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AUTOMATED EVALUATION MODELS in real estate market: A comparative analysis between linear regression and XGBoost

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  • Silviu-IonuÈ› BÄ‚BÈšAN

    (Accounting andAudit Department, Faculty of Economics and BusinessAdministration, BabeÅŸ-Bolyai University)

Abstract

The dynamic trend of the real estate market, as well as the rapid digitization process that has taken place recently, has created the need to identify techniques for automated property price valuation. This article aims to eliminate the problems of subjectivity and high resource consumption of traditional valuation by analyzing the practical implementation of automated valuation methods in the real estate market. Thus, the article performs a comparative analysis between classical prediction methods, such as linear regression, and complex prediction algorithms, such as XGBoost, which can be applied in real estate value prediction. By analyzing the accuracy of predictions on a sample of apartments in Cluj-Napoca, Romania, the study compares the two automatic valuation methods and draws the lines of their implementation. The results of this study can be used for the development of automatic valuation techniques at a global level.

Suggested Citation

  • Silviu-IonuÈ› BÄ‚BÈšAN, 2024. "AUTOMATED EVALUATION MODELS in real estate market: A comparative analysis between linear regression and XGBoost," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(26), pages 1-3.
    • Handle: RePEc:alu:journl:v:2:y:2024:i:26:p:3
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    References listed on IDEAS

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    1. Robert J. Shiller, 2015. "Irrational Exuberance," Economics Books, Princeton University Press, edition 3, number 10421.
    2. Nghiep Nguyen & Al Cripps, 2001. "Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks," Journal of Real Estate Research, Taylor & Francis Journals, vol. 22(3), pages 313-336, January.
    3. David Tretton, 2007. "Where is the world of property valuation for taxation purposes going?," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 25(5), pages 482-514, August.
    4. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, Taylor & Francis Journals, vol. 32(2), pages 139-160, January.
    5. Elli Pagourtzi & Vassilis Assimakopoulos & Thomas Hatzichristos & Nick French, 2003. "Real estate appraisal: a review of valuation methods," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 21(4), pages 383-401, August.
    6. Michel Glower & Donald R. Haurin & Patric H. Hendershott, 1998. "Selling Time and Selling Price: The Influence of Seller Motivation," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 26(4), pages 719-740, December.
    7. 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.
    8. William McCluskey & Sarabjot Anand, 1999. "The application of intelligent hybrid techniques for the mass appraisal of residential properties," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 17(3), pages 218-239, August.
    9. Carmona, Pedro & Climent, Francisco & Momparler, Alexandre, 2019. "Predicting failure in the U.S. banking sector: An extreme gradient boosting approach," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 304-323.
    10. 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.
    11. 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.
    12. Steven Peterson & Albert Flanagan, 2009. "Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal," Journal of Real Estate Research, Taylor & Francis Journals, vol. 31(2), pages 147-164, January.
    13. 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.
    14. 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.
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    More about this item

    Keywords

    real estate; mass appraisals; automated appraisal; valuation standards.;
    All these keywords.

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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