Skip to main content
Log in

Abstract

Asset and debt management are two essential managerial tasks in any firm. The traditional view holds that asset management is the primary driver of real estate investment trust (REIT) returns for the following reasons: (1) interest tax shields are not a source of incremental value for REITs and (2) the plain tangibility of real estate assets helps to diminish the financial distress costs of REITs. This paper examines empirically whether debt management also matters for the operating returns (i.e., ROA, ROE, ΔROA or ΔROE) of a portfolio of REITs. Both applying a novel dynamic decomposition method to ΔROA or ΔROE and also defining ROA and ROE under the net income and the funds from operations metrics guide the empirical approach of this paper. Our findings show that the effects of debt management on REITs’ operating profitability cannot be ruled out. However, the direction of these effects appears to be opposite to that of asset management. These results call for renewed and further investigations into the optimal capital structure questions for REITs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. While Equity REITs own and operate income-producing properties, Mortgage REITs invest in mortgages and mortgage related securities.

  2. We thank an anonymous referee for this excellent point.

  3. The National Association of Real Estate Investment Trusts (NAREIT) in the United States for a long time and REALPAC in Canada in recent years have promoted the use of FFO.

  4. Other FFO adjustments, such as including a REIT’s interest in unconsolidated partnerships and joint ventures, and adding back interest expense on convertible debt (some REITs treat convertible debt as equity), also suggest that FFO might be a more comprenhensive performance measure to asset management policies.

  5. Inselbag and Kaufold (1989) provide an excellent demonstration of the APV method with a numerically driven example of a leveraged corporate buyout. The Flow-to-Equity (FTE) and the Weighted Average Cost of Capital methods are the altenatives to the APV method. For capital budgeting or asset valuation problems without cash flow related complications, all three methods provide the same result.

  6. Previous research also reports mixed evidence. For example, Graham and Knight (2000) find evidence that FFO has higher incremental information content than NI. Fields et al. (1998) find that, while FFO is better in predicting one-year-ahead FFO and cash flows from operations (CFO), NI is better in predicting contemporaneous stock prices and one-year-ahead NI. Gore and Stott (1998) find that FFO is, in fact, more closely associated with stock returns than NI and that NI predicts dividends better than FFO does. Meanwhile, Ben-Shahar et al. (2011) report counter evidence that FFO explains better REITs’ dividend policy than NI. Vincent (1999) reports that all four measures - FFO, earnings-per-share (EPS), CFO, and earnings-before-interest-tax-depreciation-and-amortization (EBITDA) - are associated with stock returns, but their statistical significance depends on the model specifications.

  7. Note that the reverse effect could occur. That is, we could see worsened profitability of individual REITs (“within” effect), shifts of resources from more to less profitable REITs (“between” effect), entries of less profitable REITs (“entry” effect), and exits of more profitable REITs (“exit and conversion” effect) between 1989 and 2015.

  8. We are grateful to a referee for this insightful point.

  9. The availability of micro-level (i.e., establishment-level) data for manufacturing industries spawned a series of such applied microeconomic research. McGuckin (1995) describes the Longitudinal Research Database (LRD) at the U.S. Bureau of the Census upon which this research relies. For banking data at the individual bank level, see the Federal Reserve Bank of Chicago at https://www.chicagofed.org/banking/financial-institution-reports/commercial-bank-data. In sum, aggregate industry data contain important firm- and plant-level dynamics that collectively determine overall industry dynamics.

  10. We thank Brad Case for kindly providing us with data from NAREIT’s resources, Erkan Yonder for helping us in identifying and collecting some of our data from various sources, and Steve Cauley for his comments that guided us in cross checking our data vis-a-vis the CRSP/ZIMAN database.

  11. We gratefully acknowledge a referee for this point and will share these mean differences upon request.

  12. Eq. (6.a) is consistent in spirit with the weak-form market efficiency tests even though our work does not constitute a test of market efficiency. We use sample REITs’ operating profits, which are not capable of reflecting immediately all publicly available information, since firms produce them under accounting principles. They are not outcomes of market-transactions. We thank a referee for bringing this matter to our attention.

  13. A rise in the “within” effect at (t-1) under a positive sign means an increase, for example, in ΔROE and, hence, a dominance of ROE (t) over ROE (t-1) and vice versa. So, a positive “within” effect at (t-1) associates with an increase in ROE (t).

  14. Jeon and Miller (2005) provide details of the derivations. These decomposition methods can be also applied at the industry level that includes all the firms in an industry between (t-1) and (t).

  15. Consider two time periods (t-1) and (t). We classify REITs as staying, if a REIT exists in both (t-1) and (t); entering, if a REIT does not exist in (t-1) but does in (t); and exiting, if a REIT exists in (t-1) but not in (t).

  16. Diewert (2005) calls this the Laspeyres (Laspeyres, 1871) difference index.

  17. Diewert (2005) calls this the Paasche (Paasche, 1974) difference index.

  18. Note, also, that for the between effect, the lagged ROE for each REIT replaces the current ROE between Eqs. (15) and (16).

  19. Bailey et al. (1992) provide an algebraic decomposition of an industry’s total factor productivity (TFP) growth into the “within,” “between,” and “net-entry” (entry minus exit) effects. Extending Bailey et al. (1992), Haltiwanger (1997) separates the effects of firm entrants into and exit from the industry. Moreover, he also divides the “between” effect into two components – the “share” and “covariance” effects. Stiroh (2000) further decomposes Haltiwanger’s (1997) method by dividing firms into those that acquired other firms and those that did not. Finally, the Bennet (1920) dynamic decomposition combines Bailey et al.’s (1992) and Haltiwanger’s (1997) dynamic decompositions into a simple average and eliminates Haltiwanger’s (1997) “covariance” effect as it emerges because of the method of decomposition. Thus, the weighting of the four effects all employ simple averages of the initial (t-1) and final (t) year weights. See Diewert (2005) for additional details. Jeon and Miller (2005) also provide the derivation.

References

  • Bailey, M. N., Hulten, C., & Campbell, D. (1992). The distribution of productivity. Brookings Papers on Economic Activity: Microeconomics, 1, 187–267.

    Article  Google Scholar 

  • Bennet, T. L. (1920). The theory of measurement of changes in the cost of living. Journal of the Royal Statistical Society, 83, 455–462.

    Article  Google Scholar 

  • Ben-Shahar, B., Sulganik, E., & Tsang, D. (2011). Funds from operations versus net income: examining the dividend relevance of REIT performance measures. Journal of Real Estate Research, 33(3), 415–441.

    Article  Google Scholar 

  • Beracha, E., Feng, Z., & Hardin III, W. G. (2019). REIT operational efficiency: Performance, risk, and return. The Journal of Real Estate Finance and Economics, 58, 408–437.

    Article  Google Scholar 

  • Berk, J. B., Stanton, R., & Zechner, J. (2010). Human capital, bankruptcy, and capital structure. Journal of Finance, 65(3), 891–926.

    Article  Google Scholar 

  • Bhattacharya, N., Black, E. L., Christensen, T. E., & Larson, C. R. (2003). Assessing the relative informativeness and permanence of pro forma earnings and GAAP Operating earnings. Journal of Accounting and Economics, 36, 285–319.

    Article  Google Scholar 

  • Brander, J., & Lewis, T. R. (1986). Oligopoly and financial structure: The limited liability effect. American Economic Review, 76(5), 956–970.

    Google Scholar 

  • Chemmanur, T. J., Cheng, Y., & Zhang, T. (2013). Human capital, capital structure, and employee pay: An empirical analysis. Journal of Financial Economics, 110(2), 478–502.

    Article  Google Scholar 

  • Diewert, W. E. (2005). Index number theory using differences rather than ratios. The American Journal of Economics and Sociology, 64, 311–360.

    Article  Google Scholar 

  • Feng, Z., Price, S. M., & Sirmans, C. F. (2011). An overview of equity real estate investment trusts (REITs): 1993-2009. Journal of Real Estate Literature, 19, 307–343.

    Article  Google Scholar 

  • Feng, Z., Lin, Z., & Wu, W. (2020). CEO influence on funds from operations (FFO) Adjustment for real estate investment trusts (REITs). Journal of Real Estate Finance and Economics. https://doi.org/10.1007/s11146-020-09795-0

  • Fields, T., Rangan, S., & Thiagarajan, R. (1998). An empirical evaluation of the usefulness of non-GAAP accounting measure in the real estate investment trust industry. Review of Accounting Studies, 3, 103–130.

    Article  Google Scholar 

  • Ghosh, C., Roark, S., & Sirmans, C. F. (2013). On the operating performance of REITs following seasoned equity offerings: Anamoly revisited. Journal of Real Estate Finance and Economics, 46, 633–663.

    Article  Google Scholar 

  • Glover, B. (2016). The expected cost of default. Journal of Financial Economics, 119(2), 284–299.

    Article  Google Scholar 

  • Gore, R., & Stott, D. M. (1998). Toward a more informative measure of operating performance in the REIT industry: Net income vs. funds from operations. Accounting Horizons, 12(4), 323–339.

    Google Scholar 

  • Graham, J. R. (2000). How big are the tax benefits of debt. Journal of Finance, 55, 1901–1941.

    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 

  • Graham, J. R., & Leary, M. T. (2011). A review of empirical capital structure research and directions for the future. Annual Review of Financial Economics, 3, 309–345.

    Article  Google Scholar 

  • Graham, C. M., & Knight, J. R. (2000). Cash flows vs Earnings in the valuation of equity REITs. Journal of Real Estate Portfolio Management, 6(1), 17–25.

    Article  Google Scholar 

  • Haltiwanger, J. C. (1997). Measuring and analyzing aggregate fluctuations: The importance of building from microeconomic evidence. Federal reserve bank of St. Louis Review, 79, 55–77.

    Google Scholar 

  • Harrison, D. M., Luchtenberg, K. F., & Seiler, M. J. (2011). REIT performance and lines of credit. Journal of Real Estate Portfolio Management, 17(1), 1–14.

    Article  Google Scholar 

  • Howe, J. S., & Shilling, J. D. (1988). Capital structure theory and REIT security offerings. Journal of Finance, 43(4), 983–993.

    Article  Google Scholar 

  • Huang, G.-C., Liano, K., & Pan, M.-S. (2009). REIT open-market stock repurchases and profitability. Journal of Real Estate Finance and Economics, 39, 439–449.

    Article  Google Scholar 

  • Inselbag, I., & Kaufold, H. (1989). How to value recapitalizations and leveraged buyouts. Journal of Applied Corporate Finance, 87-96.

  • Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360.

    Article  Google Scholar 

  • Jeon, Y., & Miller, S. M. (2005). An 'Ideal' decomposition of industry dynamics: An application to the nationwide and state level U.S. banking industry. University of Connecticut, Working Paper #2005-25, http://ideas.repec.org/p/uct/uconnp/2005-25.html. Accessed 10 June 2021

  • Kim, H. (2020). How does labor market size affect firm capital structure? Evidence from large plant openings. Journal of Financial Economics, 138(1), 277–294.

    Article  Google Scholar 

  • Laspeyres, E. (1871). Die Berechnung einer mittleren Warenbpreissteigerung. Jahrbücher für Nationalökonomie und Statistik, 16, 296–314.

    Article  Google Scholar 

  • Lougee, B. A., & Marquardt, C. A. (2004). Earnings informativeness and strategic disclosure: An empirical examination of ‘Pro Forma’ earnings. The Accounting Review, 79(3), 769–795.

    Article  Google Scholar 

  • Matsa, D. A. (2010). Capital structure as a strategic variable: Evidence from collective bargaining. Journal of Finance, 65(3), 1197–1232.

    Article  Google Scholar 

  • McGuckin, R. H. (1995). Establishment microdata for economic research and policy analysis: Looking beyond the aggregates. Journal of Business and Economic Statistics, 13, 121–126.

    Google Scholar 

  • Modigliani, F., & Miller, M. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, 48(3), 261–297.

    Google Scholar 

  • Modigliani, F., & Miller, M. (1963). Corporate income taxes and the cost of capital: a correction. American Economic Review, 53(3), 433–443.

    Google Scholar 

  • Myers, S. M. (1974). Interactions of corporate financing and investment decisions - implications for capital budgeting. Journal of Finance, 29, 1–25.

    Article  Google Scholar 

  • Myers, S. M., & Majluf, N. S. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13(2), 187–221.

    Article  Google Scholar 

  • Paasche, H. (1974). Über die Preisentwicklung der letzten Jahre nach den Hamburger Börsennotirungen. Jahrbücher für Nationalökonomie und Statistik, 24, 168–178.

    Google Scholar 

  • Parsons, C., & Titman, S. (2008). Empirical capital structure: A review. Foundations and Trends in Finance, 3(1), 1–93.

    Article  Google Scholar 

  • Reindl, J., Stoughton, N., & Zechner, J. (2017). Market implied costs of bankruptcy. WU-Vienna University of Economics and Business https://fnce.wharton.upenn.edu/wp-content/uploads/2017/03/ZechnerBCost_17A.pdf. Accessed 9 June 2021.

    Google Scholar 

  • Stiroh, K. J. (2000). Compositional dynamics and the performance of the U.S. banking industry. Federal Reserve Bank of New York, Staff Reports #98.

  • Vincent, L. (1999). The information content of funds from operations for real estate investment trusts. Journal of Accounting and Economics, 26, 69–104.

    Article  Google Scholar 

  • Xu, R., & Ooi, J. T. L. (2018). Good growth, bad growth: How effective are REITs’ corporate watchdogs? Journal of Real Estate Finance and Economics, 57, 64–86.

    Article  Google Scholar 

Download references

Acknowledgments

We thank Erkan Yonder, Steve Cauley, the 11th ReCapNet Conference participants for their comments; Brad Case and NAREIT for data; Carolin Schmidt (the editor) for her encouragement; and two reviewers for their excellent and constructive suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dogan Tirtiroglu.

Ethics declarations

Conflict of interest

None.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Alternative Dynamic DecompositionsFootnote 14

At time t, the ROE (Rt) equals net income (NIt) divided by total equity (Et). That is,

$${R}_t=\frac{NI_t}{E_t}$$
(7)

where \({NI}_t={\sum}_{i=1}^{n_t}{NI}_{i,t},{E}_t={\sum}_{i=1}^{n_t}{E}_{i,t},\) and nt is the number of REITs. After substitution and rearrangement, we get

$${R}_t={\sum}_{i=1}^{n_t}{r}_{i,t}{\theta}_{i,t},$$
(8)

where ri,t equals the ratio of net income to equity for REIT i in period t and θi,t equals the i-th REIT’s share of portfolio/industry equity. We want to decompose the change in portfolio/industry ROE into “within,” “between,” “entry,” and “exit” effects. The change in portfolio/industry ROE equals the following:

$$\Delta {R}_t={R}_t-{R}_{t-1}={\sum}_{i=1}^{n_t}{r}_{i,t}{\theta}_{i,t}-{\sum}_{i=1}^{n_{t-1}}{r}_{i,t-1}{\theta}_{i,t-1}.$$
(9)

The number of REITs in period (t) equals the number of REITs in period (t-1) plus the number of REIT entrants minus the number of REIT exits.Footnote 15 That is,

$${n}_t={n}_{t-1}+{n}_t^{enter}-{n}_{t-1}^{exit}.$$
(10)

Rearranging terms in Eq. (10) yields

$${n}_t-{n}_t^{enter}={n}_{t-1}-{n}_{t-1}^{exit}={n}_{t/t-1}^{stay};\ or$$
(11)
$${n}_t={n}_{t/t-1}^{stay}+{n}_t^{enter}, and\ {n}_{t-1}={n}_{t/t-1}^{stay}+{n}_{t-1}^{exit}$$
(12)

Thus, Eq. (9) adjusts as follows:

$$\Delta {R}_t={\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,t}{\theta}_{i,t}+{\sum}_{i=1}^{n_t^{enter}}{r}_{i,t}{\theta}_{i,t}-{\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,t-1}{\theta}_{i,t-1}-{\sum}_{i=1}^{n_{t-1}^{exit}}{r}_{i,t-1}{\theta}_{i,t-1}.$$
(13)

Case 1: Existing Dynamic Decomposition - Laspeyres Difference Index.

While we already separate the “stay” terms from the “entry” and “exit” terms, we now need to decompose the “stay” terms into the “within” and “between” effects. Bailey et al. (1992) and Haltiwanger (1997) weight the “within” effect with the individual firm’s portfolio/industry share of equity in the initial year.Footnote 16 That is, we need to add and subtract \({\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,t}{\theta}_{i,t-1}\) from the right-hand side of Eq. (13). After some manipulation, we get

$$\Delta {R}_t={\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,t}{\theta}_{i,\Delta t}+{\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,\Delta t}{\theta}_{i,t-1}+{\sum}_{i=1}^{n_t^{enter}}{r}_{i,t}{\theta}_{i,t}-{\sum}_{i=1}^{n_{t-1}^{exit}}{r}_{i,t-1}{\theta}_{i,t-1},$$
(14)

where θi, ∆t = θi, t − θi, t − 1 and ri, ∆t = ri, t − ri, t − 1.

Then, we can rewrite Eq. (14) as follows:

$$\Delta {R}_t={\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,\Delta t}{\theta}_{i,t-1}+{\sum}_{i=1}^{n_{t/t-1}^{stay}}\left({r}_{i,t}-{R}_{t-1}\right){\theta}_{i,\Delta 1}+{\sum}_{i=1}^{n_t^{enter}}\left({r}_{i,t}-{R}_{t-1}\right){\theta}_{i,t}-{\sum}_{i=1}^{n_{t-1}^{exit}}\left({r}_{i,t-1}-{R}_{t-1}\right){\theta}_{i,t-1}$$
(15)

where we evaluate the “between,” “entry,” and “exit” effects relative to the lagged portfolio/industry ROE (Rt−1). For example, the “between” effect sums the differences between each REIT’s ROE and the portfolio’s/industry’s ROE, multiplied by that REIT’s change in equity share. In this case, we evaluate the REIT’s ROE in period (t) and the industry's ROE in period (t-1).

Case 2: Alternative Dynamic Decomposition - Paasche Difference Index.

We decompose the change in industry ROE by weighting the “within” effect by period-t individual REIT’s share of portfolio/industry equity.Footnote 17 In other words, we need to add and subtract \({\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,t-1}{\theta}_{i,t}\) to Eq. (13). After necessary manipulations, the final form equals:

$$\Delta {R}_t={\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,\Delta t}{\theta}_{i,t}+{\sum}_{i=1}^{n_{t/t-1}^{stay}}\left({r}_{i,t-1}-{R}_t\right){\theta}_{i,\Delta t}+{\sum}_{i=1}^{n_t^{enter}}\left({r}_{i,t}-{R}_t\right){\theta}_{i,t}-{\sum}_{i=1}^{n_{t-1}^{exit}}\left({r}_{i,t-1}-{R}_t\right){\theta}_{i,t-1},$$
(16)

where we evaluate the “between,” “entry,” and “exit” effects relative to the current portfolio/industry ROE (Rt).Footnote 18

Case 3: Bennet Dynamic Decomposition. Footnote 19

The Bennet dynamic decomposition computes the arithmetic average of Case 1 and Case 2 as follows:

$$\Delta {R}_t={\sum}_{i=1}^{n_{t/t-1}^{stay}}{r}_{i,\Delta t}{\overline{\theta}}_{i,t}+{\sum}_{i=1}^{n_{t/t-1}^{stay}}\left({\overline{r}}_i-\overline{R}\right){\theta}_{i,\Delta t}+{\sum}_{i=1}^{n_t^{enter}}\left({r}_{i,t}-\overline{R}\right){\theta}_{i,t}-{\sum}_{i=1}^{n_{t-1}^{exit}}\left({r}_{i,t-1}-\overline{R}\right){\theta}_{i,t-1}.$$
(17)

where \({\overline{\theta}}_i=\left({\theta}_{i,t}+{\theta}_{i,t-1}\right)/2,\kern0.5em {\overline{r}}_i=\left({r}_{i,t}+{r}_{i,t-1}\right)/2, and\ {\overline{R}}_i=\left({R}_t+{R}_{t-1}\right)/2.\)  

The Bennet dynamic decomposition includes four effects. The “within” effect equals the summation of each REIT’s change in ROE weighted by its average share of portfolio/industry equity between period (t-1) and period (t). The “between (reallocation)” effect equals the summation of the difference between each REIT’s ROE and the portfolio/industry average ROE between period (t-1) and period (t), multiplied by the change in that REIT’s share of portfolio/industry equity. The “entry” effect equals the summation of the difference between each entering REIT’s ROE in period (t) and the portfolio/industry average ROE between period (t-1) and period (t) times the entering REIT’s share of portfolio/industry equity in period (t). Finally, the “exit” effect equals the summation of the difference between each exiting REIT’s ROE in period (t-1) and the portfolio/industry average ROE between period (t-1) and period (t), multiplied by the exiting REIT’s share of portfolio/industry equity in period (t-1).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Feng, Z., Miller, S.M. & Tirtiroglu, D. Does Debt Management Matter for REIT Returns?. J Real Estate Finan Econ (2022). https://doi.org/10.1007/s11146-021-09864-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11146-021-09864-y

Keywords

Navigation