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Journal of Property Investment & FinanceEmerald Article: REITs, the stock market and economic activity

Nikiforos Laopodis

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REITs, the stock market andeconomic activity

Nikiforos LaopodisDepartment of Finance, School of Business, Fairfield University, Fairfield,

Connecticut, USA

Abstract

Purpose The purpose of this paper is to investigate the linkages among real estate investmenttrusts (REITs), the stock market, and real economic activity for the USA for the 1971-2007 period. Inview of the fact that when the economy performs well the equity and REIT markets also do well, it iseasy to see why one needs to examine the dynamic interactions among these magnitudes andunderstand the implications of market movements or policy changes on the returns of REIT.

Design/methodology/approach The empirical investigation is conducted via the vector

autoregressive (VAR) methodology coupled with Granger causality and cointegration analyses.VAR analysis permits inferences to be drawn about how a particular variable, say, the stock market,helps to explain a REITs return and to see how a shock from the same variable affects that return. Inother words, the magnitudes which are more relevant in explaining the REIT return can be deduced soas to determine the driving forces behind the return. Finally, some robustness tests are performed andsome other relative magnitudes are experimented with so as to have a more comprehensive picture ofthe dynamic interactions among the three variables.

Findings First, the equity and the mortgage REIT categories display essentially similar patternswith their interactions with the general stock market and/or industrial production movements.Specifically, in the case of the equity REIT, it is revealed that a reciprocal linkage between the twoexists, whereas for the mortgage REIT a uni-directional one run from the REIT to the stock market.Second, when substituting the general stock market returns with two sub index returns (the small- andthe mid-cap excess returns) it is found that the two REIT categories are more closely related to a subindex than the general stock market index. Overall, significant short-run interactions are seen amongthe three magnitudes since the 1970s.

Originality/value The results are important for investors and policymakers. For investors, thefinding of the close relationship between the equity and mortgage REIT categories and the generalstock market is that there may not be a profitable reallocation of portfolios within these two assetclasses. For policymakers, it can be suggested that they take notice of how changes in monetary policy(via changes in interest rates or money supply) influence REIT investments and what the impact ofthat would be on the reallocation of such investments by professional investors and managers.

Keywords Real estate, Investments, Stock markets, Equity capital, Unites States of America

Paper type Research paper

1. IntroductionThe real estate investment trust (REIT) market has grown significantly during the last

decade or so both in importance and size. The REITs extraordinary growth exceededthe US. S&P 500 index (on a total-return basis) during most of its life and it appears tohave done better than the index even after 2001 (when some REITs became part of theindex). A REIT is a closed-end investment company that either owns and/or operatesincome-generating real estate and mortgages. There are three basic types of REIT:equity, mortgage and hybrid. The dramatic growth of the sector has sparked animpressive interest in empirically investigating it and learning of its characteristics.Specifically, the equity REIT has exploded in growth since 1993 with a distant second

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the mortgage REITs. The hybrid REITs, by contrast, have declined in number sincethen.

Despite the importance of the REITs in an investors portfolio, not much is knownabout how this asset class interacts with the general stock market and the real

economy. Although some evidence about the interactions between REITs and thegeneral stock market exists (see, for instance, Okunev et al., 2000), the linkages betweenreal economic activity and REITs have not been duly investigated. So, this study is thefirst, to the best of our knowledge, to investigate these linkages within a dynamicframework. It is easy to see why one needs to examine the dynamic interactions amongthese magnitudes and understand the implications of market movements or a policychange on the returns of REITs. For example, if the general economy performs wellthen the equity and REIT markets will also do well.

Therefore, the importance of the study comes from two additional reasons. First,past studies have presented mixed evidence on the relationship between stock returnsand REIT returns. The second, and more important reason, is that studying thesemagnitudes simultaneously would enable us to draw inferences for both private andpublic policy purposes. For instance, private portfolio managers would be interested inlearning how sensitive REIT returns are to stock market movements in order toimprove the risk management of their real estate portfolios and/or also see whethermixing real estate assets with a general market portfolio would offer better risk/returnopportunities. Finally, official policymakers (i.e. monetary authorities) would beinterested in seeing how changes in interest rates affect REIT performance (and/or anyof its categories).

The empirical investigation will be conducted via the vector autoregressive (VAR)methodology coupled with Granger causality and cointegration analyses for the1971-2007 period. VAR analysis permits us to draw inferences about how (or to whatextent) a particular variable, say, the stock market, helps explain a REITs return and

to see how a shock from the same variable affects that return. In other words, we candeduce which magnitudes are more relevant in explaining the REIT return so as todetermine the driving forces behind the return. Finally, we will perform somerobustness tests and experiment with some other relative magnitudes so as to have amore comprehensive picture of the dynamic interactions among the three variables.

The remainder of the paper is organized as follows. Section 2 contains a selectivereview of the literature, while section 3 outlines the methodological design of the studyand the data. Section 4 deals with some preliminary statistical issues, the mainempirical results and conducts some robustness tests. In section 5 we present someadditional evidence on the dynamics among the three magnitudes using alternativemeasures of returns such as two sub indices of the general stock market. Finally,section 6 summarizes the findings of the study and concludes with some policy

recommendations.

2. Literature reviewThe preponderance of the empirical evidence on REIT performance, for most of the1970s and 1980s, suggests that REITs tend to outperform common stocks. Firstenberget al. (1988) report that EREITs beat the S&P 500 by about 12.5 percent over an 11-yearperiod (1974-1985). Titman and Warga (1986) also corroborate these findings for theEREITs and MREITs for the 1970s and early 1980s. More recently, Chan et al. (1990)

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find no evidence that EREIT performance exhibited superior performance since themid 1970s until 1990. Gyourko and Keim (1992), using the CAPM framework, whileconfirming the earlier results that REITS offered superior returns for most of the 1970sand early 1980s, report that this was not the case for the remainder of the 1980s (that is,

1983 to 1990). Finally, Peterson and Hsieh (1997), using the five-factor model of Famaand French (1993), find that MREITs have, on average, underperformed commonstocks and EREITs during 1976-1992.

Regarding the dynamic linkages between real estate assets and the stock market,the extant evidence is mixed. One side of this literature appears to have a consensus onthe notion that the real estate and the stock market are not integrated. For instance,Miles et al. (1990) have documented segmentation within real estate markets and stockmarkets. Liu et al. (1990) and Gyourko and Keim (1992) produced evidence to thecontrary suggesting that the two markets are integrated. More specifically, Liu et al.find that EREITs and the stock market are integrated, while Gyourko and Keim offerempirical evidence that the stock market contains significant information about realestate fundamentals. The second group of authors notes that REITs were segmented

from the equity market during the 1972-1991 period, but for the 1992-1996 period bothmarkets were integrated. Moreover, Clayton and MacKinnon (2001) find that theresponsiveness of REIT returns to the broad stock market was significantly reduces inthe 1990s. Finally, Okunev et al. (2000) find that there exists a uni-directional causalityrunning from EREIT to the stock market, when using linear tests, but, when usingnon-linear tests, they find exactly the opposite about causality.

3. Methodology and data3.1. Model specificationSince the objective of this study to investigate the dynamic relationships among (thevarious categories of) REITs, the stock market and real economic activity, while

placing as few theoretical restrictions as possible on the systems variables, we willemploy the VAR specification. The general form of a VAR model is given by thefollowing unrestricted (reduced-form) system:

Zt a cL Zt nt 1

where Zt is a vector of the n (stationary endogenous) variables, ais an n 1 vector ofconstants, c(L) is an n n matrix of (lagged) polynomial coefficients, and nt is ann 1 vector of white noise innovation terms (with Entk 0 and E(ntk, nsk 0 fort s). The disturbance term, nt, also has a covariance matrix, Ent; n

0t S. Finally,

the lag operator is defined as cL c1 c2L . . . ckLk21 of degree k2 1 and cj,

for j 1, . . . , k.More specifically, the general 3-equation VAR system can be expressed as follows:

DREITt a1 Xn

i1

l1;iDREITt2i Xn

i1

k1;iDSPt2iXn

i1

n1;iDIPt2i 11;t 1a

DSPt a2 Xn

i1

l2;iDREITt2i Xn

i1

k2;iDSPt2iXn

i1

n2;iDIPt2i 12;t 1b

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DIPt a3 Xn

i1

l3;iDREITt2i Xn

i1

k3;iDSPt2iXn

i1

n3;iDIPt2i 13;t 1c

where REIT, SP, and IP are the REIT category, stock market and industrial production,respectively, li, ni, and kiare parameters to be estimated,D is change, and 1i;ti 1 to 3)are stationary random processes describing the error terms. The nis (i 1 to n) are theoptimal orders of the autoregressive process for a given variable. Equations (1a)-(1c)serve as an appropriate framework for evaluating the dynamic short-run interactionsamong the three variables. Specifically, such dynamics among the three variables arecaptured by ki, li and ni coefficients. For instance, if one or more ofki coefficients arenonzero and statistically significant, then movements in the stock market will haveshort-run effects on the REIT category and industrial production. Similarly, if one ormore of ni coefficients are nonzero and significant, then movements in industrialproduction will have short-run effects on the specific REIT category and the stock

market.If the above-estimated coefficients are jointly found to be statistically significant,then past values of a given variable can explain variations in the other variable and thenull hypothesis (of no mutual, reciprocal impact among the three variables) can berejected. Finally, since determining the optimal lag structure of equations (1a)-(1c) is aconcern that needs to be addressed, for if the lag structure is mis-specified the empiricalresults may be biased, the use of the final prediction error (FPE) criterion will beemployed.

3.2. Data and preliminary statistical resultsThe data sample contains monthly observations on the following variables. For thefour categories of REITs namely equity, mortgage, hybrid and composite we obtained

the total index return. The samples period is from December 1971 to December 2007and the main source is the US National Association of Real Estate Investment Trust(NAREIT) web site. The other variables are the S&P 500 continuously compoundedreturns and the growth rate of industrial production. Industrial production is a proxyfor real economic activity. These variables, except for the stock return data, areobtained from the Federal Reserves online FRED database. The stock return data areobtained from the CRSP database. We compute the excess returns of each REITreturns category and the stock returns by subtracting the three-month Treasury Billfrom their total return.

Tables I and II exhibit some preliminary statistics on each series and the correlationmatrix for the entire period. Before proceeding with the estimation of the descriptivestatistics of each series, we conducted a unit-root test based on the Augmented

Dickey-Fuller (1987), methodology and found that each series contains a unit root in itslog format (results not reported here but are available upon request). By obtaining thefirst-difference of each series the unit root null hypothesis was rejected suggesting thatthey be examined further in that format. The constructed variables are now as follows:er_sp is the excess stock returns; er_trc is the excess returns on the composite totalreturn; er_tre is the excess returns of the equity total return; er_trh is the excess returnsfor the hybrid total return; er_trm is the excess returns of the mortgage total return; ipgis the industrial production growth rate.

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Some observations from the table results are worthy of special mention. First, theexcess returns on the mortgage category of REITs show the highest volatility, among

both the other REIT categories and the stock market. Second, industrial productiongrowth has the smallest standard deviation among all other variables and indicates asmooth path over time. Third, apart from the expected high correlations among thefour REIT categories, moderate and positive correlations are observed among thesecategories and the stock market. Finally, the correlations among all variables and theindustrial growth appear to be (negative) and rather small during the 26-year or soperiod.

4. Main empirical results4.1. Preliminary issuesIn VAR analyses, the ordering of the variables becomes an issue and it must be

addressed. There are various ways that we can order the variables in the system. Onemethod is to rely on economic theory and suggest an ordering, say, from the financial(side of the economy) variables to the real (side of the economy) variables or try thereverse order. Another method is to rely on statistical techniques like Grangercausality tests and see if there are patterns of sequential influences of a variable on all(or most of the other) variables, and then another variable on the other variables and soon. Preliminary statistical investigation using the second approach (for up to twelvelags) revealed several significant, reciprocal Granger-causality interactions among

DescriptivesVariable Mean Median Max Min St. dev. Skewness Kurtosis

er_sp 0.5625 0.8539 15.041 224.600 4.3280 20.6582 6.0501

er_trc 0.8743 0.0419 20.7552

13.026 4.4481 0.0633 9.0042er_tre 0.6831 0.9223 12.053 211.296 4.0112 20.3294 4.8973er_trh 0.6411 0.8227 25.405 220.114 5.5343 0.2895 10.4304er_trm 0.5675 0.7302 24.345 220.750 5.9314 20.0725 8.3555ipg 0.2293 0.2745 2.3453 23.5912 0.7073 20.8853 7.0047

Notes: er_sp is the excess stock returns; er_trc is the excess returns on total return composite; er_tre isthe excess returns of the total return equity; er_trh is the excess returns for the total return hybrid; er_trm is the excess returns of the total return mortgage; sample is monthly from 1972:1 to 2007:12

Table I.Descriptive statistics

er_trc er_tre er_trh er_trm ipg

er_sp 0.305 0.331 0.237 0.257 0.093er_trc 1 0.929 0.901 0.861 20.010er_tre 1 0.775 0.736 20.015er_trh 1 0.827 20.050er_trm 1 20.077

Notes: er_sp is the excess stock returns; er_trc is the excess returns on total return composite; er_tre isthe excess returns of the total return equity; er_trh is the excess returns for the total return hybrid; er_trm is the excess returns of the total return mortgage; sample is monthly from 1972:1 to 2007:12

Table II.Correlation matrix

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each REIT category, industrial production and the stock market which became difficultto disentangle (results available upon request). Such finding is consistent with thelinear Granger causality tests performed by Okunev and Wilson (1997) promptingthem to use nonlinear tests to uncover a true relationship(s) between the stock market

and equity REITs. As a result, we resorted to economic theory and decided to order thevariables from the real sector to the financial sector of the US economy. Besides, it isthe real growth of the economy (i.e., production of all types of real estate) that spursgrowth in the financial sector (i.e., gives rise to financial instruments like REITs).Therefore, industrial production growth is ranked first, followed by a REIT categoryand then by stock returns.

Another issue is that of selecting the optimal lag length of the structural VAR. Theuse of the FPE criterion revealed an optimal lag-length of five for the VAR thatincludes the hybrid and the mortgage categories of REITs. For the composite and theequity categories, the criterion indicated a three-month optimal lag length for the VAR.The tests were conducted over 12 lags and the smallest values of the criterion wereachieved for five or three lags, as specified above. Finally, before proceeding with theVAR estimation, we must check for possible cointegration among the three variables.The Johansen (1995) multivariate cointegration procedure for the three-variable groupsyielded no cointegration among the variables (results are available upon request) andthus the models will not contain an error-correction term and will only represent theshort-run interactions among the three series.

4.2. Main empirical resultsIn this section, we focus only on the two important outputs from VAR: variancedecompositions and impulse response graphs (the estimates from the models areavailable on request). Table III contains the variance decomposition results for twoREIT categories for selected lags. Since the results for the composite REIT are nearly

identical to those for the equity REIT, we focus on the latter. First, shocks to the excessreturns of the equity REIT explain about 86 percent of the variation of their ownmovement. Second, industrial production explains approximately 8 percent of theequity REIT excess returns and 6 percent of the stock market excess returns. Third, thestock market (excess returns) seems to explain a smaller fraction of the equity REIT(about 4 percent) relative to the industrial production variable. Fourth, in contrast tothe third observation, the equity REIT surfaces as a major determinant (with 37percent) of the error forecast variance of the stock returns.

Figure 1 shows the impulse response graphs for all above variables. Again, theresponses of the two REITs are similar and thus we only discuss the equity REIT, orthe three first graphs in the figure. A shock in the industrial production (the IPG line inthe 2nd graph) seems to exert a negative and consistent impact on the excess returns of

the EREIT which dies out in about 20 months. By contrast, the stock market (ER_Spline in same graph) is seen to have a positive effect on the EREIT, which fades after 15months. The impact of shocks on the stock market excess returns from industrialproduction changes (see IP line in third graph) emerges a negative, initially, but thenbecomes positive and dies out in less than 20 months. The effect of innovations fromthe EREIT (ER_TRE line) on the stock returns surfaces as positive and highlysignificant and then fades smoothly in about 20 months. By contrast, the impact of theEREIT on industrial production (ER_TRE line in first graph) is negative at first but

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then it turns positive and dies out well after 20 months, meaning that it takesapproximately two years for this shock to be fully absorbed by the real economy.

Continuing our analysis of the empirical findings, we next discuss Table IV, whichcontains the variance decomposition results for the hybrid and the mortgage REITs.Relative to the previous findings, we note the following. First, excess returns on themortgage REIT seem to explain only 4 percent (on average) of the variance ofindustrial production, contrary to the equity REIT which explained double thatpercentage. Second, excess stock returns explain only a negligible fraction (less than 1percent) of the error forecast variance of the MREIT relative to a 4 percent explanation

of the variance of the EREIT. Finally, innovations in MREIT are seen to explain alower fraction (about 29 percent) of the variance of excess returns in comparison of anaverage of 38 percent for the EREIT (or 39 percent for the CREIT). The impulseresponse graphs are shown in Figure 2. Two main observations can be made. First,there seems to be higher turbulence in the way the short-run responses by one variableto shocks from the other variables behave. Second, innovations from the stock marketseem to elicit alternating responses, that is, between negative and positive, to themortgage REIT. In all instances, however, these responses tend to die out within two

Period IPG ER_TRE ER_SP IPG ER_TRC ER_SP

Variance decomposition of IPG2 98.4579 0.1183 1.4236 98.0417 0.4655 1.4927

4 95.6571 2.3132 2.0295 94.1543 3.9108 1.93476 92.5163 4.8230 2.6605 91.3869 6.0877 2.52528 90.0665 6.7939 3.1394 89.3942 7.7542 2.8514

10 88.4075 8.1910 3.4013 88.6721 8.3634 2.964412 87.4401 9.0095 3.5502 87.7367 9.1556 3.1075Variance decomposition of ER_TRE

2 2.9824 95.9703 1.04724 7.2317 89.0675 3.70066 8.2623 87.8070 3.93068 8.6860 87.0999 4.2140

10 8.9154 86.7427 4.341812 9.0555 86.5494 4.3949Variance decomposition of ER_TRC

2 2.1273 96.5423 1.33034 6.2839 90.0134 3.70256 7.3171 88.8330 3.84978 7.7250 88.1718 4.1031

10 7.9603 87.8353 4.204212 8.0990 87.6586 4.2422Variance decomposition of ER_SP

2 0.6323 2.2479 97.1197 0.2338 2.0820 97.68404 3.2623 34.960 61.7771 2.4437 37.123 60.43276 5.5353 36.168 58.2950 4.7926 38.082 57.12468 6.1895 37.611 56.1990 5.4418 39.545 55.0123

10 6.5218 38.250 55.2275 5.7951 40.165 54.039712 6.7249 38.560 54.7148 6.0191 40.434 53.5466

Note: For selected lags

Table III.Variance decompositions

for EREIT and CREIT

indices

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years. In the case of the mortgage REIT, the reaction of this REIT category toinnovations from the stock market appears to be less volatile than that of the hybridREIT.

An important insight has been revealed in the above results and from the statisticalsignificance between the stock market excess returns and the equity and mortgageREITs. Specifically, the finding that the equity REITs are positively linked with the

Figure 1.Impulse response graphsfor EREIT and CREITVAR models

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stock market, and seem to mutually affect each other, suggests that there may not be areallocation effect of portfolios within these asset classes when the stock marketadvances or declines. Since an up market appears to positively influence equity REITsit is implied that the equity risk premium may not be as important (within this context).Alternatively put, given the direct relationship between the two markets a price changein one market will spill over to the other market and thus no reduction in risk, viadiversification, is possible. This result is intuitive given the fact that the two assetclasses are very closely related and that the equity REITs submarket is impacted by

the general state of the stock market.Such a lower diversification benefit implication is in accordance of the findings by

Glascock et al. (2000) who find that including REIT assets in a (multi-asset) portfoliomay not offer better profit opportunities since the 1990s. In this sense also, our findingsare consistent with similar findings by Stevenson (2002), Gyourko and Keim (1992),and Cotter and Stevenson (2006) who report that REITs and value stocks (as in theS&P 500) have similar return structures given their high levels of asset backing andcash flows.

Period IPG ER_TRH ER_SP IPG ER_TRM ER_SP

Variance decomposition of IPG2 98.8379 0.3183 0.8436 97.9217 0.4155 1.6527

4 96.9671 1.8132 1.1195 96.2143 1.3108 2.42476 94.0263 3.2230 2.7205 93.4269 1.8477 4.72528 91.1265 5.6939 3.2694 90.9642 3.5942 5.4314

10 89.4075 6.8910 3.7413 89.2421 4.9734 5.814412 88.1101 7.9495 3.9402 88.0167 6.0556 5.9375Variance decomposition of ER_TRH

2 3.3724 95.1803 1.44724 9.8617 88.3475 1.79066 10.063 87.1600 2.77068 10.540 86.6799 2.7840

10 10.684 86.3427 2.981812 10.765 86.1694 3.0649Variance decomposition of ER_TRM

2 2.8773 96.8223 0.29034 7.9039 91.4134 0.68256 7.9671 91.0430 0.99978 8.3150 90.6718 1.0031

10 8.3703 90.5053 1.124212 8.4090 90.4186 1.1722Variance decomposition of ER_SP

2 0.5473 1.7179 97.7397 0.2118 1.0120 98.77404 4.3723 27.080 68.5671 3.4237 27.333 69.23276 7.3573 26.988 65.6550 5.7926 27.762 66.48468 7.7795 29.621 62.5990 6.0718 31.065 62.8523

10 8.4328 30.200 61.3575 6.5251 32.045 61.432712 8.6279 30.840 60.5328 6.6491 32.864 60.4966

Note: For selected lags

Table IV.Variance decompositions

for HREIT and MREIT

indices

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4.3. Robustness testsIn order to see if our results remain stable throughout the entire period underexamination (1970-2007), we will conduct some robustness tests on the estimated VARmodels. More specifically, we will investigate the coefficient stability under some

Figure 2.Impulse response graphsfor HREIT and MREITVAR models

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events that significantly affected the structure/trading of the REITs in the US

economy. Such events are the Savings & Loan financial crisis of late 1980s (in 1989 in

particular), which altered the financing of real estate assets, and the 1986 Tax Reform

Act, which called for the elimination of tax-based incentives of REITs. These two

events will be examined via the dummy variable method, which takes the value of 1during the event and 0 otherwise.

It is interesting to mention that, although the use of the 1986 dummy variable itself

in each VAR equation did not surface as statistically significant, when we formed an

interaction variable (er_tre 1986 dummy]) it emerged as highly statistically

significant and positive (in the er_tre and er_trm equations in the VAR system). The

1989 dummy, by contrast, was not found significant in any format in any REIT

subcategory. Figure 3 illustrates the impulse response graphs for the 1986 event (using

the interaction variable) for the equity REIT (the mortgage REIT revealed very similar

graphs and thus was omitted). A basic observation form these graphs is that although

the nature of the interactions among all variables in the system has not changed

(relative to the graphs in Figures 1 and 2), the intensity of the reaction to which avariable responds to shocks from another variable seems to be smaller. In other words,

whereas innovations from a variable to another die out well after a year and a half, asevidenced by the first set of graphs in Figure 1 (or Figure 2, for the mortgage REIT),

such innovations now tend to die out well under a year for all variables. Thus, the main

results above remain robust even accounting for the two events in the 1980s, which

means that our main model specification was appropriate.

Figure 3.Impulse response graphsfor EREIT, stock prices,

industrial production with1986 interaction dummy

variable, 1972:1-2007:8

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5. Further evidence on the dynamics among REITs, stock market, andeconomic activityIn this section, we investigate the impact of alternative measures of stock returns, inlieu of the aggregate stock market index, on the equity and the mortgage REIT

categories only. We employ two different measures of (the excess) stock returnsnamely, the S&P 500 small-cap and the S&P 500 mid-cap indices to further examinethe dynamic interactions among REITs, stock market and real economic activity. Theidea is to see whether and how the two main REIT categories excess returns, theequity and the mortgage, are linked with each subcategory of the general equitymarket in view of some mixed evidence on the issue (see Okunev and Wilson, 1997;Cotter and Stevenson, 2006). Also, it has been suggested that the small cap index maybe more relevant as a measure of stock market activity when in conjunction withREITs (see Chiang and Lee, 2002; Stevenson, 2002).

Table V contains the variance decompositions for the mid- and small-cap indiceswhereas Figure 4 exhibits the impulse response graphs for selected lags. From thevariance decomposition results, we can detect little difference in the way the small- or

the mid-cap stock returns interact with the equity REIT or industrial production.Perhaps the only notable result is the observation that industrial production changes

Period IPG ER_TRE ER_MID IPG ER_TRE ER_SMA

Variance decomposition of IPG2 99.6279 0.0409 0.3066 93.2921 0.4155 0.35274 96.1351 2.5082 1.3565 92.6143 0.2108 2.72476 92.6063 5.8970 1.5015 90.5269 5.6577 2.62528 90.8645 7.6509 1.4856 89.7642 6.5942 2.5314

10 89.3055 9.2320 1.5725 88.5421 8.5734 2.414412 88.4561 10.090 1.5607 87.3167 8.7556 2.3375

Variance decomposition of ER_TRE2 2.2924 97.3653 0.3472 1.2373 98.4223 0.12034 8.2967 90.9925 0.7106 3.4039 96.5434 0.78256 8.3213 90.0880 1.5806 3.5671 96.8730 0.89978 8.7600 89.6729 1.5640 3.6150 95.4318 0.7631

10 8.9234 89.5040 1.5718 3.7703 95.5353 0.774212 8.9850 89.4544 1.5649 3.8090 95.6786 0.7652Variance decomposition of ER_MID

2 0.0873 0.0509 99.86974 0.1123 1.9800 97.89716 0.2873 2.0888 97.62508 0.2865 2.1221 97.5890

10 0.2898 2.1350 97.577512 0.2890 2.1370 97.5708

Variance decomposition of ER_SMA2 0.0118 0.0120 99.77404 0.1237 0.3433 97.65276 0.2426 2.5762 97.54468 0.2618 2.5065 97.4323

10 0.2851 2.2345 97.543712 0.3391 2.4464 97.4786

Note: Selected lags

Table V.Variance decompositionsfor EREIT and mid-capand small-cap indices

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account for 8 percent of the variance decomposition of the equity REIT excess returns,when linked with the mid-cap sub index, but only 3 percent when linked with thesmall-cal sub index.

A similar picture is essentially revealed by the impulse response graphs whichimply that equity REITs respond to shock from the small- or the mid-cap movementsin a non-turbulent manner, alternating between positive and negative values, butappear to decay fast. Another common finding is that the equity REITs react to

Figure 4.Impulse response graphs

for EREIT with mid- andsmall-cap indices

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innovations from industrial production changes in a negative manner and thesereactions take as much as a year to die out. This is an interesting result and it may runcontrary to common belief that a pick up in economic activity boosts stock returns.However, if we think of this interaction differently, we may see that as economic

activity picks up, interest rates may also increase, thus reducing the returns (values) ofequity stocks. Therefore, this inverse relationship may be a proxy for the impact ofinterest rates on the equity REITs. To take this comment further, it would beinteresting to examine the effect of monetary policy on equity (and mortgage) REITs.

The final observation worthy of mention entails the different reactions of thecomposite S&P500 (er_sp) and its two subcategories (er_small, and er-mid).Specifically, the reaction of the (excess returns of the) composite index to shockscoming from industrial production or the equity REITs is much stronger than that ofthe mid- or small-cap indices and tends to stabilize well after two years (relative toabout half a year for the sub indices). In a sense, these findings seem to suggest thatequity REITs are more closely related to a sub index (based on its lower degree ofresponse to a sub index) than the composite index (where its response surfaces as morevolatile). Therefore, we observe a decoupling effect between these REIT categories andthe general stock market. This conclusion is in line with (some of) the findings by Liuand Mei (1992) and Adrangi et al. (2004).

6. Summary and conclusionsIn this paper we examined empirically the dynamic interactions among REIT excessreturns, stock market excess returns (proxied by the S&P 500 index), and real economicactivity (proxied by industrial production) for the USA for the 1971-2007 period usingVAR analyses. We extended the analysis with alternative measures of market returnssuch as small- and mid-cap excess returns in an effort to further assess their impact onREIT excess returns. In our analyses, we used the four categories of REITs namely,

composite, equity, mortgage, and hybrid.Why is it important to study the interactions between movements in real estate

prices and the financial sector and the (real) macroeconomy? In general, the dynamicsof property prices and their linkages with financial stability and economic policy posesignificant challenges for not only portfolio managers and risk management, but alsofor financial policy design and implementation. For example, changing real estateprices impose pressures on credit and finance for institutions and investors alike andtend to spillover over to borrowers of securitized investments resulting in changes oftheir values. This issue is timely and highly relevant in todays financial crisis where asharp downturn in real estate has caused a wide reduction in securitized assetprofitability and quality deterioration driving many financial institutions intobankruptcy. In addition, studying the interactions between real estate and the

macroeconomy is important to government and monetary regulators and policymakersin designing effective policies for the real estate sector. Thus, it is important to examinehow changing real estate values affect the financial and real sides of an economy.

Our results basically suggest the following. First, that the equity and the mortgageREIT categories displayed essentially similar patterns with their interactions with thegeneral stock market and/or industrial production movements. There was a difference,however, in the pair-wise linkages between the two REITs and the general stockmarket returns. Specifically, in the case of the equity REIT, it was revealed that a

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reciprocal linkage between the two existed, whereas for the mortgage REIT auni-directional one running from the REIT to the stock market. Second, whensubstituting the general stock market returns with two sub index returns (the small-and the mid-cap excess returns) it was found that the two REIT categories were more

closely related to a sub index than the general stock market index. Overall then, we seesignificant short-run interactions among the three magnitudes since the 1970s.

The results also have several implications for investors and policymakers alike. Forinvestors, the finding of the close relationship between the equity and mortgage REITcategories and the general stock market is that there may not be a profitablereallocation of portfolios within these two asset classes. This, essentially, means thatthere is no significant risk diversification potential within a portfolio that includes bothasset classes. Another practical implication is that portfolio managers may use sectorindices (for example, the small-cap equity index) rather than aggregate market indicesto predict (or infer) movements in either the equity or the mortgage REIT indices asthey affect the value of their portfolios. Regarding the usefulness of the study topolicymakers, it can be suggested that policymakers take notice of how changes inmonetary policy (via changes in interest rates or money supply) influence REITinvestments and what the impact of that would be on the reallocation of suchinvestments by professional investors and managers. Again, this point is currentlyvery potent in view of the fact that the world economy is in the midst of a credit crisiswhich originated in the housing (real estate) market.

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Corresponding authorNikiforos Laopodis can be contacted at: nlaopodis@mail.fairfield.edu

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