Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/80299
Authors: 
Day, Brett
Bateman, Ian
Lake, Iain
Year of Publication: 
2004
Series/Report no.: 
CSERGE Working Paper EDM 04-04
Abstract: 
A frequent assumption of hedonic price estimation using property market data is that spatial autocorrelation of regression residuals is a feature of the error generating process. Under this assumption, spatial error dependence models that impose a specific spatial structure on the error generating process provide efficient parameter estimates. In this paper we argue that spatial autocorrelation is induced by spatial features influencing property prices that are not observed by the researcher. Whilst many of these features comprise the subtle nuances of location that might adequately be handled by modelling the error process, others may be substantive spatial features whose absence from the model is likely to induce omitted variable bias in the parameter estimates. Accordingly we propose an alternative estimation strategy. We use spatial statistics to determine the nature of spatial dependence in regression residuals. Subsequently we adopt a semiparametric smooth spatial effects estimator to account for omitted locational covariates over the spatial scale indicated by the spatial statistics. The parameter estimates from this model are found to differ significantly from those of a spatial error dependence model.
Subjects: 
Hedonics
Morans
omitted variables
smooth spatial effects
spatial error dependence
Document Type: 
Working Paper

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