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Forecasting Housing Prices under Different Submarket Assumptions

Author

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  • Chen, Zhuo
  • Cho, Seong-Hoon
  • Poudyal, Neelam C.
  • Roberts, Roland K.

Abstract

This research evaluated forecasting accuracy of hedonic price models based on a number of different submarket assumptions. Using home sale data for the City of Knoxville and vicinities merged with geographic information, we found that forecasting housing prices with submarkets defined using expert knowledge and by school district and combining information conveyed in different modeling strategies are more accurate and efficient than models that are spatially aggregated, or with submarkets defined by statistical clustering techniques. This finding provided useful implications for housing price prediction in an urban setting and surrounding areas in that forecasting models based on expert knowledge of market structure or public school quality and simple model combining techniques may outperform the models using more sophisticated statistical techniques.

Suggested Citation

  • Chen, Zhuo & Cho, Seong-Hoon & Poudyal, Neelam C. & Roberts, Roland K., 2007. "Forecasting Housing Prices under Different Submarket Assumptions," 2007 Annual Meeting, July 29-August 1, 2007, Portland, Oregon 9689, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea07:9689
    DOI: 10.22004/ag.econ.9689
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    Cited by:

    1. Olgun Kitapci & Ömür Tosun & Murat Fatih Tuna & Tarik Turk, 2017. "The Use of Artificial Neural Networks (ANN) in Forecasting Housing Prices in Ankara, Turkey," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 1(5), pages 4-14.
    2. Poudyal, Neelam C. & Hodges, Donald G. & Tonn, Bruce & Cho, Seong-Hoon, 2009. "Valuing diversity and spatial pattern of open space plots in urban neighborhoods," Forest Policy and Economics, Elsevier, vol. 11(3), pages 194-201, May.

    More about this item

    Keywords

    Demand and Price Analysis;

    Statistics

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