Estimation of Hedonic Price Functions via Additive Nonparametric Regression
AbstractWe model a hedonic price function for housing as an additive nonparametric regression. Estimation is done via a backfitting procedure in combination with a local polynomial estimator. It avoids the pitfalls of an unrestricted nonparametric estimator, such as slow convergence rates and the curse of dimensionality. Bandwidths are chosen using a novel plug in method that minimizes the asymptotic mean average squared error (AMASE) of the regression. We compare our results to alternative parametric models and find evidence of the superiority of our nonparametric model. From an empirical perspective our study is interesting in that the effects on housing prices of a series of environmental characteristics are modeled in the regression. We find these characteristics to be important in the determination of housing prices. Copyright Springer-Verlag 2005
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Bibliographic InfoPaper provided by East Carolina University, Department of Economics in its series Working Papers with number 0116.
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- Carlos Martins-Filho & Okmyung Bin, 2005. "Estimation of hedonic price functions via additive nonparametric regression," Empirical Economics, Springer, vol. 30(1), pages 93-114, January.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
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- Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.
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- repec:asg:wpaper:1006 is not listed on IDEAS
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