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

Jozef Zurada (), Alan S. Levitan () and Jian Guan ()
Additional contact information
Jozef Zurada: University of Louisville
Alan S. Levitan: versity of Louisville
Jian Guan: University of Louisville

Journal of Real Estate Research, 2011, vol. 33, issue 3, 349-388

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.

JEL-codes: L85 (search for similar items in EconPapers)
Date: 2011
References: Add references at CitEc
Citations: View citations in EconPapers (22)

Downloads: (external link)
http://pages.jh.edu/jrer/papers/pdf/past/vol33n03/03.349_388.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jre:issued:v:33:n:3:2011:p:349-388

Ordering information: This journal article can be ordered from
Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323
http://pages.jh.edu/jrer/about/get.htm

Access Statistics for this article

Journal of Real Estate Research is currently edited by Dr. Ko Wang

More articles in Journal of Real Estate Research from American Real Estate Society American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323.
Bibliographic data for series maintained by JRER Graduate Assistant/Webmaster ().