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Predicting House Prices with Spatial Dependence: Impacts of Alternative Submarket Definitions

Author

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  • Steven C. Bourassa

    (University of Louisville, School of Urban and Public Affairs)

  • Eva Cantoni

    (University of Geneva, Departement of Econometrics)

  • Martin Hoesli

Abstract

We analyze the impacts of alternative submarket definitions when predicting house prices in a mass appraisal context, using both ordinary least squares (OLS) and geostatistical techniques. For this purpose, we use over 13,000 housing transactions for Louisville, Kentucky. We use districts defined by the local property tax assessment office as well as a classification of census tracts generated by principal components and cluster analysis. We also experiment with varying numbers of census tract groupings. Our results indicate that somewhat better results are obtained with more homogeneous submarkets. Also, the accuracy of house price predictions increases as the number of submarkets is increased, but then quickly levels off. Adding submarket variables to the OLS model yields price predictions that are as accurate as when geostatistical methods are used to account for spatial dependence in the error terms. However, using both dummy variables for submarkets and geostatistical methods leads to significant increases in accuracy.

Suggested Citation

  • Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2008. "Predicting House Prices with Spatial Dependence: Impacts of Alternative Submarket Definitions," Swiss Finance Institute Research Paper Series 08-01, Swiss Finance Institute.
  • Handle: RePEc:chf:rpseri:rp0801
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    Cited by:

    1. Joao Lourenço Marques & Eduardo Castro & Arnab Bhattacharjee & Paulo Batista, 2012. "SPATIAL HETEROGENEITY ACROSS SUBMARKETS: Housing submarket in an urban area of Portugal," ERSA conference papers ersa12p1111, European Regional Science Association.

    More about this item

    Keywords

    spatial dependence; hedonic price models; geostatistical models; mass appraisal; housing submarkets.;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • D58 - Microeconomics - - General Equilibrium and Disequilibrium - - - Computable and Other Applied General Equilibrium Models
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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