Spatial Dependence,Housing Submarkets,and House Price Prediction |
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| Authors: | Steven C Bourassa Eva Cantoni Martin Hoesli |
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| Institution: | (1) School of Urban and Public Affairs, University of Louisville, 426 W. Bloom Street, Louisville, Kentucky 40208, USA;(2) Department of Econometrics, University of Geneva, 40 boulevard du Pont-d’Arve, CH-1211 Geneva 4, Switzerland;(3) HEC, University of Geneva, 40 boulevard du Pont-d’Arve, CH-1211 Geneva 4, Switzerland;(4) University of Aberdeen Business School, University of Aberdeen, Aberdeen, Scotland;(5) Bordeaux Business School, Bordeaux, France |
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| Abstract: | This paper compares alternative methods of controlling for the spatial dependence of house prices in a mass appraisal context.
Explicit modeling of the error structure is characterized as a relatively fluid approach to defining housing submarkets. This
approach allows the relevant submarket to vary from house to house and for transactions involving other dwellings in each
submarket to have varying impacts depending on distance. We conclude that—for our Auckland, New Zealand, data—the gains in
accuracy from including submarket variables in an ordinary least squares specification are greater than any benefits from
using geostatistical or lattice methods. This conclusion is of practical importance, as a hedonic model with submarket dummy
variables is substantially easier to implement than spatial statistical methods.
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| Keywords: | Spatial dependence Hedonic price models Geostatistical models Lattice models Mass appraisal Housing submarkets |
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