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Financial determinants of systematic risk in real estate investment trusts
Financial Determinants of Systematic Risk in Real Estate Investment Trusts Raman C. Pate1 and Robert A. Olsen, California State University, Chico
Financial theory and empirical evidence suggest that a firm’s systematic, or market related, risk is related to itsfirancial conditions. This study empirically investigates the financial determinants of systematic risk for Real Estate Investment Trusts (REtTs). The study is an examination of sample of 32 REITs for the period 197678. The results indicate that systematic risk varies directly with financial leverage, business risk, and advisor fee. The explanatory power of the relationship between systematic risk and fiMncia1 variables exceeds that of previous studies wherein firms were pooled across industry groups. The higher explanatory power observed even with limited data suggests that better estimates of coeficients off%tancial determinants of systematic risk may be obtained through analysis conducted on an industry by industry basis. Furthermore, such industry-specific analysis provides useful results to practicing financial managers in their financial policy considerations. With the knowledge of how the financial decisions affect the firm’s systematic risk, a manager may be able to manipulate those variables so as to reduce the systematic risk for his or her firm and thus increase the market value of thefirm’s securities.
Financial theory and evidence suggest that systematic risk is a major determinant of the value of a firm’s common shares [7, 18,251. A firm’s systematic risk as measured by the beta coefficient in the Capital Asset Pricing Model reflects the correlation between the firm’s common stock return and the market return [4, 221. Since the pioneer research of Beaver, Kettler, and Scholes [2] found significant association between beta and the fmancial characteristics of the firm, a number of studies have attempted to identify financial determinants of systematic risk [ 1, 3, 6, 10, 11, 13, 15, 16, 20, 2 1, 241. Most of these studies, however, have utilized cross sections of fums from different industries in estimating the relationship between systematic risk and financial variables. Thus, these studies have not been very successful in explaining variation in individual firm’s betas. Investigation of each industry separately by considering those financial variables important in each industry should provide better results. The authors
are indebted
to Ann E. O’Rourke
for her help in this study.
Address correspondence to Raman C. Patek School of Business, California State University, Chico Chico, CA 95926. Journal of Business Research 12,48 I-491 (1984) @ Elsevier Science Publishing Co., Inc. 1984 52 Vanderbilt Ave., New York, NY 10017
481 0148-2%3/84/$3.00
432
R. C. Pate1 and R. A. Olsen
The purpose of this study is to investigate the fmancial determinants of systematic risk for Real Estate Investment Trusts (REIT). A few research studies have been done on the systematic risk in the RElT industry [9, 12, 18, 19,231, but no research has been reported which considers the financial determinants of systematic risk for the REIT indusuy . Since REITs are financial intermediaries specializing in real estate investment, the focus on financial variables would seem especially relevant. A regression model is considered using five financial variables. Section I describes the selection and specification of the variables based on a priori notions and general financial theory considerations. Section II briefly discusses the sources of data and the final specification of the model. The specification and estimation of the empirical equation is necessarily limited by the availability of data as well as the usefulness of the variables in explaining variation in betas. The regression analysis and empirical results are presented in Section III. Section IV summarizes the conclusions.
Model SpeeIfkation In developing a regression equation for explaining variation in systematic risk for an REIT, the following financial variables were selected, based on theoretical considerations and empirical evidence. The model considered for this study may be written as:
(1) where X1 X2 X3 X4 X5
= a measure of financial leverage of an REIT,
= = = =
a a a a
measure measure measure measure
of of of of
advisor fees for an REIT, business risk for an REIT, the amount of property owned by an REIT, marketability of the common shares of an REIT.
Theory and data suggest consideration of the following measures for variables listed above. Financial theory and empirical evidence indicate a direct relationship between financial leverage and beta [2, 3, 11, 13, 15, 161. The greater the amount of fuced financial charges (interest and lease payments), the greater will be the variability in net income. The larger variability in net income should be reflected in a greater standard deviation of stockholder returns and, hence, a larger beta. The ratio of short-term debt to total assets is used as the leverage
Financial Determinants
483
measure (X,) for this study, since most REITs tend to use short-term notes rather than long-term bonds as a source of debt financing. Among different forms of advisor compensation, fmed fees, expenses, percent of assets, and percent of income am most common in REIT. Jenkins [14] has shown that the standard deviation of stockholder’s income increases as the standard deviation of advisor compensation increases. Thus, a higher advisor fee should lead to a higher standard deviation of stockholder return and a larger common stock beta. The advisor fee variable (X,) is computed for this study by dividing the standard deviation of the annual advisor fee by the REIT’s average total assets. The division by total assets is considered in order to scale the variable by the firm size, since larger firms would be expected to have greater absolute variability in advisor fees. Business risk refers to the uncertainty in the firm’s earnings before interest and taxes and is shown to be significantly correlated with beta [ 131. Since earnings available for distribution to stockhoklets as dividends will fluctuate as the firm’s earnings before interest and taxes vary, a direct association between business risk and beta is expected. In this study, business risk is measured by the ratio of standard deviation of annual earnings (before interest and taxes) and average total annual assets. The division by assets scales the variable by firm size. Theory suggests that as business risk increases beta might increase at a decreasing rate. Therefom, a natural logarithmic transformation of the above ratio is considered for the business risk variable (X,) for this study. Previous research suggests that equity trusts have less variability in earnings than mortgage trusts [ 121. Real property provides a fairly steady source of income for a REIT. Since owned property can be liquidated or refinanced, it can also provide a source of cash when necessary. Both these factors should serve to reduce variability in REII’ earnings when property ownership is high. Therefore, an inverse relationship between beta and the ratio of property owned to total assets variable (X,) is expected. Earlier studies have shown a relationship between common stock marketability and beta [3, 10, 13, 151. Marketability refers to the ability to sell shams at short notice without major price concessions. The greater the marketability, the lower the variability in the capital gain portion of a stockholder’s return. Therefore, an inverse relationship is expected between beta and marketability. A natural logarithmic transformation of the ratio of number of shares traded to shares outstanding is used as the measure of marketability variable (X5) in this study, since theory suggests that as marketability increases, return variability should decrease at a decreasing rate
R. C. Pate1 and R. A. Olsen
484
The measures selected were considered in ratio form rather than aggregate values because such transformations allow for more direct comparisons of different size firms and a better view of the interrelationships of the fum’s financial position than its aggregate data.
Data Sources The period January 1976 to December 1978 was selected for this study because of data availability and general market stability during the period. The financial information needed to compute various ratios for variables was either incomplete or not available consistently for earlier years for some fums . As a result, only those firms for which information was available were selected for the study. This resulted in the use of a sample of 32 REJTs. The REIT Factbook [ 171 provided the data for the annual percentage of property owned. Financial statement information needed for ratio calculations was obtained from COMPUSTAT fules and Moody’s Manuals. Percentage property owned, advisor fee to total assets, and short-term debt to total assets were obtained as three year company averages. The log of the ratio of the standard deviation of earnings to total assets was a natural logarithmic transformation of the standard deviation of earnings before interest and taxes divided by the three year mean of total assets of the company. Log of shares traded to shares outstanding was the natural logarithmic transformation of the three year average of the ratio of shares traded to shares outstanding. Using monthly data from CRSP files for the period of the study, betas were computed by regressing individual returns (Rit) on market return (Rmt). Firm names and their betas are presented in the Appendix. A multiple regression analysis was performed on the data of financial variables and the beta values to estimate the equation Y = bo + b,X,
+ b*X,
+ bjX3
+ b4XQ + bSX,)
(2)
where Y = betaofanREJT, X , = ratio of short-term debt to total assets of an REJT,
X, = ratio of the standard deviation of the annual advisor fee to total average assets of an REJT, X, = ratio of the log of the standard deviation of annual earnings before interest and taxes to total average assets of an REIT, X, = ratio of the property owned to total assets of an REJT, X, = ratio of the log of shares traded to shares outstanding of an REIT.
485
Financial Determinants
Table 1: Matrix of Correlations
between
Financial
Variables
Ratio of Short-Term Debt to Total Assets
Ratio of Standard Deviation of Annual Advisor Fee to Total Average Assets
Ratio of Log of Standard Deviation of Annual Earnings to Total Assets
Ratio of Property Owned to Total Assets
Ratio of Log of Shares Traded to Shares Outstanding
(Xl)
(X2)
(X3)
(X4)
(X5)
0.0680 (0.0356)a 0.1803 (0.162) -0.2114 (0.123) 0.1393 (0.224)
-0.3804 (0.016) 0.0315 (0.432) 0.0989 (0.295)
-0.2518 (0.082) 0.2766 (0.063)
-0.5712 (0.001)
Xl
X2 x3 x4
X5
a p values (for significance)
in parentheses.
Empirical Results The correlation matrix presented in Table 1 suggests that multicollinearity might exist between the percent of property owned and the log of shares traded to shares outstanding, and the standard deviation of advisor fee to total assets and the log of the standard deviation of earnings before interest and taxes to total assets. A stepwise regression routine provided the results shown in Table 2. The signs of the regression coefficient are as expected. The results suggest an inverse relationship between systematic risk and percent of property owned (X,) and marketability (X5), and a positive relationship between systematic risk and financial leverage (X,), advisor fee (X2), and business risk (X,) . Two variables, short-term debt to total assets (X,) and standard deviation of advisor fee to total assets (X2), aresignificant at the 1% level (p < 0.0 1). The log of standatd deviation of earnings before interest and taxes (X3) is significant at the 10% level. The remaining two variables, percent property owned (X,) and log of shams traded to shams outstanding (X5) are not significant at any conventional statistical level (p > 0.10). However, they do have the hypothesized sign.
-
1.294X1 (3.491)C
1.288X1 (3.491)C
t
f
1.254 (2.106)C
1.068 (2.106)c
Step 4
Step 5
19.805X2 (2.248)d
- -_ - -. .._ _
-
I._
-.
- -.-
+ 27.538X2 (2.954)c
+ 26.40X2 (2.954)c
+ 27.806X2 (2.958)c
+
Q t Statistics in parentheses. b Durbin Watson statistic = 2.585. C Denotes significance at the 1% level. d Denotes significance at the 5% level. e Denotes significance at the 10% level.
1.387X1 (3.37O)C
+
1.210 (3.38O)C
Step 3
-
1.551X, (4.125)c
+
0.620 (2.95)c
1.608X1 (4.02)c
Step 2
+
0.670 (1.49)C
Step 1
-
0.133X3 (1.985)e 0.110x3 (1.70)e 0.119X3 (1.70)e
+
+
+
Regression Equations
Table 2: Stepwise Regression Estimates= b
-
-
0.005X4 (1.25)
0.005x4 (1.25) -
0.165X5 (0.512)
6.59
8.40
8.40
11.71
16.18
F Ratio
-
0.241
0.235
0.235
0.271
0.308
I.
0.47
0.48
0.48
0.41
0.33
MSE R2 (Adjusted)
Financial Determinants
487
The coefficient of determination is high when compared with results from other studies using cross section industry samples. The highest adjusted R* = 0.4833 obtained in this study exceeds the R2 values (ranging from 0.22 to 0.43) reported in other studies which have utilized cross-section data from different industry groups and for different time periods [3, 10, 15, 16,201. Although the results of various studies are not directly comparable due to differences in the number and type of financial measures used, the high explanatory power observed in this study relative to any other previous study of financial determinants of systematic risk does suggest that better estimates of coefficients of financial determinants may be obtained when analysis is confined to a single industry. The stepwise regression results of Table 2 indicate that there may be some instability in regression coefficients initially as new variables am sequentially entered into the estimating equation. However, this instability seems to be minor and reasonable when some degree of multicollinearity exists among the independent variables. Although some low degree of collinearity exists in the data, none of the variables exhibit changes in their significance so as to cause any multicollinearity problem when new variables are added. Futhermore, the nearly stable regression coefficients observed after step 3 indicate that the estimates are fairly reliable.
Summary and Conclusions The results of the study suggest that financial leverage, business risk, and advisor fee are significantly positively correlated with systematic risk in an REIT. In addition, the results of this study suggest that investigating financial determinants of systematic risk on an industry-by-industry basis may be more advantageous than using cross sections of several industries. The explanatory power of the relationship between systematic risk and financial variables obtained in this study exceeds that of previous studies wherein firms were pooled across industry groups. The high explanatory power observed even with limited data in this study suggests that better estimates of coefficients of financial determinants of systematic risk may be obtained through analysis conducted on an industry-by-industry basis. Furthermore, such industry-specific analyses provides useful results to practicing financial managers in their policy considerations. However, the results also indicate that over 50% of the variability of returns on RElTs was unexplained. The addition of other variables specific to the REIT industry, such as diversification measures (geo-
488
R. C. Pate1 and R. A. Olsen
graphic areas and property types) may add explanatory power to the model. Further research into possible additional variables and analysis over a larger sample should be continued in view of the importance of REITs to the investment community and real estate markets.
Appendix: Sample of Real Estate Investment Trusts Name
American Century Mortage Investors Bay Colony Property BT Mortgage Investors Cameron Brown Investments Chase Manhattan Mortgage & Realty CI Realty Investors Connecticut General Mortgage & Realty Investments Continental Illinois Properties Continental Illinois Realty Equitable Life Mortgage & Realty Investors First Pennsylvania Mortgage Trust First Union Real Estate Equity and Mortgage General Growth Properties GMR Properties Growth Realty Investors
Beta 1.8013 (2.8965)” 2.6629 (3.2757) 1.8664 (2.2752) 1.8946 (2.8588) 1.4513 (2.4908) 0.8560 ( 1.8432) .8 143 (3.9019) 1.2038 (5.3047) 2.4003 (3.2716) 0.7202 (3.2809) 2.3 145 (3.6074) 0.3837 (2.5720) 1.3715 (2.5727) 1.9068 (2.5455) 2.2494 (2.8745)
489
Financial Determinants
Appendix: (Continued) Name
Mass Mutual Midland Mortgage Investors Trust Money Mortgage Investors Mortgage Trust of America
North America Mortgage Investors Northwestern Mutual Life Mortgage PNB Mortggge Realty Investors Republic B. F. Saul Real Estate Investment Trust Southmark (Citizens & Southern) State Mutual Sutro Mortgage Tri-South Mortgage Investors VMET Trust United States Realty Investments Wachovia Realty Investments Wells Fargo Mortgage & Equity Trust U t statistics in parentheses.
Beta
0.5653 (2.5690) 2.5137 (3.7895) 0.8432 (4.9479) 0.9360 (1.7451) 1.4975 (2.7002) 1.2155 (5.8471) 0.7859 (2.2619) 2.0467 (2.7934) 1.5999 (3.5076) 3.1407 (2.9230) 1.4283 (1.9085) 0.9614 (2.8716) 1.0310 (0.9345) 1.0862 (1.1249) 1.0017 (1.6228) 1.6321 (3.2559) 0.9725 (2.9042)
496
R. C. Pate1 and R. A. Olsen
References 1.
Arditti, Fred D., Risk and the Required Rate of Return, J, Finance 32 (March 1977): 19-36.
2.
Beaver, William H., Kettler, Paul, and Scholes, Myron, The Association Between Market Determined and Accounting Determined Risk Measures, Accounting Rev. 45 (October 1970): 654-682.
3.
Ben-Zion, Uri, and Shaht, SOL S., Size, Leverage, and Divident Reocrd as Determinents of Equity Risk, J. Finance 30 (September 1975): 1015-1026.
4.
Blume, Marshah, and Friend, Irwin, A New Look at the Capital Asset Pricing Model, J. Finance 28 (March 1973) 19-34.
5.
Bowman, Robert G. K., The Theoretical Relationship Between Systematic Risk and Financial (Accounting) Variables, J. Finance 34 (June 1979): 617-630.
6.
Breen, William J., and Lerner, Eugene M., Corporate Financial Strategies and Market Measures of Risk and Return, J. Finance 28 (May 1973): 339-351.
7.
Brigham, Eugene F., Financial Management. The Dryden Press, Hinsdale, Ill. 1977, p. 8.
8.
Copeland, Thomas E., and Weston, J. Fred, Financial Theory and Corporate Policy. Addison-Wesley, Reading, Mass. 1979, p. 18.
9.
Davidson, Harold A., Comparison of the Investment Performance of Common Stocks, Homebuilding Firms, and Equity REITS, Real Estate Appraiser 44 (July 1978): 35-39.
10.
Fabozzi, Frank J., and Francis, Jack Clark, Industry Effects and the Determinants of Beta, Quarterly Rev. Econ. Bus. 19 (Autumn 1979): 62-74.
11.
Hamada, Robert S., The Effect of the Firms Capital Structure On the Systematic Risk of Common Stocks, J. Finance 27 (May 1972): 435-452.
12.
Hines, Mary Alice, Risk-Return Analysis of Real Estate Investment REZT Rev. 11 (Winter 1974): p. 17-30.
13.
Idol, Charles R., The Financial Determinants Studies 9 (FalI 1978): 55-69.
14.
Jenkins, James W., Incentive Compensation and REIT Financial Leverage and Asset Risk, Financial Management 9 (Spring 1980): 81-87.
15.
Logue, Dennis E., and MerviIle, Larry J., Financial Policy and Market Expectations, FinancialManagement 1 (Summer 1972): 37-44.
16.
MeIicher, Ronald W., Financial Factors Which Influence Beta Variations Within a Homogeneous Industry Environment, J. Financial Quantitative Analysis 9 (March 1974): 231-241.
17.
National Association of Real Estate Investment Trusts, REIT FACTBOOK, (Washington, D.C.)(NationalAssociation of Real Estate Investment Trusts, Inc., 1979).
18.
Neuberger, Brian M., and Smith, Stanley A., Market Performance of Selected Real Estate Investment Trusts: 1961-1973, REZTRev. 11 (FalI 1974): 34-49.
19.
Neuberger, Brian M., and Hughes, Michael A., Operating Performance of Shortterm Trusts, 1972-l 974, REZT Rev. 13 (February 1976): l-1 0.
Trust Funds,
of Systematic Risk, BayZor Business
Einancial Determinants
491
20.
Rosenberg, Barr, and McKibben, Walt, The Prediction of Systematic and Specific Risk in Common Stocks, J. Financial QuantitativeAnalysis 8 (March 1973): 317333.
21.
Rosenberg, Barr, and Guy, James, Prediction of Beta From Investment Fundamentals, FinancialAnaZystsJ. 32 (July-August 1976): 62-70.
22.
Sharpe, WiIliam F., Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk, J. Finance 19 (September 1964): 425-552.
23.
Smith, Keith V., and Shuhnan, David, The Performance of Equity Real Estate Investment Trust, Financial Analysts J. 32 (September-October 1976): 6 l-66.
24.
Thompson II, Donald., Sources of Systematic Risk in Common Stocks, J. Bus. 49 (April 1976): 172-188.
25.
Tinic, Seha M., and West, Richard R, Investing in Securities: An Efficient Market Approach, Addison-Wesley, Reading, Mass., 1979, p. 14.
Financial theory and empirical evidence suggest that a firm’s systematic, or market related, risk is related to itsfirancial conditions. This study empirically investigates the financial determinants of systematic risk for Real Estate Investment Trusts (REtTs). The study is an examination of sample of 32 REITs for the period 197678. The results indicate that systematic risk varies directly with financial leverage, business risk, and advisor fee. The explanatory power of the relationship between systematic risk and fiMncia1 variables exceeds that of previous studies wherein firms were pooled across industry groups. The higher explanatory power observed even with limited data suggests that better estimates of coeficients off%tancial determinants of systematic risk may be obtained through analysis conducted on an industry by industry basis. Furthermore, such industry-specific analysis provides useful results to practicing financial managers in their financial policy considerations. With the knowledge of how the financial decisions affect the firm’s systematic risk, a manager may be able to manipulate those variables so as to reduce the systematic risk for his or her firm and thus increase the market value of thefirm’s securities.
Financial theory and evidence suggest that systematic risk is a major determinant of the value of a firm’s common shares [7, 18,251. A firm’s systematic risk as measured by the beta coefficient in the Capital Asset Pricing Model reflects the correlation between the firm’s common stock return and the market return [4, 221. Since the pioneer research of Beaver, Kettler, and Scholes [2] found significant association between beta and the fmancial characteristics of the firm, a number of studies have attempted to identify financial determinants of systematic risk [ 1, 3, 6, 10, 11, 13, 15, 16, 20, 2 1, 241. Most of these studies, however, have utilized cross sections of fums from different industries in estimating the relationship between systematic risk and financial variables. Thus, these studies have not been very successful in explaining variation in individual firm’s betas. Investigation of each industry separately by considering those financial variables important in each industry should provide better results. The authors
are indebted
to Ann E. O’Rourke
for her help in this study.
Address correspondence to Raman C. Patek School of Business, California State University, Chico Chico, CA 95926. Journal of Business Research 12,48 I-491 (1984) @ Elsevier Science Publishing Co., Inc. 1984 52 Vanderbilt Ave., New York, NY 10017
481 0148-2%3/84/$3.00
432
R. C. Pate1 and R. A. Olsen
The purpose of this study is to investigate the fmancial determinants of systematic risk for Real Estate Investment Trusts (REIT). A few research studies have been done on the systematic risk in the RElT industry [9, 12, 18, 19,231, but no research has been reported which considers the financial determinants of systematic risk for the REIT indusuy . Since REITs are financial intermediaries specializing in real estate investment, the focus on financial variables would seem especially relevant. A regression model is considered using five financial variables. Section I describes the selection and specification of the variables based on a priori notions and general financial theory considerations. Section II briefly discusses the sources of data and the final specification of the model. The specification and estimation of the empirical equation is necessarily limited by the availability of data as well as the usefulness of the variables in explaining variation in betas. The regression analysis and empirical results are presented in Section III. Section IV summarizes the conclusions.
Model SpeeIfkation In developing a regression equation for explaining variation in systematic risk for an REIT, the following financial variables were selected, based on theoretical considerations and empirical evidence. The model considered for this study may be written as:
(1) where X1 X2 X3 X4 X5
= a measure of financial leverage of an REIT,
= = = =
a a a a
measure measure measure measure
of of of of
advisor fees for an REIT, business risk for an REIT, the amount of property owned by an REIT, marketability of the common shares of an REIT.
Theory and data suggest consideration of the following measures for variables listed above. Financial theory and empirical evidence indicate a direct relationship between financial leverage and beta [2, 3, 11, 13, 15, 161. The greater the amount of fuced financial charges (interest and lease payments), the greater will be the variability in net income. The larger variability in net income should be reflected in a greater standard deviation of stockholder returns and, hence, a larger beta. The ratio of short-term debt to total assets is used as the leverage
Financial Determinants
483
measure (X,) for this study, since most REITs tend to use short-term notes rather than long-term bonds as a source of debt financing. Among different forms of advisor compensation, fmed fees, expenses, percent of assets, and percent of income am most common in REIT. Jenkins [14] has shown that the standard deviation of stockholder’s income increases as the standard deviation of advisor compensation increases. Thus, a higher advisor fee should lead to a higher standard deviation of stockholder return and a larger common stock beta. The advisor fee variable (X,) is computed for this study by dividing the standard deviation of the annual advisor fee by the REIT’s average total assets. The division by total assets is considered in order to scale the variable by the firm size, since larger firms would be expected to have greater absolute variability in advisor fees. Business risk refers to the uncertainty in the firm’s earnings before interest and taxes and is shown to be significantly correlated with beta [ 131. Since earnings available for distribution to stockhoklets as dividends will fluctuate as the firm’s earnings before interest and taxes vary, a direct association between business risk and beta is expected. In this study, business risk is measured by the ratio of standard deviation of annual earnings (before interest and taxes) and average total annual assets. The division by assets scales the variable by firm size. Theory suggests that as business risk increases beta might increase at a decreasing rate. Therefom, a natural logarithmic transformation of the above ratio is considered for the business risk variable (X,) for this study. Previous research suggests that equity trusts have less variability in earnings than mortgage trusts [ 121. Real property provides a fairly steady source of income for a REIT. Since owned property can be liquidated or refinanced, it can also provide a source of cash when necessary. Both these factors should serve to reduce variability in REII’ earnings when property ownership is high. Therefore, an inverse relationship between beta and the ratio of property owned to total assets variable (X,) is expected. Earlier studies have shown a relationship between common stock marketability and beta [3, 10, 13, 151. Marketability refers to the ability to sell shams at short notice without major price concessions. The greater the marketability, the lower the variability in the capital gain portion of a stockholder’s return. Therefore, an inverse relationship is expected between beta and marketability. A natural logarithmic transformation of the ratio of number of shares traded to shares outstanding is used as the measure of marketability variable (X5) in this study, since theory suggests that as marketability increases, return variability should decrease at a decreasing rate
R. C. Pate1 and R. A. Olsen
484
The measures selected were considered in ratio form rather than aggregate values because such transformations allow for more direct comparisons of different size firms and a better view of the interrelationships of the fum’s financial position than its aggregate data.
Data Sources The period January 1976 to December 1978 was selected for this study because of data availability and general market stability during the period. The financial information needed to compute various ratios for variables was either incomplete or not available consistently for earlier years for some fums . As a result, only those firms for which information was available were selected for the study. This resulted in the use of a sample of 32 REJTs. The REIT Factbook [ 171 provided the data for the annual percentage of property owned. Financial statement information needed for ratio calculations was obtained from COMPUSTAT fules and Moody’s Manuals. Percentage property owned, advisor fee to total assets, and short-term debt to total assets were obtained as three year company averages. The log of the ratio of the standard deviation of earnings to total assets was a natural logarithmic transformation of the standard deviation of earnings before interest and taxes divided by the three year mean of total assets of the company. Log of shares traded to shares outstanding was the natural logarithmic transformation of the three year average of the ratio of shares traded to shares outstanding. Using monthly data from CRSP files for the period of the study, betas were computed by regressing individual returns (Rit) on market return (Rmt). Firm names and their betas are presented in the Appendix. A multiple regression analysis was performed on the data of financial variables and the beta values to estimate the equation Y = bo + b,X,
+ b*X,
+ bjX3
+ b4XQ + bSX,)
(2)
where Y = betaofanREJT, X , = ratio of short-term debt to total assets of an REJT,
X, = ratio of the standard deviation of the annual advisor fee to total average assets of an REJT, X, = ratio of the log of the standard deviation of annual earnings before interest and taxes to total average assets of an REIT, X, = ratio of the property owned to total assets of an REJT, X, = ratio of the log of shares traded to shares outstanding of an REIT.
485
Financial Determinants
Table 1: Matrix of Correlations
between
Financial
Variables
Ratio of Short-Term Debt to Total Assets
Ratio of Standard Deviation of Annual Advisor Fee to Total Average Assets
Ratio of Log of Standard Deviation of Annual Earnings to Total Assets
Ratio of Property Owned to Total Assets
Ratio of Log of Shares Traded to Shares Outstanding
(Xl)
(X2)
(X3)
(X4)
(X5)
0.0680 (0.0356)a 0.1803 (0.162) -0.2114 (0.123) 0.1393 (0.224)
-0.3804 (0.016) 0.0315 (0.432) 0.0989 (0.295)
-0.2518 (0.082) 0.2766 (0.063)
-0.5712 (0.001)
Xl
X2 x3 x4
X5
a p values (for significance)
in parentheses.
Empirical Results The correlation matrix presented in Table 1 suggests that multicollinearity might exist between the percent of property owned and the log of shares traded to shares outstanding, and the standard deviation of advisor fee to total assets and the log of the standard deviation of earnings before interest and taxes to total assets. A stepwise regression routine provided the results shown in Table 2. The signs of the regression coefficient are as expected. The results suggest an inverse relationship between systematic risk and percent of property owned (X,) and marketability (X5), and a positive relationship between systematic risk and financial leverage (X,), advisor fee (X2), and business risk (X,) . Two variables, short-term debt to total assets (X,) and standard deviation of advisor fee to total assets (X2), aresignificant at the 1% level (p < 0.0 1). The log of standatd deviation of earnings before interest and taxes (X3) is significant at the 10% level. The remaining two variables, percent property owned (X,) and log of shams traded to shams outstanding (X5) are not significant at any conventional statistical level (p > 0.10). However, they do have the hypothesized sign.
-
1.294X1 (3.491)C
1.288X1 (3.491)C
t
f
1.254 (2.106)C
1.068 (2.106)c
Step 4
Step 5
19.805X2 (2.248)d
- -_ - -. .._ _
-
I._
-.
- -.-
+ 27.538X2 (2.954)c
+ 26.40X2 (2.954)c
+ 27.806X2 (2.958)c
+
Q t Statistics in parentheses. b Durbin Watson statistic = 2.585. C Denotes significance at the 1% level. d Denotes significance at the 5% level. e Denotes significance at the 10% level.
1.387X1 (3.37O)C
+
1.210 (3.38O)C
Step 3
-
1.551X, (4.125)c
+
0.620 (2.95)c
1.608X1 (4.02)c
Step 2
+
0.670 (1.49)C
Step 1
-
0.133X3 (1.985)e 0.110x3 (1.70)e 0.119X3 (1.70)e
+
+
+
Regression Equations
Table 2: Stepwise Regression Estimates= b
-
-
0.005X4 (1.25)
0.005x4 (1.25) -
0.165X5 (0.512)
6.59
8.40
8.40
11.71
16.18
F Ratio
-
0.241
0.235
0.235
0.271
0.308
I.
0.47
0.48
0.48
0.41
0.33
MSE R2 (Adjusted)
Financial Determinants
487
The coefficient of determination is high when compared with results from other studies using cross section industry samples. The highest adjusted R* = 0.4833 obtained in this study exceeds the R2 values (ranging from 0.22 to 0.43) reported in other studies which have utilized cross-section data from different industry groups and for different time periods [3, 10, 15, 16,201. Although the results of various studies are not directly comparable due to differences in the number and type of financial measures used, the high explanatory power observed in this study relative to any other previous study of financial determinants of systematic risk does suggest that better estimates of coefficients of financial determinants may be obtained when analysis is confined to a single industry. The stepwise regression results of Table 2 indicate that there may be some instability in regression coefficients initially as new variables am sequentially entered into the estimating equation. However, this instability seems to be minor and reasonable when some degree of multicollinearity exists among the independent variables. Although some low degree of collinearity exists in the data, none of the variables exhibit changes in their significance so as to cause any multicollinearity problem when new variables are added. Futhermore, the nearly stable regression coefficients observed after step 3 indicate that the estimates are fairly reliable.
Summary and Conclusions The results of the study suggest that financial leverage, business risk, and advisor fee are significantly positively correlated with systematic risk in an REIT. In addition, the results of this study suggest that investigating financial determinants of systematic risk on an industry-by-industry basis may be more advantageous than using cross sections of several industries. The explanatory power of the relationship between systematic risk and financial variables obtained in this study exceeds that of previous studies wherein firms were pooled across industry groups. The high explanatory power observed even with limited data in this study suggests that better estimates of coefficients of financial determinants of systematic risk may be obtained through analysis conducted on an industry-by-industry basis. Furthermore, such industry-specific analyses provides useful results to practicing financial managers in their policy considerations. However, the results also indicate that over 50% of the variability of returns on RElTs was unexplained. The addition of other variables specific to the REIT industry, such as diversification measures (geo-
488
R. C. Pate1 and R. A. Olsen
graphic areas and property types) may add explanatory power to the model. Further research into possible additional variables and analysis over a larger sample should be continued in view of the importance of REITs to the investment community and real estate markets.
Appendix: Sample of Real Estate Investment Trusts Name
American Century Mortage Investors Bay Colony Property BT Mortgage Investors Cameron Brown Investments Chase Manhattan Mortgage & Realty CI Realty Investors Connecticut General Mortgage & Realty Investments Continental Illinois Properties Continental Illinois Realty Equitable Life Mortgage & Realty Investors First Pennsylvania Mortgage Trust First Union Real Estate Equity and Mortgage General Growth Properties GMR Properties Growth Realty Investors
Beta 1.8013 (2.8965)” 2.6629 (3.2757) 1.8664 (2.2752) 1.8946 (2.8588) 1.4513 (2.4908) 0.8560 ( 1.8432) .8 143 (3.9019) 1.2038 (5.3047) 2.4003 (3.2716) 0.7202 (3.2809) 2.3 145 (3.6074) 0.3837 (2.5720) 1.3715 (2.5727) 1.9068 (2.5455) 2.2494 (2.8745)
489
Financial Determinants
Appendix: (Continued) Name
Mass Mutual Midland Mortgage Investors Trust Money Mortgage Investors Mortgage Trust of America
North America Mortgage Investors Northwestern Mutual Life Mortgage PNB Mortggge Realty Investors Republic B. F. Saul Real Estate Investment Trust Southmark (Citizens & Southern) State Mutual Sutro Mortgage Tri-South Mortgage Investors VMET Trust United States Realty Investments Wachovia Realty Investments Wells Fargo Mortgage & Equity Trust U t statistics in parentheses.
Beta
0.5653 (2.5690) 2.5137 (3.7895) 0.8432 (4.9479) 0.9360 (1.7451) 1.4975 (2.7002) 1.2155 (5.8471) 0.7859 (2.2619) 2.0467 (2.7934) 1.5999 (3.5076) 3.1407 (2.9230) 1.4283 (1.9085) 0.9614 (2.8716) 1.0310 (0.9345) 1.0862 (1.1249) 1.0017 (1.6228) 1.6321 (3.2559) 0.9725 (2.9042)
496
R. C. Pate1 and R. A. Olsen
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