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Price Jump Risk in the US Housing Market

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Abstract

Housing prices, like the prices of other speculative assets, contain a mix of both small and large changes (i.e., jumps). We apply a jump-GARCH model to monthly Case-Shiller housing price indexes of twenty cities in the U.S. during the period January 1991 through December 2011. We document the evidence of large housing price jumps in many cities, during both the financial crisis and non-crisis periods. The housing price jump intensity observed during the whole sample is largely explained by city, state and national-level fundamentals. However, consistent with the development of an asset bubble, there is further evidence of a decoupling between housing price jump intensity and fundamentals during the active or turbulent phase of the U.S. housing market that immediately preceded the onset of the Global Financial Crisis. No evidence of a decoupling from fundamentals is observed during the normal or tranquil phase of the U.S. housing market.

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Notes

  1. The recent housing crisis is a good illustration of housing price risk. Based on the Case-Shiller 10-city composite index, real house prices increased by over 70 % between 2001 and 2006, and then fell by over 30 % between 2006 and 2009.

  2. A number of studies have examined the jump component of volatility in financial markets (e.g., Bjursell et al. 2013; Miao et al. 2014).

  3. While there are studies on jump risk on stock markets, the jump risk for real estate markets might be quite different. For example, Zhou and Anderson (2012) use the tail risk measure and document significantly higher extreme risks for securitized real estate markets (i.e., REITs) than for general stock markets. In this regard, the jump risk measure used in this study is different from their tail risk measure, as we are able to exploit the time-variation of the jump risk measure.

  4. The 20 MSAs are Phoenix, AZ, Los Angeles, CA, San Francisco, CA, Denver, CO, Washington, DC, Miami, FL, Tampa, FL, Atlanta, GA, Chicago, IL, Boston, MA, Detroit, MI, Minneapolis, MN, Charlotte, NC, Las Vegas, NV, New York, NY, Cleveland, OH, Portland, OR, Dallas, TX, and Seattle, WA.

  5. We also applied the basic ARJI model to all 50 states using FHFA quarterly data, and obtained the convergence of estimation only for six states (Arizona, Iowa, Kansas, New Hampshire, Oklahoma, and Virginia), representing a convergence rate of 12 % which is much lower than the rate of 40 % based on the Case-Shiller housing price index reported in this paper.

  6. Per capita GMP is another variable we tried in estimation. However, this variable essentially measures something close to that by per capita personal income and indeed both are highly correlated. Thus, we drop it from the estimation results.

  7. In both models, only six variables (at MSA and state levels) are MSA-specific. All the five national variables are identical across the MSA units. This explains the six degree of freedom given in Hausman test.

  8. The random effects models are also estimated but rejected based on the Hausman test.

  9. We thank two anonymous referees for suggesting the further analysis.

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Acknowledgments

We gratefully acknowledge helpful comments on earlier versions from session participants at the 2013 American Real Estate Society annual meetings and particularly two anonymous referees. An earlier version of this paper was awarded the 2013 American Real Estate Society Best Paper Award in Real Estate Investments.

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Correspondence to Jian Yang or Jin Zhang.

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Webb, R.I., Yang, J. & Zhang, J. Price Jump Risk in the US Housing Market. J Real Estate Finan Econ 53, 29–49 (2016). https://doi.org/10.1007/s11146-015-9518-z

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