Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods
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 neighborsĂ 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.
Year of publication: |
2009
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Authors: | Hoesli, Martin ; Bourassa, Steven ; Cantoni, Eva |
Institutions: | European Real Estate Society - ERES |
Saved in:
freely available
Extent: | application/vnd.ms-powerpoint text/html |
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Series: | ERES. |
Type of publication: | Book / Working Paper |
Source: |
Persistent link: https://www.econbiz.de/10011154283
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