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Master Thesis

University of Amsterdam | Business Economics: Real Estate Finance

The Effect of the Maturity Structure of Debt

on the Performance of US Equity REITs

Name: Florian Schüßler Uva-ID: 10825452

Supervisor: Prof. Erasmo Giambona Date of Submission: 29.06.2015

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Statement of Originality

This document is written by Student Florian Schüßler (Schuessler) 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 serves the purpose of analysing the effect of the maturity structure of debt on the performance of US equity REITs. In the context of prevailing capital structure theories and mainly due to the corporate tax exemption of REITs, the usage of financial leverage seems unrational. As revealed by other scientific papers one reason could be that the financial leverage is chosen according to the debt maturity structure of the REIT. Therefore it seems inevitable to ask for the effect of debt maturity structure and not financial leverage on the performance of REITs. In order to empirically assess the effect, this thesis conducts a panel data analysis of 66 US equity REITs in the time frame of 2005 Q2 until 2015 Q1 and measures the statistical explanatory power of a wide range of debt maturity related variables on five different performance measures. The findings of this thesis are manifold and in line with the findings of other authors. Whereas the usage of short-term debt does not seem to have a statistically significant negative effect on REIT performance in general, it does very well so in financially distressed times such as the grand financial crisis of 07/08. Furthermore, the study indicates that REITs, which deviate more than 10% from its respective REIT property type group, perform worse in economic terms. The results of the empirical analysis also allow to comment on the importance of the maturity structure of a REIT’s leases and hints at a lower economic performance of REITs who cannot cover their time-weighted outstanding debt with their time-weighted incoming leases.

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Table of Contents

1.INTRODUCTION ... 4

1.1INTRODUCTION TO TOPIC AND RESEARCH QUESTION ... 4

1.2COURSE OF INVESTIGATION ... 5

2.LITERATURE REVIEW ... 6

2.1CAPITAL STRUCTURE THEORIES IN THE CASE OF REITS ... 6

2.2THE DYNAMICS OF FINANCIAL LEVERAGE AND DEBT MATURITY ... 9

3.METHODOLOGY ... 12 3.1ECONOMETRIC MODELS ... 12 3.2VARIABLE DESCRIPTION ... 14 4. DATA ... 18 4.RESULTS ... 21 5.1REGRESSION RESULTS ... 21 5.1.1 Quarterly Returns ... 21

5.1.2 Quarterly Returns including Dividends ... 23

5.1.3 Return on Average Assets ... 24

5.1.4 Return on Average Equity ... 26

5.1.5 Tobin’s Q ... 28 5.2PRELIMINARY CONCLUSION ... 29 5.ROBUSTNESS SECTION ... 30 7. CONCLUSION ... 33 REFERENCE LIST ... 36 APPENDIX ... 38

 

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1. Introduction

1.1 Introduction to Topic and Research Question

With their famous contribution to corporate finance literature, Franco Modigliani and Merton H. Miller (1958) had started the discussion about the role of a firm’s optimal capital structure – the composition or structure of a firm’s liabilities. Although the capital structure is mostly associated with the financial leverage aspect of a firm, it can also be seen multidimensional, as the maturity of the debt financing vehicles also determines the financing of a firm’s operations. However, Modigliani and Miller focus on the financial leverage and find that rational investors in an efficient market under the absence of asymmetric information and taxes are indifferent in the choice of degree of financial leverage. They argue that the market value of a firm is solely dependent on in the income stream its assets generate, but completely independent of the firm’s capital structure. Therefore the value of an unlevered and a levered firm would be equivalent ceteris paribus.

However, many of their underlying assumptions are more than questionable in reality, so that there may be reasons for firms to alter their capital structure in reality. Hence, it seems important to understand that the capital structure of a firm cannot only be altered by adjusting its financial leverage, but it can also be influenced by changing the underlying debt maturity structure1. There is vast empirical as well as theoretical literature concerning theories behind the usage of debt and the determinants and effects of financial leverage. Although the cross-sectional variation in leverage ratios across firm’s can be partially explained by the pecking order theory or trade-off theory, these theories find limited applicability in the analysis of real estate investment trusts (REITs). REITs, which were established as an investment vehicle for individual real estate investors, are exempted from corporate tax in exchange for fulfilling strict legal requirements about ownership structure, asset allocation and dividend distribution. As a result of the corporate tax exemption, interest payments of debt positions are not tax deductible, thus leaving no real financial benefit to the usage of debt. In fact, the value of an unlevered REIT should exceed the value of a levered REIT, as explained later on. Surprisingly, in practice, almost every REIT uses financial leverage for its operations.

As explained earlier, financial leverage is one channel through which management can influence the capital structure of a firm like a REIT. Using debt positions with different

1 Giambona et al. (2008) state: “the choice of leverage and maturity must be made jointly considering both the

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maturities can also be used as a tactical or strategic tool to alter the firm’s capital structure. Since capital structure theory does not explain how the two components interact, especially for REITs, authors like Barclay et al. (2001), Giambona et al. (2008) or Alcock et al. (2012) tackled this question with surprising but also somewhat contradicting outcomes. Whereas Giambona et al. (2008) supports the findings of Barclay et al. (2001) that the two components act as substitutes in a firm’s capital structure choice, Alcock et al. (2012) finds that the debt maturity structure is a determinant of leverage in the case of REITs. These findings put the question of the role of a REITs debt maturity structure into a whole new perspective and constitute the motivation to analyze the effect of the debt maturity structure on the performance of REITs. According to Giambona et al. (2008) the “homogeneity of REIT regulations and operations reduces the need for control variables when modeling capital structure”, thus shaping it as an optimal research opportunity (p.115).

The question at hand also seems relevant from an investor’s point of view. If the debt maturity structure determines the degree of financial leverage, than this fact should also have implications on the financial analysis of REITs as a whole. Riddiough and Brown (2003) state, “a more detailed analysis of debt maturity, perhaps in conjunction with the firm’s leasing structure, would also be interesting” (p.335). As REITs are so-called pure-plays2 the lease maturity structure of their real estate assets has a big influence on the firm’s overall profitability as well. Therefore this thesis will analyze the effect of the debt maturity structure also in conjunction with the lease maturity on the overall performance of US Equity REITs.

1.2 Course of Investigation

In order to conduct a proper analysis of the mentioned research question, this paper will be structured in the following way. Section II provides a literature review regarding underlying theories and relating empirical scientific papers, which are of utmost importance to the understanding of this analysis. In order to fully understand the rationale of this thesis, as well as its empirical approach, there will be a short recapitulation of capital structure theory. Also, trade-off theory as well as pecking-order theory will be explained as well to understand the rational and dynamics in the usage of debt. As this thesis is rather concerned with the effect of debt maturity, literature concerning the effect of financial leverage will be examined keeping in mind the findings of Alcock et al. (2012).

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The effect of debt maturity structure will then be tested in an empirical analysis with the help of a panel data set constructed from accounting data. Therefore, section III will deal with the applied methodology and performance measures, which will help to assess the hypothesis developed at the basis of most recent literature. In section IV there will be a presentation of the dataset used for the empirical analysis. Subsequently, section V depicts the results of the empirical analysis and section VI provides potential robustness issues, before section VII will conclude the thesis and provide the reader with potential limitations of the thesis.

2. Literature Review

2.1 Capital Structure Theories in the Case of REITs

As previously described, the seminal work of Modigliani & Miller (1958) represents the beginning of theory of optimal capital structure. However, before reviewing their main conclusion, it is essential to understand the underlying simplifying assumptions, which have to be assessed in the case of REITs. In the theoretical environment of Modigliani & Miller, it is assumed that capital markets are efficient, so that there is an “atomistic competition” (p. 296). Furthermore markets are assumed to be transparent, which induces perfect information symmetry. In addition to that there are no bankruptcy costs, agency costs or corporate taxes. Under these restrictive assumptions they demonstrated that the value of a firm using no financial leverage should be equivalent to the value of a firm, which does use financial leverage. They argued, the value of a company is purely dependent on the cash flows generated by the firm’s assets discounted by the weighted average cost of capital.

The theory states that with rising financial leverage in a firm’s financing, the risk exposure to shareholders rises as well. As a result, shareholders require a higher return on equity, while the cost of debt also rises with additional financial leverage. The simultaneous increase in cost of equity, cost of debt and the underlying debt-to equity ratio results in a constant weighted average cost of capital:

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Figure 1: Constant Weighted Average Cost of Capital Across Different D/E Ratios

As mentioned before, the heavily restricting assumptions, upon which their findings rely, do rarely hold in reality. Interestingly, Modigliani & Miller also proposed an alternative version of their theorem, in which they dropped the assumption of a general tax exemption. Because of an effective tax shield, which allows corporations to deduct interest payments as operating expenses, the value of firms with interest bearing debt positions (2) should in theory exceed the value of firms (1) with no debt (D) at ceteris paribus3.

Now, when analysing the case of REITs with help of the Modigliani-Miller theorem including taxes, it becomes obvious that their corporate tax exemption dramatically changes the implications of the theorem. If one assumes effective tax rates on bonds to be larger than effective tax rates on stocks due to the fact that capital gain taxes can be deferred and a large portion of dividends can be excluded from taxable income, then the value of an unlevered REIT (3) should actually exceed the value of a levered REIT (4):

VU= E(EBIT )(1−τps) ρ (3) 3 VU= E(EBIT )(1−τc)(1−τps) ρ (1) VL= E(EBIT )(1−τc)(1−τps) ρ + kdD[(1−τpB) − (1−τc)(1−τps)] kb (2)

VU = Value of unlevered firm

VL= Value of levered firm

τC, τpS,τpB= Taxes on corporations, stocks and bonds, respectively

E(EBIT ) = Expected EBIT (Earnings before Interest and Tax)

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VL= E(EBIT )(1−τps) ρ + kdD(τps−τpB) kb (4) Yields VU > VL, as τpB > τps (5)

The implication of (5) is striking as it proposes that REITs should in fact use no financial leverage at all. Yet, most REITs of any kind – equity, mortgage or hybrid – use financial leverage in their course of business4. Hence, the usage of debt for firms like equity REITs cannot be explained rationally by the Modigliani Miller theorem.

With the findings of Alcock et al. (2012), who argue that the maturity of debt – as another multidimensional component of a firm’s capital structure – is a determinant of financial leverage, the puzzling evidence of the precedent finding may be explained at least partially (cf. p.47). If the maturity structure of a firm’s debt determines the amount of leverage, and leverage has an implication on the value of a firm, then it is the debt maturity that ultimately partially drives the value of the firm.

But before giving up on the explanation of the existence (and/or) variation of leverage across REITs, maybe other prevailing capital structure theories such as pecking-order theory or trade-off theory can explain the precedent discrepancy. According to the trade-off theory, which originates from Kraus & Litzenberger (1973), a firm chooses its financial leverage by trading off the bankruptcy costs of debt agsinst tax saving benefits of corporate interest payment deductions. The deadweight costs associated with debt are costs of financial distress, which consist of so-called “bankruptcy penalties” that increase with additional external financing (p. 918). Although trading off marginal benefits and marginal costs in a firm’s financing decisions, the trade-off theory also finds limited applicability in the case of REITs. One the one hand, the corporate tax exemption of REITs disables the ability to benefit from tax savings. On the other hand, one could argue that the income generating assets of REITs, which by regulation have to equal 75 percent of a REITs total assets’, can be characterized as durable collateral leading to marginal expected financial distress costs. Hence, the trade-off between non-existent benefits and marginally existent costs does not explain the usage of debt for the case of REITs. Due to the fact that the trade-off theory also fails to explain the

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existence and variation in leverage ratios across REITs, the pecking-order theory may finally help understanding the motivation of REIT managers to employ a levered capital structure. Whereas trade-off theory captures the effect of the existence of corporate taxes as well as bankruptcy costs, the pecking order theory originating from Myers & Majluf (1984) tackles the issue of information asymmetries. A firm can raise capital by using internal funds such as retained earnings, issue debt or raise equity at the capital markets. According to this theory management will prioritize internal funds over debt and debt over equity for the following reasons: When issuing equity, equity investors infer adverse selection, i.e. they believe the management issues equity because the share price is over-valued and not because they have ran out of internal funds. As a result equity investors would put a discount on the firm’s share price, so that firms would always prefer internal funds to equity. Furthermore equity investors expect the management to issue debt until the financial distress cost is too high. For the case of REITs this theory also finds limited applicability, as the source for internal funds will be exhausted more than quickly as REITs are required to pay out 90% or their net income as dividends. Therefore, REITs would issue debt until the marginal costs of financial distress exceed the discount on the share price caused by suspicious equity investors. Hence, REITs would employ low to moderate financial leverage at best.

The preceding chapter tried to explain the existence and variation in the usage of financial leverage for US Equity REITs. However, REITs seem to be unique in the sense that their corporate tax exemption and strict regulations cause the theories to fail in explaining prevailing capital structures of REITs. Therefore, it seems inevitable to further examine the multidimensionality of capital structure, especially in the case of REITs.

2.2 The Dynamics of Financial Leverage and Debt Maturity

Similar to Giambona et al. (2008), Barclay et al. (2001) had already indicated that under certain conditions, long-term debt could be substituted for a high leverage and vice versa as both works to “limit management’s flexibility to use free cash flow to overinvest”. Though, when discussing related literature, it is important to point out the difference between determinants and effects of the debt maturity structure of a firm or REIT.

Barclay & Smith (1995) empirically examine the determinants of corporate debt maturity and find that firms, which have few growth opportunities, are large or regulated, have more long-term debt. In line with these findings, Giambona et al. (2008) indicate that high growth opportunities as proxied by Tobin’s Q have a negative impact on debt maturity of a firm.

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These findings are also in line with the findings of Johnson (2003), who indicate that firms with a lot of growth opportunities tend use short-term debt over long-term debt. On the contrary, Ertugrul & Giambona (2011) find that the dominant REITs in a property segment choose more long-term debt to signal rival firms their solvency or lower information asymmetry.

Diamond (1991) states: “The optimal maturity structure trades off a preference for short maturity due to expecting their credit rating to improve, against liquidity risk” (p.1). Brick & Ravid (1991) on the other hand oppose that the optimal debt maturity structure for all firms is dependent on the prevalent term structure of interest rates, i.e. an increasing term structure favors long-term debt. Giambona et al. (2008) also find that a REIT’s debt maturity depends on the median debt maturity of the segment in which the REIT operates.

Consistent with the findings of Shleifner & Vishny (1992) for firms in general, Giambona et al. (2008) also reveal that a REITs debt maturity structure correlates with its asset liquidation value. They find that firms specializing in low liquid assets use more leverage and shorter debt maturity. However, Garcia-Teruel et al. (2010) find that firms with a low accounting quality face shorter debt terms because of information asymmetry problems. Summing up, a REITs debt maturity structure is empirically proven to be at least partially determined by growth opportunities, information asymmetry, asset liquidation value, competitive position, and standard segment practice.

Having reviewed scientific literature with respect to the determinants of variation in debt maturity structures, there will now be a discussion of existing literature on the effects of the debt maturity structure. However, it is important to mention that there is no literature regarding the effect on operational performance of REITs. As mentioned earlier, Alcock et al. (2012) revealed, that the debt maturity structure determines the leverage ratio in US REITs as a result of the corporate tax exemption. Gopalan, Song and Yerramilli (2014) find that firms with shorter debt terms have greater refinancing (rollover) risks, which in turn decreases their credit quality / rating. Interestingly, this shows a discrepancy in the prevaling literature between Gopalan et al. (2014) and Ertugul & Giambona (2011) as the latter argue that incumbent REITs or large REITs, which are likely to have a comparably good credit rating, tend to use short-term debt over long-term debt.

Although the work of Schiantarelli & Sembenelli (1997) focuses on the effect of debt maturity structure on firm performance in general, their findings support Gopalan et al. (2014) by drawing the conclusion that “more profitable firms have more long-term debt” (p.1). Also,

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Almeida et al. (2011) find that “firms whose long-term debt was largerly maturing right after the third quarter of 2007 reduced investment by 2,5% more (on a quarterly basis) than otherwise similar firms whose debt was scheduled to mature well after 2008” (p.1). In line with their findings, Sun et al. (2014), who analysed the effect of financial distress cost on REIT values in the context of the financial crisis 2007/08, also find that REITs with a bigger portion of debt due in the crisis per form worse in terms of cumulative returns over that period. This supports the rollover refinancing risk argument of Gopalan et al. (2014). However, they Sun et al. (2014) find no significant effect of debt maturity on cross-sectional cumulative returns during any of the analysed “two-year non-overlapping sample periods from 1993 to 2006” (cf. p. 14). These findings indicate that the effect of the debt maturity structure is dependent on a temporal context, in which debt maturity influences the performance of REITs through the exogenous effect of financial distress channeled through rollover risks rather than debt maturity as a (endogenous) management instrument to match lease maturity and eventually increase profitability.

The academic paper of Sun et al. (2014) represents the latest scientific work in the field of capital structure including the effect of debt maturity structure on performance in the context of US equity REITs. Although the authors focus more on the financial distress effect of short debt maturities on cumulative returns, equity issuances and property sales in the context of the crisis, their work delivers an ideal starting point for this thesis. This thesis serves the purpose to examine the general effect of debt maturity - as a part of the capital structure of REITs – on REIT performance metrics other than cumulative returns, equity issuances and property sales. I set the hypothesis, that US equity REITs with a higher short-term debt to total debt ratio are of lower economic performance in general (within and outside of financially distressed times). Also, I expect REITs whose weighted-average incoming lease revenues do not suffice to pay the outstanding time-weighted debt, to perform worse in economic terms. The next section will therefor elaborate on the methodology and performance measures, with which these hypotheses will be empirically analysed.

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3. Methodology

3.1 Econometric Models

In order to adequately assess the developed hypothesis over a bigger time frame, it is necessary to collect a vast amount of quantitatve data on accounting data of US equity REITs. Although the most recently discussed paper of Sun et al. (2014) uses a cross-sectional OLS regression approach, this thesis will employ a thorough analysis of panel data, also called longitudinal data, in order to benefit from the advantages of panel data regression methodologies. However, the rationale behind the OLS model, which is applied to the panel data set, is analogous to Sun et al. (2014), but slightly edited in order to account for the possibility of heterogenous effects over time, especially during and after the financial crisis of 07/08.

It is also important to acknowledge on how REIT performance is quantified as this is also of great importance. Similar to the mentioned academic paper, this thesis will analyze the effect of debt maturity on different dependent variables. Whereas Sun et al. (2014) test the effect of debt maturity on cumulative returns according to their cross-sectional dataset and methodology design, this thesis will analyze the effect on quarterly returns, with and without dividends. The two versions of the return variable only seem rational as REITs, which act as pass-through entities for cash flows to shareholders, pay vast amounts of dividends, which should not be neglected in the quantification of performance. In addition to quarterly returns, this thesis will also analyse the effect on accounting performance measures as described by Feng et al. (2011): return on assets, return on equity as well as Tobin’s Q.

The critical independent variables, whose coefficient will help to comment on the developed hypotheses, are short-term debt over total debt, short-term debt over total debt minus that

ratio of the relating REIT property type group. I also tested the models shown below after

replacing the previously mentioned critical variables with the variable debt maturity over

lease maturity in order comment on the importance of matching lease schedules with debt

schedules. The construction and description of variables originating from accounting data will be explained in much more detail later on.

In order to control for other variables, which could potentially have an influence on the described independent variables and could thus lead to an ommited variable bias, it is necessary to account for this by including different types of control variables. Since panel

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data allows for as many cross-correlation matrices as there are time periods, the usage of such was considered as unuseful. Instead, this thesis uses the insights of Sun et al. (2014) to construct the econometric models. However, in addition to the control variables proposed by Sun et al. (2014) this thesis also includes time dummy variables for the financial crisis and respective interaction terms to account for the possibility of heterogeneous effects during financially distressed times as well as dummies indicating an operating partnership or whether a REIT is self-advised. In order to avoid perfect multicollinearity, the dummy variable for the retail property type was excluded, as this was the most frequent property type. Also the time dummy for the period before the financial crisis was dropped in order to interpret the coefficients of the other time dummy variables with respect to the time period preceding the financial crisis.

The benefit of panel data is that it allows for additional econometric OLS methodologies such as fixed or random effect models. In order to exploit this advantage over cross-sectional OLS designs, the following econometric models are also estimated by means of including fixed or random effects. In order to decide which effects (fixed or random) to include in the respective models, I used the Hausman specification test5 (cf. Hausman, 1978). Each of the following models was then once tested with time-invariant property type and management dummies as well as with fixed (or random) effects after dropping time-invariant variables. These two versions of the models were made for three regression sets using the three different debt maturity related variables alternatively. Inference of coefficents was made using clustered standard errors. According to Stock & Watson (2010) clustered standard errors “allow for heteroskedasticity and for arbitrary correlation within a cluster, (…), but treat the errors uncorrelated across entities” (p. 364). The regression models are as follows:

5

The hausman specification test has the null hypothesis (H0), that the random effect estimator is consistent and

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3.2 Variable Description

This chapter serves the purpose of explaining the variables created from accounting data included in the previously described models in more detail as well as respective expectations for them.

Return on Average Assets & Return on Average Equity:

Feng et al. (2011) state: “Return on assets, return on equity and Tobin’s Q are widely used in the corporate finance oriented real estate literature to proxy for firm performance and value” (p.322). Therefore this thesis employs these return variables in order to assess the effect of debt maturity on firm performance. The profitability ratios are directly reported in SNL database and are calculated by dividing US GAAP net income by average assets or average equity in that period, respectively.

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Tobin’s Q:

Tobin’s Q is the ratio between the market value of a firms assets and its replacement value. Therefore it is often characterized as growth opportunities of a firm, as it puts the trust of the financial market participants into the REIT management in relation to the underlying physical value of a firm’s assets. Therefore I expect a positive effect on the any of the profitbality ratios in model 1-4. In accordance to Feng et al. (2011) the variable is constructed by subtracting the book value of equity and adding the market value (total capitalization) of equity from total assets. Subsequently, this measure is standardized by total assets (cf. p.322). Size:

The inclusion of this variable accounts for the fact that the size as measured by the dollar value of a REITs’ total assets may have a significant impact on any of the profitability ratios. The theory behind this is that larger REITs could benefit from economies of scale. Although empirical evidence regarding this theory is ambiguous, I expect a positive sign. The variable size is measured as the natural logarithm of total assets.

Market Leverage:

Although the scope of this thesis is rather concerned with the debt maturity aspect of the capital structure, the amount of debt to equity still has to be included in the model in order to differentiate between the effect of financial leverage and the effect of debt maturity. In the case of REITs the usage of debt does not induce the benefit of tax savings and would therefore only increase the probability of financial distress. Analogous to Sun et al. (2014) I therefore expect a negative sign. As opposed to book leverage, market leverage is calculated by dividing the book value of debt by the market value of equity (market capitalization). Cash/ Total Assets:

Including the a standardized variable to account for cash only seems rational as its effect could at least partially erode the effect of financial distress or rollover risk induced by a high amount of short-term debt. On the other hand cash or cash equivalents are not producing any profits at the same, therefore I expect a negative sign. This ratio is calculated by dividing the sum of US GAAP cash and cash equivalents by the dollar value of a REITs total assets. FFO per Share:

This variable measures the funds from operations per share outstanding in the respective period. Although the variable is reported in the SNL database as well, it seems crucial to

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understand its importance in the REIT context and how it is calculated. As opposed to the earnings measure earnings per share (Net Income/Shares Outstanding), which is regularly used in general corporate finance, funds from operation are a more accurate earnings measure in the case of REITs to account for its vast depreciation expenses, which rather represent an accounting expense than a cash outflow. Therefore, funds from operation per share are calculated by adding depreciation expenses to net income and dividing this figure by the number of shares outstanding. Analogous to the findings of Sun et al. (2014) I expect a negative sign.

Variable Rate Debt to Total Debt:

This ratio is also directly reported in the SNL database and is calculated by dividing the amount debt, for which the interest payment rely on a variable interest rate, by the total debt of a REIT. As variable rates can suffer severe increases in financially distressed times, including this variable will also capture the effect of financial distress and purify the effect of debt maturity in general, which I assume to be negative. Analogous to Sun et al. (2014) I expect a negative sign within financially distressed times. As opposed to that, I expect a positive sign outside of the financial crisis, where debt with variable rates may benefit from improving market conditions.

Short Term Debt Variables:

The short-term debt variables used in this thesis are “Short Term Debt over Total Debt” as well as “Short Term Debt over Total Debt minus this Ratio of the respective REIT group”. The latter is calculated by first creating the average of the ratio “short term debt over total debt” per REIT group per quarter and then subtracting the group average ratio from the ratio of the specific REIT. REIT groups were created on behalf of their property type foci (cf. figure 3). It is important to mention that the data availability in the SNL database for this ratio was very limited for the examined time period. Therefore both ratios were calculated by dividing the figure “debt maturing in one year” by “total debt”. For both versions of the variable I expect – analogous my hypothesis – a negative sign within financially distressed as well as normal economic time periods.

Debt Maturity / Lease Maturity:

This variable serves the purpose on putting the maturity structure of debt in the context of the maturity structure of leases, which represent the main source of income for REITs. Both

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variables are calculated as time-weighted averages6. Figure 4 depicts how this ratio has changed over time across property type groups. A ratio equaling one would mean that a REIT matches his leases perfectly with his outstanding debt payments. A ratio bigger than one would mean that the total amount of leases would be received in a shorter period than the REIT would have to pay its total outstanding debt. This would require a REIT to renew its leases to that extent that they will suffice to pay the remaining outstanding debt. On the other hand, a ratio smaller than one means all debt, which the REIT has to pay in the future, is already covered by the incoming leases (rental revenues). Hence, I expect a negative effect of this variable on all performance metrics.

REIT Characteristic Dummies & Crisis Time Dummies:

Property type dummy variables were created in order to account for a potential constant heterogeneity in the profitablity across REITs with different property type foci. Dummy variables for the property types residential, retail7, office&industrial8, specialty9, diversified and hotel, were created. Furthermore dummy variables were included to indicate UP REITs (operating partnerships) or REITs with own asset management services (self-advised). In order to account for a constant difference in the profitability across REITs during financially distressed (2007 Q3-2009 Q2) and recovering time periods (2009 Q3-2015 Q1), I included the crisis time dummies.

Interaction Terms:

On the one hand this thesis serves the purpose to examine the general effect of a REITs debt maturity structure on its performance, on the other the dataset being used to assess this effect covers a ten year time period, in which there was also the biggest financial crisis since the great depression. Therefore it seems rational to allow for heterogenous effects in all capital structure related variables, which could have been deterred as a result of a capital contraction in financially distressed times otherewise.

6 The variables were calculated analogous to the description in footnote 11 of Giambona et al.

(2008)

7 Under “retail“ REITs with the property type focus on either Shopping Centers, Regional

Malls or other Retail were pooled together

8 Under “office&industrial“ REITs with the property type focus of either office or industrial

were pooled together.

9 Under „specialty“ REITs with the property type focus of either Health Care, Specialty or

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4. Data

The data required for the empirical analysis was retrieved from two different sources. Data on stock market prices as well as paid dividends for all necessary REITs were downloaded from COMPUSTAT database in a quarterly frequency. Respective accounting data was retrieved from SNL database in a quarterly frequency. The examined time period ranges from 2005 Q2 until 2015 Q1. Due to limited availability on data regarding debt maturity (debt maturing in one year) the final data set does not contain all US equity REITs, which were existent in the beginning of 2005 in the US, but 66 REITs, for which data on debt maturing in one year was reported in 2005 Q2 in the SNL database. Observations, for which any of the dependent variables were missing, were deleted from the data set. The final panel data set contains 2,647 observations and is partially unbalanced due to the fact that not all REITs reported all accounting variables in all quarters from 2005 Q2 until 2015 Q1 continuously. However, most REITs did report the majority of the variables on a continuous basis, which are required for the proposed econometric models.

Panel A: Descriptive Statistics of REIT Characteristics

Panel A depicts that the majority of REITs contained in the final data set focus on retail properties as well as office & industrial properties. Only four of the 66 REITs focus on residential properties, whereas the number of REITs, who focus on Hotels or follow a diversification strategy are twice as many. Regarding operating partnerships, Panel A reveals that 14 out of 66 REITs are umbrella partnership REITs (UPREITs). In addition to that it is observable that the majority of the REITs contained in the data set provides their own asset management services responsible for investment acquistions and disposition decisions.

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Panel B: Decriptive Statistics of Dependent Variables

In order to understand the proportions of the dependent variables, Panel B depicts the mean, standard deviation, minimum and maximum values as well as the number of observations of those variables. All 66 REIT entities reported data throughout an average of 39-40 quarters. However, it is also observable that some REITs did not report return on average equity (2586) in all time periods.

Interestingly but not unexpected in the context of REITs is, that the overall average quarterly

return including dividends exceeds the pure average quarterly return by 1,25%. Also both

stock return variables show a minimum value of close to -100%, which is probably caused by the financial crisis of 07/08. On the other hand both variables also show maximum values of close to +380%, which can be argued for in the case of recovering times after markets had exaggerated the impact of the crisis. However, visual observation of the very few cases with extreme values did not indicate data anomalies, so that these observations were neither winsorized, nor dropped from the dataset. Whereas the overall average return on average

assets equals 2,56%, the overall average return on equity shows an overall average value of

6,38%, which is slightly less than the average figure reported by Feng et al. (2011). Both variables also range from negative figures to large positive figures caused by extreme economic conditions. However, one can observe that the standard deviation of the return on

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(32,37%) by far. The overall average Tobin’s Q, which depicts the growth opportunities of a firm, equals 1,97. This shows that the market values a REIT almost twice as much as the replacement costs of its total assets, although the variable also ranges from 0,73 to 4,64.

Panel C: Decriptive Statistics of Continuous Independent Variables

Panel C depicts the descriptive statistics of all continuous independent variables, which enter the regression models. Except for the variable Debt Maturity/Lease Maturity the number of observations, which enter the regression analysis, is about 2500. This is due to the fact that only 43 REITs reported continuously on the debt maturing one, two, three, four and five years (and thereafter) as well as leases to be received in one, two, three, four and five years (and thereafter). Whereas the overall average mean of the size variable is hard to determine due to the fact that is the natural logarithm of total assets, it is observable that the book- and market

leverage variables average about 55% and 40% with an overall standard deviation of 19,7%

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equivalents, although this figure increases up to a maximum value of 66%. The overall average of the REIT-specific earnings measure FFO per share equals 0,63 and ranges from -13 up to +12,77. Whereas an overall average of 19% of the total debt outstanding is linked to a variable interest rate, only 5% of total debt is considered as short-term, so due one year at that point in time. The overall average of the short-term debt ratio in comparison the respective REIT group is by definition zero (-9,51e-11≈ 0) and ranges from -0,27 up to 0,9. Interestingly Panel C depicts, that the overall average ratio of debt maturity/lease maturity equals approximately one, which means that REITs tend to timely match their outstanding debt with leases, which are to be received. However, this variable also ranges from low values such as 0,22 up to 1,74.

4. Results

In order to keep the analysis of the regression results in the scope of this thesis, I will mostly comment on the effects of capital structure related variables, but depict interesting findings for other explanatory variables as well.

5.1 Regression Results

5.1.1 Quarterly Returns

Panel D depicts regression results for the dependent variable quarterly return without

dividends. Whereas the coefficent of market leverage has a negative sign in all six models,

they only seem statistically significant 1a, 1c, 1d and 1e. This is according to the expectations set before and indicates that higher levered REITs perform on average worse in terms of their stock price appreciation. The negative signs of the market leverage interaction term coefficients also show that high leverage was even more punished by the market during crisis. However this effect changed significantly after the crisis, so that market leverage is preferred in times of financial recovery. None of the coefficients of any short-term debt ratio variables seem statistically significant in general. However, the interaction term with short-term debt over total debt is statistically significant at a 99% confidence level during the crisis and possesses a negative sign meaning REITs with a higher ratio of short-term debt compared to total debt performed worse on the stock market during the crisis.

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Panel D: Regression Results for Quarterly Returns (excluding Property Type, Management & Time Dummies)

These findings are in line with the findings of Sun et al. (2014), who do not find a statistically significant effect previous to the financial crisis of 07/08, but a significant effect during the crisis. Whereas the coefficients on the ratio of variable-rate debt to total debt seem insignificant, the interaction terms during the crisis are significantly negative and thus indicate the negative impact of interest rates, which are linked to the worsening market conditions, on the overall REIT stock price.

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In general, it can be observed a REIT’s debt maturity structure only seems to be relevant in times of crisis, whereas the financial leverage has a statistically significant impact during financially distressed times as well as in economically thriving times. Interestingly, the effect of the variable debt maturity over lease maturity is statistically significant at a 95% confidence level and possesses – as expected – a negative sign. As explained, a ratio bigger than one would mean that the total amount of leases would be received in a shorter period than the REIT would have to pay its total outstanding debt, which would require a REIT to rollover its lease, so that it suffices to pay its remaining outstanding debt. The negative coefficent deludes that the market participants or potential shareholders of REIT stocks prefer a lower debt maturity over lease maturity ratio, so that a REIT is able to pay the total remaining outstanding debt easily with the leases, which are going to be received certainly.

5.1.2 Quarterly Returns including Dividends

Panel E depicts regression results for the dependent variable quarterly return including dividends. Similar to the case of quarterly stock price returns without dividends the effect of market leverage is statistically significant and negative for the same models as mentioned earlier. In general, the regression results for the two versions of quarterly dividends yield the same results. Including dividends into quarterly returns only seem to amplify the effect sizes marginally.

Although the coefficents for property type, management as well as crisis time dummies are not reported in any panels due to design reasons10, it seems interesting to mention that quarterly returns without and including dividends are on average statistically significant higher in times of financially distressed time periods. This price appreciation may be explained by the fact, that stock market participants demanded REIT stocks more as a reason of their regular dividend payments also in times of crisis. Especially due to ensured incoming cash flows in form of rental revenues, stock market participants could have considered REITs as a good investment in times of financially distressed times. Although only statistically significant in model 1c & 1d as well as model 2c & 2d, the negative coefficient of the after-crisis dummy variable could indicate that stock market participants started to divest their REIT stocks right after times of capital contraction had ended, thus decreasing REIT stock prices.

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Panel E: Regression Results for Quarterly Returns including Dividends (excluding Property Type, Management & Time Dummies)

5.1.3 Return on Average Assets

Panel F depicts the regression results for the dependent variable return on average assets. Similar to the case of quarterly return variables, the effect of market leverage is statistically significant to a 99% confidence level and possesses a negative sign. The negative sign seems

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logical as more outstanding debt induces more interest payments, which reduces the resulting

Panel F: Regression Results for Return on Average Assets (excluding Property Type, Management & Time Dummies)

net income and thus the ratio net income over average assets. However, the responding interaction terms during the crisis are statistically significant and possess positive signs, which indicates that the negative effect of market leverage on return on average assets is dampened during the crisis.

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None of the critical variables concerning the debt maturity structure of REITs except for the ratio of variable-rate debt over total debt seem to have a statistically significant impact on the return on average assets, although all possess the economic sign, which was expected ex ante. The coefficient on the variable-rate debt variable in general is statistically significant for models 3 a,b,e & f and possesses a positive sign. This makes sense as variable rate debt position benefit from decreasing interest rate payments in improving market conditions outside of financially distressed times. Interestingly, also none of the interaction terms are close to statistical significance again except for the coefficient of variable-rate debt during the crisis.

The latter possesses a negative sign and seems logical as the net income of REITs with larger portions of variable-rate debt suffer from increasing interest rate payments due to worsening economic conditions. Also, the effect of variable-rate debt on return on average assets seems significant and negative after the crisis, which might be caused by higher interest rates even after the financial crisis. Also, the statistical significant and negative sign on the during-crisis dummy variable indicates that the return on average assets was on average 2-3% lower than before the crisis. Furthermore, the return on average assets of REITs with the property type focus office & industrial and diversified have a lower return on average assets on a 95% confidence level.

5.1.4 Return on Average Equity

Panel G depicts the regression results of the dependent variable return on average equity. Surprisingly and opposed to the prior described regression results, only a few of the explanatory variables in this model seem to have a statistically significant influence on the dependent variable. However, all p-values of Wald Chi-Squared statistics or F-statistics are approximately equal to zero, so that one can conclude a joint significance of the independent variables. Whereas the only statistically significant coefficient of a capital structure related variable is the negative coefficient of market leverage and a positive sign on the after crisis interaction term with the debt maturity over lease maturity variable, the signs of the short-term debt variables do appear to be economically significant.

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Panel G: Regression Results for Return on Average Equity (excluding Property Type, Management & Time Dummies)

Both, short-term debt over total debt as well the difference-to-REIT-group variable, indicate a negative effect on return on average equity, although they are not statistically significant. An explanation for the negative effects of the short-term debt variables on return on average equity as well as on average assets could be that lower performing REITs are only able to receive credit lines with shorter maturities. In addition, the coefficient of the variable debt maturity over lease maturity has a negative sign and marginal confidence level of 87,2%11.

This indicates the bigger the amount of incoming leases, which covers the remaining outstanding debt, the higher the return on average equity. Surprisingly, this negative effect

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seemed to be offset in times after the financial crisis as indicated by the respective interaction term.

5.1.5 Tobin’s Q

Panel H depicts the regression results of the dependent variable Tobin’s Q. Similar to the aforementioned regression results, the sign of the coefficient of market leverage is negative and statistically significant at a 99% confidence level for all models 5 a-f. This indicates that firms with higher leverage yield lower growth opportunities. Stunningly, the negative effect seems to be dampenend or partially offset during the crisis, as the interaction term is statistically significant positive. However, the latter is only the case in model 5 c & d, so that these results should be interpreted with caution.

None of the effects of debt maturity related variables are close to a statistical significance. Though, the ratio of short-term debt to total debt appears to have a negative effect on the growth opportunities of a REIT during and after the crisis, although only the latter can be stated at a 90% confidence level. Also, the models indicate a negative effect of the ratio of variable-rate debt to total debt on growth opportunities during and after the crisis. As Tobin’s Q somewhat relates to the trust, which stock market participants place into a REIT or firm in general, one can argue that stock market participants penalize REITs more, the more debt linked to a variable interest rate they have. The growth opportunities of REITs were on average statistically significantly lower during the financial crisis as suggested by crisis time dummies. Also REITs, which are self-advised and focus on hotels, office & industrial as well as specialty properties appear to have on average lower growth opportunities.

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Panel H: Regression Results for Tobin’s Q (excluding Property Type, Management & Time Dummies)

5.2 Preliminary Conclusion

After having commented on the regression results for the five different performance measures, one can observe that results are somewhat ambiguous. Although the findings show that none of the critical explanatory variables except for debt maturity over lease maturity have a statistically significant explanatory power on any of the performance measures during normal times (in general). The debt maturity over lease maturity variable shows to have a significant negative effect on quarterly returns, indicating that shareholders prefer REITs to

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match its outstanding debt with incoming leases. However, the effect of other debt maturity related variables such as short-term debt over total debt does seem to have a negative impact on quarterly returns during financially distressed times as indicated by the highly significant interaction term, but not during normal times. The same holds true for variable-rate debt, which captures the effect of financial distress costs. These results are in line with the findings of Sun et al. (2013), who state “the decline was greater for REITs with more variable interest rate debt and more debt coming due in 2008 and 2009, which is consistent with financial distress costs” (p.34). Therefor it seems important to mention again, that the effect of a REIT’s debt maturity seems to be highly dependent on a temporal context. Whereas the debt maturity does not seem to significantly explain the variation in performance measures in general, its effect becomes visible during financially distressed times.

Although the short-term debt minus REIT group average variable does not show a statistical significance in any of the models, it does show an economic significance as its negative sign implies, that REITs with a higher deviation to their respective REIT group are less performant. However, these results should be interpreted with caution, as their marginal significance levels range from approximately 30% up to 80% across the models. Although not related to the debt maturity aspect of a REITs capital structure, the highly statistically significant negative signs on market leverage in general across almost all models indicates that financial leverage seems to have a negative impact on the evaluated performance measures. This finding supports the theoretical argument derived from Modigliani & Miller, that there should be no usage of financial leverage in the case of a corporate tax exemeption, as it just adds deadweight costs of financial distress.

5. Robustness Section

In this section I will use the insights from my preliminary regression results in order to test my results and to give a more detailed insight in the dynamics of debt maturity related variables and performance measures. Therefore I will test the models on quarterly returns

including dividends, return on average assets and Tobin’s Q with the help of dummy variable

specifications of the three debt maturity related variables. The robustness section limits itself to these three mentioned dependent variables, as the preliminary regression results have shown either similar results in the case of quarterly returns (without dividends) or somewhat unexciting results in the case of return on average equity.

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Figure 2: Frequency Distribution of Short-Term Debt over Total Debt as well as Reit Group Average Comparison Variable

After visualising the frequency of the short-term debt over total debt variable it became obvious that the majority of REITs do not hold more debt maturing in one year than 10% of its total debt. Therefore a dummy variable was created, which indicated observations, for which the ratio was above 10%. Figure 2 also depicts, that the majority of REITs does not deviate in absolute terms more than 10% from the respective REIT group average. Therefore a dummy variable, which indicated observations, whose absolute group deviation was above 10%, was created. For both dummy variable specifications, the respective interaction terms for financially distressed times as well as time periods after the financial crisis were created and included in the regression models.

In order to give more insight in the maturity matching of outstanding debt and incoming leases, I also created a dummy variable indicating observations, for which the ratio was bigger than 120%. The rationale behind this is that REITs, which are not able to cover their outstanding debt with guaranteed incoming leases are less performant. I assumed a buffer of 20% in excess of 100% as REITs could in the worst case use their accumulated cash or cash equivalents to cover remaining outstanding debt positions. I assume that REITs with a ratio in excess of 120% suffer from a maturity mismatch problem and should perform worse.

Panel I depicts the regressions results with the models including the previously described dummy variable specifications. The results are similar to the original model specifications. Although the debt maturity structure related dummy variables (DST, DGD, MMI) do not show a statistically significant explanatory power in any models in general, except for the maturity mismatch indicator in explaining the variation in return on average assets, they do show the expected economic signs and in some cases have marginal confidence levels of up to 88,22%. The maturity mismatch indicator (MMI) in general is statistically significant and possesses a negative sign in the case of return on average assets, which supports the negative but insignificant effect in the original model specification.

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Panel I: Robustness Test with Dummy Model Specifications for Critical (Capital Structure related) Explanatory Variables

Similar to the original model specification, the dummy variable indicating a short-term debt to total debt ratio of bigger than 10% does have a statistically significant negative effect on quarterly returns during financially distressed times, but not in general. The same holds true for the group deviation variable. The negative sign on the interaction term during the crisis (DGD x During Crisis) deludes that REITs who deviated more than 10% from its respective REIT group did even worse during financially distressed times, than already in normal times. Surprisingly, the effect of the maturity mismatch indicator variable (MMI) during the crisis is statistically significant and possesses a negative sign in the case of quarterly returns. This means that the otherwise negative effect of a maturity mismatching in general is offset by a positive effect during the financial crisis. This outcome may be reasoned by the fact that the market at that time preferred REITs, whose outstanding debt is not in near future. A ratio bigger than 100% can either be caused by a shortcome of leases, or an extensive portion of

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debt, which is to be paid in the far future, i.e. outside of the crisis thus increasing the time-weighted average of outstanding debt. However, these are rather speculations and require further research.

7. Conclusion

This thesis served the purpose of evaluating the effect of debt maturity structure on the performance of US equity REITs. On the basis of current topic related literature I developed the hypotheses that a) US equity REITs with a higher short-term debt to total debt ratio are of lower economic performance in general (within and outside of financially distressed times) and b) REITs, whose weighted-average incoming lease revenues do not suffice to pay the outstanding time-weighted debt, perform worse in economic terms.

The empirical analysis, which sets the basis for my comment on this hypotheses, was done with the help of panel data regression analysis consisting of 66 REITs in the time frame of 2005 Q2 until 2015Q1 in a quarterly frequency. The performance measures, whose variation was explained by a vast set of explanatorty variables including the critical debt maturity related variables, were quarterly returns (including and excluding dividends), return on average assets, return on average equity as well as growth opportunities, which is quantified by Tobin’s Q.

The results of the empirical analysis are manifold. Although the majority of the debt maturity related variables were statistically insignificant in general, i.e. in normal times, their economics signs were as expected negative. Also, by including interaction terms with financially distressed times as well as time periods after the financial crisis, the models allowed for heterogenous effects of these debt maturity related variables. As it turns out, this inclusion seems crucial, as many effects are only statistically significant in times of financial distress. These results indicate that the effect of debt maturity is highly dependent on a temporal context, meaning on present market conditions. Whereas the ratio of short-term debt over total debt does not seem to influence a REIT’s performance during normal times, it does very well so during times of a financial crisis, in which rollover risks arises. Although not statistically significant in any models, a larger deviation from the average of the respective REIT group, seems to have a negative impact on firm performance as well. The robustness section showed that REITs, which deviated more than 10% from its REIT group average ratio performed statistically significant worse at the stock market during the financial crisis, i.e. in the case of quarterly returns including dividends.

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Although only statistically significant in the context of quarterly returns (including as well as excluding dividends) the ratio of debt maturity over lease maturity possesses a negative algebraic sign in all models, deluding a lower economic performance of REITs, which increase this ratio. In order to give more insight into the matching of outstanding debt and incoming leases, the robustness section supports the idea that REITs with a maturity mismatch problem, i.e. a ratio over 120%, do have on average a lower return on average assets.

Although Sun et al. (2014) rather focus on the effect of debt maturity structure on REIT values, net property sales as well as equity issuances, the findings of this thesis support parts of their findings. Similar to their study, this thesis did not reveal a significant explanatory power of debt maturity structure on the REIT values outside of financially distressed times. In line with their findings, this thesis does find a negative effect of short-term debt during the financial crisis of 07/08 and even gives more insight in the importance of REIT group behavior, which only seems to be of significant explanatory power during times of crisis as well. As demanded by Riddiough & Brown (2003) this thesis also puts the debt maturity “in conjunction with the firm’s leasing structure” (p.335) and indicates that a maturity mismatch of outstanding debt and incoming leases leads to an inferior performance in general.

It is important to mention that any theses can suffer from methodological issues or data problems. Although the analysis was conducted meticulous, one cannot fully exclude the possibility of endogeneity issues. Furthermore it is important to understand that this panel data set was unbalanced. Especially in the case of data concerning the debt and lease maturity structure only 43 reported the required data, but also not necessarily throughout the entire time period. In consideration of the amount of coefficients to be estimated, this could lead to a slight deterioration in the estimation of the coefficients. The missing statistical significance could also be caused by endogeneity issues. Therfore the topic of debt maturity structure in context of REITs will require further research with a more detailed statistical analysis or a different set of methodologies in the future.

The implications of these findings can be of great importance for the REIT managers responsible for the cash flow management of outstanding debts and incoming leases. If market participants in general punish REITs with maturity mismatch problems or REITs with a large proportion of short-term debt during financially distressed times, then this knowledge can beneficial in the tactical and strategic decision making process. Further research dealing with the topic of debt maturity structure in the context of REITs should deal in more detail in

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conjunction with the maturity structure of leases. If a REIT truly determines debt maturity before financial leverage and a maturity mismatch leads to lower economic performance, then it is questionable how and why this maturity mismatch is created.

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Reference List

Alcock, J., Glascock, J., & Steiner, E. (2012b). Manipulation in U.S. REIT Investment Performance Evaluation: Empricial Evidence. Journal of Real Estate Finance and Economics, pp. 212-247

Alcock, J., Steiner, E., & Tan, K. J. (2012). Joint Leverage and Maturity Choices in Real Estate Firms: The Role of the REIT Status. Journal of Real Estate Financial Economics, pp. 58-67

Almeida, H., Campello, M., Laranjeira, B., & Weisbenner, S. (2011). Corporate Debt Maturity and the Real Effects of the 2007 Credit Crisis. Critical Finance Review, pp. 4-52 Barclay, M. J., Marx, L. M., & Smith, C. W. (19. December 2001). The joint determination of leverage and maturity. 9. Journal of Corporate Finance., pp. 149-167

Barclay, M., & Smith, C. J. (1995). The Maturity Structure of Corporate Debt. Journal of Finance. pp. 103-143

Brick, I., & Ravid, R. (1991). Interest Rate Uncertainty and the Optimal Debt Maturity Structure. Journal of Finance. p.1 - 35

Brown, D. T., & Riddiough, T. J. (2003). Financing Choice and Liability Structure of Real Estate Investment Trusts. Real Estate Economics. pp. 313-346

Diamond, D. W. (1991). Debt Maturity Structure and Liquidity Risk. The Quarterly Journal of Economics., pp. 1-50

Feng, Z., Price, S. M., & Sirmans, C. (2011). An Overview of Equity Real Estate Investment Trusts (REITs): 1993-2009. Journal of Real Estate Literature. pp. 307-343

García-Terual, P. J., Martímez-Solano, P., & Sánchez-Ballesta, J. P. (2010). Accruals Quality and Debt Maturity Structure. A Journal of Accounting, Finance and Business Studies. pp. 188-207

Giambona, E., & Ertugrul, M. (2011). Property Segment and REIT Capital Structure. Journal of Real Estate Finance and Economics. pp. 1- 43

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Giambona, E., Harding, J. P., & Sirmans, C. (2008). Explaining the Variation in REIT Capital Structure: The Role of Asset Liquidation Value. Real Estate Economics. pp. 111-137

Gopalan, R., Song, F., & Yerramilli , V. (2014). Debt Maturity Structure and Credit Quality. Journal of Financial and Quantitative Analysis. pp. 817 - 842

Hausman, J. (1978). Specification Tests in Econometrics. Econometrica. pp. 1251 - 1271 Howe, J., & Shilling, J. (1988). Capital Structure Theory and REIT Security Offerings. The Journal of Finance. pp. 983 - 993

Johnson, S. A. (2003). Debt Maturity and the Effects of Growth Opportunities and Liquidigty Risk on Leverage. The Review of Financial Studies., pp. 209-236

Kraus, A., & Litzenberger, R. H. (1973). A State-Preference Model of Optimal Financial Leverage. pp. 911 - 918

Leland, H., & Toft, K. (1996). Optimal Capital Structure, endogenous bankruptcy, and the term structure of credit spreads. Journal of Finance. p. 987 - 1019

Modigliani, F., & Miller, M. H. (1958). The Cost of Capital, Corporation Finance and the Theory of Investment. 48. The American Economic Review. pp. 261 - 297

Myers, S. C., & Majluf, N. S. (1984). Corporate Financing and Investment Decisions When Firms have Information that Investors do not have. Journal of Financial Economics. pp. 187 - 221

Schiantarelli, F., & Sembenelli, A. (1997). The Maturity of Debt - Determinants and Effects on Firms' Performance. (T. W.-P. Department, Hrsg.) , pp. 1 - 30

Shleifer, A., & Vishny, R. W. (1992). Liquidation Values and Debt Capacity: A Market Equilibrium Approach. The Journal of Finance. pp. 1343 - 1366

Stock, J., & Watson, M. (2007). Introduction to Econometrics. pp. 109 - 349

Sun, L., Titman, S., & Twite, G. (2013). REIT and Commercial Real Estate Returns: A Post Mortem of the Financial Crisis. pp. 8 - 36

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Appendix

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7. Additional Graphs regarding Debt Maturity Variables

Figure 3: Mean Short-Term / Total Debt Ratio by Property Type across Time

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