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Property Segment and REIT Capital Structure

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Abstract

This paper relies on an increasing number of industry equilibrium studies linking a firm to its industry peers to help explain the observed REIT capital structure variation within property segments beyond what is possible with the traditional partial equilibrium trade-off and pecking order theories, which assume that each firm operates in isolation from other market participants and are not particularly suitable to REITs because of the regulated setting within which these firms operate. We build several proxies for a REIT’s position within its property segment. Consistent with the competitive equilibrium model of Maksimovic and Zechner (1991), we find that a REIT’s volatility of operating performance relative to the median volatility of operating performance of its segment peers is an important determinant of its leverage ratio. We also find that a REIT’s leverage ratio depends on the median leverage ratio in its segment. Leverage is also related to a REIT’s status as an incumbent and its role as a leader in the property segment.

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Notes

  1. See Giambona et al. (2008) for evidence of variation in the lease structure within property segments. For instance, for retail REITs these authors report that the average lease maturity is about 81 months with a standard deviation of 43 months.

  2. This result is consistent with the argument that median segment leverage ratio is a proxy for target leverage. We discuss this possibility in “Multivariate Regression Results” section.

  3. Cf. Howe and Shilling (1988) and Boudry et al. (2007) for evidence on the limitations of the trade-off theory applied to the case of REITs. Similarly, see Ghosh et al. (1999) and Buttimer et al. (2005) for the role of the pecking order theory in explaining capital structure decisions for REITs.

  4. Relatedly, Ooi et al. (2009) find that REIT financing decisions are mainly influenced by an attempt to time favorable market conditions.

  5. Mainly for modeling reasons, these theoretical papers model the behavior of single product line firms. This assumption is particularly suitable to REITs. General finance studies (see for instance MacKay and Phillips, 2005, Hou and Robinson, 2006, and Maksimovic and Phillips, 2008) usually rely on the firm primary 4- or even 3-digit SIC code to identify its product market. Because the average COMPUSTAT firm — the standard unit of analysis of finance studies — operate across different 4 or even 3-digit SIC codes, its degree of “conglomeration” is high and certainly higher than that for REITs, which by law must operate within the 6798 4-digit SIC code and mainly invest in one property type (see Table 2) albeit possibly across different metropolitan areas.

  6. Despite the fact that REITs have inflexible production structure relative to a standard manufacturing firm, they do choose product-service functions that differentiate themselves from other market participants. Giambona et al. (2008) report that there is significant variation in the lease maturity structure within each property segment. For instance, for retail REITs those authors report that the average lease maturity is about 81 months with a standard deviation of 43 months. Similarly, they report significant variation in the lease maturity structure for office, industrial and apartment REITs. Furthermore, we have found that 35% of the equity REITs currently in the SNL database has chosen not to be self-managed.

  7. Contracts forcing stockholders to commit to a certain investment decision are ruled out from the model by assuming that the investment decision is not publicly observable (Maksimovic and Zechner, 1991).

  8. Despite the fact that the source of uncertainty is operating costs in the study of Maksimovic and Zechner (1991), this is mainly a modeling expedient. Even a shock on the demand side of the real estate market — for instance, a change in rental rates volatility — which is more plausible in this market, would be sufficient to trigger the results in their model.

  9. The theory also predicts that in the time series we should observe that as rivals cut rental rates, then the leader relies on its debt capacity to start a price “war” and push the rival into financial distress. Unfortunately, our data does not allow for a test of this prediction.

  10. The NCREIF regions in our sample are as follows: East North Central, Mideast, Mountain, Northeast, Pacific, Southeast, Southwest, and West North Central. An example of property-geography segment would be Hotel — Northeast. In our sample, we have a total of 56 property-geography segments. Therefore, we have few firms in each property-geography segment. However, when we calculate Leader and Relative Age variables, we use a larger sample as described below. Nevertheless, due to data limitation, our property-geography segment results should be interpreted with caution.

  11. A leader with low leverage could reduce rental rates to force rivals out of the market. This action could be sustained by using unexploited debt capacity.

  12. There are 774 observations of REITs in the property types we focus on and with net book of value of real estate data available from SNL.

  13. To calculate segment median age, we use all SNL REITs for which age is not missing (777 observations).

  14. The number of observations for variables based on property-geography segments is 412 since we remove property-geography segments with only one REIT.

  15. The number of REIT-year observations in Table 3 drops to 399 and 321 when we measure median leverage at the property and geography-property segment level, respectively, because we are using change variables.

  16. We note, however, that firm fixed-effects also capture the effect of omitted variables that are fixed for each firm during the sample period. If some of these variables are proxies for segment characteristics, then the evidence discussed in the text should not be taken completely at face value.

  17. This positive coefficient could also obtain if general market conditions depress market value for REITs. In this case, the denominator for our measure of market leverage would go down for all REITs causing the positive coefficient that we observe for Change Segment Median Leverage. To check the robustness of this result, we have replicated the second regression in Table 3 using book leverage. We find that the coefficient on Change Segment Median Leverage goes down to +0.24 but is still very highly significant. We therefore exclude that real estate market dynamics alone might be explaining our evidence in Table 3.

  18. Roberts (2002) reports a similar result.

  19. Maksimovic and Zechner (1991) argue that when a firm chooses a production structure, this has implications for its risk structure based on whether it is similar to the structure chosen by other firms in the industry. Firms choosing a technology similar to the median technology of the industry create a “natural hedge” against production cost shocks affecting the industry. This is so because, if the majority of firms in the industry are affected by the same production cost shock, it will be easier for them to transfer this shock into the final price. Therefore, the volatility of cash flows will be lower for those firms operating with a capital-to-labor ratio similar to the industry practice. In the resulting competitive equilibrium, Maksimovic and Zechner (1991) show the existence of two types of firms. The type with its production function close to the “natural hedge” will have less volatile cash flows and a lower leverage ratio while the opposite will be true for the type with a production function distant from the natural hedge. MacKay and Phillips (2005) use the proximity of a firm’s capital-to-labor ratio to the industry median as a proxy for the volatility of cash flows. Because the capital-to-labor ratio choice is less obvious for the case of REITs, we have decided to use a more direct proxy, which is the volatility of a firm’s cash flow relative to its property segment peers. Our approach as well as MacKay and Phillips’s are both consistent with the spirit of Maksimovic and Zechner (1991). We believe, however, that our approach is less sensitive to how accurately one can measure a firm’s proximity of its production function to the standard practice in the industry. In fact, we measure directly the REIT’s volatility of its funds from operations relative to industry peers rather than how the proximity of its production function to the standard practice in the industry will affect this volatility. Nevertheless, our results are robust to including a proxy for “natural hedge,” built in the same spirit as MacKay and Phillips (2005). The “natural hedge” proxy however is not statistically significant.

  20. An alternative interpretation of this result is that larger REITs are more profitable, and therefore rely more on internal equity financing to avoid the information asymmetry costs of external funding consistent with the pecking order hypothesis. Ambrose and Linneman (2001), Bers and Springer (1997), Capozza and Senguin (1998), and, more recently, Ambrose et al. (2005) all document that larger REITs are more profitable. We find that the average funds from operations (FFO) per share is $3.05 for our property-geography segment leader REITs compared to $2.56 for non leaders. A simple t-test shows that the difference also is statistically significant. In our regressions, the Leader dummy is significant, even after we control for both the size and the profitability of REITs. Therefore, we conclude that our results are more supportive of the argument that leaders use less leverage to signal solvency and to deter rival firms from attempting predatory actions against them than they are of pecking order arguments.

  21. Incumbent firms with low leverage could reduce rental rates to force rivals out of the market. This action could be sustained by increasing leverage.

  22. We estimate a target leverage ratio following Kayhan and Titman (2007). We first regress leverage ratio on lagged values of market-to-book ratio, log of firm size, FFO per share, real estate investment over total assets, and property segment dummies. The predicted value from this regression is our estimate for target leverage ratio. We include the estimated target leverage ratio in our regressions. Even after we control for target leverage ratio, the coefficient estimate for segment median leverage (both for property and property-geography segments) remains positive and significant.

  23. These authors note that Hart (1993, pp. 36-37) provide a rationale for this outcome. Consider a three period world. At t = 0 the management implements an investment I, which is financed with a combination of debt and equity. At t = 1, the investment generates an uncertain cash flow CF 1 . Because CF 1 is stochastic, free cash flow at t = 1 will be high at least in some states of the world. The management could use the free cash flow (in the good states) to implement a negative NPV investment at the intermediate state t = 1 (e.g., empire building). Therefore, if at t = 0 stockholders are mainly concerned with the possibility of overinvestment, then they should increase leverage as much as possible. In this case, cash flow has to be used to pay back debt and overinvestment is mitigated. The empirical prediction in this case is that volatility of cash flow is positively associated with debt. On the other hand, firms that are more concerned with the possibility of inefficient liquidation associated to debt should use less debt. “So the theory can explain why firms with stable cash flows have more debt but can also explain the opposite” (i.e., that firms with more volatile cash flows have more debt) (Hart, 1993, p. 37).

  24. Results discussed in the robustness section are not tabulated in the interest of saving space, but they are available upon request from the authors.

References

  • Almazan, A., & Molina, C. A. (2005). Intra-industry capital structure dispersion. Journal of Economics and Management Strategy, 14, 263–297.

    Article  Google Scholar 

  • Ambrose, B. W., & Linneman, P. (2001). REIT organizational structure and operating characteristics. Journal of Real Estate Research, 21, 141–162.

    Google Scholar 

  • Ambrose, B. W., Highfield, M. J., & Linneman, P. D. (2005). Real estate and economies of scale: the case of REITs. Real Estate Economics, 33, 323–350.

    Article  Google Scholar 

  • Barclay, M. J., Morellec, E., & Smith, C. J. (2006). On the debt capacity of growth options. Journal of Business, 79, 37–59.

    Article  Google Scholar 

  • Bers, M., & Springer, T. M. (1997). Economies of scale for real estate investment trusts. Journal of Real Estate Research, 14, 275–290.

    Google Scholar 

  • Boudry, W. I., Kallberg, J. G., & Liu, C. H. (2007). An analysis of REIT security issuance decisions. Working Paper.

  • Brander, J. A., & Lewis, T. R. (1986). Oligopoly and financial structure: the limited liability effect. American Economic Review, 85, 415–435.

    Google Scholar 

  • Brander, J. A., & Lewis, T. R. (1988). Bankruptcy costs and the theory of oligopoly. Canadian Journal of Economics, 21, 221–243.

    Article  Google Scholar 

  • Buttimer, R., Hyland, D. C., & Sanders, A. B. (2005). REITs, IPO waves and long-run performance. Real Estate Economics, 33, 51–87.

    Google Scholar 

  • Capozza, D. R., & Senguin, P. J. (1998). Managerial style and firm value. Real Estate Economics, 26, 131–150.

    Article  Google Scholar 

  • Faulkender, M., & Petersen, M. A. (2006). Does the source of capital affect capital structure? Review of Financial Studies, 19, 45–79.

    Article  Google Scholar 

  • Fries, S., Miller, M., & Perraudin, W. (1997). Debt in industry equilibrium. Review of Financial Studies, 10, 39–67.

    Article  Google Scholar 

  • Ghosh, C., Nag, R., & Sirmans, C. F. (1999). An analysis of seasoned equity offerings by equity REITs, 1991 to 1995. Journal of Real Estate Finance and Economics, 19, 175–192.

    Article  Google Scholar 

  • Giambona, E., Harding, J., & Sirmans, C. F. (2008). Explaining the variation in REIT capital structure: the role of asset liquidation value. Real Estate Economics, 36, 111–137.

    Article  Google Scholar 

  • Gompers, P., Ishii, J., & Metrick, A. (2003). Corporate governance and equity prices. Quarterly Journal of Economics, 118, 107–155.

    Article  Google Scholar 

  • Graham, J. R., & Harvey, C. R. (2001). The theory and practice of corporate finance: evidence from the field. Journal of Financial Economics, 60, 187–243.

    Article  Google Scholar 

  • Hart, O. D. (1993). Theories of optimal capital structure: A managerial discretion perspective. In M. M. Blair (Ed.), The deal decade. Washington: The Brookings Institution.

    Google Scholar 

  • Hou, K., & Robinson, D. T. (2006). Industry concentration and average stock returns. Journal of Finance, 61, 1927–1956.

    Article  Google Scholar 

  • Howe, J. S., & Shilling, J. D. (1988). Capital structure theory and REIT security offerings. Journal of Finance, 43, 983–993.

    Article  Google Scholar 

  • Johnson, S. A. (2003). Debt maturity and the effects of growth opportunities and liquidity risk on leverage. Review of Financial Studies, 16, 209–236.

    Article  Google Scholar 

  • Kayhan, A., & Titman, S. (2007). Firms’ histories and their capital structures. Journal of Financial Economics, 83, 1–32.

    Article  Google Scholar 

  • MacKay, P., & Phillips, G. M. (2005). How does industry affect firm financial structure? Review of Financial Studies, 18, 1433–1466.

    Article  Google Scholar 

  • Maksimovic, V., & Zechner, J. (1991). Debt, agency costs and industry equilibrium. Journal of Finance, 46, 1619–1643.

    Article  Google Scholar 

  • Maksimovic, V., & Phillips, G. (2008). The industry life cycle, acquisitions and investment: does firm organization matter? Journal of Finance, 63, 673–708.

    Article  Google Scholar 

  • Ooi, J. T. L., Ong S.-E., & Li., L. (2009). An analysis of the financing decisions of REITs: The role of market timing and target leverage. Journal of Real Estate Finance and Economics. Forthcoming.

  • Petersen, M. (2007). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies. Forthcoming.

  • Poitevin, M. (1989). Financial signaling and the “Deep-Pocket” argument. The RAND Journal of Economics, 20, 26–40.

    Article  Google Scholar 

  • Rajgopal, S., Shevlin, T., & Zamora, V. (2006). CEO’s outside employment opportunities and the lack of relative performance evaluation in compensation contracts. Journal of Finance, 61, 1813–1844.

    Article  Google Scholar 

  • Roberts, M. (2002). The dynamics of capital structure: An empirical analysis of partially observable system. Working Paper. Duke University.

  • Rogers, W. (1993). Regression standard errors in clustered samples. Stata Technical Bulletin, 13, 19–23.

    Google Scholar 

  • Telser, L. G. (1966). Cutthroat competition and the long purse. Journal of Law and Economics, 9, 259–277.

    Article  Google Scholar 

  • Titman, S. (1984). The effect of capital structure on a firm’s liquidation decision. Journal of Financial Economics, 13, 137–151.

    Article  Google Scholar 

  • Wald, J. K., & Long, M. S. (2007). The effect of state laws on capital structure. Journal of Financial Economics, 83, 297–319.

    Article  Google Scholar 

  • Welch, I. (2004). Capital structure and stock returns. Journal of Political Economy, 112, 106–131.

    Article  Google Scholar 

  • White, H. (1980). A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica, 48, 817–838.

    Article  Google Scholar 

  • Williams, J. (1995). Financial and industrial structure with agency. Review of Financial Studies, 8, 431–474.

    Article  Google Scholar 

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Acknowledgements

We are grateful to Joseph Golec, Karthik Krishnan, Crocker Liu, Dogan Tirtiroglu, and an anonymous referee for providing insightful comments that have greatly improved this paper. We claim sole responsibility for any remaining errors.

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Authors and Affiliations

  1. Department of Accounting and Finance, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA, 02125, USA

    Mine Ertugrul

  2. Finance Group, University of Amsterdam, Roetersstraat 11, Amsterdam, 1018 WB, The Netherlands

    Erasmo Giambona

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  1. Mine Ertugrul
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  2. Erasmo Giambona
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Correspondence to Erasmo Giambona.

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Ertugrul, M., Giambona, E. Property Segment and REIT Capital Structure. J Real Estate Finan Econ 43, 505–526 (2011). https://doi.org/10.1007/s11146-009-9229-4

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