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Evaluating Alternative Methods of Forecasting House Prices: A Post-Crisis Reassessment

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  • William D. Larson

    (George Washington University)

Abstract

This paper compares the performance of different forecasting models of California house prices. Multivariate, theory-driven models are able to outperform a theoretical time series models across a battery of forecast comparison measures. Error correction models were best able to predict the turning point in the housing market, whereas univariate models were not. Similarly, even after the turning point occurred, error correction models were still able to outperform univariate models based on MSFE, bias, and forecast encompassing statistics and tests. These results highlight the importance of incorporating theoretical economic relationships into empirical forecasting models.

Suggested Citation

  • William D. Larson, 2010. "Evaluating Alternative Methods of Forecasting House Prices: A Post-Crisis Reassessment," Working Papers 2010-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Feb 2011.
    • Handle: RePEc:gwc:wpaper:2010-004
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    File URL: https://www2.gwu.edu/~forcpgm/2010-004.pdf
    File Function: Second version, 2011
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    References listed on IDEAS

    as
    1. Anthony Pennington-Cross, 2005. "Aggregation bias and the repeat sales price index," BIS Papers chapters, in: Bank for International Settlements (ed.), Real estate indicators and financial stability, volume 21, pages 323-335, Bank for International Settlements.
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    Cited by:

    1. Berndt, Antje & Hollifield, Burton & Sandås, Patrik, 2014. "How Subprime Borrowers and Mortgage Brokers Shared the Pie," Working Paper Series 286, Sveriges Riksbank (Central Bank of Sweden).
    2. repec:zbw:rwirep:0294 is not listed on IDEAS
    3. Zietz, Joachim & Traian, Anca, 2014. "When was the U.S. housing downturn predictable? A comparison of univariate forecasting methods," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 271-281.
    4. Philipp an de Meulen & Martin Micheli & Torsten Schmidt, 2011. "Forecasting House Prices in Germany," Ruhr Economic Papers 0294, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
    5. an de Meulen, Philipp & Micheli, Martin & Schmidt, Torsten, 2011. "Forecasting House Prices in Germany," Ruhr Economic Papers 294, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

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      More about this item

      Keywords

      house prices; forecasting; forecast comparison; forecast encompassing;
      All these keywords.

      JEL classification:

      • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
      • 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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