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High-Frequency Volatility Forecasting of US Housing Markets

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Abstract

We propose a logistic smooth transition autoregressive fractionally integrated [STARFI (p, d)] process for modeling and forecasting US housing price volatility. We discuss the statistical properties of the model and investigate its forecasting performance by assuming various specifications for the dynamics underlying the variance process in the model. Using a unique database of daily data on price indices from ten major US cities, and the corresponding daily Composite 10 Housing Price Index, and also a housing futures price index, we find that using the Markov-switching multifractal (MSM) and FIGARCH frameworks for modeling the variance process helps improving the gains in forecast accuracy.

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Notes

  1. See: https://www.federalreserve.gov/releases/z1/20190920/html/b101h.htm.

  2. For example, consider the following case: if low-valued houses’ values are relatively volatile, then policies that encourage low-income renter households to become homeowners should be evaluated in light of the house price risk that they would bear.

  3. It must be realized that, the features of market efficiency, long-memory, nonlinearity, and non-normality, as observed traditionally for other asset markets, are however not likely due to the usage of high-frequency data of the housing market. In fact, these features have also been reported for low- frequency (monthly, quarterly, and annual) data for the US by studies such as Canarella et al. (2012, 2019), Barros et al. (2015), Balcilar et al. (2015), Gupta and Majumdar (2015).

  4. Liu et al. (2007) find that assuming other base distributions, such as lognormal and gamma, makes little difference in empirical applications.

  5. As mentioned by Granger (1999) the issue associated with the non-differentiability may be just a technicality due to the fact that it should always be possible to find a smooth function which is arbitrarily close to the non-smooth one.

  6. We would like to thank an anonymous referee for pointing this out to us.

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Acknowledgments

We would like to thank an anonymous referee for many helpful comments. However, any remaining errors are solely ours.

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Authors and Affiliations

  1. Department of Economics, University of Münster, Münster, Germany

    Mawuli Segnon

  2. Department of Economics, University of Pretoria, Pretoria, South Africa

    Rangan Gupta & Keagile Lesame

  3. Department of Economics, University of Nebraska at Omaha, Omaha, NE, 68182, USA

    Mark E. Wohar

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  1. Mawuli Segnon
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  2. Rangan Gupta
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Correspondence to Mawuli Segnon.

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Segnon, M., Gupta, R., Lesame, K. et al. High-Frequency Volatility Forecasting of US Housing Markets. J Real Estate Finan Econ 62, 283–317 (2021). https://doi.org/10.1007/s11146-020-09745-w

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