The Effect of Market Asset Returns, Economic Conditions, and Firm Fundamentals on Net Lease Capitalization Rates

AbstractThis study fills a void in the literature by examining real estate capitalization rates for single-tenant net lease (STNL) properties. First, we examine cap rate variation in relation to market and firm-level fundamentals using individual transaction data in a multistage regression approach. Second, our single-tenant dataset, which allows us to control for characteristics such as industry and tenant credit ratings, gives us unique insight into not only the pricing of cap rates, but also their underlying drivers and their relationship to market fundamentals and returns on alternative assets. Using this unique dataset of more than 8,000 single-tenant net lease retail property transactions, we develop a quarterly cap rate index controlling for Metropolitan Statistical Area (MSA) and industry fixed effects, property and lease characteristics, and localized influences such as population density and household income. Third, we examine the effect of excess corporate bond spreads, excess stock returns, stock market indicators, firm financials, and economic and demographic indicators. Finally, we examine the effect on cap rates of MSA characteristics such as size, wealth, poverty, crime, gross domestic product, and growth. The findings show that, besides the systematic risk from stock and bond returns, national and metropolitan economic forces and firm fundamental factors explain variation in cap rates.Keywords: cap ratesnet leaseexcess returnmacroeconomic factors AcknowledgmentsWe thank the editor and the anonymous referees for their insightful feedback, which has substantially improved this article. We also thank the participants at the 2023 ARES Conference for their helpful comments.Disclosure StatementNo potential conflict of interest was reported by the author(s).Notes1 As a percentage of total investible wealth, real estate is a significant percentage of domestic publicly traded equities. According to Siblis Research Ltd, the total market capitalization of the U.S. stock market was $53.4 trillion as of December 31, 2021 (https://siblisresearch.com/data/us-stock-market-value/) while, according to the National Association of Real Estate Investment Trusts (NAREIT), the total value of U.S. commercial real estate was $20.7 trillion as of June 2021 (https://www.reit.com/data-research/research/nareit-research/estimating-size-commercial-real-estate-market-us-2021).2 The cap rate is defined as the ratio of a property’s net operating income to its current market value; most often the cap rate is based on expected net operating income.3 Common multifactor models in the finance literature include the Fama-French three-factor model (1992) and the arbitrage pricing theory (APT) model (Ross, Citation1976).4 All the studies discussed in this section use either the full cap rate or the excess cap rate (typically defined as the full cap rate minus the three-month TB yield) as the dependent variable in their analysis.5 To examine the relationship between real estate and capital markets, additional studies have used alternative return measures such as publicly traded REITs or National Council of Real Estate Investment Fiduciaries (NCREIF) returns data. Some results: real estate returns are driven by fundamental macroeconomic factors such as the growth rate in real per capita consumption, the real T-bill rate, the term structure of interest rates, and unexpected inflation (Naranjo & Ling, Citation1997); exchange‐traded real estate is integrated with the market for exchange‐traded non‐real estate stocks and the growth rate in real per capita consumption is a common variable (Ling & Naranjo, Citation1999); fundamental and nonfundamental factors such as debt capital market conditions, unemployment, NAREIT and NCREIF returns, stock market volatility, and investor sentiment are significant predictors of ex ante risk premiums (Beracha et al., Citation2019); cross-sectional dispersion of real estate returns is explained by macroeconomic factors such as the term and credit spreads, inflation, and the short rate of interest (Plazzi et al., Citation2008); and REITs with properties in high-density locations have lower implied capitalization rates (Fisher et al., Citation2020).6 The estimated model relates the excess cap rate to the spread between the current BAA bond rate and the current three-month T-bill rate, the one-period lagged BAA bond rate and the one-period lagged three-month T-bill rate, the two-period lagged BAA bond rate and the two-period lagged three-month T-bill rate, the current S&P 500 stock market return rate the current three-month T-bill rate, the one-period lagged S&P 500 stock market return rate and the one-period lagged three-month T-bill rate, the two-period lagged S&P 500 stock market return rate and the two-period lagged three-month T-bill rate. The effect of growth is assumed to be captured by fixed effects MSA variables.7 The Gordon model, rearranged to solve for the cost of equity capital, is based on a dividend (D1) received starting in period 1, a current stock price of P0, and a dividend stream growing at a constant growth rate g into the foreseeable future. The cost of equity capital using this model is rE=D1P0+g.8 For a detailed explanation of this approach from a financial management perspective, see Emery et al. (Citation2018, pp. 132–134).9 Most properties with gross leases were also ground lease transactions. A small percentage of gross leases occurred in fee simple ownership. In the interest of obtaining reliable regression estimates, these observations were deleted from the sample.10 As Letdin et al. (Citation2023) pointed out, this positive relationship is likely due to additional maintenance costs as building size increases.11 Cap rates vary widely depending on the strength of the operator and with a franchise, the cash flow (to cover rents) is riskier than a dealer-operated property. Investors might see the corporate flag for a property and assume that they are protected, but they are not. Corporate owners represent less risk and, therefore, we would expect franchisee cap rates to be higher.12 Industry Dummies include: Auto, Bank, Cellular, Education, Fitness, Gas Station, Government, Grocery, Industrial, Large Retail, Medical, Multi, Office, Pharmacy, Restaurant, and Small Retail. Deal Type Dummies include: Fee Simple, Ground Lease, and Leasehold. Lease Type Dummies include: GL, N, NN, and NNN. Ownership Type Dummies include: Corporate, Franchisee, and Other.13 As shown in Appendix A, we ran our baseline regression using the BBB corporate bond spread (in place of the AAA spread), consistent with Jud and Winkler (Citation1995), and found weaker statistical significance, but similar economic interpretations. For instance, the R2 of column 1 in Appendix A is 0.363, which is much lower than the R2 of 0.610 in column 4 of Table 2, Panel A. This suggests that there is substantial corporate default risk information in BBB spreads that is not found in real estate cap rates.14 Jud and Winkler (Citation1995) used dummy variables to capture differences in MSA characteristics.15 These variables are potentially endogenous, as they may be based on some of the same real estate transactions as in our dataset.16 It is important to note that these three variables exhibit strong correlations. Relative to Housing Supply Elasticity, the Wharton Land Regulation Index and Land Share Unavailable for Development exhibit correlations of –55% and –76%, respectively, for MSAs in our sample. Furthermore, the first principal component explains 67% of the variation among the three variables. Despite the correlations, however, when all three are included in regression, the R2 increases to 0.38 (unreported result).17 Because the data appear to be error free (not mistyped, misreported, etc.), the observations beyond the 5th and 95th percentile are deemed to be legitimate data that should be included in the statistical analysis. Therefore, we report the findings based on the untransformed data.