Estimation of Hedonic Price Functions via Additive Nonparametric Regression
We 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
(This abstract was borrowed from another version of this item.)
|Date of creation:|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (252) 328-6006
Fax: (252) 328-6743
Web page: http://www.econ.ecu.edu/wp/
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:wop:eacaec:0116. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Krichel)
If references are entirely missing, you can add them using this form.