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Housing Price Forecastability: A Factor Analysis

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  • Lasse Bork
  • Stig V. Møller

Abstract

We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS) and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out‐of‐sample predictive power over and above the predictive power contained by the price–rent ratio, autoregressive benchmarks and regression models based on small datasets.

Suggested Citation

  • Lasse Bork & Stig V. Møller, 2018. "Housing Price Forecastability: A Factor Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 46(3), pages 582-611, September.
    • Handle: RePEc:bla:reesec:v:46:y:2018:i:3:p:582-611
    DOI: 10.1111/1540-6229.12185
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    Cited by:

    1. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Mark E. Wohar, 2020. "Mortgage Default Risks and High-Frequency Predictability of the U.S. Housing Market: A Reconsideration," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 26(2), pages 111-117, December.
    2. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    3. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
    4. Kucharska-Stasiak Ewa, 2019. "Valuation Schools and the Evolution of the Income Approach. An Evaluation of Change Trends," Real Estate Management and Valuation, Sciendo, vol. 27(2), pages 66-76, June.
    5. Bork, Lasse & Møller, Stig V., 2015. "Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection," International Journal of Forecasting, Elsevier, vol. 31(1), pages 63-78.
    6. repec:ipg:wpaper:2014-585 is not listed on IDEAS
    7. 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.
    8. Theodore Panagiotidis & Panagiotis Printzis, 2016. "On the macroeconomic determinants of the housing market in Greece: a VECM approach," International Economics and Economic Policy, Springer, vol. 13(3), pages 387-409, July.
    9. Paul E. Carrillo & Erik Robert De Wit & William D. Larson, 2012. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the U.S. and the Netherlands," Working Papers 2012-11, The George Washington University, Institute for International Economic Policy.
    10. Christou, Christina & Gupta, Rangan & Hassapis, Christis, 2017. "Does economic policy uncertainty forecast real housing returns in a panel of OECD countries? A Bayesian approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 50-60.
    11. Tripathi, Sabyasachi, 2019. "Macroeconomic Determinants of Housing Prices: A Cross Country Level Analysis," MPRA Paper 98089, University Library of Munich, Germany.
    12. Taufiq Choudhry, 2020. "Economic Policy Uncertainty and House Prices: Evidence from Geographical Regions of England and Wales," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(2), pages 504-529, June.
    13. Osman Dou{g}an & Raffaele Mattera & Philipp Otto & Suleyman Tac{s}p{i}nar, 2024. "A Dynamic Spatiotemporal and Network ARCH Model with Common Factors," Papers 2410.16526, arXiv.org.
    14. George Milunovich, 2020. "Forecasting Australia's real house price index: A comparison of time series and machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1098-1118, November.
    15. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05r, Department of Economics, University of Birmingham.
    16. Paul E. Carrillo & Eric R. Wit & William Larson, 2015. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the United States and the Netherlands," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(3), pages 609-651, September.

    More about this item

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • G1 - Financial Economics - - General Financial Markets

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