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A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

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  • Clapp, John M

Abstract

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value. Copyright 2003 by Kluwer Academic Publishers

Suggested Citation

  • Clapp, John M, 2003. "A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation," The Journal of Real Estate Finance and Economics, Springer, vol. 27(3), pages 303-320, November.
  • Handle: RePEc:kap:jrefec:v:27:y:2003:i:3:p:303-20
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    Cited by:

    1. 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.
    2. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2005. "Spatial Dependence, Housing Submarkets, and House Prices," FAME Research Paper Series rp151, International Center for Financial Asset Management and Engineering.
    3. Liv Osland & Inge Thorsen & Jens Petter Gitlesen, 2007. "Housing Price Gradients in a Region with One Dominating Center," Journal of Real Estate Research, American Real Estate Society, vol. 29(3), pages 321-346.
    4. Franz Fuerst & Patrick McAllister, 2008. "Green Noise or Green Value? Measuring the Price Effects of Environmental Certification in Commercial Buildings," Real Estate & Planning Working Papers rep-wp2008-09, Henley Business School, Reading University.
    5. W. Brunauer & S. Lang & P. Wechselberger & S. Bienert, 2010. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," The Journal of Real Estate Finance and Economics, Springer, vol. 41(4), pages 390-411, November.
    6. Clapp, John M. & Wang, Yazhen, 2006. "Defining neighborhood boundaries: Are census tracts obsolete?," Journal of Urban Economics, Elsevier, vol. 59(2), pages 259-284, March.
    7. David Ling & Milena Petrova, 2008. "Avoiding Taxes at Any Cost: The Economics of Tax-Deferred Real Estate Exchanges," The Journal of Real Estate Finance and Economics, Springer, vol. 36(4), pages 367-404, May.
    8. David Dale-Johnson & Christian L. Redfearn & W. Jan Brzeski, 2004. "From Central Planning to Centrality: Krakow's Land Prices after Poland's Big Bang," Working Paper 8593, USC Lusk Center for Real Estate.
    9. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2007. "Spatial Dependence, Housing Submarkets, and House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 143-160, August.
    10. Wolfgang Brunauer & Stefan Lang & Peter Wechselberger & Sven Bienert, 2008. "Additive Hedonic Regression Models with Spatial Scaling Factors: An Application for Rents in Vienna," Working Papers 2008-17, Faculty of Economics and Statistics, University of Innsbruck.
    11. Fuerst, Franz, 2007. "Office Rent Determinants: A Hedonic Panel Analysis," MPRA Paper 11445, University Library of Munich, Germany.
    12. Liv Osland & Inge Thorsen, 2006. "Testing for the Impact of Local Labour Market Characteristics on House Prices," ERSA conference papers ersa06p490, European Regional Science Association.
    13. Claudio A. Agostini & Gastón Palmucci, 2008. "Capitalización Heterogénea de un Bien Semipúblico: El Metro de Santiago," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 45(131), pages 105-128.
    14. Christian L. Redfearn, 2005. "The Cost of Imposing Monocentricity: Uncovering the Dynamics of Emerging Centrality in Post-Socialist Krakow's Land Markets," Working Paper 8586, USC Lusk Center for Real Estate.
    15. Liang Peng, 2012. "Repeat Sales Regression on Heterogeneous Properties," The Journal of Real Estate Finance and Economics, Springer, vol. 45(3), pages 804-827, October.
    16. Löchl, Michael & Axhausen, Kay W., 2010. "Modelling hedonic residential rents for land use and transport simulation while considering spatial effects," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 3(2), pages 39-63.
    17. 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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