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Predicting turning points in the housing market

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  • Croce, Roberto M.
  • Haurin, Donald R.

Abstract

We identify leading indicators of changes in the housing market and compare their performance in predicting turning points. Being able to predict turning points is of importance to the home building industry, homeowners, and makers of housing policy. Our leading indicators include the Wells Fargo/NAHB Housing Market Index, two of its forward looking components, and an index of consumer sentiment regarding purchasing a home. Our comparison tests include Granger causality and a Bayesian predictor of the probability of a turning point. We find that the measure of consumer sentiment performs relatively well compared to the HMI in predicting home permits, housing starts, and new home sales.

Suggested Citation

  • Croce, Roberto M. & Haurin, Donald R., 2009. "Predicting turning points in the housing market," Journal of Housing Economics, Elsevier, vol. 18(4), pages 281-293, December.
    • Handle: RePEc:eee:jhouse:v:18:y:2009:i:4:p:281-293
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    Cited by:

    1. Basse, Tobias & Desmyter, Steven & Saft, Danilo & Wegener, Christoph, 2023. "Leading indicators for the US housing market: New empirical evidence and thoughts about implications for risk managers and ESG investors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. Gregory Bauer, 2014. "International House Price Cycles, Monetary Policy and Risk Premiums," Staff Working Papers 14-54, Bank of Canada.
    3. Tim Meyer, 2019. "On the Directional Accuracy of United States Housing Starts Forecasts: Evidence from Survey Data," The Journal of Real Estate Finance and Economics, Springer, vol. 58(3), pages 457-488, April.
    4. Agnello, Luca & Schuknecht, Ludger, 2011. "Booms and busts in housing markets: Determinants and implications," Journal of Housing Economics, Elsevier, vol. 20(3), pages 171-190, September.
    5. MeiChi Huang, 2019. "A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1547-1563, April.
    6. Zhou, Zhengyi, 2018. "Housing market sentiment and intervention effectiveness: Evidence from China," Emerging Markets Review, Elsevier, vol. 35(C), pages 91-110.
    7. Julien Chevallier & Bangzhu Zhu & Lyuyuan Zhang, 2021. "Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 537-575, February.
    8. Akcay, Belgin & Yucel, Eray, 2014. "Unveiling the House Price Movements and Financial Development," MPRA Paper 59377, University Library of Munich, Germany, revised 19 Oct 2014.
    9. MeiChi Huang, 2022. "Time‐varying impacts of expectations on housing markets across hot and cold phases," International Finance, Wiley Blackwell, vol. 25(2), pages 249-265, August.
    10. Huang, MeiChi, 2014. "Bubble-like housing boom–bust cycles: Evidence from the predictive power of households’ expectations," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(1), pages 2-16.
    11. Huang, MeiChi, 2018. "Time-varying diversification strategies: The roles of state-level housing assets in optimal portfolios," International Review of Economics & Finance, Elsevier, vol. 55(C), pages 145-172.
    12. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    13. MeiChi Huang, 2019. "Risk diversification gains from metropolitan housing assets," Review of Financial Economics, John Wiley & Sons, vol. 37(4), pages 453-481, October.
    14. Hyejung Moon & Jungick Lee, 2013. "Forecast evaluation of economic sentiment indicator for the Korean economy," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Proceedings of the Sixth IFC Conference on "Statistical issues and activities in a changing environment", Basel, 28-29 August 2012., volume 36, pages 180-190, Bank for International Settlements.
    15. Meichi Huang, 2013. "Housing bubble implications: The perspective of housing price predictability," Economics Bulletin, AccessEcon, vol. 33(1), pages 586-596.
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    17. Steffen Heinig & Anupam Nanda & Sotiris Tsolacos, 2016. "Which Sentiment Indicators Matter? An Analysis of the European Commercial Real Estate Market," ICMA Centre Discussion Papers in Finance icma-dp2016-04, Henley Business School, University of Reading.

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    More about this item

    Keywords

    R31 R21 C53 E37 Forecasting Housing starts Building permits Home sales Turning points;

    JEL classification:

    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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