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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, Global Social Science Institute, 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. Monica Palma & Claudia Cappello & Sandra De Iaco & Daniela Pellegrino, 2019. "The residential real estate market in Italy: a spatio-temporal analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2451-2472, September.
    4. Hany Guirguis & Vaneesha Boney Dutra & Zoe McGreevy, 2022. "The impact of global economies on US inflation: A test of the Phillips curve," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 575-592, July.
    5. 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.
    6. 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.
    7. John W. Galbraith & Douglas J. Hodgson, 2018. "Econometric Fine Art Valuation by Combining Hedonic and Repeat-Sales Information," Econometrics, MDPI, vol. 6(3), pages 1-15, June.
    8. Christos Giannikos & Hany Guirguis & Tin Shan Suen, 2012. "Modelling the Blind Principal Bid Basket Trading Cost," European Financial Management, European Financial Management Association, vol. 18(2), pages 271-302, March.
    9. Xufeng Jiang & Zelu Jia & Lefei Li & Tianhong Zhao, 2022. "Understanding Housing Prices Using Geographic Big Data: A Case Study in Shenzhen," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    10. Stanley McGreal & Paloma Taltavull de La Paz, 2013. "Implicit House Prices: Variation over Time and Space in Spain," Urban Studies, Urban Studies Journal Limited, vol. 50(10), pages 2024-2043, August.
    11. Enwei Zhu & Stanislav Sobolevsky, 2018. "House Price Modeling with Digital Census," Papers 1809.03834, arXiv.org.
    12. Schulz, Rainer, 2002. "Real estate valuation according to standardized methods: An empirical analysis," SFB 373 Discussion Papers 2002,55, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    13. 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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