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Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods

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  • Steven C. Bourassa

    ()
    (University of Louisville)

  • Eva Cantoni

    ()
    (University of Geneva)

  • Martin Hoesli

    ()
    (University of Geneva)

Abstract

This paper compares alternative methods for taking spatial dependence into account in house price prediction. We select hedonic methods that have been reported in the literature to perform relatively well in terms of ex-sample prediction accuracy. Because differences in performance may be due to differences in data, we compare the methods using a single data set. The estimation methods include simple OLS, a two-stage process incorporating nearest neighbors’ residuals in the second stage, geostatistical, and trend surface models. These models take into account submarkets by adding dummy variables or by estimating separate equations for each submarket. Based on data for approximately 13,000 transactions from Louisville, Kentucky, we conclude that a geostatistical model with disaggregated submarket variables performs best.

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File URL: http://aux.zicklin.baruch.cuny.edu/jrer/papers/pdf/past/vol32n02/01.139_160.pdf
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Bibliographic Info

Article provided by American Real Estate Society in its journal journal of Real Estate Research.

Volume (Year): 32 (2010)
Issue (Month): 2 ()
Pages: 139-160

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Handle: RePEc:jre:issued:v:32:n:2:2010:p:139-160

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Postal: American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323
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Web page: http://www.aresnet.org/

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Postal: Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323
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Web: http://aux.zicklin.baruch.cuny.edu/jrer/about/get.htm

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Cited by:
  1. Katja Hanewald & Michael Sherris, 2011. "House Price Risk Models for Banking and Insurance Applications," Working Papers 201118, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
  2. Fernandes, Guilherme Barreto & Artes , Rinaldo, 2013. "Spatial correlation in credit risk and its improvement in credit scoring," Insper Working Papers wpe_321, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
  3. Baranzini, Andrea & Schaerer, Caroline, 2011. "A sight for sore eyes: Assessing the value of view and land use in the housing market," Journal of Housing Economics, Elsevier, vol. 20(3), pages 191-199, September.

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