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REIT Debt Pricing and Ownership Structure

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Abstract

We show that ownership by institutional investors with increased incentives to monitor decreases the cost of both public and private debt in the REIT industry. Our study focuses on four types of “incentivized” investors: long-horizon institutional investors, public pension funds, institutions with significant portfolio allocations to particular REITs (motivated institutional owners), and institutional investors that specialize in REITs. In addition, we confirm our findings using a composite index that incorporates only that part of incentivized ownership that is free from the effects of total institutional ownership. Finally, we provide evidence that some of the empirical relationships that we observe can be attributed to monitoring effects and cannot be entirely explained by the intentional selection of REITs with low agency risk into institutional portfolios.

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Notes

  1. The ability to affect change also requires the capacity to understand when change is needed, i.e. a degree of sophistication. The literature has long recognized that institutions tend to be sophisticated investors with greater resources and access to information (Bartov et al. 2000; Jiambalvo et al. 2002; Collins et al. 2003; Gibson et al. 2004; Hribar et al. 2004; Ke and Petroni 2004; Amihud and Li 2006).

  2. While we are the first to construct a firm-level measure of REIT industry specialization of institutional owners, prior literature provides ample evidence that institutions that focus on specific industries tend to form skills that allow them to better evaluate firm performance in these industries. See, for instance, Kacperczyk et al. (2005). Specific to REITs, Beracha et al. (2019) show a positive relation between the share of the institutions’ portfolio invested in REITs and the return generated on those securities.

  3. To maintain their tax status, REITs cannot retain more than 10% of their income. This all but precludes growth through retained earnings. Instead, some REITs choose to signal to the capital markets their needs for growth capital by supporting their stock price through excess dividends or share repurchases.

  4. Loan syndicators, such as commercial banks, may manipulate the cost of debt by charging higher fees. This second measure (All-In Spread) addresses this issue.

  5. Following existing literature, we eliminate observations for convertible bonds and bonds with time to maturity of less than one year. See, for instance, Chichernea et al. (2019) and references therein.

  6. Calculated as the bond’s yield to maturity less the yield of the nearest constant maturity treasury available in the FRED H.15 dataset.

  7. We follow Avramov et al. (2007) and convert S&P and S&P equivalent bond ratings as follows: AAA= 1, AA+= 2, AA = 3, AA-= 4, A+= 5, A = 6, A-= 7, BBB+= 8, BBB= 9, BBB-= 10, BB+= 11, BB= 12, BB-= 13, B+= 14, B = 15, B-= 16, CCC+= 17, CCC= 18 , CCC-= 19, CC= 20, C = 21, and D = 22.

  8. To be precise, we select the following Loan Types from the Facility file: 364-day Facility, Revolver/Line < 1 Yr, Revolver/Line >= 1 Yr, and Term Loan.

  9. Buyout loans are associated with the LBO, SBO, or MBO Primary Purpose in the Facility File.

  10. Our results are also robust to identifying long-horizon institutions as those having below median churn rate.

  11. https://crr.bc.edu/data/public-plans-database/.

  12. The list of the pension funds identified in the S34 data is available from the authors upon request.

  13. Although prior literature does not establish a classification threshold, we confirm in untabulated tests that our findings are robust to using a measure of REIT specialist ownership where an institution is classified as REIT specialist if its WREIT is in the top decile of the distribution in a given quarter.

  14. Note that the numeric rating of 10 corresponds to the S&P rating of BBB-, which is the lowest investment-grade rating.

  15. To alleviate concerns due to potential multicollinearity, we calculate variance inflation factors for each coefficient and observe values of 4.30 and 2.80 for LIO and TIO, respectively. In a separate untabulated analysis, we also estimate the LIO coefficient in regression without controlling for TIO and find that the LIO coefficient equals − 0.14 with significance at the 1% level.

  16. e.g. The Olea and Pflueger (2013) Effective F Statistic equals 9.62 in the bond spread model with PFIO as the instrumented variable, which is less than the critical value of 10.60 for the 5% confidence level and 30% bias threshold so that we cannot reject the null hypothesis of weak instruments at these levels.

  17. When the dependent variable is the bond rating, we use ordered logit in the second stage. In this case, we obtain the residual from the first stage and use it as a control function in the second stage. Both the predicted variable substitution and the residual inclusion methods are valid when the first stage is linear and the second stage is nonlinear (Newey 1987), but the residual inclusion method is more efficient (Terza et al. 2008).

  18. This is a test of the joint hypotheses of correct model specification and the orthogonality conditions, and a rejection of the null calls into question either or both of these hypotheses. We are aware that the Hansen-Sargan test requires the investigator to believe that at least some of the instruments satisfy exclusion restriction (Ruud 2000). Whereas we cannot say with certainty, it is very unlikely that volatility of investor flows to institutional investors (especially short-term) directly affects REIT cost of debt. Therefore, we are reasonably assured that in our case at least FLOW SD1 satisfies the exclusion criterion.

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Acknowledgments

We thank the editor (C.F. Sirmans), two anonymous referees, and conference participants at the 2020 FMA annual meeting (virtual conference) for their valuable comments. All remaining errors are the authors’ alone.

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  1. John B. and Lillian E. Neff Department of Finance, College of Business and Innovation, The University of Toledo, Toledo, OH, 43606, USA

    Collin Gilstrap, Alex Petkevich, Ozcan Sezer & Pavel Teterin

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Gilstrap, C., Petkevich, A., Sezer, O. et al. REIT Debt Pricing and Ownership Structure. J Real Estate Finan Econ 64, 546–589 (2022). https://doi.org/10.1007/s11146-020-09806-0

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