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

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  • Andrey D. Pavlov

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.

Suggested Citation

  • Andrey D. Pavlov, 2000. "Space-Varying Regression Coefficients: A Semi-parametric Approach Applied to Real Estate Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 28(2), pages 249-283.
  • Handle: RePEc:bla:reesec:v:28:y:2000:i:2:p:249-283
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    Cited by:

    1. Yongheng Deng & Andrey D. Pavlov & Lihong Yang, 2005. "Spatial Heterogeneity in Mortgage Terminations by Refinance, Sale and Default," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 33(4), pages 739-764, December.
    2. Julie Le Gallo, 2004. "Hétérogénéité spatiale : principes et méthodes," Économie et Prévision, Programme National Persée, vol. 162(1), pages 151-172.
    3. Karl L. Guntermann & Crocker Liu & Adam Nowak, 2014. "Repeat Sales Methods for Growing Cities and Short Horizons," Working Papers 14-20, Department of Economics, West Virginia University.
    4. Stephen A. Samaha & Wagner A. Kamakura, 2008. "Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(4), pages 717-751, December.
    5. Dann Payares Ayola, 2012. "Estimación del potencial de valorización del suelo en Barranquilla en el periodo 2001-2011. Estimación de efectos fijos en datos de panel," REVISTA DE ECONOMÍA DEL CARIBE 010851, UNIVERSIDAD DEL NORTE.
    6. 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.
    7. 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.
    8. 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.
    9. Chau-Sa Ho & Diane Hite, 2008. "The benefit of environmental improvement in the southeastern United States: Evidence from a simultaneous model of cancer mortality, toxic chemical releases and house values," Papers in Regional Science, Wiley Blackwell, vol. 87(4), pages 589-604, November.
    10. 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.
    11. Cohen, Jeffrey P. & Coughlin, Cletus C. & Clapp, John M., 2015. "Local Polynomial Regressions versus OLS for Generating Location Value Estimates: Which is More Efficient in Out-of-Sample Forecasts?," Working Papers 2015-14, Federal Reserve Bank of St. Louis, revised 29 Oct 2015.
    12. Yoo, James & Ready, Richard, 2016. "The impact of agricultural conservation easement on nearby house prices: Incorporating spatial autocorrelation and spatial heterogeneity," Journal of Forest Economics, Elsevier, vol. 25(C), pages 78-93.
    13. 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.
    14. Patric H. Hendershott & Thomas G. Thibodeau & Halbert C. Smith, 2009. "Evolution of the American Real Estate and Urban Economics Association-super-1," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(4), pages 559-598.
    15. repec:kap:jgeosy:v:20:y:2018:i:1:d:10.1007_s10109-017-0257-y is not listed on IDEAS
    16. McMillen, Daniel P., 2001. "Nonparametric Employment Subcenter Identification," Journal of Urban Economics, Elsevier, vol. 50(3), pages 448-473, November.

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