This research examines credit risk pricing using variations in caps rates for single net lease (STNL) homes. Although there is plenty of research on macro- and microeconomic factors that affect capitalization rates, more research must be done on assessing risk pricing for individual tenants. With a unique set of more than 8,200 single-tenant retail property transactions between 2001 and 2019 across the United States, we quantify the implications of pricing the risk posed by tenant credit traits. Our findings suggest that tenant characteristics significantly contribute to explaining the variation in cap rates; in addition, investors can be particularly vulnerable to the risk of expected income. The impact of the expected tenant’s income uncertainty, income growth, and asset quality are also examined.

This is a glimpse of subscription content you can access through your work institution.

Data Availability

None.

Notes

1. For instance, Ambrose and Nourse (1993) utilize data from the American Council of Life Insurance Companies Cher-machine and Wheaton. (2009), Chervachidze and Wheaton (2013) employ retail, office, etc. information for thirty U.S. metropolitan areas, Hendershott and MacGregor (2005) utilize the National Council on Real Estate Fiduciaries data for office, industrial, and retail properties as well as Chaney and Hoesli (2015) make use of data for residential multifamily properties.
2. The data was sourced via Verum Properties, LLC, a leading real estate data provider, focusing solely on properties with a single tenant and transactions. See Sect. ” Hypothesis Development” for more detailed information.
3. The authors start with simple regressions by regressing any variables on cap rates. They discover that betas of stock held by the tenants and the lease term in total plus options are the best explanations for this cap rate. They then employ a multiple regression model to analyze these variables in conjunction. Their model yields an Adjusted R2 of 0.884 and indicates that these variables can cause 88.4 percent of the variation in these rates. The study provides a substantial explanation for the variance in the cap rate. However, an adjusted R2 above 0.80 is also an indicator that there is multicollinearity inside the model. The authors offer no specifics on the collinearity between the variables used in the model. Furthermore, with an average sample size of 26 variables, the model’s capacity to accurately describe cap rate variance could be limited.
4. In a parallel investigation, Liu and Liu (2013) study how bankruptcy announcements affect the performance of the REIT landlord REIT company.
5. Corgel et al. (2015) Jud and Winkler (1995), McDonald and Dermisi (2009), Gunnelin and. (2004), Saderion et al. (1994), Chaney and Hoesli (2015).
6. Hendershott, as well as MacGregor (2005), have found a median or trend reversion pattern in both rents for property as well as dividends. The authors also note that dividends above the trend make the property more attractive and result in the cap rate for the property to fall.
7. Levkovich et al. (2018) have also observed that the depreciation rates for commercial real property in the Netherlands differ significantly across property types. The majority of properties built between 1960-1990 suffer adverse depreciation effects. However, buildings built before WWI are likely to see an appreciation in value due to the vintage effect.
8. See the NAREIT website on “REITs by the Numbers” at https://www.reit.com/data-research/data/reits-numbers.
9. Research has shown that US REITs sell a tiny portion of their properties yearly. For instance, Eichholtz and Yonder (2015) report that a typical US REIT sells around 5 percent of its properties, based on the property value in dollars between 2003 and 2010. Feng et al. (2022) discovered that most REITs employ a long-term investment strategy that involves holding the bulk of their properties for an extended period. This suggests that the property age of REIT firms is likely to remain relatively high in the near time frame since it is an aggregate measure of the properties of portfolios owned by REITs. However, we recognize that self-selection biases can occur as managers decide to invest in older properties. This bias in selection is addressed with the Heckman sampling method. The results are the same.
10. For, see a list of REIT headquarters by location: https://www.reitsacrossamerica.com/us-reits-headquartered-state.
11. In particular, older CEOs generally tend to remain cautious by implementing a low-growth strategy (Child 1974), restraining technology adoption (Kitchell, 1997), spending less on R&D (Barker & Mueller, 2002), and making fewer acquisitions (Yim & Ooi, 2013) and taking fewer risk (Serfling, 2014; Andreou et al., 2017; Belenzon et al. 2019,). In a recent study, Zhang and Ooi (2022) examined the acquisitions of properties made by 150 REIT CEOs. They concluded that younger CEOs are likelier to take on more frequent and aggressive property acquisitions to demonstrate to the market that they can negotiate deals.
12. See the article, “Older condos plagued by high maintenance costs,” at https://www.marketwatch.com/story/older-condos-plagued-by-high-maintenance-costs-2014-06-12.
13. See a TD Insurance article titled “How property insurance is calculated” at https://www.tdinsurance.com/products-services/home-insurance/tips-advice/premium-calculations.
14. Older buildings tend to be less efficient in energy efficiency when contrasted to modern buildings as they generally conform to outdated standards. See https://www.forbes.com/sites/pikeresearch/2016/04/13/energy-efficient-building/?sh=41a4e8d548f8.
15. Kenneth R. French’s Data Library: http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.
16. See https://www.eia.gov/consumption/commercial/data/2018/pdf/CBECS_2018_Building_Characteristics_Flipbook.pdf.
17. In particular, we run the following regression over time on every REIT throughout the period.
18. Ri,t=b0+b1Rm,t+b2SMBt+b3HMLt+b4RMWt+b5CMAt+et,=0+1,+2+3+4+5+e where Ri,t, is the excess stock return of REIT i, Rm,t, is the risk-free stock return of the market, SMBt (Small minus Big), HMLt (High minus Low), MOMt (Momentum), and RMWt (Robust minus Weak), and CMAt (Conservative minus Aggressive) are the return to zero investment factor-mimicking portfolios designed to capture size, book-to-market effects, momentum, profitability. And risk to investment in the year and investment risk in the year. We then use the market return, which is the year-long average of SMB, HML, MOM RMW, CMA Risk factors, and factors estimated as loadings of the model, to calculate the expected return estimate Ri,t.
19. Leskinen et al. (2020) offer an overview of the literature on green certifications for commercial properties.
20. This regression has fewer observations, which is smaller than other regressions because the information about repairs and maintenance costs is unavailable for a few REITs.
21. Research has shown that the volatility of REIT return is different from other asset types (e.g., Cotter & Stevenson (2006); Fei et al. (2010)). Additionally, the extreme risks of REITs are more significant than those of non-REIT companies (Zhou & Anderson, 2012). The pricing of REITs is influenced by their unique risks (e.g., Ooi et al., 2009; Chiang et al., 2009; Cakici et al., 2014.) but not the risk of distress (Shen, 2021).
22. The U.S. geographic regions determined by the National Council of Real Estate Investment Fiduciaries (NCREIF) are (1) NE (Northeast) comprising ME, VT, NH, NY, CT, RI, MA, PA, NJ, DE; (2) ME (Mideast) comprising MD, WV, VA, KY, NC, SC, DC; (3) SE (Southeast): comprising TN, GA, FL, AL, MS; (4) EN (East et al.) comprising MI, IL, OH, IN, WI; (5) WN (West et al.) which includes MN, IA, MO, KS, NE, S D, ND; (6) SW (Southwest) which includes TX, OK, AR, LA, (7) MT (Mountain): MT, ID, WY, UT, CO, NM, AZ, NV; and (8) PC (Pacific) including WA, OR, CA, AK, HI.
23. Check out some of the most recent asset pricing research studies from the REIT literature (e.g., Ling et al. (2019), Beracha et al. (2019b), Chen et al. (2020), Milcheva et al. (2021), Shen (2021), Shen et al. (2021) as well as Zhu as well as Lizieri (2022)) in similar studies.
24. We look at the equal-weighted monthly excess returns on portfolios’ stocks to ensure robustness. We observe quantitatively comparable results. However, we removed them from the paper to make it easier for readers.
25. To increase the robustness of our model, we use our capital asset pricing (CAPM) model. Fama, the French (1993) Five-factor model as well as Fama and French (1993) five-factor model, and Fama the French (2015) five-factor model. We observe quantitatively comparable results for the small coefficients and the statistical significance of the alphas with these models. The paper should include these results to make it easier for readers.