Spatial Dependence, Housing Submarkets, and House Price Prediction

  • Steven C. Bourassa
  • Eva Cantoni
  • Martin Hoesli


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.


Spatial dependence Hedonic price models Geostatistical models Lattice models Mass appraisal Housing submarkets 



We thank John Clapp, Xavier de Luna, and two anonymous referees for useful comments.


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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Steven C. Bourassa
    • 1
  • Eva Cantoni
    • 2
  • Martin Hoesli
    • 3
    • 4
    • 5
  1. 1.School of Urban and Public AffairsUniversity of LouisvilleLouisvilleUSA
  2. 2.Department of EconometricsUniversity of GenevaGeneva 4Switzerland
  3. 3.HEC, University of GenevaGeneva 4Switzerland
  4. 4.University of Aberdeen Business SchoolUniversity of AberdeenAberdeenScotland
  5. 5.Bordeaux Business SchoolBordeauxFrance

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