: age categories 10-20 years

old and more than 20 years old, population density in neighborhood, percentage of neighborhood

area in commercial uses in 1998, percentage of neighborhood area in institutional uses in 1998,

percentage of neighborhood area empty or vacant in 1998, homicides per 100,000 residents in

neighborhood in 2001 and 2004, and dummies indicating a location within 500m of Av Ciudad

de Cali, a location within 500m of AV 68 and a location within 500m of Avenida Longitudinal.

Their model has an R² of 69.4%. 78

Regarding Hong Kong, two relevant studies have been analyzed. Jim and Chen (2009) study the value of harbor and mountain views. In their hedonic pricing model, they use 1,474 transaction prices of houses from 2005 and 2006 as dependent variable. After taking the log of the dependent variable and regressing this on its attributes, statistically signiﬁcant attributes at the

5% level or less are: log of floor level on which apartment is situated, log of total floor area, log of number of bedrooms, distance to nearest mountain, and dummies for the presence of a pool, clubhouse, panoramic sea view, partially obstructed sea view, panoramic mountain view, street view and accessibility by public transport. Insignificant attributes at the 5% level or less are: log of the duration between occupation permit and transaction date, distance to nearest seashore, and dummies for partially obstructed mountain view and view of buildings. Their model has an adjusted R² of 94.4%. Another study in Hong Kong, by Hui et al. (2007) estimates the value of neighboring and environmental attributes in relation to the market value of high-rise houses.

Market value refers to recorded transaction prices of 2,957 houses (second-hand) between the third quarter of 2000 and the second quarter of 2001. Statistically signiﬁcant attributes at the

5% level or less are: a time dummy for 2001, age of building, number of ﬂoor level above ground level, number of rooms, number of bathrooms, presence of clubhouse in the estate, dummy for an MTR/KCR station that can be reached within 10 minutes, shortest time to CBD by any means of transport, number of secondary schools in the district, noise level in the estate, and log of air pollution index in the district. Statistically insigniﬁcant attributes at the 5% level or less are dummies for the presence of sea view, and a minimum of 20,000 square meters of green belt area in the vicinity that can be reached within 10 min or 300m in distance. Their model has an adjusted R² of 61.5%.

With regard to Guangzhou, Jim and Chen (2007) asses the values attributed to environmental externalities on transaction 521 prices of houses in 2004. They find that statistically significant attributes, at the 5% level or less are: log of floor area, log of plot ratio of the residential 79

precinct (indicating high-density environment and high-rise buildings), log of distance to shop- ping center, log of the security condition around the residential precinct, and dummies for view of green space and a water body. Insigniﬁcant attributes at the 5% level or less are: log of number of bedrooms, log of number of bathrooms, log of story on which apartment is located, percentage green space provision rate in the residential precinct, distance to the nearest park, and dummies indicating the compass direction of the apartment, the quality of the building, location within commercial area, located within residential area, located close to main street, located in old town area, view of street and the view of buildings. Their model has an adjusted R² of

72.5%.

In Seoul, Bae et al (2003) estimate the impact of a new subway line on nearby housing values. Housing prices are average transaction prices of apartments with the same attributes in a complex for the years 1989, 1995, 1997, and 2000. By adding a log transformation to 956 of such transaction prices, they ﬁnd that statistically signiﬁcant attributes on the 5% level or less are:

ﬂoor space, building year, type of heating dummies, distance from the greenbelt, distance from the Han river, dummies for Kangseo, Nambu, and Kangdong school districts, population density in the neighborhood, job density in the neighborhood, distance from the new subway station, distance from sub center Kangnam, distance from sub center Yeongdungpo, and dummies for a sale in 1995, 1997, or 2000 (reference is the year 1989). Statistically insigniﬁcant attributes at the 5% level or less are: total households in the apartment complex, parking spaces per household, and distance from the central business district. Their model has an R² of 95.4%.

All in all, in most comparable studies, the dependent variable measures transaction prices of houses. Regarding independent variables, the number of rooms is a variable that is used in most city models. Intuitively, the more rooms (assumed to have an average surface), the higher the surface area will be. Within this regard, the number of rooms could be an indicator for the total surface area of a house. This assumption is veriﬁed in three studies (i.e. Cape Town, Knoxville, 80

and Guangzhou), in which the number of rooms is insigniﬁcant at the 5% level or less if surface area is also a variable. Other variables that are commonly used in city models are: age of a house, housing type (if the scope of research is broader than analyzing one housing type), parcel surface, and locational indicators (e.g. municipality dummies, north, east, south, west, or central city location, periphery location, and distance to nearest urban forest, rail station, airport, ring- road and CBD). Other detailed information, which might only be relevant for the city being analyzed, is collected in several studies, such as mountain view, broker names, distance to agricultural land, distance to coastline, vacancy rate in neighborhood, percentage Hispanics in neighborhood, location in old town center and the presence of a clubhouse in an apartment.

4.5 Sub-conclusion

This chapter starts by answering the sub-question: “What method(s) should be used to determine market value?”, and then proceeds by answering the sub-question: “ Which housing and spatial characteristics are potentially relevant for explaining market values of houses in the

Netherlands ”. The TCM is widely used in appraising natural resources. It takes into account the

time and cost that people make to visit a recreational site. The ABM assumes that the price of

goods in a market can be a substitute for goods that cannot be traded in markets (environmen-

tal qualities). By doing so, people reveal their willingness to pay with averting expenditures to

counteract harmful results. Both the TCM and the ABM are used to value environmental quali-

ty (i.e. qualities that cannot be bought or traded in a market), but not market values of houses.

The HPM assumes that an object (which can or cannot be traded in the open market), can be

decomposed by its attributes. By using this method, the market value of owner-occupied housing

in the Netherlands can be decomposed in prices for physical and spatial attributes of houses.

This is directly linked to the research question of this master thesis, making this the method to

be used. A disadvantage of the HPM is that an extensive dataset is needed, which may be diﬃ- cult to collect. However, the HPM has often been used with regard to housing value. Studies that assume the HPM and that focus on housing have been analyzed to identify relevant attributes. Results indicate that, on a national, district, and city level, the dependent variable often refers to the transaction price of houses. National studies indicate that housing type, age, and size are often signiﬁcant variables. Some locational indicators such as dummies for provinces or the distinction between an urban or rural house might be used. Decreasing the scale to district models, a conclusion is that they provide more detailed information, by including lot size, distance to CBD, and distances to local amenities such as roads, schools and railway stations.

Decreasing the scale even further to city models, city-speciﬁc information can be captured.

Apart from using the same variables as in national and district models, they capture additional locational information, such as distances to a broad range of local amenities, the presences of mountain view or clubhouses in apartments, distances to coastline, vacancy rates in a neighborhood, and percentages of nationalities in a neighborhood.

The structural characteristics of houses that are present in most national, district, and city- level studies (i.e. housing type, age, size, and lot size) should be captured in a hedonic pricing model for the Netherlands. In addition, locational information should be used, which diﬀers per comparable study, but often includes distances to public and commercial places and the distance to the CBD.

DATASET ANALYSIS AND OPTIMIZATION

Assuming the conclusions of previous chapters, data can be collected, selected, and operationalized in accordance. DataQuote has provided a dataset, which includes listing prices, rents, and structural attributes of 7.8 million real estate objects in the Netherlands between the third quarter of 2009 and the third quarter of 2012 (or data collected “unknown” / “missing” ). Listing price is inferior to transaction price (see chapter 3), but DataQuote does not have the transaction prices currently 48 . To this dataset, a correction for the kind of object, to exclude all non- residential objects and missing values 49 , has been made. This has resulted in a dataset of 1.2

million residential real estate objects. This dataset has been further divided into owner-occupied

and rental dwellings. Cases without a subdivision into owner-occupied or rental dwellings, where

the price variable could be the annual rent, as well as the listing price of a “cheap” house (lower

48 DataQuote expects to have all actual transaction prices of the sample period in the near future. Then, listing prices can be substituted with transaction prices, after which the model will estimate transaction prices.

49 Residential real estate as “kind of object” cannot be derived from structural attributes, resulting in the exclusion of all missing values.

than EUR 50,000), are removed from the dataset. This has resulted in a dataset of 0.4 million owner-occupied dwellings. Correcting for duplicate cases, because this might impose a bias as identical houses are listed through time against diﬀerent prices, resulted in a dataset of 0.2 million unique owner-occupied dwellings. The most recent owner-occupied dwelling, in case there were duplicates, has been included in the dataset because this listing price is assumed to be closest to the transaction price.

The remainder of this chapter is organized as follows: section 5.1 discusses further dataset reduction as a result of false and/or unverified information. Section 5.2 operationalizes and defines each of the variables. Section 5.3 describes a method to derive housing type in case this variable is missing. Section 5.4 gives summary statistics of each of the variables in the dataset, the location of cases within the Netherlands and an analysis of sample selection bias. This chapter concludes with a description of the final dataset in section 5.5.

5.1 Dataset Cleaning

The dataset with data on 0.2 million owner-occupied dwellings in the Netherlands has been further “cleaned” to exclude cases with false information. A new variable, which is calculated by dividing the volume by the surface, measures the average height per ﬂoor of a house. This selection variable is only included in the dataset for cases that have a value on this variable ranging between 1.9 and 4.0 meter (excluding 502 cases; less than 0.3% of dataset, and 16,394 cases;

8.4% of dataset, respectively). The lower bound is derived from “Bouwbesluit 2012” 50 , which states that the minimal net height of the living ﬂoor of a dwelling must be 2.6 meter, but that in exceptions (reconstructions) this may be 2.1 meter. This regulatory constraint stems from 2012.

Houses that are built before the Second World War were subjective to diﬀerent and less regula-

50 Regulations set by the Dutch central government for buildings.

tions. Also, there might be rooms beneath stairs and roofs that force the net height downwards.

In accordance with DataQuote, a minimal value of 1.9 meter has been set. Also, the maximal value of 4.0 meter has been set in accordance with DataQuote on the basis of eﬃciency in height in relation to price and direct comparability with other houses.

In addition, DataQuote mentions that a listing price higher than EUR 2,500,000 may not be relevant for the AVM. The cases in this category are a small portion of the dataset (176 cases; less than 0.1% of the dataset), and often have values relating to missing data (e.g.: 9,999,999,

99,999,999, and 999,999,999). In addition, DataQuote veriﬁed that the most expensive house in the Netherlands is valued at EUR 10 million in 2012, so values above this price are false.

Finally, frequency tables of all variables (municipality name, municipality code, province, house number, zip code including the two letters, zip code excluding the two letters, date listed, date listed quarter and year, price listed, type of transaction, housing type, building year, surface of living ﬂoor, parcel surface, volume of living ﬂoor, X and Y coordinates, and monumental status) have been analyzed to detect false or inaccurate information. In a discussion with

DataQuote, this has resulted in excluding houses with a building year before 1700 (121 cases; less than 0.1% of the dataset), a building year after 2012 (3,268 cases; 1.7% of the dataset), a parcel surface in square meters with the, probably missing, values: 999, 9999, 99999, and 999999

(177 cases; less than 0.1% of the dataset), and all parcel surfaces that have a value of more than

1,500 square meters 51 (11,583 cases; 5.9% of the dataset). This has resulted in a dataset that will be used for further analysis of 167,963 cases. From these cases, 32.2% has a missing housing type. From comparable studies (see chapter 4), it is concluded that housing type is one of the important variables. Therefore, this value is extracted by using the random forest classiﬁcation method (see section 5.3).

51 Parcel surfaces of more than 1,500 square meters have not been verified by DataQuote

5.2 Operationalization and Deﬁnition of Variables

DataQuote analyzes the validity of data by comparing diﬀerent data sources with each other

(see table 4).

Table 4: Definition and operationalization of variables Variable name Source Deﬁnition Operationalization

Listing price Several 53 Price associated with the listing EUR Listing date Several 53 Date associated with the listing dd -mm -yyyy Updated listing Script 52 Update of all listing pric es to 2012 Q4 EUR Municipality code CBS Numerical indication of the municipality GM0007 -GM1987 Municipality name CBS Name of the governmental municipality - House number BAG Assigned number from governmental body 1-999 Zip code Several 53 Configuration of 4 numbers and two letters 1000 AA -9999 ZZ Province CBS Territorial classification in 12 areas 1-12 COROP CBS Territorial classification in 40 areas 1-40 Type of transaction Several 53 v.o.n. if first sale since construction, k.k. else 0=k.k., 1=v.o.n. Ho using type Several 53 Apartment, terraced, (semi -)detached, corner 0-4 Building year BAG Year in which the house was constructed 1700 -2012 Surface BAG Usable surface 54 m² Parcel surface BAG Total surface of ground, excluding living floor m² Volume BAG Gr oss volume 55 m3 X and Y coordinates ACN Longitude and latitude of the house Longitude+lattitude Monumental status BAG, RACM Heritage protected by National government 0=no, 1=yes

52 Updated to 2012Q4 (see procedure in section 3.2.3, with the exception that housing types are known).

53 BAG as primary data source, verified by ACN and/or Agentschap NL.

54 Includes rooms for building installations, all inside rooms of a building that are destined for vertical traffic (net floor surface) and parking space. It excludes surface of constructions that separate building functions, non- accessible pipe shafts, static building components, glass-panel correction, and spaces that are lower than 1.5 meter (BAG and Ministerie van VROM, 2007). For more information, see: NEN 2580.

55 Does not provide exceptions (e.g. different calculation rules) for rooms in the dwelling, other than determining the surface of the floor and multiplying this with the height. The height includes the thickness of floors and the roof, which is 30 cm if unknown (Waarderingskamer, 2013). For more information, see: NEN 2580.

Listing price, listing date, type of transaction, and housing type come from a main source

(Funda), which is veriﬁed or enriched with data from other websites 56 . Only NVM appraisers can publish houses for sale on Funda. To get the NVM certiﬁcation, one must pass several examina- tions. In addition, one must pay an entrance fee of EUR 15,000, pay annual contribution of EUR

250, pay annual service fees of EUR 1,650, pay EUR 25 per object placed on Funda and pay for annual recertification that is about EUR 450. The costs and efforts to get the NVM certification are considerable. Once an NVM appraiser, one must accept the honor code, which lists that an

NVM appraiser must act in the best interests of the principal and that he or she acts in a knowledgeable way. However, the listing price an NVM appraiser reports on Funda may or may not be reliable. The NVM appraiser acts in the best interests of the principal. The listing price may then be a function of several subjective variables, such as the principal’s necessity to sell the house, the principal’s and appraiser’s expectations of the ower-occupied housing market and the opinion of the appraiser regarding the listing price. This may obscure the reliability of a fair listing price. NVM (2013) mentions that: “NVM does not provide guidance how an NVM appraiser should come to the listing price that he or she reports on Funda. NVM also does not state which housing attributes should be considered in a valuation and how much weight should be given to them. The NVM appraiser has to consider this in his or her best knowledge. The prices on Funda may in exceptional cases be an indisputable selling price or a bid-from price” . It is assumed that choosing the latest listing price among all duplicates in the dataset provides the fairest listing price. In addition, DataQuote mentions that indisputable selling prices and bid- from prices are stated in descriptions of the concerning cases and that these cases are not present in the dataset.

56 Funda as the primary data source, verified and/or enriched with data from from huislijn.nl, woningnet.nl, vbo.nl, niki.nl, vastgoedpro.nl and era.nl.

Listing date, type of transaction and housing type refer to facts. It is assumed that an NVM appraiser acts according to the honor code and reveals these objective variables. According to

DataQuote, Funda is less reliable with respect to building year, surface of the living floor, parcel surface and volume of the living floor. DataQuote mentions that appraisers seem to inflate these

figures to bring the property to market under better conditions. DataQuote verifies housing type with “Fotowĳzer woningen, uniformering begrippen en definities woningen” (NVM, Vastgoed-

PRO, VBO Makelaar, VNG and Waarderingskamer, 2013). They define a detached house as a single family dwelling that is not attached to another object. A semi-detached house is a single family dwelling where the central building is attached to the central building of a comparable dwelling (they do not have to share the same roof). A terraced house is defined as a single family dwelling whereby partitioning walls are adjacent to those of other objects and where houses are located in similar lines and planes. A corner dwelling is a single family house that is adjacent to its neighboring house, which is located in the beginning or end of a sequence and which has (additional) ground on the sides of the house. At last, they define an apartment as a dwelling that is part of a multiple family building. Municipality names may be inconsistent (e.g.

Den-Haag and ‘s-Gravenhage both refer to the same municipality). This is why municipality codes have been added, which is a code consisting of 4 numbers that CBS has agreed on with the Ministry of the Interior and Kingdom Relations. CBS describes province and COROP-area as a governmental area of the Dutch territory, which is formed by the European NUTS- formation 57 (CBS, 2013f). Data from the CBS is considered reliable because the CBS is not tied

to commercial organizations and because the described data comes from (inter)national govern-

mental bodies, which act to present data as objective as possible.

57 Nomenclature des Unités Territoriales Statistiques, is a regional classification in country parts, provinces and COROP-areas of the European statistical office Eurostat so that member states of the European Union have clearly separated areas.

Housing number, building year, surface, parcel surface, volume, monumental status, and zip codes are derived from Basisregistraties Adressen Gebouwen (BAG) 58 . Housing number, building year, surface, parcel surface, and volume are considered objective attributes of a dwelling.

Whether an owner-occupied dwelling has a monumental status is data that is provided by BAG and the Rijksdienst voor Archeologie, Cultuurlandschap en Monumenten (RACM) 59 . Infor-

mation from BAG is considered reliable because the Kadaster is legally responsible to provide

information as objective as possible (Kadaster, 2013). In addition, the minister of Infrastructure

and the Environment oversees the BAG database and also the RACM is a governmental body.

Tables to translate zip codes into X and Y coordinates come from Adrescoördinaten Neder-

land (ACN). ACN is part of BAG, which is managed by the Kadaster. By using graphic infor-

mation systems (GIS), an X and Y coordinate can be assigned to all addresses in the Nether-

lands. This information is assumed to be reliable for the same reasons that the BAG database is

reliable.

5.3 Estimating Housing Type

In the dataset after cleaning (see section 5.1), 32.2% has a missing value on housing type.

Deleting 32.2% of the data (54,084 cases) would be unnecessary if housing type can be derived.

Derivation is possible by using random forest classification. This is a method that consists of several classification trees 60 . Breiman (2001) defines a random forest as: “a classifier consisting of

a collection of tree-structured classiﬁers, where the random vectors are independent identically

58 A database, containing municipal information on all addresses and objects in a municipality. BAG is managed by the Kadaster and the minister of Infrastructure and the Environment is responsible for the database.

59 Governmental body that strives to protect and conserve National heritage.

60 Wilkinson (2012) mentions that classification trees involve categorical dependent variables and that trees begin with one node and branch to many. A node is a cluster of cases, which is to be split by further branches in the tree (Wilkinson, 2012).

distributed 61 and where each tree casts a unit vote for the most popular class at input x” . Breiman

(2001) shows that, the error rate of a forest depends on the correlation between the individual

trees and the strength of individual trees. This is conforming intuition because a higher correla-

tion between trees increases the probability to derive a false category and a tree with a low

individual error rate is stronger than one with a high individual error rate. In addition, Breiman

(2001) mentions several advantages of random forests, including: unexcelled accuracy among

current algorithms, eﬃcient use on large data bases, estimation of variables that are important

in the classiﬁcation, generation of an internal unbiased estimate of generalization error as the

forest building progresses and eﬀective and maintaining accurate in estimating missing data.

Liam and Wiener (2002) provide the R code for classiﬁcation by using random forest.

DataQuote has provided a dataset, in which housing types of 4,154 houses have been manually

veriﬁed. In addition, the updated price, building year, surface, parcel surface and volume of all

these houses are known. After correcting the updated price (excluding cases < EUR 50,000 and

> EUR 2,500,000), building year (excluding cases with building years < 1700 and > 2012), and

volume per surface unit (excluding cases < 1.9 and > 4.0), 3,519 houses remained. Of this total,

610 are apartments, 1,493 are detached, 559 are semi-detached, 272 are corner houses, and 585

are attached. The script automatically selects (with replacement) 63.2% of this data as the

training set and 36.8% as test set 62 . The training set is used to develop a classiﬁcation model for housing types, while the test set is used to predict housing types and to compare these with actual housing types to derive the prediction error. The best results are acquired by predicting housing types in 2 stages. In the ﬁrst stage, a distinction has been made between the housing

61 Each random variable has the same underlying distribution and all variables are mutually independent: , which implies: , which is the conditional independence Pr () Pr () = Pr () Pr () Pr () = Pr() (Clauset, 2011).

62 The original dataset (excluding 32.2% of 167,963 cases) has not been used as test set because these housing types are considered less reliable as the manually verified housing types in the cleaned dataset of 3,519 cases.

types “apartment” , “detached”, and “other” . This has been done because the categories “semi- detached”, “corner”, and “attached” are rather similar. Predicting all housing types in a single stage seems to be inferior to a prediction in 2 stages. Variables that are used to estimate a model for the training set are: volume, surface, parcel surface, parcel surface per unit of surface, and building year. Parcel surface per unit of surface has been calculated to further distinguish between “corner” and “attached” houses, because they are similar except that “corner” houses have

a (slightly) larger parcel. Results of the ﬁrst stage (see table 5) are based on 500 trees and the

number of variables tried at each split is 2 (both automatically selected as best model). Results

indicate that the housing type “apartment” can be predicted with 100% accuracy. The housing

type “detached” has 18.5% of cases predicted incorrectly (as “other” ), while this is 14.8% of the

cases for the category “other” (as “detached”) . The test set estimate of the error rate is 13.8%.

Table 5: First stage random forest categorization in number of cases and percentages Apartment Detached Other Classiﬁcation Apartment 610 ( 100.0% ) 0 ( 0.0% ) 0 ( 0.0% ) 0.0% Detached 0 ( 0.0% ) 1,217 ( 81.5 %) 276 ( 18.5 %) 18.5 % Other 0 ( 0.0% ) 210 ( 14.8 %) 1,206 ( 85.2 %) 14.8 %

In the second stage of the random forest, which is also based on 500 trees and 2 variables tried at each split, the category “other” is further speciﬁed into “semi-detached”, “corner”, and

“attached” . Results of the second stage (see table 6) indicate the diﬃculties in predicting the subcategories of “other” .

Table 6: Second stage random forest categorization; specification of “other” in number of cases and percentages Semi -detached Corner Attached Classiﬁcation Semi -detached 461 (82.5 %) 48 (8.6 %) 50 (8.9 %) 17.5 % Corner 102 (37.5 %) 53 ( 19.5 %) 117 ( 43.0 %) 80.5 % Attached 83 ( 14.2 %) 42 ( 7.2 %) 460 ( 78.6 %) 21.4 %

Within this category, “semi-detached” has the least incorrectly predicted cases (17.5%). Espe- cially the subcategory “corner” is hard to predict (80.5% of predicted cases incorrect). This is because the category “corner” is similar to “semi-detached” , and “attached” . In addition, the tree of “corner” is weaker than the other (sub)categories because it has less observations in the training set. Regression results (see chapter 6) should indicate whether the housing type “corner” has a diﬀerent economic signiﬁcance than the housing types “semi-detached”, and “attached”. If this is the case, adjustments may be necessary. The overall prediction error of the second stage is

31.2%.

The variables that are used to estimate the models in the ﬁrst and second stage can be graphically presented in a variable importance plot (see ﬁgure 4).

Figure 4: Variable importance of stage 1 (left) and 2 (right) of the random forest classification method

This plot ranks the variables from top-to-bottom as most-to-least-important based on their mean decrease in Gini. Liam and Wiener (2002) mention that the mean decrease in Gini measures the total decrease in node impurities from splitting on a variable, averaged over all trees. A higher mean decrease in Gini then translates into higher node purity, so a more important variable. Parcel surface and parcel surface per unit of surface are important variables in both stages of the categorization process of housing type. Volume, surface and building year are less important variables.

5.4 Summary Statistics and Sample Selection Bias

There is reason to believe that the sample, consisting of 167,963 cases, is non-random. Listed houses on Funda, Huislijn, Woningnet, VBO, Niki, VastgoedPRO, and Era are analyzed by

DataQuote, which leads to systematically excluding listed houses on other websites. In addition, prices, building years, and parcel surfaces are capped at lower and upper bounds, leading to systematically excluding houses outside the range from lower to upper bound. The Heckman 2- step procedure to correct for sample selection bias is an obvious solution. In the ﬁrst step, a regression has to be done on listing price and its attributes. Also, a probit model has to be developed in this step whether a house is listed or not, which is also dependent on attributes.

This probit regression cannot be modeled because the decision to bring a house up for sale is highly subjective and this is information is unknown. To provide any guidance whether there is sample selection bias, summary statistics of the dataset can be compared with summary statistics of CBS Statline for the Netherlands.

5.4.1 Summary Statistics of Interval and Ratio Variables

Summary statistics of interval and ratio variables (see table 7) indicate that the mean of the updated listing price is EUR 266,542 (median: EUR 221,162) with a standard deviation of EUR

165,058. This can be compared with the average transaction price of houses that sold in 2012Q4 that is reported by CBS (2013g), which is EUR 219,746. According to CBS (2013g), the average transaction price of houses that sold in 2012 was EUR 226,661, which is closer to the average listing price of the dataset. CBS (2013h) also mentions that the average WOZ/assessed value of houses in the Netherlands is EUR 232,000. The median updated listing price is closer to the average prices as listed by the CBS. This may be due to outliers that force the average price upwards. A further difference may be the incomparability of listing and transaction prices (median difference of 4.6%, see subsection 3.2.3) and the fact that the average transaction price as reported by CBS may be based on few transactions (35,704 in 2012Q4 and 117,261 in 2012). In addition, with regard to the updating process of the listing price to 2012Q4, price trends in a giving year, per housing type in a COROP region differ. If these price trends are based on few cases, which is unknown information, outliers have a larger influence. This could make the updating process less reliable, leading to an over- or understatement of the price in 2012Q4. Also, the dataset may indeed be a sample from the population that is not representative.

Table 7: Summary statistics of variables on a continuous scale of the cleaned dataset Variable name Mean Median Standard Minimum Maximum Deviation

Updated listing price 266,542 221,162 165,057 50,185 2,492,041.0 Building year 1971 1978 33 1700 2012 Surface 120 115 49 14 850 Parcel surface 216 150 258 0 1,49 8 Volume 371 350 165 35 2,900

In addition to price, CBS (2013i) mentions that, of the 6,980,600 houses (no distinction between owner-occupied and rental houses) in the Netherlands in 2012 with known building year, 1,705,200 (24.4% of all cases) fall within the building year category of 1960 to 1975 and

1,641,000 (23.5% of all cases) fall within the building year category of 1975-1990. This may be in line with summary statistics of the dataset, which indicate a mean building year of 1971 (median: 1978). CBS does not provide any indications for the mean surface, parcel surface and/or volume for houses in the Netherlands.

5.4.2 Summary Statistics of Nominal and Ordinal Variables

Type of transaction ( “v.o.n.” as ﬁrst sale of a house and “k.k.” when house is second handed), monumental status, housing type, and the location of cases by their zip codes or within their respective province, can also be analyzed to indicate whether there may be signs of sample selection bias. There are 157,170 cases that are up for sale as k.k. (10,793 cases are v.o.n.) in the dataset. There is no information available of this variable for all owner-occupied houses in the

Netherlands or for samples of data in a given year. Considering monumental status, 1,224 houses in the dataset are characterized as monuments (0.7% of the total dataset), while DataQuote has a list of 101,570 monuments in the Netherlands. The total number of monuments in the Nether- lands cannot be divided through the number of houses in the Netherlands because the list also includes non-dwellings. Information regarding the amount of houses with a monumental status in the Netherlands is unavailable.

Regarding the type of owner-occupied houses, CBS (2013) reports that in 2012: 1,003,800 houses were categorized as detached, 891,800 are semi-detached, 882,500 are on corners,

1,926,300 are attached, and 2,276,200 houses were categorized as apartments. This corresponds to 14.4%, 12.8%, 12.6%, 27.6%, and 32.6%, respectively. The dataset has this division of 21.4%,

15.8%, 5.0%, 26.5%, and 31.3%, respectively on the housing types. The dataset is underrepre- 95

sented in the housing type category “corner” and overrepresented in the category “detached” . The

underrepresentation may be due to the fact that the second stage of the random forest incorrect-

ly predicts 80.5% of the housing type “corner” . In addition, 18.6% of the housing type “de-

tached” is predicted incorrectly.

All cases of the dataset can be plotted on a graph to see the distribution of cases across the

Netherlands and whether there are municipalities with no cases (see ﬁgure 5).

#CASES

Figure 5: Distribution of cases across the Netherlands (left) and heat map (right) of the Netherlands (source: derived from TargetMaps)

There is a clustering of cases in Amsterdam, but all other cases are quite evenly distributed

across the Netherlands. Municipalities (or part of municipalities) with no information are (from

top to bottom): Bedum, Scheemda, Reiderland, Winschoten, Warnsveld, Zevenhuizen-

Moerkapelle, Moordrecht, Nieuwkerk aan den Ijssel, Arcen en Velden, Nuenen, Sevenum, Helden, 96

Meijel, and Blaricum. Almost all municipalities are covered within the dataset. Considering provinces, the most cases are located in Zuid-Holland and Noord-Holland, followed by Noord-

Brabant, and Gelderland (see appendix A). The dataset deviates more than 2% from the population in terms of location (sample % - population %) in Limburg (sample is underrepresented by 4.8%) and in Noord-Holland (sample is overrepresented by 4.5%).

5.5 Sub-conclusion

A dataset of 167,963 cases will be used for further analysis. This dataset contains unique

(non-duplicate) owner-occupied houses with a listing price between EUR 50,000 and EUR

2,500,000 with an average ﬂoor height between 1.9m and 4.0m, a building year between 1700 and 2012, and a parcel surface smaller than 1,500 m². In addition, the dataset does not contain missing values, except for housing type, and frequency tables do not indicate false information.

Deﬁnitions, sources, and operationalization of all variables (listing price, listing date, updated listing price, municipality code, municipality name, house number, zip code, province, COROP, type of transaction, housing type, building year, surface, parcel surface, volume, X and Y coordinates, and monumental status) is a step before summary statistics can be drawn from the dataset. The sources, except for listing price, are all assumed to be reliable because they have a mission to provide objective information and because they are governmental bodies, tied to governments and/or independent research institutes (CBS, BAG, CAN, and RACM). In addition, data from these sources has been veriﬁed by DataQuote after comparing with other sources. The source for listing price mainly includes Funda. This is a website where appraisers can list a house and give a description about its attributes. The listing price is dependent on expectations from the seller and the appraiser, which might be subjective and unreliable. If a house is listed more than once on Funda, only the latest listing price has been recorded in the

dataset, so that there is corrected for mispricing in previous listings of the same house. This listing price might still be unreliable, because the house may not be listed after a while (taking it oﬀ the market), without selling it. However, transaction prices that reﬂect actual behavior, after a potential negotiation, are unknown.

To correct for the missing values of housing type, a random forest classification method has been used. In the first stage, housing type is classified as “apartment”, “detached” or “other” . The overall error rate in this stage is 13.8%. The second stage predicts the category “other” as: “semi- detached”, “corner”, and “attached” . These subcategories are harder to predict (overall error rate of 31.2%). Based on these results, housing type has been predicted for the 32.2% missing values in the dataset.

After substituting missing values of housing type, summary statistics can be drawn from the dataset. A formal Heckman 2-step procedure to test for sample selection bias cannot be done because the independent variables for a probit model that estimates whether a house is listed or not, cannot be derived. To give an indication whether the dataset is representative, summary statistics of the dataset can be compared to average data from all (owner-occupied) houses in the Netherlands. The average updated listing price of the dataset is EUR 266,542 (median: EUR

221,162), while the average transaction price of houses in the Netherlands that sold in 2012Q4 is

EUR 219,746 (over 2012: EUR 226,661). The average building year of the dataset is 1971 (median 1978), while the average building year of houses (rental and owner-occupied) in the Nether- lands is between the category 1960-1990. Comparing the housing types of the dataset with housing types of owner-occupied houses in the Netherlands, reveals that the dataset is overrepresented (sample % - population %) in the category “detached” (7.0%), while underrepresented in the category “corner” (6.8%). The underrepresentation in the category “corner” could stem from the random forest classiﬁcation that incorrectly predicts 80.5% of “corner” houses. At last, a plot of the cases, on municipality level, on a map of the Netherlands reveals that all but 14 munici- 98

palities are represented in the dataset. A plot of the number of cases per province can be compared with the number of owner-occupied houses in the Netherlands per province. This plot reveals that the dataset and the population are rather similar with regard to their percentage provincial distribution, with the exception that there is a diﬀerence of more than 2% (sample %

- population %) in Limburg (underrepresentation by 4.8%) and Noord-Holland (overrepresenta- tion by 4.5%).

All in all, it cannot be concluded that the sample is directly comparable to the population.

However, on the basis of price, building year, housing type, and a plot of cases per municipality and province, it is assumed that the sample more or less resembles the population. Questionable for this representation is the listing price of the dataset (after correcting for diﬀerence between listing and transaction price).

MODEL DEVELOPMENT AND RESULTS

The ﬁnal dataset (see chapter 5) can be used to estimate models for market value of owner- occupied houses in the Netherlands. After potential modiﬁcations, multivariate hedonic regressions can be applied to derive results.

This chapter is organized as follows: section 6.1 gives the transformations that have been applied to certain variables. A conceptual model with all relevant, potentially transformed, variables is the outcome of this section. Section 6.2 describes the theoretical background of multivariate regression, so that the estimated regression parameters are useful. Section 6.3 uses the theoretical background to estimate four models. The ﬁrst model is without any variables that relate to location, while model 2, 3, and 4 are with the inclusion of province, COROP, and municipality dummies, respectively. To correct for possible overﬁtting 63 , a training, validation,

and test procedure will be done on the dataset. Section 6.4 explains the code of model 5, which

is based on estimating local models on the basis of the 30 houses in the database that are the

63 This occurs when a model is too closely fit to the dataset (overtrained), so that it might lose predictive performance.

100

most closely to the subject. Section 6.5 concludes this chapter by choosing the best performing model that will be presented to DataQuote.

6.1 Transforming Variables and Conceptual Model

To reduce the skewness of certain variables, they have been transformed by taking the natural logarithm. This is the case for “Updated listing price” , “Surface”, and “Volume” . To allow for nonlinearity, the variable “Building year” has been divided into category dummies. CBS (2013) uses the following building year categories: before 1906, 1906-1945, 1945-1960, 1960-1975, 1975-

1990, 1990-2000, 2000 and after. Based on the history of the Dutch owner-occupied housing market after the Second World War (see section 2.3), important years or periods are: 1945 (end of Second World War, building houses as fast as possible), 1960 (from reconstruction and high density expansion to clustered de-concentration and higher quality), second half 1970s (cuts in subsidies, oil crisis, collapse of owner-occupied market), second half 1980s (economic recovery, increase in owner-occupied houses), 1990s (eﬃciency and deregulation, more responsibility to municipalities, Grossing and Balancing Act), and 2007-2008 (ﬁnancial crisis) and after. Assum- ing the categories of the CBS before the Second World War, this leads to the category dummies: before 1906, 1906-1945, 1945-1960, 1960-1975, 1975-1985, 1985-1990, 1990-2007, 2007 and after.

All in all, the “Log of Updated Listing Price” is the dependent variable, while independent variables are: “Building year” , “Log of surface” , “Log of volume”, and dummies for: “Type of transaction” , “Monumental status” , “Housing type” and possibly “Location” (province, COROP and municipality, respectively). This can be summarized in a conceptual model (see ﬁgure 6).

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<1906 1906-1945 1945-1960 1960-1975 1975-1985 1985-1990 1990-2007 Building year Buildingyear >2007

Log of surface

Log of volume

K.k. Log of updated listing price

V.o.n.

Type of of Type transaction transaction

Yes status status Monumental Monumental

Apart ment Detached Semi -detached

type type Corner Housing Attached

Figure 6: Conceptual model (with the exclusion of “Location”), where “Log of surface” and “Log of volume” are continuous variables, while all other variables are category dummies

6.2 Multivariate regression

The ﬁrst model, without the inclusion of a locational variable, that will be estimated is:

ln ( ) = + + ln() + ln() + + + + , with i=1-167,963, in

which , ,, , , , and refer to the updated listing price, building year, sur- 102

face, volume, transaction type, monumental status, and housing type of house i, respectively.

The betas are the to be estimated regression coeﬃcients and is the error term corresponding to house i. Other models include an additional location indicator. The model can be rewritten

to: ln ( ) = + , in which is the transpose of a 7x1 column vector, resulting in a row

vector of 1x7 (including a constant of 1 for the corresponding intercept ) variables, and is a column vector of 7x1 estimated regression parameters. A further reduction leads to: ln ( ) =

+ , where ln() and are 167,963x1 row vectors, and where X is a 167,963x7 matrix.

Given this equation, for every value of the actual betas ( ) one can implement a row vector of such that the equation holds for each case. Assumptions have to be made to give the model a meaning. The residual for house i is the estimation of the error term: = ln( ) − () ,

in which the observed log of the updated listing price is subtracted from the estimated log of the

updated listing price, by using . This residual should be as close to zero as possible for all

cases. This has been done by minimizing the sum of squares of the residuals (ordinary least

squares): , , . To minimize this function, () = = (ln( ) − )²

the ﬁrst order condition is given by: , , resulting in: , −2 − = 0 =

, , which leads to: , , . Whether the estimator is a = ( ) good approximation of the observed depends on assumptions that are made on the distribution of and its relation to (De Jong et al., 2013). The Gauss-Markov theorem gives four conditions for the estimator to be the best linear unbiased estimator (BLUE) for under ordinary least squares for large samples: E {} = 0 for i=1-167,963; and are independent;

≠ variance {} = for i=1-167,963, and the covariance , = 0 for i,j=1-167,963 when i j (De

Jong et al., 2013). The ﬁrst condition states that, on average, the regression line is correct. The

second condition implies that and are independent, but does not rule out a dependency for ≠ i j, so: E {} = 0. The third condition implies that error terms have ﬁnite variance, which is 103

(assumed to be homoskedastic). The fourth term states that there is zero correlation between the error terms. Under the four Gaus-Markov conditions, approximate a normal distribution under OLS regression. Endogenous variables are those variables that correlate with the error term. Causes of endogeneity might be reverse causality (causal relation from independent to dependent variable), measurement error and/or omitted variables. In addition to the Gaus-

Markov conditions, it will be tested whether the error term is mean independent from transformations of “Size” : E( )=0, where is the respective transformation. | τ

6.3 Results before Optimization

Models can be developed according to the theory of multivariate regression. To derive at models, the relation between interval or ratio variables and the dependent variable should be analyzed to test whether a transformation of independent variables leads to a better linear association (subsection 6.3.1). In addition, parametric variables may be correlated (subsection

6.3.2), so that further adjustments need to be made (subsection 6.3.3). To prevent from overfit- ting and to attain as much predictive power as possible, the final dataset is divided in a training, validation, and test set (subsection 6.3.4). To assess whether people value locational characteristics, dummies for location have been added. The first model is the benchmark model, which does not contain a locational indicator (subsection 6.3.5). The second model includes an indicator for the province in which a house is located (subsection 6.3.6). The third and fourth model includes locational indicators for COROP number and municipality, respectively.

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6.3.1 Relation between Independent Continuous Variables and the Dependent

Variable

The independent ratio and interval variables ( “Log of volume” and “Log of surface” ) can be plotted against the dependent variable ( “Log of updated listing price” ) in a scatterplot to analyze whether there is a linear association or whether these independent variables should be transformed. Results of the scatterplots (see figure 5 and 6) indicate that a linear association is ap- proximately true for part of the data points. For other data points, this linear association does not hold. The linear regression line (see blue line in figure 5 and 6) is the result of regressing the independent variable on the dependent variable, by assuming OLS. Compared to a nonparametric LOWESS line 64 (see red line in figure 7 and 8), and its 95% confidence bands (see dashed red line in figure 5 and 6), the linear regression line is not assumed to serve as a good approximation. “Log of updated listing price” is underestimated in the linear regression model for low values of “Log of surface” (<4.3, which is below 74m²) and “Log of volume” (<5.5, which is below

245m³), for a range of “Log of surface” between 4.9 (134m²) and 6.2 (493m²), and for a range of

“Log of volume” between 6.1 (446m³) and 7.8 (2,441m³).

64 Referring to locally weighted scatterplot smoothing, which uses nonparametric regressions methods to plot a best fitting line. For more information, see Cleveland (1981).

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Figure 7: Scatterplot of the dependent variable versus the independent variable “Log of surface” (x-axis)

Figure 8: Scatterplot of the dependent variable (y-axis) versus the independent variable “Log of volume” (x- axis)

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6.3.2 Correlations

Bivariate correlations 65 between “Log of updated listing price” , “Log of surface”, and “Log of volume” can be analyzed to express the association between the variables in a number. The positive association between “Log of updated listing price” and “Log of surface”, and “Log of updated listing price” and “Log of volume” is already known from scatter plots (see subsection

6.3.1). Results indicate that the bivariate correlation between “Log of updated listing price” and

“Log of surface” is 0.760, while the bivariate correlation between “Log of updated listing price” and “Log of surface” is 0.743 (see table 8). With regard to the correlations of “Log of surface” and “Log of volume” with “Log of updated listing price” , the null hypothesis states that there is no actual correlation ( ), while the alternative hypothesis states that there is a correla- : = 0 tion ( ). The significance is defined as the probability that the corresponding correla- : ≠ 0 tion is a result of chance. The significances are smaller than 0.05 (5% level), meaning that is rejected. There are indeed positive and significant correlations between the two independent variables and the dependent variable. In addition, the correlation between “Log of surface” and

“Log of volume” is 0.959. This correlation is also signiﬁcant, meaning that if one of these variables changes, the other variable changes in almost the same proportion and in the same direction. This is conforming intuition; if the surface increases, the volume should also increase be-

65 A measure between -1 and 1 that expresses the strength of the linear dependence of one variable on another. It is a measure for how two variables move in relation to each other. Perfect positive correlation (value of 1) means that there is a linear equation that describes the relation between the variables perfectly, where one variable increases as the other increases (i.e. all point lie on this line). Perfect negative correlation (value of -1) means that the point still fall on a linear line, but one variable increases when the other variable decreases. A value of 0 indicates that there is no linear correlation between the variables. The Pearson correlation coefficient is calculated as:

, , , ] = , where the upper (left) = [ ( − )( − )]/[ ( − )² ( − )² () term of the division is the covariance between x and y ( ) and the lower (right) term is the product of the standard deviations of x and y ( ). 107

cause by deﬁnition, volume is the surface multiplied by a height. To account for this interaction eﬀect, partial correlations can be derived.

Table 8: Bivariate correlations between the three variables that can be used to perform parametric analysis Log of updated Log of Log of listing price surface volume Log of updated Pearson Correlation 1.000 0.760 0.743 listing price Significance .000 .000 Log of surface Pearson Correlation 0.760 1.000 0.959 Significance .000 .000 Log of volume Pearson Correlation 0.743 0.959 1.000 Significance .000 .000

The partial correlation is the unique correlation, after correcting for one or more controlling variables. The partial correlation between “Log of updated listing price” and “Log of surface” , after controlling for “Log of volume” is 0.252, which has a signiﬁcance of .000 (signiﬁcant at the

5% level). The partial correlation between “Log of updated listing price” and “Log of volume” , after controlling for “Log of surface” is 0.073, which has a signiﬁcance of .000 (signiﬁcant at the

5% level). Based on this, it is preferred to include “Log of surface” and exclude “Log of volume” .

In addition, both variables can be converted into linearly uncorrelated principal components. All in all, they cannot be used both in regression procedures because their collinearity leads to an increase in the standard error, when performing regression analysis.

6.3.3 Principal Component Analysis

According to Pearson (1901), Principal Component Analysis (PCA) is a form of multidimen- sional scaling to reduce the dimensions of data, while retaining as much information as possible.

Components are linear transformations that choose the variables, which are used to perform

PCA, such that the most variance lies on the ﬁrst axis, the second most variance on the second

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axis, etc. (the number of components is equal to the number of variables used for PCA). All axes are orthogonal, meaning that they are perpendicular to each other in a k-dimensional space, where k is the number of variables used in PCA. The perpendicularity results into the fact that components are uncorrelated, which solves issues associated with highly correlated variables. This lack of correlation is achieved by shifting the origin axis to the centroid of the data and by then rotating the axis. The direction of the first axis is determined both by the shape of the data cloud and by the eigenvector of the largest eigenvalue of the correlation matrix 66 . The second axis is, by definition, perpendicular to the first, and goes in the direction of the second biggest eigenvalue of the correlation matrix. Loadings on components should be analyzed to interpret a component. They are defined as the correlations between the component and the original variables.

“Log of surface” and “Log of volume” are highly correlated variables (correlation of 0.96), which can be reduced to one or two uncorrelated components. After performing PCA, the ﬁrst component explains 97.97% of the total variance and this component has a standard deviation of

0.56. The second component explains 2.01% of the total variance and has a standard deviation of 0.20. The loadings of the ﬁrst component are 0.71 on “Log of surface” and 0.71 on “Log of volume” , while for the second component they are -0.71 on “Log of surface” and 0.71 on “Log of volume”. The ﬁrst component can be summarized as “Size” , which follows from the loadings.

There are three different rules of thumb to choose which component(s) should be retained. First, the total variance explained by a component needs to be sufficient, whereas sufficiency is determined on an ad hoc basis because it varies per field of study and per dataset (Jackson and

66 If the correlation matrix C is a matrix of kxk, which has the largest eigenvalue λ, the eigenvector V is calculated such that: . Eigenvalues are defined by Hoffman and Kunze (1971) as scalars associated with a linear × = × system of equations (e.g. matrix equations). They also state that decomposition of eigenvalues and eigenvectors is always possible if the matrix is kxk (square).

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Chen, 2004). Second, Kaiser (1958) states that components with eigenvalues that are less than the average eigenvalue, which is one when considering the correlation matrix, should be dropped

(Kaiser’s Criterion). According to Jackson (1993), Kaiser’s Criterion leads to the issue of retaining too many components when the number of variables that are considered for PCA are below

20. Third, a scree plot (components on x-axis and corresponding eigenvalues on y-axis) can be used to detect an elbow in the plot, because variables are ranked from left to right from biggest eigenvalue to lowest. Cattell’s scree test states that all components after the elbow should be dropped (Cattell, 1966).

There are two components, so the Cattell’s scree test does not provide any guidance. A scree plot of eigenvalues of the components in relation to the number of components can be constructed, which indicates that the eigenvalue of the second component is below the average of 1 (see

ﬁgure 9). In addition, the second component only explains 2.01% of the total variance. This leads to the conclusion to retain component 1, and to exclude component 2. The correlation between component 1 ( “Size” ) and “Log of updated listing price” is 0.76, while the correlation with the original variables ( “Log of surface” and “Log of volume” ) is 0.99.

Figure 9: Scree plot of the eigenvalues (y-axis) versus the number of components (x-axis), where the red line is the average eigenvalue of 1, and where dots indicate the eigenvalues of the components

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Principal Component Regression refers to the use of one or more components in regression analysis instead of the independent variables on which PCA was performed.

6.3.4 Cross-Validation with Training, Validation and Test Sets

The final dataset of 167,963 cases has been split randomly, without replacement, into a training, validation, and test set. Each of these sets contain 1/3 of the cases (55,987 cases per set, constitutes the loss of 2 cases). The cases per set are unique, because they are randomly chosen without replacement. By doing so, all cases are distributed over 3 sets, so that there are no duplicates within or across sets. The sets are constructed to test whether estimated regression coefficients of the training set are consistent with the validation set. In addition, the regression coefficients of the validation set are used to predict the “Log of updated listing price” of the test set, given the values of the variables in the test set (cross-validation). The predicted values can then be compared to the actual “Log of updated listing price” , resulting in the prediction accuracy. The goal of this subsampling is to not overfit the model on the training and validation data, so that it can be used to accurately predict the market value of houses, which are not in the training and validation sample. Summary statistics of variables on a ratio scale reveal that the individual sets are a representation of the total dataset (see table 9). An analysis of nominal and ordinal variables (and mapping cases) also indicates this.

Table 9: Summary statistics of variables on a ratio scale from the total dataset and the training, validation and test set TOTAL DATASE T Mean Median Standard Minimum Maximum Deviation Log of updated listing 12.36 12.31 0.48 10.82 14.73 Size 0.00 0.02 0.56 -3.08 3.0

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TRAINING SET Mean Median Standard Minimum Maximum Deviation Log of updated listing 12.36 12.30 0.48 10.82 14.73 Size 0.00 0.02 0.56 -3.08 2.71

VALIDATION SET Mean Median Standard Minimum Maximum Deviation Log of updated listing 12.36 12.31 0.48 10.84 14.68 Size 0.00 0.02 0.56 -3.08 2.95

TEST SET Mean Median Standard Minimum Maximum Deviation Log o f updated listing 12.36 12.31 0.48 10.82 14.71 Size 0.00 0.03 0.56 -2.93 2.95

6.3.5 Model 1: Excluding Locational Indicators

The first model excludes direct locational indicators. This model follows the conceptual model (see figure 4). Statistical significance implies that the regression coefficients approximate their true value (i.e., have a low standard error). Results (see table 10) indicate that in the training set, all variables except the building year category “After 2007” are statistically significant at the

1% level or less. In the validation set, all variables are statistically significant at the 1% level or less. However, building year category “After 2007” has a small t-value (high standard error in relation to its coefficient) in the validation set. In addition, its coefficient deviates from the coefficient in the training set. All other categories and variables have rather similar coefficients, standard errors and t-values when comparing the training with the validation set. Coefficients for “Building year” categories and “Type of transaction – v.o.n.” seem to deviate more than the other coefficients. To test whether or not there is a statistical difference between coefficients in the training and validation set, a Z score has been calculated. This Z score is defined as:

, where and refer to the estimated coeﬃcient of = (, − ,)/((,) + (,)) , , 112

variable i in the training and validation set, respectively. The null-hypothesis states that there is no difference between the coefficient of variable i in the training and validation set, while the alternative hypothesis states that there is a difference. Results indicate that, at the 95% level, most coefficients of variables in the training set are statistically similar to those in the validation set (i.e. assume the null-hypothesis), except: “Monumental status”, and housing types “Corner” and “Apartment” . The sampling has been done at random without replacement, meaning that those differences can be contributed to chance (i.e., bad luck in the sampling process).

Table 10: Results of the training and validation set for model 1 Variable name Coefficient Std. err. t-value Coefficient Std. err. t-value Training Training Training Validation Validation Validation Intercept 12.385710 *** 0.004091 3,027.686 12.377951 *** 0.004135 2,993.163 Building year Before 1906 0.053005 *** 0.007332 7.229 0.063638 *** 0. 007297 8.721 1945 -1960 -0.134234 *** 0.004973 -26.994 -0.118088 *** 0.004989 -23.669 1960 -1975 -0.204246 *** 0.004074 -50.138 -0.196061 *** 0.004105 -47.756 1975 -1985 -0.165567 *** 0.004453 -37.181 -0.155093 *** 0.004469 -34.707 1985 -1990 -0.066238 *** 0.005764 -11.493 -0.054417 *** 0.005806 -9.373 1990 -2007 -0.03 3744 *** 0.003799 -8.882 -0.025082 *** 0.003818 -6.570 After 2007 0.007844 0.006753 1.162 0.020727 *** 0.006616 3.133 Size 0.662404 *** 0.002807 235.950 0.659889 *** 0.00280 1 235.589 V.o.n. transaction -0.034573 *** 0.006397 -5.404 -0.046506 *** 0.006290 -7.393 Monumental status 0.184100 *** 0.014124 13.035 0.181239 *** 0.014455 12.538 Housing type Apartment 0.150092 *** 0.004225 35.522 0.146520 *** 0.004242 34.538 Detached 0.14 4114 *** 0.004071 35.403 0.145912 *** 0.004081 35.757 Corner -0.0 29398 *** 0.006177 -4.759 -0.024945 *** 0.006170 -4.043 Attached -0.061939 *** 0.003870 -16.006 -0.062410 *** 0.003905 -15.982 Nr. of observations 55,987 55,987 Adjusted R ² 0.6513 0,6517 The dependent variable is the logarithm of the updated listing price. For the dummy variables, the remaining categories (building year category “1906-1945”, type of transaction “k.k.”, monumental status “no”, and housing type “semi-detached”) act as reference values *** = significant at 1%.

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To test the economic effect of non-dummy independent variables on “Log of updated listing price” , a one standard deviation in an independent variable multiplied by its coefficient results in the marginal effect. A one standard deviation in the “Size” measure of the validation set equals

0.562747. Multiplying this by its coefficient (0.659889) yields a marginal effect on “Log of updated listing price” of 0.371350 or 37.14%. The economic significance of all dummy variables in the regression can be derived in relation to their reference category and a house that scores 0 on all variables, except the dummy variable of interest. This reveals the economic significance of a dummy variable in relation to the intercept. For example: a house that has a monumental status has, in relation to the intercept in the validation set, an additional monetary value of

(..) . EUR 47,193 (with a standard error of EUR 4,145), while a − = house that is sold as “v.o.n.” has a value that is EUR 10,793 lower (standard error of EUR

1,422) than its reference category (“k.k.” ), in relation to the intercept. It is clear that a decrease of EUR 10,793 on the monetary value of the intercept is economically signiﬁcant. To the contrary, the building year category “After 2007” is less economic signiﬁcant (EUR 4,974, with a standard error of EUR 1,610).

With regard to the residuals of the validation set, they have been standardized 67 and results show that they have a mean of 0 (also after transforming “Size” ) and ﬁnite variance. This leads to the conclusion that, on average, the estimated regression coeﬃcients are a good approximation of their true value (they are BLUE). In addition, they approximate a normal distribution

(see ﬁgure 10).

67 Dividing the residuals by their standard deviation in order to make comparisons: , where E{ }=0. ( − )/ 114

Figure 10: Scatterplots of the standardized residuals versus transformations of “Size” and histogram of standardized residuals of model 1

All in all, the validation set of the first model has a goodness-of-fit (R²) of 65.17%. The estimated regression coefficients of the validation set can be used to predict “Log of updated listing price” in the test set. This estimation can then be compared to the actual value of “Log of updated listing price” to conclude how well the validation set of model 1 predicts. The percentage difference between the predicted (fitted) value and the actual value of “Log of updated price” in the test set is calculated by subtracting the exponent of the fitted value (because it is estimated as a log variable) from the actual value, and by then dividing this by the actual value:

() . The median percentage diﬀerence is 17.51%. This is = ( − )/ assumed to be too high, which is veriﬁed by DataQuote. Regarding the test set, 73.58% of the

55,987 cases have a percentage difference lower than 30%, 55.65% of the cases have a percentage difference lower than 20%, 30.35% of the cases have a percentage difference lower than 10% and 115

15.46% has a percentage diﬀerence lower than 5%. There are outliers in the dataset that impose a bias on the results (see ﬁgure 11).

Figure 11: Scatterplot of the ﬁtted versus the actual updated listing prices of model 1 (the red line indicates the mean of the listing price, conditional on its independent variables)

6.3.6 Model 2: Including Province as Locational Indicator

The second model includes the same variables as the first model and has a dummy that refers to province in which a house is located. Results indicate similar coefficients, standard errors and t-values for variables in the training and validation set (see table 11). All variables are statistically significant at the 1% level or less in both the training and the validation set. Variables with low t-values, which indicate less statistical significance than other variables, are: building year category “1985-1990” and the provinces “Friesland” and “Limburg”. Less economic significant variables are: building year categories “1985-1990” and “1990-2007”, “Type of transaction – v.o.n.”, and province “Friesland” . In relation to the first model, the intercept and housing type

“Apartment” decreased in coeﬃcient and t-value and the housing type “Attached” increased its

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Table 11: Results of the training and validation set for model 2 Variable name Coeﬃcient Std. err. t-value Coeﬃcient Std. err. t-value Training Training Training Validation Validation Validation

Intercept 12.125760 *** 0.006623 1,830.909 12.124336 *** 0.006635 1,827.212 Building year Before 1906 0.055439 *** 0.006391 8.675 0.065202 *** 0.006360 10.252 1945 -1960 -0.099664 *** 0.004350 -22.910 -0.085754 *** 0.004366 -19.643 1960 -1975 -0.151706 *** 0.003589 -42.270 -0.144373 *** 0.003617 -39.916 1975 -1985 -0.109587 *** 0.003915 -27.992 -0.104092 *** 0.003927 -26.507 1985 -1990 -0.025661 *** 0.005047 -5.085 -0.016969 *** 0.005086 -3.337 1990 -2007 0.021679 *** 0.003362 6.448 0.031278 *** 0.003382 9.248 After 2007 0.058181 *** 0.005912 9.841 0.072932 *** 0.005795 12.585 Size 0.648481 *** 0.002471 262.477 0.647679 *** 0.002468 262.442 V.o. n. transaction -0.036330 *** 0.005582 -6.508 -0.044815 *** 0.005489 -8.164 Monumental status 0.183558 *** 0.012311 14.910 0.183736 *** 0.012603 14.579 Housing type Apartment 0.049880 *** 0.003804 13.111 0.043278 *** 0.003832 11.294 Detached 0.160094 *** 0.003555 45.032 0.158937 *** 0.003562 44.626 Corner -0.046378 *** 0.005386 -8.611 -0.048705 *** 0.005382 -9.050 Attached -0.118086 *** 0.003420 -34.529 -0.121117 *** 0.003452 -35.088 Province Flevoland 0.066126 *** 0.008149 8.115 0.060628 *** 0.008104 7.482 Friesland 0.023004 *** 0.007572 3.038 0.020761 *** 0.007540 2.753 Gelderland 0.207855 *** 0.006394 32.507 0.203220 *** 0.006405 31.729 Groningen -0.098775 *** 0.009838 -10.040 -0.101738 *** 0.009942 -10.234 Limburg 0.060830 *** 0.008530 7.131 0.056055 *** 0.008488 6.604 Noord -Brabant 0.234355 *** 0.006340 36.964 0.234515 *** 0.006346 36.954 Noord -Holland 0.462705 *** 0.006248 74.059 0.462000 *** 0.006244 73.987 Overijssel 0.109408 *** 0.007071 15.474 0.109518 *** 0.007041 15.555 Utrecht 0.411991 *** 0.006696 61.523 0.406801 *** 0.006692 60.788 Zeeland 0.159502 *** 0.008864 17.994 0.158648 *** 0.009015 17.598 Zuid -Holland 0.302858 *** 0.006210 48.769 0.296250 *** 0.006214 47.673 Nr. of observations 55,987 55,987 Adjusted R ² 0.7355 0.7356 The dependent variable is the logarithm of the updated listing price. For the dummy variables, the remaining categories (building year category “1906-1945”, type of transaction “k.k.”, monumental status “no”, housing type “semi-detached”, and province “Drenthe”) act as reference categories or values. *** = signiﬁcant at 1%.

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absolute value of the coefficient and t-value. Z-scores can be calculated in the same way as in model 1, to test whether the coefficients of variables in the training set are statistically similar to the coefficients on the same variables in the validation set or not. Results indicate the null- hypothesis (the coefficient of a variable in the training set is statistically similar to the coefficient of the same variable in the validation set) is assumed for every variable in model 2 at the

95% level.

The residuals of the validation set have a mean of 0 (also after transforming “Size” ), finite variance and they approximate a normal distribution (see figure 12). So, on average, the estimated regression coefficients are a good approximation of their true value.

Figure 12: Scatterplots of the standardized residuals versus transformations of “Size” and histogram of standardized residuals of model 2

All in all, the validation set of the second model has an R² of 73.56%. This model has been used to predict “Log of updated listing price” in the test set. The median percentage diﬀerence

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between actual and fitted values of “Updated listing price” in the test set is 14.78%. This is comparable to model 1, which is assumed to be too high (verified by DataQuote). Regarding the test set, 80.56% of the 55,987 cases have a percentage difference lower than 30%, 63.14% of the cases have a percentage difference lower than 20%, 35.41% of the cases have a percentage difference lower than 10% and 18.33% has a percentage difference lower than 5%. There are outliers in the dataset that impose a bias on the results (see figure 13).

Figure 13: Scatterplot of the fitted versus the actual updated listing prices of model 2 (the red line indicates the mean of the listing price, conditional on its independent variables)

6.3.7 Model 3: Including COROP as Locational Indicator

The third model includes the same variables as the ﬁrst model and has a dummy that refers to the COROP number in which a house is located. This reduces the scale in relation to model

2. Results indicate similar coeﬃcients, standard errors and t-values for variables in the training and validation set (see table 12). All variables, except COROP number 2, 8, and building year category “1985-1990” in the validation set, are statistically signiﬁcant at the 1% level or less in

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both the training and the validation set. COROP number 8 is statistically signiﬁcant at the 5%

level and building year category “1985-1990” is statistically signiﬁcant at the 5% level in the

validation set. Variables with low t-values, which indicate less statistical signiﬁcance than other

variables, are: building year category “1985-1990” , housing type “Apartment”, and COROP

number 2, 3 and 8. Less economic signiﬁcant variables are: building year categories “1985-1990”

and “1990-2007” , housing type “Apartment”, and COROP number 2. In relation to the second

model, the intercept, housing type “Apartment”, and building year categories “1960-1975” and

“1975-1985” decreased in coeﬃcient and t-value, while building year categories “1990-2007” and

“2007 and after” increase in coeﬃcient and absolute value of the t-value. Z-scores can be calcu-

lated in the same way as in model 1 and 2, to test whether the coeﬃcients of variables in the

training set are statistically similar to the coeﬃcients on the same variables in the validation set

or not. Results indicate the null-hypothesis (the coeﬃcient of a variable in the training set is

statistically similar to the coeﬃcient of the same variable in the validation set) is assumed for all

variables in model 3 at the 95% level, except COROP number 38 (“Midden-Limburg” ).

Table 12: Results of the training and validation set for model 3 Variable name Coeﬃcient Std. err. t-value Coeﬃcient Std. err. t-value Training Training Training Validation Validation Valid.

Intercept 11.991193 *** 0.012750 940.464 11.98 8680 *** 0.013187 909.095 Building year Before 1906 0.057824 *** 0.006002 9.634 0.065061 *** 0.005974 10.892 1945 -1960 -0.092470 *** 0.004088 -22.618 -0.080452 *** 0.004102 -19.612 1960 -1975 -0.134785 *** 0.003398 -39.670 -0.128653 *** 0. 003423 -37.582 1975 -1985 -0.090267 *** 0.003703 -24.380 -0.086516 *** 0.003714 -23.295 1985 -1990 -0.019750 *** 0.004752 -4.156 -0.012261 ** 0.004789 -2.560 1990 -2007 0.036989 *** 0.003185 11.614 0.044650 *** 0.003204 13.935 After 2007 0.0 81288 *** 0.005569 14.598 0.094956 *** 0.005460 17.390 Size 0.645332 *** 0.012750 276.901 0.644599 *** 0.002327 277.028 V.o.n. transaction -0.042797 *** 0.005241 -8.166 -0.052320 *** 0.005155 -10.150 Monumental status 0.169331 *** 0.011555 14.654 0.173746 *** 0.011827 14.690

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Housing type Apartment 0.017973 *** 0.003624 4.959 0.011478 *** 0.003653 3.142 Detached 0.159266 *** 0.003338 47.707 0.158618 *** 0.003342 47.456 Corner -0.047017 *** 0.005053 -9.305 -0.047880 *** 0.005049 -9.483 At tached -0.126065 *** 0.003215 -39.216 -0.129081 *** 0.003246 -39.769 COROP 2: Delfzijl and vicinity 0.024319 0.034228 0.711 0.005543 0.034205 0.162 3: Remaining Groningen 0.044600 *** 0.015891 2.807 0.048612 *** 0.016281 2.986 4: Nort h-Friesland 0.136257 *** 0.014108 9.658 0.127662 *** 0.014475 8.819 5: Southwest -Friesland 0.218697 *** 0.016383 13.349 0.224044 *** 0.016457 13.614 6: Southeast -Friesland 0.129062 *** 0.014903 8.660 0.141124 *** 0.015358 9.189 7: North -Drenthe 0.149600 *** 0.015056 9.936 0.150292 *** 0.015363 9.783 8: Southeast -Drenthe 0.032487 ** 0.015332 2.119 0.040474 ** 0.015769 2.567 9: Southwest -Drenthe 0.204518 *** 0.015946 12.826 0.208564 *** 0.016395 12.759 10 : North -Overijssel 0.302091 *** 0.013881 21.763 0.312619 *** 0.014287 21.882 11: Southwest -Overijssel 0.281340 *** 0.016161 17.408 0.280617 *** 0.016395 17.116 12: Twente 0.167342 *** 0.013713 12.204 0.169391 *** 0.014088 12.024 13: Veluwe 0.417749 *** 0.013584 30.752 0.41622 3*** 0.014030 29.667 14: Achterhoek 0.227632 *** 0.013758 16.545 0.229652 *** 0.014175 16.201 15: Arnhem/Nijmegen 0.346564 *** 0.013117 26.421 0.349900 *** 0.013572 25.780 16: Southwest -Gelderland 0.329075 *** 0.014869 22.131 0.321517 *** 0.015 191 21.165 17: Utrecht 0.546714 *** 0.012835 42.595 0.546227 *** 0.013275 41.146 18: Kop van Noord -Holland 0.280255 *** 0.013813 20.289 0.276905 *** 0.014297 19.369 19: Alkmaar and vicinity 0.482339 *** 0.014569 33.108 0.485724 *** 0.014924 32. 547 20: IJmond 0.485099 *** 0.016080 30.168 0.508419 *** 0.016456 30.895 21: Agglomerate Haarlem 0.686400 *** 0.014510 47.304 0.676508 *** 0.015044 44.970 22: Zaanstreek 0.388092 *** 0.015358 25.270 0.385675 *** 0.015598 24.726 23: Groot -Amsterdam 0.719327 *** 0.012888 55.813 0.721652 *** 0.013323 54.164 24: Het Gooi and Vechtstreek 0.684310 *** 0.014184 48.244 0.692367 *** 0.014514 47.703 25: Agglomerate Leiden 0.619440 *** 0.013745 45.066 0.617234 *** 0.014142 43.645 26: Agglomerate ‘s-Gravenhage 0.444674 *** 0.013127 33.875 0.445430 *** 0.013562 32.845 27: Delft and Westland 0.493430 *** 0.015140 32.591 0.491131 *** 0.015644 31.394 28: East Zuid -Holland 0.455428 *** 0.014338 31.763 0.459584 *** 0.014888 30.870 29: Groot -Rijnmond 0.384528 *** 0.012963 29.664 0.379494 *** 0.013407 28.306 30: Southeast Zuid -Holland 0.339162 *** 0.014024 24.185 0.336712 *** 0.014419 23.352 31: Zeeuwsch -Vlaanderen 0.152895 *** 0.018911 8.085 0.167674 *** 0.019632 8.541 32: Remaining Zeeland 0.323085 *** 0.014297 22.597 0.322822 *** 0.014786 21.833

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33: West Noord -Brabant 0.342195 *** 0.014107 24.257 0.346606 *** 0.014583 23.767 34: Mid Noord -Brabant 0.359180 *** 0.013593 26.425 0.364288 *** 0.013998 26.024 35: Northeast Noord-Brabant 0.375975 *** 0.013300 28.268 0.382730 *** 0.013750 27.835 36: Southeast Noord-Brabant 0.365403 *** 0.013236 27.607 0.368319 *** 0.013672 26.939 37: North Limburg 0.212487 *** 0.017165 12.379 0.211717 *** 0.017419 12.154 38: Mid Limburg 0.204218 *** 0.016796 12.159 0.209343 *** 0.016969 12.337 39: South Limburg 0.169503 *** 0.015151 11.187 0.165812 *** 0.015531 10.676 40: Flevoland 0.192306 *** 0.013564 14.178 0.191422 *** 0.013962 13.710 Nr. of observations 55,987 55 ,987 Adjusted R ² 0.7675 0.7676 The dependent variable is the logarithm of the updated listing price. For the dummy variables, the remaining categories (building year category “1906-1945”, type of transaction “k.k.”, monumental status “no”, housing type “semi-detached”, and COROP “1: Oost-Groningen”) act as reference categories or values. *** = signiﬁcant at 1%. **=signiﬁcant at 5%.

The residuals of the validation set have a mean of 0 (also after transforming “Size” ), ﬁnite

variance and they approximate a normal distribution (see ﬁgure 14).

Figure 14: Scatterplots of the standardized residuals versus transformations of “Size” and histogram of standardized residuals of model 3

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So, on average, the estimated regression coefficients are a good approximation of their true value. All in all, the validation set of the second model has an R² of 76.76%. This model has been used to predict “Log of updated listing price” in the test set. The median percentage difference between actual and fitted values of “Updated listing price” in the test set is 13.95%. This is comparable to model 1 and model 2, which is assumed to be too high. Regarding the test set,

82.32% of the 55,987 cases have a percentage difference lower than 30%, 65.44% of the cases have a percentage difference lower than 20%, 37.45% of the cases have a percentage difference lower than 10% and 19.46% has a percentage difference lower than 5%. There are outliers in the dataset that impose a bias on the results (see figure 15).

Figure 15: Scatterplot of the fitted versus the actual updated listing prices of model 3 (the red line indicates the mean of the listing price, conditional on its independent variables)

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6.3.8 Model 4: Including Municipality as Locational Indicator

The fourth model includes the same variables as the ﬁrst model and has a dummy that refers to the municipality in which a house is located. This reduces the scale in relation to model 3.

Results indicate similar coefficients, standard errors and t-values for variables in the training and validation set (see appendix D). All variables, except building year category “1985-1990” , housing type “Apartment”, and several municipalities, are statistically significant at the 1% level or less in both the training and the validation set. Less economic significant variables are: building year category “1985-1990” housing type “Apartment”, and several municipalities with an absolute coefficient of less than 0.03. In relation to the third model, the intercept increased in coefficient and decreased in t-value, housing types “Apartment” and “Detached” , “Monumental status” , and building year categories “1960-1975” and “1975-1985” decreased in coefficient and t- value, while housing types “Corner” and “Attached”, and building year categories “1990-2007” and “2007 and after” increase in coefficient and absolute value of the t-value. Calculated Z-scores

(as in model 1, 2 and 3) yield that most coeﬃcient estimates of variables are statistically similar.

They should be similar because samples are taken at random without replacement. Therefore, it is assumed that any diﬀerences are due to chance.

The residuals of the validation set have a mean of 0 (also after transforming “Size” ), finite variance and they approximate a normal distribution (see figure 16). So, on average, the estimated regression coefficients are a good approximation of their true value.

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Figure 16: Scatterplots of the standardized residuals versus transformations of “Size” and histogram of standardized residuals of model 4

All in all, the validation set of the second model has an R² of 80.05%. This model has been used to predict “Log of updated listing price” in the test set. The median percentage difference between actual and fitted values of “Updated listing price” in the test set is 12.55%. This is comparable to model 1, 2 and 3, which is assumed to be too high (verified by DataQuote).

Regarding the test set, 86.22% of the 55,987 cases have a percentage diﬀerence lower than 30%,

70.28% of the cases have a percentage difference lower than 20%, 41.40 % of the cases have a percentage difference lower than 10% and 21.81% has a percentage difference lower than 5%.

There are outliers in the dataset that impose a bias on the results (see ﬁgure 17).

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Figure 17: Scatterplot of the ﬁtted versus the actual updated listing prices of model 4 (the red line indicates the mean of the listing price, conditional on its independent variables)

6.4 Results after Optimization

The previous models show that adding locational indicators improve the goodness-of-fit in the training and validation set in comparison to a model that does not have a locational indicator. In addition, including the municipality in which a house is located leads to a better goodness-of-fit in the training and validation set than including COROP numbers or provinces. Also, including COROP numbers leads to a better goodness-of-fit in both samples than including provinces. So, a decreasing locational scale, in terms of breadth, increases the goodness-of-fit. In addition, decreasing the scale also leads to a decrease in median percentage difference between

ﬁtted and actual updated listing price, within the test set. A model can be constructed that takes into account few variables (not complex) at a (very) local scale (subsection 6.4.1). Results should indicate whether this model predicts better than the previous models (subsection 6.4.2).

Then, this model can be further adjusted for outliers and “locality” (subsection 6.4.3).

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6.4.1 Model 5: Combining Strengths of Simplicity and Locality

The ﬁfth model performs local linear regressions. On the basis of a house’s X and Y coordinates, the thirty most nearby houses from the target are selected (based on the proposition of

Pythagoras). Only houses which are as close to the target house as possible (in distance) should be accounted for. This is because (very) local information is captured in the prices of these houses. Expanding the number of houses that are used for a local regression leads to an increase in average distance to the house and a blurring of the importance of local information (see distinction in R² between model 2, 3, and 4). Thirty nearby houses are arbitrarily chosen (subject for further research) to maintain locational specificity while also having “enough” cases for regression analysis (trade-off). This model is an Automated Valuation Model (AVM), because it depends on a code and an underlying dataset, which is equal to the final dataset (see chapter 5).

The code is written in a statistical software package, called: R-project 68 and starts with loading the ﬁnal dataset, a conversion table with zip codes, house numbers and X and Y coordinates and the basic cleaning rules (see section 5.1). Then, the user submits the zip code, number of nearby houses that should be used in the regression (K=30 by default), house number, housing type, surface, parcel surface 69 , volume, building year (exact or category), and whether the house

has a monumental status (as either “monument” or “no monument”) . The AVM computes the X

and Y coordinate of the target house (based on zip code and house number). In addition, hous-

ing type is derived from the random forest classiﬁcation method (see section 5.3) if the user does

not submit it or ﬁlls in an unknown housing type. Also, the variable “Size” is computed for the

target house (see subsection 6.3.3). A script automatically calculates the distance from all houses

in the dataset to the target house, based on X and Y coordinates and selects the given number

68 Language and environment for statistical modeling (see: http://www.r-project.org/ ).

69 Parcel surface is an input because this variable may not be close to zero for houses in certain areas.

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of houses that are most nearby. If building year is given as a number by the user, a script trans-

lates this into the used categories (see section 6.1). After this is done, there are several scenarios

in the AVM that are passed through in the following order, to derive a prediction of the target:

1. Rule : between 3 and K-5 monuments, two or more housing types with at least 3 cases per

type, and two or more building year categories with at least 3 cases per category.

Model : ln ( ) = + + ln( ) + + + + , where , , , , , and refer to “Updated listing price”, “Size”, and “Building year” of house i (where i reaches from 1 to K), while , , and refer to dummies for building year categories, housing types, and monumental status, respectively

(the number of dummies for housing types and building year categories depend on the variety

of housing types and building years in the sample of K houses).

2. Rule : between 3 and K-5 (K=30 by default) monuments and two or more housing types with

at least 3 cases per type.

Model : . ln ( ) = + + ln( ) + + + 3. Rule: between 3 and K-5 (K=30 by default) monuments and two or more building year

categories with at least 3 cases per category.

Model : . ln ( ) = + + ln( ) + + + 4. Rule: between 3 and K-5 (K=30 by default) monuments.

Model : . ln ( ) = + + ln( ) + + 5. Rule : less than 3 or more than K-5 monuments, two or more housing types with at least 3

cases per type and two or more building year categories with at least 3 cases per category.

Model : . ln ( ) = + + ln( ) + + + 6. Rule : less than 3 or more than K-5 monuments and two or more housing types with at least

3 cases per type. 128

Model : . ln ( ) = + + ln( ) + + 7. Rule: less than 3 or more than K-5 monuments and two or more building year categories

with at least 3 cases per category.

Model : . ln ( ) = + + ln( ) + +

The AVM assumes that all prices are updated (see subsection 3.2.3 with the addition that

housing type is known) and that new listings (with the independent variables) are properly

recorded. Data that does not match with the rules of dataset cleaning (see section 5.1) is auto-

matically deleted. In addition, the AVM is able to report the average price of a house in a zip

code, the average price of a house in a street and three comparable houses, with the same hous-

ing type, in the direct vicinity. This information will be used by DataQuote in valuation reports.

6.4.2 Results Model 5

The AVM can be used to value all houses in the ﬁnal dataset (167,963 houses). There are 8

houses with a diﬀerent X coordinate than is expected from the conversion table that links an X

and Y coordinate to a house on the basis of zip code and house number. These houses are re-

moved from the dataset, resulting in 167,955 cases. The percentage diﬀerence between ﬁtted

price and actual price can be calculated (recall: percentage difference = () ). The ( − )/ percentage difference of all 167,955 cases ranges from 0.00% to 1300.15% (see figure 18). The

average and the median percentage diﬀerence are 13.97% and 10.04%, respectively, while the

percentage diﬀerence has a standard deviation of 14.94%.

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Figure 18: Scatterplot of the actual versus the ﬁtted listing prices of model 5 (the red line indicates the mean of the listing price, conditional on its independent variables)

An issue arises because the percentage diﬀerence of a target cannot be determined because its current listing price is not recorded in the model. Therefore the estimation of the listing price by the AVM cannot be compared. To give an indication of the reliability of an estimated value,

STenV 70 and NWWI 71 (2012) state that the “accuracy” of a valuation performed by an AVM should be higher than or equal to 85%. If not, the target will be appraised by other AVM pro- viders that can guarantee this accuracy. They define accuracy as: 100% minus the median percentage difference between fitted and listing price of the objects that are used to get to the valuation. With regard to the AVM, all of the 167,955 cases can be seen as targets and they can

70 Foundation for valuations and validations of registered property in the Netherlands. STenV aims to set and test standards and regulation for valuation purposes and to adjust them to those of the International Valuation Stand- ards (IVS) so that they are internationally acknowledged.

71 Foundation that aims to increase the level of expertise, reliability and transparency of housing appraisals in the Netherlands.

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all be appraised after each other. The updated listing price of the target is removed, so that the accuracy is based on the 30 most nearby houses. Results indicate that the minimum and maximum accuracy are 60.00% and 100.00%, respectively. The average accuracy is 92.53%, while the median accuracy is 93.00%. The accuracy has a standard deviation of 2.86%. The amount of houses that have a percentage diﬀerence that is lower than 30% is 89.77% of the dataset

(150,781 out of 167,955 cases).

Aggregating percentage differences of all individual cases to municipality level by taking the median, gives insight in the predictive performance within a municipality (see appendix E columns 1-4). The median percentage difference between fitted and actual listing price per municipality ranges from 7.00% (in the municipalities: Urk, Wageningen, Bunschoten, Leiderdorp,

Spijkenisse and Zoetermeer) to 26.00% (in the municipalities: Vlieland and Vaals). The average and median of this percentage diﬀerence per municipality is 11.71% and 11.00%, respectively, while the standard deviation of the percentage diﬀerence per municipality is 2.59%. In addition, accuracy in a municipality can be calculated as an aggregation (taking the mean) of all recorded objects in a municipality. The minimum accuracy is 87% (in the municipalities Terschelling and

Valkenburg aan de Geul) and the maximum accuracy is 96% (in the municipalities Urk and

Vlist). The average and median accuracy are 92.34% and 92.50%, respectively and this accuracy has a standard deviation of 1.39%. The accuracy in a municipality can be plotted against the number of cases that are recorded in the dataset in a given municipality to conclude whether adding more cases in a municipality is beneficial (see figure 19). It follows that increasing the number of cases towards 1,000-1,200 in a municipality is beneficial (see figure 19, red line).

However, the goodness-of-ﬁt is 11.56% for a LOWESS line and 4.69% for a linear regression line, both indicating a weak relation between accuracy and number of cases.

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Figure 19: Scatterplot of accuracy per municipality versus the number of houses in a municipality

6.4.3 Adjusting Model 5 and Results after Adjustments

From the results of model 5 on an individual basis, three adjustments can be made to (possibly) increase the accuracy. First, cases that have a percentage diﬀerence above a certain thresh- old can be eliminated. Excluding cases with a percentage diﬀerence of more than 50%, results in deleting 13,446 cases (8.0% of the dataset). These cases have been manually analyzed by

DataQuote, which has led to the conclusion that these cases have false information on one or more variables. Second, if one of the K objects has exactly the same housing type, surface, volume, building year, and parcel surface as the target, that price is assumed to be the price of the target. This stems from the intuition that if a neighboring house is sold, this price most accurately reﬂects the true price of the target. Third and in addition to the second adjustment, the distance of K objects to the target can be weighted: weight , where distance is = 1/ the distance in meters. For example: the municipality Vlieland has 6 cases. Selecting cases that are further away (in other municipalities) decreases the relevance because neighborhood infor-

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mation is not captured anymore. Therefore, houses that are closer by should have more weight than houses that are further away.

Implementing these three rules leads to better results on an individual level (see table 13).

Table 13: Results of model 5 before and after implementing the three adjustments Percentage Number of % of total Number of % of total difference objects Before dataset Before objects After dataset After > 100% 403 adjustments 0.24%adjustments 0 adjustments 0.00%adjustments >95% 485 0.29% 0 0.00% >90% 570 0.34% 0 0.00% >85% 700 0.42% 0 0.00% >80% 859 0.51% 0 0.00% >75% 1,038 0.62% 0 0.00% >70% 1,322 0.79% 0 0.00% >65% 1,684 1.00% 0 0.00% >60% 2,239 1.33% 0 0.00% >55% 7,450 4.44 % 229 0.15% >50% 13,446 2.42% 782 0.51% >45% 13,898 8.01 % 2,017 1.31% >40% 14,119 8.41 % 3,991 2.58% >35% 15,892 9.46 % 7,203 4.66% >30% 17,174 10.23% 12,236 7.92% >25% 25,465 15.16% 19,717 12.76% >20% 38,014 22.63% 31,205 20.20% >15% 56,858 33.85% 48,677 31.50% >10% 84,246 50.16% 74,532 48 .24% >5% 122,458 72.91% 110,570 71.56% >0% 167,931 99.99% 154,478 99.98% ≥ 0% 167,955 100.00% 154,509 100% The percentage of cases that have a percentage difference of 10% or less increased, while there are fewer cases in the higher categories of percentage difference. These results are due to better selection criteria and the removing of cases that are based on false information. Imple- menting the first rule leads to a median percentage difference of 9.78% instead of 10.04%. Also the median accuracy of all cases increased from 93.00% to 93.56%. The standard deviation after

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implementing the ﬁrst rule is 12.51% for the percentage diﬀerence and 2.72% for the accuracy

(14.94% and 2.86%, respectively before implementing this rule). Deleting cases with a percentage difference above 50% leads to better local regressions (lower median percentage difference and higher median accuracy). Implementing the second rule does not lead to any improvement, nor to a decline in percentage difference or in accuracy. The intuition is that most objects are unique in the dataset. Similar houses will often be those that are attached because it is assumed that they will be more homogeneous than all other housing categories. They must be built in the same period, according to a “same-for-all” design. Although there are 43,854 attached houses (of the total of 163,886 houses), they are scattered among 43,795 zip codes. This corresponds to an average of 1.00 attached houses per zip code. Because this figure is so low, the second rule has a negligible influence of the percentage difference and the accuracy. In addition, because the distance between attached houses is relatively large, there might be other comparable houses in the direct vicinity which are taken into account at the expense of similar attached houses that are further away. Expanding the dataset with cases will be an improvement for the second rule.

With regard to the third rule, weighting leads to a further reduction in median percentage diﬀerence and an increase in accuracy. The median percentage diﬀerence is now 9.57% instead of

9.78%. Also, the median accuracy increased from 93.56% to 94.00% and the standard deviations of the median percentage diﬀerences and median accuracies for individual cases decreased from

12.51% and 2.72% to 10.61% and 2.60%, respectively. A better individual performance in terms of median percentage diﬀerence, median accuracy and standard deviation of both percentage diﬀerence and accuracy also leads to better performance on municipal level (see appendix E, columns 5-7).

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6.5 Sub-conclusion

After collecting potentially relevant variables, some variables have been transformed to decrease their skewness. This has led to the variables: “Log of updated listing price” , “Log of surface”, and “Log of volume” . To allow for nonlinearity, the variable “Building year” has been divided into category dummies. The variables “Type of transaction” , “Monumental status” , “Housing

type” and locational indicators are all nominal variables, of which the categories can be included in models as dummies.

Under the Gauss-Markov theorem for large samples, the estimated regression coeﬃcients under OLS are the best linear unbiased estimators. Four models have been developed under the assumptions of OLS regression, all having “Log of updated listing price” as dependent variable. In

addition, the ﬁnal dataset is divided into three equally sized sets: a training, validation, and test

set. The training, validation, and test procedure prevents from overﬁtting a model on one da-

taset. The regression results of the training set are compared with those of the validation set.

Then the estimated regression coeﬃcients of the validation set are used to predict “Log of updat-

ed listing price” in the test set. The percentage diﬀerence between the predicted (ﬁtted) and

actual value of “Log of updated listing price” can then be calculated to derive how well the model

predicts unknown datasets. Model 1 contains the independent variables “Building year” , “Size” ,

“Type of transaction” , “Monumental status”, and “Housing type”. Because the variables “Log of

surface” and “Log of volume” have a correlation of 0.959, they have been combined by using

principal component analysis. The ﬁrst component explains 97.97% of the total variance, has

loadings of 0.71 on both “Log of surface” and “Log of volume” and has an eigenvalue that is

greater than 1. This component is referred to as “Size” . The second component only explains

2.03% and has an eigenvalue than is less than 1. Therefore, this component will not be used in

regression analysis. Model 2, 3, and 4 contain the same variables as model 1, with the addition

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of a dummy for “Province” , “COROP number”, and “Municipality”, respectively. This is to test whether adding more regional or locational information of a house has a value (i.e. decreasing the scale from nation-wide to municipality). Results indicate that the validation set of model 1 has an R² of 65.17%. The R² of model 2, 3, and 4 are 73.56%, 76.76%, and 80.05%, respectively.

This leads to the conclusion that adding regional or locational information and decreasing the scale form province to municipality leads to a better goodness-of-ﬁt (performance). Regional or locational information has value because it leads to better regression performance. Also, the median percentage diﬀerence of model 1, 2, 3, and 4 is 17.51%, 14.78%, 13.95%, and 12.55%.

To keep the simplicity of the earlier models, while also acknowledging (very) local information, a fifth model has been developed. Model 5 selects the K (30 by default) most nearby houses of the target in the final dataset, based on X and Y coordinates. Parcel surface is now added as a variable because it may not approximate a constant in local regressions. Model 5 performs a regression on K houses to derive the value of the target. By doing so, locational information is already captured in the prices of nearby houses. The median percentage difference of this model is 10.04%. To further increase the performance of model 5, three rules have been added. First, cases that have a percentage difference above 50% have been excluded (cases based on false information). Second, if the target has exactly the same housing type, surface, volume, building year, and parcel surface as one of the K houses, that price is used as the price for the target. Third, the distance of the target to K houses is weighted so that the houses that are closer to the target get more weight. Results of these adjustments lead to a median percentage difference of 9.57%.

With regard to prediction (median percentage diﬀerence), model 5 performs better than model 1, 2, 3, and 4. Model 5 includes (very) locational information, depending on the distance to K nearby houses. Therefore, model 5, after the adjustments, has been automated (AVM) and presented to DataQuote. 136

Intuitively, adding more and more cases to the final dataset will further decrease the scale in which K nearby houses will be selected. However, results indicate that there is a nonlinear relation. Although the goodness-of-fit of this relation is rather low (11.56%), increasing the number of cases in a municipality above 1,100 is not considered beneficial.

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DISCUSSION

After analyzing the results of model 5, a discussion of the practical usefulness of this model aligns it with the theoretical background (section 7.1). Thereafter, a discussion of boom and bust cycles links housing markets to other (international) markets (section 7.2). By assuming this linkage, macro-economic trends can be analyzed that can be a cause of boom and bust cycles (section 7.3). It gives the reader insight into variables that cause the overall economy (of which housing is a substantial share) to move in a certain direction, so that boom and bust cycles can be recognized in advance.

7.1 Practical Usefulness

STenV and NWWI (2012) state that, as of September 1, 2012, parties with an AVM to value houses in the Netherlands must comply with several rules. The AVM must be able to value a house on the basis of zip code, house number, housing type, surface (as in NEN 2580), volume, building year, and/or parcel surface. Within this regard, value refers to the transaction price of

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a house. In addition, the AVM must be able to select 3 unique comparable objects that have sold no longer ago than 5 years, have the same housing type and were not in ownership of housing associations (on average, they sell at a discount). If the AVM cannot select at least 3 unique comparable objects, the AVM provider is given the choice to search for the additional unique comparable objects by themselves or to not value the target. If the latter is chosen, the target will be valued by the next AVM provider. Also, the accuracy of the value that comes from an

AVM must at least be 85%. Otherwise, the next party with an AVM will be contacted to value the target.

The AVM (model 5 after adjustments) that has been developed for DataQuote is based on listing prices instead of transaction prices. Transaction prices and transaction dates of all houses in the ﬁnal dataset will be bought in the near future. Then, transaction prices and transaction dates can easily be substituted for the listing prices and listing dates. The script (see subsection

3.2.3) can be used to update all transaction prices to 2012Q4 (or a more relevant quartile, based on availability from the CBS). This is considered as an improvement, because the listing dates may not be as reliable as thought in ﬁrst instance. They have been collected via a script of

DataQuote, which is not made accessible. There is a clustering of 26,369 cases in May 2012 and

64,412 cases in June 2012 (see appendix F). Together, these months contain about 54% of cases.

There is no known event to the author that explains this clustering. Although the quarterly price developments may be small, transaction dates may be more reliable.

The AVM has been further developed to automatically search the dataset for three comparable houses with the same housing type, based on the subjective weights 0.5 for “Log of surface” ,

0.4 for “Log of volume”, and 0.1 for “Building year”, which are set by DataQuote. A rule has been implemented to select only those comparable houses that have a listing date (substituted by transaction date in the near future) that is equal to the current date minus 5 years. The

AVM automatically reports the accuracy for the target. 139

7.2 Boom and Bust Cycles

It is clear that housing is a major part of the GDP of countries (see section 2.1 and 2.2). On

average, real estate constitutes the third largest asset class. Within real estate, housing is the

major contributor (48%). To understand the link between housing in a country and the macro-

economy, the deﬁnition of GDP is essential. It includes “ the sum of total consumption, invest-

ment, government spending and net exports ” (European Commission, 2011). Because housing has

a substantial share, a housing market bust decreases GDP substantially (Leung, 2003). Because

of the bust, there is a lower demand for houses. Muellbauer (2012) mentions that housing serves

as collateral for raising cheap debt. He then reasons that during a bust, people may default on

housing loans, which leads to major losses on a bank’s balance sheet. The lower demand for

houses and the decrease in available capital of banks, leads to less home construction, decrease

in home prices, decrease in consumer wealth, decrease in consumption, increase in risk from the

standpoint of a bank and tightened credit standards (Muellbauer, 2012). The increase of risk

and tightened credit standards make it more expensive to borrow. This is not only for individu-

als seeking a mortgage, but also for persons or companies that are active in other markets and

are seeking for debt. Assuming the deﬁnition of GDP, a housing market bust ultimately leads to

a reduction in total consumption, investment, government spending and net exports. It eﬀective-

ly makes a country poorer. This in turn could lead to reduction in jobs, which leads to unem-

ployment. Vice versa, an increase in GDP is possible during a housing market boom. Together

with the trends of internationalization and globalization, the economic systems of countries

become interdependent on each other (Aizenman and Jinjarak, 2009). This global economic

integration leads to global booms and busts. The 2007 bust of the housing market in the US has

led to a collapse of the economic system, not only in the US but also in other countries. This

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conﬁrms the statement that there is an interdependency of economic systems in various countries and that the housing market aﬀects other markets.

7.3 Macro-economic Trends

Now that it is clear that housing markets affect local and global economies, a discussion of macro-economic variables that relate to housing prices is needed. The script to update listing prices to a defined quarter (see subsection 3.2.3) partly captures macro-economic trends. Re- gressing the quarterly increase or decrease in prices per housing type in a province or COROP region on its components surpasses the scope of this master thesis. Leung (2003) and Bou- chouicha and Ftiti (2012) mention that the interaction between housing markets and the macroeconomy has long been ignored and that literature is scarce. Hiebert and Sydow (2011) describe that, for the euro area, excess return to housing (returns on housing minus the return on the risk-free rate) depend on the rental yield, the real interest rate and real disposable per capita income. From their analysis, it follows that in the long term (16-32 quarters), excess return to housing mainly depends on the rental yield. The strength of this relationship varies across countries on the shorter-term, which is defined as less than a year (Hiebert and Sydow, 2011; Igan et al., 2011). The rental yield is, from the perspective of a lender, an indicator of cash-flow news, which depends on expectations (Hiebert and Sydow, 2011). This relation is further described by

Tomura (2013), who describes that over-optimism could lead to shocks in the macro-economy in the form of boom-bust cycles. On the other hand, Adams and Füss (2010) describe that housing prices react limited to news (price stickiness) and that the reaction is lagged. Their reasoning is that homeowners resist selling their house if the price drops significantly and that as a result, they hypothesize that house prices only decrease through inflation. By using data of 15 countries over a period of 30 years, they find that economic activity (principal component of money sup-

141

ply, consumption, production GDP, and employment), construction costs and the long term interest rates are statistically significant in explaining house prices, which are adjusted for inflation. Aizenman and Jinjarak (2009) find that, for the OECD countries, real estate prices are driven by GDP per capita growth or decline, inflation, financial depth (credit as a percentage of

GDP provided by the banking sector), institution (measure of law and order), urban population growth and the rental yield. They also find that there are less clear conclusions for non-OECD countries. Nneji and Ward (2013) conclude that, in the US, changes in house price developments in the long-run (from 1960 to 2011) depend on short term interest rates, the term spread (difference between long and short interest rate), and disposable personal income. In their study, inflation rates were not found statistically significant at the 10% level or less. They also found that, on the short term, economical and statistical significance differed between bust, boom, and steady-state periods. A bust period results in only the constant to be statistically significant at the 10% level or less. Bouchouicha and Ftiti (2012) link housing price developments in the UK and US to the long and short term interest rate, inflation, and employment growth.

All in all, inﬂation rates, GDP growth or decline and long term interest rates are considered as consistent variables in literature. Employment growth and short term interest rates are also used frequently to derive relevant variables that explain changes in house prices. Current literature is indeed scarce, but by analyzing trends in inﬂation, GDP growth, long term interest rates and possibly employment growth and short term interest rates, appraisers are able to make more consistent valuations of houses with respect to the macro-economy. Assuming the trend of globalization, markets increasingly become interlinked. As a result, macro-economic indicators will become more important. Combining knowledge on macro-indicators with knowledge on boom and bust cycles in housing markets is essential to derive conclusions whether house prices will continue to rise or whether they will drop.

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SUMMARY, CONCLUSION AND RECOMMENDATION

This master thesis first summarizes (section 8.1) and then concludes by answering all sub- questions in accordance, so that, finally, the problem definition can be answered (section 8.2).

During the writing of this master thesis, several topics for further research have been found, which will be shortly described (section 8.3).

8.1 Summary

Relative to other sectors, real estate sectors have a limited amount of transactions. In addition, perfect and complete information is often missing or hard to obtain, every object is unique, market participants do not have to act independently of each other and they do not have to maximize proﬁts, there are entry and exit barriers, and investments in real estate are illiquid.

All of these imperfections lead to false trades, which are transactions at the non-market value.

Hence, real estate, as a percentage of GDP, is the third largest asset class besides stocks and bonds, but acting on imperfections could provide an issue. Within the real estate sector, housing constitutes the majority in terms of percentage of total value. By focusing on the Dutch owner- 143

occupied housing market, it is clear that the same imperfections of real estate sectors contribute to this market. In addition, there has always been a strong intervention of the central government through spatial planning policy and subsidies since the Housing Act in 1901, preventing the free market mechanism. Decentralization, deregulation and the Grossing and Balancing Act limited central government intervention in time, but regional and local governments still have considerable power by setting-out policy objectives and regulations. This makes an estimation of market value diﬃcult. By deﬁning market value, measurement becomes possible. Five models have been constructed to estimate market value of owner-occupied houses in the Netherlands.

These five models increasingly take into account (very) locational information. From these five models, it is clear that locational information has value. The fifth model, which presumably captures the most locational information, has the highest goodness-of-fit and the best performance (measured by percentage difference between actual and fitted market value). This model has been automated, so that it can be used in practice.

8.2 Conclusion

To answer the first and second sub-questions ( “What is value of houses?” and “Which values with respect to housing are used in practice?”), a distinction has been made between factual and evaluative judgments. Factual judgments refer to descriptions based on statements that involve reference to verifiable data (objectivism). Evaluative judgments are descriptions of sentiments or emotions, where something is valuable if it evokes pleasurable feelings and maximizes a person’s utility (subjectivism). Each discipline has its own definitions and measurement techniques of value, ranging between objectivism and subjectivism. Valuation becomes troublesome in an interdisciplinary context, such as housing, where economic, social, and political disciplines interact. With respect to the dependent variable ( “value” of houses), a distinction has been made

144

between relevant individualistic economic values (use value, investment value, and insurable value) and collective economic values (market, liquidation, and assessed value). An advantage of individualistic economic values is that estimates directly give a point-estimate of the worth

(price) of a particular object to a buyer. Yet, this point-estimate does not give the seller any indication whether this price is fair, high or low. To overcome this problem, the willingness-to- pay of a collection of buyers should be analyzed. Liquidation and assessed value (as collective economic values) can be derived from market value. Market value of owner-occupied houses in the Netherlands is therefore the dependent variable. The International Valuation Standards

Council defines market value as “the expected selling price of the specified real property rights in arm’s-length transaction, as of the date of the appraisal, and assuming a reasonable exposure to the market” . Arm’s-length transaction presumes that the buyer and seller are motivated, well informed, and act reasonably in their own self-interest without undue duress. From the market value definition, it is unclear whether “expected selling price” refers to listing or transaction price. The hypothesis that there is no difference between listing and transaction prices has been rejected. Adjusting for market trends in the conciliation period leads to a median difference between listing and transaction price of -4.6%. Therefore, the definition should further elaborate on the use of listing or transaction price. Transaction prices are actual observations for willingness to pay, while listing prices are appraisals that estimate a value on which to base negotiations. Appraisals can deviate in value from the transaction price, which might be attributable to the knowledge and experience of appraisers, but also to the influence of price negotiations. As transaction prices involve real market behavior, they are preferred over listing prices. Apart from the value of the dependent variable, independent variables can also capture a variety of values.

Non-economic values, which could be present in independent variables, such as environmental, aesthetic, and cultural heritage value are hard to estimate. Literature points out the diversity in

145

deﬁnitions and variables that can be used to measure these values. Because these values are highly subjective, some studies contradict other studies.

To answer the third and fourth sub-questions ( “What method(s) could be used to determine value of houses?” and “Which housing and spatial characteristics are potentially relevant for explaining market values of houses in the Netherlands?”), comparable studies have been analyzed. The Hedonic Pricing Method has been chosen as it decomposes value into its characteristics. This method is used in most comparable studies and has advantages compared to the Trav- el Cost Method and Averting Behavior Method. In addition, it can be used to answer the fifth sub-question and to partly answer the problem definition. Comparable studies can be classified as country-wide, district, and city level models. Most of these models include housing type, age, and size as independent variables to estimate market value of owner-occupied houses. Decreasing the scale leads to the inclusion of more specific information of the target place. Most district models also include lot size, distance to central business district and sometimes a few distances to local amenities. Most city models include distances to a variety of local amenities (more detailed than on district level), view, and specific building information (e.g. presence of a clubhouse) in addition to the variables that are often used in district models.

To answer the ﬁfth sub-question (“Which housing and spatial characteristics of the Dutch housing market are statistically and economically signiﬁcant in explaining market value?”), data has been collected for the Dutch owner-occupied housing market. All listing dates and prices have been updated towards fourth quarter of 2012 to make analysis and comparisons possible.

By constructing four models, the hypothesis that states that locational information does not have value can be tested. Model 1 contains the independent and statistically signiﬁcant (at the

5% level or less) variables: “Building year” , “Size” , “Type of transaction” , “Monumental status”, and “Housing type” . Model 2, 3, and 4 contain the same variables as model 1, with the addition of a dummy for “Province” , “COROP number”, and “Municipality”, respectively. Results indicate 146

that the validation set of model 1 has an R² of 65.17%. The R² of model 2, 3, and 4 are 73.56%,

76.76%, and 80.05%, respectively. This leads to the rejection of the hypothesis because adding regional or locational information and decreasing the scale form province to municipality leads to a better goodness-of-fit (performance). Also, a training, validation, and test procedure to test for prediction accuracy leads to a median percentage difference between predicted value and actual value of model 1, 2, 3, and 4 of: 17.51%, 14.78%, 13.95%, and 12.55%, respectively. This supports the hypothesis that locational information has value. Across the four models, economically significant variables are most locational indicators, the intercept, “Size”, “Monumental status”, “Detached”, “1960-1975”, and “Attached” . The economic significance of the intercept,

“1960-1975” and “Attached” weakens when adding locational indicators, while the economic signiﬁcance of “Detached” increases. The economic signiﬁcance of “Size” and “Monumental status” stays constant over the four models.

To answer the sixth and seventh sub-question ( “How can an AVM be developed for the Dutch owner-occupied housing market?” and “What is the accuracy of the AVM?” ), the advantages of the previous models have been used to construct model 5. Model 5 keeps the simplicity of model

1 and 2, while also acknowledging (very) local information. It selects the K (30 by default) most nearby houses of a target based on X and Y coordinates. By performing a regression on K houses to derive the value of the target, locational information is already captured in the prices of nearby houses. The median percentage diﬀerence between predicted value and actual value of this model is 10.04%, which is lower than those of model 1, 2, 3, and 4 that are between 17.51% and 12.55%. To further increase the performance of model 5, three rules have been added. First, cases that have a percentage diﬀerence between predicted and actual value that are above 50% have been excluded (cases based on false information). Second, if the target has exactly the same housing type, surface, volume, building year, and parcel surface as one of the K houses, that price is used as the price for the target. Third, the distance of the target to K houses is 147

weighted so that the houses that are closer to the target get more weight. Results of these adjustments lead to a median percentage diﬀerence between predicted and actual value of 9.57%.

In addition, accuracy for AVMs is defined as: 100% minus the median percentage difference between fitted and listing price of the objects that are used to get to the valuation. This should at least be 85%. The median accuracy of model 5 after implementing the three rules is 94.00%.

To answer the last sub-question (“Which macro-economic trends may aﬀect the valuation of houses in the Netherlands?”), it has been recognized that markets are increasingly interlinked

(globalization). Therefore, macro-economic trends may affect housing markets and they can be a sign of boom or bust cycles. Macro-economic variables that affect the change in value of houses are the inflation rate, GDP growth or decline, and long term interest rates. In addition, employment growth and short term interest rates are also used frequently to derive changes in value of houses.

All in all, the answers to these sub-questions have led to the development of an AVM for the owner-occupied housing market in the Netherlands that is consistent and accurate.

8.3 Recommendations for Further Research

It may be obvious that listing date and listing price should be changed to transaction date transaction price. Then the model estimates the outcomes of actual market behavior and this is also required by regulation on AVMs in the Netherlands. In addition, housing type can be further speciﬁed in 25 housing types, for example: gallery ﬂat, maisonette, and farmhouse (see

NVM et al., 2013) and eﬀorts could be made to update prices on municipality level instead of on

COROP number.

When the prices are not updated to the current quarter, time series analysis can be done.

Then, macro-economic variables can be included to estimate their eﬀect on housing value. Also,

148

the quarterly price adjustments in housing prices of the CBS (see CBS, 2013g) can be regressed against macro-economic variables, so that forecasting price adjustments will become possible.

A comparable model can be constructed for the rental sector in the Netherlands. This imposes diﬃculties because most houses are regulated, which restricts free-market competition. To escape this restriction, liberalized rental houses can be used to construct a model. Time series of liberalized rents can be analyzed with respect to macro-economic variables as well. Once this is done, there is a possibility to combine the owner-occupied AVM, the liberalized rental sector

AVM and the forecasting of housing prices and rents. From this combination, it is possible to advice people whether they should buy or rent, depending on the time that they expect to live in a house.

149

REFERENCES

ABN AMRO. (2013). Roadshow presentation: results ﬁrst half 2013 – ABN AMRO . Retrieved May 20, 2013 from: www.abnamro.com/en/images/050_Investor_Relations/025_Presentations/20130517_Investor_Roadsh ow_presentation.pdf

Adams, Z., & Füss, R. (2010). Macroeconomic determinants of international housing markets. Journal of Housing Economics , 19 (1), 38-50.

Aedes. (2007). Antwoord aan de samenleving [Answer to Society] . Hilversum: Aedes.

Aizenman, J., & Jinjarak, Y. (2009). Current account patterns and national real estate markets. Journal of Urban Economics , 66 (2), 75-89.

Akaike, H. (1974). A new look at the statistical model identiﬁcation. Automatic Control, IEEE Transactions on , 19 (6), 716-723.

Al-hagla, K. S. (2010). Sustainable urban development in historical areas using the tourist trail approach: A case study of the Cultural Heritage and Urban Development (CHUD) project in Saida, Lebanon. Cities , 27 (4), 234-248.

Andersson, D.E., Shyr, O.F. & Fu, J. (2010). Does high-speed rail accessibility inﬂuence residential property prices? Hedonic estimates from southern Taiwan. Journal of Transport Geography 18 , 166-174.

150

Aravossis, K. G., & Karydis, V. (2004). Combination of monetary valuation techniques and application to environmental impact receptors. Fresenius Environmental Bulletin , 13 (3), 283- 288.

Attﬁeld, R. (1998). The Ethics of The Global Environment . Edinburgh: Edinburgh University Press.

Austin, P. C., & Tu, J. V. (2004). Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. Journal of clin- ical epidemiology , 57 (11), 1138-1146.

Bae, C.C., Jun, M. & Park, H. (2003). The impact of Seoul’s subway Line 5 on residential property values. Journal of Transport Policy 10 , 85-94.

BAG & Ministerie van VROM. (2007). Oppervlakte verdiepingsdocument voor gemeenten: Moge- lĳkheden om in de implementatiefase BAG de oppervlakte van verblĳfsobjecten te bepalen . Re- trieved May 2, 2013 from: http://www.kadaster.nl/web/ﬁle?uuid=a7032e26-5f31-4cc3-9e2c- ea6993515826&owner=23cbe925-35ce-4a72-ac8c-a33a0c19ae1e&contentid=6988 .

Banzhaf, S.H. (2010). Economics at the fringe: Non-market valuation studies and their role in land use plans in the United States. Journal of environmental management , 91 (3), 592-602.

Bartik, T. J. (1988). Evaluating the beneﬁts of non-marginal reductions in pollution using information on defensive expenditures. Journal of Environmental Economics and Manage- ment , 15 (1), 111-127.

Bateman, I. J., Carson, R. T., Day, B., Hanemann, M., Hanley, N., Hett, T. & Swanson, J. (2002). Economic valuation with stated preference techniques: a manual. Economic valuation with stated preference techniques: a manual .

Beechey M, Gruen D & Vickrey J. (2000). The Eﬃcient Markets Hypothesis: A Survey . Reserve Bank of Australia.

Beron, K., Murdoch, J., & Thayer, M. (2001). The beneﬁts of visibility improvement: new evidence from the Los Angeles metropolitan area. The Journal of Real Estate Finance and Eco- nomics , 22 (2-3), 319-337.

Black’s Law Dictionary. (2013). Retrieved March 3, 2013 from: http://www.blackslawdictionary.com

Boelhouwer, P. (2003). Social Housing Finance in the Netherlands: the road to independence. Housing Finance International , 17 (4), 17-21.

Bouchouicha, R., & Ftiti, Z. (2012). Real estate markets and the macroeconomy: A dynamic coherence framework. Economic Modelling , 29 (5), 1820-1829.

Bourassa, S.C., Hoesli, M. & Peng, V.S. (2003). Do housing submarkets really matter? Journal of Housing Economics 12 , 12-28. 151

Bouwbesluit. (2012). Algemeen deel van de toelichting bĳ het Bouwbesluit 2012, samengesteld uit de toelichtingen bĳ de Staatsbladen 2011, 416 en 2011, 676 . Retrieved March 9, 2013 from: http://www.rijksoverheid.nl/onderwerpen/bouwregelgeving/bouwbesluit-2012

Brasington, D. M., & Hite, D. (2005). Demand for environmental quality: A spatial hedonic analysis. Regional science and urban economics , 35 (1), 57-82.

Breiman, L. (2001). Random forests. Machine learning , 45 (1), 5-32.

Brounen, D. (2011). Real Estate Finance for Engineers. University of Tilburg.

Brounen, D. & Kok, N. (2011). On the economics of energy labels in the housing market. Jour- nal of Environmental Economics and Management 62 , 166-179.

Brown, T. C. (1984). The concept of value in resource allocation. Land Economics , 60 (3), 231- 246.

Buchanan, N. H. (2008). How realistic is the supply/demand equilibrium story?: A simple demonstration of false trading and its implications for market equilibrium. Journal of Socio- Economics , 37 (1), 400-415.

Burton, M.G. (1996). A Random Walk Down Wall Street . New York: Norton & Company.

Calcasa. (2012). History . Retrieved December 3, 2012, from http://www.calcasa.nl/nl/over- calcasa/historie

Callicott, J. (1992). Rolston on intrinsic values: a deconstruction. Environmental Ethics 14 , 129– 143.

Cannon, M.Y. (2002). The role of the real estate appraiser and assessor in valuing real property for ad valorem assessment purposes. Appraisal Journal 70(2).

Cannon, S., Miller, N. G., & Pandher, G. S. (2006). Risk and Return in the US Housing Market: A Cross‐Sectional Asset‐Pricing Approach. Real Estate Economics , 34 (4), 519-552.

Carr, R.A., Hellier, P. K., Geursen, G. M. & Rickard, J. A. (2003). Customer repurchase inten- tion: a general structural equation model. European Journal of Marketing , 37 (11/12), 1762- 1800.

Cattell, R. B. (1966). The scree test for the number of factors. Multivariate behavioral research , 1(2), 245-276.

CBS. (2012). Indeling van Nederland in 40 COROP-gebieden: Gemeentelĳke indeling van Neder- land op 1 januari 2012 . Retrieved February 20, 2013 from: http://www.cbs.nl/NR/rdonlyres/5243D99C-0F3F-43C1-BC38- ADEC2FDD9DEB/0/2012indelingcoropkaart.pdf

152

CBS. (2013a). Prognose bevolking; geslacht, leeftĳd, herkomst en generatie, 2013-2060 . Retrieved April 3, 2013 from: http://statline.cbs.nl/StatWeb/selection/?DM=SLNL&PA=81584NED&VW=T

CBS. (2013b). Prognose huishoudens naar type; leeftĳd, herkomstgroepering, 2013-2060 . Re- trieved April 3, 2013 from: http://statline.cbs.nl/StatWeb/selection/?DM=SLNL&PA=81645NED&VW=T

CBS. (2013c). Dynamische koopkrachtontwikkeling . Retrieved April 5, 2013 from: http://statline.cbs.nl/StatWeb/selection/?DM=SLNL&PA=71015NED&VW=T

CBS. (2013d). Gemiddeld inkomen; particuliere huishoudens naar diverse kenmerken . Retrieved April 5, 2013 from: http://statline.cbs.nl/StatWeb/selection/?DM=SLNL&PA=70843NED&VW=T

CBS. (2013e). Werkloze en werkzame beroepsbevolking per maand . Retrieved April 5, 2013 from: http://statline.cbs.nl/StatWeb/selection/?DM=SLNL&PA=80479NED&VW=T

CBS. (2013f). NUTS, regionale indeling voor Europese statistieken. Retrieved March 22, 2013 from: http://www.cbs.nl/nl-NL/menu/informatie/decentrale-overheden/informatie-voor- gemeenten/gemeenten-en-regionale-indelingen/regionale-indelingen/landelijk-dekkende-indelingen/nuts- regionale-indeling-voor-europese-statistieken.htm

CBS. (2013g). Bestaande koopwoningen; verkoopprĳzen prĳsindex 2010=100 . Retrieved June 28, 2013 from: http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=81884NED&D1=6&D2=288,292,296,300, 304-305&HDR=T&STB=G1&VW=T

CBS. (2013h). Waarde onroerende zaken van woningen en niet-woningen . Retrieved June 28, 2013 from: http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=37610&D1=1,3&D2=0&D3=l&VW=T

CBS. (2013i). Woningen; hoofdbewoner/huishouden . Retrieved June 29, 2013 from: http://statline.cbs.nl/StatWeb/publication/?DM=SLNL&PA=7409WBO&D1=1,60- 66&D2=0&D3=0&D4=l&VW=T

Central Intelligence Agency. (2012). The World Factbook . Derived from: https://www.cia.gov/library/publications/the-world-factbook/rankorder/2001rank.html

Cesario, F. J. (1976). Value of time in recreation beneﬁt studies. Land economics , 52 (1), 32-41.

Chattopadhyay, S. (1999). Estimating the demand for air quality: New evidence based on the Chicago housing market. Land Economics , 22-38.

Cho. S., Poudyal, N.C. & Roberts, R.K. (2008). Spatial analysis of the amenity value of green open space. Journal of Ecological Economics 66 , 403-416.

Clap, J.M., Bardos, K.S. & Wong, S.K. (2012). Empirical estimation of the option premium for residential redevelopment. Journal of Regional Science and Urban Economics 42 , 240-256.

153

Clauset, A. (2011). A brief primer on probability distributions . Retrieved June 25, 2013 from: http://tuvalu.santafe.edu/~aaronc/courses/7000/csci7000-001_2011_L0.pdf

Clawson, M., & Knetsch, J. L. (1966). Economics of outdoor recreation . Baltimore, MD: Johns Hopkins University Press.

Cleveland, W. S. (1981). LOWESS: A program for smoothing scatterplots by robust locally weighted regression. The American Statistician , 35 (1), 54-54.

Colom, M. C., & Molés, M. C. (2008). Comparative analysis of the social, demographic and economic factors that inﬂuenced housing choices in Spain in 1990 and 2000. Urban Stud- ies , 45 (4), 917-941.

Cotteleer, G. & Peerlings, J.H.M. (2011). Spatial planning procedures and property prices: The role of expectations. Landscape and Urban Planning, 100(1-2), 77-86.

Cournot, A.A. (1838). Recherches sur les Principes Mathematiques de la Theorie des Richesse . Translated by Bacon, N.T., New York: Macmillan, 1929.

Cowles, A. & Jones, H. (1937). Some A Posteriori Probabilities in Stock Market Action. Econo- metrica 5 (3): 280–294.

Damigos, D. (2006). An overview of environmental valuation methods for the mining industry. Journal of cleaner production , 14 (3), 234-247.

De Jong, F.C.J.M., Spalt, O.G. & Rodriguez, J.C. (2013). Empirical Methods in Finance . Til- burg University.

Dekkers, J. E., & van der Straaten, J. W. (2009). Monetary valuation of aircraft noise: A hedonic analysis around Amsterdam airport. Ecological economics , 68(11), 2850-2858.

Derksen, S., & Keselman, H. J. (1992). Backward, forward and stepwise automated subset selection algorithms: Frequency of obtaining authentic and noise variables. British Journal of Math- ematical and Statistical Psychology , 45 (2), 265-282.

Dieleman, F., & Hooimeijer, P. (2005). 3. Institutional Changes on Housing Markets in the Netherlands. Regions, Land Consumption And Sustainable Growth: Assessing the Impact of the Public And Private Sectors , 39.

Dixon, T., Thompson, B., McAllister, P., Marston, A., & Snow, J. (2005). Real estate and the new economy: The impact of information and communications technology . Blackwell Publishing Ltd, UK.

Dobbs, R., Lund, S., Roxburgh, C., Manyika, J., Kim, A., Schreiner, A., Boin, R., Chopra, R., Jauch, S., Kim, H., McDonald, M. & Piotrowski, J. (2010). Farewell to cheap capital? The implications of long-term shifts in global investment and saving. McKinsey Global Institute.

154

Droes, M. I. (2011). House price uncertainty in the Dutch owner-occupied housing market. Tjalling C. Koopmans Institute Discussion Paper Series .

Dwyer, J. F., Peterson, G. L., & Darragh, A. J. (1983). Estimating the value of urban forests using the travel cost method. Journal of Arboriculture , 9(7), 182-185.

Efthymiou, D. & Antoniou, C. (2013). How do transport infrastructure and policies aﬀect house prices and rents? Evidence from Athens, Greece. Journal of Transportation Research Part A 52 , 1-22.

Elsinga, M., Haﬀner, M. & Hoekstra, J. (2007). Balance between landlord and tenant? A comparison of the rent regulation in the private rental sector in ﬁve countries. In ENHR Interna- tional Conference .

Eshet, T., Baron, M. G., Shechter, M., & Ayalon, O. (2007). Measuring externalities of waste transfer stations in Israel using hedonic pricing. Waste Management , 27 (5), 614-625.

Espey, M., & Lopez, H. (2000). The impact of airport noise and proximity on residential property values. Growth and Change , 31 (3), 408-419.

European Commission Eurostat. (2011). National accounts – GDP . Retrieved February 2, 2012, from www.epp.eurostat.ec.europa.eu

Englin, J., & Shonkwiler, J. S. (1995). Modeling recreation demand in the presence of unobservable travel costs: toward a travel price model. Journal of environmental economics and management , 29 (3), 368-377.

Eshtet, T., Baron, M.G., Shechter, M. & Ayalon, O. (2006). Measuring externalities of waste transfer stations in Israel using hedonic pricing. Journal of Waste Management 27 , 614-625.

European Commission. (2013). Household budget survey (HBS) . Retrieved June, 5, 2013 from: http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/Glossary:Household_budget_survey_( HBS)

Fallis, G. (1985). Housing Economics . Butterworth, Toronto, 1985.

Fama, E. (1965). The Behavior of Stock Market Prices. Journal of Business 38: 34–105.

Fama, E. (1970). Eﬃcient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance 25 (2): 383–417.

Frankena, W. K. (1967). Value and valuation. The encyclopedia of philosophy , 8, 229-232.

Findlay, J. N. (1970). Axiological ethics. Vol. 3010, Macmillan.

Fleming, C. M., & Cook, A. (2008). The recreational value of Lake McKenzie, Fraser Island: An application of the travel cost method. Tourism Management , 29 (6), 1197-1205.

155

Freeman III, A.M. (1974). On estimating air pollution control beneﬁts from land value studies . Journal of Environmental Economics and Management 1, 74–83.

Freeman III, A.M. (1979). Hedonic prices, property values and measuring environmental beneﬁts. Scandinavian Journal of Economics 81, 154–173.

Freeman III, A. M. (1985). Methods for assessing the beneﬁts of environmental pro- grams. Handbook of natural resource and energy economics , 1, 223-270.

Freeman III, A. M. (2003). Air pollution and property values: A methodological comment. The Review of Economics and Statistics , 53 (4), 415-16.

Gabriel, K. R. (1971). The biplot graphic display of matrices with application to principal component analysis. Biometrika , 58 (3), 453-467.

Garrod, G., & Willis, K. (1992). The amenity value of woodland in Great Britain: a compromise of economic estimates. Environmental and Resource Economics 24:415-434.

Garrod, G., & Willis, K. G. (1999). Economic valuation of the environment: methods and case studies (p. 384). Cheltenham: Edward Elgar.

Gautier, P. A., Siegmann, A., & Van Vuuren, A. (2009). Terrorism and attitudes towards minor- ities: The eﬀect of the Theo van Gogh murder on house prices in Amsterdam. Journal of Urban Economics , 65 (2), 113-126.

Geoghegan, J., Wainger, L. A., & Bockstael, N. E. (1997). Spatial landscape indices in a hedonic framework: an ecological economics analysis using GIS. Ecological economics , 23 (3), 251-264.

Giedion, S. (1941). Space, Time and Architecture – The Growth of a New Tradition . Harvard University Press, 5ed. Cambridge, Massachusetts.

Glower, M., Haurin, D. R., & Hendershott, P. H. (1998). Selling time and selling price: The inﬂuence of seller motivation. Real estate economics , 26 (4), 719-740.

Green, P. E. (1984). Hybrid models for conjoint analysis: An expository review. Journal of Marketing Research , 155-169.

Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for research and practice. The Journal of Marketing , 3-19.

Green, R. K., & Wachter, S. M. (2005). The American mortgage in historical and international context. The Journal of Economic Perspectives , 19 (4), 93-114.

Gruis, V., & Nieboer, N. (2006). Social housing investment without public Finance. Public ﬁ- nance and management , 6(1), 122-144.

156

Haﬀner, M., & de Vries, P. (2009, February). Dutch house price fundamentals. In Australian Tax Research Foundation Housing and Tax Symposium (Vol. 11).

Haﬀner, M., & Heylen, K. (2011). User costs and housing expenses. Towards a more comprehensive approach to aﬀordability. Housing studies , 26 (04), 593-614.

Halifax. (2012). Index methodology . Retrieved December 4, 2012, from http://www.lloydsbankinggroup.com/media1/economic_insight/halifax_house_price_index_page.asp

Hart, S. L. (1971). Axiology--theory of values. Philosophy and phenomenological research , 32 (1), 29-41.

Hanley, N., & Spash, C. L. (1993). Cost-beneﬁt analysis and the environment . Cheltenham: Edward Elgar.

Heckman, J. J. (1979). Sample selection bias as a speciﬁcation error. Econometrica: Journal of the econometric society , 153-161.

Hellema, D., Wiebes, C. & Witte, T. (1998). Doelwit Rotterdam: Nederland en de oliecrisis . Den Haag, SDU.

Herrin, W. E., Knight, J. R., & Sirmans, C. F. (2004). Price cutting behavior in residential markets. Journal of housing economics , 13 (3), 195-207.

Hiebert, P., & Sydow, M. (2011). What drives returns to euro area housing? Evidence from a dynamic dividend–discount model. Journal of Urban Economics , 70 (2), 88-98.

Hitchcock, J.R., Bourne, L.S. & Bell, J.K. (1978). Urban Housing Markets :Recent directions in research and policy . University of Toronto Press, Toronto, 1978.

Hoehn, J. P., Berger, M. C., & Blomquist, G. C. (1987). A hedonic model of interregional wages, rents and amenity values. Journal of Regional Science , 27 (4), 605-620.

Hofman, E., Halman, J. I., & Ion, R. A. (2006). Variation in housing design: identifying customer preferences. Housing Studies , 21 (6), 929-943.

Hoﬀman, K., & Kunze, R. (1971). Characteristic values. § 6.2 in Linear Algebra.

Holbrook, W. (1960). Note on the Correlation of First Diﬀerences of Averages in a Random Chain. Econometrica 28 (4): 916–918.

Holmans, A. E. (1994). House prices, land prices, the housing market and house purchase debt in the UK and other countries . Economic Modelling, 11(2), 157-199.

Hui, E., Chau, C. K., Pun, L., & Law, M. Y. (2007). Measuring the neighboring and environmental eﬀects on residential property value: Using spatial weighting matrix. Building and environment , 42 (6), 2333-2343.

157

Hutter, M., & Shusterman, R. (2006). Value and the valuation of art in economic and aesthetic theory. Handbook of the Economics of Art and Culture , 1, 169-208.

Hutter, M. & Throsby, D. (2008). Beyond price: value in culture, economics, and the arts. New York: Cambridge University Press.

Igan, D., Kabundi, A., Nadal De Simone, F., Pinheiro, M., & Tamirisa, N. (2011). Housing, credit, and real activity cycles: Characteristics and comovement. Journal of Housing Econom- ics , 20 (3), 210-231.

International Monetary Fund. (2010). Kingdom of the Netherlands – Netherlands: 2009 article IV consultation . International Monetary Fund Country Report No 10/34.

Jackson, D. A. (1993). Stopping rules in principal components analysis: a comparison of heuris- tical and statistical approaches. Ecology , 2204-2214.

Jackson, D. A., & Chen, Y. (2004). Robust principal component analysis and outlier detection with ecological data. Environmetrics , 15 (2), 129-139.

Jencks, C. (1980). Post-modern classicism: The new synthesis. Architectural Design (Vol. 28).

Jim, C.Y. & Chen, W.Y. (2006). Impacts of urban environmental elements on residential housing prices in Guangzhou (China). Journal of Landscape and Urban Planning 78 , 422-434.

Jim, C.Y. & Chen, W.Y. (2007). Consumption preferences and environmental externalities: A hedonic analysis of the housing market in Guangzhou. Journal of Geoforum 38 , 414-431.

Jim, C.Y. & Chen, W.Y. (2009). Value of scenic views: Hedonic assessment of private housing in Hong Kong. Journal of Landscape and Urban Planning 91 , 226-234.

Jim, C. Y. & Chen, W. Y. (2010). External eﬀects of neighbourhood parks and landscape elements on high-rise residential value. Land Use Policy , 27 (2), 662-670.

Kadaster. (2013). Over het Kadaster . Retrieved March 3, 2013 from: http://www.kadaster.nl/web/over-kadaster.htm

Kahn, J.A. (2000). Explaining the Gap Between New Home Sales and Inventories. Current Issues in Economics and Finance, vol. 6(6).

Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psy- chometrika , 23 (3), 187-200.

Kaplan, A. (1966). The aesthetics of the popular arts. The Journal of Aesthetics and Art Criti- cism , 24 (3), 351-364.

Karagöl, T. (2007). A Study of Housing Prices in Ankara. Doctoral dissertation, Middle East Technical University.

158

Karvel, G. R. & Unger, M. A. (1991). Real Estate: Principles and Practices . South Western Publishing Co., USA.

Khan & Arshad, M. (1986). Conformity with Large Speculators: A Test of Eﬃciency in the Grain Futures Market. Atlantic Economic Journal 14 (3): 51–55.

Keller, E.F. (1985). Reﬂections on Gender and Science . Yale University Press, New Haven, Conn.

Keng, T.Y. (1997). A hedonic model for house prices in Malaysia . Retrieved from Paciﬁc Rim Property Research Journal: http://www.prres.net/papers/tan_an_hedonic_model_for_house_prices_in_malaysia.pdf

Kennedy, J. (1993). Preparing for the Twenty-ﬁrst Century . New York: Random House.

Knight, J. R. (2002). Listing price, time on market, and ultimate selling price: Causes and eﬀects of listing price changes. Real estate economics , 30 (2), 213-237.

Kong, F., Yin, H., & Nakagoshi, N. (2007). Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, Chi- na. Landscape and Urban Planning , 79 (3), 240-252.

Kosić, M. (2008). Real estate market conditions in the Netherlands, Slovenia and Croatia . Uni- versity of Ljubljana, Faculty of Economics.

Krause, L.A. & Bitter, C. (2012). Spatial econometrics, land values and sustainability: Trends in real estate valuation research. Cities .

Kromhout, B. & Oving, R. (2008). Niet meer dan een belasting technische maatregel. Historisch Nieuwsblad .

Lancaster, K.J. (1966). A new approach to consumer theory. Journal of Political Economy 74 , 132–157.

Lazear, E. P. (1986). Retail Pricing and Clearance Sales. The American Economic Review .: 14- 32.

Leung, C. (2004). Macroeconomics and housing: a review of the literature. Journal of Housing Economics , 13 (4), 249-267.

Liaw, A., & Wiener, M. (2002). Classiﬁcation and Regression by random Forest. R news , 2(3), 18-22.

Lindqvist, S. (2011). Transaction cost and transparency on the owner-occupied housing market: An international comparison . Building and Real Estate Economics, Department of Real Estate and Construction Management at the Royal Institute of Technology, Stockholm.

159

Lipczynski, J., Wilson, J. O. S., & Goddard, J. A. (2009). Industrial organization: Competition, strategy, policy (3rd ed.). England: FT Prentice Hall.

Longley, P. (1984). Comparing discrete choice models: some housing market examples.

Louwes, S.L. (2012). Exchange by private treaty. Review of Agricultural Economics, 2(3), 377- 379.

Lusht, K. M. (2001). Real estate valuation: principles and applications. New York: McGraw-Hill Primis Custom Publishing.

MacDonald, D.H., Crossman, N.D., Mahmoudi, P., Taylor, L.O., Summers, D.M. & Boxall, P.C. (2010). The value of public and private green spaces under water restrictions. Journal of Land- scape and Urban Planning 95 , 192-200.

Magni, C. A. (2009). Splitting up value: A critical review of residual income theories. European Journal of Operational Research , 198 (1), 1-22.

Marr, P. (2013). Principal Component Analysis . Shippensburg University, Pennsylvania.

Marshall, M. (1920). Principles of Economics . London: Macmilland, 8 th edition.

McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior .

McFadden, D. (1978). Modelling the choice of residential location (pp. 75-96). Institute of Trans- portation Studies, University of California.

McFadden, D. (1986). The choice theory approach to market research. Marketing Science, 5, 275-279.

McLeod, P. B. (1984). The demand for local amenity: An hedonic price analysis. Environment and Planning , 16 (3), 389-400.

Melichar, J. & Kaprová, K. (2013). Revealing preferences of Prague’s homebuyers toward greenery amenities: The empirical evidence of distance-size eﬀect. Journal of Urban Planning 109 , 56-66.

Mills, E. S. (1990). Housing tenure choice. The Journal of Real Estate Finance and Econom- ics , 3(4), 323-331.

Mills, E.S. (1998). Economic theory and housing. Handbook of Regional and Urban Economics, volume 2, Urban Economics . Elsevier Science B.V., Amsterdam. (959-988).

Molen van der, M. (2012). Kwartaalbericht woningmarket: November 2012. Rabobank Kennis en Economisch Onderzoek. Retrieved November 5, 2012, from: https://www.rabobank.com/en/images/Kwartaalbericht_woningmarkt_november_2012_totaal_tcm64- 173146.pdf

160

More, T. A., Stevens, T., & Allen, P. G. (1988). Valuation of urban parks. Landscape and urban planning , 15 (1), 139-152 .

More, T. A., Averill, J. R., & Stevens, T. H. (1996). Values and economics in environmental management: a perspective and critique. Journal of Environmental Management , 48 (4), 397- 409.

Morrell, P., & Lu, C. H. Y. (2000). Aircraft noise social cost and charge mechanisms–a case study of Amsterdam Airport Schiphol. Transportation Research Part D: Transport and Envi- ronment , 5(4), 305-320.

Muellbauer, J. (2012). Housing and the Macroeconomy. International Encyclopedia of Housing and Home , 301-314, Elsevier, San Diego.

Munizzo, M. A., & Musial, L. V. (2009a). Residential Sales Comparison and Income Approaches . Cengage Learning.

Munizzo, M. A., & Musial, L. V. (2009b). Residential Site Valuation and Cost Approach . Cen- gage Learning.

Musso, A., Neri, S., & Stracca, L. (2011). Housing, consumption and monetary policy: How diﬀerent are the US and the euro area?. Journal of Banking & Finance , 35 (11), 3019-3041.

Nagel, T. (1986). The View from Nowhere . New York: Oxford University Press.

Nahman, A. (2011). Pricing landﬁll externalities: Emissions and disamenity costs in Cape Town, South Africa. Journal of Waste Management 31 , 2046-2056.

Naoi, M., Seko, M. & Sumita, K. (2009). Earthquake risk and housing prices in Japan: Evidence before and after massive earthquakes. Journal of Regional Science and urban Economics 39 , 658-669.

Nash, R. (1989). The Rights of Nature . Madison, WI: University of Wisconsin Press.

National Bureau of Statistics in China (2012). National accounts. Retrieved January 4, 2013, from: http://www.stats.gov.cn/english/statisticaldata/yearlydata/

Navrud, S. (Ed.). (1992). Pricing the European environment . Oslo, Norway: Scandinavian Uni- versity Press.

Neelawala, P., Wilson, C., & Athukorala, W. (2013). The impact of mining and smelting activities on property values: a study of Mount Isa city, Queensland, Australia. Australian Journal of Agricultural and Resource Economics , 57 (1), 60-78.

Nelson, J.A. (1996). Feminism, Objectivity and Economics . Routledge, London.

161

Nelson, R. R. (2008a). Economic development from the perspective of evolutionary economic theory. Oxford development studies , 36 (1), 9-21.

Nelson, J. A. (2008b). Economists, value judgments, and climate change: a view from feminist economics. Ecological Economics , 65 (3), 441-447.

Nneji, O., Brooks, C., & Ward, C. W. (2013). House price dynamics and their reaction to macroeconomic changes. Economic Modelling , 32 , 172-178.

Nolan. (1995). Ethics and morality – Philosophy and religion . Oxford University Press.

Nuryanti, W. (1996). Heritage and postmodern tourism. Annals of Tourism Research 23(2): 249– 260.

NVB. (2007). Gedragscode Hypothecaire Financieringen . Retrieved November 10, 2012, from: http://www.nvb.nl/publicaties/1671/gedragscode-hypothecaire-ﬁnancieringen.html

NVB. (2011). Toelichting GHF 2011 . Retrieved November 10, 2012, from: http://www.nvb.nl/media/document/000457_toelichting-gedragscode-hyp-ﬁnanciers-per-1-aug-2011.pdf

NVM, VastgoedPro, VBO Makelaar, VNG & Waarderingskamer. (2013). Fotowĳzer woningen: Uniformering begrippen en deﬁnities woningen .

OECD. (2005). Recent house price developments: The role of fundamentals . Retrieved December 10, 2012 from: http://www.oecd.org/ﬁnance/monetary/35756053.pdf

OECD. (2011). Gross domestic product (GDP) . Retrieved December 15, 2012 from: http://stats.oecd.org/

Ommeren, van, J., & Leuvensteĳn, van, M. (2005). New Evidence of the Eﬀect of Transaction Costs on residential Mobility*. Journal of Regional Science , 45 (4), 681-702.

Oorschot, van, W. (2006). Dutch Welfare State: Recent Trends and Challenges in Historical Perspective. European Journal of Sociology 8, 57.

Opoku, R. A., & Abdul-Muhmin, A. G. (2010). Housing preferences and attribute importance among low-income consumers in Saudi Arabia. Habitat International , 34 (2), 219-227.

Ovaskainen, V., Neuvonen, M., & Pouta, E. (2012). Modelling recreation demand with respondent-reported driving cost and stated cost of travel time: A Finnish case. Journal of Forest Eco- nomics .

Park, M., Hagishima, A., Tanimoto, J., & Chun, C. (2013). Willingness to pay for improvements in environmental performance of residential buildings. Building and Environment .

Pearson, K. (1901). LIII. On lines and planes of closest ﬁt to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science , 2(11), 559-572.

162

Peterson, B. (1993). Working capital and ﬁxed investment: New evidence on ﬁnancing constraints. Rand Journal of Economics , vol. 24 (autumn), pp. 328-342.

Prüfer, J.O. (Assistant Professor). (2011). Microeconomics for international business administration . Tilburg University.

Randall, A. & Stoll, J. (1983). Existence value in a total valuation framework . Boulder, CO: Westview Press.

Randall, A. (1994). A diﬃculty with the travel cost method. Land economics , 88-96.

Rao, J. N. K. (1966). On the comparison of sampling with and without replacement. Revue de l'Institut International de Statistique , 125-138.

Rapoport, A. (1982). The meaning of the built environment . Beverly Hills: Sage.

Ravetz, A. (1980). Remaking Cities . London: Croom Helm.

Riera, A. (2000). Mass tourism and the demand for protected natural areas: A travel cost approach . Journal of Environmental Economics and Management, 39, 97–116.

Rĳksoverheid. (2011). Kabinet introduceert kooprecht voor huurder corporatiewoning . Retrieved December 24, 2012 from: http://www.rijksoverheid.nl/documenten-en- publicaties/persberichten/2011/12/23/kabinet-introduceert-kooprecht-voor-huurder- corporatiewoning.html

Rockwell Publishing. (2006). Real estate principles . Rockwell Publishing Company, USA.

Rodríguez, D.A. & Mojica, C.H. (2009). Capitalization of BRT network expansions eﬀects into prices of non-expansion areas. Journal of Transportation Research Part A 43 , 560-571.

Romijn, G. & Besseling, P. (2008). Economic impacts of regulation and subsidisation of the rental housing market . CPB document 165, The Hague (CPB).

Rosen, S. (1974). Hedonic prices and implicit markets: product diﬀerentiation in pure competition. The journal of political economy , 82 (1), 34-55.

Rouwendal, J., & Van Der Straaten, W. (2008). The costs and beneﬁts of providing open space in cities . Tinbergen Institute Discussion Paper.

Ruijgrok, E. C. M. (2006). The three economic values of cultural heritage: a case study in the Netherlands. Journal of cultural heritage , 7(3), 206-213.

Ruskin, J. (1849). The Seven Lamps of Architecture.

Samuelson, P. (1965). Proof that properly anticipated prices ﬂuctuate randomly. Industrial Management Review 6, 41–9.

163

Saphores, J. & Li, W. (2012). Estimating the value of urban green areas: A hedonic pricing analysis of the single family housing market in Los Angeles, CA. Journal of Landscape and Urban Planning 104 , 373-387.

Schram, J. F. (2006). Real Estate Appraisal . Rockwell Publishing.

Schummer, J., MacLennan, B., & Taylor, N. (2009). Aesthetic values in technology and engineering design. Philosophy of technology and engineering , 1031-1068.

Selim, H. (2009). Determinants of house price in Turkey: Hedonic regression versus artiﬁcial neural network. Journal of Expert Systems with Applications 36 , 2843-2852.

Smith, L. B., Rosen, K. T., Markandya, A., & Ullmo, P. A. (1984). The demand for housing, household headship rates, and household formation: An international analysis. Urban Stu- dies , 21 (4), 407-414.

STenV. (2013). Advies van de brancheorganisaties NVM, VastgoedPRO en VBO Makelaar aan het Waarborgfonds Eigen Wonen inzake de normen voor taxateurs en taxatierapporten en de controle erop, ten behove van ﬁnancieringen met NHG. Retrieved June 29, 2013 from: http://www.stenv.nl/userdata/downloads_bestanden/branchebrede%20normering%20NHG%202011.pdf

STenV & NWWI. (2012). Verbetering modelmatige rapporten . Retrieved August 1, 2013 from: http://www.nwwi.nl/site/wp-content/uploads/Verbetering-Modelwaarden.pdf

Stevenson, S., Young, J. & Gurdgiev, C. (2010). A comparison of the appraisal process for auction and private treaty residential sales. Journal of Housing Economics 19 , 145-154.

Stone, C. (1974). Should Trees Have Standing? Toward Legal Rights for Natural Objects . Palo Alto, CA: Tioga Publishing Co.

Sue, E.D.W. & Wong, W. (2010). The political economy of housing prices: Hedonic pricing with regression discontinuity. Journal of Housing economics 19 , 133-144.

Sullivan, L. H. (1896). The tall oﬃce building artistically considered. Lippincott's Maga- zine , 57 (3), 406.

Throsby, D. (2003). Determining the value of cultural goods: How much (or how little) does contingent valuation tell us?” Journal of Cultural Economics 27:275-285.

Thurstone, L. L. (1927). A law of comparative judgment. Psychological Review, 34 , 273-286.

Tiwari, P. & White, M. (2010). International Real Estate Economics . Palgrave MacMillan, UK.

Tomura, H. (2013). Heterogeneous beliefs and housing-market boom-bust cycles. Journal of Economic Dynamics and Control , Volume 37, p.735-755.

164

Tweed, C., & Sutherland, M. (2007). Built cultural heritage and sustainable urban development. Landscape and Urban Planning , 83 (1), 62-69.

Tyrväinen, L. (1997). The amenity value of the urban forest: an application of the hedonic pricing method. Landscape and Urban planning , 37 (3), 211-222.

Tyrväinen, L., & Miettinen, A. (2000). Property prices and urban forest amenities. Journal of environmental economics and management , 39 (2), 205-223.

Tyrväinen, L., & Väänänen, H. (1998). The economic value of urban forest amenities: an application of the contingent valuation method. Landscape and Urban Planning , 43 (1), 105-118.

Urban, W. M. (1909). Valuation: its nature and laws, being an introduction to the general theory of value. S. Sonnenschein & Co.

U.S. Department of Commerce, Bureau of Economic Analysis. (2011). U.S. Economy at a glance: Perspective from the BEA Accounts. Retrieved December 11, 2011 from: https://www.bea.gov/newsreleases/glance.htm

Vandevyvere, W., & Zenthöfer, A. (2012). The Housing market in the Netherlands . Directorate General Economic and Monetary Aﬀairs, European Commission (No. 457).

Vecco, M. (2010). A deﬁnition of cultural heritage: From the tangible to the intangible. Journal of Cultural Heritage , 11 (3), 321-324.

Venti, S. & Wise, D. (1991). Aging and the Income Value of Housing Wealth. Journal of Public Economics. 44 :3, pp. 371–97.

Verbruggen, J., Kranendonk, H., Van Leuvensteĳn, M. & Toet, M. (2005). Welke factoren bepalen de ontwikkeling van de huizenprĳs in Nederland? CPB Document, No. 81 (April).

Vermeulen, W., & Rouwendal, J. (2007). Housing supply in the Netherlands . Den Haag, the Netherlands: CPB.

Vermeulen, W., & van Ommeren, J. (2009). Does land use planning shape regional economies? A simultaneous analysis of housing supply, internal migration and local employment growth in the Netherlands. Journal of Housing economics , 18 (4), 294-310.

Vigaray, M.D. and Hota, M. (2008). Schwartz’s Values, Consumer Values and Segmentation: The Spanish Fashion Apparel Case . LEM Working Paper Series.

Voke, M., Fairley, I., Willis, M., & Masters, I. (2013). Economic evaluation of the recreational value of the coastal environment in a marine renewables deployment area. Ocean & Coastal Management .

Vries, P. de. (2009). Measuring and explaining house price developments . Amsterdam, IOS Press BV. 165

VROM. (1989). Volkshuisvesting in the jaren negentig: van bouwen naar wonen . Tweede Kamer 1988-1989, no. 20.691, Den Haag (Sdu Uitgevers).

Waarderingskamer. (2013). Meetinstructie bruto inhoud woningen . Retrieved June 22, 2013 from: http://www.waarderingskamer.nl/documents/meetinstructie%20bruto%20inhoud%20woningen%202013% 2001%2001.pdf

Walker, R. (1998). New public management and housing associations: from comfort to competition. Policy & Politics 26(1): pp. 71-87.

Walker, B., Marsh, A., Wardman, M. and P. Niner. (2002). Modelling tenants’ choices in the public rented sector: a stated preference approach. Urban Studies, 39 : 665-688.

Whitehead, J. C., Hoban, T. J., & Van Houtven, G. (1998). Willingness to pay and drinking water quality: An examination of the averting behavior approach. In : 68th Annual Conference of the Southern Economic Association. Baltimore.

Wilkinson, L. (2012). Classiﬁcation and regression trees . Retrieved June 24, 2013 from: http://cda.psych.uiuc.edu/multivariate_fall_2012/systat_cart_manual.pdf

Xu-Doeve, W.L.J. (2010). The overvalued housing market in the Netherlands: A conspiracy of silence . Prepared for the 5 th Conference of the Irving Fisher Committee on Central Bank Sta- tistics (IFC) and held at the Bank for International Settlements (BIS) in Basel, Switzerland. Retrieved from http://www.bis.org/ifc/events/5ifcconf/xu.pdf

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APPENDICES

Appendix A: Comparing Sample with Population II

Appendix B: Model 4 Regression Results III

Appendix C: Model 5 Regression Results XVI

Appendix D: Counting Cases by Listing Date per Province XXVIII

Appendix A: Comparing Amount of Owner-Occupied Houses per Province in the Sample with the Amount of Owner-Occupied Houses per Province in the Population

Sample distribution of owner-occupied houses across provinces in the Netherlands

GR FR DR

NH FL OV #CASES UT GE ZH

NB ZE LI

Sample distribution versus distribution of the population of owner-occupied houses per province

Province Distribution of Distribution in Diﬀerence sample the Netherlands (%sample - %population) Drenthe (DR) 5,828 (3.5% ) 134,908 (3.3% ) 0.2% Flevoland (FL) 5,527 (3.3% ) 99,917 (2.5% ) 0.8% Friesland (FR) 7,550 (4 .5% ) 174,737 (4.3% ) 0.2% Gelderland (GE) 20,986 (12.5% ) 494,338 (12.1% ) 0.4% Groningen (GR) 2,841 (1.7% ) 144,118 (3.5% ) -1.8% Limburg (LI) 4,470 (2.7% ) 302,921 (7.4% ) -4.8% Noord -Brabant (NB) 23,458 (14.0% ) 636,579 (15.6% ) -1.6% Noord -Holland (NH) 32, 123 (19.1% ) 596,117 (14.6% ) 4.5% Overijssel (OV) 10,304 (6.1% ) 279,325 (6.8% ) -0.7% Utrecht (UT) 15,608 (9.3% ) 297,785 (7.3% ) 2.0% Zeeland (ZE) 3,907 (2.3% ) 117,000 (2.9% ) -0.5% Zuid -Holland (ZH) 35,361 (21.1% ) 806,063 (19.7% ) 1.3% Total 167,963 (100% ) 4,083,808 (100% )

Appendix B: Model 4 Regression Results

Variable name Coeﬃcient Std. err. t-value Coeﬃcient Std. err. t-value Training Training Training Validation Validation Validation

Intercept 12.028064 *** 0.031817 378.037 12.050000 *** 0.03561 0 338.355 Building year Before 1906 0.059902 *** 0.005604 10.688 0.069080 *** 0.005583 12.373 1945-1960 -0.084623 *** 0.003855 -21.952 -0.073780 *** 0.003864 -19.095 1960-1975 -0.124233 *** 0.003252 -38.199 -0.119600 *** 0.003281 -36.442 1975-1985 -0. 072267 *** 0.003554 -20.336 -0.066180 *** 0.003564 -18.568 1985-1990 -0.002784 0.004491 -0.620 0.002659 0.004528 0.587 1990-2007 0.054000 *** 0.003058 17.657 0.059820 *** 0.003080 19.427 After 2007 0.095576 *** 0.005252 18.198 0.106900 *** 0.005161 20.710 Size 0.642991 *** 0.002193 293.220 0.643000 *** 0.002194 293.089 V.o.n. transaction -0.050566 *** 0.004897 -10.326 -0.058630 *** 0.004822 -12.159 Monumental status 0.156070 *** 0.010775 14.484 0.150200 *** 0.011050 13.600 Housing type Apartment 0.003378 0.003508 0.963 -0.001489 0.003548 -0.420 Corner -0.045015 *** 0.004708 -9.562 -0.045240 *** 0.004707 -9.612 Attached -0.123005 *** 0.003038 -40.490 -0.124300 *** 0.003070 -40.484 Detached 0.154175 *** 0.003138 49.130 0.155100 *** 0.003146 49.296 Municipality GM0007Bellingwedde -0.084761 *** 0.053464 -1.585 -0.194500 *** 0.067750 -2.871 GM0010Delfzijl -0.109686 *** 0.072205 -1.519 -0.187400 *** 0.058130 -3.223 GM0014Groningen 0.317326 0.217439 1.459 0.157500 0.113600 1.386 GM0015Grootegast -0.020358 0.06 5670 -0.310 -0.095720 0.069630 -1.375 GM0017Haren 0.434849 *** 0.062448 6.963 0.441200 *** 0.064600 6.830 GM0018Hoogezand-Sappemeer -0.015856 0.036320 -0.437 -0.045880 0.040270 -1.139 GM0022Leek 0.085237 ** 0.040398 2.110 0.099080 ** 0.042450 2.334 GM0024Loppersum -0.065930 0.078406 -0.841 -0.080170 0.084200 -0.952 GM0025Marum 0.034696 0.046834 0.741 0.019520 0.048690 0.401 GM0034Almere 0.170940 *** 0.032554 5.251 0.151100 *** 0.036230 4.171 GM0037Stadskanaal 0.027609 0.037778 0.731 -0.000796 0.040900 -0.0 19 GM0040Slochteren -0.013152 0.043750 -0.301 -0.030160 0.047920 -0.629 GM0047Veendam -0.014498 0.035803 -0.405 -0.062500 0.039850 -1.568 GM0048Vlagtwedde -0.082497 0.040838 -2.020 -0.066350 0.043570 -1.523 III

GM0050Zeewolde 0.227681 *** 0.037473 6.076 0.1 94600 *** 0.040400 4.816 GM0051Skarsterl 0.183813 *** 0.037488 4.903 0.182700 *** 0.040900 4.467 GM0053Winsum 0.041417 0.072206 0.574 -0.010370 0.076950 -0.135 GM0055Boarnsterhim 0.162467 *** 0.038516 4.218 0.156900 *** 0.041140 3.815 GM0056Zuidhorn -0.0936 49 0.049079 -1.908 -0.075840 0.051710 -1.467 GM0058Dongeradeel 0.072280 0.040497 1.785 0.005117 0.042760 0.120 GM0059Achtkarspelen 0.078830 0.039823 1.979 0.060310 0.042930 1.405 GM0060Ameland 0.567863 *** 0.050013 11.354 0.514300 *** 0.055270 9.306 GM0063het Bildt 0.021934 0.054940 0.399 -0.024760 0.059000 -0.420 GM0064Bolsward 0.042852 0.051016 0.840 0.070430 0.050210 1.403 GM0070Franekeradeel 0.008332 0.053451 0.156 -0.067170 0.054090 -1.242 GM0072Harlingen 0.242041 0.093386 2.592 0.050720 0.074140 0.684 GM0074Heerenveen 0.079443 ** 0.042986 1.848 0.097050 ** 0.048420 2.005 GM0079Kollumerland en Nieuwkruisland 0.001862 0.043554 0.043 0.002960 0.044610 0.066 GM0080Leeuwarden 0.072034 0.033295 2.164 0.032390 0.036960 0.876 GM0081Leeuwarderadeel 0.069 881 0.052159 1.340 -0.002045 0.050920 -0.040 GM0082Lemsterland 0.244955 *** 0.038926 6.293 0.184100 *** 0.041610 4.424 GM0083Menaldumadeel -0.015711 0.047160 -0.333 -0.055080 0.050920 -1.082 GM0085Ooststellingwerf 0.040407 0.039813 1.015 0.007597 0.042770 0.178 GM0086Opsterland 0.094959 0.040507 2.344 0.068130 0.042310 1.610 GM0088Schiermonnikoog 0.689403 *** 0.069735 9.886 0.576600 *** 0.064610 8.925 GM0090Smallingerland 0.045230 0.034431 1.314 0.043150 0.038070 1.134 GM0091Sneek 0.173346 *** 0.036131 4. 798 0.166300 *** 0.039070 4.257 GM0093Terschelling 0.740146 *** 0.112157 6.599 0.670400 *** 0.095050 7.053 GM0096Vlieland 0.862247 *** 0.128202 6.726 0.867200 *** 0.129700 6.688 GM0098Weststellingwerf 0.074353 0.039902 1.863 0.038740 0.043380 0.893 GM0104Nijefurd 0.138688 ** 0.041196 3.366 0.090410 ** 0.044470 2.033 GM0106Assen 0.087495 0.034211 2.557 0.052070 0.037630 1.384 GM0109Coevorden -0.019026 0.036266 -0.525 -0.027360 0.039800 -0.687 GM0114Emmen -0.002164 0.033457 -0.065 -0.027690 0.037120 -0.746 GM0118Hoogeveen 0.108040 ** 0.035094 3.079 0.091310 ** 0.038570 2.367 GM0119Meppel 0.234413 *** 0.035802 6.548 0.197400 *** 0.039480 5.000 GM0140Littenseradiel 0.003800 0.051564 0.074 -0.030470 0.050200 -0.607 GM0141Almelo 0.018951 0.034206 0.554 -0.000072 0.037820 -0.002 GM0147Borne 0.172681 * 0.051019 3.385 0.102400 * 0.055900 1.831 GM0148Dalfsen 0.230592 *** 0.040962 5.629 0.235300 *** 0.042850 5.492

GM0150Deventer 0.254232 *** 0.033734 7.536 0.238400 *** 0.037300 6.390 GM0153Enschede 0.095873 * 0.035093 2.73 2 0.070000 * 0.038470 1.820 GM0158Haaksbergen 0.087723 0.082408 1.064 0.044720 0.080250 0.557 GM0160Hardenberg 0.074941 * 0.036474 2.055 0.068200 * 0.040380 1.689 GM0163Hellendoorn 0.252289 *** 0.035830 7.041 0.238400 *** 0.039230 6.077 GM0164Hengelo 0.1038 84 ** 0.033745 3.078 0.082210 ** 0.037330 2.202 GM0166Kampen 0.235266 *** 0.034824 6.756 0.196400 *** 0.038100 5.155 GM0168Losser 0.113342 ** 0.042328 2.678 0.088290 ** 0.044350 1.991 GM0171Noordoostpolder 0.047814 0.034736 1.376 0.006088 0.037950 0.160 GM0173Oldenzaal 0.206236 *** 0.038793 5.316 0.199700 *** 0.040600 4.919 GM0175Ommen 0.257012 *** 0.042021 6.116 0.196400 *** 0.044360 4.427 GM0177Raalte 0.211851 *** 0.037591 5.636 0.167800 *** 0.040610 4.133 GM0180Staphorst 0.157583 ** 0.046847 3.364 0.114500 ** 0.049260 2.324 GM0183Tubbergen 0.115376 * 0.049534 2.329 0.090200 * 0.053060 1.700 GM0184Urk 0.294543 ** 0.093376 3.154 0.191500 ** 0.084190 2.275 GM0189Wierden 0.192615 *** 0.042173 4.567 0.153100 *** 0.045000 3.403 GM0193Zwolle 0.335942 *** 0.032820 10.236 0.321400 *** 0.036490 8.808 GM0196Rijnwaarden 0.104672 ** 0.044865 2.333 0.105200 ** 0.047450 2.218 GM0197Aalten 0.118590 ** 0.038153 3.108 0.099240 ** 0.040380 2.458 GM0200Apeldoorn 0.319622 *** 0.039726 8.046 0.294200 *** 0.044010 6.685 GM0202Arnhem 0.246495 *** 0.033264 7.410 0.219800 *** 0.036890 5.960 GM0203Barneveld 0.343145 *** 0.036448 9.415 0.339500 *** 0.040380 8.407 GM0209Beuningen 0.276595 *** 0.039735 6.961 0.259600 *** 0.043290 5.997 GM0213Brummen 0.233012 *** 0.041451 5.621 0.268100 *** 0.046080 5.818 GM0214Buren 0.312162 *** 0.038455 8.118 0.271500 *** 0.041610 6.526 GM0216Culemborg 0.302350 *** 0.039080 7.737 0.270700 *** 0.041550 6.515 GM0221Doesburg 0.189488 *** 0.042480 4.461 0.173900 *** 0.043570 3.990 GM0222Doetinchem 0.239108 *** 0.034294 6.972 0. 198600 *** 0.038040 5.220 GM0225Druten 0.183589 *** 0.039818 4.611 0.155800 *** 0.045000 3.462 GM0226Duiven 0.202983 *** 0.036170 5.612 0.189200 *** 0.039400 4.803 GM0228Ede 0.379080 *** 0.033360 11.363 0.335100 *** 0.036990 9.060 GM0230Elburg 0.318099 *** 0.0 38051 8.360 0.300200 *** 0.041850 7.174 GM0232Epe 0.349958 *** 0.037733 9.275 0.310500 *** 0.041140 7.548 GM0233Ermelo 0.213208 *** 0.052798 4.038 0.214700 *** 0.059030 3.637 GM0236Geldermalsen 0.401251 *** 0.042319 9.482 0.363700 *** 0.045140 8.058 GM0241Groesbeek 0.290152 *** 0.055768 5.203 0.255200 *** 0.060940 4.187

GM0243Harderwijk 0.297683 *** 0.052165 5.707 0.278000 *** 0.053550 5.191 GM0244Hattem 0.307833 *** 0.075062 4.101 0.394000 *** 0.084180 4.680 GM0246Heerde 0.263307 *** 0.050002 5.266 0.239500 *** 0. 054080 4.428 GM0252Heumen 0.335520 *** 0.042326 7.927 0.336000 *** 0.044600 7.535 GM0262Lochem 0.230585 *** 0.036951 6.240 0.185300 *** 0.040650 4.558 GM0263Maasdriel 0.272813 *** 0.041458 6.580 0.242800 *** 0.044470 5.459 GM0265Millingen aan de Rijn 0.16449 3* 0.047878 3.436 0.096400 * 0.051300 1.879 GM0267Nijkerk 0.426772 *** 0.036076 11.830 0.418400 *** 0.039280 10.654 GM0268Nijmegen 0.384488 *** 0.032834 11.710 0.363400 *** 0.036540 9.943 GM0269Oldebroek 0.315936 *** 0.038212 8.268 0.289400 *** 0.041560 6.964 GM0273Putten 0.473005 *** 0.039468 11.985 0.449400 *** 0.042610 10.548 GM0274Renkum 0.458755 *** 0.035836 12.802 0.469900 *** 0.039070 12.027 GM0275Rheden 0.357513 *** 0.034782 10.279 0.301300 *** 0.038290 7.868 GM0277Rozendaal 0.643184 *** 0.065685 9.792 0.6 39300 *** 0.071730 8.913 GM0279Scherpenzeel 0.491761 *** 0.047164 10.427 0.462300 *** 0.046630 9.914 GM0281Tiel 0.233198 *** 0.035269 6.612 0.220300 *** 0.038920 5.659 GM0282Ubbergen 0.303815 *** 0.052156 5.825 0.345800 *** 0.053550 6.458 GM0285Voorst 0.36111 3*** 0.040393 8.940 0.334200 *** 0.042380 7.886 GM0289Wageningen 0.397342 *** 0.035563 11.173 0.358000 *** 0.039170 9.139 GM0293Westervoort 0.178893 *** 0.042996 4.161 0.191900 *** 0.044600 4.303 GM0294Winterswijk 0.081819 * 0.035968 2.275 0.066830 * 0.039770 1.681 GM0296Wijchen 0.313835 *** 0.035441 8.855 0.293100 *** 0.039260 7.466 GM0297Zaltbommel 0.327314 *** 0.039224 8.345 0.286000 *** 0.041610 6.875 GM0299Zevenaar 0.201649 *** 0.036005 5.601 0.143500 *** 0.040200 3.569 GM0301Zutphen 0.225689 *** 0.035205 6.4 11 0.209800 *** 0.038510 5.449 GM0302Nunspeet 0.440881 *** 0.038456 11.465 0.385800 *** 0.041280 9.345 GM0303Dronten 0.058625 0.035282 1.662 0.050690 0.038600 1.313 GM0304Neerijnen 0.294359 *** 0.045921 6.410 0.183400 *** 0.050210 3.654 GM0305Abcoude 0.8484 32 *** 0.046529 18.234 0.870000 *** 0.051300 16.959 GM0307Amersfoort 0.421954 *** 0.032830 12.853 0.390800 *** 0.036490 10.709 GM0308Baarn 0.653757 *** 0.039309 16.631 0.600900 *** 0.042680 14.076 GM0310De Bilt 0.696599 *** 0.037028 18.813 0.665200 *** 0.040170 16.561 GM0311Breukelen 0.575573 *** 0.047876 12.022 0.555700 *** 0.049870 11.144 GM0312Bunnik 0.585523 *** 0.042996 13.618 0.578200 *** 0.045130 12.810 GM0313Bunschoten 0.470324 *** 0.047883 9.822 0.542000 *** 0.054090 10.022 GM0317Eemnes 0.580795 *** 0.0462 15 12.567 0.579000 *** 0.047920 12.083

GM0321Houten 0.474855 *** 0.035880 13.234 0.437800 *** 0.039250 11.154 GM0327Leusden 0.524030 *** 0.037654 13.917 0.493500 *** 0.040530 12.174 GM0329Loenen 0.779594 *** 0.045931 16.973 0.768100 *** 0.046440 16.540 GM0331Lopik 0.402780 *** 0.044864 8.978 0.389200 *** 0.048160 8.081 GM0333Maarssen 0.516097 *** 0.055781 9.252 0.561400 *** 0.052570 10.679 GM0335Montfoort 0.430161 *** 0.043369 9.919 0.469200 *** 0.047240 9.932 GM0339Renswoude 0.352592 *** 0.061073 5.773 0.384000 ** * 0.055260 6.949 GM0340Rhenen 0.391141 *** 0.039224 9.972 0.393500 *** 0.042310 9.302 GM0342Soest 0.567572 *** 0.034941 16.244 0.505400 *** 0.038640 13.079 GM0344Utrecht 0.548635 *** 0.032226 17.025 0.528600 *** 0.035930 14.711 GM0345Veenendaal 0.384830 *** 0.034479 11.161 0.375600 *** 0.037690 9.965 GM0351Woudenberg 0.410157 *** 0.045928 8.930 0.358800 *** 0.047240 7.595 GM0352Wijk bij Duurstede 0.479258 *** 0.038722 12.377 0.466300 *** 0.041450 11.250 GM0353IJsselstein 0.433511 *** 0.037803 11.468 0.414400 *** 0.041240 10.049 GM0355Zeist 0.596011 *** 0.034018 17.521 0.577600 *** 0.037790 15.285 GM0356Nieuwegein 0.405016 *** 0.034654 11.688 0.370800 *** 0.038300 9.682 GM0358Aalsmeer 0.528159 *** 0.037554 14.064 0.536100 *** 0.039890 13.438 GM0361Alkmaar 0.354427 *** 0.033176 10.683 0.344700 *** 0.036740 9.382 GM0362Amstelveen 0.718321 *** 0.033683 21.326 0.716300 *** 0.037200 19.259 GM0363Amsterdam 0.771635 *** 0.032023 24.097 0.747400 *** 0.035750 20.906 GM0364Andijk 0.154071 ** 0.055763 2.763 0.199800 ** 0.060950 3.278 GM0365Graft-De Rijp 0.450194 *** 0.052784 8.529 0.423600 *** 0.055260 7.666 GM0366Anna Paulowna 0.194739 *** 0.048661 4.002 0.190800 *** 0.047690 4.001 GM0370Beemster 0.393901 *** 0.045914 8.579 0.389300 *** 0.050540 7.702 GM0373Bergen (NH.) 0.700553 *** 0.03 5971 19.475 0.634500 *** 0.039110 16.223 GM0375Beverwijk 0.330478 *** 0.037886 8.723 0.337600 *** 0.040720 8.289 GM0376Blaricum 0.943629 *** 0.045375 20.796 0.981600 *** 0.049250 19.931 GM0377Bloemendaal 0.902259 *** 0.038728 23.298 0.823300 *** 0.043780 18.80 5 GM0381Bussum 0.672687 *** 0.036547 18.406 0.672100 *** 0.039240 17.129 GM0383Castricum 0.496234 *** 0.037030 13.401 0.473900 *** 0.040600 11.672 GM0384Diemen 0.626054 *** 0.039938 15.676 0.590100 *** 0.042780 13.793 GM0385Edam-Volendam 0.538581 *** 0.037697 14.287 0.502700 *** 0.040650 12.364 GM0388Enkhuizen 0.248666 *** 0.038223 6.506 0.242300 *** 0.040860 5.930 GM0392Haarlem 0.611765 *** 0.032730 18.691 0.586600 *** 0.036440 16.096 GM0393Haarlemmerliede en Spaarnwoude 0.619726 *** 0.062441 9.925 0.620400 *** 0.057330 10.821 GM0394Haarlemmermeer 0.526615 *** 0.033102 15.909 0.511200 *** 0.036730 13.920

VII

GM0395Harenkarspel 0.225933 *** 0.042320 5.339 0.217400 *** 0.048420 4.490 GM0396Heemskerk 0.346050 *** 0.056671 6.106 0.339700 *** 0.066100 5.139 GM0397Heemstede 0.846579 *** 0.065679 12.890 0.737300 *** 0.062050 11.882 GM0398Heerhugowaard 0.264849 *** 0.056676 4.673 0.244600 *** 0.066100 3.701 GM0399Heiloo 0.593584 *** 0.037539 15.812 0.569900 *** 0.041440 13.753 GM0400Den Helder 0.133933 ** 0.035364 3.787 0.083240 ** 0.038770 2.147 GM0402Hilversum 0.542528 *** 0.033685 16.106 0.520400 *** 0.037110 14.023 GM0405Hoorn 0.257574 *** 0.033960 7.585 0.215100 *** 0.037890 5.676 GM0406Huizen 0.590506 *** 0.034861 16.939 0.593500 *** 0.038250 15.517 GM0412Niedorp 0.207876 *** 0.041 728 4.982 0.192400 *** 0.048680 3.953 GM0415Landsmeer 0.658885 *** 0.043180 15.259 0.652500 *** 0.047450 13.751 GM0416Langedijk 0.339442 *** 0.037679 9.009 0.307000 *** 0.040680 7.546 GM0417Laren 0.953675 *** 0.039147 24.361 0.960400 *** 0.042040 22.847 GM0420Medemblik 0.236089 *** 0.036507 6.467 0.202800 *** 0.039690 5.110 GM0424Muiden 0.800933 *** 0.047171 16.979 0.676900 *** 0.051700 13.092 GM0425Naarden 0.744095 *** 0.040518 18.365 0.752500 *** 0.043010 17.496 GM0431Oostzaan 0.633539 *** 0.045912 13.799 0.5988 00 *** 0.046820 12.789 GM0432Opmeer 0.194882 *** 0.045923 4.244 0.193400 *** 0.050910 3.800 GM0437Ouder-Amstel 0.743366 *** 0.043179 17.216 0.701800 *** 0.047920 14.647 GM0439Purmerend 0.381917 *** 0.034081 11.206 0.339700 *** 0.037540 9.048 GM0441Schagen 0.4 21447 *** 0.040849 10.317 0.362700 *** 0.043580 8.324 GM0448Texel 0.487670 *** 0.043768 11.142 0.405100 *** 0.046650 8.683 GM0450Uitgeest 0.414135 *** 0.045112 9.180 0.415700 *** 0.045280 9.180 GM0451Uithoorn 0.517626 *** 0.038056 13.602 0.475700 *** 0.041090 11.578 GM0453Velsen 0.498946 *** 0.033991 14.679 0.495800 *** 0.037600 13.186 GM0457Weesp 0.547990 *** 0.038606 14.194 0.511300 *** 0.042620 11.997 GM0458Schermer 0.346240 *** 0.049087 7.054 0.340000 *** 0.049870 6.816 GM0459Wervershoof 0.254024 *** 0.050001 5.080 0.224400 *** 0.054080 4.148 GM0462Wieringen 0.166357 *** 0.057619 2.887 0.239800 *** 0.057330 4.182 GM0463Wieringermeer 0.104180 ** 0.045118 2.309 0.115800 ** 0.049550 2.337 GM0473Zandvoort 0.678780 *** 0.037248 18.223 0.646200 *** 0.041290 15.649 GM0476Zijpe 0.265161 *** 0.044389 5.974 0.229400 *** 0.050550 4.538 GM0478Zeevang 0.383370 *** 0.046513 8.242 0.390900 *** 0.048420 8.073 GM0479Zaanstad 0.344480 *** 0.032925 10.462 0.312200 *** 0.036510 8.551 GM0482Alblasserdam 0.359875 *** 0.040008 8.995 0.348200 *** 0.042930 8.110 GM0484Alphen aan den Rijn 0.363794 *** 0.033632 10.817 0.344100 *** 0.037420 9.196

VIII

GM0489Barendrecht 0.396243 *** 0.037382 10.600 0.381200 *** 0.040500 9.413 GM0491Bergambacht 0.448394 *** 0.047510 9.438 0.363100 *** 0.047920 7.577 GM0497Bodegraven 0.497073 *** 0.040511 12.270 0.443900 *** 0.044110 10.064 GM0498Drechterland 0.223037 *** 0.041327 5.397 0.188800 *** 0.042840 4.406 GM0499Boskoop 0.391235 *** 0.112131 3.489 0.400400 *** 0.088990 4.500 GM0501Brielle 0.393744 *** 0.041888 9.400 0.3969 00 *** 0.044350 8.950 GM0502Capelle aan den IJssel 0.348512 *** 0.034241 10.178 0.311800 *** 0.037990 8.209 GM0503Delft 0.442266 *** 0.033926 13.036 0.402500 *** 0.037750 10.661 GM0504Dirksland 0.252135 *** 0.051010 4.943 0.191700 *** 0.055250 3.470 GM0505Dordrecht 0.221019 *** 0.033130 6.671 0.193000 *** 0.036700 5.259 GM0511Goedereede 0.357997 *** 0.048255 7.419 0.400900 *** 0.057330 6.993 GM0512Gorinchem 0.370653 *** 0.072213 5.133 0.303000 *** 0.063260 4.790 GM0513Gouda 0.294111 *** 0.052802 5.570 0.279400 *** 0.058130 4.806 GM0518's-Gravenhage 0.379676 *** 0.032146 11.811 0.351300 *** 0.035860 9.796 GM0523Hardinxveld-Giessendam 0.335852 *** 0.054948 6.112 0.288000 *** 0.064620 4.457 GM0530Hellevoetsluis 0.344572 *** 0.035305 9.760 0.301600 *** 0.038900 7.752 GM0531Hendrik-Ido-Ambacht 0.351527 *** 0.037844 9.289 0.352700 *** 0.041180 8.565 GM0532Stede Broec 0.174009 *** 0.042655 4.079 0.152800 *** 0.046440 3.291 GM0534Hillegom 0.478112 *** 0.038106 12.547 0.443000 *** 0.041610 10.647 GM0537Katwijk 0.589919 *** 0.034628 17.036 0.576600 *** 0.038140 15.118 GM0542Krimpen aan den IJssel 0.412070 *** 0.036849 11.183 0.374600 *** 0.040310 9.294 GM0545Leerdam 0.423600 *** 0.040836 10.373 0.385800 *** 0.045130 8.548 GM0546Leiden 0.520725 *** 0.033594 15.501 0.499900 *** 0.037160 13 .454 GM0547Leiderdorp 0.487302 *** 0.038419 12.684 0.469100 *** 0.041000 11.442 GM0553Lisse 0.506189 *** 0.039729 12.741 0.485200 *** 0.044000 11.027 GM0556Maassluis 0.435546 *** 0.041107 10.596 0.410400 *** 0.046260 8.872 GM0559Middelharnis 0.393917 *** 0.04 2649 9.236 0.316600 *** 0.047020 6.734 GM0568Bernisse 0.369099 *** 0.040841 9.037 0.368100 *** 0.044600 8.254 GM0569Nieuwkoop 0.432088 *** 0.038464 11.234 0.412600 *** 0.042850 9.628 GM0571Nieuw-Lekkerland 0.266809 *** 0.059809 4.461 0.315800 *** 0.060940 5.18 2 GM0575Noordwijk 0.761442 *** 0.035504 21.447 0.722300 *** 0.039280 18.387 GM0576Noordwijkerhout 0.518946 *** 0.040614 12.777 0.500200 *** 0.043010 11.630 GM0579Oegstgeest 0.640898 *** 0.036820 17.406 0.593800 *** 0.039720 14.949 GM0580Oostﬂakkee 0.233761 ** 0.045114 5.182 0.123000 ** 0.049870 2.466 GM0584Oud-Beijerland 0.427788 *** 0.038048 11.243 0.407800 *** 0.041780 9.760 GM0585Binnenmaas 0.340471 *** 0.037445 9.092 0.292600 *** 0.040730 7.183

GM0588Korendijk 0.378467 *** 0.046514 8.137 0.347300 *** 0.046080 7.536 GM0589Oudewater 0.511726 *** 0.049528 10.332 0.470800 *** 0.054650 8.614 GM0590Papendrecht 0.329449 *** 0.036316 9.072 0.301000 *** 0.039490 7.621 GM0595Reeuwijk 0.589094 *** 0.040300 14.618 0.587800 *** 0.044990 13.064 GM0597Ridderkerk 0.361923 *** 0. 035915 10.077 0.325000 *** 0.039100 8.314 GM0599Rotterdam 0.353084 *** 0.032388 10.902 0.315000 *** 0.036070 8.732 GM0600Rozenburg 0.286790 *** 0.049540 5.789 0.229700 *** 0.054650 4.202 GM0603Rijswijk 0.329796 *** 0.037491 8.797 0.314400 *** 0.041310 7.611 GM0606Schiedam 0.182551 *** 0.033830 5.396 0.178300 *** 0.037280 4.783 GM0608Schoonhoven 0.435684 *** 0.041876 10.404 0.414800 *** 0.044000 9.428 GM0610Sliedrecht 0.425568 *** 0.043551 9.772 0.337400 *** 0.046820 7.205 GM0611Cromstrijen 0.431883 *** 0.040838 10 .576 0.405300 *** 0.044990 9.009 GM0612Spijkenisse 0.244698 *** 0.034134 7.169 0.216900 *** 0.037720 5.750 GM0613Albrandswaard 0.433309 *** 0.037846 11.449 0.401100 *** 0.040730 9.846 GM0614Westvoorne 0.551280 *** 0.041461 13.296 0.493700 *** 0.043780 11.277 GM0617Strijen 0.343219 *** 0.054949 6.246 0.320300 *** 0.058130 5.510 GM0620Vianen 0.460088 *** 0.041085 11.198 0.415300 *** 0.045140 9.201 GM0622Vlaardingen 0.275314 *** 0.033552 8.206 0.244300 *** 0.037130 6.580 GM0623Vlist 0.400387 *** 0.049534 8.083 0.3980 00 *** 0.056600 7.033 GM0626Voorschoten 0.623358 *** 0.036340 17.154 0.584000 *** 0.039610 14.744 GM0627Waddinxveen 0.361819 *** 0.036823 9.826 0.331700 *** 0.040140 8.262 GM0629Wassenaar 0.851465 *** 0.036201 23.520 0.812900 *** 0.039180 20.749 GM0632Woerden 0.478463 *** 0.036191 13.221 0.454200 *** 0.039510 11.495 GM0637Zoetermeer 0.360601 *** 0.033013 10.923 0.327600 *** 0.036670 8.934 GM0638Zoeterwoude 0.521626 *** 0.046222 11.285 0.530800 *** 0.052120 10.183 GM0642Zwijndrecht 0.278815 *** 0.035398 7.877 0.278 700 *** 0.039060 7.135 GM0643Nederlek 0.385711 *** 0.043171 8.934 0.360900 *** 0.046260 7.801 GM0644Ouderkerk 0.258440 *** 0.054959 4.702 0.295600 *** 0.055250 5.351 GM0653Gaasterl 0.109989 *** 0.046513 2.365 0.134700 *** 0.050200 2.682 GM0654Borsele 0.074693 ** 0.043350 1.723 0.110000 ** 0.045750 2.405 GM0664Goes 0.283703 *** 0.045106 6.290 0.216100 *** 0.047920 4.510 GM0668West Maas en Waal 0.161614 *** 0.042993 3.759 0.129800 *** 0.045280 2.866 GM0677Hulst 0.080712 0.044167 1.827 0.050920 0.047920 1.063 GM0678Kapelle 0.311210 *** 0.051011 6.101 0.362400 *** 0.051690 7.010 GM0683Wymbritseradiel 0.211466 *** 0.040834 5.179 0.178900 *** 0.042930 4.168 GM0687Middelburg 0.265457 *** 0.036888 7.196 0.256100 *** 0.040100 6.387

GM0689Giessenlanden 0.427023 *** 0.051022 8. 369 0.379200 *** 0.052570 7.213 GM0693Graafstroom 0.297173 *** 0.067586 4.397 0.323500 *** 0.069620 4.647 GM0694Liesveld 0.357976 *** 0.051010 7.018 0.360100 *** 0.058990 6.104 GM0703Reimerswaal 0.095337 *** 0.049985 1.907 0.178500 *** 0.052570 3.395 GM0707Zederik 0.421620 *** 0.043742 9.639 0.372200 *** 0.045910 8.107 GM0710W 0.159600 *** 0.045640 3.497 0.147300 *** 0.047030 3.133 GM0715Terneuzen 0.077428 ** 0.036347 2.130 0.080950 ** 0.040030 2.022 GM0716Tholen 0.082624 * 0.045364 1.821 0.091480 * 0.048680 1.879 GM0717Veere 0.486431 *** 0.037736 12.890 0.445700 *** 0.041730 10.682 GM0718Vlissingen 0.248173 *** 0.034288 7.238 0.214100 *** 0.037970 5.639 GM0733Lingewaal 0.345199 *** 0.046832 7.371 0.336600 *** 0.049250 6.835 GM0736De Ronde Venen 0.631137 *** 0.036080 17.493 0.586500 *** 0.039610 14.807 GM0737Tytsjerksteradiel 0.086728 * 0.036767 2.359 0.067200 * 0.039920 1.683 GM0738Aalburg 0.186343 *** 0.051017 3.653 0.185100 *** 0.050920 3.635 GM0743Asten 0.239971 *** 0.048676 4.930 0.207300 *** 0.052130 3.977 GM0744Baarle-Nassau 0.286692 *** 0.062449 4.591 0.208800 *** 0.066100 3.158 GM0748Bergen op Zoom 0.188588 *** 0.037316 5.054 0.189700 *** 0.040690 4.663 GM0753Best 0.314312 *** 0.039651 7.927 0.324300 *** 0.042390 7.652 GM0755Boekel 0.253702 *** 0.051575 4.919 0.216100 *** 0.055900 3.865 GM0756Boxmeer 0.175737 *** 0.042483 4.137 0.144000 *** 0.046440 3.101 GM0757Boxtel 0.409480 *** 0.039158 10.457 0.345000 *** 0.042460 8.126 GM0758Breda 0.461904 *** 0.034823 13.264 0.444700 *** 0.038690 11.494 GM0762Deurne 0.280445 *** 0.038 466 7.291 0.255900 *** 0.041610 6.149 GM0765Pekela -0.161346 *** 0.046510 -3.469 -0.187300 *** 0.048960 -3.826 GM0766Dongen 0.254866 *** 0.039152 6.510 0.228100 *** 0.043100 5.291 GM0770Eersel 0.406401 *** 0.044187 9.197 0.302600 *** 0.046090 6.565 GM0772Eindhoven 0.340020 *** 0.032533 10.452 0.322400 *** 0.036250 8.894 GM0777Etten-Leur 0.313150 *** 0.063973 4.895 0.262900 *** 0.067750 3.880 GM0779Geertruidenberg 0.298916 *** 0.043173 6.924 0.274100 *** 0.044470 6.165 GM0784Gilze en Rijen 0.283983 *** 0.042820 6.6 32 0.262500 *** 0.045000 5.834 GM0785Goirle 0.357609 *** 0.065676 5.445 0.320900 *** 0.066100 4.855 GM0786Grave 0.191700 *** 0.058672 3.267 0.176900 *** 0.062040 2.851 GM0788Haaren 0.381336 *** 0.049098 7.767 0.365000 *** 0.052130 7.001 GM0794Helmond 0.254498 *** 0.033316 7.639 0.221800 *** 0.037070 5.983 GM0796's-Hertogenbosch 0.409021 *** 0.033001 12.394 0.389600 *** 0.036660 10.626 GM0797Heusden 0.310474 *** 0.035684 8.701 0.297600 *** 0.039210 7.590

GM0798Hilvarenbeek 0.371510 *** 0.042652 8.710 0.379600 *** 0. 048170 7.880 GM0808Lith 0.203994 ** 0.052790 3.864 0.128100 ** 0.059930 2.137 GM0809Loon op Zand 0.341364 *** 0.039640 8.612 0.269100 *** 0.043380 6.202 GM0815Mill en Sint Hubert 0.206613 *** 0.052166 3.961 0.248500 *** 0.057330 4.335 GM0823Oirschot 0.392429 *** 0.041733 9.403 0.352100 *** 0.043580 8.079 GM0824Oisterwijk 0.493580 *** 0.036739 13.435 0.506400 *** 0.040690 12.446 GM0826Oosterhout 0.342803 *** 0.034753 9.864 0.310100 *** 0.038420 8.072 GM0828Oss 0.244540 *** 0.033703 7.256 0.238100 *** 0.037260 6.391 GM0840Rucphen 0.243047 *** 0.052168 4.659 0.241100 *** 0.055260 4.363 GM0844Schijndel 0.321800 *** 0.041596 7.736 0.275400 *** 0.045430 6.061 GM0845Sint-Michielsgestel 0.388831 *** 0.038723 10.041 0.387500 *** 0.042240 9.173 GM0846Sint-Oedenrode 0.424740 *** 0.041334 10.276 0.428500 *** 0.045590 9.399 GM0847Someren 0.246465 *** 0.056667 4.349 0.229900 *** 0.051710 4.446 GM0848Son en Breugel 0.404711 *** 0.043765 9.247 0.374300 *** 0.045920 8.151 GM0851Steenbergen 0.222088 *** 0.040503 5.483 0.198200 *** 0.044470 4.456 GM0852Waterland 0.659919 *** 0.040847 16.156 0.635900 *** 0.045430 13.997 GM0855Tilburg 0.279585 *** 0.032588 8.579 0.260800 *** 0.036260 7.192 GM0856Uden 0.358424 *** 0.035133 10.202 0.338200 *** 0.038640 8.752 GM0858Valkenswaard 0.385886 *** 0.036603 10.542 0.369300 *** 0.039940 9.245 GM0860Veghel 0.301689 *** 0.036009 8.378 0.266300 *** 0.039220 6.790 GM0861Veldhoven 0.389376 *** 0.035465 10.979 0.348100 *** 0.038710 8.994 GM0865Vught 0.521843 *** 0.037544 13.899 0.533600 *** 0.040640 13.130 GM0866Waalre 0.410233 *** 0.040409 10.152 0.411600 *** 0.042850 9.607 GM0867Waalwijk 0.297099 *** 0.034975 8.495 0.298300 *** 0.038010 7.847 GM0870Werkendam 0.370753 *** 0.042160 8.794 0.343000 *** 0.045130 7.600 GM0873Woensdrecht 0.245979 *** 0.047887 5.137 0.232200 *** 0.048960 4.743 GM0874Woudrichem 0.426446 *** 0.043962 9.700 0.340800 *** 0.046260 7.368 GM0879Zundert 0.299082 *** 0.045113 6.630 0.297300 *** 0.045430 6.544 GM0880Wormerland 0.386240 *** 0.042164 9.160 0.400600 *** 0.044850 8.932 GM0881Onderbanken -0.059243 0.128205 -0.462 -0.105100 0.095020 -1.106 GM0882Landgraaf 0.001838 0.042644 0.043 0.024020 0.046630 0.515 GM0888Beek 0.022386 0.059812 0.374 -0.004207 0.059920 -0.070 GM0889Beesel 0.110914 0.067581 1.641 0.043860 0.071720 0.611 GM0893Bergen (L.) 0.1214 36 ** 0.048264 2.516 0.091880 * 0.052580 1.747 GM0899Brunssum -0.035418 0.043748 -0.810 -0.026420 0.048420 -0.546 GM0905Eijsden 0.061998 0.072209 0.859 0.015490 0.069610 0.222

XII

GM0907Gennep 0.217957 *** 0.052791 4.129 0.200900 *** 0.052570 3.821 GM0917Heerlen 0.012920 0.038590 0.335 -0.016500 0.041850 -0.394 GM0928Kerkrade -0.023301 0.041327 -0.564 -0.059570 0.043010 -1.385 GM0935Maastricht 0.401920 *** 0.035925 11.188 0.351400 *** 0.039280 8.946 GM0936Margraten 0.186412 *** 0.062469 2.984 0.227800 *** 0.0599 30 3.801 GM0938Meerssen 0.160854 * 0.058674 2.741 0.094130 * 0.055250 1.704 GM0944Mook en Middelaar 0.344817 *** 0.042825 8.052 0.324500 *** 0.044350 7.317 GM0946Nederweert 0.198325 *** 0.048271 4.109 0.204400 *** 0.047690 4.287 GM0951Nuth 0.080827 0.052795 1.531 0.020080 0.058990 0.340 GM0957Roermond 0.084689 0.045112 1.877 0.064770 0.046260 1.400 GM0962Schinnen -0.037417 0.075078 -0.498 -0.057320 0.129600 -0.442 GM0965Simpelveld -0.054404 0.075072 -0.725 -0.051700 0.080250 -0.644 GM0971Stein 0.063864 0. 072217 0.884 0.096090 0.074150 1.296 GM0981Vaals -0.155288 0.155477 -0.999 -0.070540 0.157000 -0.449 GM0983Venlo 0.084903 0.038116 2.227 0.027220 0.042310 0.643 GM0984Venray 0.186376 *** 0.037452 4.976 0.153500 *** 0.040240 3.815 GM0986Voerendaal 0.12902 5** 0.048664 2.651 0.109300 ** 0.052130 2.096 GM0988Weert 0.253768 *** 0.035442 7.160 0.239100 *** 0.038850 6.155 GM0994Valkenburg aan de Geul 0.252684 *** 0.053467 4.726 0.150300 *** 0.057330 2.622 GM0995Lelystad 0.110127 ** 0.033621 3.276 0.074510 ** 0.03728 0 1.999 GM1507Horst aan de Maas 0.106714 0.049534 2.154 0.080120 0.050910 1.574 GM1509Oude IJsselstreek 0.095688 *** 0.038392 2.492 0.108900 *** 0.041840 2.603 GM1525Teylingen 0.558641 *** 0.037149 15.038 0.573500 *** 0.039640 14.468 GM1581Utrechtse Heuvelrug 0.570197 *** 0.034666 16.448 0.538100 *** 0.038000 14.160 GM1586Oost Gelre 0.196740 *** 0.045645 4.310 0.195400 *** 0.048420 4.036 GM1598Koggenland 0.254533 *** 0.039464 6.450 0.246400 *** 0.042600 5.782 GM1621Lansingerland 0.439625 *** 0.035086 12.530 0.4 19000 *** 0.038870 10.778 GM1640Leudal 0.102577 * 0.043756 2.344 0.091870 * 0.050550 1.817 GM1641Maasgouw 0.029426 0.048265 0.610 0.030940 0.048170 0.642 GM1651Eemsmond -0.092912 ** 0.051561 -1.802 -0.124300 ** 0.050920 -2.442 GM1652Gemert-Bakel 0.210318 *** 0.043754 4.807 0.233700 *** 0.045750 5.109 GM1655Halderberge 0.175153 *** 0.038859 4.507 0.153400 *** 0.042310 3.627 GM1658Heeze-Leende 0.312399 *** 0.043369 7.203 0.275100 *** 0.047470 5.796 GM1659Laarbeek 0.249988 *** 0.038593 6.478 0.223500 *** 0.042770 5. 225 GM1663De Marne -0.138846 *** 0.069740 -1.991 -0.203000 *** 0.074150 -2.737 GM1667Reusel-De Mierden 0.340412 *** 0.042326 8.043 0.274600 *** 0.044860 6.121

XIII

GM1669Roerdalen 0.054171 0.046843 1.156 0.032220 0.049870 0.646 GM1671Maasdonk 0.359735 *** 0.0576 39 6.241 0.383300 *** 0.058140 6.594 GM1672Rijnwoude 0.460456 *** 0.044182 10.422 0.438200 *** 0.048420 9.049 GM1674Roosendaal 0.204104 *** 0.051571 3.958 0.163100 *** 0.058130 2.807 GM1676Schouwen-Duiveland 0.366438 *** 0.038338 9.558 0.321400 *** 0.041340 7. 774 GM1680Aa en Hunze 0.065871 0.039472 1.669 0.064120 0.044000 1.457 GM1681Borger-Odoorn -0.039947 0.038657 -1.033 -0.032790 0.041300 -0.794 GM1684Cuijk 0.213354 *** 0.037999 5.615 0.190200 *** 0.041390 4.595 GM1685Landerd 0.271251 *** 0.043760 6.199 0.2 39300 *** 0.045430 5.267 GM1690De Wolden 0.201526 *** 0.039817 5.061 0.176800 *** 0.042040 4.206 GM1695Noord-Beveland 0.252612 *** 0.041325 6.113 0.242200 *** 0.044480 5.446 GM1696Wijdemeren 0.705722 *** 0.037592 18.773 0.666500 *** 0.041180 16.187 GM1699Noordenveld 0.123648 ** 0.038726 3.193 0.093730 ** 0.041620 2.252 GM1700Twenterand 0.071515 0.044867 1.594 0.009592 0.045920 0.209 GM1701Westerveld 0.131225 ** 0.040618 3.231 0.105800 ** 0.044240 2.393 GM1702Sint Anthonis 0.219879 *** 0.056670 3.880 0.211000 *** 0.058130 3.630 GM1705Lingewaard 0.270702 *** 0.035150 7.701 0.253000 *** 0.038740 6.530 GM1706Cranendonck 0.171010 ** 0.051019 3.352 0.151000 ** 0.062050 2.434 GM1708Steenwijkerland 0.164180 *** 0.035428 4.634 0.175200 *** 0.038770 4.520 GM1709Moerdijk 0.228 200 *** 0.036266 6.292 0.185600 *** 0.040100 4.628 GM1711Echt-Susteren 0.052350 0.051017 1.026 -0.030760 0.049870 -0.617 GM1714Sluis 0.203615 *** 0.041075 4.957 0.162200 *** 0.044230 3.667 GM1719Drimmelen 0.298700 *** 0.038791 7.700 0.315300 *** 0.041780 7.54 5 GM1721Bernheze 0.341747 *** 0.040199 8.501 0.312600 *** 0.042310 7.388 GM1722Ferwerderadiel -0.050128 0.057617 -0.870 -0.045150 0.059920 -0.753 GM1723Alphen-Chaam 0.310592 *** 0.048265 6.435 0.299700 *** 0.050210 5.970 GM1724Bergeijk 0.312240 *** 0.049543 6.302 0.292500 *** 0.050920 5.744 GM1728Bladel 0.323706 *** 0.044400 7.291 0.321200 *** 0.046080 6.971 GM1729Gulpen-Wittem 0.159697 ** 0.058745 2.718 0.128000 ** 0.053540 2.391 GM1730Tynaarlo 0.214954 *** 0.035997 5.971 0.175700 *** 0.039220 4.481 GM1731Midden-Drenthe 0.033095 0.036243 0.913 0.046580 0.039330 1.184 GM1734Overbetuwe 0.256836 *** 0.034907 7.358 0.217600 *** 0.038450 5.660 GM1735Hof van Twente 0.133214 ** 0.038651 3.447 0.104400 ** 0.042380 2.464 GM1740Neder-Betuwe 0.311785 *** 0.042989 7.253 0.19 6300 *** 0.045590 4.306 GM1742Rijssen-Holten 0.280703 *** 0.042013 6.681 0.221200 *** 0.042460 5.211 GM1771Geldrop-Mierlo 0.339289 *** 0.047179 7.191 0.281100 *** 0.044860 6.267

XIV

GM1773Olst-Wijhe 0.195098 *** 0.042644 4.575 0.153200 *** 0.043780 3.500 GM1774Dinkelland 0.168145 ** 0.040970 4.104 0.110900 ** 0.043590 2.544 GM1783Westland 0.453037 *** 0.033642 13.467 0.432200 *** 0.037180 11.626 GM1842Midden-Delfland 0.551173 *** 0.048669 11.325 0.513400 *** 0.052120 9.850 GM1859Berkelland 0.119684 ** 0.036878 3.245 0.087570 ** 0.039720 2.205 GM1876Bronckhorst 0.223844 *** 0.036270 6.172 0.221100 *** 0.040350 5.480 GM1883Sittard-Geleen 0.072652 0.040299 1.803 0.021800 0.044110 0.494 GM1884Kaag en Braassem 0.544906 *** 0.041737 13.056 0.493300 *** 0.042460 11.619 GM1891Dantumadiel 0.056506 0.044395 1.273 0.028490 0.050210 0.567 GM1896Zwartewaterland 0.228769 *** 0.047167 4.850 0.201500 *** 0.049560 4.067 GM1916Leidschendam-Voorburg 0.477072 *** 0.033729 14.144 0.459100 *** 0.037310 12.305 GM1926Pijnacker-Nootdorp 0.471907 *** 0.035576 13.265 0.460600 *** 0.038590 11.935 GM1955Montferland 0.204084 *** 0.036383 5.609 0.163000 *** 0.038800 4.199 GM1987Menterwolde -0.090912 *** 0.047164 -1.928 -0.133200 *** 0.048970 -2.720 Nr. of observations 55.987 55.987 Adjusted R² 0.8007 0.8005 The dependent variable is the logarithm of the updated listing price. For the dummy variables, the remaining categories (building year category “1906-1945”, type of transaction “k.k.”, monumental status “no”, housing type “semi-detached” and municipality “GM0003Appingedam”) act as reference categories or values. *** = significant at 1%. ** = significant at 5%. * = significant at 10%.

Appendix C: Model 5 Regression Results

Before adjustments After removing outliers and

acknowledging distance Ob- Median Median Objects Median Median Municipality jects % diﬀerence accuracy % diﬀerence accuracy

GM0003Appingedam 121 15% 90% - - - GM0007Bellingwedde 68 18% 89% - - - GM0009Ten Boer 9 10% 93% - - - GM0010Delfzĳl 44 8% 94% - - - GM0014Groningen 10 16% 92% - - - GM0015Grootegast 35 14% 91% - - - GM0017Haren 53 16% 89% - - - GM0018Hoogezand-Sappemeer 408 11% 93% - - - GM0022Leek 238 12% 92% - - - GM0024Loppersum 27 10% 92% - - - GM0025Marum 110 15% 93% - - - GM0034Almere 2729 8% 94% 2703 8% 94% GM0037Stadskanaal 384 11% 92% - - - GM0040Slochteren 133 14% 92% - - - GM0047Veendam 468 11% 93% - - - GM0048Vlagtwedde 204 12% 91% - - - GM0050Zeewolde 375 10% 92% 367 9% 93% GM0051Skarsterl 339 13% 91% 322 12% 92% GM0053Winsum 38 9% 95% - - - GM0055Boarnsterhim 282 15% 90% 122 13% 91% GM0056Zuidhorn 115 15% 93% - - - GM0058Dongeradeel 236 15% 91% - - - GM0059Achtkarspelen 260 11% 93% - - - GM0060Ameland 90 16% 88% - - - GM0063het Bildt 61 14% 91% - - - GM0064Bolsward 89 11% 94% 88 10% 95% GM0070Franekeradeel 84 15% 90% 81 19% 90% GM0072Harlingen 33 10% 93% 33 9% 94% GM0074Heerenveen 155 13% 92% 150 12% 92% GM0079Kollumerland en Nieu- 184 12% 93% - - - wkruisland

XVI

GM0080Leeuwarden 1337 9% 93% - - - GM0081Leeuwarderadeel 103 14% 91% - - - GM0082Lemsterland 263 15% 90% 251 15% 91% GM0083Menaldumadeel 108 13% 93% - - - GM0085Ooststellingwerf 261 12% 92% 252 12% 93% GM0086Opsterland 250 12% 92% 73 10% 93% GM0088Schiermonnikoog 43 20% 90% - - - GM0090Smallingerland 786 10% 93% - - - GM0091Sneek 520 10% 93% 511 10% 93% GM0093Terschelling 21 21% 87% 3 20% 88% GM0096Vlieland 6 26% 93% - - - GM0098Weststellingwerf 249 14% 92% 237 13% 93% GM0104Nijefurd 195 17% 91% 180 13% 92% GM0106Assen 895 8% 94% - - - GM0109Coevorden 440 11% 92% 427 10% 93% GM0114Emmen 1246 11% 92% 1226 11% 93% GM0118Hoogeveen 587 9% 94% 578 9% 94% GM0119Meppel 486 9% 93% 483 10% 93% GM0140Littenseradiel 106 13% 91% 67 12% 92% GM0141Almelo 799 10% 93% 784 10% 93% GM0147Borne 83 15% 91% 80 16% 92% GM0148Dalfsen 216 12% 92% 210 12% 92% GM0150Deventer 1059 9% 93% 1044 9% 94% GM0153Enschede 656 11% 93% 637 12% 93% GM0158Haaksbergen 29 12% 91% 29 15% 91% GM0160Hardenberg 395 12% 92% 385 10% 93% GM0163Hellendoorn 487 11% 93% 467 10% 94% GM0164Hengelo 993 10% 94% 977 9% 94% GM0166Kampen 729 9% 94% 714 8% 94% GM0168Losser 183 13% 92% 178 11% 93% GM0171Noordoostpolder 728 10% 93% 714 10% 93% GM0173Oldenzaal 330 11% 93% 321 11% 93% GM0175Ommen 212 14% 90% 200 11% 93% GM0177Raalte 366 10% 93% 359 9% 94% GM0180Staphorst 100 12% 94% 96 10% 95% GM0183Tubbergen 92 12% 91% 86 11% 93% GM0184Urk 20 7% 96% 17 9% 96% GM0189Wierden 192 11% 92% 187 12% 92% XVII

GM0193Zwolle 1884 8% 94% 1862 8% 95% GM0196Rijnwaarden 153 13% 92% 147 13% 93% GM0197Aalten 319 13% 92% 312 12% 93% GM0200Apeldoorn 216 16% 90% 205 13% 91% GM0202Arnhem 1450 9% 93% 1429 9% 94% GM0203Barneveld 386 13% 91% 370 12% 93% GM0209Beuningen 218 9% 93% 211 9% 94% GM0213Brummen 187 12% 93% 182 12% 93% GM0214Buren 302 12% 91% 292 12% 92% GM0216Culemborg 302 9% 94% 295 9% 94% GM0221Doesburg 199 10% 94% 194 9% 94% GM0222Doetinchem 772 9% 94% 761 9% 94% GM0225Druten 203 10% 93% 196 10% 93% GM0226Duiven 449 9% 94% 443 8% 94% GM0228Ede 1358 11% 93% 1332 10% 93% GM0230Elburg 295 11% 93% 288 9% 94% GM0232Epe 333 11% 92% 325 12% 93% GM0233Ermelo 85 17% 89% 83 17% 91% GM0236Geldermalsen 199 10% 93% 191 10% 94% GM0241Groesbeek 58 16% 90% 52 18% 91% GM0243Harderwijk 71 10% 93% 70 9% 93% GM0244Hattem 24 10% 92% 22 9% 93% GM0246Heerde 99 14% 92% 97 14% 93% GM0252Heumen 183 12% 92% 178 12% 93% GM0262Lochem 380 14% 91% 366 13% 92% GM0263Maasdriel 180 12% 92% 176 13% 93% GM0265Millingen aan de Rijn 87 11% 93% 87 10% 94% GM0267Nijkerk 473 10% 93% 455 9% 94% GM0268Nijmegen 1943 9% 93% 1918 9% 94% GM0269Oldebroek 296 12% 93% 294 13% 93% GM0273Putten 233 13% 92% 224 13% 92% GM0274Renkum 557 13% 92% 534 12% 93% GM0275Rheden 680 13% 91% 656 13% 92% GM0277Rozendaal 28 16% 89% 28 13% 91% GM0279Scherpenzeel 113 10% 92% 110 10% 94% GM0281Tiel 570 9% 94% 561 9% 94% GM0282Ubbergen 87 14% 90% 82 16% 92% GM0285Voorst 221 15% 91% 210 14% 91% XVIII

GM0289Wageningen 534 7% 95% 532 7% 95% GM0293Westervoort 195 10% 93% 192 10% 93% GM0294Winterswijk 454 11% 92% 438 11% 93% GM0296Wijchen 533 9% 93% 520 9% 94% GM0297Zaltbommel 307 13% 93% 295 11% 93% GM0299Zevenaar 473 12% 92% 461 11% 92% GM0301Zutphen 613 12% 92% 589 10% 93% GM0302Nunspeet 313 13% 92% 295 12% 93% GM0303Dronten 566 10% 93% 560 9% 94% GM0304Neerijnen 135 14% 91% 131 14% 92% GM0305Abcoude 122 13% 92% 117 10% 93% GM0307Amersfoort 2088 8% 94% 2059 8% 95% GM0308Baarn 258 15% 90% 250 14% 91% GM0310De Bilt 386 13% 91% 371 12% 93% GM0311Breukelen 121 14% 92% 113 13% 93% GM0312Bunnik 153 10% 93% 153 11% 94% GM0313Bunschoten 91 7% 95% 87 9% 95% GM0317Eemnes 115 14% 91% 112 14% 91% GM0321Houten 508 8% 94% 500 8% 95% GM0327Leusden 359 10% 94% 353 8% 94% GM0329Loenen 120 15% 92% 112 15% 91% GM0331Lopik 140 9% 94% 138 8% 95% GM0333Maarssen 83 12% 92% 80 12% 93% GM0335Montfoort 140 10% 93% 135 9% 94% GM0339Renswoude 68 10% 93% 68 9% 95% GM0340Rhenen 266 11% 93% 261 11% 93% GM0342Soest 623 11% 93% 610 11% 93% GM0344Utrecht 4651 8% 94% 4601 8% 95% GM0345Veenendaal 840 9% 94% 820 9% 95% GM0351Woudenberg 143 12% 93% 140 12% 93% GM0352Wijk bij Duurstede 294 9% 93% 293 10% 94% GM0353IJsselstein 310 8% 94% 301 8% 94% GM0355Zeist 851 11% 92% 822 11% 93% GM0356Nieuwegein 680 8% 94% 669 8% 94% GM0358Aalsmeer 388 11% 93% 376 11% 94% GM0361Alkmaar 1501 9% 94% 1477 9% 94% GM0362Amstelveen 1037 10% 93% 998 10% 93% GM0363Amsterdam 8877 8% 93% 8729 8% 94% XIX

GM0364Andijk 68 13% 93% 66 13% 93% GM0365Graft-De Rijp 91 15% 91% 87 14% 94% GM0366Anna Paulowna 122 14% 93% 112 11% 93% GM0370Beemster 123 13% 91% 119 13% 93% GM0373Bergen (NH.) 490 14% 91% 460 13% 93% GM0375Beverwijk 368 10% 93% 357 10% 93% GM0376Blaricum 119 17% 88% 114 14% 90% GM0377Bloemendaal 264 13% 91% 252 13% 92% GM0381Bussum 471 14% 90% 443 12% 92% GM0383Castricum 379 10% 93% 373 10% 94% GM0384Diemen 237 8% 94% 236 8% 94% GM0385Edam-Volendam 361 11% 92% 351 11% 93% GM0388Enkhuizen 330 9% 94% 327 9% 94% GM0392Haarlem 2173 10% 93% 2126 9% 93% GM0393Haarlemmerliede en 63 14% 90% 61 14% 93% Spaarnwoude GM0394Haarlemmermeer 1620 9% 94% 1583 8% 94% GM0395Harenkarspel 135 11% 95% 128 10% 95% GM0396Heemskerk 57 9% 94% 54 5% 95% GM0397Heemstede 49 13% 90% 48 16% 88% GM0398Heerhugowaard 45 10% 95% 45 9% 95% GM0399Heiloo 318 11% 92% 305 12% 93% GM0400Den Helder 608 10% 93% 600 10% 94% GM0402Hilversum 1163 11% 93% 1130 10% 93% GM0405Hoorn 848 9% 94% 836 8% 94% GM0406Huizen 645 11% 92% 624 10% 93% GM0412Niedorp 154 11% 93% 147 10% 94% GM0415Landsmeer 142 12% 93% 139 12% 94% GM0416Langedijk 323 11% 92% 311 11% 93% GM0417Laren 237 15% 90% 214 15% 91% GM0420Medemblik 440 13% 92% 426 12% 93% GM0424Muiden 91 16% 91% 90 15% 92% GM0425Naarden 249 12% 91% 242 12% 92% GM0431Oostzaan 121 11% 93% 117 12% 94% GM0432Opmeer 118 10% 92% 111 10% 94% GM0437Ouder-Amstel 164 10% 93% 163 9% 94% GM0439Purmerend 888 8% 94% 877 7% 94% GM0441Schagen 199 11% 92% 192 10% 94%

GM0448Texel 152 14% 91% 140 13% 92% GM0450Uitgeest 166 10% 93% 161 11% 94% GM0451Uithoorn 317 8% 94% 307 8% 95% GM0453Velsen 909 9% 93% 886 9% 93% GM0457Weesp 292 9% 94% 289 9% 95% GM0458Schermer 82 17% 91% 77 12% 92% GM0459Wervershoof 102 11% 93% 100 12% 94% GM0462Wieringen 64 11% 93% 64 9% 93% GM0463Wieringermeer 131 12% 92% 124 10% 94% GM0473Zandvoort 341 13% 91% 330 13% 92% GM0476Zijpe 138 14% 91% 129 13% 93% GM0478Zeevang 101 12% 91% 95 13% 94% GM0479Zaanstad 1822 10% 93% 1772 9% 94% GM0482Alblasserdam 252 10% 93% 247 10% 94% GM0484Alphen aan den Rijn 1087 8% 94% 1070 8% 95% GM0489Barendrecht 367 9% 93% 358 9% 94% GM0491Bergambacht 133 12% 92% 132 12% 93% GM0497Bodegraven 231 10% 92% 226 10% 93% GM0498Drechterland 215 13% 93% 212 11% 94% GM0499Boskoop 23 9% 93% 23 6% 94% GM0501Brielle 203 11% 93% 196 11% 94% GM0502Capelle aan den IJssel 779 8% 94% 770 8% 94% GM0503Delft 918 8% 94% 901 8% 95% GM0504Dirksland 95 11% 92% 90 12% 92% GM0505Dordrecht 1576 10% 93% 1546 10% 93% GM0511Goedereede 87 17% 90% 84 16% 92% GM0512Gorinchem 38 10% 92% 36 8% 94% GM0513Gouda 60 12% 93% 59 12% 91% GM0518's-Gravenhage 5791 10% 92% 5674 10% 93% GM0523Hardinxveld-Giessendam 46 10% 93% 42 15% 93% GM0530Hellevoetsluis 569 9% 94% 563 9% 94% GM0531Hendrik-Ido-Ambacht 303 10% 94% 296 8% 94% GM0532Stede Broec 157 10% 94% 153 10% 94% GM0534Hillegom 297 10% 93% 288 9% 94% GM0537Katwijk 711 8% 94% 700 8% 95% GM0542Krimpen aan den IJssel 385 9% 94% 378 8% 94% GM0545Leerdam 180 10% 93% 175 10% 94% GM0546Leiden 1128 8% 94% 1109 9% 94% XXI

GM0547Leiderdorp 284 7% 95% 283 7% 95% GM0553Lisse 227 10% 92% 223 9% 93% GM0556Maassluis 181 10% 94% 180 10% 94% GM0559Middelharnis 181 12% 91% 177 12% 92% GM0568Bernisse 181 12% 92% 173 11% 93% GM0569Nieuwkoop 253 13% 93% 244 11% 94% GM0571Nieuw-Lekkerland 52 12% 93% 51 12% 92% GM0575Noordwijk 522 10% 93% 502 9% 94% GM0576Noordwijkerhout 244 9% 94% 235 8% 95% GM0579Oegstgeest 426 9% 94% 419 9% 94% GM0580Oostﬂakkee 139 12% 92% 135 13% 93% GM0584Oud-Beijerland 298 8% 94% 294 8% 95% GM0585Binnenmaas 352 11% 93% 339 10% 94% GM0588Korendijk 145 12% 93% 143 10% 93% GM0589Oudewater 95 10% 93% 90 10% 94% GM0590Papendrecht 433 9% 94% 419 9% 94% GM0595Reeuwijk 206 13% 92% 194 12% 93% GM0597Ridderkerk 489 8% 95% 478 8% 95% GM0599Rotterdam 3411 10% 93% 3335 10% 93% GM0600Rozenburg 88 9% 94% 87 8% 96% GM0603Rijswijk 314 10% 93% 312 10% 93% GM0606Schiedam 1065 9% 94% 1059 9% 94% GM0608Schoonhoven 207 10% 95% 199 7% 95% GM0610Sliedrecht 152 9% 95% 149 8% 95% GM0611Cromstrijen 197 12% 92% 191 11% 93% GM0612Spijkenisse 823 7% 94% 816 7% 95% GM0613Albrandswaard 337 9% 93% 330 9% 94% GM0614Westvoorne 200 11% 93% 188 10% 94% GM0617Strijen 78 12% 91% 76 11% 92% GM0620Vianen 195 12% 92% 189 12% 94% GM0622Vlaardingen 1123 10% 93% 1106 9% 93% GM0623Vlist 76 9% 96% 75 8% 96% GM0626Voorschoten 422 10% 93% 412 10% 93% GM0627Waddinxveen 364 8% 95% 358 8% 95% GM0629Wassenaar 504 13% 91% 480 13% 92% GM0632Woerden 458 9% 94% 450 9% 94% GM0637Zoetermeer 1689 7% 95% 1677 7% 95% GM0638Zoeterwoude 119 12% 93% 115 9% 94% XXII

GM0642Zwijndrecht 579 10% 93% 569 9% 94% GM0643Nederlek 179 9% 94% 172 9% 94% GM0644Ouderkerk 82 12% 90% 78 9% 94% GM0653Gaasterl 129 11% 92% 128 11% 92% GM0654Borsele 154 14% 91% 149 15% 92% GM0664Goes 127 12% 93% 123 10% 94% GM0668West Maas en Waal 168 13% 92% 161 12% 93% GM0677Hulst 141 14% 92% 131 16% 92% GM0678Kapelle 86 11% 93% 81 7% 96% GM0683Wymbritseradiel 217 16% 91% 191 13% 91% GM0687Middelburg 416 12% 93% 402 12% 93% GM0689Giessenlanden 93 15% 93% 88 10% 93% GM0693Graafstroom 50 13% 92% 45 13% 92% GM0694Liesveld 60 13% 92% 59 13% 92% GM0703Reimerswaal 98 14% 91% 90 12% 91% GM0707Zederik 137 13% 91% 132 13% 91% GM0710W 156 13% 91% 150 13% 91% GM0715Terneuzen 426 13% 91% 409 12% 92% GM0716Tholen 132 15% 91% 127 13% 92% GM0717Veere 290 17% 89% 253 13% 92% GM0718Vlissingen 821 11% 93% 789 11% 94% GM0733Lingewaal 117 11% 91% 113 11% 93% GM0736De Ronde Venen 458 10% 94% 449 10% 94% GM0737Tytsjerksteradiel 435 13% 92% - - - GM0738Aalburg 98 14% 92% 94 11% 94% GM0743Asten 100 17% 90% 91 14% 92% GM0744Baarle-Nassau 48 12% 92% 47 13% 92% GM0748Bergen op Zoom 367 12% 93% 362 10% 93% GM0753Best 241 9% 93% 236 9% 94% GM0755Boekel 85 12% 93% 83 12% 93% GM0756Boxmeer 185 10% 94% 181 11% 94% GM0757Boxtel 261 12% 92% 252 11% 93% GM0758Breda 670 11% 92% 659 11% 93% GM0762Deurne 285 10% 93% 279 10% 94% GM0765Pekela 125 13% 93% - - - GM0766Dongen 252 11% 92% 242 11% 93% GM0770Eersel 161 10% 93% 159 10% 93% GM0772Eindhoven 2625 10% 93% 2566 9% 94% XXIII

GM0777Etten-Leur 46 15% 91% 46 16% 92% GM0779Geertruidenberg 185 12% 91% 183 11% 92% GM0784Gilze en Rijen 178 12% 94% 171 10% 95% GM0785Goirle 51 17% 89% 49 14% 90% GM0786Grave 56 14% 92% 52 12% 93% GM0788Haaren 95 13% 91% 88 14% 92% GM0794Helmond 1302 9% 94% 1293 9% 94% GM0796's-Hertogenbosch 1802 9% 94% 1766 8% 94% GM0797Heusden 487 9% 94% 482 10% 94% GM0798Hilvarenbeek 136 12% 91% 129 11% 92% GM0808Lith 80 20% 90% 73 17% 91% GM0809Loon op Zand 246 14% 92% 244 13% 93% GM0815Mill en Sint Hubert 67 12% 91% 63 11% 93% GM0823Oirschot 216 11% 93% 208 10% 93% GM0824Oisterwijk 384 14% 91% 362 12% 93% GM0826Oosterhout 686 10% 93% 673 11% 94% GM0828Oss 1102 9% 93% 1081 9% 94% GM0840Rucphen 76 12% 92% 76 14% 91% GM0844Schijndel 179 13% 92% 169 11% 93% GM0845Sint-Michielsgestel 261 12% 92% 253 11% 94% GM0846Sint-Oedenrode 194 13% 92% 185 14% 92% GM0847Someren 90 15% 91% 89 14% 92% GM0848Son en Breugel 152 12% 92% 150 10% 93% GM0851Steenbergen 221 13% 92% 214 13% 92% GM0852Waterland 214 11% 93% 205 11% 94% GM0855Tilburg 2508 9% 93% 2478 9% 94% GM0856Uden 629 8% 94% 622 8% 94% GM0858Valkenswaard 430 11% 92% 416 12% 92% GM0860Veghel 476 9% 94% 471 9% 94% GM0861Veldhoven 579 11% 93% 557 10% 93% GM0865Vught 365 10% 93% 356 10% 93% GM0866Waalre 231 12% 92% 225 11% 94% GM0867Waalwijk 681 12% 92% 658 11% 93% GM0870Werkendam 198 11% 93% 196 11% 93% GM0873Woensdrecht 110 13% 90% 105 14% 90% GM0874Woudrichem 116 16% 90% 109 15% 92% GM0879Zundert 174 10% 92% 169 10% 93% GM0880Wormerland 184 13% 93% 180 12% 95% XXIV

GM0881Onderbanken 19 12% 91% 19 14% 92% GM0882Landgraaf 159 15% 90% 153 13% 91% GM0888Beek 48 15% 90% 47 16% 91% GM0889Beesel 39 11% 93% 38 12% 93% GM0893Bergen (L.) 95 11% 92% 92 10% 94% GM0899Brunssum 137 13% 91% 134 12% 92% GM0905Eijsden 31 9% 94% 31 9% 94% GM0907Gennep 87 11% 94% 85 10% 95% GM0917Heerlen 303 11% 92% 299 12% 92% GM0928Kerkrade 199 14% 91% 197 13% 92% GM0935Maastricht 465 12% 92% 443 12% 92% GM0936Margraten 60 13% 92% 57 11% 92% GM0938Meerssen 68 11% 93% 65 11% 92% GM0944Mook en Middelaar 152 12% 93% 148 11% 94% GM0946Nederweert 109 10% 93% 107 10% 94% GM0951Nuth 73 13% 91% 69 11% 92% GM0957Roermond 130 11% 93% 129 11% 94% GM0962Schinnen 27 15% 91% 27 15% 91% GM0965Simpelveld 32 19% 89% 31 19% 89% GM0971Stein 32 14% 90% 30 14% 91% GM0981Vaals 10 26% 89% 8 18% 90% GM0983Venlo 279 16% 90% 263 12% 92% GM0984Venray 344 8% 95% 339 8% 95% GM0986Voerendaal 72 9% 93% 70 13% 94% GM0988Weert 542 10% 94% 534 10% 94% GM0994Valkenburg aan de Geul 83 18% 87% 73 21% 90% GM0995Lelystad 1115 10% 93% 1104 9% 94% GM1507Horst aan de Maas 91 12% 91% 90 14% 92% GM1509Oude IJsselstreek 301 11% 93% 290 11% 94% GM1525Teylingen 417 10% 93% 404 9% 94% GM1581Utrechtse Heuvelrug 715 13% 91% 675 11% 93% GM1586Oost Gelre 129 13% 92% 122 11% 94% GM1598Koggenland 243 11% 93% 234 11% 94% GM1621Lansingerland 611 9% 93% 597 9% 94% GM1640Leudal 151 11% 92% 150 10% 93% GM1641Maasgouw 126 15% 91% 121 12% 92% GM1651Eemsmond 86 13% 93% - - - GM1652Gemert-Bakel 164 12% 93% 159 10% 94% XXV

GM1655Halderberge 276 13% 92% 272 13% 92% GM1658Heeze-Leende 174 15% 91% 165 12% 93% GM1659Laarbeek 267 11% 93% 261 9% 94% GM1663De Marne 29 11% 93% - - - GM1667Reusel-De Mierden 175 11% 92% 169 12% 92% GM1669Roerdalen 113 12% 92% 109 12% 93% GM1671Maasdonk 63 13% 92% 62 14% 93% GM1672Rijnwoude 123 10% 93% 122 10% 93% GM1674Roosendaal 84 13% 92% 82 12% 93% GM1676Schouwen-Duiveland 302 13% 91% 290 12% 92% GM1680Aa en Hunze 213 14% 91% - - - GM1681Borger-Odoorn 317 12% 91% 106 14% 91% GM1684Cuijk 312 10% 93% 306 10% 94% GM1685Landerd 148 11% 93% 146 10% 94% GM1690De Wolden 246 14% 91% 239 13% 92% GM1695Noord-Beveland 186 13% 91% 166 11% 93% GM1696Wijdemeren 365 15% 91% 341 13% 94% GM1699Noordenveld 272 14% 92% - - - GM1700Twenterand 146 11% 94% 141 9% 94% GM1701Westerveld 222 13% 92% 219 11% 93% GM1702Sint Anthonis 75 12% 93% 73 9% 94% GM1705Lingewaard 584 10% 93% 577 10% 93% GM1706Cranendonck 76 13% 92% 72 14% 92% GM1708Steenwijkerland 592 12% 93% 568 11% 93% GM1709Moerdijk 455 13% 91% 440 11% 93% GM1711Echt-Susteren 95 12% 93% 91 10% 94% GM1714Sluis 188 18% 90% 169 14% 92% GM1719Drimmelen 284 13% 92% 275 12% 93% GM1721Bernheze 250 12% 93% 245 11% 94% GM1722Ferwerderadiel 66 13% 92% - - - GM1723Alphen-Chaam 106 13% 92% 100 11% 93% GM1724Bergeijk 95 10% 93% 94 10% 94% GM1728Bladel 143 12% 92% 140 12% 93% GM1729Gulpen-Wittem 78 17% 92% 71 12% 94% GM1730Tynaarlo 513 13% 91% - - - GM1731Midden-Drenthe 467 11% 92% 15 9% 94% GM1734Overbetuwe 668 11% 93% 655 10% 93% GM1735Hof van Twente 220 13% 91% 207 13% 92% XXVI

GM1740Neder-Betuwe 185 15% 91% 179 13% 92% GM1742Rijssen-Holten 217 10% 92% 212 11% 93% GM1771Geldrop-Mierlo 140 10% 93% 137 10% 93% GM1773Olst-Wijhe 196 10% 94% 192 10% 94% GM1774Dinkelland 208 10% 94% 199 9% 95% GM1783Westland 1121 9% 94% 1101 9% 94% GM1842Midden-Delﬂand 107 9% 94% 104 9% 94% GM1859Berkelland 401 12% 92% 392 11% 93% GM1876Bronckhorst 421 14% 91% 402 13% 92% GM1883Sittard-Geleen 222 14% 92% 211 13% 92% GM1884Kaag en Braassem 244 15% 90% 219 11% 93% GM1891Dantumadiel 136 13% 92% - - - GM1896Zwartewaterland 116 11% 93% 111 11% 94% GM1916Leidschendam-Voorburg 1152 10% 92% 1128 10% 93% GM1926Pijnacker-Nootdorp 557 9% 94% 546 8% 94% GM1955Montferland 501 12% 93% 483 11% 93% GM1987Menterwolde 125 13% 92% - - -

XXVII

Appendix D: Counting Cases by Listing Date per Province

08 07

2012

0 01

39 74 77 366 388 1066 16

606 585 2794 3790186 6044 15738 121 1215 264438 417 193 549 1369 526 113 1843346 3780 199 5903 1379 159 412914 861 577 4217 1430 187 3954591 6511 12846 374 544 1401 1719199 3534 9961 143 321 278 404 547 1322 421 719 447 1639 2825369 2327 7445 502 219 807168 666 637 674 2169 675 1045 96 200 476 329 1054 691 13 523 678 1322 90

0 07

2011

03 02 448 58 23 25 6 8 50 6 39467 241 213 188 10 23 14 13 29 16 66 261 139 403 23 3 8 4 11 48 8 19 132 148

1143 619 11 27 7 14 55 19 87 359 152

1 1406 1289 436 1567 74 1941 50 36 93 34 171 351 88 37 354170 118 88 9952 501 53 2 1286 1333 100 4 341 698 322 74 4 783 297 48 1 9 53 16 164 393 4 51 14 1176 194 15 127 23 107801 84 16 464 97 58 18 18 18 123 347 18 263 46 37 41 150 122 2 1449 890 43 77 45 67 140 60 80 691 305

56 944 77 39 54 33 49 64 42 97 497 233

08 07 72 9 87 5 89 13 626 84 16135 14 19 5193 248 15 174 386 60569 1 67 23 1 3 49 1 491 53225 27 28 1 164 12 12 1189 33 3 1 2010

06 XXVIII 05 Drenthe Zeeland Limburg Utrecht Friesland

Overijssel Flevoland Flevoland

Groningen

Gelderland 4 0 Zuid Holland - Zuid Noord Holland - Noord

Brabant - Noord