A varying coefficient approach to estimating hedonic housing price functions and their quantiles

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Original languageEnglish
Pages (from-to)1979-1999
Journal / PublicationJournal of Applied Statistics
Issue number11
Online published30 Sept 2016
Publication statusPublished - Sept 2017


The varying coefficient (VC) model introduced by Hastie and Tibshirani [26] is arguably one of the most remarkable recent developments in nonparametric regression theory. The VC model is an extension of the ordinary regression model where the coefficients are allowed to vary as smooth functions of an effect modifier possibly different from the regressors. The VC model reduces the modelling bias with its unique structure while also avoiding the ‘curse of dimensionality’ problem. While the VC model has been applied widely in a variety of disciplines, its application in economics has been minimal. The central goal of this paper is to apply VC modelling to the estimation of a hedonic house price function using data from Hong Kong, one of the world's most buoyant real estate markets. We demonstrate the advantages of the VC approach over traditional parametric and semi-parametric regressions in the face of a large number of regressors. We further combine VC modelling with quantile regression to examine the heterogeneity of the marginal effects of attributes across the distribution of housing prices.

Research Area(s)

  • Hedonic price function, heterogeneity, housing, kernel estimation, quantile regression, varying-coefficient