IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this article or follow this journal

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

  • Jozef Zurada

    ()

    (University of Louisville)

  • Alan S. Levitan

    ()

    (versity of Louisville)

  • Jian Guan

    ()

    (University of Louisville)

Registered author(s):

    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.

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

    File URL: http://pages.jh.edu/jrer/papers/pdf/past/vol33n03/03.349_388.pdf
    File Function: Full text
    Download Restriction: no

    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

    as
    in new window

    Handle: RePEc:jre:issued:v:33:n:3:2011:p:349-388
    Contact details of provider: Postal: American Real Estate Society Clemson University School of Business & Behavioral Science Department of Finance 401 Sirrine Hall Clemson, SC 29634-1323
    Web page: http://www.aresnet.org/Email:

    Order Information: Postal: Diane Quarles American Real Estate Society Manager of Member Services Clemson University Box 341323 Clemson, SC 29634-1323
    Web: http://pages.jh.edu/jrer/about/get.htm Email:


    No references listed on IDEAS
    You can help add them by filling out this form.

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    When requesting a correction, please mention this item's handle: RePEc:jre:issued:v:33:n:3:2011:p:349-388. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (JRER Graduate Assistant/Webmaster)

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.