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Evaluating House Price Forecasts

Author

Listed:
  • John M. Clapp

    () (University of Connecticut, Storrs, CT 06269-2041)

  • Carmelo Giaccotto

    () (University of Connecticut, Storrs, CT 06269-2041)

Abstract

House prices, unlike stock prices, appear to be predictable with some degree of accuracy. We use an autoregressive process to model the time series behavior of a city-wide house price index, and then produce one-quarter ahead forecasts for individual properties. Better real estate decisions require forecasting models with desirable properties for prediction errors (PEs). We propose that managers use a battery of tests to compare PEs; in particular, non-parametric smoothing of the empirical distribution of PEs can add important information to statistics that focus on first and second moments. The decision-making framework is fitted with housing transactions from Dade County, Florida, from 1976 through the second quarter of 1997. PEs from two forecasting models, hedonic and repeat sales, show some departure from the desirable properties of any one-step-ahead forecast. Also, both show some informational inefficiency, but the hedonic is more efficient than the repeat. Nonparametric smoothing shows that the hedonic method dominates the repeat over an important range of PEs; thus, a case can be made that many risk-averse managers would prefer a forecast based on the hedonic method.

Suggested Citation

  • John M. Clapp & Carmelo Giaccotto, 2002. "Evaluating House Price Forecasts," Journal of Real Estate Research, American Real Estate Society, vol. 24(1), pages 1-26.
  • Handle: RePEc:jre:issued:v:24:n:1:2002:p:1-26
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    Citations

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    Cited by:

    1. Changha Jin & Terry V. Grissom, 2008. "Forecasting Dynamic Investment Timing under the Cyclic Behavior in Real Estate," International Real Estate Review, Asian Real Estate Society, vol. 11(2), pages 105-125.
    2. Hany Guirguis & Christos Giannikos & Randy Anderson, 2004. "The US Housing Market: Asset Pricing Forecasts Using Time Varying Coefficients," The Journal of Real Estate Finance and Economics, Springer, vol. 30(1), pages 33-53, October.
    3. Joe Tak-Yun Wong & Eddie Hui & William Seabrooke & John Raftery, 2005. "A study of the Hong Kong property market: housing price expectations," Construction Management and Economics, Taylor & Francis Journals, vol. 23(7), pages 757-765.
    4. Ghysels, Eric & Plazzi, Alberto & Valkanov, Rossen & Torous, Walter, 2013. "Forecasting Real Estate Prices," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 509-580, Elsevier.
    5. repec:gam:jecnmx:v:6:y:2018:i:3:p:32-:d:154105 is not listed on IDEAS
    6. repec:bla:eufman:v:18:y:2012:i:2:p:271-302 is not listed on IDEAS
    7. Enwei Zhu & Stanislav Sobolevsky, 2018. "House Price Modeling with Digital Census," Papers 1809.03834, arXiv.org.
    8. Seoung Hwan Suh & Kabsung Kim, 2014. "Global financial crisis and early warning system of Korean housing market," Chapters, in: Susan Wachter & Man Cho & Moon Joong Tcha (ed.), The Global Financial Crisis and Housing, chapter 4, pages 62-81, Edward Elgar Publishing.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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