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

Martin Hoesli, Steven Bourassa and Eva Cantoni

ERES from European Real Estate Society (ERES)

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.

JEL-codes: R3 (search for similar items in EconPapers)
Date: 2009-01-01
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Citations: View citations in EconPapers (1)

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Journal Article: Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods (2010)
Journal Article: Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2009_153

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