The change in liquidity for US REITs due to the REIT Act

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The change in liquidity for US REITs due

to the REIT Act

University of Amsterdam, Amsterdam Business School MSc Finance: Finance & Real Estate Track

Master Thesis

The change in liquidity for US REITs due to the REIT Act Groeneveld, Koen

Thesis: 1 July, 2017

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Statement of Originality

This document is written by Koen Groeneveld who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

This thesis investigates the effect of the US REIT Act (16 may 2012) on US REIT liquidity using a Fama-Macbeth procedure. This effect on liquidity risk should affect US REIT returns as predicted in the literature (Pastor and Stambaugh, 2003). Using a sample period of four years (2010-2014) and using 70 US REITs, we found no evidence that US REIT returns are affected by a change in US liquidity due to the US REIT Act. Furthermore, we found no evidence of a liquidity risk premium for REITs and no evidence that this liquidity risk premium is affected by the US REIT Act. However, we can explain REIT returns with more than 53% percent using 13 different risk factors.

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1. Introduction

Investors have become more interested in the Real Estate asset class over the past few years because investors’ diversification potential increases for larger mixed-asset portfolios (Brounen et al., 2009). Therefore, investors have more interest in understanding how the Real Estate asset class is priced. US Real Estate Investment Trusts (REITs) are public listed firms who invest in Real Estate. Understanding the pricing of US REITs can explain the diversification benefits for investors.

This master thesis explains Real Estate Investment Trusts (REITs) returns due to the effect of liquidity pricing. REITs make a profit from investing in properties. The returns of the REITs should depend on the returns of their investments. The returns of the investments are affected by the liquidity of these investments. Cheng et al. (2013) also argue that illiquidity of a REIT’s investment is an important source of investment risk. This investment liquidity risk is reflected in the stock returns. Pastor and Stambaugh (2003) argue that REIT returns should be affected by the risk of their investment. Investors should be rewarded for bearing higher (liquidity) risk (Pastor and Stambaugh, 2003). If liquidity risk for investing in REITs increases, than REIT returns should be higher. Cheng et al. (2013) argue that the liquidity in the REIT market and the liquidity in the direct Real Estate market affect the stocks differently in risk and return. Stefek & Suryanarayanan (2011) also argue that the public REIT market can react more quickly than the direct Real Estate market. Therefore, liquidity risk in the REIT market can be measured more quickly than in the direct Real Estate market.

This thesis explains the returns for US REITs due to different (liquidity) risk factors in the REIT market. The US REIT market is highly legislated and therefore changes in legislation affects the liquidity of US REITs. Liquidity risk should have changed due to the US REIT Act on 16 may 2012. This change in liquidity risk should be reflected in the US REIT returns.

REITs are considered to be highly legislated and the legislation constantly changes. Examples of legislations for REITs are the distribution requirement and the leverage restrictions. REITs have a distribution requirement of 80-100% of their earnings which means that they need to invest all of their profit in new and profitable projects. REITs also have leverage restrictions of 40-60% of all their assets which means that REITs have a restriction on the amount of debt they can issue. The new legislation rule on 16 May 2012, which is called the Update & Streamline REIT Act (U.S. REIT Act), influences the REIT returns and REIT

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liquidity. The US REIT Act consists of several improvements which are highlighted in the introduction and will be discussed further in the literature review. There are four changes in legislation. First, the asset management capabilities are improved with a modification of the Dealer Sales Safe Harbor. Secondly, there is an extension of the mutual funds’ interest-related dividend rule to REITs. Thirdly, the REIT income test and the REIT asset test are improved. Fourthly, modifications of the earnings and profit rule minimize duplicative taxation of REITs’ shareholders.

The changes in legislation have an impact on the capital flow to the US REIT market because it is more attractive for investors to invest in the REIT market as their net returns of investing in the REIT market increases. More capital on the REIT market will lead to more liquidity for REITs on the REIT market (Subrahmanyam, 2007). Subrahmanyam (2007) argues that the REIT market can be considered as a substitute for the stock market resulting in a capital flow to the REIT market when net returns get higher. Table 1 shows an increase of average market capitalization and an increase of the median of the market capitalization.

Table 1: The market capitalization over time

2008-2010 2010-2012 2012-2014 2014- Average Market Capitalization 1.817.906 (2.863.261) 3.059.997 (4.877619) 4.248.497 (6.567.617) 5.607.545 (8.270.309) Median 862.142 1.386.134 2.065.359 2.818.947

Table 1: Market Capitalization of REITs over Time. Standard Deviations are displayed in the brackets. Data retrieved from Wharton-Reuters database. 70 REITs are included in the sample.

Table 1 shows a growing market capitalization for US REITs. The US REIT Act could partly have caused an increase in the average market capitalization. After the financial crisis, the market capitalization of REITs was low but the average market capitalization has increased over time. The legislation change in 2012 can maybe explain the increase in market capitalization from 2010-2012 till 2012-2014. The implementation of the US REIT Act could have effected the liquidity position of the US REITs due to a capital inflow on the REIT market.

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This thesis explains the change in liquidity risk for REITs due to a change in legislation. This liquidity change can explain changes in REIT returns. The (liquidity) risk premium, which is a compensation for investors who bear extra (liquidity) risk, can also change due to the REIT Act. The changes in risk, return and risk premium can be researched. The research question is: Is there a change in liquidity risk and/or a change in the liquidity risk premium

due to the US REIT Act in 2012 and can these changes explain REIT returns?

This thesis tries to link the financial capital asset pricing models to REITs returns. The thesis tries to explain returns by a liquidity risk factor. This thesis analyzes the change in liquidity risk on REITs and the change in liquidity risk premium for REITs rather than other risk factors and premiums. Research in liquidity changes due to a change in legislation is scarce and this research will provide more insight on the affect of a change in legislation. This thesis can contribute to the explanation of liquidity change in REITs due to a change in the US REIT Act. More broadly, this thesis can explain US policy decisions.

2. Literature review

The liquidity of a REIT can be measured in several ways (IPF, 2015). This thesis will explain REIT returns with three liquidity measures: the Amihud factor, the turnover ratio and the bid-ask spread. First, these liquidity measures will be discussed theoretically. Secondly, the impact on REIT liquidity due to the REIT Act will be discussed. Thirdly, the chosen holding period will be discussed. Fourthly, previous research of changing legislation for REITs and the affects on REIT liquidity will be discussed.

2.1 Different Liquidity measures

This section describes the different liquidity measures that are used to research liquidity. The paper of Bond & Chang (2012) describes three different liquidity measures which can explain different aspects of liquidity: the price impact of liquidity, the volume based impact and the transaction cost of liquidity. The Amihud liquidity factor measures the price impact of transaction volumes. Amihud (2002) argues that the Amihud liquidity variable measures the daily change in price with a change in trading volume. The turnover ratio measures the volume based impact of liquidity: the number of times the outstanding volume is transacted within a time period. Demsetz (1968) argues that the price of immediacy would be smaller for stocks with high trading frequency since frequent trading reduces the cost of inventory

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control. The bid-ask spread measures the transaction cost impact of liquidity on returns. The bid-ask spread measures the difference between the bid price and the ask price in the market. The bid-ask spread gives an approximation of trading cost, in addition to taxes and fees, that an investor needs to pay.

2.1.1 The Amihud liquidity factor

IPF (2015) argues that the price impact of liquidity can be measured using the Amihud liquidity measure. The Amihud liquidity factor measures the price impact of transaction volumes (Amihud, 2002). This liquidity measure captures the market depth and measures the price impact of liquidity on REIT returns. The Amihud liquidity factor measures the impact of a change in REIT returns due to higher liquidity risk. The Amihud liquidity factor is computed from returns on a daily basis to detect the price impact of changes in volume activity. The Amihud (illiquidity) factor is calculated by the formula:

Illiquidityt = 1/n * ∑ni=1|Rr, n| / Volumer

Illiquidityt is the illiquidity value on time t (month). |Rr, n| is the absolute value of the daily

(n) REIT returns (Rr) which will be constructed to an average REIT return per month. Volumer

is the total trading volume per REIT on time t. The illiquidity measure can be calculated for every REIT at time t.

If the value of the Amihud factor increases, than the REIT becomes more illiquid. If the volume activity increases, than the value of the illiquidity factor decreases. So if the volume activity increases, than the REIT becomes less illiquid. The absolute value of returns measures the total impact on returns. If the absolute value of the daily REIT returns increases, than the value of the illiquidity factor increases leading to more illiquid REITs.

The paper of Hoesli et al. (2016) explains the liquidity risk factor with a capital asset pricing models which looks like a financial capital asset model (Fama & French, 2005). The paper of Hoesli et al. (2016) explains US REIT returns with a capital asset pricing model and the Amihud liquidity risk factor. The paper of Hoesli et al. (2016) finds a time-varying Amihud liquidity risk factor: in good times there is more liquidity. This thesis researches an upward period in the economy and therefore it is expected that the liquidity, measured by the Amihud liquidity factor, increases from 2010 till 2014.

A previous study of Marcato & Ward (2007) argues that liquidity influences expected returns, either because investors might be prepared to pay a premium for liquid stocks when

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the market is down or because investors perceive liquidity as an additional return in different phases of the market: in downturns they can sell their stock. Thus relative to stocks and bonds the illiquid REITs should have higher returns and thus a (liquidity) risk premium.

Bond & Chang (2012) research the explanatory power of the three liquidity measures of REITs to the REIT market. They use a timeframe from 1993 till 2010. Bond & Chang (2012) argue that the Amihud liquidity measure can explain REIT returns with a range from 6,64% to 22.8% on different factor loadings.

2.1.2 The turnover ratio

IPF (2015) argues that the turnover liquidity measure can be used to estimate some volume liquidity risk which can be explained by the type of market stocks are listed. The turnover ratio measures the activity of the market which tends to be more liquid if the markets’ transaction activity is high. The turnover ratio can be measured as:

Turnovert = Volt / (SOt * Pricet),

where Volt is the amount of trading volume of every REIT on time t and SOt is the total shares outstanding of every REIT on time t. Pricet is the price of a REIT’s stock at time t. The turnover ratio is constructed for every REIT and for every month. Amihud and Mendelson (1986) show that the turnover measure is negatively correlated with illiquidity costs. They argue that market makers tend to charge higher transaction costs to cover the risk of holding their position when the turnover ratio is low. If the turnover ratio increases, meaning there is more trading volume in the market, than the REIT is more liquid. Demsetz (1968) argue that the price of immediacy would be smaller for stocks with high trading frequency because that would reduce the cost of inventory. If the turnover ratio is high, meaning that the trading activity in the market is high, investors would decrease their holding periods and that reduces the cost of inventory (Constantinides, 1986). The level of information asymmetry is explained by Glosten and Milgrom (1985) who argue that highly traded shares have lower levels of information asymmetry. If shares are highly traded, than the price reveals more information of investors’ value for the stock. Bond & Chang (2012) argue that the REIT returns can be explained by the turnover ratio of REITs with a range from 26,74% to 37,81%.

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2.1.3 The bid-ask spread

The third liquidity measure is the quoted spread which measures the difference between the lowest ask price Pa_{and the highest bid price P}B_{for a REIT’s stock of every month (IPF, 2015).} The bid-ask spread can be defined as:

Spreadt = Askt – Bidt,

where Askt is the ask price on time t for a REIT and Bidt is the bid price on time t for a REIT. The spread is the difference between the ask and bid price for every REIT on time t. Bhasin et al. (1997) argue, using the bid-ask spread as liquidity measure, that the change in legislation in 1990 has increased REIT liquidity from 1990 to 1994. However, Marcato & Ward (2007) argue that this liquidity measure is poor from an investor point of view who is more interested in the price impact side of liquidity (the Amihud factor). IPF (2015) argues that for small REITs the quoted spread represent a good proxy for the execution cost of a trade, for larger REITs other costs may also needed to be added. Marcato & Ward (2007) argue that an increase in the bid-ask spread results in lower liquidity in the market. Bhasin et al. (1997) measure transaction costs on public listed firms which can be applied on public listed REITs. Amihud & Mendelson (1986) already argued that the illiquidity can be measured by cost of immediate execution. On the one hand, immediate buying reflects a premium in the ask price and on the other hand, immediate selling reflects a concession. So the investor either waits to transact at the ask price or immediate execute at the bid price. The spread between the ask and bid price measures the sum of buying at a premium and selling at a discount. Selling at a discount would be done in downturns of the economy where liquidity decreases. Amihud & Mendelson (1986) argue that the expected returns increases in the bid-ask spread: a higher bid-ask spread means lower liquidity and higher returns. IPF (2015) argues that the illiquidity measure is always positive: the higher the spread, the more illiquid the market is. Bond & Chang (2012) estimated an explanatory power of the quoted spread measure on REITs returns with a range from 15,93 to 48,29 percent. The three liquidity measures are summarized in table 2.

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Table 2: The three liquidity measures

Liquidity measures Formula Measure Description

Amihud 1/n * ∑n

i=1|Rr, n| / Volumer Price impact of liquidity

Turnover Volt / (SOt * Pricet) Volume based liquidity

Ask-Bid Spread Askt – Bidt Transaction cost of liquidity

Table 2: Summary of the liquidity measures and their formulas.

2.2 The impact of the US REIT Act on REIT liquidity

The US REIT Act in 2012 is beneficial for investors to invest in REITs because their net returns increase. The impact of the four changes in legislation on REIT liquidity will be discussed. First, the asset management capabilities are improved with a modification of Dealer Sales Safe Harbor. The Dealer Sales Safe Harbor is a spot which gives a ‘Safe Harbor’ when the 10 percent rule is met. The 10 percent rule state that the fair market value of all “properties” sold during the year does not exceed 10 percent of the fair market value of all of the REIT’s assets as of the beginning of the year. If the 10 percent rule is exceed, than the REIT is subject to a 100% tax on ‘net income derived from prohibited transactions’. The 10 percent rule prevents managers to manage their assets to achieve long-term growth. The modification in the US REIT Act changed this. The 10 percent rule will be used in the future as a threshold on a 3-year average basis with a cap of 20% in one year. Therefore, a REIT can sell 20% of her property in year 1, but need to sell less than 10% in the upcoming two years to qualify for the Dealer Sales Safe Harbor. This will effect the liquidity position as REITs have a 3-year range to decide when to sell their properties. REITs can sell more properties in a downward economy in order to earn money in bad times. This effect will lead to more liquidity for REITs as REITs can decide when to sell their properties.

Secondly, there is an extension of the mutual funds’ interest-related dividend rule to REITs. Interest paid to a non-US portfolio investor who invests in US debt securities is exempt from withholding tax. Investing in mutual funds of REITs lead to a higher tax position for the non-US investor because the withholding tax needs to be paid. REITs offer managed investment portfolios of US debt to non-US investors. However, investors don’t invest in REITs due to the higher tax cost. The new rule extends the same temporary provision that would apply to mutual funds exempting interest-related dividends of a public traded US REIT

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(not owned by a non-US investor with a stake of 50% or higher). This will attract investors to invest in REITs and will lead to a higher capital flow to the REIT market.

Thirdly, the REIT income & Asset test is improved. REITs were obligated to an income test and needed at least 75 percent of a REIT’s annual gross income to be from real estate-related sources like rents or interest on mortgages in order to ensure that the REITs will be real estate focused, and at least 95 percent of a REIT’s annual gross income must be from rents, interest on mortgages and other non-real estate sources such as interest on bank deposits (Income Test). The Asset Test states that at each quarter at least 75 percent of a REIT's total assets should be real estate assets, cash, cash items, and government securities. Some REIT debt securities that another REIT may hold were classified as real estate assets which led to equal priorities between debt holders and equity holders. The US REIT act changed the classification of debt securities that another REIT may hold from a real estate asset to a loan. It also limited this classification to hold only 25 percent of a REIT debt security for another REIT. REITs which held a debt security have now less real estate asset securities instead of debt securities. The difference between the income and asset test (20%) is the income of other non-real estate sources. This can now be the debt securities that can be hold in another REIT. The capital structure of REITs changes to less equity and more debt which decreases for example the weighted cost of capital and increases total value of a REIT. Furthermore, the personal property use of REITs is no longer classified as a real estate asset class. Therefore, there’s need to invest more in real estate sources to met the Income Test and Asset Test leading to a more attractive position for REITs as REITs will invest in more profitable projects.

Fourthly, modifications of the earnings and profits rule minimize duplicative taxation of REITs’ shareholders. There are differences in holding periods for depreciation for non-residential real estate (40 years), non-residential real estate (39 years) and certain energy investments in commercial real estate (5 years). For purposes of calculating a corporation’s dividend -earnings & profits calculation- real estate is written off over 40 years. REITs determine their dividends not only on distribution requirement but also on historical practices and the amount of cash available. The earnings & profits calculation is therefore frequently overstated because the REIT’s distribution to shareholders is taxable to extend to the earnings & profits. REITs distributions therefore frequently exceed taxable income. ‘The E&P Disallowance Rule is a necessary rule to ensure that a REIT has sufficient E&P to satisfy

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the requirement to distribute at least 90 percent of its taxable income as a dividend, but as it is currently written, its application can cause this doubling up of tax on REIT shareholders’. This is caused by the difference in depreciation that occurred in the E&P calculation. The US REIT Act (2012) changes this to minimize duplicative taxes and therefore attract more shareholders when net income of shareholders should increase.

2.3 Holding period

Collet et al. (2003) argue that the holding period for an asset class depends on the sort of asset class. Optimal portfolio allocation depends on the variances and co-variances of REIT returns with investors’ portfolio. US REITs are listed on the US stock exchange market. The stock price of a REIT should reflect future growth opportunities such as new profitable project. REITs have multiple direct Real Estate investments which should reflect immediately in the stock price. Consistently, Hordijk & Teuben (2016) argue that the value of the property seems to have no impact on the holding period. REIT returns for investing in properties are already reflected in the stock price. Therefore, this thesis can focus on a timeframe of four years: two years prior to the REIT Act and two years after the REIT Act instead of focusing on the holding period of properties which is used for private real estate.

2.4 Previous changes in REIT legislation and their impact on REIT

liquidity

Bhasin et al. (1997) researched changes in REIT legislation and the impact of this legislation on REIT liquidity. Bhasin et al. (1997) argue that there is some change in REIT liquidity post 1993. Bhasin et al. (1997) measure REIT liquidity with the bid-ask spread from 1990 till 1994. The market capitalization of the REIT Industry was growing rapidly in this period from $8.9 billion to $15.7 billion dollars. Much of this growth can be explained by the switch from private to public real estate ownership of investors. This increased the participation of institutional investors who wanted to have more ‘liquid’ assets in their portfolio relative to private Real Estate. This participation was strengthened due to the availability of larger market sized REITs which offered investors the opportunity to purchase meaningful shares of REITs. According to Bhasin et al. (1997) there was also another reason which led investors to become more interested over time in the REIT market. A change in legislation was made in 1993. The ‘five or fewer’ rule which stated that no more than 50% of a REITs stock can be

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held by five or fewer investors was relaxed. The change in legislation led to more institutional involvement of pension funds which could hold more stakes in the REITs through individual pension funds. Therefore, institutional involvement increased market capitalization. The interest of institutional involvement led to more informed parties and therefore bid-ask spreads lowered meaning higher liquidity for REITs in the period 1990-1994. Clayton and MacKinnon (2000) also argue that there is an increase in liquidity in the period 1990-1994 and that this is due to the increase in institutional involvement. Therefore, information asymmetry between REITs and investors decreased and REIT liquidity increased.

3. Methodology and Hypothesis

3.1 Methodology

In this thesis I use the methodology as given by Hoesli et al. (2016) & Peng (2016). Hoesli et al. (2016) argue that the returns of US REITs can be explained by the Amihud liquidity risk factor and a capital asset model. The paper of Peng (2016) gives a good insight in the methodology to determine changing risk factors. The paper uses the CAPM model to determine the returns on the commercial real estate market. The paper uses a times series approach and a cross section approach in estimating factor loadings on returns. Estimated factor loadings will be collected in the times series approach to determine risk. Peng (2016) contributes to this thesis as this thesis tries to research changing risk factors due to the REIT Act.

Fama-Macbeth regressions will be used to regress REIT returns from t to t+n which characteristics are independent of stochastic elements of the state-of-the-world at time t that affect investors’ tastes for a given level of liquidity at t (Fama & Macbeth, 1973). This means that the relationship between the risk factors and the REIT returns is causal because there can be controlled for specific REIT characteristics for every month in time. The sensitivity between REIT returns and REIT liquidity, measured by the Amihud liquidity risk factor, can be determined using a time-series regression. The time-series regression model is displayed as model (1) at page 16. Time-series regressions result in a single beta for every REIT. This beta measures the sensitivity between a REIT return at time t and the REIT’s (liquidity) risk factor at time t. 70 betas of 70 REITs for 13 risk factors will be collected using a rolling window and will be merged to the REIT returns. Changes in risk exposure (due to the

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REIT Act) can be detected by including a dummy for the REIT Act in the model which will be further discussed in section 3.3 at page 18. The impact on REIT returns of a change in (liquidity) risk due to the REIT Act can be measured using an interaction variable that includes a dummy of the REIT Act and a (liquidity) risk factor. The cross-sectional regression measures the sensitivity between the risk factors and the REIT returns with the average REIT returns to determine a risk premium for every risk factor that is included in the model. The cross-sectional regression model is displayed as model (2) at page 16. The change in risk premium due to the REIT Act can be researched with a dummy of the REIT Act.

3.2 Empirical model

This thesis tries to explain REIT returns in a financial capital asset pricing model. The Fama-Macbeth methodology will be used to determine different factor loadings on liquidity risk and to determine a change in liquidity risk premium. The time-series regression model is displayed as model (1). The cross-sectional regression model is displayed as model (2). The symbols in the model will be further discussed in the variable description, section 3.3.

The impact on REIT returns can be explained by a change in liquidity risk due to the REIT Act. This impact can be tested with an interaction variable that includes a dummy for the REIT Act and a liquidity measure. The interaction variables can be used in the time-series and cross-sectional regression and the dummy counts a zero if REITs posses information before 16 may 2012 and a 1 if REITs posses information after 16 may 2012. The estimated sample period is from May 2010 till May 2014. The results will be biased if the crisis is included: returns and liquidity are lower. Furthermore, a lot of REITs were gone bankrupt which leads to a survival bias. This survival bias leads to relative better performing REITs that will survive the crisis instead of the REITs that became bankrupt during the crisis. To tackle the ‘survival’ problem there can be taken a sample period starting two years prior and two years after the implementation of the REIT Act which excludes the crisis. The observed dummy coefficients (ßAct) can help us to test any significant difference in risk. Any change in total risk premium due to the REIT Act can be tested with ƀAct.

There will be done 70 time-series regressions using a rolling window to collect 70 betas for every risk factor resulting in 70x13 (910) betas that can be used in the cross-sectional regression. The different risk factors can be summed up in model (1) as variable x. X shows all the risk factors that are included in model (1). The ß0, r, t is a constant for every

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time-series regression and εr is an error term for every time-series regression. The Rr, t are the REIT returns at time t; every month.

(1) Rr, t = ß0,r,t + x’*ßr + εr Lp Turnover Spread Act Lp*Act Turnover*Act X= Spread*Act Capitalization Rm SMB HML RMW CMA

A time-series regression will be done which estimate betas for every REIT for the different factor loadings in X. These betas can be collected and used in the cross sectional regression in model (2). Ṝr is the average return per REIT over time, α0,r,ß is the constant in the cross sectional regression, ƀx’ are the estimated betas for the risk factors, λx is the risk premium for every risk factor that need to be determined and αr is the error term in the cross-sectional regression. (2) Ṝr = α0, r, ß + ƀx’ * λx + αr, where ƀx’ = ƀLp ƀTurnover ƀSpread ƀAct

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ƀLp*Act ƀTurnover*Act ƀSpread*Act ƀCapitalization ƀRm ƀSMB ƀHML ƀRMW ƀCMA

3.3 Variable description

The variables are described and interpreted in this section. The Rr, t are the REIT returns over time which are derived from the Wharton Reuters Database (WRDS) site and indirectly from CRSP/Ziman REIT database. Data is collected over a monthly period of 4 years resulting in 48 observations for every REIT. The sample period counts 48 monthly observations from 2010 till 2014 for 70 REITs. In the cross sectional regression, the average return per REIT is calculated.

The three different liquidity measures should all effect stock returns and thus should be included in the model. Time-series regressions are first made independent for every liquidity measure. Thereafter all the measures will be included stepwise to detect any problems of multicollinearity. The outcomes can be compared using significant evidence.

The Lp is the priced liquidity component which measures the price impact that liquidity has on REIT returns. As described in the literature, section 2.1, the Amihud liquidity measure is used which is calculated as: Illiquidityt = (1/n * ∑ni=1|Rr, n|) / Volumer. The priced

liquidity component should have a positive impact on REIT returns. If illiquidity rises, REIT returns should increase as investors need to be compensated for bearing higher liquidity risk. The change in legislation should have increased the liquidity of the REIT market as market capitalization increased. More liquidity means less liquidity risk for a REIT and therefore REIT returns should be lower. Thus, the change in legislation should have decreased the Amihud liquidity factor. The Amihud factor, which measures illiquidity, should therefore be lower: the priced liquidity risk should be lower due to more liquidity. There can be tested whether

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there is a decrease in the Amihud liquidity factor due to the REIT Act and if this decrease statistically decreases REIT returns.

The second liquidity measure is the turnover ratio which measures the volume liquidity risk. Some markets tend to be more liquid than others and this is reflected in the turnover ratio. As mentioned in section 2.1 page 9, the Turnover (ratio) is defined as:

Turnovert = Volt / (SOt * Pricet),

where Volt means trading volume of every REIT at time t (month), SOt means the total shares outstanding at time t and Pricet means the price at time t for the REIT. The turnover ratio is calculated for every month in time for every REIT. There can be tested whether the REIT market becomes more liquid due to the REIT and if this change affects REIT returns. There can also be tested how the increase in volume based liquidity decreases returns. The expectation is that the REIT market is more liquid after the change in legislation due to the increase in market capitalization. The increase in volume based liquidity leads to lower REIT returns as investors should be less rewarded for bearing lower liquidity risk. The relation between the turnover ratio and the REIT returns should be negative.

The third liquidity measure is the quoted spread which measures the transaction based costs due the difference on ask and bid prices which reflects the immediate cost of execution of selling and buying. The quoted spread can be defined as: Spreadt = Askt – Bidt, where the Askt is the ask price at time t for a REIT and Bidt is the bid price p at time t for a REIT. The spread is measured for every REIT for every point in time t (month). There should be an increase in liquidity due to the REIT Act. The spread should have decreased as liquidity have increased. The decrease in the spread due to the REIT Act should decrease the REIT returns. There should be a positive relationship between the liquidity spread measure and the REIT returns: smaller spreads lead to more liquidity and that should be reflected in lower REIT returns. If the REIT market is more liquid, there are more transactions in the market. Therefore, information asymmetry between buyers and sellers should have decreased. Lower information asymmetry should lead to a lower spread between buying and selling.

A dummy is included in the model which measures the change in REIT returns and the change in risk premium. The dummy is included in the model as ACT and measures the impact on returns due to the REIT Act in the time-series regression. The REIT Act dummy should be negative in the time-series regression as REIT returns could have decreased as a result of increasing REIT liquidity. In the cross-sectional regression, the dummy measures the

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change in risk premium due to the REIT Act. The REIT Act dummy should be negative in the cross-sectional regression as the increase in liquidity should decrease the risk premium. Investors should be rewarded less if liquidity risk decreases.

There are three interaction variables included which measure the affect on REIT returns by a change in liquidity risk due to the REIT Act. First of all, there is an interaction variable included which measures the affect on REIT returns due to a change in priced liquidity due to the REIT Act. This is stated in the model as Lp*ACT which is the dummy ACT

times the Amihud liquidity measure. This interaction variable should be positive as a significant decrease in illiquidity due to a change in the REIT Act should lead to lower REIT returns. Secondly, there is an interaction variable which measures the impact of a change in volume liquidity due to the REIT Act on REIT returns. This is stated in the model as

Turnover*ACT. It is expected that the REIT Act will lead to significant higher volume liquidity

and therefore returns should decrease. Thirdly, an interaction variable is included which measures the change in transaction based liquidity due to the REIT Act on REIT returns. This is stated in the model as Spread*ACT and it is expected to have a negative coefficient. The REIT Act should increase the transaction based liquidity significantly and therefore REIT returns should decrease.

The λx is the risk premium and is included in model (2). This risk premium is calculated in the cross-sectional regression as a coefficient because the betas that are estimated and the average REIT returns over time are known in model (2).

The control variables in both models are the excess market return (Rm), the

Small-minus-Big factor (SMB), the High-minus-Low factor (HML), Robust-minus-Weak factor

(RMW) and Conservative-minus-Aggressive (CMA) factor which all affect stock returns. Fama

and French (1992) argue that common stock returns are related to firm size and book-to-market ratios. Therefore the Fama and French 5-factor model is included as control variable. The Fama and French 5-factor model is retrieved from the Fama and French website and indirectly from CRSP.

An additional control variable is the size of the Real Estate investment fund (Liow, 2009). The variable Capitalization is included in the models as the market capitalization of the REIT. REITs with a large portfolio should have more liquidity because of their diversification benefits and the larger pool of buying and selling properties. Their transaction liquidity risk should be much lower as they could sell or buy assets more easily due to the

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available capital. Stocks of a large fund should have a smaller liquidity premium compared to stocks of a smaller fund. Liow (2009) argues that larger REITs have higher returns and this can be explained by the liquidity risk premium. REITs with the same assets and the same returns but with a relatively smaller portfolio will discount with a higher rate resulting in a lower valuation and thus lower capital gains for investments.

3.4 Hypothesis

This thesis wants to test three theories. First of all, this thesis tries to test whether there is a change in liquidity for REITs over time caused by the REIT Act and if this liquidity change affects REIT returns. Secondly, this thesis tries to test whether there is a liquidity risk premium for REITs. Thirdly, this thesis tries to test whether there is a change in liquidity risk premium due to the REIT Act. The main hypotheses can be summarized and explained.

The first hypothesis state that there is no change in liquidity for REITs due to the REIT Act and the liquidity change didn’t affect REIT returns. This means that there is no difference in the liquidity risk after the change in the REIT Act. This change in liquidity risk doesn’t affect REIT returns. The expectation is that the REIT ACT led to more liquidity in the REIT market. Therefore, REIT returns should have decreased. This hypothesis can be tested using an interaction variable of the three liquidity risk factors and a dummy of the REIT Act. The hypothesis is tested in the time-series regressions.

The second null-hypothesis states that there is no liquidity risk premium for REITs. The null-hypothesis goes against the theory that liquidity risk should be priced. Pastor and Stambaugh (2003) argue that there is a cross-sectional risk premium that explains returns for all stocks. However, Hoesli et al. (2003) finds that there is no liquidity risk premium for US REITs in an upward economy. The hypothesis can be tested in a cross-sectional regression. The risk premium of the three liquidity risk factor should be significant.

The third null-hypothesis states that there is no change in (liquidity) risk premium before due to the REIT Act. This can be tested with a dummy of the REIT Act in the cross sectional regression. A significant coefficient indicates an increase or decrease of the risk premium due to the REIT Act. The interaction variables can test whether the change in risk premium is caused by the change in liquidity risk premium.

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4. Data

Data for the monthly REIT returns is downloaded from the Wharton-Reuters Data Service website and indirectly from the CRSP’s & Ziman REITs database. The data is downloaded using a 4 year timeline from 2010-2014. For every REIT (70 REITs) this will result in almost 48 observations. There have been collected 3.360 REIT returns. Furthermore, observations who observed no information of the total return variable (displayed in STATA with a dot) were also deleted. The next paragraph shows the construction of the database.

First, the master dataset is merged with the dataset where the Amihud liquidity measure is calculated (CRSP/Ziman REITs dataset for daily returns). First, the absolute value of every REIT’s return is calculated. Secondly, this absolute return will be averaged over all the trading days to collect an average REIT return per month. Thirdly, the average REIT return per month will be divided by the volume activity of that month which results in an Amihud value for every month and every REIT. The formula of the Amihud liquidity factor is also displayed in section 2.1.

The data for the turnover and spread liquidity measures can be retrieved from the same CRSP’s Ziman REIT database as the masterset. The variables that are collected from this database are shares outstanding, REITs’ stock’s price, the bid & ask price of REITs’ stock, REITs’ market capitalization and information about REIT type. The turnover liquidity measure & the bid-ask price liquidity measure can now be calculated using the formula in section 2.1.

The dataset can now be set as a panel data with one (cross)panel for all the REITs and a time panel for the Date. The date is encoded to a numeric variable and therefore it can be used to calculate returns prior to a certain point in time and after. The Fama-Macbeth regressions can only be done when there is a panel data: a time-series regression and a cross-sectional regression.

To detect any outliers in the sample there can be looked at maximums and minimums in a summary statistic that is displayed in appendix 1. Appendix 1 shows a high maximum of REIT returns. Appendix 2 plots a graph to detect possible outliers which includes the financial crisis. Appendix 2 shows huge outliers of returns partly due to the crisis. The outliers don’t impact the results which will be estimated from 2010 till 2014. There is one observation in the sample period who outperform the other observations and therefore this observation will be deleted (1 July 2013).

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Furthermore, the Fama-French 5-factor model is added from the Fama and French website. The Fama-French 5-factor model is a control variable which is earlier discussed in section 3.3. It provides common stock returns for the total market which can help explain REIT returns.

5. Summary statistics

5.1 Descriptive statistics

The model has 13 independent variables. High correlation between these independent variables could bias coefficients. Table 3 shows a correlation table to detect some degree of relationship between the variables. If the degree of relationship between the independent and dependent variable is high, than the independent variable has more explanatory power to explain the dependent variable (REIT returns).

Table 3: Correlation table of all the variables

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (1) Rr, t 1 (2) Amihud 0.00 1 (3) Turnover -.07 -.03 1 (4) Spread 0.02 0.06 -.06 1 (5) Act -.02 -.01 -.13 0.01 1 (6) Ami*Act 0.01 0.47 -.10 0.00 0.82 1 (7) Turn*Act 0.01 -.03 0.18 -.06 0.49 0.40 1 (8) Spr.*Act 0.01 0.05 -.04 0.73 0.12 0.09 -.03 1 (9) Cap. 0.02 -.04 -.16 -.05 0.10 0.08 -.12 -.04 1 (10) Rm -.07 -.03 -.03 0.00 0.06 -.10 0.02 0.01 0.01 1 (11) SMB -.04 -.00 0.03 0.01 0.06 0.05 0.04 0.01 -.02 0.53 1 (12) HML -.06 -.00 -.05 0.01 0.31 0.10 0.15 0.05 0.05 0.30 0.13 1 (13) RMW -.07 0.01 0.03 0.01 -.27 -.19 -.13 -.02 -.02 -.45 -.44 -.26 1 (14) CMA 0.08 -.02 -.00 0.01 0.08 -.03 0.05 0.02 0.01 0.21 0.09 0.63 -.04 1

Table 3: Correlation table of the variables. Description and respectively number are displayed on the vertical left. The numbers on the horizontal match the description on the vertical left.

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Table 3 shows that the correlation between the returns and the liquidity measures is low and respectively -0.07 (Turnover), 0.00 (Amihud) and 0.02 (Spread). These correlations are consistent with the paper of Hoesli et al. (2016) who found a correlation of 0.003 between the Amihud liquidity variable and REIT returns. Furthermore, the correlation between the three liquidity measures is different. The correlation between the bid-ask spread and the other liquidity measures is low and respectively 0.06 (Amihud) and -.06 (Turnover). Table 3 also shows that the correlation between the turnover liquidity measure and the Amihud liquidity measure is also low (-.03). Consistently, Hoesli et al. (2016) find also low correlations between their liquidity measures. The correlations between the indepedent variables are low and therefore coefficients shouldn’t be biased.

Furthermore, the high correlations of the interaction variables Amihud/Amihud*Act/Act (0.60 & 0.82), Turnover/Turnover*Act/Act (0.18 & 0.49) and Spread/Spread*Act/Act (0.73 & 0.12) can be explained by the construction of these variables. The interaction variables have high correlations with the part of the variables that are included which has the same values over time. The 5-factor control variables have a low correlation with REIT returns. The 5-factor model has also low correlations with the other independent variables. Correlation describes some relationship between two variables; however it doesn’t measure causality which can be tested with regressions. Before doing so, the data can be described using a summary statistics.

Table 4 will provide summary statistics to provide additional knowledge of the variables. Key elements like the mean, median, minimum, maximum and standard deviation will be described.

Table 4: Summary statistics for all the variables

Mean Median Minimum Maximum Std. Deviation Returns 0.01193 0.01470 -.43000 1.2000 0.07540

Amihud 9.08e-06 7.29e-08 1.31e-09 0.0072 0.00015

Turnover 0.10039 0.04788 -.09112 4.6047 0.23066

Spread 0.05743 0.01000 -.00999 7.4800 0.34042

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Mean Median Minimum Maximum Std. Deviation Amihud*Act 3.53e-06 0 0 0.00366 0.00007 Turnover*Act 0.03446 0 -.09112 0.69741 0.07252 Spread*Act 0.02998 0 0 7.4800 0.25480 Cap. 4.851.184 2.520.344 2909.8 55.200.000 6.892.883 Rm 1.2511 1.55 -7.89 11.35 4.0734 SMB 0.11480 0.07 -4.1 5.07 1.9852 HML -.08383 -.25 -4.52 5.03 1.9152 RMW 0.13717 0.22 -3.64 3.51 1.6158 CMA 0.203 0.04 -3.33 5.38 1.2550

Table 4: Summary statistics of the dependent and independent variables. The mean, median, minimum, maximum and standard deviation is displayed. The sample period is from May 2010 till May 2014.

Table 4 shows that the Amihud has low values and that the market capitalization has high values. This will affect the magnitude of the coefficients in the regression. To see the effect of the legislation, it is useful to provide some summary statistics prior to the US REIT Act (from 2010-2012) and after the US REIT Act (2012-2014). Table 5 will show the mean, median and standard deviation prior and after the change in legislation for some key variables.

Table 5: The change in summary statistics prior and after the REIT Act

2010-2012 2012-2014

Mean Median SD Mean Median SD Returns 0.00001 0.0125 0.0902 0.01306 0.0163 0.0559

Amihud 0.00005 8.63e-08 0.00018 7.08e-06 6.29-08 0.0001

Turnover 0.1304 0.05774 0.3079 0.0691 0.0384 0.0890

Spread 0.0539 0.0100 0.3206 0.0611 0.0100 0.3600

Cap. 4.099.985 2.213.669 5.784.314 5.634.539 2.918.457 7.809.787

Table 5: Summary statistics prior and after the US REIT Act. Mean, median and standard deviation are displayed for the variables REIT returns, Amihud factor, turnover ratio, the bid-ask spread and the market capitalization.

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Table 5 compares statistics before the change in legislation with the statistics after the change in legislation. Table 5 shows that the median and mean of the REIT returns increased. Table 5 also shows a decrease in standard deviation for the REIT returns leading to a decrease in REIT return volatility. The turnover liquidity measure has decreased in the mean and the median. A decrease in the turnover liquidity measure indicates a decrease in liquidity. This contradicts the expectation that the turnover liquidity measure should have increased due to the REIT Act. Furthermore, the Amihud liquidity measure stays the same. The bid-ask spread stayed the same; only the mean increased. This may decrease REIT liquidity as measured by the bid-ask spread. These are average numbers which doesn’t control for specific REIT characteristics. There cannot be concluded that liquidity has increased or decreased due to the REIT Act. To detect possible relationships between the liquidity measures and the REIT returns, graphs can be plotted.

5.2 Patterns over time

The summary statistics can show us patterns during the sample period. REIT returns and liquidity risk factors can be plotted over time to detect possible relationships between REIT returns and liquidity risk. In appendix 2, all the observations of REIT returns (70 per month) are displayed against time. Appendix 2 shows the volatility of the REIT returns over time. The volatility of the REIT returns increased during the crisis and decreased after. Figure 1 shows the average REIT returns over time in the sample period.

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-. 1 -. 0 5 0 .0 5 .1 .1 5 Ave ra g e R EI T re tu rn s

July 2010 July 2011 July 2012 July 2013 July 2014 Date

Average REIT returns over time

Figure 1: Average REIT returns over time in the sample period. The vertical red line shows the day that the REIT Act was implemented (16 may 2012). Figure 1 seems to show a decrease in REIT return volatility after the REIT Act was implemented.

The REIT Act is implemented on 16 may 2012 which is indicated with a vertical red line in figure 1. First of all, figure 1 shows that the average REIT returns doesn’t seem to increase after the change in legislation. However, volatility of the average REIT returns seems to decrease after the change in legislation. The volatility of REIT returns, which measures the total risk of a REIT, seems to have decreased. The decrease of total risk on REIT returns could be caused by a decrease of liquidity risk. On the other hand, more confidence in the economy could have caused lower volatility in general for all market. Figure 1 shows that REIT returns hasn’t changed but that the total risk of investing in these REITs has decreased. This will collaborated by the summary statistics in section 5.1, table 5. This will indicate that there maybe is an increase in total risk premium for REITs. REIT returns seems to be constant over time while total risk seems to decrease. The risk premium should be higher as investors will bear less total risk but still get the same REIT returns in their portfolio. The thesis has already argued that there can be an increase in market capitalization in the REIT market and therefore liquidity risk should have decreased. The different liquidity measures can be plot against the average REIT returns to detect any patterns between the liquidity risk factors and the REIT returns. First, the Amihud liquidity factor can be displayed against the average REIT returns over time to detect any patterns. Figure 2 shows the average REIT returns against the average of the Amihud liquidity factor over time.

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0 .0 0 0 0 2 .0 0 0 0 4 .0 0 0 0 6 .0 0 0 0 8 .0 0 0 1 Ave ra g e o f th e Ami h u d f a ct o r -. 1 -. 0 5 0 .0 5 .1 .1 5 Ave ra g e R EI T R e tu rn s

Average REIT Returns Average Amihud factor

Average REIT Returns against the average Amihud factor

Figure 2: Average REIT returns plotted against the average Amihud liquidity measure over time.

Figure 2 shows no clear relationship between the REIT returns and the Amihud liquidity factor. However, the Amihud liquidity factor has high values around July 2011 and January 2013. The REIT returns decreased thereafter (at September 2011 and at May 2013). It seems that an increase in the Amihud factor resulted in an decrease of REIT returns. It seems that an increase in illiquidity resulted in a decrease in REIT returns. This contradicts with the risk premium theory who would expect that returns would increase if liquidity risk increases. Figure 2 shows that there seems to be no liquidity risk premium: investors don’t get higher returns for bearing more liquidity risk (higher illiquidity). This can also be seen in the correlation table in section 5.1, table 3, which shows a correlation of 0.0001 between the Amihud factor and the REIT returns.

The turnover ratio measures the volume based liquidity in the REIT market. In section 2.1, there is argued that the turnover liquidity measure negatively affects REIT returns: if the turnover liquidity measure increases, meaning that there is more liquidity in the REIT market, than the REIT returns should decrease. The relationship between the turnover liquidity measure and REIT returns over time is displayed in figure 3.

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.0 5 .1 .1 5 .2 .2 5 Ave ra g e T u rn o ve r ra ti o -. 1 -. 0 5 0 .0 5 .1 .1 5 Ave ra g e R EI T re tu rn s

Average REIT returns Average Turnover ratio

Average REIT returns against the average Turnover ratio

Figure 3: Average REIT returns against the average turnover liquidity measure over time.

Figure 3 shows the average REIT returns against the average turnover ratio over time. The average turnover ratio seems to decrease after the change in legislation. The volume activity could have decreased due to the change in legislation. This leads to a less liquid REIT market. On the other hand, the increase in market capitalization in the REIT market results in a lower value of the turnover ratio as measured in section 2.1, page 9. Furthermore, the REIT returns have a relationship with the turnover liquidity at some points in time: when turnover ratio increases, the REIT returns decrease (September 2011, June 2012). However, the decrease of the turnover ratio in the long run doesn’t affect REIT returns.

The bid-ask spread measures the transaction based activity in the REIT market. The spread liquidity measure and the REIT returns should have a positive relation. When the spread gets bigger, meaning less liquidity in the REIT market, than the REIT returns increase. The average spread measure can be plotted against the average REIT returns in figure 4 to detect any relationship.

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.0 2 .0 4 .0 6 .0 8 .1 .1 2 Ave ra g e Sp re a d -. 1 -. 0 5 0 .0 5 .1 .1 5 Ave ra g e R EI T re tu rn s

July 2010 July 2011 July 2012 July l2013 July 2014 Date

Average REIT Returns Average Spread

Average REIT returns against the average Spread

Figure 4: Average REIT returns against the average spread liquidity measure over time.

Figure 4 shows a positive relation between the average spread liquidity measure and the average REIT returns. When the spread liquidity measure increases, the average REIT returns also increase. This holds also for a drop in the spread measure: REIT returns decrease. The bid-ask spread measures the REIT market liquidity. If the spread gets bigger, meaning the REIT market becomes less liquid, than the REIT returns increase. This indicates that investors want to earn an extra return for bearing extra REIT market liquidity risk. Table 3 shows the correlation between the bid-ask spread and the REIT returns (0.02). There can be argued that the relationship isn’t as strong as showed in figure 4, because of the low correlation.

Concluding, there cannot be seen any change in liquidity risk or REIT returns after the change in legislation. The graphs give relationships between the REIT returns and the liquidity measures over time. Regressions can argue whether the relationships between REIT returns and the liquidity risk factors are significant.

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6. Results

6.1 Fama-Macbeth regressions

6.1.1 Time-series regressions

Time-series regressions will be done to collect betas over time for each REIT. The sensitivity for all the liquidity and control variables with the returns will be calculated for each REIT. There will be done 70 regressions in total to collect betas for every REIT using a rolling window. First, regressions are independently made for every liquidity measure and betas will be collected for every REIT to detect differences in sensitivity. There will be done four rolling windows in total; one for each liquidity measure against the REIT returns and one including all liquidity measures and all control variables.

6.1.1.1 Model using only the Amihud liquidity factor

Appendix 4 shows that there is no evidence that the US REIT Act has increased the liquidity position of the REITS as only 10 time-series regressions of the 70 are significant. The time-series regressions, which measures the sensitivity of the REIT returns with the market returns, shows 8 negative significant TS regressions and two positive significant TS regressions. For some REITs, there is a negative relationship between the Amihud liquidity measure and the REIT returns. For these REITs, their return increases if illiquidity decreases. This contradicts the expectation of the risk premium theory: lower liquidity risk should decrease returns.

6.1.1.2 Model using only the Turnover liquidity measure

Appendix 5 shows a part of the time series regression results which includes only the turnover factor. Appendix 6 provides a summary of signs and significance levels of the time-series regressions which measures the sensitivity between the REIT returns and the market liquidity of the REIT market. Appendix 6 shows that the time-series regressions result in 31 significant negative TS regressions and one positive significant TS regression. For 31 REITs, there is a negative relation between the turnover liquidity ratio and their returns over time. For these REITs, their return increases if their liquidity decreases. So if the trading volume increases, meaning the REIT market becomes more liquid, than the REIT returns tend to be lower. This indicates that there is a risk premium: returns get lower for investors when the

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liquidity risk decreases. The investors want to bear less liquidity risk for lower REIT returns. A time-series regression which includes the interaction variable of the turnover liquidity measure and the REIT Act shows 15 negative TS regression results (appendix 6). For 15 REITs, the REIT Act has increased the liquidity of these REITs and therefore their returns have decreased. This TS regressions result is consistent with the risk premium theory: lower liquidity risk should be reflected in lower returns.

6.1.1.3 Model using only the spread liquidity measure

The liquidity of the market can also be researched with the spread liquidity measure which measures the transaction based activity in the REIT market. If the spread gets bigger, than there is more information asymmetry and the market have less transactions. Appendix 9 shows that there are 8 significant time-series regressions. Five REITs have a significant negative relationship between their returns and their bid-ask spread. If their bid-ask spread increases (meaning less liquidity), than will the returns decrease. This contradicts the theory of the risk premium. On the other hand, three REITs have a significant positive relationship between their return and their liquidity. If the bid-ask spread increases, than their return increases. This collaborates with the theory of the risk premium. Founding only 8 significant relationships out of the 70 TS regressions could question whether there is an impact of liquidity measured by the bid-ask spread on REIT return.

Appendix 10 shows that the REIT Act doesn’t affect the spread liquidity measure in a linear regression model. Appendix 9 shows that there is only one significant and negative relation between a REIT’s return and its change in liquidity due to the REIT Act. The other TS regressions found no significant results. This means that there is no impact on REIT returns by a change in liquidity (as measured by the bid-ask spread) due to the REIT Act.

6.1.1.4 Model using all liquidity measures and control variables

To explain the change in liquidity due to the REIT Act and whether this change affects REIT return, time-series regressions are performed. The full model includes the three liquidity measures; the Amihud liquidity measure, the Turnover liquidity measure and the spread liquidity measure, a dummy for the REIT Act, three interaction variables between the REIT Act dummy and the three liquidity measures, the control variable size and a controlling model; the Fama-French 5 factor model. The time-series regression model is described in

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section 3.2 model (1). Appendix 13 shows an example of how the betas are displayed after the rolling window process has been done. 70 time-series regressions are performed with 13 beta coefficients for each REIT. The first hypothesis can be tested in this time-series regression model. The null-hypothesis state that the REIT Act hasn’t changed liquidity and therefore REIT returns aren’t affected. Table 6 shows the three interaction variables that measure the effect of liquidity change due to the REIT Act on REIT returns. Table 6 shows the signs and significance levels of the coefficients from all the 70 time-series regressions.

Table 6: Summation of the time-series regression results

No. of REITs for Amihud*Act

No. of REITs for Turnover*Act

No. of REITs for Spread*Act No significance - (45) + (12) - (31) + (32) - (25) + (35) 10% - (6) + (1) - (2) + (1) - (2) + (3) 5% - (2) + (0) - (2) + (1) - (1) + (0) 1% - (4) + (1) - (1) + (1) - (0) + (0)

Table 6: Time-series regression results using model (1). The interaction terms between the dummy of the REIT Act and the three liquidity measures are displayed. Significance levels and signs are described from

70 regressions. The time-series regression model equation is: Rr, t = ß0, r, t + x’*ßr + εr, where x’ are all the

variables described in X (section 3.2, model (1), page 16).

6.1.1.5 Testing the first hypothesis

First, there is little evidence that the REIT Act changed the Amihud liquidity factor and therefore REIT returns are affected. Table 6 shows 14 significant relationships between the change in the Amihud factor due to the REIT Act and the REIT returns. For 12 REITs, there is a negative relationship between this change in (Amihud) liquidity and their returns. An increase in illiquidity due to the REIT Act leads to lower returns. This contradicts the risk premium theory. Furthermore, there are a lot of TS regression results insignificant what could question these results. The hypothesis that the liquidity (using the Amihud factor) hasn’t changed due to a change in the REIT Act cannot be rejected in general but for some

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REITs it can. The most of the REITs that had a (negative) significant relation between the change in liquidity and their REIT returns were REIT type 2. These are equity REITs which tend to be more affected by a change in liquidity than mortgage REITs. Furthermore, these equity REITs invest mostly in property type 9 which is retail property.

Secondly, there is almost no evidence that liquidity (as measured by the turnover ratio) has significantly changed due to the REIT Act and therefore returns aren’t affected. 8 REITs have a significant relation between the change in liquidity due to the REIT Act and their returns. The hypothesis that returns aren’t affected by the change in liquidity (as measured by the turnover ratio) due to the REIT Act cannot be rejected in general. Using the turnover ratio, there cannot be distinguished whether mortgage or equity REITs are more affected by this change.

Thirdly, there is no evidence that REIT returns are affected by a change in liquidity (measured by the bid-ask spread) due to the REIT Act. Table 6 shows only 6 significant TS regression results out of the 70 regressions. The hypothesis that returns aren’t affected by a change in liquidity (measured by the spread) due the REIT Act cannot be rejected in general.

The time-series regression results give an indication which risk factors affect the REIT returns over time. There is controlled for difference in REIT characteristics using the rolling process. The betas that are collected from the last regression (full model) can be merged to the master-set. The second step of the Fama-Macbeth procedure will be done using the results that are retrieved. The time-series regressions showed some changes in liquidity risk for some REITs due to the REIT act. The time-series regressions also showed some affect on the REIT returns due to a change in liquidity for some REITs. The cross-sectional regression will show the change in risk/return over time due to the REIT Act. The cross-sectional regression tries to estimate a changing risk premium due to the REIT Act.

6.1.2 Cross-Sectional regression

The cross-sectional regressions will be done using the betas for every REIT against the average REIT returns. For each risk factor there will be calculated a risk premium that could be determined over the sample period. The average of every REIT’s return will be calculated over the sample period. This will be regressed against the estimated betas from the time-series regressions. The liquidity measure will calculate whether there is indeed a liquidity risk premium in the REIT market. Secondly, there will be determined whether there is a change

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in (liquidity) risk premium due to the change in the REIT Act in the REIT market. A dummy for the REIT Act can be used to determine a change in total risk premium. Interaction variables between the liquidity measures and the REIT Act dummy can be used to determine whether this change in risk premium is caused by a change in liquidity risk premium due to the REIT Act. The second and third hypothesis will be tested.

An average return of every REIT is regressed against the estimated betas in the TS regressions. The tested model is Ṝr = α0, r, ß + ƀx’ * λx + αr. This model estimates risk premiums. Table 7 shows the cross-sectional regression results of the average REIT returns using the sample period 2010-2014.

Table 7: The cross-sectional regression results

Average REIT return Coefficient Std. Err. T-Statistic

Amihud 6.66e-10 6.52e-10 1.02

Amihud*Act -3.89e-10 3.62-10 -1.07

Turnover -0.0003 0.0001 -2.86

Turnover*Act -0.0002 0.0001 -2.34

Spread -2.03e-08 5.18e-08 -0.39

Spread*Act -0.0001 0.0001 -0.77 REIT ACT -0.0093 0.0121 -0.77 Cap. -628.04 608.59 -1.03 Rm 0.5596 0.2778 2.01 SMB 0.0995 0.1124 0.89 HML -0.3142 0.2062 -1.52 RMW -0.2285 0.1599 -1.43 CMA 0.0424 0.1293 -0.33 Constant 0.0080 0.0021 3.87

Obs. = 70 F-stat. = . R-Squared = 0.5337 Root MSE = 0.00491

Table 7: Cross-Sectional regression results of the REIT returns from 2010-2014 against the betas collected from the time-series results. The coefficients can be interpreted as the risk premium for a risk factor. The

cross-sectional model equation is: Ṝr = α0, r, ß + ƀx’ * λx + αr, where X are all the variables that are displayed

The change in liquidity for US REITs due to the REIT Act