Space-Varying Regression Coefficients: A Semi-parametric Approach Applied to Real Estate Markets
This paper presents a method for estimating home values by non-parametrically incorporating the physical location of the properties. Specifically, I allow the parameters of the observed covariates to vary in space. This approach mitigates one of the biggest deficiencies inherent in hedonic pricing models-omitted variables. I demonstrate the advantages of the proposed method using real estate transaction data from Los Angeles County. The estimation finds a substantial spatial variation of the marginal values of the hedonic characteristics and provides an insight into the segmentation of the market. The proposed method is an extension of semi-parametric multi-dimensional k-nearest-neighbor smoothing. It alleviates a fundamental problem known as the curse of dimensionality by incorporating parametric components into a non-parametric estimation. Copyright American Real Estate and Urban Economics Association.
Year of publication: |
2000
|
---|---|
Authors: | Pavlov, Andrey D. |
Published in: |
Real Estate Economics. - American Real Estate and Urban Economics Association - AREUEA. - Vol. 28.2000, 2, p. 249-283
|
Publisher: |
American Real Estate and Urban Economics Association - AREUEA |
Saved in:
Saved in favorites
Similar items by person
-
Space-varying regression coefficients : a semi-parametric approach applied to real estate markets
Pavlov, Andrey D., (2000)
-
Competing risks of mortgage termination : who refinances, who moves, and who defaults?
Pavlov, Andrey D., (2001)
-
Investment timing for new business ventures
Blazenko, George W., (2010)
- More ...