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

Author

Listed:
  • 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.

Suggested Citation

  • Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, American Real Estate Society, vol. 32(2), pages 139-160.
  • Handle: RePEc:jre:issued:v:32:n:2:2010:p:139-160
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    Cited by:

    1. Zhang, Wei-Bin, 2016. "Economic Globalization and Interregional Agglomeration in a Multi-Country and Multi-Regional Neoclassical Growth Model," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 34, pages 95-121.
    2. 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.
    3. Richard Arnott & Huiling Zhang, 2015. "The Aggregate Value of Land in the Greater Los Angeles Region," Working Papers 201506, University of California at Riverside, Department of Economics.
    4. 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.
    5. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.
    6. Fernandes, Guilherme Barreto & Artes, Rinaldo, 2016. "Spatial dependence in credit risk and its improvement in credit scoring," European Journal of Operational Research, Elsevier, vol. 249(2), pages 517-524.
    7. repec:kap:jgeosy:v:20:y:2018:i:1:d:10.1007_s10109-017-0257-y is not listed on IDEAS
    8. Füss, Roland & Koller, Jan A., 2016. "The role of spatial and temporal structure for residential rent predictions," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1352-1368.
    9. Marko Kryvobokov, 2011. "Defining apartment neighbourhoods with Thiessen polygons and fuzzy equality clustering," ERES eres2011_142, European Real Estate Society (ERES).
    10. Daniel Melser & Adrian D. Lee, 2014. "Estimating the Excess Returns to Housing at a Disaggregated Level: An Application to Sydney 2003–2011," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(3), pages 756-790, September.
    11. 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.
    12. Marcelo Cajias, 2017. "Is there room for another hedonic model? –The advantages of the GAMLSS approach in real estate research," ERES eres2017_226, European Real Estate Society (ERES).

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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