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The Application of Classical and Neural Regression Models for the Valuation of Residential Real Estate

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  • Mach Łukasz

    (Ph.D. Opole University of Technology Faculty of Economics and Management Department of Economics, Finances and Regional Research Luboszycka 7, 45-036 Opole, Poland)

Abstract

The research process aimed at building regression models, which helps to valuate residential real estate, is presented in the following article. Two widely used computational tools i.e. the classical multiple regression and regression models of artificial neural networks were used in order to build models. An attempt to define the utilitarian usefulness of the above-mentioned tools and comparative analysis of them is the aim of the conducted research. Data used for conducting analyses refers to the secondary transactional residential real estate market.

Suggested Citation

  • Mach Łukasz, 2017. "The Application of Classical and Neural Regression Models for the Valuation of Residential Real Estate," Folia Oeconomica Stetinensia, Sciendo, vol. 17(1), pages 44-56, June.
    • Handle: RePEc:vrs:foeste:v:17:y:2017:i:1:p:44-56:n:4
    DOI: 10.1515/foli-2017-0004
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    References listed on IDEAS

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    1. Mariusz Kubus, 2016. "Locally Regularized Linear Regression In The Valuation Of Real Estate," Statistics in Transition New Series, Polish Statistical Association, vol. 17(3), pages 515-524, September.
    2. Limsombunchai, Visit, 2004. "House Price Prediction: Hedonic Price Model vs. Artificial Neural Network," 2004 Conference, June 25-26, 2004, Blenheim, New Zealand 97781, New Zealand Agricultural and Resource Economics Society.
    3. R D Hurrion & S Birgil, 1999. "A comparison of factorial and random experimental design methods for the development of regression and neural network simulation metamodels," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(10), pages 1018-1033, October.
    4. Patrick L. Brockett & Linda L. Golden & Jaeho Jang & Chuanhou Yang, 2006. "A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(3), pages 397-419, September.
    5. Mariusz Kubus, 2016. "Locally Regularized Linear Regression in the Valuation of Real Estate," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 17(3), pages 515-524, September.
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