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Testing the Dividend Discount Model in Housing Markets: the Role of Risk

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Abstract

Tests of the dividend discount model (DDM) applied to housing have studied the trade-off between the capitalization rate (CAP rate) and subsequent house price appreciation. Even allowing for attenuation bias because of actual appreciation does not equal expected appreciation, evidence for the DDM is not strong. This research has included an implicit assumption that risks associated with housing investment are common across housing markets. In addition, many previous tests have used the Bureau of Labor Statistics (BLS) Rent Index to construct the CAP rate although recent research by Ambrose et al. (2015) has questioned this data. The American Housing Survey is used to construct estimates of the CAP rate which is then combined with standard appreciation measures to estimate total return and its variance over time for larger Metropolitan Statistical Areas (MSA) in the U.S. Using statistically constructed estimates of the CAP rate and adding variance in total return to conduct tests of the DDM produces far stronger results than those obtained in previous studies of a cross section of cities in the U.S. But, when the BLS Rent Index is used to measure CAP rates and risk, the results are not consistent with DDM.

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Notes

  1. The CAP rate is often estimated based on the BLS Rent Index, which has been shown to be problematic by Ambrose et al. (2015), and Lai and Van Order (2010).

  2. Verbrugge (2008) has shown that these two series diverge significantly. Verbrugge and Poole (2010) further show that the divergence between BLS Rent Index and OER (Owner Equivalent Rent) in 2000 and after 2004 is mainly explained by the inter-city shelter inflation.

  3. In some literature, rent growth and housing appreciation are implicitly treated as the same. However, rental appreciation turns out to diverge from price appreciation largely (Verbrugge 2008).

  4. Both Ihlanfeldt and Martinez-Vazquez (1986) and Sivitanides et al. (2001) mention this problem.

  5. Some larger MSAs are not considered (like Denver) because they have very few rental properties in surveys from specific years.

  6. 38 cities are generated when matching SMSA from AHS with MSA from FHFA City HPI.

  7. Value range of the CAP rate in our estimation is consistent with those from Phillips (1988), Campbell et al. (2009) and Bracke (2015).

  8. Panel autocorrelation regression shows that 1% increase in current CAP rate could predict 0.03% increase in the future 2-year CAP rate.

  9. Alternative method of testing the divided pricing hypothesis is the one originally for stock market proposed by Campbell and Shiller (1988). Hwang et al. (2006) extend this method to panels of housing markets in South Korea differentiated by size and locations. However, in this paper, we aim to investigate the role of housing risks in cross-market tests in a large country.

  10. Appreciation rate is first difference of logarithm housing price and thus can be assumed to be expressed as two components: component that can be explained by CAP rate and random idiosyncratic noise following normal distribution; then variance of total housing return could be approximated by Taylor expansion of CAP risk.

  11. One possible endogeneity is that the measured sampling variance of the CAP rates should fall with sample sizes. We regress the standard deviations of CAP rates on the sample size, and find the coefficient is close to zero and insignificant. This indicates that the measure error of the CAP risk is very trivial and unrelated with the unobserved factors that affect the housing price appreciation.

  12. Some of the unique risk of investing in residential real estate in individual cities may be diversified away if the assets are held in a well-diversified portfolio. This may be the case for the multifamily properties whose CAP rates are estimated here. This diversification benefit should vary with the correlation between individual city returns and the market portfolio. This would have a potential effect on differences in required returns among cities. Thus far the only published estimates of the correlation between total returns to housing and the S&P500 have used national average house price change to measure appreciation return and either the BLS rent index change or a fixed percent of asset price to measure rental return. While the returns to housing investment for individual cities estimated here could be used to compute these city-specific correlation effects, given the small sample size, their differences would not be statistically significant and they would be subsumed in a model with city fixed effects.

  13. When computing the BLS CAP rates, we use the hedonic housing price index from American Housing Survey (AHS HPI). This aims to exclude the bias from using the appreciation rates from the same FHFA HPI in the regression. We also use the FHFA HPI to replace the AHS HPI in calculation of BLS CAP rates, and still find the results not consistent with the prediction of DDM.

  14. Some larger MSAs are not considered (like Denver) because they have very few rental properties in surveys from specific years.

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Acknowledgements

We are deeply indebted to Anthony M. Yezer for his insightful guidance. We are also grateful to the discussion from Paul Carrillo, Leah Brooks, Ming Hwang, Mike Bradley, Dean Gatzlaff and participants at the GWU Microeconomics Seminar, the UCONN 50th Anniversary Real Estate Symposium and 2015 NARSC Annual Conference.

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Authors and Affiliations

  1. School of International Trade and Economics, University of International Business and Economics, Chaoyang District, Beijing, 100052, China

    Ge Bao

  2. China Investment Corporation, New Poly Plaza 19fl, Dongcheng District, Beijing, 100010, China

    Guoliang Feng

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  1. Ge Bao
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Correspondence to Guoliang Feng.

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This research is supported by “the Fundamental Research Fund for the Central Universities” in University of International Business and Economics (16QN03). The opinions expressed herein are solely those of the authors and do not necessarily reflect the opinions of the institutions with which they are associated.

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The authors declare that they have no conflict of interest.

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Bao, G., Feng, G. Testing the Dividend Discount Model in Housing Markets: the Role of Risk. J Real Estate Finan Econ 57, 677–701 (2018). https://doi.org/10.1007/s11146-017-9626-z

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