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The contribution of statistical models in the field of real estate valuation

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  • Tothăzan Helga Flavia

    (Department of Accounting, Faculty of economics and Business Administration, Babes-Bolyai University Cluj-Napoca, Cluj-Napoca, Romania)

Abstract

Testing a model in property evaluation can be a difficult task due to the large variety of these models. The most popular models used in valuation are regression and neural networks. This paper applied a systematic review study and presents 11 types of regression models and 9 types of neural network models applied in real estate valuation. Our aim is to provide a tool for model selection applied in real estate valuation. The selection criteria were based on their applicability, user preferences and price estimation performance. The findings were slightly different from our expectations. Multi-Layer Perceptron (MLP) and Multiple Linear Regression (GLM) are the most applied and popular models in valuation.

Suggested Citation

  • Tothăzan Helga Flavia, 2022. "The contribution of statistical models in the field of real estate valuation," Timisoara Journal of Economics and Business, Sciendo, vol. 15(1), pages 111-126.
    • Handle: RePEc:vrs:timjeb:v:15:y:2022:i:1:p:111-126:n:1007
    DOI: 10.2478/tjeb-2022-0007
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    References listed on IDEAS

    as
    1. Jozef Zurada & Alan S. Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, American Real Estate Society, vol. 33(3), pages 349-388.
    2. Jozef Zurada & Alan Levitan & Jian Guan, 2011. "A Comparison of Regression and Artificial Intelligence Methods in a Mass Appraisal Context," Journal of Real Estate Research, Taylor & Francis Journals, vol. 33(3), pages 349-388, January.
    3. A. Quang Do & G. Grudnitski, 1993. "A Neural Network Analysis of the Effect of Age on Housing Values," Journal of Real Estate Research, American Real Estate Society, vol. 8(2), pages 253-264.
    4. Allan Din & Martin Hoesli & Andre Bender, 2001. "Environmental Variables and Real Estate Prices," Urban Studies, Urban Studies Journal Limited, vol. 38(11), pages 1989-2000, October.
    5. repec:eme:jv0000:eb008022 is not listed on IDEAS
    6. Hans R. Isakson, 1998. "The Review of Real Estate Appraisals Using Multiple Regression Analysis," Journal of Real Estate Research, American Real Estate Society, vol. 15(2), pages 177-190.
    7. Stephen A. Samaha & Wagner A. Kamakura, 2008. "Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(4), pages 717-751, December.
    8. W.J. McCluskey & M. McCord & P.T. Davis & M. Haran & D. McIlhatton, 2013. "Prediction accuracy in mass appraisal: a comparison of modern approaches," Journal of Property Research, Taylor & Francis Journals, vol. 30(4), pages 239-265, December.
    9. Helen X.H. Bao & Alan T.K. Wan, 2004. "On the Use of Spline Smoothing in Estimating Hedonic Housing Price Models: Empirical Evidence Using Hong Kong Data," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(3), pages 487-507, September.
    10. repec:eme:jpvi00:14635789710163775 is not listed on IDEAS
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    JEL classification:

    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

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