IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/27645.html
   My bibliography  Save this paper

Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics

Author

Listed:
  • Antipov, Evgeny
  • Pokryshevskaya, Elena

Abstract

To the best knowledge of authors, the use of Random forest as a potential technique for residential estate mass appraisal has been attempted for the first time. In the empirical study using data on residential apartments the method performed better than such techniques as CHAID, CART, KNN, multiple regression analysis, Artificial Neural Networks (MLP and RBF) and Boosted Trees. An approach for automatic detection of segments where a model significantly underperforms and for detecting segments with systematically under- or overestimated prediction is introduced. This segmentational approach is applicable to various expert systems including, but not limited to, those used for the mass appraisal.

Suggested Citation

  • Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Mass appraisal of residential apartments: An application of Random forest for valuation and a CART-based approach for model diagnostics," MPRA Paper 27645, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:27645
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/27645/1/MPRA_paper_27645.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. Carlo Bagnoli & Halbert C. Smith, 1998. "The Theory of Fuzzy Logic and its Application to Real Estate Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 16(2), pages 169-200.
    2. Young-Lung Lee & Chang Jung & Yih Yeh Kuang, 2003. "Fair Evaluation of real Estate Value in Urban Area via Fuzzy Theory," ERES eres2003_198, European Real Estate Society (ERES).
    3. Carlos Martins-Filho & Okmyung Bin, 2005. "Estimation of hedonic price functions via additive nonparametric regression," Empirical Economics, Springer, vol. 30(1), pages 93-114, January.
    4. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, American Real Estate Society, vol. 22(3), pages 313-336.
    5. George H. Lentz & Ko Wang, 1998. "Residential Appraisal and the Lending Process: A Survey of Issues," Journal of Real Estate Research, American Real Estate Society, vol. 15(1), pages 11-40.
    6. Han-Bin Kang & Alan K. Reichert, 1991. "An Empirical Analysis of Hedonic Regression and Grid-Adjustment Techniques in Real Estate Appraisal," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 19(1), pages 70-91.
    7. Laakso, Seppo, . "Urban Housing Prices and the Demand for Housing Characteristics. A Study on Housing Prices and the Willingness to pay for Housing Characteristics and Local Public Goods in the Helsinki Metropolitan Ar," ETLA A, The Research Institute of the Finnish Economy, number 27.
    8. A. Prinzie & D. Van Den Poel, 2007. "Random Forrests for Multiclass classification: Random Multinomial Logit," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/435, Ghent University, Faculty of Economics and Business Administration.
    9. Elaine M. Worzala & Margarita Lenk & Ana Silva, 1995. "An Exploration of Neural Networks and Its Application to Real Estate Valuation," Journal of Real Estate Research, American Real Estate Society, vol. 10(2), pages 185-202.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Random forest; mass appraisal; CART; model diagnostics; real estate; automatic valuation model;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:27645. 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: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    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 CitEc recognized a reference but did not link an item in RePEc 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 RePEc Author Service 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.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.