Examining the Deviation to Net Asset Value For

Home , Net asset value

DEGREE PROJECT, IN REAL ESTATE AND CONSTRUCTION MANAGEMENT BUILDING AND REAL ESTATE ECONOMICS MASTER OF SCIENCE, 30 CREDITS, SECOND LEVEL STOCKHOLM, SWEDEN 2016

Examining the Deviation to Net Asset Value

for Swedish Listed Property Companies

Applying a rational and irrational approach

Tomas Shaw

Matilda Wåhlin

TECHNOLOGY

DEPARTMENT OF REAL ESTATE AND CONSTRACTION MANAGEMENT

ROYAL INSTITUTE OF TECHNOLOGY

DEPARTMENT OF REAL ESTATE AND CONSTRUCTION MANAGEMENT

Master of Science thesis

Title Examining the Deviation to Net Asset Value for Swedish Listed Property Companies Authors Tomas Shaw, Matilda Wåhlin Department Real Estate and Construction Management Master Thesis number TRITA-FOB-ByF-MASTER-2016:29 Archive number 435 Supervisor Herman Donner Keywords Deviation to NAV, Panel data, Real Estate

Abstract Net asset value (NAV) is commonly used to represent the value of a property company. For listed property companies a secondary valuation occurs simultaneously as the company’s stocks are traded on the stock market. Historically, a deviation between the NAV and the market capitalisation has been found for property companies implying that the stock market values the company differently. This thesis examines the deviation to NAV for 14 Swedish listed property companies during 2006-2015. The examination explains the deviation from the basis of a rational and an irrational approach. The thesis investigates empirically which factors that have affected the deviation by the use of a panel data regression analysis.

The rational approach investigates the impact of company-specific, share-specific and corporate governance variables. The results of the thesis show that the rational variables can explain the deviation to NAV to some extent. The main contribution comes from company- specific variables. Larger companies, companies focused on fewer locations, companies with a better reputation among asset managers and companies with a higher amount of insider ownership are negatively correlated to the discount to NAV. These company characteristics thus suggest a decrease in discounts to NAV (increase in premiums). At the same time companies with a higher loan to value, focus on property type and systematic risk increase the discount to NAV (decrease in premiums). The final rational model produces an adjusted R-square of 37.4% for the Swedish listed property market during the investigated period.

The irrational approach investigates the impact of noise traders. The results show that the contribution of market sentiment is significant. The confidence indicator for the households has the greatest impact on the discount to NAV and an inclusion of the variable increases the adjusted R-square to 53.6%. An investigation into the justification of using the Noise Trader Theory is conducted and concludes that the use of a proxy for market sentiment is justified. Examensarbete

Titel Substansrabatter och substanspremier hos svenska börsnoterade fastighetsbolag Författare Tomas Shaw, Matilda Wåhlin Institution Fastigheter och Byggande Examensarbete Master nivå TRITA-FOB-ByF-MASTER-2016:29 Arkiv nummer 435 Handledare Herman Donner Nyckelord Substansrabatt, Substanspremie, Panel data

Sammanfattning Substansvärdet (NAV) används ofta för att representera värdet av ett fastighetsbolag. För börsnoterade fastighetsbolag sker samtidigt en sekundär värdering eftersom deras aktier köps och säljs på aktiemarknaden. Historiskt sett har fastighetsbolagens substansvärden skilt sig från börspriserna av deras aktier vilket tyder på att aktiemarknaden värderar bolagen annorlunda och det uppstår då en substansrabatt eller substanspremie. Denna uppsats utvärderar detta fenomen för 14 svenska börsnoterade fastighetsbolag under åren 2006-2015 utifrån en rationell och en irrationell utgångspunkt. Uppsatsen testar empiriskt vilka faktorer som påverkar skillnaden under perioden genom en regressionsanalys med paneldata.

Den rationella utgångspunkten undersöker effekterna av variabler knutna till företaget, aktien samt företagets bolagsstyrning. Resultatet visar att rationella variabler kan förklara substansrabatter och substanspremier till en viss grad. Det största bidraget kommer från de företagsspecifika variablerna. Större företag, företag fokuserade på ett mindre antal orter, företag med ett bättre rykte och företag vars styrelse har ett stort aktieinnehav tenderar att ha en minskad substansrabatt alternativt en ökad substanspremie. Å andra sidan tenderar företag med hög belåningsgrad, ett fåtal fastighetstyper och hög systematisk risk att ha en ökad substansrabatt alternativt en minskad substanspremie. Den slutliga modellen av rationella variabler genererar ett justerat R-square om 37,4% för svenska börsnoterade fastighetsbolag.

Den irrationella utgångspunkten i denna uppsats undersöker variabler knutna till ett irrationellt handlande. Resultatet visar signifikant utfall för irrationellt handlande, där en konfidensindikator för hushållen visar störst inverkan och genererar ett justerat R-square om 53,6%. Uppsatsen undersöker möjligheten att använda irrationellt handlande som förklaringsvariabler till varför substansrabatter och substanspremier uppstår. Resultatet visar att det är motiverat att inkludera irrationella förklaringsvariabler.

Acknowledgement

This master thesis is written as the final part of the master program in Real Estate and Construction Management and the Civil Engineering programme at KTH, the Royal Institute of Technology.

The deviation to NAV for listed property companies has been studied broadly on an international level. The aim of this thesis is to investigate this phenomenon in the Swedish context. Hopefully, the contribution of the report could be of interest to academia, property companies as well as investors in terms of investment strategies and stock market behaviour.

First and foremost, we would like to thank our supervisor Herman Donner for his inspiration, positive guidance and long discussions throughout the process of writing this thesis.

We also want to thank Han-Suck Song and Mats Wilhelmsson for additional guidance as well as for their contribution in discussions.

Feel free to contact us regarding questions about the thesis at [email protected] (Tomas Shaw) and [email protected] (Matilda Wåhlin).

Stockholm 2nd of June 2016,

Tomas Shaw and Matilda Wåhlin

Table of Contents

1. Introduction ...... 1 1.1 Background ...... 1 1.2 The Aim of the Thesis ...... 4 1.3 Research Question ...... 4 1.4 Limitations ...... 4 2. Theoretical Framework ...... 5 2.1 Modelling Financial Markets ...... 5 2.2 The Property Market Versus the Stock Market ...... 7 2.3 The Calculation of the NAV Spread ...... 8 2.4 Literature Review ...... 9 2.5 Expertise Review ...... 24 3. Method ...... 26 3.1 Methodology ...... 26 3.2 The Model ...... 26 3.3 Regression Analysis ...... 29 3.4 Reliability ...... 33 3.5 Validity ...... 34 4. Data Description ...... 35 4.1 Selection ...... 35 4.2 Variables ...... 37 4.3 Descriptive Statistics ...... 41 5. Results and Discussion ...... 48 5.1 Results – Rational Approach ...... 48 5.2 Discussion – Rational Approach ...... 52 5.3 Results – Irrational Approach ...... 57 5.4 Discussion - Irrational Approach ...... 63 6. Conclusion ...... 67 7. References ...... 70 8. Appendix ...... 73

1. Introduction

1.1 Background The value of a property company is to a large extent derived from its underlying assets. The underlying assets, the properties in the company’s portfolio, generate value to the firm primarily through rental income but also indirectly through capital gains. For a company that mainly operates in the field of owning and letting real estate it is thus justified that the value of the company is derived from its underlying assets using a net asset value (NAV) approach (Rehkugler et al., 2012). The NAV of a company is calculated as the sum of the market value of all assets minus liabilities and other claims to the company (Ke, 2015). Since the introduction of IAS-40 accounting standards in 2005 all European companies are obliged to report their investment properties at their fair value in their annual reports (Nordlund, 2008). The NAV of a property company can therefore be constructed fairly easy via information available in the companies’ annual reports, as the combined fair value of the property portfolio minus all debt and other liabilities. This means that the company displays a justified estimate of their company value on an annual basis. Simultaneously, listed property companies have their shares traded on the stock market, thus enabling that a secondary pricing of the company occurs. This pricing mechanism values the company via supply and demand of the company shares on the stock market. It has been found that the share prices of property companies, the market capitalization, deviate from the companies’ NAV over time, implying that the stock market values the company differently. This deviation varies widely between companies, sectors and over time (Ke, 2015).

The deviation to NAV from market capitalisation is not a phenomenon that is purely related to property companies. In general it is referred to as the closed-end fund puzzle and is an intriguing problem in finance that has been researched on numerous occasions during recent decades (Dimson and Minio-Paluello, 2002). A closed-end fund is a mutual fund that issues a fixed number of shares that are traded at the stock market. Similar to listed property companies, closed-end funds derive most of their value from the underlying assets and are not obliged to buy back (redeem) issued shares. This allows the shares to be priced purely on the stock market and thus differ from the underlying assets of the fund, the NAV (Dimson and Minio-Paluello, 2002). The closed-end fund puzzle is the empirical finding that shares of closed-end funds are being traded at a price that differs from the NAV of the fund, and therefore creating a discount or a premium to NAV. Historically, the closed-end fund puzzle

1 research has shown that these types of funds have been traded at a significant discount. This is in line with the results of research focusing on discounts on property companies (Barkham and Ward, 1999, Morri et al., 2005 and Rehkugler et al., 2012). According to the efficient market theory this should not be possible since it says that all investors access the same information and act rationally, which would lead to efficiently priced shares (Lee et al., 1991). The puzzle has led to a large body of research on the matter including the most famous works of Malkiel (1977), Lee et al. (1991) and De Long et al. (1990), who all look into how this deviation can be explained.

In broad terms there are mainly two approaches for research into the discount to NAV in closed-end funds and subsequently within the segment of listed property companies: the “rational approach” and the “noise trader approach”. The rational approach tries to link the discount to company-specific factors such as company size, management quality, leverage ratio and/or portfolio focus via the use of regression analysis techniques. These studies, although relatively intuitive appealing, do not manage to explain more than 30% - 50% of the variance of discounts at its best (Rehkugler et al., 2012). The noise trader approach is mainly based on the works of De Long et al. (1990) and Lee et al. (1991) and basically assume that there are two different types of investors: “rational” and “noise traders”. The noise trader acts irrationally, and this theory implies that market sentiment can play a role in that asset prices can diverge from its fundamental values in the long run (Barkham and Ward, 1999). Even though irrational variables significantly help to explain the variation of the discount, explanatory variables describing market sentiment are hard to define and interpret as they typically are based on proxies to imitate a market sentiment, thus not perfectly corresponding to the true variable.

Studies examining the discount to NAV among property companies have mainly focused on the UK and US property markets. The largest contribution to understanding the UK market is achieved by Barkham and Ward (1999), Morri et al. (2005) and Ke (2015). For the US market Capozza and Lee (1995) and Clayton and MacKinnon (2000) are the main contributors. In recent years the research has shifted to observe a pan-European context. Brounen and Laak (2005) started this trend which was continued by Rehkugler et al. (2012). These studies vary in their approach, some focusing on explaining the discount using only rational variables while some only focus on market sentiment. The importance of investigating the existence of noise traders when applying an irrational approach cannot be neglected but very few studies investigate this fully. Muller and Pfnuer (2013) do this on a

European property sector level by investigating if five implications of the Noise Trader Theory can be supported for the property market. Three studies combine the two approaches: Barkham and Ward (1999) manage to explain 33% with an approach in which a proxy for market sentiment is included. Rehkugler et al. (2012) manage to explain as much as 76% by using a semi-rational approach. Ke (2015) uses traditional rational variables plus variables accounting for corporate governance in order to capture the impact of the company’s management. This in combination with a market average discount as a proxy for market sentiment explains 43% of the deviation as best.

To the best of our knowledge, no research has focused purely on examining the NAV spread for the Nordic context. This thesis aims to fill that gap by looking at annual data for 14 Swedish listed property companies during 2006-2015. The ambition is to explain the NAV spread from the basis of a rational and irrational approach by using a panel data regression methodology. The Nordic property market and Sweden in particular is interesting to examine since it is unique in the sense that the property market is very liquid. According to Leimdörfer (2015), a leading advisory within property related transactions in the Nordic countries, Sweden has been the most liquid property market in Europe during the last decade. In addition, the Nordic property market, and Sweden in particular, is characterized by a high level of transparency as well as stable and high quality institutional environments (Leimdörfer, 2015). This is something that might imply better NAV estimates in comparison to other countries. In addition, it allows investors to have all available information thus setting an interesting platform for examining the NAV spread. The contribution of the report could be of interest to property companies as well as investors in terms of investment strategies and stock market behaviour.

The traditional view when observing the NAV spread is to look at discounts. Previous research has embraced this point of view mainly motivated by the fact that historically closed-end funds and listed property companies have been traded at a discount. However, during this time period the shares of property companies have been traded at both discounts and premiums. For example, 2006 and 2014-2015 are characterized by premiums thus implying an alternating behaviour of the NAV spread. This thesis observes a larger time spectra of ten years and aims to explain the NAV spread in a volatile environment. The period that will be studied, 2006-2015, is particularly interesting since it starts with the end of a boom followed by the large crash in 2007-2008 and sums up with gaining momentum

3 during the last years. The NAV spread for property companies has thus shifted from a premium to discount and then back to a premium.

1.2 The Aim of the Thesis This thesis aims at extending the existing literature on the closed-end fund puzzle for property companies on the unique Swedish property market by applying variables and approaches used in previous research. The thesis aims to explain the NAV spread from the basis of a rational and an irrational approach by the use of a panel data regression methodology. In addition the existence and impact of noise traders will be investigated in the thesis, justifying the use of market sentiment in explaining the discount. The data is collected directly from annual reports, which in combination with a standardized method for calculating the NAV spread ensure consistency.

1.3 Research Question The thesis aims to answer the following questions for Swedish listed property companies during the investigated time period of 2006-2015:

1. How large is the NAV spread (discount or premium to NAV) for the investigated property companies? 2. What can explain the NAV spread for listed property companies? a. Can a rational approach explain the NAV spread? b. Can Noise Trader Theory and market sentiment explain the NAV spread? 3. Is the noise trader model justified for explaining the NAV spread?

1.4 Limitations This thesis will only evaluate Swedish listed property companies and is thus limited to a maximum of approximately 30 listed companies. Via a number of criteria the amount of investigated property companies is narrowed down to 14. An issue can be raised whether a larger data set could increase the contribution of the report. In order to solve this issue the report could choose to use quarterly data thus providing a larger number of observations. However, quarterly reports have been found to be deficient in terms of information and reporting, and consequently annually data is used. A cornerstone in this report is the appraised value of the property portfolios undertaken by the property companies and displayed in their annual reports. This report assumes that these valuations are done in a professional way and therefore represent the market value of the property. The validity of the results could be increased by taking possible errors or biases in appraisals into account.

2. Theoretical Framework This chapter will introduce and discuss topics and theories needed to understand the NAV spread as well as discuss and present previous research into the phenomenon. The chapter aims to create a framework in which the NAV spread among property companies can be examined further. First, relevant theories concerning asset pricing are presented as these forms the basis for establishing the two approaches, rational and irrational, to examine the NAV spread. Second, a short explanation into the different characteristics of the property and the stock market is presented as well as the basic techniques for calculating the NAV spread. This is followed by a literature review of the closed-end fund puzzle and the different approaches applied to explain the NAV spread in listed property companies. The chapter concludes with an expertise review consisting of the view of professionals within the field.

2.1 Modelling Financial Markets

2.1.1 The Rational Investor and the Efficient Market Hypothesis The traditional models explaining financial markets and asset pricing assume that investors are rational. The meaning of the word rational is in this sense essentially twofold.

- First, when new information is given rational investors update their beliefs correctly. - Second, based on the new information rational investors make rational decisions based on fundamentals. (Barberis and Thaler, 2003)

One of the more influential theories in the traditional framework is the efficient market hypothesis, developed in part by Fama (1970). The efficient market hypothesis states that since investors are rational the market becomes efficient in its pricing. This statement has one major implication for asset pricing:

- As investors are rational and act rational the price of an asset is correctly set with respect to its fundamental value. The main argument for this is that if a mispricing occurs momentarily due to an actor acting less than fully rational the market will correct for this instantly by the use of arbitrage. (Barberis and Thaler, 2003)

The efficient market hypothesis acts as the base of the literature into the rational approach to the NAV spread, covered in chapter 2.4.2.

2.1.2 Behavioural Finance and the Noise Trader Theory The traditional framework, resting on the belief that every investor acts in a rational manner, is not confirmed by data in a satisfying way (De Long et al. 1990). In the light of this, the field of behavioural finance started to gain momentum in economic research in the late 20th century. Behavioural finance aims to explain financial markets by stating that financial phenomena can be better understood using models in which some investors are not fully rational. It is hypothesised that there are two types of investors: rational and irrational.

- In contrast to the rational investor, the irrational investor does not trade in consideration of fundamentals but instead on market sentiment. Such market sentiment can be anything from the emotional condition of the investor, rumours in mainstream media, guidance of a friend or just gut instinct. The actions of irrational investors, also called noise traders, distort the pricing mechanisms of assets by producing noise. (Barberis and Thaler, 2003)

Under the efficient market hypothesis the existence of noise traders and distortion of price can only exist for a short period of time. The Noise Trader Theory, put forward by De Long et al. (1990) and extended by Lee et al. (1991), abandons the premises of a perfectly efficient stock market and instead postulates that noise traders exist over time and that their noise distorts the pricing mechanism in such a way that market prices deviate from fundamentals permanently. If asset prices are influenced by noise under the efficient market hypothesis, rational investors will take part in arbitrage. This makes sure that prices return to the levels warranted by current information and fundamentals. Contrary, De Long et al. (1990) argue that rational investors are not able to take part in the arbitrage situation that is created by noise traders because of a number of fundamental assumptions. De Long et al. (1990) put forward three assumptions initially. However, Mueller and Pfnuer, (2013) divide the first assumption into two, which results in the following:

- First and second, rational investors are risk averse (A1) and have reasonably short investment horizons (A2).

- Third, the incorrect beliefs that noise traders use in order to select their portfolios are unpredictable and create a risk in asset pricing. This means that rational investors cannot with great certainty calculate the outcome of the noise on the asset pricing. In other words, noise trader sentiment is stochastic. As rational investors are risk averse, the implication becomes that they reduce the extent to which they bet against noise

traders in response to this increased risk. This means that rational investors need to take both the fundamental risk and the risk caused by noise traders into account. (A3)

- Fourth, the noise trader sentiment is not restricted to just a few individuals. The noise trader sentiment is systematic in its nature and influences all irrational traders collectively. The imitative and herd behaviour of noise traders snowballs the total effect of the noise traders making them a significant market power that can shift share prices considerably over time (A4). (De Long et al., 1990, Mueller and Pfnuer, 2013)

Lee et al. (1991) basically apply the Noise Trader Theory to the closed-end fund puzzle and add a fourth (fifth when using the Mueller and Pfnuer (2013) approach) assumption to the Noise Trader Theory in that off differing clienteles.

- Fifth, noise traders and rational investors hold different types of assets. Noise traders can be generalised as small investors. Small investors usually hold more liquid assets like stocks. Rational investors can on the other hand be generalised as institutional investors. Institutional investors are able to hold assets with a higher transaction cost, less liquidity but stable return. Lee et al. (1991) argue that for the closed-end fund puzzle small investors hold the closed-end fund stocks while institutional investors hold the underlying asset (A5).

De Long et al. (1990) thus postulate that the presence of noise traders in financial markets can permanently deviate price from its fundamental value. The implication is that the closed- end fund stocks are subject to noise trader risk and thus need to be priced higher in equilibrium. The Noise Trader Theory acts as the base of the literature into the irrational approach to the NAV spread, covered in chapter 2.4.4.

2.2 The Property Market Versus the Stock Market The differing characteristics of the property and stock market are of importance to this thesis as differences allow for a differing pricing to occur. Large property portfolios are typically held for longer time horizons and require special knowledge. This means that typical real estate owners are large investors such as institutional investors and property companies (Barkham and Ward 1999). Unlike real estate, shares on the stock market can be sold quickly and easily to low transaction cost, and investors do not need to take the burden of the purchased assets (Geltner and Miller, 2006).

Although the property market is viewed as being illiquid, the Nordic market and Sweden in particular has proved to be very liquid compared to other countries. Sweden has the highest property investment market turnover ratio in Europe, with an average of 6.3% between 2005- 2014 (Leimdörfer, 2015). However, there is a time lag of about three to six months from the decision to sell property is taken until the assets are actually sold. (Leimdörfer, 2011)

2.3 The Calculation of the NAV Spread When share prices of property companies (the market capitalization) deviate from the companies’ NAV over time, it is implied that the stock market values the company differently. In this situation, a discount or a premium to NAV arises. As this thesis investigates the differing behaviour of the NAV spread and the explanations for this, it is of utmost importance to understand how it is computed. The discount to NAV is calculated as the percentage difference between the market capitalisation (MC) and the net asset value (NAV) of the firm, see equation 2.1. If the NAV is higher (lower) than the market capitalization, the fund is traded at a discount (premium). (Barkham and Ward, 1999)

DISCOUNT = (NAV-MC)/NAV Equation 2.1 The market capitalization is the total value of shares outstanding and is calculated by multiplying a company’s outstanding shares with the current market price of one share (Investopedia, 2016). The net asset value (NAV) of a company is calculated as the sum of the market value of all assets minus liabilities and other claims to the company (Ke, 2015).

To ensure that an equivalent calculation of the NAV is pursued, the European Public Real Estate Association (EPRA) provides guidelines for how this calculation should be done. EPRA acts as a non-profit association and was founded in 1999. They work for consistency and transparency in financial reporting in the listed property sector. EPRA continuously observes the NAV spread at a pan-European perspective and has set up two definitions connected to the NAV named EPRA NAV and EPRA NNNAV. The EPRA NAV is intended to reflect the true business of an investment property company, where the assumption is that assets are held for the long term. Therefore EPRA NAV excludes deferred taxes related to future disposals and the fair value of hedging instruments, as both of these are not expected to materialise. The EPRA NNNAV however, is a ’spot’ fair value measure and incorporates management’s view of the fair value of deferred tax and hedging instruments. It also adjusts to fair value debt, which is held at amortised cost in EPRA NAV. (EPRA, 2014)

2.4 Literature Review

2.4.1 The Closed-end Fund Puzzle The starting point for the research into premiums and discounts to NAV of property companies, and thus in this thesis, lies in the discovery of the closed-end fund puzzle. The closed-end fund puzzle (CEFP) is the empirical finding that shares of closed-end funds are being traded at a price that differs from the NAV of the funds (Dimson and Minio-Paluello, 2002). The NAV spread, as it similarly is referred to, has been found to deviate considerably cross-sectionally, between funds, as well as over time as shown and investigated most famously by Malkiel (1977) and Lee et al. (1991). Historically the deviation has mainly been in the form of a discount implying that investors are only willing to buy shares of closed-end funds for prices less than their fundamental value (Dimson and Minio-Paluello, 2002). The NAV of closed-end funds, and property companies for that matter, can easily be computed and is accessible for investors. This means that the existence of NAV spread goes against the efficient market hypothesis. Consequently, it is considered to be one of the most intriguing problems in finance and has thus inspired a vast body of literature and hypothesis of its origin, which can be applied to the property market (Barkham and Ward, 1999). Dimson and Minio-Paluello (2002) provide a useful overview of the fundamental literature concerning research into the closed-end fund puzzle.

The initial explanations for the NAV spread for closed-end funds attempt to explain the discount within the framework of the efficient market hypothesis, which was presented in more detail in chapter 2.1.1. This rational approach focuses on that purely company-specific factors can warrant that the NAV spread exists. However, as mentioned by several studies, the purely rational approach only manages to explain a part of the NAV spread (Dimson and Minio-Paluello, 2002). In combination with the gaining popularity of behavioural economics in the later 20th century, a shift towards explaining the discount via the noise trader approach, which was presented more in detail in chapter 2.1.2, was imposed. The noise trader approach has managed to explain some parts of the discount, which has not been captured by earlier methods. In summation there are two ways to look at the discount, via a rational approach and noise trader approach (Barkham and Ward, 1999).

Since the late 20th century the theories and hypothesis regarding the explanation for the closed-end fund puzzle has started to be applied to the listed property sector and REITs. The closed-end fund puzzles’ relationship with discounts and premiums to NAV in property

9 companies comes from the striking resemblance between a listed property company and a closed-end fund in terms of some of their main characteristics (Barkham and Ward, 1999). Three main characteristics can be argued for:

- First, a closed-end fund is a mutual fund that mainly invests assets in other companies’ securities and manages these for long-term income and profit (Dimson and Minio-Paluello, 2002). Listed property companies involved in owning and letting real estate pursue similar business objectives focused on holding the underlying asset for long-term income and profit (Rehkugler et al., 2012).

- Second, the investments of closed-end funds are usually characterized by low levels of liquidity, which is in similarity with the general low liquidity of direct property investments.

- A third resemblance can be found in one of the main characteristics of the closed-end fund, the inelastic supply of shares. The inelastic supply of shares comes from that closed-end funds issue a fixed number of shares that are traded at the stock market. Once an initial private offering (IPO) has taken place the closed-end fund does not redeem issued shares (Investopedia, 2016). In that sense the closed-end fund can be compared to a listed property company that in the same way as the closed-end fund is under no obligation to redeem issued shares and rarely issue new ones (Barkham and Ward, 1999). This implies that investors that want to buy and sell the stock have to do this at the secondary market at the price set by supply and demand on the stock market. This means that shares are priced on the stock market and subsequently can differ from the NAV of the fund (Rehkugler et al., 2012).

In addition to these similarities the underlying asset of property companies, the properties themselves, are appraised on an annual, even quarterly basis. This is an implication as of the introduction of IAS-40 accounting standards in 2005. Since 2005 all European companies are obliged to report their property portfolios at their fair value in their annual reports (Nordlund, 2008). For a property company the NAV can thus be constructed fairly easy via information available in the companies’ annual reports, as the combined fair value of the property portfolio minus all debt and other liabilities. This means that the company displays a justified estimate of their company value on an annual basis and further motivates why the deviation to NAV is interesting to study within the listed property sector.

2.4.2 The Rational Approach to the Deviation to NAV The literature attempting to explain the discount in closed-end funds starts by having a strong focus on a framework set up under the efficient market hypothesis and rests on standard economic theories. The divergence of the NAV from the market capitalization is reasoned to be motivated by pure economic company related characteristics (Dimson and Minio-Paluello, 2002). Suggested factors are considered to be either of an endogenous or exogenous nature. The endogenous approach looks at company-specific factors that could motivate a deviation while the exogenous approach looks outward to external factors to the company that subsequently the company management cannot influence (Rehkugler et al., 2012).

In previous research the rational endogenous approach is often included as the base of most studies. This is a result of its intuitive feel and that it can be computed and analysed with ease. The data used to compute the variables are often key figures that the company is obliged to include in financial reports. The rational approach applied to the deviation to NAV in the real estate sector has been treated in a number of studies, but have mainly focused on REITs (Real Estate Investment Trusts) or the UK, US and recently pan-European markets (Rehkugler et al., 2012).

The main method for investigating the NAV spread has focused on finding a relationship between the discount and different hypothesized explanatory variables through regression analysis on series of cross-sectional set of data over time. This type of data can be considered panel data, but a number of previous studies fail to comment and structure their models after this and instead perform cross-sectional regressions using OLS (Ordinary Least Squares). Malkiel (1977) is one of the first to study the discounts to closed-end funds and investigates the explanatory power of several company-specific factors. He mainly links the discount to unrealized capital appreciation, number of stock available for trading and amount of foreign assets. In order to test these factors Malkiel employs a cross-sectional regression on a set of 24 closed-end fund stocks for each of the examined years (1967-1974). Adams and Venmore- Rowland (1989) was among the first to study the discount to NAV in property companies. Although their study is purely theoretical they inspire further research into the problem as they postulate several reasons for the discount to exist. Their main contribution lies in their reasoning of the effects of financial gearing, liquidity and especially how capital gains tax can be a direct factor to the discount. Barkham and Ward (1999) are the next to test the discount in the property sector. They apply a two-step approach in which the first part is of a rational character and the second focuses on a noise trader approach. By the use of data from

44 listed property companies between 1993-1995 they link 15% of the movement of the discount to tax, firm size, holding of trading stock and historical return. Brounen and Laak (2005) investigate the discount in 72 property companies in a large pan-European study. Although extensive, the study only investigates the discount for one year, thus only looking at a snapshot of the problem. Their main findings are that firm size, historical return and free float (amount of stocks available for trading) decrease the discount, while risk and leverage increase the discount. Morri et al. (2005) provide a purely rational approach to 26 UK listed property companies between 1999 and 2004. The study discusses a noise trader approach but that is not tested empirically. Their main findings are a negative relationship between NAV discounts and leverage, return and equity, while the company’s beta increases the discount to NAV. Rehkugler et al. (2012) extends the literature by using a semi-rational approach to explain the discount. The first part of their study looks at rational variables to explain the deviation to NAV in approximately 40 property companies over 8 years in Europe. They link REIT status, stock price volatility, sectoral and regional concentration, leverage and free float to the discount. The latest contribution to the discount literature within the listed property sector is made by Ke (2015). This thesis focuses on explaining the discount via traditional rational variables such as size, leverage et cetera but aims to highlight the impact of corporate governance mechanisms. It manages to show that some of the corporate governance variables, such as insider ownership and non-executive directors on the managing board, impact the deviation to NAV. At a maximum 43% of the discount could be explained. However, in order to explain 43% of the movement in NAV spread, an irrational variable accounting for the average market discount was used. (Ke, 2015)

The NAV spread is moreover researched on a large scale at an American level where the main focus is to observe the NAV spreads in REITs. REITs and listed property companies differ in some aspects but do also have similarities, which mean that the REIT research can be applicable in this thesis although with care. Capozza and Lee (1995) contribute largely to the research into the NAV spread in REITs via a rational approach applied to 75 REITs in the US from 1985-1992. The main findings are that focus on property type and size affect the discount. Clayton and MacKinnon (2000) apply a two-step approach to 98 US REITs over three years. The first step is a rational approach that via endogenous rational variables only can explain 7% of the NAV spread in which size plays the biggest part. A summary of the previous literature into the rational approach to the discount to NAV is presented in table 2.1 below.

Table 2.1 – Summary literature rational approach

Article Year Time Period Companies Region Method Approach R-square Comment

Examining the closed-end fund Malkiel 1977 1967-1974 24 - OLS Rational 38% puzzle

Adams & The first study applying closed-end Venmore- 1989 - - - Discussion Rational - fund puzzle to the property Rowland market

Capozza & 1995 1985-1992 75 US OLS Rational - Examining REITs Lee

Barkham & Rational/Noise Rational and testing noise trader 1999 1993-1995 44 UK OLS 15% / 33% Ward trader assumptions

Clayton & Rational/Noise Examining REITs with a rational 2000 1996-1999 98 US OLS 7% / 44% MacKinnon trader and irrational approach

Cronqvist OLS + Rational Discount explained by 2001 1990-1996 32 SWE 65% et al. FGLS (Diversification) diversification and agency costs

Brounen & Large data set but only for one 2005 2002 72 EU OLS Rational 51% Laak year

Morri et al. 2005 2000-2003 26 UK OLS Rational 51% Ungearing NAV spread approach

Semi-rational approach. Creating Rehkugler Rational/Noise 2012 2000-2007 40 (28) EU SEM 76% country-specific sentiment index et al. trader plus running a SEM

Rational Ke 2015 2005-2013 41 UK OLS 19% / 43% Corporate governance variables (corporate gov.)

OLS: Ordinary Least Squares, FGLS: Feasible Generalized Least Squares, SEM: Structural Equation Model

2.4.3 Rational Variables The rational approach emphasises that endogenous and exogenous rational variables can warrant a share price that differs from the NAV of the company. If a variable warrants a market capitalisation that is higher (lower) than the NAV it is said to decrease (increase) the discount, thus have a negative (positive) effect on the discount to NAV. In the section below the rationale for the most commonly used variables when performing regression analysis on the discount to NAV is presented and discussed. This chapter concludes with a summary of the findings visualized in table 2.2.

Early research put a large emphasis on tax liabilities as a factor that causes a large part of the discount. NAV of closed-end funds are based on the market value of the securities they hold.

If a fund holds securities that have grown in value, i.e. have appreciated, the sale of these securities would incur capital gains tax, which also is applicable for property companies (Barkham and Ward, 1999). However, Malkiel (1977) shows mathematically that only a 6% discount can be motivated by capital gains taxes, thus shattering the initial thought that the NAV spread was purely a tax issue. Later, investigations into the NAV spread in REITs, that are not subjected to capital gains tax, show that REITs still suffer from discount to NAV, although generally smaller which is in line with Malkiel’s findings. Ke (2015) uses the ratio of contingent tax liabilities as of the total balance sheet value. This was significant in their initial model but insignificant in later models. Barkham and Ward (1999) apply the same approach 15 years earlier and find a significant positive relationship with the discount, as expected, for capital gains tax liabilities for listed property companies in the UK.

Earlier studies also discuss the impact of agency costs and expense ratios on the NAV spread. The results of these contradict each other. Gemmil and Thomas (2002) find that higher management expenses contribute to a larger discount. Malkiel (1977) however, do not find a significant relationship between management fees and discounts. There are some problems in the application of agency costs as a variable affecting the discount to NAV. Neither current nor future agency costs can explain the wide fluctuation in discount since management fees normally are a percentage of the NAV and do not fluctuate as much as the discount does. In addition, according to Lee et al., (1991) agency costs do not seem to explain much of the cross-sectional variation in discounts.

The core of most literature with a rational or a semi-rational approach is made out by endogenous company-specific factors. These are purely company related and thought to warrant the market capitalisation to differ from the NAV. Company size is probably the most intuitive such company-specific factor and probably the most frequent in previous literature. The results of the impact of company size on the discount are inconsistent. Property is an asset that can be considered illiquid. For a company with large property holdings this will likely lead to a problem if the company was forced to sell its entire stock instantly. An instant sale of a large amount of properties would lead to a considerable increase in normal flow of properties thus considerably lower the price of the property. Hence, the total value of company’s assets is not necessarily the sum of the values of individual properties. This would lead to that a larger company size would infer a discount to NAV for listed property companies (Barkham and Ward, 1999). On the other hand Adams and Venmore-Rowland (1989) argue that property companies with a large portfolio access capital more easily than

14 smaller companies. In addition, high value properties can act as a barrier since they require a large amount of capital. Smaller property companies cannot gain access to these properties, which then creates an inefficient pricing. This makes it possible for larger institutions to earn abnormal returns from larger properties. For this reason, larger companies with greater power might be associated with a lower discount to NAV. Ke (2015) and Brounen and Laak (2005) concur that larger companies have smaller discounts. Brounen and Laak (2005) lift the idea that a larger company might benefit from increased popularity, recognition and higher transparency than smaller companies and thus a lower discount.

Studies have found that companies with high levels of leverage tend to have a higher discount to NAV (Ke, 2015, Brounen and Laak, 2005). Rehkugler et al. (2012) confirm the belief that leverage leads to larger discounts as leverage increases the risk for the investor. Partly in contrast, Adams and Venmore-Rowland (1989), Barkham and Ward (1999) and Morri et al. (2005) argue that leverage can affect the NAV spread in both ways. According to them a higher leverage means an amplified NAV spread.

The trade-off between diversification benefits and loss of economies of scale and other benefits spurring from corporate focus is widely discussed in the literature covering NAV spreads among listed property companies. Previous research measure the degree of corporate focus on property type and geographical location via the construction of a Herfindahl- index. A Herfindahl index is a measurement of the degree of property type focus and geographical concentration and is calculated as shown in equation 2.2.

! ! HERFINDAL!"#$/!"#$%&"' !!! �!,!,! Equation 2.2

The Herfindahl index is based on the value of property type/location for company i at time t. r represents the set of property types or locations, Sr,i,t is the proportion of firm i’s assets invested in property type or location r at time t. The Herfindahl index can vary between close to 0 and 1, where a low Herfindahl index means diversified focus while a number of 1 implies a company focused on one property type or location (Ke, 2015). In regards to property type, previous literature suggest that a larger focus reduces the discount to NAV (Brounen and Laak, 2005 and Ke, 2015). Cronqvist et al. (2001) and Boer et al. (2005) show findings that concur and show that unfocused publicly traded real estate companies are less transparent and more expensive to manage and therefore less successful, which indicate a higher discount. A more focused strategy in this regard could increase both a firm’s return

15 and risk (Rehkugler et al., 2012). However, the impact of company concentration on geographical location is inconsistent. From purely a theoretical standpoint most researchers argue that the effect of geographical concentration should be similar focus on property type. Most studies do not find a significant result and the concentration variable has found to both increase and reduce the discount. The results of Brounen and Laak (2005), which has a pan- European perspective, show no relationship between the geographical spread and discounts. They argue that the non-existent relationship could be due to the relatively high degree of regional focus that is presented in their sample.

The variable free float can be used as a proxy for stock liquidity, in other words, how easily the share can be converted to cash (Brounen and Laak, 2005). Investors prefer a liquid share before an illiquid, since an illiquid share would reduce the possibility to sell it. Therefore a higher free float should lead to lower discounts since it will be easier to buy or sell the share. According to the results of Brounen and Laak (2005), real estate companies with higher free float are linked to lower discounts to NAV. In the study made by Rehkugler et al. (2012), free float is statistically insignificant in the final model, even if pre-test ascribes the variable to lower discounts and some explanatory power.

Brounen and Laak (2005) introduce the belief that an EPRA membership can affect the discount to NAV. EPRA is an organization that works for better transparency and stability in financial reporting for property companies by producing standards and indices. A membership in EPRA should reduce the discount to NAV since the membership entails more stability and transparency for the investor. The results of Brounen and Laak (2005) support the hypothesis of a smaller discount for a company that is a member of EPRA.

Some explanations for the NAV spread to be motivated under the efficient market hypothesis are thought to be of a more exogenous nature, even though they are company related (Rehkugler et al., 2012). These factors are related to the share of the listed property company and are made out by the reputation of the management, volatility and the stock beta. According to Malkiel (1995) a good reputation of the firm and its management should have a negative relationship to the discount. However, reputation is hard to measure. In order to measure this variable a proxy variable has been used in previous literature. This proxy constitutes a history of good performance. This is calculated as the average daily stock return of the share during the last year. Malkiel (1995), Barkham and Ward (1999), Brounen and Laak (2005) and Ke (2015) expect that this variable would be negatively related to the

16 discount. After the regression analysis, all authors find a significant and negative relationship. The stock price volatility is another hypothesized variable that drives the share price away from the NAV. Its importance was first discussed by Adams and Venmore-Rowland (1989) and was picked up by Rehkugler et al. (2012). Both studies predict a positive relationship with NAV discounts, i.e. larger volatility leads to larger discounts. This can be explained by the fact that higher volatility means insecurity for the investor. Volatility is the most explanatory rational variable in the report of Rehkugler et al. (2012). On the other hand, Brounen and Laak (2005) find no significant relationship although showing the right coefficient. In addition to measuring the total risk of the firm, here captured as volatility, the risk associated with the market is also tested for explaining the discount. The Beta of the stock measures the stock’s sensitivity against the market movement and is hypothesized to be positive, as risk tends to lower market values. Although a positive relationship is indicated in the results of Brounen and Laak (2005) a significant relationship cannot be proven. However, Morri et al. (2005) conclude that a higher systematic risk is associated with higher discounts.

Ke (2015) highlights the impact of corporate governance factors on the discount to NAV. The first corporate governance factor, board size, contains the amount of members in a company’s board. According to Ke (2015), previous studies on REITs seem to suggest that a smaller board can be interpreted as a proxy of a good board, thus having a positive impact on firm performance. Ke (2015) also tests board independence which is constructed by the amount of independent members of the board. The meaning of the word independent is being independent to the company and its governance or independence to larger shareholders. A dependent board might obtain a part of their preferred level of private risk in the company to benefit themselves. Since they are entitled to private benefits of control, they may, for example, implement diversifying investments in property types that are not in line with the company’s business, and support such projects too long despite being less profitable or even unprofitable for the company. (Cronqvist et al., 2001) Insider ownership describes the amount of the company’s shares that are owned by the board as a percentage of the total. Ke (2015) find that a higher insider ownership would lead to higher levels of discount to NAV. This result is different from the results of Capozza and Seguin (2003), which study investigates the impact of insider ownership on the discount to NAV in US REITs. They find that higher levels of insider ownership convey a signal of higher quality management and therefore a higher REIT valuation. However, Malkiel (1995) argues that insider ownership reduces the likelihood that a fund will be taken over and liquidated at the NAV, which

17 increases the discount. It is not common that real estate companies are taken over with the aim to be liquidated. However, insider ownership may reduce the prospect of a take-over bid being launched (the opportunity for profitable arbitrage) and therefore widen the discount. On the other hand, if the directors of the company are crucial shareholders, there is a reduced risk of conflicts of interest between the non-directorial shareholders and the management, which would lead to lower discounts (Cronqvist et al, 2001). The last corporate governance variable is similar to insider ownership and free float and proxies ownership concentration. The variable, top three, describes the ownership as the percentage of stocks that is held by top three substantial institutions such as pension funds, insurance companies, private equity funds or other firms that are predominately owned by managers or directors. Although not confirmed empirically, larger ownership by institutions should lead to a negative discount, as these investors are often very rational in their investment strategy (Ke, 2015).

Table 2.2 – Summary rational variables

ADAMS & CAPOZZA & BARKHAM CLAYTON & CRONQVIST BROUNEN & REHKUGLER ARTICLE VENMORE- MORRI ET AL. KE LEE &WARD MACKINNON ET AL. LAAK ET AL. ROWLAND YEAR 1989 1995 1999 2000 2001 2005 2005 2012 2015 SIZE - + + + O - O O - LTV EXAGGERATES N/A O + O O + + + RETURN N/A N/A - N/A N/A - + O - VOLATILITY + N/A N/A N/A N/A N/A N/A - + SYSTEMATIC RISK N/A N/A N/A N/A N/A O - O N/A TYPE N/A + N/A O - - N/A - - LOCATION N/A N/A N/A N/A O O N/A - N/A FREE FLOAT O N/A - N/A O - N/A + N/A EPRA N/A N/A N/A N/A N/A - N/A O N/A BOARD SIZE N/A N/A N/A N/A N/A N/A N/A N/A O INSIDER OWNERSHIP N/A N/A O N/A + N/A N/A N/A - TOP THREE N/A N/A N/A N/A N/A N/A N/A N/A + UNREALIZED + N/A + N/A N/A N/A O N/A O CAPITAL GAINS AGENCY COST N/A N/A O N/A + N/A O N/A O Positive significant relationship (+), Negative significant relationship (-), Insignificant relationship (O), Not Accounted for (N/A) 2.4.4 The Irrational Approach to the Deviation to NAV The irrational approach to the NAV spread is mainly linked with behavioural finance and the Noise Trader Theory, which was presented in chapter 2.1.2. As previously stated the main part of the theory rests on the abandonment of the premises of a perfectly efficient stock market and instead postulates that noise traders exist over time and that their noise distorts the pricing mechanism in such a way that market prices deviate from fundamentals permanently (Dimson and Minio-Paluello, 2002). For closed-end funds as well as property companies the fundamental values are accessible and thus give an interesting platform for testing the theory of De Long et al. (1990). Lee et al. (1991) elaborate and investigate the findings of De Long et al. (1990) on closed-end funds. They show that the noise trader

18 approach is more successful in explaining the closed-end fund puzzle than the rational approach.

Table 2.3 summarizes previous literature on the irrational approach. There are mainly two ways of applying the Noise Trader Theory. Rehkugler et al. (2012) engage in the first type that focuses on combining rational factors and irrational variables such as market sentiment indicators to be able to explain the NAV spread via noise trader behaviour of investors. Rehkugler et al. (2012) create a country-specific market sentiment indicator that is combined with rational variables in a structural equation model. The complete set was given by REIT status, volatility, regional and sectoral concentration, leverage, free float as well as a market sentiment variable. To some extent also Barkham and Ward (1999) and Ke (2015) make use of this approach. They both add average sector discount to their calculation with rational variables in order to explain a part of the NAV spread via noise trader behaviour of investors.

The second approach follows from the five assumptions (A1-A5) of the Noise Trader Theory that was presented in chapter 2.1.2. Essentially this approach investigates the presence of noise traders on closed-end funds or property market and if found to be true it justifies the use of market sentiment in models trying to explain the NAV spread. These assumptions lead to a number of consequences such as long-term mispricing and the continued existence of noise traders. Figure 2.1 shows the complete summary of assumptions and consequences as modelled by Mueller and Pfnuer (2013). In turn these consequences lead to five implications (I1-I5) that can be tested. These implications are: (I1) NAV spreads have a negative long- term average, (I2) alternation of premium/discount, (I3) correlation among NAV spreads, (I4) correlation with other sentiment indicators and (I5) equity issues in premium periods. Barkham and Ward (1999) (although not I1) and Mueller and Pfnuer (2013) apply the approach to test the Noise Trader Theory on the property sector by investigating these five implications. Several studies focus on examining one or two of the implications but do not apply a holistic approach on the same data set. The framework for analysing the existence of noise traders can be summarized in the following figure 2.1.

ASSUMPTIONS NTM CONSEQUENCES OBSERVEABLE IMPLICATIONS Long-term mispricings (I1) NAV spread, negative long- (A1) Risk aversion (i) Continued existence of noise traders term average Noise trader sentiment: (A2) Finite investment - Additional risk, not diversifiable (I2) Alternation of horizons (i) - Affects direct and indirect investments differently premium/discount (iii)

(A3) Noise trader Equal probability for optimistic and pessimistic (I3) Correlation among NAV sentiment is stochastic sentiment spreads

(A4) Noise trader (I4) Correlation with other Homogeneity of sentiment sentiment is systematic sentiment-indicators (A5) Divergent Rational investors have higher funds in direct (I5) Equity issues in premium investment preferences investments periods (ii)

Noise trader model published by Mueller and Pfnuer (2013), based on the NTM of De Long et. al, (1990) and Lee et.al, (1991), Barkham and Ward (1999) (i) Assumption regarding rational investors (ii) Divergent investment preferences of institutional and private investors, whereas institutional investors are identified with rational investors while private investors are identified with noise traders (iii) Mean reversion

Figure 2.1 – Methodology for the justification of the irrational approach

Mueller and Pfnuer (2013) test the Noise Trader Theory by looking at the property sector and the five implications as stated in figure 2.1. They manage to test and find positive significant results for all observable implications. For (I1), (I2) and (I5) an aggregated data analysis is made in which all cross-sectional entities are bundled together. For (I3) and (I4), a panel data GLS (General Least Squares) regression model with random effects is used. Barkham and Ward (1999) try to investigate the existence of noise traders in the same way as Mueller and Pfnuer. They investigate if market sentiment is important in explaining the discount by testing four of the implications implied by the Noise Trader Theory (I2-I5).

The first implication (I1) of the Noise Trader Theory is that NAV spread has a negative long- term average. The rationale for this lies in that if noise traders exist they create long-term mispricing due to the permanent deviation of price from fundamentals due to systematic noise. Thus an additional risk is created by the noise trader which means that the stock, in contrast with the underlying asset, should be burdened with a risk premium which in turn leads to a negative long-term average discount (Dimson and Minio-Paluello, 2002). In order to test whether this long-term average exists or not a market sector average NAV spread is computed for the studied period. Liow and Li (2006) show a long-term discount among Asian and Pacific property companies between 1995 and 2003. Mueller and Pfnuer (2013) show a long-term average discount of 4.62% for EU-REITs between 2005-2010. Barkham and Ward (1999) estimate an equilibrium long-term average discount of 25% for UK property companies between 1993-1995 via a Vector Error Correction Model.

The second implication (I2) is that the NAV spread should alternate from a premium to a discount around a mean. The reason for this lies in that even though the value of the property company shares fluctuates they will be linked to the fundamental value of the underlying assets. When the market is overvalued (premiums) noise traders dominate the market, pushing the premium up until a certain point where they decide to sell their shares making the premiums lower. At a certain discount rational investors will engage in buying shares reverting the discount back to its equilibrium. Thus the NAV spread shows clear mean reversion behaviour (Barkham and Ward, 1999, Liow and Li, 2006 and Mueller and Pfnuer, 2013). In order to test mean reversion several approaches can be pursued. Mueller and Pfnuer (2013) test the presence of mean reversion by testing autocorrelation on the NAV spreads for nine lags and the application of a Box-Ljung test. They can reject that the autocorrelations are random and also find that short autocorrelations are positive while longer are negative, which is in line with what is expected.

The third implication (I3) is that sectors of noise trader sentiment will show high levels of correlation. This is a direct result of the fourth assumption (A4), which implies that noise trader sentiment is systematic. The excess volatility and divergence of price is thus correlated across companies (Barkham and Ward, 1999). Mueller and Pfnuer (2013) use an approach that investigates the correlation of a single firm’s NAV spread at a given time against an average of the European market and the national market at the same time via a panel data regression analysis. They find clear links of correlation.

The fourth implication (I4) is that discounts will be correlated with other indicators of sentiment not related to real estate. The rationale for this also lies in that noise trader sentiment is systematic. Thus the actions of noise traders should be captured by other types of sentiment indicators correlated with the real estate sector. Here Barkham and Ward (1999) select indicators for market sentiment based on the best availability in the UK. The indices chosen are an index of industrial confidence, index of consumer confidence and index of inflation expectations. The results show strong explanatory power for the expected inflation and industrial confidence while the consumer confidence indicator is dropped. Mueller and Pfnuer (2013) try other indicators such as SENTIX Economic Index, Ifo Economic Climate and the European Commission of Economic Sentiment indicator on EU and National level. They find significant relationships especially with the SENTIX index, which explained as much as 24% of the movement (Mueller and Pfnuer, 2013).

The fifth implication (I5) is that IPOs (initial public offering) will sell to noise traders, as rational investors will wait for the discount to re-establish before investing. This should mean that during times of NAV premiums, the IPO activity of listed property companies should increase. This is investigated by looking at the correlation of IPO activity to the reversed discount. The results of Barkham and Ward (1999) indicate a considerable relationship.

Table 2.3 – Summary literature irrational approach

Article Year Time Period Companies Region Method Approach R-Square Comment

World The irrational approach creates Lee et al. 1991 1960-1986 20 Noise trader wide and tests noise trader assumptions

Barkham Rational/Noise Rational and testing noise trader 1999 1993-1995 44 UK OLS 15% / 33% and Ward trader assumptions

Clayton and Rational/Noise Examining REITs, with a rational 2000 1996-1999 98 US OLS 7% / 44% MacKinnon trader and irrational approach

Semi-rational approach. Creating Rehkugler Rational/Noise 2012 2000-2007 40 (28) EU SEM 76% country-specific sentiment index et al. trader plus running a SEM

N/A, 25% Testing the five assumptions of the Mueller and 2013 2005-2010 40-80 EU GLS etc Noise trader against other Noise Trader Theory on REITs. Pfnuer sentiment Similar to Barkham and Ward OLS: Ordinary Least Squares, SEM: Structural Equation Model, GLS: Generalized Least Squares

2.4.5 Market Sentiment (Irrational Variables) In contrast to the rational variables, the variables trying to catch the effect of market sentiment are affected by the fact that a market sentiment is not directly measureable and thus should be considered as a latent variable. In order to capture the behaviour of a latent variable, different proxies are used in which data is visible and accessible. These proxies try to imitate the behaviour of the latent variable they are trying to explain, in this case market sentiment. The results given by these types of variables are thus dependent on how well the latent variable and its proxy are linked. In previous studies the following variables have been discussed to have explanatory power toward the NAV spread or that there is a considerable market sentiment power driving the NAV spread. (Rehkugler et al., 2012)

Barkham and Ward (1999) suggest that the Noise Trader Theory implies that NAV spreads should be correlated with other indicators of sentiment not related to real estate. They investigate expectations about inflation, consumer confidence and industrial optimism. The inflation indicator is constructed as the index of inflation expectations and measures the rate of inflation in the next month expected by a variety of individuals in the economics profession. The consumer confidence indicator is constructed by a questionnaire asking

22 consumers what they think about their family’s financial situation in a year. The index is given as the percentage of optimists (more money than now) and the percentage of pessimists (less money than now). The industry confidence reports the percentage of firms that are more optimistic than the quarter before. Barkham and Ward (1999) find inflation expectations and industry confidence to be significant.

Barkham and Ward (1999), Rehkugler et al. (2012) and Mueller and Pfnuer (2013) make use of IPO data in property companies to show that a part of the NAV spreads show a cyclical movement and are affected by market sentiment. They both state and show that the relationship between the IPOs of property companies and a reversed discount can be shown graphically, see figure 2.2. IPOs occur when companies feel that the investors value the market higher than given by fundamentals which means that when discounts are larger more IPOs take place (Rehkugler et al., 2012). Rehkugler et al. also perform a regression analysis on the data of IPO and find that it can explain around 40% of the movements in discounts.

Figure 2.2 – Average discount and all property company IPOs (Sahi 1996, published in Barkham and Ward in 1999)

The country-specific market sentiment variable constructed by Rehkugler et al. (2012) incorporates a part of stock market sentiment as well as real estate market sentiment. In order to distinguish the impact of a specific country a country-element is added to the sentiment variable. This creates an index aimed to represent a country-specific real estate stock market sentiment indicator. For the stock market sentiment the SENTIX database is used, which is an index measuring investor sentiment in Europe and globally. For the real estate sentiment the growth rates of appraisal-based property prices available from EPIX50 is used as a proxy.

2.5 Expertise Review There are several parties that observe and examine the NAV spread within the real estate industry. The aim of this section is to find explanatory variables from the working professional’s point of view. European Public Real Estate Association (EPRA) continuously observes the NAV spread at a pan-European perspective. EPRA has also founded the definitions of EPRA NAV and EPRA NNNAV to provide a measure of the fair value of a company. (EPRA, 2014) Their definition of EPRA NNNAV is used in this thesis. In addition to EPRA, Leimdörfer, a leading financial adviser within real estate in Nordic countries, continuously observe changes in the NAV spread of Nordic listed property companies. The results of the research are published annually in a research report. In the latest edition from October 2015 it can be seen that Swedish listed property companies had a discount to NAV during the whole period of 1997-2004 and the time period 2004-2015 has been characterized by premiums. (Leimdörfer, 2015)

2.5.1 Explanation of the NAV Spread Leimdörfer (2006) proposes that the main argument for a discount on property portfolios is that the investors cannot choose the portfolio composition themselves. On the other hand, a portfolio of real estate diversifies the risk, which makes it possible for the investor to reduce its yield and thereby pay a higher price (Leimdörfer 2006).

An interview was held with Erik Bodin, head of research at Leimdörfer, in order to find indicators that can affect the NAV spread in the Nordic property market. Bodin (2016) emphasises that the share price tends to rise if the company is working with a visible management such as proactive work with real estate development in order to create value. Leimdörfer’s annual research report contains a survey of asset manager’s view on Swedish listed property companies. The survey contains the asset manager’s view on perceived management skills, which results can be formed as a variable where better management should lead to a higher premium to NAV. Bodin (2016), says that it is more doubtful whether asset managers actually are acting after their answers of the survey but that the management should be a variable in its own right since the market’s confidence in the company’s management should be highly relevant for the pricing of its shares. Stability in growth, the variation in profit during a certain time period without regard to an eventual increase in property value, does also affect the price of a share. Bodin (2016) means that a three-to-five- year-period of growth results in a more favourable valuation at a given time.

Bodin (2016) highlights the impact of the company’s method of financing. Advantageous conditions, such as the borrowing rate, should increase the premium to NAV. The strength of a company can also be measured when the interest rate environment changes. If a company is sensitive to changes in the interest rate environment, the company will perform better when the interest rate is low, but worse when the interest rate is high, which in turn might affect the discount to NAV. (Bodin 2016)

The owners of a company’s shares have an effect on the transaction price and therefore the discount to NAV. A higher amount of shares held by the CEO might decrease the discount to NAV since a higher amount of shares could lead to an increased commitment and therefore a performance improvement of the company. Another impact is the amount of foreign ownership. A higher amount of foreign ownership means a higher demand, which might lead to an increase in the premium to NAV. A company for which there is a demand from abroad would benefit from an excess demand for its share compared to a company for which there is no demand from abroad (provided identical domestic demand for both firms). (Bodin 2016)

2.5.2 Time Lag on the Real Estate Market According to Bodin (2016), the stock market reacts about one year before the real estate market. In general, increased property share prices lead to increased property values. However, if the price of a share increases today, the increase in real estate values becomes visibly about one year later. Several reasons may describe this phenomenon. First, valuers tend to lag behind the market in property valuations. Second, expectations about increasing share values will increase the share price today. As an example, if the rents are expected to rise in the future, the price of property shares will increase today since investors want to buy now in order to gain positive return when the rents actually increase. However, an increase in property value due to an increase in rents will not appear before the rents are increased.

3. Method

3.1 Methodology This thesis uses a quantitative approach in which a data set will be analysed in order to find relationships between explanatory variables and the NAV spread. In order to construct a justified theoretical framework, the choice of approach is supported by previous research on the closed-end fund puzzle and discount to NAV for property companies in particular. Preceding research is used as a base, mainly in terms of method of analysation and selection of companies and variables thus suggesting a deductive approach for this thesis. The deductive approach tests earlier theories and research against real life observations in order to confirm their validity. (Saunders et al., 2009) The data is measured annually and is mainly collected from the companies’ annual reports.

3.2 The Model The analysis of the NAV spread among Swedish listed property companies is pursued in two ways in this thesis. First, a rational approach is applied in which a panel data regression analysis on the selected variables is conducted in order to explore the factors that might explain the longitudinal (cross-sectional and over time) variation in NAV spreads. As this thesis provides a large amount of variables, several sub-models are tested in which variables with similar characteristics are analysed together. The sub-groups are presented in figure 3.1 below and are labelled as company-specific, share-specific, corporate governance and expertise. A more detailed description of the variables is presented in chapter 4.2. The rational approach starts by testing the company-specific variables against the NAV spread. The other sub-groups are then added to the panel regression one group after each other. The necessity to eliminate variables from the model is done with respect to statistical results in combination with outcomes and analyses from previous research. Throughout the thesis the panel data is examined by using regression analysis with both fixed and random effects, as described in further detail in chapter 3.3. Through theory and via a Hausman test, an appropriate model is chosen.

DISCOUNT TO NAV EXPLAINED BY RATIONAL VARIABLES

COMPANY-SPECIFIC SHARE-SPECIFIC CORPORATE GOVERNANCE EXPERTISE SIZE RETURN BOARD_SIZE MANAGEMENT LTV VOLATILITY BOARD_INDEPENDENCE TYPE SYSTEMATIC_RISK (β) INSIDER_OWNERSHIP LOCATION TOP_THREE FREE_FLOAT EPRA_MEMBER (dummy)

PANEL DATA REGRESSION ANALYSIS Figure 3.1 – Model for the rational approach

Second, the irrational approach applied in this thesis investigates the existence and impact of noise trader sentiment on property company shares and thus if noise trader sentiment plays a role in explaining the NAV spread. A panel data regression will be carried out in which proxies for market sentiment will be regressed one by one with the final set of rational variables against the NAV spread in order to investigate the explanatory power of such variables. The justification of the use of proxies for market sentiment is investigated by examining the five implications of the Noise Trader Theory as presented and investigated by Lee et al. (1991) for closed-end funds and later applied for the property sector by Barkham and Ward (1999) and Mueller and Pfnuer (2013), see chapter 2.4.4. As of the heterogeneity of the five implications, the analysis of the implications needs to apply different methods. In figure 3.2 below, the assumptions, implications and the methods to evaluate the five implications are presented.

INVESTIGATION INTO THE EXISTENCE OF NOISE TRADERS

FIVE ASSUMPTIONS ASSUMPTION 1 ASSUMPTION 2 ASSUMPTION 3 ASSUMPTION 4 ASSUMPTION 5 Risk aversion Short time horizons Noise trader sentiment is Noise trader sentiment is Differing clienteles unpredictable (stochastic) systematic

IMPLICATION 1 IMPLICATION 2 IMPLICATION 3 IMPLICATION 4 IMPLICATION 5 NAV spread, negative long- Alternation of premium or Cross-sectional correlation of Correlation with other Equity issues in premium term average discount NAV spreads sentiment indicators periods (IPO activity) DISCOUNT TO NAV DISCOUNT TO NAV DISCOUNT TO NAV C_INDICATOR_MI IPO_LISTED C_INDICATOR_H IPO_DELISTED INFLATION_INDICATOR INT_RATE_EXPECTATION

METHOD 1 METHOD 2 METHOD 3 METHOD 4 METHOD 5 GRAPHICALLY (AVERAGE OF GRAPHICALLY AND MEAN CORRELATION MATRIX PANEL DATA REGRESSION GRAPHICALLY AND DISCOUNT) REVERSION ANALYSIS REGRESSION ANALYSIS

Figure 3.2 – Model for testing the irrational approach

Implication 1 – NAV spread will have a negative long-term average In order to investigate the first implication (I1) an average sector NAV spread is calculated for the entire investigated time period. Similar to Mueller and Pfnuer (2013) the average

27 sector NAV spread is used to represent a long-term average. It is calculated by computing an aggregated average NAV spread of all companies together for each year (YNAV) and then computing the arithmetic average. The computation for the aggregated NAV spread per year relates to the contribution of every company to the total NAV spread by recognising their part of the total market capitalisation. The computation of the aggregated average NAV spread per year (YNAV) is shown in equation 3.1 and the arithmetic average of YNAV is shown in equation 3.2.

! YNAVt = !!! (��!" / ��!) ∗ ��!" Equation 3.1

! Long-term Average = ( !!! ��! )/� Equation 3.2

Where: j = company 1 to n in sample; t = time (year); MCjt = market capitalisation of company j at time t; MCt = total market capitalisation of all companies at time t; YNAVt = aggregated average NAV spread per year.

Implication 2 – Alternation of premium or discount In order to test the alternating behaviour of the NAV spread, the second implication (I2), the method of Mueller and Pfnuer (2013) is applied. They state that an alternating behaviour can be associated by a mean-reverting behaviour. Similar to Mueller and Pfnuer (2013), this is investigated by observing the autocorrelation of the annual aggregated average NAV spread calculated in implication 1. This is tested by calculating the correlation between the current year (2015 in this thesis) and all previous in the investigated time period. The data set of this thesis consists of ten yearly observations for each company (except for Corem), which allows for a maximum of eight lags. In addition, a Box-Ljung test-statistic is performed in order to assess the respective significance level for rejecting the H0 of no autocorrelation. The Box- Ljung test is performed in Stata and is based on the equation:

! ! !! Qk = n(n+2) Equation 3.3 !!! !!!

Where k = number of lags being tested; lags 1 to k; n = length of time series and rj = correlation coefficient for lag of j.

Implication 3 – Levels of discounts will be highly correlated across funds The third implication is investigated by a correlation matrix. The correlation matrix is computed by deriving a company’s correlation towards another company with respect to its NAV spread. Each value in this correlation matrix represents the correlation between the two

28 companies’ NAV spread over the studied period. For each company an average correlation towards all the other companies is computed, which is then averaged for the total amount of companies.

! AVERAGE_CORRi = !!! ��!" Equation 3.4

! AVERAGE_CORR = !!! ��_��! Equation 3.5

Where: i = the current company, j = the other companies 1 to n in sample; CORRELATIONij

= correlation between company i and j over the time period; AVERAGE_CORRi = average correlation of company i to all other companies.

Implication 4 – Correlation with other sentiment indicators Similar to both Mueller and Pfnuer (2013) and Barkham and Ward (1999) a panel data regression with the discount to NAV as independent variable and other sentiment indicators as explanatory variables is undertaken in order to investigate implication 4 (I4). The approach in this thesis follows the same sentiment indicators used in Barkham and Ward (1999) as similar sentiment indicators were available and could be computed with ease for a Swedish context. These indicators constitute of the household’s confidence, the manufacturing industry’s confidence, inflation expectations and with an addition of interest rate expectations, further described in section 4.2.2. For each sentiment indicator the panel data set is analyzed by a regression analysis using both fixed and random effects. Via a Hausman test an appropriate model design is chosen.

Implication 5 – IPOs occur when there is a premium The fifth implication (I5) is tested graphically by analyzing the relationship over time between IPOs and the annual average sector NAV spread. Second, a simple regression analysis is conducted between IPO activity and the aggregated average NAV spread for the examined time period. The graphical analysis is pursued in Barkham and Ward (1999), while the approach that investigates the relationship between IPO activity and average NAV spread through a regression analysis is applied by Mueller and Pfnuer (2013) and Rehkugler et al. (2012).

3.3 Regression Analysis Regression analysis is applied in the analysis of the rational approach and partly in the analysis of the irrational approach. Based on a set of data, the regression analysis estimates relationships among variables to create functions that best fit the observed data. The

29 functions, or equations, show a relationship between a dependent variable and one or more independent variables and explain how the typical value of the dependent variable changes when any one of the independent variables is changed and holding everything else equal (Studenmund, 2014).

3.3.1 Panel Data Regression Methods The data set in this thesis is based on 14 property companies, where each one of them has ten observations for every variable, one for every year between 2006 and 2015. Therefore, the data set supports a panel data regression approach, which is applied in the analysis of the rational variables and for implication 4 in the analysis of the irrational variables. Implication 5 in the irrational approach is investigated with a regular regression analysis (OLS). Panel data (longitudinal data) has both cross-sectional elements (entities) and observations over time. There are several advantages with the use of panel data. A panel data set gives more informative data, more variability, less collinearity among variables, more degrees of freedom and more efficiency (Baltagi, 2001). This is the result as of increased number of observations due to combining several time periods for each individual entity.

There are several ways to deal with panel data in the estimation of the equation and in the choice of method to handle it. It is the type of data that determines the choice of method. The methods are regression using pooled OLS, fixed effects and random effects. However, panel data modelling is complex. The choice of which model to use has no clear guidelines and the process of interpretation and presentation of the result is challenging (Park, 2011). The simplest method to analyse panel data is by pooled OLS, which causes some limitations. Most importantly, it assumes that the average values of the variables and the relationships between them are constant, both over time and cross-sectionally. Therefore, the fixed effects and random effects model is broadly applied in financial research. (Brooks, 2014)

The Fixed Effects Model is defined by Wooldridge (2006) as the unobserved effects panel data model where the unobserved effects is allowed to be arbitrarily correlated with the explanatory variables in each time period. The fixed effects model can be set up as:

yit = β1xit + ai + uit Equation 3.6

Where β1 is a k x 1 vector of parameters to be estimated by the explanatory variables and xit is a k x 1 vector of observations on the explanatory variables. ai + uit form the error term and ai is the unobserved effect that captures all unobserved time-constant factors that affect y. In

30 the case of this thesis it will be interpreted as the discount heterogeneity, which represents all factors affecting discount levels (yit) that do not change over time. During a short investigated time period, 10 years in this thesis, some variables might be roughly constant over time. The variable EPRA_MEMBER does rarely change for a company during the investigated period.

Since the fixed effects model allows correlation between ai and the explanatory variables, it is implied that time constant explanatory variables such as EPRA_MEMBER cannot be used in the fixed effects model. The fixed effects estimator uses a transformation to remove the unobserved effect ai prior to estimation and time-constant explanatory variables are removed along with ai. The second part of the error term uit is the idiosyncratic error, or time-varying error, which represent the unobserved factors that affect yit and change over time. (Wooldridge, 2006)

The Random Effects Model is defined by Wooldridge (2006) as the unobserved effects panel data model where the unobserved effect (ai) is assumed to be uncorrelated with the explanatory variables (xit) in each time period.

In contrast to the fixed effects model, the random effects model includes an intercept β0

(equation 3.7) and thus can make the assumption that the unobserved effect ai has zero mean.

The goal of the fixed effects model is to remove ai as of it is thought to be correlated with the explanatory variables. The random effects model assumes that ai is uncorrelated with the explanatory variables and can thus include time-constant variables. (Wooldridge, 2006)

yit = β0 + β1xit + ai + uit Equation 3.7

According to the definitions made by Wooldridge (2006), the fixed effects model allows arbitrarily correlation between ai and the explanatory variables while random effects does not. Therefore, the fixed effects model is widely thought to be a more convincing tool for estimating ceteris paribus effects. It is fairly common that researchers apply both random and fixed effects and then formally test for statistical differences in the coefficients on the time varying explanatory variables. This can be tested by the Hausman test where the idea is that one uses the random effects estimates unless the Hausman test rejects. In practice, a failure to reject means either that the random effects and the fixed effects estimates are sufficiently close so that it does not matter which is used, or sampling variation is so large in the fixed

31 effects estimates that one cannot conclude practically significant differences are statistically significant. (Wooldridge, 2006)

3.3.2 Panel Data Characteristics A short panel data set has many entities but few time periods, which means that the short panel data set is wide in width (cross-sectionally) and short in length (time-series). Conversely, a long panel data is set narrow in width and long in length. Both long and short data might lead to errors during examination. (Park, 2011) The panel data set of this thesis has neither a short nor long panel data set, thus exempting it from those kinds of errors. In a fixed panel, the same entities are observed during each time period. Conversely, in a rotating panel, the entities change from one period to the next. (Park, 2011) The thesis investigates the same companies during the measurement period, which indicates a fixed panel. A panel data set can further be balanced or unbalanced. A balanced panel data set follows when each entity (cross-sectional unit) has the same number of time series observations. In other words, if each entity has different numbers of time series observations, the panel data set is unbalanced, which comes with computing and estimation problems. (Brooks, 2014) In this master thesis, one of the entities Corem had its IPO during the investigation period. This fact leads to an unbalanced panel data set, since a major part of the data before an IPO is unavailable. However, most software packages, used in the analysis of panel data, can handle an unbalanced panel data set. (Park, 2011) Missing observations should be automatically accounted for by the software package. (Brooks, 2014)

3.3.3 Issues in Regression Analysis Due to the complexity of panel data regression modelling it is important to not become purely focused on statistical results such as exclusively searching for significant variables. Therefore the weight of the statistical results and tests during the data analysis is not purely decisive, but rather considered in combination with outcomes of previous research and logical reasoning. Several key issues and properties with the collected data are favourable to investigate prior to and during the regression analysis. One issue is multicollinearity, which refers to a high correlation among two or more independent variables in a multiple regression model and is investigated by looking at the correlation between variables. (Wooldridge, 2006) Multicollinearity leads to issues that involve low t-scores indicating that the significance levels of coefficients are lower than it should be (Studenmund, 2014). Another issue that is treated in this thesis is the stationary time series process, which is a process whose probability distributions are stable over time. By stable over time it is meant that if any

32 collection of random variables in the sequence is shifted ahead in h time periods, the joint probability distribution is remained unchanged. (Wooldridge, 2006). As this thesis analyses panel data, the regressions are made with the command xtreg in Stata. For fixed effects models, xtreg returns incorrect R-squares. In order to obtain the correct R-square for regressions performed with fixed effects models, the command areg is used. (Park, 2011)

3.4 Reliability Reliability is a key issue when talking about the quality of the research undertaken in a study. In quantitative research reliability is often represented by reproducibility. In order to be able to draw conclusions from the findings of a study the observer should be able to reproduce the research and reach similar results. Hence, reliability plays a key role in the configuration of the model used in this report. It refers to both the approach used to collect the data but also which methods that has been used in processing and analysing the data (Saunders et al., 2009). The data used to compute the variables used in this report are publicly available and described in detail in section 4.2.2 “Independent variables” with regard to their definition, source and calculation techniques. This report revolves around examining NAV spreads cross-sectionally and over time. The ability to produce reliable calculations of the NAV spreads is facilitated by the fact that Swedish property companies report their property portfolio at their fair value in accordance with IAS-40 since 2005, and therefore during the whole investigation period of the thesis. However, the NAV is still subject of systematic and unsystematic valuation errors due to the external valuation consultants’ assessment. Geltner (1993) shows on the other hand that random valuation errors tend to be diversified away and thus that property companies with a large portfolio only have a small part of systematic valuation errors. The comparison between companies discount to NAV can be problematic as their annual reports are published at different times and most certainly also the appraisals. However, the appraisals are aimed to value the company at the same date, which should help to minimize errors caused by different valuation dates. The methods undertaken to analyse the data are stated earlier in chapter 3. In addition chapter 5 takes the reader through the results step-by-step thus providing a basic guideline for how the thesis was undertaken which makes the study relatively easy to reproduce if needed.

3.5 Validity Validity is concerned with the conceptual and theoretical relevance of the study. It treats the extent to which the methods and data used in the study are able to describe what they are aimed to describe (Saunders et al., 2009). Regression analysis is commonly used to quantify estimates of economic relationships that previously have been argued for in theory (Studenmund, 2014). For the literature concerning the NAV spread, regression analysis is mainly applied as the technique for analysis. The validity of a report that mainly applies a regression analysis lies in that the relationship between the factors impacting the independent variable and the independent variable itself is valid. In this report the choice to incorporate a variable in the study is mainly founded on a theoretical and proven significance in previous research. However, internal validity issues concerning the casual relationships between the dependent variable (here NAV spread) and the independent variables can always be debated. Omitted variables can affect the independent variables causing an insignificant variable to have an apparent causality. Validity also addresses whether the results of the study can be generalised to other markets or the complete set of entities. The external validity, as this is often addressed as, is considered to be relatively for this thesis. Generalisation over the entire Swedish listed property companies is seen as possible. A selection process of property companies has been made in order to avoid a selection bias. Most of the listed property companies are thus incorporated in the report except the ones with only a brief history of being listed. This could lead to a selection bias as the report only focuses on the more robust companies that are listed throughout the examined period. On the other hand, if property companies with a brief history on the stock exchange would have been incorporated in the report, the panel data would have been strongly unbalanced which would have led to a problematic analysis. Lastly, a cornerstone in this report is the appraised value of the property portfolios. This report assumes that these valuations are done in a professional way and therefore represents the market value of the property. The validity of the results could be increased by taking possible errors or biases in appraisals into account.

4. Data Description In this chapter the analysed data is presented more in detail. The data is measured on a yearly basis and has mainly been collected from annual reports for each company. Data has also been collected from Nasdaq OMX Nordic, the Swedish share organization Aktiespararna, EPRA and Leimdörfer. The chapter starts by discussing the selecting process of the time period, companies and variables used in this thesis. It continues with presenting how the variables are defined and computed. The chapter concludes with a presentation of the descriptive statistics of the variables.

4.1 Selection

4.1.1 Time Period Selection The data is measured annually through the time period between 2006 and 2015. This interval contains both booms and recessions to make it possible to investigate the relationship between the discount to NAV and the general economy through time. The IAS-40 accounting standards were introduced in 2005, which also makes it rational to start the investigation period after that to ensure more reliable calculations of the NAV.

4.1.2 Company Selection It is important that the sample of the companies investigated is selected randomly, within the framework and in line with the aim of the report. This thesis will only treat Swedish listed property companies and is thus limited to a maximum of approximately 30 listed companies. These are narrowed down to 14 investigated property companies via a selection process with a number of criteria. First, only companies listed on Nasdaq OMX Nordic were considered. Second, only property companies with a 75% of ownership and letting are included in the sample. Third, the property company should be listed on Nasdaq OMX Nordic during a larger part of the investigated period, 2006-2015. Companies that do not have enough accounting materials during this period, such as companies that have not been listed during the majority of the investigation period and therefore do not have enough information in their annual reports, are excluded. Corem was listed on Nasdaq OMX Nordic during the second half of 2007 but is included in the thesis as it has been listed for the majority of the investigated period. As for the last criteria, the company must own and operate 75% of its stake in Sweden. The selected companies and the criteria for selecting these are shown in figure 4.1.

Figure 4.1 – Company selection criteria

4.1.3 Variable Selection The selection of variables to be investigated is mainly based on and justified by previous research. In order to be consistent with previous research on the matter of deviations to NAV, the dependent variable (y) will be made out by the discount to NAV. A positive value implicates a discount to NAV while a negative value implicates a premium to NAV. Each parameter affecting the discount to NAV (y) will act as an independent variable (x). These variables are selected from previous research and partly through expertise from the real estate industry. The selection process starts with an overall perspective and variables with high explanatory power in terms of statistical and theoretical arguments have been chosen. Four groups of rational variables have arisen during the process:

Company-specific refers to endogenous variables, which is the group of variables that forms the base of most research. Share-specific refers to company-specific variables that have exogenous tendencies. This means variables that the company management cannot influence. Similarly to the company-specific group, share-specific variables are examined frequently in previous research. Corporate governance refers to the variables introduced by Ke (2015). The expertise variable is a result of the gained insight from the expertise review. The irrational variables in this thesis consist of IPO activity, confidence indicators and interest and inflation expectations. These are collected in order to test the Noise Trader Theory. Each rational and irrational variable and its assumptions will be described in table 4.1.

4.2 Variables

4.2.1 The Dependent Variable - Discount to Net Asset Value (NAV) The dependent variable is constructed as the discount to NAV. A negative value of y implicates a premium to NAV. The discount to NAV is the difference between the share price and the NAV per share at a specific time, see figure 4.2. The share price is collected from Nasdaq OMX Nordic and is calculated as an average of the daily closing price (not adjusted) during a time period of 10 days before and after the year-end. Atrium Ljungberg, Balder and Wallenstam are trading its A shares internally, which means that there is no share price information available. In this case, the price of A shares is assumed to be the same as for the B shares. Hufvudstaden’s C shares can be found at the OMX but since transactions of these occur infrequently, the price of C shares is assumed to be the same as for the B shares.

NAV per share is computed via data from the companies’ annual reports. It is common for companies to report a calculated NAV in their annual reports. However, the NAV calculation differs between companies and through time within a company. This thesis has thus chosen to construct a calculation method of the NAV, which is applied for all companies to ensure an equivalent calculation in order to be able to compare companies over time. This definition is aimed to capture EPRA’s definition of a present NAV (EPRA NNNAV) combined with the ability to construct the NAV without more information than what is available in the annual reports. The definition of NAV is as follows:

The deferred tax is estimated as the increase in real estate value plus/minus the difference in untaxed reserves, plus/minus the difference in derivatives, plus/minus the difference in carry- forwards. The Swedish corporate tax rate has been lowered twice during the investigation period: in 2009 from 28.0% to 26.3% and in 2013 it was reduced again to 22%. (Ekonomifakta, 2016). The deferred tax liabilities recognized in the financial statements for 2008 does not take place until the following year, in 2009, and thus according to the rules for 2009. Therefore, 26.3% can be expected already in 2008 (Castellum Annual Report, 2008). This must be taken into account in the calculation of the estimated tax, where a percentage of 5% is used to calculate the actual tax for the company to pay (Atrium Ljungberg Annual Report, 2014).

Figure 4.2 – The discount to NAV calculation

4.2.2 Independent Variables In table 4.1 the independent variables used in this thesis are defined. If any assumptions are made when collecting the data this is stated. In addition the source is disclosed as well as the expected signs and the comments to these. The expected signs are based on previous literature and the discussion that was presented in the literature review, chapter 2.4.3 for rational variables and chapter 2.4.5 for irrational variables.

Table 4.1 – Independent variables

EXPECTED SIGN +/- + - - - - - + +

COMMENTS TO EXPECTED SIGN Due to the widespread views on this variable, the expected sign is either positive or negative. A high degree of leverage leads to an increased risk for the investor and thus higher discounts. Focused publicly traded real estate companies are more transparent and less expensive to manage and therefore more successful. As for TYPE, focused publicly traded real estate companies are more transparent and less expensive to manage and therefore more successful. However, this has not been confirmed in previous reserach and an argument against this is that the risk is not diversified. A company with a higher free float has a higher liquidity and therefore trades for a smaller discount. Being an EPRA member is supposed to lead to a lower discount as of more stability and transparency for the investor. Higher return indicates a better reputation and therefore a lower discount to NAV. Higher volatility in the company’s stock price leads to an increased risk for the investor and thus higher discounts. Since the systematic risk explains how much the share's price changes if the market changes, a high risk could mean both really good performance in good times and really bad performance in bad times. This makes it harder to find an expected sign but in a diversification perspective it would be better with a low systematic risk, which means that a lower systemic risk would generate a lower discount to NAV. Also, Morri et. al (2005) concluded that a higher systematic risk is associated with higher discounts. ASSUMPTION No No For the most part, the division between the different property types are captured by using the distribution of the real property values of the company per type. When this value was not available the yearly rent per property type was used and in some cases also the area of a property type. Although there are differences between the techniques, different techniques have never been mixed in the calculation for the same company. Therefore, this should not influence the results in a crucial manner. Same assumption as for TYPE The amount of shares that is owned by private persons, organisations or other companies that own < 5% of the company's shares. No The return on equity for a company's stock during the last year. The variable consists only of the shares that are traded on the market. This is necessary when a company has more than one type of ordinary share (A, B and/or C). For the companies Atrium Ljungberg, Balder, Wallenstam and Huvudstaden, the calculated volatility is based on their B shares. For Klövern, the calculated volatility is based on the B share for the years 2014-2015 and the A shares for the years 2006- 2013. Return on market portfolio: OMX Nordic price index as an average of each year. Return on property shares is estimated by: (the price change during one year plus the dividend of each share of that year) / (the closing price of last year) The covariance and variance is calculated over the last three years before the current year. However, Catena and Diös were listed on the Swedish stock exchange in 2006 which means that for both Catena and Diös the covariance of the return on property shares and the return on market portfolio is assumed to be the same as for 2008 for the years 2006- 2007. The same method is applied for Corem. SOURCE The companies' annual reports The companies' annual reports The companies' annual reports The companies' annual reports The companies' annual reports EPRA The companies' annual reports Nasdaq OMX Nordic Return on market portfolio: Nasdaq OMX Nordic Return on property shares: Aktiespararna and Nasdaq OMX Nordic + The 2 ) 2 + … + + … + 2 ) 2 location +( 2 type ) 1 +( 2 ) to show how much the 1 ), Mkt ) / Var(R Mkt ,R i The degree of focus on geographical DEFINITION Total balance sheet value, Equity + Liabilities (MSEK) Loan to Value, total debt over total balance sheet value (%) Herfindahl index (type degree of focus on property type(s) of the company. The property types used in this study are office, retail, residential, logistics/industrial and other (%) Herfindahl index (location + … location(s) of the company. The geographical regions used in this study are Stockholm region, Gothenburg region, Öresund region and other. This is supported by that 80% of the property values are located in these regions. (Leimdörfer, 2008) and that companies usually report in this way (%) The amount of shareholding < 5% to capture the amount of shares that are more frequently traded on the market as a measurement of liquidity (%) 1 if member of EPRA, 0 otherwise (dummy variable) Average return on equity as a proxy for reputation among investors (%) The standard deviation of the adjusted daily closing price during one year (%) Cov(R price of the share is changed if the market is changed by 1% (%) VARIABLE SIZE LTV TYPE LOCATION FREE_FLOAT EPRA_MEMBER RETURN VOLATILITY SYSTEMATIC_RISK (β) 39

Table 4.1 – Independent variables (continuation)

EXPECTED SIGN + - + + - - + - - + + COMMENTS TO EXPECTED SIGN Smaller board can be interpreted as a proxy of a good board, thus having a positive impact on firm performance. A board that is very dependent might have objectives that are not in line with the company's. A dependent board might obtain a part of their preferred level of private risk in the company to benefit themselves. Insider ownership might increase the discount because it reduces the likelihood that a fund will be taken over and liquidated at the NAV. If a large portion of the company's shares is owned by the top three biggest owners, the liquidity of the shares would decrease, which would increase the discount to NAV. Better management should lead to better performance and reputation and thus lower discounts to NAV. Because of the presence of premiums in good times, real estate companies decide to do their IPO in order to access more money. Because of the presence of discounts in a recession, real estate companies avoide to do their IPO. A larger confidence leads to a lower discount to NAV since the manufacturing industry believes in good growth and can pay more to receive advantage from it. A larger confidence leads to a lower discount to NAV since the households believe in good growth and can pay more to receive advantage from it. The results of Barkham and Ward (1999), shows that the index of inflation is positively correlated to the discount, i.e. when the inflation is expected to increase, investors will only buy at a discount. An increase in interest rate will lead investors to invest in less interest rate sensitive asset classes. ASSUMPTION No Amount of members of the board that are independent to the company and its governance. Being dependent towards the larger shareholders is not defined as being dependent in this thesis as of data collection issues. When computing this value preference shares as well as ordinary shares were used. In this master thesis, all types of investors can be included in top three, which differs from Ke (2015) where only substantial institutions are counted. This is because it is common that CEOs or founders of the company own shares privately. The main idea with this variable is, similar to free float, to test liquidity. This means that it is important to include all types of investors who probably do not sell their shares often, or maybe never. The result, used as data for this parameter, shows the proportion (%) of positive and negative perceptions of management skills of the companies among asset managers. Indifferent asset manager's are excluded from the result and only asset managers familiar with each company were asked. (Leimdörfer, 2015) In some cases, different assets manager's views regarding a company's management skills differ from each other. In these cases in this thesis, the management variable is calculated as the difference between the percentage of positive and negative perception. No No No No The variable is constructed as the expected inflation in 12 months The variable is constructed as the expected interest rate in 12 months SOURCE The companies' annual reports The companies' annual reports The companies' annual reports The companies' annual reports Leimdörfer's "Asset manager's view of the listed property companies" (years 2006-2015) Nasdaq OMX Nordic Nasdaq OMX Nordic "Konjunkturbarometern" made of The National Institute of Economic Research (NIER), a Swedish national administrative authority that analyses the Swedish and international economy. "Konjunkturbarometern", The National Institute of Economic Research (NIER) The National Institute of Economic Research (NIER) The National Institute of Economic Research (NIER) DEFINITION Number of members of the board (amount) Ratio of independent members of the board (%) The board's total shareholding of the company's total (%) The total shareholding of the top three largest shareholders as a percentage of the total amount of shares. A proxy for ownership concentration (%) How asset managers perceive the management skills of the companies as a proxy for reputation (%) The amount of real estate companies that are listed on the stock exchange during one year (amount) The amount of real estate companies that are de-listed and on the stock exchange during one year (amount) Representing the expectations of the manufacturing industry. Based on a questionnaire which includes questions about the total backlog, current status review of stocks of finished goods, current assessment and expectations about production volume (index) Representing the expectations of the households. Based on a questionnaire about personal finances, the Swedish economy at present and in twelve months, and whether it is conceivably to buy consumer durables (index) Expected inflation rate in 12 months (%) Expected interest rate in 12 months (%) VARIABLE BOARD_SIZE BOARD_INDEPENDENCE INSIDER_OWNERSHIP TOP_THREE MANAGEMENT IPO_LISTED IPO_DELISTED C_INDICATOR_MI C_INDICATOR_H INFLATION_INDICATOR INT_RATE_EXPECTATION 40

4.2.3 Excluded Variables Rational variables that have been treated in the literature review but are not included in the regression analysis models of this thesis are unrealized capital gains tax and agency costs and expense ratios. In Sweden, capital gain tax can be avoided or postponed by making share deals instead of asset deals (Leimdörfer, 2015). Moreover, calculation and interpretation problems lead to the exclusion of these variables. Variables connected to unique characteristics of REITs are not included in the thesis since there are no Swedish REITs. The irrational variable county-senti is excluded since it is most useful in an analysis between different countries. Due to limitations in time and the data collecting process, only one of the expertise variables is applied in the panel data regression analysis of this thesis: management as a proxy for reputation. However, the remaining variables are included in the discussion.

4.3 Descriptive Statistics

4.3.1 The Discount to NAV Figure 4.3 describes the NAV spread for the 14 investigated property companies during the examined time period, 2006-2015. The vertical axis describes the discount to NAV. A positive value represents a discount while a negative value represents a premium. For the majority of the companies the NAV spread starts at a premium in 2006. Following the financial crisis in 2007-2008, property company shares are traded at a high discount. Balder had the highest discount during 2008, followed by Wallenstam and Fabege. In the recovery period thereafter, the NAV spread of a single company acts more independent and does not follow other companies to the same extent as during and around the crisis years (2006-2010). The other peak of discounts occurs in 2011, due to the European debt crisis, but is not as dramatic as in 2008. Until now, the trend has once again turned and most of the observed companies trade at premium values. An exception is Klövern, which has a high discount to NAV compared to the other property companies in 2014. During this year, a new class of ordinary shares was issued and a reversed split was undertaken with implications that the NAV spread might be affected for Klövern. Another exception is Catena, which has a high premium to NAV in 2010. In November of 2010 Catena decided to sell all their properties except for one. This property was very lucrative thus probably distorting the NAV spread relationship during this period.

1,00

0,80

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0,00 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 -0,20 DISCOUNT TO NAV -0,40

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Atrium Ljungberg Castellum Catena Corem Diös

Fabege FastPartner Balder Heba Hufvudstaden

Klövern Kungsleden Wallenstam Wihlborgs AVERAGE

Figure 4.3 – Cross-sectional company NAV spread over time

100%

80%

60%

40%

20%

0% 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 -20%

DISCOUNT TO NAV -40%

-60%

-80%

-100%

Figure 4.4 – Annual NAV spread distribution and average discount

The vertical line in figure 4.4 shows the annual maximum and minimum discount (maximum premium), calculated for all companies. The thicker horizontal line marks the average NAV spread for the treated year. As can be seen, the development of the average discount indicates

42 a cyclical movement. The discount to NAV starts at a premium in 2006 and follows cyclical patterns thereafter. It is characterized by strong discounts in 2008 and premiums in 2010. This is repeated again with discounts in 2011 and premiums from 2014 and onwards.

Table 4.2 shows that property company shares are traded on average at a 25% premium in 2006, which is the highest average premium during the investigation period. The maximum average discount during the period is found during the financial crisis in 2008. Property company shares were traded at an average discount of 28% during this year, although the largest discount amounted to 61%. The large distribution in 2010 is due to special circumstances regarding Catena.

Table 4.2 – Statistical summary of the discount to NAV

YEAR AVERAGE ST.DEV MAX MIN SPREAD 2006 -0.25 0.17 0.04 -0.57 0.62 2007 0.16 0.13 0.41 -0.06 0.47 2008 0.28 0.20 0.61 -0.12 0.73 2009 0.10 0.11 0.35 -0.06 0.41 2010 -0.12 0.27 0.13 -0.97 1.10 2011 0.13 0.20 0.40 -0.44 0.83 2012 0.08 0.18 0.37 -0.21 0.58 2013 0.04 0.20 0.37 -0.38 0.74 2014 -0.15 0.23 0.41 -0.52 0.92 2015 -0.08 0.24 0.34 -0.51 0.86

Figure 4.5 reflects how the NAV spreads differ between focus on property types. If more than 50% of a company’s property value is derived from a certain property type, the company’s NAV spread is added to that category. If not it is reflected in the mixed category. Each sector’s average NAV spread follows the same pattern. However, different levels of discount and premiums are shown between different property types. Property companies focused on the industrial sector show lower discounts in the recession of 2007-2008 and higher premium during 2010. The residential sector shows higher discounts in the recession of 2007-2008 and no premiums in 2010. The retail, office and mixed sector follow the same pattern as the average discount to NAV. However, from 2011 and onwards, these patterns are dissolved.

0,50

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0,10

0,00 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 -0,10 DISCOUNT TO NAV -0,20

-0,30

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Of all Retail Ofice Industrial Residential Mixed

Figure 4.5 – Sector specific discount to NAV against the average discount of all companies

4.3.2 Statistical Summary of the Dependent Variable, Time and Company As can be seen in table 4.3 below, there are 14 companies investigated in the analysis during the 10 year period from 2006-2015. This amounts to 140 observations but since the data for Corem starts at 2008 two observations are lost. The table presents the minimum, maximum, mean and standard deviation for the discount to NAV. It should be noted that the dependent variable y is the discount to NAV, which means that a negative value of y means a premium to NAV. The DISCOUNT fluctuates between -97.2% and 61.2% and has a standard deviation of 24.7% during the investigation period.

Table 4.3 – Statistical summary COMPANY, TIME and DISCOUNT

VARIABLE OBS MEAN STD. DEV. MIN MAX COMPANY 138 7.550725 4.052705 1 14 TIME 138 2010.558 2.861826 2006 2015 DISCOUNT 138 0.0214191 0.2469835 -0.9718061 0.6121523

4.3.3 Statistical Summary of the Rational Approach Table 4.4 below shows how the value of the rational variables varies over time and between companies during the investigated time period by a statistical summary including minimum, maximum, mean and standard deviation.

Table 4.4 – Statistical summary rational variables

VARIABLE OBS MEAN STD. DEV. MIN MAX SIZE 138 18599.41 12182.73 710.8 73376 LTV 138 0.6259165 0.1017677 0.3230513 0.7940353 RETURN 138 0.142029 0.1273567 -0.187 0.714 VOLATILITY 138 0.058485 0.0489752 0.0075755 0.4670338 SYSTEMATIC_RISK 138 0.7417552 1.729672 -3.394658 14.02003 TYPE 138 0.4452373 0.1825161 0.2274 1 LOCATION 138 0.6316099 0.2925204 0.2112841 1 FREE_FLOAT 138 0.5316261 0.2611853 0.102 1 EPRA_MEMBER 138 0.5217391 0.501347 0 1 BOARD_SIZE 138 6.797101 1.340876 4 10 BOARD_INDEPENDENCE 138 0.7900535 0.156727 0.5 1 INSIDER_OWNERSHIP 138 0.1654978 0.1912838 0 0.7929546 TOP_THREE 138 0.4450848 0.2253685 0.057 0.858 MANAGEMENT 133 0.432632 0.2316832 -0.29 0.89

SIZE changes from 710 million SEK to 73 376 million SEK, which indicates that there is a substantial difference between the largest and the smallest company’s total balance sheet value. In addition, LTV between the companies differs considerably, as well as RETURN, VOLATILITY and SYSTEMATIC_RISK. The average company is leveraged to a level of 62.6% and has had a return on equity of 14.2% over the time period. The average company has had a daily stock price volatility of 5.8% and a beta of 0.74. The Herfindahl index for TYPE (property type focus) moves between 22.7% and 100%. 100% indicates that at least one company’s property portfolio consists of only one property type at a given time. The Herfindahl index for LOCATION (geographical focus) moves between 21.1% and 100%. However, the outcome of the location variable is highly dependent on the geographical sectioning that was determined when the definition of the variable was set. The liquidity of the property shares, as captured by FREE_FLOAT, moves between 10.2% and 100%. 100% means that at least one company has no shareholders with more than 5% of the total amount shares in its possession, which should imply a high liquidity of stocks. EPRA_MEMBER is a dummy variable that is changing from 1 to 0 between the companies and through time. During the investigation period, about 50% of the companies in the sample are members of EPRA at a given time.

The size of a board (BOARD_SIZE) varies between four and ten members and the average board size is approximately seven persons. BOARD_INDEPENDENCE varies between 50% and 100%. 100% means that for at least one company, all of the board members are

45 independent to the company and its management at a given time. The lowest amount of 50% is anticipated since a majority of the board members of a listed company must be independent to the company and its management according to Swedish law (Kollegiet för Svensk Bolagsstyrning, 2016). INSIDER_OWNERSHIP, the board’s total shareholding as a percentage of total amounts of shares, is found to vary between 0% and 79.3%. This means that for at least one company’s board, no board member owns shares in the company at a given time. The maximum of TOP_THREE amounts to 85.8%, which means that three shareholders together own almost all property shares for at least one company at a given time. However, the average is found at a level of 22.5%. The variable MANAGEMENT show the percentage difference between positive and negative opinions on each company’s management skills, where it deviates between -29% as worst and 89% as best.

4.3.4 Statistical Summary of the Irrational Approach Table 4.5 below shows how the value of the irrational variables varies over time and between companies during the investigated time period by a statistical summary including minimum, maximum, mean and standard deviation.

Table 4.5 – Statistical summary irrational variables

VARIABLE OBS MEAN STD. DEV. MIN MAX IPO_LISTED 138 1.195652 1.176775 0 3 IPO_DELISTED 138 0.3043478 0.6462547 0 2 C_INDICATOR_MI 138 100.729 12.25871 69.8 114.2 C_INDICATOR_H 138 98.3913 10.4169 75.8 112.9 INFLATION_INDICATOR 138 0.0218841 0.0077545 0.007 0.034 INT_RATE_EXPECTATION 84 0.0325667 0.0083784 0.022 0.0469

The variables IPO_LISTED and IPO_DELISTED show that the maximum amount of newly listed property companies during one year accounts to three and the maximum amount of delisted companies during one year accounts to two. The confidence indicators, for both the manufacturing industry (C_INDICATOR_MI) and the households (C_INDICATOR_H), vary during the time period. This is logical as those variables are greatly influenced by the financial situation during the investigated time period. The expected inflation, INFLATION_INDICATOR, varies between 0.7% and 3.4% and the interest rate, INT_RATE_EXPECTATION, between 2.2% and 4.7%. The variable INT_RATE_EXPECTATION lacks data for the years of 2006-2009, which lowers the amount of observations to 84.

4.3.5 Correlations When performing regression analysis it is central to examine the correlation between the variables to avoid multicollinearity. Table 4.6 and table 4.7 show the correlation matrix for the rational and irrational variables respectively. Variables that are correlated to a large extent, more than 40%, are marked in bold. As can be seen in table 4.6, a few interesting correlations can be distinguished. There is a strong and positive correlation between EPRA_MEMBER and SIZE as well as EPRA_MEMBER and FREE_FLOAT. There is a strong and negative correlation between TOP_THREE and FREE_FLOAT.

Table 4.6 – Correlation Matrix of Rational Variables

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) DISCOUNT (1) 1.00 SIZE (2) -0.05 1.00 LTV (3) 0.06 0.03 1.00 RETURN (4) -0.05 -0.11 0.00 1.00 VOLATILITY (5) -0.33 0.09 -0.07 0.24 1.00 SYSTEMATIC_RISK (6) 0.07 -0.21 0.04 0.33 -0.02 1.00 TYPE (7) -0.04 -0.25 -0.47 -0.04 0.19 -0.05 1.00 LOCATION (8) -0.08 -0.18 -0.28 -0.10 0.13 0.01 0.33 1.00 FREE_FLOAT (9) -0.11 0.57 0.03 -0.08 0.06 -0.05 -0.15 -0.04 1.00 EPRA_MEMBER (10) -0.07 0.69 0.00 -0.13 0.09 -0.12 -0.29 -0.07 0.73 1.00 BOARD_SIZE (11) 0.00 0.04 -0.33 -0.12 0.13 -0.11 -0.05 0.33 0.28 0.34 1.00 BOARD_ INDEPENDENCE (12) -0.16 -0.17 -0.38 -0.10 0.11 -0.10 0.14 0.08 -0.02 -0.20 0.22 1.00 INSIDER_ OWNERSHIP (13) 0.18 -0.25 0.26 0.08 -0.13 -0.03 0.24 -0.25 -0.42 -0.32 -0.48 -0.27 1.00 TOP_THREE (14) 0.02 -0.45 -0.02 0.10 0.00 0.00 0.10 -0.07 -0.94 -0.63 -0.26 0.06 0.33 1.00 MANAGEMENT (15) -0.05 0.43 0.24 0.19 0.01 0.05 -0.43 -0.17 0.27 0.32 0.06 -0.29 -0.29 -0.19 1.00

The correlation matrix for irrational variables is shown in table 4.7. The confidence indicators correlate highly with each other. In addition INTEREST_RATE_ EXPECTATIONS correlate highly to three other irrational variables: IPO_LISTED, C_INDICATOR_MI and INFLATION_INDICATOR.

Table 4.7 – Correlation Matrix of Irrational Variables

(1) (2) (3) (4) (5) (6) (7) DISCOUNT (1) 1.00 IPO_LISTED (2) -0.31 1.00 IPO_DELISTED (3) 0.14 -0.21 1.00 C_ INDICATOR_MI (4) -0.43 0.54 -0.09 1.00 C_INDICATOR_H (5) -0.42 0.40 -0.06 0.89 1.00 INFLATION_ INDICATOR (6) 0.08 -0.39 0.13 0.29 0.44 1.00 INTEREST_RATE _ EXPECTATIONS (7) 0.30 -0.79 0.10 -0.56 -0.17 0.71 1.00 47

5. Results and Discussion The chapter describes the results and is divided into two parts: the rational approach and irrational approach. Each part of the results is followed by a discussion. It should be noted that the dependent variable y is the discount to NAV, which means that a negative value of y means a premium to NAV. Since y corresponds to the discount to NAV, the following presentation of the results is based on the perspective that the explanatory variables increase or decrease the discount to NAV. Therefore, it is important to remember that when it is written that an explanatory variable decreases the discount to NAV it can also mean an increase in the premium to NAV. Conversely, when it is written that an explanatory variable increases the discount to NAV it can also mean a decrease in the premium to NAV. It depends on whether a company’s share is traded at a discount or a premium at the specific time and how it is developed from there. The independent variables x are described in more detail in table 4.1 under chapter 4.2.2.

5.1 Results – Rational Approach

5.1.1 Regression Table 5.1 below presents the results of the regression analysis, based on the four sub-groups of rational variables, performed in Stata. For each model, one additional sub-group of rational variables is added in order to investigate the explanatory power of the variables to the NAV spread. The regressions have been performed with both fixed and random effects. Due to the complexity of panel data regression modelling it is important not to become purely focused on statistical results such as exclusively searching for significant variables. Therefore the weight of the statistical results and tests during the data analysis is not purely decisive, but rather considered in combination with outcomes of previous research and logical reasoning. This implies that along the process, some variables are dropped from the model with respect to statistical results such as explanatory power and significance as well as outcomes in previous research.

Most of the companies that are EPRA members have been members during the entire investigated period. The property of the variable EPRA_MEMBER is therefore seen to be of a fixed character. The implication becomes that this variable cannot help to explain the deviation in a fixed effects model, which means that it can only be tested with the random effects model. For a more detailed discussion see chapter 3.3.1.

Table 5.1 – Results of regressions on rational variables

Model 1 Model 2 Model 3 Model 4 VARIABLE FIXED RANDOM FIXED RANDOM FIXED RANDOM FIXED RANDOM SIZE -0.0000096 -0.00000313 -0.00000807 -0.00000861 -0.00000679 0.00000167 - 0.00000805 0.00000132 (-3.01)*** (-1.04) (-2.43)** (-0.04) (-2.03)** (0.72) (-2.47)** (0.56) LTV 1.187751 0.3734772 1.46097 0.2416846 2.137685 0.1181817 2.120894 0.0634364 (2.38)** (1.08) (2.8)*** (0.89) (3.7)*** (0.42) (3.46)*** (0.22) TYPE 0.6615398 0.1030835 0.6386727 0.1199094 0.8663089 0.0816621 0.5002712 0.0088836 (2.52)** (0.55) (2.43)** (0.76) (3.24)*** (0.50) (1.88)* (0.05) LOCATION -0.7495138 -0.1508593 -0.6200513 -0.0428506 -0.6857717 -0.0504936 -0.601512 -0.0647991 (-2.96)*** (-1.19) (-2.19)** (-0.46) (-2.38)** (-0.6) (-2.22)** (-0.77) FREE_FLOAT -0.1131535 -0.0167575 -0.2101765 -0.0972848 -0.5104588 -0.1195886 -0.4040059 -0.1124648 (-0.41) (-0.1) (-0.75) (-0.85) (-1.74) (-1.12) (-1.39) (-1.07) EPRA_MEMBER -0.0637086 (-0.74) RETURN 0.0313564 -0.0009952 0.043587 -0.0097365 (0.19) (-0.01) (0.26) (-0.05) VOLATILITY -0.243157 -1.443586 -0.5048445 -1.648784 -0.1739641 -1.743364 (-0.44) (-3.08)*** (-0.91) (-3.53)*** (-0.33) (-4.04)*** SYSTEMATIC_RISK 0.0259683 0.0117854 0.0222348 0.0132874 0.0697278 0.0411548 (1.94)* (0.92) (1.68)* (1.02) (3.27)*** (1.98)** BOARD_SIZE 0.0363672 0.0392544 0.0233362 0.0367564 (1.55) (2.02)** (1.02) (1.84)* BOARD_INDEPENDENCE 0.0452797 -0.1467745 -0.0080958 -0.167848 (0.22) (-0.97) (-0.04) (-1.10) INSIDER_OWNERSHIP -0.7474256 0.1829167 -0.6186565 0.241603 (-2.66)** (1.21) (-2.10)** (1.50) TOP_THREE

MANAGEMENT -0.2490638 -0.0651156 (-2.13)** (-0.60) AVERAGE_DISCOUNT

CONSTANT -0.3044625 -0.0628804 -0.5334627 -0.0269854 -1.023924 -0.117593 -0.7430306 0.0116981 (-0.74) (-0.22) (-1.26) (-0.11) (-2.22)** (-0.34) (-1.57) (0.03) R-SQUARE (xtreg) 0.1725 0.1657 0.2055 0.0859 0.2657 0.0746 0.3523 0.1335 Adj. R-SQUARE (areg) 0.221 - 0.2328 - 0.2720 - 0.3741 - Hausman test 0.0072 0.0008 0.0009 0.0031 (t or z-value) *** 1% sign. ** 5 % sign. * 10% sign. Not the expected sign Table 5.1 shows the panel data regression results for the rational variables, performed with fixed and random effects. Although the coefficients of the variables differ slightly between the fixed and random effects models, the signs of the coefficients of each explanatory variable tend to be the same, which implies a robust model. The values of the Hausman test is shown in table 5.1 and a value less than 5% implies that the hypothesis of random effects can be rejected and that the fixed effects model is preferred. The Hausman tests indicate that a fixed effects model is recommended for all the models. Therefore, the results will be presented in respect to the results of the fixed effects model approach hereafter.

The pattern of the discount to NAV, shown in figure 4.3 in chapter 4.3.1 is not entirely cyclical nor has it any upward or downward trend. If any collection of random variables in the sequence is shifted ahead in h time periods, the joint probability distribution will not remain unchanged. This implies that stationarity should not be problematic in this data set.

Along the process of performing model 1-4, variables that are highly correlated to other variables are dropped in order to avoid problems with multicollinearity. This applies to the variables EPRA_MEMBER and TOP_THREE, which is discussed later in this chapter.

In model 1 the discount to NAV is regressed to company-specific variables. The model is able to explain the NAV spread with an adjusted R-square of 22.1% for the fixed effects model. Most variables explain the discount to NAV at a significant level of at least 5%. The output of table 5.1 reports significant and positive relations between the discount and both LTV and TYPE, implying that an increase in loan to value and property type focus increases the discount to NAV. For TYPE, the sign of the coefficient is not in line with what is expected. Significant and negative relations are found between the discount and both SIZE and LOCATION. This implies that larger companies as well as companies with higher geographical concentration tend to have lower discounts, which is in line with what is expected. In the fixed effects model EPRA_MEMBER cannot be used as it seems to be of a fixed character.

In model 2, three share-specific variables are added and EPRA_MEMBER is dropped. The adjusted R-square increases from 22.1% in model 1 to 23.3% model 2 for the fixed effects model, which implies an addition of a small explanatory value for the share-specific variables. EPRA_MEMBER is removed because of its high correlation to SIZE in order to avoid multicollinearity. Members of EPRA are commonly larger companies that guarantee a certain level of standard. It can be argued that SIZE contains properties that members of EPRA have and EPRA_MEMBER can therefore be dropped. Variables with significant results in model 1 retain the sign of their coefficients in model 2, implying the same impact on the discount to NAV. In addition the variables are still found to be significant at a level of at least 5%. However, the results of the share-specific variables in model 2 are inferior to the results of the company-specific variables. RETURN and VOLATILITY are insignificant and do not obtain their expected signs. SYSTEMATIC_RISK is found significant at a level of 10% and has the expected sign. An increase in RETURN or SYSTEMATIC_RISK suggests an increase in discount, while an increase in VOLATILITY proposes a decrease according to model 2.

In model 3, three of four corporate governance variables are added. The adjusted R-square increases from 23.3% in model 2 to 27.2% model 3 for the fixed effects model. TOP_THREE is not applied as of its high correlation to FREE_FLOAT. The correlation amounts to -0.94,

50 which means that only one of them should be included in model 3 in order to avoid problems with multicollinearity. There are strong similarities between the computations of the two variables where the main idea is to measure liquidity. This is better caught by FREE_FLOAT since it excludes investors who rarely sell by rejecting investors with a shareholding of 5% or more. A regression with FREE_FLOAT does also generate a better statistical result compared to a regression with TOP_THREE. Moreover, the study of Ke (2015) shows an insignificant result of TOP_THREE. Variables with significant results in model 2 retain the sign of their coefficient and significance levels in model 3, implying the same impact on the discount to NAV. An exception is TYPE, which becomes significant at a level of 1% in model 3 compared to 5% in model 2. There is also an increase in the coefficient for LTV, implying a higher effect of LTV on the discount to NAV in model 3. Regarding the addition of corporate governance variables, BOARD_SIZE and BOARD_INDEPENDENCE have positive and insignificant coefficient signs, suggesting a higher discount to NAV. The impact of BOARD_SIZE is in line with the expectations while the results of BOARD_INDEPENDENCE and INSIDER_OWNERSHIP are not. INSIDER_OWNERSHIP shows a negative significant relationship to the discount.

In model 4 the expertise variable MANAGEMENT is added. The adjusted R-square increases from 27.2% in model 3 to 37.4% in model 4 by the inclusion of the MANAGEMENT variable as a proxy for reputation. This is an increase of 10.2%, which implies a high explanatory power of the expertise variable. MANAGEMENT is aimed to capture reputation among investors, which also is the intention of RETURN. RETURN lacks significant results in all previous models and it does not generate the expected sign in previous fixed effects models. This might imply that MANAGEMENT better reflects reputation among investors. Consequently, RETURN is dropped. MANAGEMENT has a negative and significant relation to the discount. The majority of the variables from model 3 keep their expected signs and their significance. However, TYPE can no longer explain the discount at a significance level of 1% but rather at 10%. The addition of MANAGEMENT to the regression analysis changes the sign of BOARD_INDEPENDENCE towards the negative and expected sign. The impact of SYSTEMATIC risk is also affected by the addition of the MANAGEMENT variable and becomes significant at a level of 1% in model 4.

5.2 Discussion – Rational Approach The results of the panel data regressions, shown in table 5.1, suggest that for the Swedish listed property sector, the NAV spread can be explained by rational variables to a certain extent. The rational approach manages to explain 37.4% of the NAV spread movement, which is in line with what previous studies are able to explain with similar variables. The adjusted R-square is increasing for every model, which indicates an explanatory power in every sub-group of rational variables. According to the results, it can be concluded that the group of share-specific variables has the lowest explanatory power since the adjusted R- square only increases by 1.2% from 22.1% to 23.3% between model 1 and model 2. The largest increase in the adjusted R-square is found between model 3 and model 4 where it increases by 10.2% from 27.2% to 37.4%. The group of corporate governance variables shows a doubtful contribution to the explanation of the NAV spread. The majority of the variables lack significance and increases the adjusted R-square by 3.9% when the three corporate governance variables are added. The results of the rational approach produced by this thesis show that the company-specific variables together with SYSTEMATIC_RISK, INSIDER_OWNERSHIP and MANAGEMENT have the highest explanatory power.

The size of the company is found to affect the discount to NAV for Swedish listed property companies during 2006-2015. The impact of company size is shown to be negative and significant, implying that a larger size leads to lower discounts. The reasoning behind this might be that larger companies can benefit from economies of scales. Their greater strength, in the sense of increased resources or larger financial forces, enable them to enter markets that are impossible for smaller companies to enter, as suggested by Adams and Venmore- Rowland (1989). It can also be that larger companies benefit from increased recognition and higher transparency and thus get a lower discount as suggested by Brounen and Laak (2005). An indication of transparency is memberships in EPRA. Being a member of EPRA suggest larger transparency in the companies’ reporting, which can be seen in the quality of annual reports. As EPRA_MEMBER correlates highly with SIZE it implies that larger Swedish listed property companies are aiming to have more transparent reports. Thus a larger size should decrease the discount to NAV as investors find it attractive due to increased recognition and transparency. Castellum, a large company and a long-term member of EPRA, consistently shows the highest premiums or lowest discounts during the period, which is in line with the argumentation. The study of Brounen and Laak (2005) is the only that has tested the effects of a membership in EPRA. Their results support the hypothesis of a smaller

52 discount for a company that is a member of the index. EPRA is well known globally and a membership might have a stronger impact on international investors since a membership of EPRA can increase the company’s recognition internationally.

The discount to NAV seems to be affected by the level of loan to value of a property company. The result of the thesis shows a significant and consistent relationship between the NAV spread and LTV in all models using fixed effects. Adams and Venmore-Rowland (1989) and Morri et al. (2005) suggest that higher leverage means an amplified NAV spread. Even though the result of the LTV variable shows a consistent positive relation the argumentation for an amplified impact of loan to value on the NAV spread makes sense. The reasoning behind this is that if the borrowing rate of a loan is lower than the company’s yield, the return on equity can increase if a larger share of the company’s assets is financed with the bank loan. This should be applicable for the companies during this time period since it is characterized by a low interest-rate environment. On the other hand, the results of this thesis show an increased discount to NAV when the loan to value increase, which is in line with Rehkugler et al. (2012) who confirms the belief that leverage leads to larger discounts as leverage increases the risk for the investor. This indicates that when investing Swedish property companies, stock investors value companies higher if they have a lower loan to value, given everything else equal. This is in line with the increasing debate of a bubble on the Swedish property market further motivating why investors are risk adverse.

The result for TYPE does not generate the negative expected sign in any models. Previous research, such as Brounen and Laak (2005) and Ke (2015), suggest that a larger focus reduces the discount to NAV. The findings of Cronqvist et al. (2001) and Boer et al. (2005) confirm that unfocused publicly traded real estate companies are less transparent and more expensive to manage and therefore less successful, which indicates a higher discount. Despite the positive sign in the results, TYPE is significant in all fixed effects models at a significant level of at least 10%. A possible argumentation for the positive sign shown in the results can be found in the diversification perspective. A larger property type focus leads to less diversification and thus a higher risk, which leads to a larger discount to NAV. However, LOCATION, which has not shown a significant result in most previous research got the negative expected sign and is significant in all fixed effects models. This might suggest that a property portfolio with a high geographical concentration is more accessible, due to shorter distances to property managers and developers, and therefore easier and less expensive to manage. A combination of the results of TYPE and LOCATION imply that companies with a

53 higher geographical focus and a greater spread in the amount of different property types have a smaller discount. The investigated period of this thesis consists of high premiums, which is historically unusual, and two financial crises. This implies large fluctuations in the NAV spread. As a result, the diversification perspective has been particularly important during the investigation period in this thesis compared to parts of previous research that have been examining other time periods. This economic environment prevailing in the years between 2006 and 2015 might result in new trends of strategies for real estate investments. In order to reach higher returns, property companies might choose to invest in fewer carefully selected locations with potential future rental growth to allow for efficient management. To compensate for the risk given by a high geographical concentration, property type (segment) diversification is used to spread the risk. FastPartner, Wihlborgs and Castellum are companies that visualize this trend. They have a clear geographical focus and several property segments. Wihlborgs has a premium to NAV during the whole investigation period except for the financial crisis in 2007. FastPartner and Castellum have discounts in 2007 and 2011. Since 2012 the companies’ shares have been traded at a premium. FastPartner’s portfolio consists of retail, residential, industrial, office and other properties where the industrial segment has been the greatest. By observing the property type focus from 2012 and onwards, it can be seen that FastPartner decreases its focus on the industrial segment and increases its focus on the office segment thus diversifying its property portfolio. In addition, Kungsleden has changed from being diversified geographically to be more focused during the latter part of the investigated time period and has a premium to NAV since 2014.

FREE_FLOAT as the measurement of the stock liquidity is not found to be significant for the NAV spread among Swedish listed property companies. However, it shows the expected sign in all models, implying that higher liquidity decreases the discount. Brounen and Laak (2005) confirm that companies with higher free float are linked to lower discounts. However, Rehkugler et al. (2012) also had difficulties to get a statistically significant result.

Malkiel (1995), Barkham and Ward (1999), Brounen and Laak (2005) and Ke (2015) find a significant and negative relationship between reputation and discount. However, RETURN is insignificant and does not generate the expected sign in this thesis. As mentioned in the results, this might imply that MANAGEMENT better reflect reputation among investors. MANAGEMENT is significant at a level of 5% in the fixed effects version of model 4, which confirms this hypothesis and is in line with similar findings of Malkiel (1995), Barkham and Ward (1999), Brounen and Laak (2005) and Ke (2015). MANAGEMENT is one of the

54 rational variables with highest explanatory power since the inclusion of the variable increases the adjusted R-square from 27.2% to 37.4%, see model 3 and 4 in table 5.1.

The result of the regression analysis shows an insignificant and negative relationship between the annual averaged daily closing stock price volatility and the discount to NAV. Rehkugler et al. (2012) support the findings that volatility increases the discount, while Brounen and Laak (2005) find no significant relationship although showing the expected sign. Volatility has a connection to risk. On the other hand, an investor cannot earn abnormal returns without taking the risk. This implies that a volatile share is suitable for investors who prefer to trade at a higher risk. During the investigated time period, stock prices have experienced both boom and bust periods implying a volatile behaviour of the market in general. Despite the financial crisis in 2007-2008, the time period is characterized by a boom environment. This should suggest that larger risk should have decreased the discount (higher premiums). The results of this thesis indicate such a relationship between NAV spread and VOLATILITY. However, no definitive conclusions about this can be drawn as VOLATILITY is insignificant in all fixed effects models.

The results produced by this thesis show that the beta of the company, the SYSTEMATIC_RISK, is found significant and positive. It was expected that it would be better with a low systematic risk in a diversification perspective since the systematic risk explains how much the share price changes if the market changes. Thus, Swedish listed property companies with a larger sensitivity against the market are traded at larger discounts during the investigation period. The results produced by Morri et al. (2005) concluded the same, that a higher systematic risk is associated with higher discounts. A possible explanation for this might lie in that the variable captures the trait of being unique. A unique company does not develop in the same way as the market and is hypothesised to have a premium to NAV as of high publicity and recognisability. As of this investors are willing to buy the share of the company during its bad times in order to reap possible extreme benefits in the future.

Three of four corporate governance variables are included in model 4. TOP_THREE was not included because of its high correlation to FREE_FLOAT in order to avoid problems with multicollinearity. TOP_THREE was also insignificant in the result produced by Ke (2015). Ke (2015) suggests that a smaller board can be interpreted as a proxy for a good board. BOARD_SIZE is found to be insignificant in this thesis, which is the same result as for Ke (2015) although Ke still believes that it has explanatory power. It can therefore be concluded

55 that BOARD_SIZE does not seem to have a large impact on the discount to NAV. The results produced by Ke (2015) show a negative significant sign for BOARD_INDEPENDENCE (5% significance level) and a positive significant sign for INSIDER_OWNERSHIP (1% significance level). However, BOARD_INDEPENDENCE shows no significant result in this thesis. It is important to remember that Ke’s definition of independent is independence to the company and its governance or independence to larger stakeholders. The definition used in this thesis is independence to the company and its governance. This implies that the result produced by Ke (2015) cannot be compared entirely with the result of this thesis. In addition according to Swedish law, a majority of the board members have to be independent to the company and its management, leading to the belief that the variable might not affect the discount greatly between the companies and over time. INSIDER_OWNERSHIP is significant at a 5% level in the fixed effects version of model 3 and 4 but does not have the expected sign. This implies that a high level of insider ownership reduces the discount. This is not unreasonable since a board that together own a large amount of the company’s shares increase the commitment to the company and therefore improves its result, thus decreasing the discount to NAV. As the competitive environment between property companies is increasing, higher requirements on the company’s governance and management are expected.

Bodin (2016) mentioned method of financing and interest rate sensitivity as parameters that might affect the discount to NAV. As can be seen in figure 4.3 in chapter 4.3.1, the European debt crisis in 2010 was not as dramatic as the financial crisis in 2007-2008 for Swedish listed property companies. After the financial crisis and its recovery period, from 2011 and onwards, the discount to NAV is more widespread among the companies. This might indicate that less interest rate sensitive companies with good preparedness actually manage to do better and recover faster, which causes a vaguer trend among the companies compared to after the financial crisis in 2007-2008. The deep fall in the discount to NAV, following the financial crisis in 2007-2008, occurred as share prices fell rapidly and property values lagged behind. The time lag between the stock market and the property market was mentioned by Bodin (2016), who described that the stock market reacts about one year before the real estate market. A variable capturing the impact of companies’ method of financing and interest rate sensitivity could have increased the explanatory power of the thesis.

5.3 Results – Irrational Approach

5.3.1 Irrational Explanatory Model In order to test the impact of noise traders on the discount to NAV, proxies for market sentiment are added to model 4, see table 5.2. By the addition of the market sentiment it is possible to investigate the change in explanatory power from model 4 to model 5, 6 and 7 and thereby the effect of noise traders on the market. The first proxy for market sentiment that is included is the variable AVERAGE_DISCOUNT, which is the yearly average of all discounts to NAV in the sample. It is created to capture the market environment at each year and its impact on the discount to NAV. From the results in table 5.2 below it can be seen that the AVERAGE_DISCOUNT is significant, has its expected sign and almost doubles the adjusted R-square from 37.4% to 75.2%. An increase of 1% of the average discount increases the company’s discount to NAV with 1.1%. Most variables keep the same sign after adding AVERAGE_DISCOUNT, although BOARD_SIZE and BOARD_INDEPENDENCE change signs. SIZE and SYSTEMATIC_RISK are not significant at the same level as in previous models. LTV is insignificant in model 5 and the impact of LTV on the discount to NAV is reduced from a coefficient value of 2.1 to 0.34. On the other hand, LOCATION and MANAGEMENT become more significant at a level to 1% in model 5.

In model 6 and 7, confidence indicators for the household and the manufacturing industry are added to model 4 respectively as proxies for market sentiment. As can be seen for model 6 in table 5.2 below, the inclusion of C_INDICATOR_H increases the adjusted R-square from 37.4% to 53.6%. The impact of the confidence indicator for the households is negative and significant. Most variables that are statistically significant in model 4 remain significant in model 6. As can be seen for model 7 in table 5.2 below, the inclusion of C_INDICATOR_MI increases the adjusted R-square from 37.4% to 49.9%. The impact of the confidence indicator for the manufacturing industry is negative and significant. In model 6, the results of the other explanatory variables are similar to the results in model 7. Although, some differences can be found between the models 6 and 7, the main difference is that BOARD_INDEPENDENCE is found to have a positive correlation to the discount to NAV in model 7 and a negative correlation in model 6. In addition, in model 6 the explanatory variables are found to be more statistically significant than in model 5 and 7.

Table 5.2 – Results of regressions on final models

Model 4 Model 5 Model 6 Model 7 VARIABLE FIXED FIXED FIXED FIXED SIZE - 0.00000805 - 0.00000350 - 0.00000940 - 0.00000556 (-2.47)** (-1.68)* (-3.34)*** (-1.88)* LTV 2.120894 0.3386934 1.196914 1.125714 (3.46)*** (0.83) (2.18)** (1.94)* TYPE 0.5002712 0.5463844 0.3882263 0.413746 (1.88)* (3.26)*** (1.69)* (1.74)* LOCATION -0.601512 -0.8070942 -0.7351205 -0.6141465 (-2.22)** (-4.71)*** (-3.13)*** (-2.53)** FREE_FLOAT -0.4040059 -0.2057083 -0.302557 -0.2327844 (-1.39) (-1.12) (-1.21) (-0.89) EPRA_MEMBER

RETURN

VOLATILITY -0.1739641 -0.4600696 -0.1964627 -0.3419076 (-0.33) (-1.39) (-0.43) (-0.72) SYSTEMATIC_RISK 0.0697278 0.0236223 0.0617767 0.0608553 (3.27)*** (1.70)* (3.36)*** (3.18)*** BOARD_SIZE 0.0233362 -0.0129745 -0.0005726 -0.011796 (1.02) (-0.88) (-0.03) (-0.06) BOARD_INDEPENDENCE -0.0080958 0.1186672 -0.0602312 0.0673318 (-0.04) (0.98) (-0.36) (-0.39) INSIDER_OWNERSHIP -0.6186565 -0.1119039 -0.4527984 -0.3445452 (-2.10)** (-0.59) (-1.78)* (-1.28) TOP_THREE

MANAGEMENT -0.2490638 -0.2943982 -0.2002502 -0.2583269 (-2.13)** (-3.99)*** (-1.98)** (-2.47)** AVERAGE_DISCOUNT 1.110673 (12.86)*** C_INDICATOR_H -0.0096399 (-6.22)*** C_INDICATOR_MI -0.0076368 (-5.28)*** CONSTANT -0.7430306 0.3904618 1.051616 0.7294599 (-1.57) (1.26) (2.1)** (1.44) R-SQUARE (xtreg) 0.3523 0.7456 0.5243 0.4862 Adj. R-SQUARE (areg) 0.3741 0.7519 0.5360 0.4989 Hausman test 0.0031 0.0000 0.0958 0.0000 (t or z-value) *** 1% sign. ** 5 % sign. * 10% sign. Not the expected sign

5.3.2 Investigation into the Justification of the Noise Trader Model The investigation into the justification of the noise trader model for explaining the NAV spreads of Swedish listed property companies is split into five parts as visualised in figure 5.1. The five implications are based on the model of Mueller and Pfnuer (2013) as discussed in chapter 2.4.4. Based on the collected data and methodology the obtained results are presented in turn.

INVESTIGATION INTO THE EXISTENCE OF NOISE TRADERS

METHOD 1 METHOD 2 METHOD 3 METHOD 4 METHOD 5 GRAPHICALLY (AVERAGE OF GRAPHICALLY AND MEAN CORRELATION MATRIX PANEL DATA REGRESSION GRAPHICALLY AND DISCOUNT) REVERSION ANALYSIS REGRESSION ANALYSIS Figure 5.1 – Model for testing the irrational approach

Results – Implication 1 – NAV spread will have a negative long-term average Figure 5.2 describes the aggregated average discount to NAV (combined NAV spread) where a positive value indicates a discount and a negative value indicates a premium. The aggregated average discount to NAV is found to fluctuate between 28% premium and 28% discount during the investigated period. The highest premium is found in 2006 and the highest discount in 2008. The long-term average, as visualised in figure 5.2 during the investigation period, amounts to a discount of 2.45%.

0,40 0,30 0,20 0,10 0,00 -0,10 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 -0,20 DISCOUNT TO NAV -0,30 -0,40 AVERAGE LONG TERM AVERAGE

Figure 5.2 – Aggregated average NAV spread and long-term average NAV spread

Results – Implication 2 – Alternation of premium or discount The alternating behaviour of the NAV spread is investigated in the same way as in Mueller and Pfnuer (2013). This comes with the thought that an alternating behaviour is the same as a mean reverting behaviour. Mean reversion is tested in Mueller and Pfnuer (2013) by investigating the autocorrelation of the NAV spread. Table 5.3 presents the results of the autocorrelation of the NAV spread and the Box-Ljung Statistic-test that comes with it.

A mean reverting time series show positive autocorrelation regarding short time lags, but negative autocorrelation regarding longer time lags. Positive values for lag 1, 4 and 8 are found, as well as negative values for 2, 3, 5 and 7. However, the Box-Ljung statistic-test fails to reject the null hypothesis that all lags are not autocorrelated. This implies that there is no autocorrelation and thus does not support a mean reversion behaviour according to the test applied by Mueller and Pfnuer (2013).

Table 5.3 – Autocorrelation of NAV Spreads

BOX-LJUNG STATISTIC LAG AUTOCORRELATION STD.ERROR VALUE SIGNIFICANCE 1 0.0954 0.3116772 0.12124 0.7277 2 -0.3531 0.2949708 1.991 0.3695 3 -0.1351 0.2911638 2.3041 0.5117 4 0.2422 0.2539833 3.4768 0.4814 5 -0.1017 0.3451150 3.7252 0.5896 6 -0.2705 0.2408394 5.9202 0.4322 7 -0.2584 0.1254676 8.5918 0.2833 8 0.1202 - 9.4593 0.3050

Results – Implication 3 – Levels of discounts will be highly correlated across funds The cross-sectional correlation among NAV spreads for each company towards another is shown via a correlation matrix in table 5.4. Each cell shows the correlation between the two companies’ NAV spread for the entire time period. The bottom row (“Average”) describes the average correlation of a company towards all other companies. The average of this value is then computed as the arithmetic average to show the average correlation between all companies, which can be seen in table 5.5.

Table 5.4 – Correlation matrix for NAV spreads

2006-2015 (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) Atrium Ljungberg (1) 100% Castellum (2) 94% 100% Catena (3) 34% 38% 100% Corem (4) -29% -37% -38% 100% Diös (5) 80% 74% 21% -26% 100% Fabege (6) 84% 74% 30% -58% 87% 100% FastPartner (7) 82% 80% 19% -58% 89% 88% 100% Balder (8) 87% 79% 28% -54% 91% 94% 94% 100% Heba (9) 79% 74% 21% -8% 78% 85% 64% 73% 100% Hufvudstaden (10) 91% 89% 55% -62% 68% 80% 82% 82% 61% 100% Klövern (11) 38% 26% 41% 22% 22% 39% -3% 23% 54% 24% 100% Kungsleden (12) 61% 41% 5% 37% 60% 66% 35% 58% 79% 32% 75% 100% Wallenstam (13) 65% 73% 24% -56% 76% 66% 92% 79% 44% 71% -30% 6% 100% Wihlborgs (14) 92% 93% 42% -58% 82% 83% 93% 92% 65% 94% 16% 46% 86% 100% Average 66% 61% 25% -33% 62% 63% 58% 64% 59% 59% 27% 46% 46% 63%

As can be seen in table 5.4 the correlations between the companies are high. For example Atrium Ljungberg (1) correlates with Castellum, Hufvudstaden and Wihlborgs by more than

90% over the entire time period. On average Atrium Ljungberg correlates with the other companies by 66% over the time period. The average correlation among all companies is computed via an arithmetic average of the last row, this value corresponds to a correlation across companies of 47%, see table 5.5. The results are somewhat misleading as Corem (4) has a negative correlation with the others and is thus lowering the correlation value. In addition, special circumstances regarding Klövern (11), Catena (3) and Kungsleden (12) lead to that the average correlation is computed without these four companies. Corem and Klövern have a high level of insider ownership, while Catena and Kungsleden have distinctly changed their strategy during the investigation period. Table 5.5 provides the results of the average correlation for all companies with the full set of companies and without these four companies. It also shows the difference in time periods by highlighting the bust period of 2006-2009 and the recovery/boom period 2010-2015. Without the four companies the correlation across companies is 81% for the entire period and even as much as 92% for the period between 2006 and 2009. For the period between 2010 and 2015 the correlation across funds is 75%, which is still considered high.

Table 5.5 – Average correlation of NAV spreads across property companies

AVERAGE CORRELATION Year 2006-2015 Year 2006-2009 Year 2010-2015 ALL COMPANIES 47% 65% 46% MINUS COREM 61% 92% 48% MINUS COREM & KLÖVERN 67% 92% 58% MINUS COREM, KLÖVERN & CATENA 75% 93% 72% MINUS COREM, KLÖVERN, CATENA & KUNGSLEDEN 81% 92% 75%

Results – Implication 4 – Correlation with other sentiment indicators A panel data regression analysis is applied in order test the impact of other sentiment indicators on the discount to NAV. The regressions on the different sentiment indicators are presented in table 5.6. A model using fixed effects or random effects does not change the outcome of the regression analysis considerably, which ensures a robust model. The Hausman test indicates fixed effects as the best model for the household’s confidence indicator while the confidence indicator for the manufacturing indicator indicates that random effects should be used. The results show a high correlation between the discount to NAV and each of the two confidence indicators. The expectations of the manufacturing industry and the household show a negative and significant relationship at a level of 1% to the discount to NAV as well as an adjusted R-square of 29.5% respectively 28.2%. Thus the confidence indicators are able to explain the NAV spread movements across companies relatively well

61 on their own. The variable consisting of expectations of future interest rate is significant at a level of 1%, while the inflation indicator is not.

Table 5.6 – Results of regressions on other sentiment indicators

Model 1 Model 2 Model 3 Model 4 VARIABLE FIXED RANDOM FIXED RANDOM FIXED RANDOM FIXED RANDOM C_INDICATOR_MI -0.0086697 -0.0087058 (-6.00)*** (-6.02)*** C_INDICATOR_H -0.0098637 -0.0099165 (-5.74)*** (-5.77)*** INFLATION_INDICATOR 2.588499 2.75765 (1.12) (1.06) INT_RATE_EXPECTATIONS 8.465536 8.465536 (3.58)*** (3.58)*** CONSTANT 0.8947132 0.8992801 0.9919219 0.9980079 -0.0419947 -0.0380335 -0.2924515 -0.2924515 (6.10)*** (6.06)*** (5.83)*** (5.82)*** (-0.70) (-0.60) (-3.68)*** (-3.30)*** R-SQUARE (xtreg) 0.2262 0.2262 0.2112 0.2112 0.0101 0.0101 0.1570 0.0000 Adj. R-SQUARE (areg) 0.2952 - 0.2816 - 0.0984 - 0.4331 - Husman -0.93 0.0136 -1.20 1.0000 (t or z-value) *** 1% sign. ** 5 % sign. * 10% sign. Not the expected sign

Results – Implication 5 – IPOs occur when there is a premium Figure 5.3 below shows IPO activity (the bars) in relation to the discount to NAV (the line). Positive values on the NAV spread indicate a premium while negative values indicate a discount. The relationship between IPO activity and the discount to NAV can easily be seen graphically: when shares of property companies are traded at a premium, property companies tend to do their IPO’s, which is in line with expectations.

In addition the regression analysis, carried out by an OLS, shows a negative and significant relationship between the aggregated average NAV spread and the IPO activity. An increase in average discount decreases the amount of IPOs as can be seen in table 5.7.

Figure 5.3 – Property NAV spreads and IPO activity

Table 5.7 – Results of regression on IPO activity IPO_LISTED CONSTANT Adj. R-SQUARE - 0.0644665 0.0984986 0.2333 (-6.53)*** (5.96)*** (t-value) *** 1 sign. ** 5% sign. * 10% sign. Not the expected sign

5.4 Discussion - Irrational Approach

5.4.1 Irrational Explanatory Model The addition of a proxy for market sentiment considerably increases the explanatory power of the models in this thesis. The model based on only rational variables (model 4) manages to explain the discount with an adjusted R-square of 37.4%. The inclusion of proxies for market sentiment considerably increases the adjusted R-square, which indicates a high explanatory power connected to noise traders. However, one has to be careful when interpreting the results when using proxies in order to measure the impact of a factor. One has to be certain that the proxy variable captures what it is aimed to capture.

When adding the average market discount, AVERAGE_DISCOUNT, as a proxy for market sentiment an adjusted R-square of 75.2% is achieved. The proxy aims to show that even though companies have different fundamental values the company NAV spread is strongly linked to the average NAV spread movement, implying that investors are more prone to invest in the property sector as a whole and not in a certain company looking at its fundamental value. The results imply a large impact of sector-wide sentiment on the NAV spread of a property company. Barkham and Ward (1999) as well as Ke (2015) use the average market discount and obtain similar results. The use of the average market discount as a proxy for market sentiment is however constrained. The average market discount is computed via a transformation of the dependent variable (the averaged discount to NAV). This implies that the average discount can help to explain the economic climate at the given time and thus indicate the existence and justification of noise traders on the market, but not help to explain it. In that sense the confidence indicators better capture the attitude of investors and thus the ability to explain the impact of market sentiment on the NAV spread. These variables are constructed via a survey of the household’s confidence and manufacturing industry’s confidence respectively. They produce more robust results for the impact of market sentiment as they aim to show how a certain stakeholder envisions the future. Barkham and Ward (1999) suggest that market sentiment should be market-wide and even economy-wide and thus that a proxy should be of this nature as well. This statement supports the use of the confidence indicators as a proxy for market sentiment even though they do not correspond to be purely a real estate market sentiment. The confidence indicator for the households considerably increases the outcome of the regression analysis. The adjusted R-square increases to 53.6% and the variable is statistically significant on a 1% level. Private investors are often hypothesized to be noise traders, which further motivates the

63 use of this variable to describe the NAV spread. The confidence indicator for the household also increases the strength of the model by making other variables more significant. The use of the confidence indicator for the manufacturing industry provides similar results. The adjusted R-square increases to 49.9% and the variable is statistically significant on a 1% level. However, it does not increase the significance of the other variables to the same extent as the confidence indicator of the household. In addition, the logical reasoning is not as strong since the individuals answering the manufacturing industry survey should on average be higher educated than the average household. Therefore this thesis mainly points at the inclusion and impact of the confidence indicator of the households to the set of rational variables. Barkham and Ward (1999) find the confidence indicator of the manufacturing industry to be more significant than the household indicator, in contrast to this report. A possible reason can be that the study of Barkham and Ward is conducted during 1993-1995, which is more than 20 years prior this thesis. The stock market and the general economy have undergone large changes during these years, which might imply that more noise traders are active at the present time. Today, investor platforms such as Avanza make it much easier for the average consumer in a household to invest in stocks and change its portfolio instantly. This would imply a larger activity of small investors, often seen as noise traders.

The highest R-square obtained in previous research amounts to 76% and was obtained by Rehkugler et al. (2012). The model used by Rehkugler (2012) was created with a similar semi-rational approach as applied in this thesis. This thesis produces an adjusted R-squared of 53.6% when the confidence indicator of households is added, which implies an existence and strong impact of noise traders on the Swedish property market.

5.4.2 Investigation into the Justification of the Noise Trader Model The noise trader theory and model rely on several assumptions as discussed in chapter 2.4.4. In order to be able to test the appropriateness of the Noise Trader Theory and its impact on the discount to NAV, five implications are investigated. Following, the results of the investigation of the five implications are discussed in turn.

For Swedish listed property companies the long-term average NAV spread is found to be a discount. This is in line with the first implication (I1) and the results are also in line with what is found in Mueller and Pfnuer (2013). A strengthening fact for the results is that during this time period real estate has been considered to be a very good investment, especially in the Nordics. In spite of this the property companies trade at a long-term average NAV

64 discount during the period. The results support the belief that the action of noise traders distorts the price away from its fundamental value and that rational investors are unable to arbitrage the price back to its fundamental value. This is due to noise trader risk and implies that the price should be lower than the fundamental value long-term. The results of the investigation of the first implication are in line with the Noise Trader Theory. However, the first implication alone cannot explain the noise trader model because the long-term average discount also can arise because of rational factors.

The method used by Mueller and Pfnuer (2013), undertaking an investigation into if an alternating behaviour can be supported by a mean reverting behaviour, cannot be justified for the data in this thesis (I2). However, figure 4.4 in chapter 4.3 shows that the average NAV spread has a cyclical behaviour during the period in which the NAV spreads alternate around a mean. Although the mean reverting behaviour for NAV spreads was clearly not found for Swedish listed companies during the investigated time period, alternating discounts and premiums can be seen for the property companies.

All companies do not have the same fundamental value. However, the discounts to NAV of the companies follow a trend, which also can be seen by observing the correlation between the companies’ discount to NAV. A high level of correlation implies that some investors treat all property companies equally without regard to their fundamental value. Conversely, a low level of correlation would imply that a company’s discount to NAV would be based on its individual fundamental value. The correlations between the companies are high in the results of this thesis. This indicates an existence of noise traders on the market who rather observe property companies as a whole group than the companies’ unique fundamentals, which is in line with the third implication (I3). Over the whole estimated period the companies correlate by an average of 47% as shown in table 5.5. The correlation among the companies during the bust period of 2006-2009 amounts to 65%, which is higher than the correlation of 46% during the recovery/boom period of 2010-2015. This might imply that noise traders are more active in bust periods and rational investors will act more cautiously. Corem, Klövern, Catena and Kungsleden are removed one by one as special circumstances affect these companies. Corem has a negative correlation with the rest of the companies. Moreover, Corem and Klövern have a high level of insider ownership, where either the CEO or the members of the board own a large amount of the company’s shares. Catena and Kungsleden have distinctly changed their strategy during the investigation period. Catena sold all of their properties except for one at a given time during the examined period. Kungsleden also had a high amount of sales during

65 the latter part of the investigation period as they changed from being diversified geographically to be more focused. When removing these outliers the correlation increases drastically. It amounts to 81% for the entire time period and as high as 92% during the bust period. The results of the investigation of the third implication (I3) are in line with the Noise Trader Theory and support that noise trader sentiment has a systematic effect.

The results of the panel data regression of the NAV spreads towards other types of market sentiment indicators show that the NAV spread correlate to a high extent with several non- real estate market sentiments. This is in line with the fourth implication (I4), which implies that systematic effects of sentiment affect the NAV spread. Among the market sentiments, the confidence indicators have a significant relationship to the discount to NAV. Irrational investors act with the belief that the current general economic environment will continue in the future and do not base their beliefs on fundamentals. As the confidence indicators capture the general economic environment, a high correlation between the confidence indicators and the discount to NAV implies an existence of noise traders. The inflation indicator seems to have no impact on the discount to NAV as no significant relationship can be found. The impact of interest rate expectations might be considerable but is hard to fully capture since it lacks data for the years of 2006-2009. The variable helps to show the impact of non-real estate market sentiment but conclusions regarding the whole time period cannot be drawn.

When property companies observe an overvalued real estate market (a premium to NAV), they know that irrational investors are willing to pay overprices for stocks. This implies that during a boom, where irrational investors buy real estate shares to premiums, the IPO activity increases. Rational investors however will wait to buy until the premium has been equalized. This is supported by the results of the fifth implication (I5). The results indicate a higher amount of IPOs when property companies are traded at lower discounts.

To summarize, the results of the investigation into the justification of the noise trader model support the existence of noise traders on the Swedish listed property market. Although implication 2 cannot be justified for the data in this thesis, the other implications (I1, I3, I4 and I5) can be confirmed and it can therefore be concluded that noise traders exists and have an impact on the discount to NAV.

6. Conclusion The aim of this thesis is to examine the deviation to NAV for 14 Swedish listed property companies during 2006-2015. The thesis tests if the NAV spread can be described by a rational approach as well as from the basis of the Noise Trader Theory. The contribution of the report could be of interest for academia, property companies as well as investors in terms of investment strategies and stock market behaviour. The results show a time period that has been characterized by a volatile NAV spread with both high premiums and discounts. The largest average discount is found in 2008 and amounts to 28% and the largest average premium is found in 2006 and amounts to 25%.

The rational approach investigates the impact of company-specific, share-specific and corporate governance variables by the use of a panel data regression analysis. The results show that the rational variables can explain the deviation to NAV to some extent. The main contributions of the rational approach produced by this thesis show that the company-specific variables together with the amount of systematic risk, insider ownership and perceived management skills have the highest explanatory power. For the company-specific variables, larger company size, lower leverage, a greater spread in different property types as well as fewer geographical locations decreases the discount to NAV (increases the premium to NAV). The board’s total shareholding of the company’s stock (insider ownership) and management skills have a negative impact on the discount to NAV (increased premium). Systematic risk is positively correlated to the discount to NAV. The final rational model produces an adjusted R-square of 37.4% for the Swedish listed property companies in this thesis during the investigated period, which is in line with the results of previous research.

The results of this thesis show that a larger company decreases the discount, which is thought to be a result of higher recognition, higher transparency and benefits from economies of scales. A higher loan to value ratio is found to increase the discount since it increases the risk for the investor. The results of focus on property type and geographical location differ from most previous research. A trend that might result from the especially volatile behaviour during the investigated time period is that property companies choose to invest in fewer carefully selected locations in order to receive rental growth and allow for efficient management. To compensate for the risk given by a high geographical concentration, a larger spread in different property types is used to spread the risk. This motivates why a higher focus on property type increases the discount to NAV, while a higher geographical

67 concentration decreases the discount to NAV. A higher level of insider ownership increases the commitment among board members and therefore the result of the company. It can also be concluded that the discount to NAV is lowered for a company with a higher quality of management skills. As the competitive environment between property companies increases, higher requirements on the company’s governance and management are expected. The results of this thesis show that a company with a lower systematic risk tend to have lower discounts. This makes sense as a share that does not follow the general market is preferable from a diversification perspective.

The findings of the irrational approach applied in this thesis can be divided into two parts. First, three proxies for market sentiment, average market discount, confidence indicator for the households and the manufacturing industry are added to the final model of rational variables. The results show that the contribution of market sentiment is significant and the explanatory power of the final rational model increases considerably. A confidence indicator is logically well founded to use as a proxy for market sentiment since it produces more robust results as it aims to show how certain stakeholders envisions the future. As private investors often are hypothesized to be noise traders, the confidence indicator for the households should have the greatest impact on the discount to NAV. The adjusted R-square amounts to 53.6%, which is higher than for the confidence indicator of the manufacturing industry of 49.9%.

Second, an investigation into the justification of using Noise Trader Theory is conducted. This thesis is one of few studies that investigates this type of justification on the same data set and concludes that the use of a proxy for market sentiment is justified for the Swedish listed property market. This is conducted by investigating the impact of five implications (I1-I5) as suggested by previous literature. Implication one (I1) that the NAV spread has a long-term average discount is supported in this thesis. Implication two (I2) that the NAV spread alternates between premiums/discounts cannot be supported via methods from previous studies. Implication three (I3) that the correlation of the NAV spread between the companies is high can be supported by the thesis. Implication four (I4) that the NAV spread correlates to a high extent with several non-real estate markets sentiment can also be supported by the thesis. Implication five (I5) that a higher amount of IPOs occur during times of lower discounts (higher premiums) is supported by the thesis.

Four of five implications support the Noise Trader Theory. Although implication two cannot be confirmed, alternating discounts and premiums can be seen for the property companies

68 during the period. The thesis thus supports the belief that the action of noise traders distorts the price away from its fundamental value and that rational investors are unable to arbitrage the price back to its fundamental value. In other words it justifies the use of proxies for market sentiment in models trying to explain the NAV spread.

In conclusion, the rational approach can explain the NAV spread to some extent. The company-specific and expertise variables are the most effective rational variables. The investigation of the Noise Trader Theory concludes that the use of a proxy for market sentiment is justified for the Swedish listed property market. The inclusion of a proxy for market sentiment considerably increases the explanatory power of the models used in the thesis.

7. References

Academic References Adams, A.T. & Venmore-Rowland, P., 1989. Property Share Valuation. Journal of Property Valuation, 8, pp.127–142. Baltagi, B., 2001. Econometric Analysis of Panel Data, Wiley, John & Sons. Barberis, N. & Thaler, R., 2003. A Survey of Behavioral Finance. In Handbook of Economic Finance. Barkham, R. & Ward, C., 1999. Investor sentiment and noise traders : Discount to net asset value in Listed Property Companies in the U.K., Journal of Real Estate Research, 18(2), pp.291–312. Boer, D., Brounen, D. & Op’t Veld, H., 2005. Corporate Focus and Stock Performance Evidence from International Property Share Markets. The Journal of Real Estate Finance and Economics, 31(3), pp.263–281. Brooks, C., 2014. Introductory Econometrics for Finance, Third edition Brounen, D. & Laak, M., 2005. Understanding the Discount : Evidence from European property shares. Journal of Real Estate Portfolio Management, 11, pp.241–252. Capozza, D.R. & Lee, S., 1995. Property Type, Size and REIT Value. Journal of Real Estate Research, 10(4), p.363. Capozza, D.R. & Seguin, P.J., 2003. Inside Ownership , Risk Sharing and Tobin ’ s q - Ratios : Evidence from REITs. Real Estate Economics, 31(3), pp.367–404. Clayton, J. & MacKinnon, G., 2000. Explaining the discount to NAV in REIT Pricing: Noise or information. Real Estate Research Institute, Working Paper. Cronqvist, H., Högfeldt, P. & Nilsson, M., 2001. Why Agency Costs Explain Diversification Discounts. Real Estate Economics, 29(1), pp.85–126. Dimson, E. & Minio-Paluello, C., 2002. The Closed-End Fund Discount, Charlottesville EPRA, 2014. Best Practices Recommendations., (January). Available at: http://www.epra.com/media/EPRA_BPR_2011_1371135424024.pdf. Fama, F., 1970. Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), pp.383–417. Geltner, D., 1993. Estimating Market Values from Appraised Values without Assuming an Efficient Market. Journal of Real Estate Research, 8(3), pp.325–345. Geltner, D. & Miller, N., 2006. Commercial Real Estate: Analysis and Investments, Second edition Gemmil, G. & Thomas, D., 2002. Noise Trading, Costly Arbitrage, and Asset Prices: Evidence from Closed-end Funds. Journal of Finance, 57(6), pp.2571–2594.

Ke, Q., 2015. What affects the discount to net asset value in the UK-listed property companies? Journal of Property Research, 32(3), pp.240–257. Lee, C., Shleifer, A. & Thaler, R., 1991. Investor Sentiment and the Closed-end Fund Puzzle. Journal of Finance, 46(1), pp.75–109. Leimdörfer, 2006. Fastighetsmarknaden och de noterade fastighetsbolagen, Stockholm. Leimdörfer, 2011. Property shares - property or shares?, Stockholm. Leimdörfer, 2015. Same same but different, The Nordic property markets in a European perspective, Stockholm. Liow, K.H. & Li, Y., 2006. Net asset value discounts for Asian-Pacific real estate companies: Long-run relationships and short-term dynamics. Journal of Real Estate Finance and Economics, 33(4), pp.363–388. De Long, B. et al., 1990. Noise Trader Risk in Financial Markets. Journal of Political Economy, 98(4), pp.703–738. Malkiel, B.G., 1995. The Structure of Closed-End Discounts Revisited. Journal of Portfolio Management, pp.32–38. Malkiel, B.G., 1977. The Valuation of Closed-End Investment-Company Shares. Journal of Finance, 32(3), pp.847–859. Morri, G., McAllister, P. & Ward, C., 2005. Explaining deviations from NAV in UK property companies: rationality and sentimentality. Mueller, M.G. & Pfnuer, A., 2013. A review of the noise trader model concerning the NAV Spread in REIT Pricing: Evidence from the pan EU REIT market. Journal of Real Estate Portfolio Management, 19(3), pp.189–205. Nordlund, B., 2008. Valuation and Performance Reporting in Property Companies According To IFRS, Stockholm. Park, H.M., 2011. Practical Guides To Panel Data Modeling : A Step by Step Analysis Using Stata. Rehkugler, H., Schindler, F. & Zajonz, R., 2012. The net asset value and stock prices of European real estate companies. Zeitschrift für Betriebswirtschaft, 82(S1), pp.53–77. Saunders, M., Lewis, P. & Thornhill, A., 2009. Research Methods for Business Students, Third edition Studenmund, A., 2014. Using Econometrics, A Practical Guide Sixth edition Wooldrige, J., 2006. Introductory Econometrics: A Modern Approach, Third edition. Interviews Erik Bodin, Director and Head of Research, Leimdörfer, March 9th 2016, Stockholm

Web Based References Ekonomifakta, 2016, ”Bolagsskatt”, Available at: http://www.ekonomifakta.se/Fakta/Skatter/Skatt-pa-foretagande-och-kapital/Bolagsskatt/ (Accessed March 11th 2016) Investopedia, 2016, “Closed-End fund”, Available at: http://www.investopedia.com/terms/c/closed-endinvestment.asp (Accessed February 9th 2016) Investopedia, 2016, “Market Capitalization”, Available at: http://www.investopedia.com/terms/m/marketcapitalization.asp (Accessed February 10th 2016) Kollegiet för Svensk Bolagsstyrning, 2016, “Styrelse”, Available at: http://www.bolagsstyrning.se/bolagsstyrning/svensk-bolagsstyrning/den-svenska-modellen- for-bolagsstyrning/styrelse (Accessed February 19th 2016) Data Collection Aktiespararna, Data retrieved February 2016 Atrium Ljungberg, 2006-2015, Annual Report Castellum (2006-2015), Annual Report Catena (2006-2015), Annual Report Corem (2008-2015), Annual Report Diös (2006-2015), Annual Report European Public Real Estate Association – EPRA (Data retrieved April 2016) Fabege (2006-2015), Annual Report FastPartner (2006-2015), Annual Report Heba (2006-2015), Annual Report Hufvudstaden (2006-2015), Annual Report Klövern (2006-2015), Annual Report Kungsleden (2006-2015), Annual Report Leimdörfer (2006-2015), Asset managers’ view of the listed property companies Nasdaq OMX Nordic (Data retrieved February 2016) The National Institute of Economic Research – NIER (Data retrieved February 2016) Wallenstam (2006-2015), Annual Report Wihlborgs (2006-2015), Annual Report

8. Appendix Appendix 1 – Calculation Example Net Asset Value, Atrium Ljungberg 2015

Calculation Example Net Asset Value (EPRA NNNAV) Atrium Ljungberg 2015

Units in million SEK

Equity 13 953

Goodwill (263.1)

Deffered tax accounting for:

difference in property values 3274.3

difference in untaxed reserves 137.7

difference in derivative 136.6

difference in carry-forwards (30)

Sum deffered tax 3518.6

Estimated tax

(Deffered tax/22%)×5% (799.7)

NAV 16408.8

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