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Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty

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Abstract

We analyze the role of macroeconomic uncertainty in predicting synchronization in housing price movements across all the United States (US) states plus District of Columbia (DC). We first use a Bayesian dynamic factor model to decompose the house price movements into a national, four regional (Northeast, South, Midwest, and West), and state-specific factors. We then study the ability of macroeconomic uncertainty in forecasting the comovements in housing prices, by controlling for a wide-array of predictors, such as factors derived from a large macroeconomic dataset, oil shocks, and financial market-related uncertainties. To accommodate for multiple predictors and nonlinearities, we take a machine learning approach of random forests. Our results provide strong evidence of forecastability of the national house price factor based on the information content of macroeconomic uncertainties over and above the other predictors. This result also carries over, albeit by a varying degree, to the factors associated with the four census regions, and the overall house price growth of the US economy. Moreover, macroeconomic uncertainty is found to have predictive content for (stochastic) volatility of the national factor and aggregate US house price. Our results have important implications for policymakers and investors.

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Notes

  1. Similar observations for the US Real Estate Investment Trusts (REITs) have also been drawn by Ajmi et al. (2015), and Akinsomi et al. (2016).

  2. In particular, for sign identification, national factor for Alaska is restricted to be positive, whereas the sign restriction on regional factor loadings is chosen arbitrarily. We achieve scale normalizations by following Sargent and Sims (1977), Stock and Watson (1989, 1993), and Del Negro and Otrok (2007) and restrict \({\sigma ^{2}_{n}}\) and \({\sigma ^{2}_{r}}\) to unity. The signs and scale normalization do not have any economic content and do not affect any economic inference (Neely and Rapach 2011).

  3. The prior for idiosyncratic state-specific shocks follows an inverse-gamma distribution with parameters 6 and 0.001. The prior for the AR polynomial follows a normal distribution with tighter centering on zero (at the geometric rate of 0.5). The priors for factor loadings are standard normal.

  4. The data is available for download from: http://www.freddiemac.com/research/indices/house-price-index.pagehttp://www.freddiemac.com/research/indices/house-price-index.page.

  5. The factors are available for download from: https://www.sydneyludvigson.com/data-and-appendixes.

  6. The data is downloadable from: https://sites.google.com/site/cjsbaumeister/research.

  7. The MU and FU indices are available for download from: www.sydneyludvigson.com/data-and-appendixes.

  8. We use the R language for statistical computing (R Core Team 2019) to carry out our forecasting experiments, and the add-on package “MultivariateRandomForest” (Rahman 2017) to estimate random forests.

  9. We also analyzed the relative importance of the predictor variables using the full sample of data. The importance of a predictor measures how often a predictor is used for splitting. Results show that, across the different model configurations and forecast horizons, the three measures of macroeconomic uncertainty have a relative importance of about 10 % percent, which corresponds to a top average rank of 1.3 among all predictors (detailed results are available upon request from the authors).

  10. The splitting function is the same as in the case of univariate random forests, but the region-impurity measure is the Mahalanobis distance (for a detailed description and a recent application in the forecasting literature, see Behrens et al. 2018).

  11. The reader is referred to https://www.federalreserve.gov/releases/z1/20200611/z1.pdf for further details.

  12. Complete details of the estimation results for the volatility models are available upon request from the authors.

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Authors and Affiliations

  1. Department of Economics, University of Pretoria, Pretoria, 0002, South Africa

    Rangan Gupta

  2. Department of Economics, Northeastern Illinois University, 5500 N St Louis Ave, BBH 344G, Chicago, IL, 60625, USA

    Hardik A. Marfatia

  3. Department of Economics, Helmut Schmidt University, Holstenhofweg 85, P.O.B. 700822, 22008, Hamburg, Germany

    Christian Pierdzioch

  4. Centre for Econometric & Allied Research, University of Ibadan, Ibadan, Nigeria

    Afees A. Salisu

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  1. Rangan Gupta
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  2. Hardik A. Marfatia
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  3. Christian Pierdzioch
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Correspondence to Hardik A. Marfatia.

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

The research of C. Pierdzioch was supported by the German Science Foundation (Project: Exploring the experience-expectation nexus in macroeconomic forecasting using computational text analysis and machine learning; Project number: 275693836). The usual disclaimer applies.

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Gupta, R., Marfatia, H.A., Pierdzioch, C. et al. Machine Learning Predictions of Housing Market Synchronization across US States: The Role of Uncertainty. J Real Estate Finan Econ 64, 523–545 (2022). https://doi.org/10.1007/s11146-020-09813-1

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