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The Impact of Tenant Diversification on Spreads and Default Rates for Mortgages on Retail Properties

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Abstract

We use an empirical model of commercial mortgage spreads to examine how tenant diversification impacts credit spreads for mortgages on retail properties. We find that mortgages on properties with a highly diversified tenant base have spreads that are up to 7.1 basis points higher than spreads on mortgages for single-tenant properties, but that mortgages on properties with moderate levels of tenant diversification have spreads that are up to 5.2 basis points lower than mortgages on single-tenant properties. The spread discount for mortgages on properties with moderate levels of tenant diversification disappears when the lease of the property’s largest tenant expires before the loan matures. Despite the spread discount that is given to properties with moderate levels of tenant diversification, we find that the likelihood with which a mortgage goes into default increases as tenant diversification increases.

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Notes

  1. While Fuerst and Marcato (2009) do not find that the diversification factor is able to significantly reduce alpha performance, they do claim that all risk factors in their model are to some extent important in explaining portfolio returns. They also note that all identified risk factors “should be considered for benchmarking purposes because the ability to generate extra performance and its level is dependent on exposure to these factors.”

  2. The finding that loan portfolio diversification yields no benefits to banks is not universal. For example, Bebczuk and Galindo (2008) find that banks do get some benefits from holding a diversified loan portfolio.

  3. Because a large number of loans in the Trepp Data Feed are conduit loans, we consider a loan’s characteristics at securitization to be a fair proxy for the loan’s characteristics at origination.

  4. Our primary results are qualitatively similar when we examine a pooled dataset consisting of retail, office, and industrial/warehouse properties.

  5. An alternative measure for diversification that is employed by Fuerst and Marcato (2009) is the number of tenants that occupy a given property. Our data do not directly report the number of tenants for each property. However, using data on the percent of square footage occupied by each of the largest 3 tenants enables us to determine if a property has 1, 2, 3, or greater than 3 tenants. In doing so, we observe that L1% is correlated with the number of tenants. We also find that L1% is highly correlated with the 3-tenant Herfindahl-Hirschman Index (HHI), which we use later in the paper to examine the robustness of our results.

  6. We winsorize all continuous variables at the 1 % and 99 % levels to control for extreme outlying observations.

  7. The correlation coefficient between the number of loans in each year and the average spread in each year is −0.94.

  8. See Titman et al. (2005) for a detailed explanation about the expected impact of these variables on mortgage spreads.

  9. Petersen (2009) and Thompson (2011) discuss the importance of clustering standard errors in panel datasets.

  10. Table 1 shows that L1% is correlated with property values. In unreported results, we segment properties into three different groups based on their total rentable area and perform the same analysis to ensure that our results are not driven by property size. We find that medium and large properties exhibit a similar U-shaped relationship between tenant diversification and spreads, while the smallest properties have spreads that either do not change or increase as tenant diversification increases.

  11. Tests that the sum of the coefficients on the tenant diversification dummies and the lease rollover interaction term for this model and other models that appear later in the paper are reported in the Appendix.

  12. As was the case with L1%, we find that HHI is positively correlated with the number of tenants that occupy a property.

  13. Ambrose et al. (2003) note that nonbank lenders tend to make low LTV loans while bank lenders are more willing to accept a loan package with a higher LTV. Titman et al. (2005) also document a clientele effect, as they find that some originators attract mortgages with higher LTVs.

  14. See Archer et al. (2002), Ciochetti et al. (2002), Ambrose and Sanders (2003), Ciochetti et al. (2003), Christopoulos et al. (2008), Yildirim (2008), and Titman and Tsyplakov (2010).

  15. The sample size in our default analysis is smaller than the sample size in our spread model because we drop loans that are missing data on the DSCR or the property’s U.S. Census division.

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Acknowledgments

We thank JPMorgan Chase for providing access to data from the Trepp Data Feed and for providing valuable feedback on the paper. We thank John Clapp, Richard Green, Gianluca Marcato, Andy Naranjo, Edward Reardon, Sergey Tsyplakov, and seminar participants at the 2015 AREUEA-ASSA meetings in Boston, The Homer Hoyt Institute, and Providence College for their helpful comments. All errors and omissions are our own.

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Authors and Affiliations

  1. The Smeal College of Business, The Pennsylvania State University, University Park, PA, 16802, USA

    Brent Ambrose

  2. School of Business, Providence College, Providence, RI, 02918, USA

    Michael Shafer

  3. Zicklin School of Business, Baruch College, New York, NY, 10010, USA

    Yildiray Yildirim

Authors
  1. Brent Ambrose
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  2. Michael Shafer
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  3. Yildiray Yildirim
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Corresponding authors

Correspondence to Brent Ambrose or Michael Shafer.

Appendix: Linear Restriction Tests

Appendix: Linear Restriction Tests

Tests with L1% Dummies:

  1. (1)

    coeff[D(0 ≤ L1% < 20)] + coeff[D(0 ≤ L1% < 20) × D(L1 Rollover)] = 0

  2. (2)

    coeff[D(20 ≤ L1% < 40)] + coeff[D(20 ≤ L1% < 40) × D(L1 Rollover)] = 0

  3. (3)

    coeff[D(40 ≤ L1% < 60)] + coeff[D(40 ≤ L1% < 60) × D(L1 Rollover)] = 0

  4. (4)

    coeff[D(60 ≤ L1% < 80)] + coeff[D(60 ≤ L1% < 80) × D(L1 Rollover)] = 0

  5. (5)

    coeff[D(80 ≤ L1% < 100)] + coeff[D(80 ≤ L1% < 100) × D(L1 Rollover)] = 0

Tests with HHI Dummies:

  1. (1)

    coeff[D(HHI quantile = 1)] + coeff[D(HHI quantile = 1) × D(L1 Rollover)] = 0

  2. (2)

    coeff[D(HHI quantile = 2)] + coeff[D(HHI quantile = 2) × D(L1 Rollover)] = 0

  3. (3)

    coeff[D(HHI quantile = 3)] + coeff[D(HHI quantile = 3) × D(L1 Rollover)] = 0

  4. (4)

    coeff[D(HHI quantile = 4)] + coeff[D(HHI quantile = 4) × D(L1 Rollover)] = 0

  5. (5)

    coeff[D(HHI quantile = 5)] + coeff[D(HHI quantile = 5) × D(L1 Rollover)] = 0

Tests for Tables 3 and 5

 

L1% Dummies

HHI Dummies

Restriction

chi2

p-val

chi2

p-val

1

13.31

0.0004

11.03

0.0011

2

3.71

0.0558

2.36

0.1262

3

0.09

0.7603

2.04

0.1551

4

0.57

0.4523

0.09

0.7689

5

0.05

0.8257

0.01

0.9267

Tests for Panel B of Table 6

 

L1% Dummies

HHI Dummies

Restriction

chi2

p-val

chi2

p-val

1

13.60

0.0002

12.46

0.0004

2

3.73

0.0533

2.74

0.0976

3

0.09

0.7618

2.17

0.1409

4

0.81

0.3680

0.26

0.6086

5

0.08

0.7748

0.00

0.9638

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Ambrose, B., Shafer, M. & Yildirim, Y. The Impact of Tenant Diversification on Spreads and Default Rates for Mortgages on Retail Properties. J Real Estate Finan Econ 56, 1–32 (2018). https://doi.org/10.1007/s11146-016-9579-7

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