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Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods

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  • Martin Hoesli
  • Steven Bourassa
  • Eva Cantoni
Abstract
This paper compares alternative methods for taking spatial dependence into account in house price prediction. We select hedonic methods that have been reported in the literature to perform relatively well in terms of ex-sample prediction accuracy. Because differences in performance may be due to differences in data, we compare the methods using a single data set. The estimation methods include simple OLS, a two-stage process incorporating nearest neighborsí residuals in the second stage, geostatistical, and trend surface models. These models take into account submarkets by adding dummy variables or by estimating separate equations for each submarket. Submarkets are defined at different levels of aggregation. We conclude that a geostatistical model with disaggregated submarket variables performs best.

Suggested Citation

  • Martin Hoesli & Steven Bourassa & Eva Cantoni, 2009. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," ERES eres2009_153, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2009_153
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    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2009-153
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    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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