IDEAS home Printed from https://ideas.repec.org/a/jre/issued/v30n42008p395-422.html
   My bibliography  Save this article

An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment

Author

Listed:
  • Jian Guan

    (University of Louisville)

  • Jozef Zurada

    (University of Louisville)

  • Alan S. Levitan

    (University of Louisville)

Abstract

This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system based approach to estimate prices for residential properties. The data set consists of historic sales of homes in a market in Midwest USA and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

Suggested Citation

  • Jian Guan & Jozef Zurada & Alan S. Levitan, 2008. "An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment," Journal of Real Estate Research, American Real Estate Society, vol. 30(4), pages 395-422.
    • Handle: RePEc:jre:issued:v:30:n:4:2008:p:395-422
    as

    Download full text from publisher

    File URL: http://pages.jh.edu/jrer/papers/pdf/past/vol30n04/01.395_422.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tien Foo Sing & Jesse Jingye Yang & Shi Ming Yu, 2022. "Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)," The Journal of Real Estate Finance and Economics, Springer, vol. 65(4), pages 649-674, November.
    2. Susanna Levantesi & Gabriella Piscopo, 2020. "The Importance of Economic Variables on London Real Estate Market: A Random Forest Approach," Risks, MDPI, vol. 8(4), pages 1-17, October.
    3. Renigier-Biłozor, Malgorzata & Janowski, Artur & d’Amato, Maurizio, 2019. "Automated Valuation Model based on fuzzy and rough set theory for real estate market with insufficient source data," Land Use Policy, Elsevier, vol. 87(C).
    4. Steven Peterson & Albert B. Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, American Real Estate Society, vol. 31(2), pages 147-164.

    More about this item

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jre:issued:v:30:n:4:2008:p:395-422. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: JRER Graduate Assistant/Webmaster (email available below). General contact details of provider: http://www.aresnet.org/ .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.