Chapter 2 Literature Review
2.2 Agency Problem in REITs
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2.2 Agency Problem in REITs
There are two competing property management structures for the corporate organization of REITs: internal and external management. Since one notable characteristic of REIT is the separation of ownership and control, agency problem is likely to occur between shareholders and management. Jensen and Meckling (1976) defined the agency relationship as a contract when the principal engage the agent to perform some service on their behalf which involves delegating some decision making authority to the agent. If the incentive or reward mechanism is not well designed, then there is good reason to believe that the agent will not always act for the best interests of the principal. In this case, the agency cost is inevitable.5 In addition, the authors suggest that the agency conflicts will affect firm performance, and increasing management’s ownership can help mitigate agency problems. Therefore, agency theory implies that suppose agency inflicts appear in externally-managed REITs, their market performance will also be influenced by the ownership structure.
Conflicts of interest refer to situations where the interests for management and shareholders are misaligned: acting on their self-interests, managers make decisions that will not be in the best interests of shareholders. Sagalyn (1996) identified twelve types of conflicts of interest, which cut across all spheres of REIT decision making, i.e., offering formation, investment management, transaction activity, and property management.6 The author also argues that a misalignment of incentives exists for externally-managed REITs, while the potential for conflicts of interest will decline with internal management.
On the other hand, agency theory suggests that when corporate managers have a significant ownership stake, managerial incentives are more closely aligned with shareholders and agency costs are reduced (Jensen and Meckling, 1976). Cannon and Vogt (1995) found that self-administered REITs outperformed advisor REITs over the
5 Agency costs include the monitoring expenditures by the principal, the bonding expenditures by the agent, and the residual loss (Jensen and Meckling, 1976).
6Types of conflicts of interest (COI) contain allegiance, sponsor control, outside partners, over-compensation, resource allocation, competitive affiliates, tie-in business, captivity, tax timing, expense preference behavior, and malingering (Sagalyn, 1996).
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1987 to 1992 sample period even after adjusting for the differences of market risks.
Ownership structure has considerably more effect on the performance of advisor REITs, but less effect on self-administered REITs. The authors suggest that self-administered REITs have been able to reduce agency problems effectively by other approaches, for instance, more standardized financial reporting or incentive-based compensation structures. The same findings of underperformance for externally-managed REITs are demonstrated by Howe and Shilling (1990), Hsieh and Sirmans (1991).
More recently, Capozza and Seguin (2000) exhibited that externally-managed REITs consistently underperformed internally-managed REITs due to the high financial leverage over 1985 to 1992. Ambrose and Linneman (2001) examine differences between externally-advised and internally-advised REITs with respect to operating structure, growth prospects, operating revenue and expenses, cash flow and profitability, equity returns, betas and capital costs. The results almost consistent with those found by Capozza and Seguin (2000), and indicate that internally-advised REITs continue to outperform externally-advised REITs. Furthermore, the authors found that internally-advised REITs have significantly higher betas than externally-advised REITs. It reflects the market’s perception of these firms as internally-advised (unproven) growth stocks.
In Taiwan, most of T-REIT managements are related to the originating companies (i.e. parent companies). It is likely to induce conflicts of interest and result in the loss of investors’ interests. By examining the trends of REIT price and Net Asset Value (NAV), Wang and Chang (2009) suggest that some T-REITs may exist conflicts of interest due to the close business relationships between property management and original owners. In more recent studies, Tsai, Chen, and Chang (2011) found that REITs in Taiwan are not defensive since investors have not yet been familiar with the characteristics of REITs market. However, we conjecture the potential agency problem may be the main reason for the limited development of T-REITs market. Since literature on the agency problem for T-REIT is relatively limited, this study attempts to empirically verify the hypothesis of agency problem.
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Chapter 3
Research Methodology and Data Information
This chapter is divided into two sections. The first section presents the econometric methodology applied in this research for empirical analysis. The second section introduces the current development of T-REITs market, describes the data used in empirical tests, and performs preliminary analyses by means of descriptive statistics and time-series graphs.
3.1 Research Methodology
7In order to detect the existence of long-run equilibrium relationship between REITs and direct real estate, we employ cointegration test proposed by Johansen (1988). If there exists a cointegration relationship between these two variables, we could analyze the short-term relation by estimating Vector Error Correction Model (VECM). If there is no cointegration relationship, however, we should examine the interrelation between the variables through Vector Autoregressive (VAR) model.
Finally, Granger causality test is applied in this research to clarify the lead-lag relation between REITs and direct real estate.
3.1.1 Cointegration
The concept of cointegration was first introduced by Engle and Granger (1987).
According to Engle and Granger’s original definition, cointegration refers to variables that are integrated of the same order. More specifically, if a time series is non-stationary, it could become stationary after taking d time difference, which means to be integrated of the d order, i.e., a I(d) variable. When two non-stationary time series are integrated of the same order and a linear combination relationship of them is stationary, the time series are cointegrated. In other words, there exists a long-run equilibrium relationship between the variables. Engle and Granger detect whether variables are cointegrated by testing the stationary of the residuals. If the residuals are
7 The econometric methods applied in this research are referred to Enders (2004), p. 264–310; 320–372.
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stationary, then the two variables are said to be cointegrated. If the residuals are non-stationary, however, then the two variables are not cointegrated.
However, the Engle and Granger cointegration approach still have several important defects. First, the results of cointegration test may be contrasting depending on the choice of the variable selected for normalization. In other words, the results may not be consistent. Second, when using three or more variables in cointegration tests, we expect that there may be more than one cointegrating vector. This approach, however, has no systematic procedure for indicating multiple cointegration relationships. Finally, since the Engle and Granger procedure relies on a two-step estimator, any error introduced by the researcher in Step 1 is carried into Step 2.
Therefore, Johansen cointegration test is employed in this research, which can avoid aforementioned problems.
The Johansen cointegration approach is a maximum likelihood estimation of a fully specified error correction model, which is based on VAR model. This method is more robust for interpreting the multiple long-run equilibrium relationship between variables. Assuming a VAR model of order p and n variables can be expressed as:
X= AX+ AX+ ⋯ + AX+ ε (1) where: X= the (n．1) vector (X, X, ⋯ , X );
ε= an independently and identically distributed n-dimensional vector with zero mean and variance matrix ∑
After adding and subtracting AX to the right-hand side, we can continue in this fashion to obtain
ΔX = πX+ ∑πΔX
+ ε (2)
where π = −I − ∑ A
and π = − ∑ A
The key feature to note in equation (2) is rank of the matrix π, which is equal to the number of independent cointegrating vectors. If rank π = 0, the matrix is null and equation (2) is the usual VAR model in first difference. If rank π = 1, the system exists a single cointegrating vector.
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The number of distinct cointegrating vectors can be obtained by checking the significance of the characteristic roots of π. In practice, we can obtain only estimates ofπ and its characteristic roots. In order to determine whether there exists cointegration relationship, we can test the number of characteristic roots by using the following two test statistics:
λ$%&' r = −T∑ $ln (1 − λ*) (3)
λ+%,(r, r + 1) = −Tln(1 − λ*$) (4) where: T = the number of usable observations;
λ*= the estimated values of the characteristic roots (i.e. eigenvalues) obtained from the estimated Π matrix
The trace statistic tests the null hypothesis that the number of cointegrating vectors is less than or equal to r. On the other hand, the maximum eigenvalue statistic tests the null hypothesis that the number of cointegrating vectors is equal to r.
3.1.2 Vector Error Correction Model
A critical characteristic of cointegrated variables is that their time paths are influenced by the extent of any deviation from long-run equilibrium. After all, if the system is to return to long-run equilibrium, the movements of at least some of the variables must respond to the magnitude of the disequilibrium. Hence, if cointegration relationship exists between two series, according to Granger representation theorem, an error correction term must be added to correct the short-term dynamics influenced by the deviation from the long-run relationship. VECM is a special form of VAR model for I(1) that are cointegrated, making the variables move toward to the direction of long-run equilibrium. To examine the relationship between cointegration and error correction, it is important to study the properties of the simple VAR model:
Y= aY+ aZ+ ε0 (5) Z= aY− aZ+ ε1 (6) where ε0 and ε1 are white-noise disturbances that may be correlated with each other and, for simplicity, intercept terms have been ignored.
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To ensure that the variables are cointegrated of order (1,1), we must place following restrictions on the coefficients of equation (5) and (6):
a= 2(1 − a) − aa3/(1 − a) (7)
a > −1 (8)
aa+ (a) < 1 (9) To see how these coefficient restrictions bear on the nature of the solution, write equation (5) and (6) as
7∆Y
∆Z8 = 7a− 1 a
a a− 18 7Y
Z8 + 9ε0
ε1: (10)
After a bit of manipulation, equation (10) can be written in the form
∆Y = −2aa/(1 − a)3Y+ aZ+ ε0 (11)
∆Z = aY− (1 − a)Z+ ε1 (12) Equation (11) and (12) form an error-correction model. If both a and a
differ from zero, we can normalize the cointegrating vector with respect to either variables. Normalizing with respect to Y, we get
∆Y = α0(Y− βZ) + ε0 (13)
∆Z = α1(Y− βZ) + ε1 (14) where: α0= −aa/(1 − a);
β = (1 − a)/a; α1= a
Notice that α0 and α1 have the interpretation of speed of adjustment parameters. The larger α0 is, the greater the response of to the previous period’s deviation from long-run equilibrium. At the opposite extreme, very small values of α0 imply that the short-term of the variable Y is unresponsive to last period’s equilibrium. If both α0 and α1 are equal to zero, the long-run equilibrium relationship does not appear and the model is not one of error-correction or cointegration.
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3.1.3 Vector Autoregressive Model
If those series are not cointegrated, the vector autoregressive model is a general framework to explore the dynamic interrelationships among economic variables. All the variables in a VAR model are treated symmetrically. In particular, each variable has an equation explaining its evolution based on its own lags and the lags of all the other variables in the model. In this case, VAR model can identify the lags short-term impact on the dependent variable by analyzing the correlation between the lags of the dependent variable and of other variables. Therefore, this study applies the VAR approach to examine the reactions of REIT to direct real estate and the reactions of direct real estate to REIT.
In the bivariate case, we can let the time path of Y be affected by current and past realizations of the Z sequence and let the time path of Z be affected by current and past realizations of the Y sequence. Based on this concept, we estimate a VAR in the standard form:
Y= α;+ α<Y+ α<Z<+ e (15)
Z= α;+ α<Y+ α<Z<+ e (16)
It is assumed that (1) both Y and Z are stationary; (2) the error term (i.e. e
and e) are composites of the two shocks ε> and ε?.
In addition, there are two useful techniques employed by VAR analysis to understand the interrelationship between variables. One is impulse response function which can quantify and graphically depict the time path of the short-term impact varies under the long-run fluctuations. In other words, it will present how the variables react to shocks. The other is variance decomposition which allows us to assess the relative contributions of different shocks to the forecast error variance, that is, it will be informative to present the sources of volatility.
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3.1.4 Granger Causality
In addition to cointegration test, we can gain some additional insights into the interrelation between two series by performing Granger causality tests both of REIT on direct real estate and of direct real estate on REIT. The main purpose of this methodology is to examine the existence of lead-lag relations between two variables.
In other words, it can investigate the ability of one series to predict another, conditional on its own past value.8 For instance, if current and past value of Y is helpful to forecast future values of Z, it is said that Y does Granger cause Z, alternatively called Y leads Z. Moreover, if there is an interaction between the two variables, then the result indicates the feedback relation between variables.
Suppose two variables in VAR model are stationary, but does not have a cointegration relationship, the Granger causality equation is defined as:
∆Yt= α0+ ∑ αp i∆Zti
i1 + ∑ βp j∆Ytj
j1 + εt, (17)
where Y is the dependent variable; Z is independent variable, p is lag terms. The null hypothesis is α = α = ⋯ ⋯ = αA = 0. If the results reject the null hypothesis that Z sequence does not lead Y sequence, then the inclusion of Z sequence in the equation is useful in predicting Y sequence.
If there is a cointegration between two variables, the result of causality test would be biased by using equation (17) directly. In order to avoid the distortion, the deviation from the long-run equilibrium level should be taken into consideration.
Hence, we employ VECM to estimate by adding error correction term λµB into the above VAR model, becoming equation (18).
∆Y= α;+ ∑ α ∆Z+ ∑ β ∆Y+ λµB+ ε (18)
8 Such causality, based on predictability, is not to be confused with causality based on cause and effect, which can only be tested by performing controlled experiments (Myer and Webb, 1993).
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3.2 Data Information
This section is divided into three subsections, including the introduction of T-REITs market, data source and data analysis. The first subsection introduces the current development of T-REITs market and general information of REITs launched in Taiwan. Next, the source and information of data applied in this study are described in the second subsection. Finally, we do some preliminary analyses by presenting the descriptive statistics and depicting the time series trends of these variables.
3.2.1 Introduction of T-REITs Market
In Taiwan, the first case of REIT (Fubon No. 1) was offered to the public in March 2005. By the end of 2010, there are eight REITs issued and the total market capitalization of T-REITs has reached NT$ 62.17 billion, while the number of T-REITs ceased to increase since May 2007. As shown in Table 3-1, the highest percentage of market capitalization is Cathay No.1, whereas Kee Tai Star and Trident possess relatively lower market capitalization, which are liquidated in mid-2011. In other words, the market capitalization of T-REITs has shrunk gradually.
Table 3-1 Market Value of T-REITs Market
T-REIT Stock Symbol Issuing Date Market Value (billion)
Percentage of Total Market Capitalization
Fubon No. 1 01001T 03/10/2005 6.94 11.16%
Cathay No. 1 01002T 10/03/2005 16.47 26.49%
Shin Kong No. 1 01003T 12/26/2005 11.46 18.43%
Fubon No. 2 01004T 04/13/2006 7.96 12.80%
Trident 01005T 06/26/2006 4.77 7.67%
Kee Tai Star 01006T 08/14/2006 2.53 4.07%
Cathay No. 2 01007T 10/13/2006 8.09 13.01%
Gallop No. 1 01008T 05/15/2007 3.95 6.35%
Total 62.17 100.00%
Note: As of December 31, 2010, there are seven T-REITs listed on the Taiwan Stock Exchange and one traded in the OTC market (i.e. Kee Tai Star).
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The basic information of T-REITs is summarized in Table 3-2 and ordered chronologically by issuing date. Panel A and Panel B present the details of investment security and investment property regarding each T-REIT, respectively. In terms of the types of REIT, a REIT is classified in one of three categories by investing direct or indirectly in properties. All the REITs in Taiwan are equity REITs, which invest directly in real estate, own and manage the properties, and therefore are responsible for the properties’ asset value.
Another distinction method is whether a REIT is closed-ended or open-ended.9 According to the “Clauses of the Real Estate Securitization Act,” the REIT funds shall be only subject to closed-end funds; provided, that open-end funds attached with repurchasing time, quantity or other limits may be collected with the approval of the competent authority. Since the sophisticated evaluation of NAV is required for open-end fund, all the T-REITs are closed-end funds.
As shown in Panel B, most T-REIT properties are commercial office buildings or shopping centers, and located in Taipei City. This is the reason why we choose commercial real estate in Taipei City on behalf of direct real estate market to examine the relationship with T-REITs market. However, investors’ interests and the performance of T-REITs may be influenced by the market capitalization or the concentrated risk of properties. In this study, we thus attempt to investigate whether T-REIT price could reflect the fundamentals of direct real estate market. In addition, the economic implication and the deficiencies in T-REITs market would be discussed based on the empirical results.
9 Closed-end fund shall mean a fund where its investors may not request the trustee to repurchase the beneficiary securities held by them during the duration of the fund. Open-end fund shall mean a fund where its investors may request the trustee to repurchase the beneficiary securities held by them during the duration of the fund.
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Table 3-2 Summary Information of T-REITs
Panel A: Investment Security Part
Cathay No. 1 10/03/2005 13.93 Land Bank of Taiwan
Shin Kong No. 1 12/26/2005 11.30
Mega
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Note: 1. The information is summarized from the prospectuses of eight T-REITs.
2. The raising capital is presented in billion NT dollars.
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T-REIT Investment Property Original Owner Major Tenant Location Property Type
Fubon No. 1
3. Foreign Office in Taiwan
Taipei City
1. My Humble House Hospitality Management Consulting 2. Eslite Corporation
3. Cathay United Bank, Insurance
Taipei City
2. Taishin Bank, 104 Corporation 3. THAI Taiwan, Hitachi Asia,
Clariant Corporation
1. Fubon Securities, Bank, Carat Media
2. Fubon Financial Holding, Bank,
Taipei City
1. Commercial Office Building
2. Industrial-Office
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1. Chinatrust Life, Shin Kong Bank, OCBC Bank 2. City Lake Hotel, President
Chain Store
2. ABB Group, Yang Ming Marine Transport Corporation
3. Johnson & Johnson, Mary Kay Taiwan
Taipei City Commercial Office Building
Note: The information is summarized from the prospectuses of eight T-REITs.
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3.2.2 Data Source
The aim of this study is to explore the relationship between REITs and direct real estate markets in the U.S. and Taiwan, respectively. Since equity REIT is the major investment form both in the U.S. and in Taiwan, we thus take equity REIT price indices as the proxy of REITs markets. On the other hand, the types of investment property are diversified in the U.S. REITs market, while most T-REITs focus on commercial properties. For the comparability of empirical results, this study chooses commercial real estate markets on behalf of direct real estate markets.
Table 3-3 summarizes the data information used in this study. For the empirical analysis of the U.S., the FYSE/NAREIT Equity REITs Index (NAREIT) and the transaction-based Index (TBI) are employed. To avoid the appraisal smoothing problem exhibit in the conventional National Council of Real Estate Investment Fiduciaries (NCREIF) Property Index (Fisher, Geltner, and Pollakowski, 2007), this study applies TBI which is established by MIT/CRE Commercial Real Estate Data Laboratory (MIT/CRE CREDL).10 On the other hand, the T-REITs price index from the Taiwan Economic Journal (TEJ) is applied for the REITs market in Taiwan. For the direct real estate market, we employ the transaction price of commercial real estate provided from the one big (Y) realty company in Taiwan. Since most T-REIT properties are located in Taipei City, the transaction price of direct real estate discussed in this study is that of commercial property in Taipei City.
Table 3-3 summarizes the data information used in this study. For the empirical analysis of the U.S., the FYSE/NAREIT Equity REITs Index (NAREIT) and the transaction-based Index (TBI) are employed. To avoid the appraisal smoothing problem exhibit in the conventional National Council of Real Estate Investment Fiduciaries (NCREIF) Property Index (Fisher, Geltner, and Pollakowski, 2007), this study applies TBI which is established by MIT/CRE Commercial Real Estate Data Laboratory (MIT/CRE CREDL).10 On the other hand, the T-REITs price index from the Taiwan Economic Journal (TEJ) is applied for the REITs market in Taiwan. For the direct real estate market, we employ the transaction price of commercial real estate provided from the one big (Y) realty company in Taiwan. Since most T-REIT properties are located in Taipei City, the transaction price of direct real estate discussed in this study is that of commercial property in Taipei City.