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A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context

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  • Jozef Zurada

    ()
    (University of Louisville)

  • Alan S. Levitan

    ()
    (versity of Louisville)

  • Jian Guan

    ()
    (University of Louisville)

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    Abstract

    The limitations of traditional linear multiple regression analysis (MRA) for assessing value of real estate property have been recognized for some time. Artificial intelligence (AI) based methods, such as neural networks (NNs), have been studied in an attempt to address these limitations, with mixed results, weakened further by limited sample sizes. This paper describes a comparative study where several regression and AI-based methods are applied to the assessment of real estate properties in Louisville, Kentucky, U.S.A. Four regression-based methods (traditional MRA, and three non-traditional regression-based methods, Support Vector Machines using sequential minimal optimization regression (SVM-SMO), additive regression, and M5P trees), and three AI-based methods (NNs, radial basis function neural network (RBFNN), and memory-based reasoning (MBR)) have been applied and compared under various simulation scenarios. The results, obtained using a very large data sample, indicate that non-traditional regression-based methods perform better in all simulation scenarios, especially with homogeneous data sets. AI-based methods perform well with less homogeneous data sets under some simulation scenarios.

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    Bibliographic Info

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

    Volume (Year): 33 (2011)
    Issue (Month): 3 ()
    Pages: 349-388

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    Handle: RePEc:jre:issued:v:33:n:3:2011:p:349-388

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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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    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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