Determinants of U.S. REIT Bond Risk Premia with Explainable Machine Learning

Jakob Kozak, Maximilian Nagl, Cathrine Nagl, Eli Beracha and Wolfgang Schäfers

ERES from European Real Estate Society (ERES)

Abstract: Corporate bonds are an important source of funding for real estate investment trusts (REITs). The outstanding unsecured debt of U.S. equity REITs, which is an approximation for outstanding bond debt, was $450 billion in 2022, while REIT net asset value was $1.1 trillion in the same year. This highlights the importance of corporate bonds for U.S. REITs. However, the literature on bond risk premia focuses only on corporate bonds in general and neglects the specific structure and functioning of issuing REITs. Specifically, U.S. REITs must distribute 90% of their taxable income to shareholders, which prevents them from building capital internally through retained earnings. Since corporate bonds represent a general claim on corporate assets and cash in the case of default, we hypothesize that the drivers of REIT bond risk premia differ from those of the general corporate bond market. Therefore, this paper aims to fill this gap by examining yield spreads, which are the difference between the yield on a REIT bond and the U.S. Treasury yield having the same maturity. Based on findings in the empirical asset pricing literature on the superior performance of artificial neural networks in the adjacent fields of stock and bond return prediction, this paper applies an artificial neural network to predict REIT bond yield spreads. We use a data set of 27,014 monthly U.S. REIT bond transactions from 2010 to 2021 and 33 explanatory variables including bond characteristics, equity and bond market variables, macroeconomic indicators, and, as a novelty, REIT balance sheet data, REIT type, and direct real estate market total return. Preliminary results show that the neural network predicts REIT bond yield spreads with an out-of-sample mean R2 of 36.3%. Feature importance analysis using explainable machine learning methods shows that default risk, captured by REIT size, economy-wide default risk spread, and interest rate volatility, is highly relevant to the prediction of REIT bond yield spreads. We also find evidence for tax and illiquidity risk premia. Interestingly, equity market-related variables are only important in times of economic recession. Real estate market return is an important feature and is negatively related to the predictions of REIT bond yield spreads. These findings underline that bond risk premia for REITs have additional drivers compared to those in the general corporate bond market.

Keywords: Fixed Income; Machine Learning; REIT; Risk Premium (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2023-01-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
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