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Space-Varying Regression Coefficients: A Semi-parametric Approach Applied to Real Estate Markets

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  • Andrey D. Pavlov
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    Abstract

    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.

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    Bibliographic Info

    Article provided by American Real Estate and Urban Economics Association in its journal Real Estate Economics.

    Volume (Year): 28 (2000)
    Issue (Month): 2 ()
    Pages: 249-283

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    Handle: RePEc:bla:reesec:v:28:y:2000:i:2:p:249-283

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    Cited by:
    1. McMillen, Daniel P. & Smith, Stefani C., 2003. "The number of subcenters in large urban areas," Journal of Urban Economics, Elsevier, vol. 53(3), pages 321-338, May.
    2. Sunding, David L. & Swoboda, Aaron M., 2010. "Hedonic analysis with locally weighted regression: An application to the shadow cost of housing regulation in Southern California," Regional Science and Urban Economics, Elsevier, vol. 40(6), pages 550-573, November.
    3. Hanson, Andrew & Schnier, Kurt & Turnbull, Geoffrey K., 2012. "Drive 'Til You Qualify: Credit quality and household location," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 63-77.
    4. McMillen, Daniel P., 2001. "Nonparametric Employment Subcenter Identification," Journal of Urban Economics, Elsevier, vol. 50(3), pages 448-473, November.
    5. Robert J. Hill & Michael Scholz, 2014. "Incorporating Geospatial Data in House Price Indexes: A Hedonic Imputation Approach with Splines," Graz Economics Papers 2014-05, University of Graz, Department of Economics.
    6. Julia Koschinsky & Nancy Lozano-Gracia & Gianfranco Piras, 2012. "The welfare benefit of a home’s location: an empirical comparison of spatial and non-spatial model estimates," Journal of Geographical Systems, Springer, vol. 14(3), pages 319-356, July.

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