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Valuation of Owner-Occupied Housing in the Netherlands

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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 ©2013 – W.P.A. van Sprundel and DataQuote B.V. All rights reserved. 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 char- acteristics. 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 in- formation, 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 ............................................
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