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Volatility in U.S. Housing Sector and the REIT Equity Return

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Abstract

This study examines how housing sector volatilities affect real estate investment trust (REIT) equity return in the United States. I argue that unexpected changes in housing variables can be a source of aggregate housing risk, and the first principal component extracted from the volatilities of U.S. housing variables can predict the expected REIT equity returns. I propose and construct a factor-based housing risk index as an additional factor in asset price models that uses the time-varying conditional volatility of housing variables within the U.S. housing sector. The findings show that the proposed housing risk index is economically and theoretically consistent with the risk-return relationship of Merton's conditional Intertemporal Capital Asset Pricing Model (ICAPM) (1973), which predicts an average maximum of 1.83 percent of annual housing risk premium in REIT equity return. Moreover, the housing risk index explains a significant portion of the cross-sectional variation of sectoral REIT returns. In cross-section, the positive risk-return relationship remains significant after controlling for VIX, Fama–French four factors, and a broad set of macroeconomic and financial variables. I also find that the proposed housing beta accurately forecasts U.S. macroeconomic and financial conditions.

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Data Availability

REIT index data and the U.S. macroeconomic series are publically available in https://www.reit.com/data-research/reit-indexes and Federal Reserve Bank of St. Louis website. The monthly REIT returns of five REIT sectors are from Yahoo finance.

Code Availability

Replication codes (R language) are available upon a proper request to the author.

Notes

  1. More details of U.S. REIT asset categories and investment opportunities are available on https://www.reit.com/what-reit/reit-basics.

  2. The first factor explains around 73 percent of total variation of data. The one-factor T-GARCH model is also applied to estimate the return and variance dynamics of German stocks by Kaiser (1996) that is also similar to Diebold and Nerlove (1989) and Engle, Ng, and Rothschild (1990).

  3. We estimate latent factors \({f}_{t}\) using the two-step principal components decomposition approach proposed by Bernanke et al. (2005) and later developed by Stock and Watson (2016) and Giannone, Reichlin, and Small (2008).

  4. The level of significance is estimated using the corresponding p-values for all Pearson correlation coefficients.

    p < .001 ‘***’, p < .01 ‘**’, p < .05 ‘*’.

  5. As Hastie et al. (2009) and Skinner et al. (1986) show If \({x}_{1},{x}_{2,}...,{x}_{n}\) are the original series of n number of correlated variables, then a weighted variable y can be generated from a linear combination of these n variables using \(y={\lambda }_{1}{x}_{1}+{\lambda }_{2}{x}_{2}+...+{\lambda }_{n}{x}_{n}\) where the\({\lambda }_{i}\)’s (\(i=1,2...n\)) are the are the corresponding eigenvectors or principal component coefficients.

  6. PCA identifies how these 10 housing volatility series can be summarized in some fashion to construct a single index of meaningful aggregate volatility measure for further analysis. This measure captures maximum possible of the variation of these ten series.

  7. Recent empirical studies that support the positive relationship between REIT returns and observed macroeconomic factors within an APT framework has shown by, amongst others, Clayton and MacKinnon (2001), Heaney and Sriananthakumar (2012), Marfatia et al. (2017), Sing, Tsai and Chen (2016), and Zhou (2012).

  8. We also run the regression for two sub-samples using the endogenous identification approach of structural breakpoints proposed by Bai and Perron (1998). The results show that the positive relationship between the housing risk index and REIT returns is not affected by the choice of two sample periods, and the results are similar to those of complete sample analysis.

  9. We also examine the delayed effect of housing supply shock on REIT return using lagged values of the housing risk index. Consistent with Glaeser et al. (2008) and Gyourko (2009), we find significant evidence of the lagged impact of housing shock, where the impact of a maximum eleven lag is significant for residential and multifamily, and retail, twelve lag is significant for lodging and resorts, office, and diversified REIT. Results are available upon request.

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Acknowledgements

I thank the editor and referee for their constructive and extensive comments on the earlier version of the paper that helped me deliver this revised version. The paper is based on my Ph.D. research at Northern Illinois University and does not necessarily reflect the views of the institutions I am affiliated. I thank Prof. Jeremy R. Groves, Prof. Carl Campbell, and Prof. Ai-ru (Meg) Cheng for valuable feedback and suggestions. Any remaining errors are mine.

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  1. Department of Economics, Shahjalal University of Science and Technology, Sylhet, Bangladesh

    Masud Alam

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Alam, M. Volatility in U.S. Housing Sector and the REIT Equity Return. J Real Estate Finan Econ (2022). https://doi.org/10.1007/s11146-022-09897-x

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