Spatial Dependence, Housing Submarkets, and House Price Prediction

Posted: 28 Mar 2007

See all articles by Steven C. Bourassa

Steven C. Bourassa

Florida Atlantic University

Eva Cantoni

University of Geneva

Martin Hoesli

University of Geneva - Geneva School of Economics and Management (GSEM); Swiss Finance Institute; University of Aberdeen - Business School

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.

Keywords: models, mass appraisal, housing submarkets

Suggested Citation

Bourassa, Steven C. and Cantoni, Eva and Hoesli, Martin Edward Ralph, Spatial Dependence, Housing Submarkets, and House Price Prediction. Journal of Real Estate Finance and Economics, Vol. 35, No. 2, 2007, Available at SSRN: https://ssrn.com/abstract=975087

Steven C. Bourassa (Contact Author)

Florida Atlantic University ( email )

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Eva Cantoni

University of Geneva ( email )

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Switzerland

Martin Edward Ralph Hoesli

University of Geneva - Geneva School of Economics and Management (GSEM) ( email )

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Swiss Finance Institute

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University of Aberdeen - Business School ( email )

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