IDEAS home Printed from https://ideas.repec.org/a/vrs/remava/v27y2019i3p109-123n9.html
   My bibliography  Save this article

Simulative Verification of the Possibility of using Multiple Regression Models for Real Estate Appraisal

Author

Listed:
  • Kokot Sebastian

    (Faculty of Economics and Management, University of Szczecin)

  • Gnat Sebastian

    (Faculty of Economics and Management, University of Szczecin)

Abstract

The possibility of using multiple regression models in real estate valuation is the subject of disputes, both in theory and in practice. Econometric modelling is a difficult process, since a number of issues of substantive and numerical nature occur during that process. Modern technologies enable quick and easy model estimation with the use of virtually any quality of data. Naturally, it provokes property appraisers to use such models in the practice of real property valuation, particularly in mass appraisal, frequently without taking those issues into account. Consequently, the models obtained and applied in practice turn out to be of poor quality and, objectively speaking, should not serve as the basis for determining real estate value. The specificity of the real estate market and of the real properties themselves as objects traded in that market additionally exert a negative impact on the quality of the obtained models.

Suggested Citation

  • Kokot Sebastian & Gnat Sebastian, 2019. "Simulative Verification of the Possibility of using Multiple Regression Models for Real Estate Appraisal," Real Estate Management and Valuation, Sciendo, vol. 27(3), pages 109-123, September.
    • Handle: RePEc:vrs:remava:v:27:y:2019:i:3:p:109-123:n:9
    DOI: 10.2478/remav-2019-0029
    as

    Download full text from publisher

    File URL: https://doi.org/10.2478/remav-2019-0029
    Download Restriction: no
    File URL: https://libkey.io/10.2478/remav-2019-0029?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. 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. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sebastian Gnat & Mariusz Doszyn, 2020. "Parametric and Non-parametric Methods in Mass Appraisal on Poorly Developed Real Estate Markets," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1230-1245.
    2. Vladimir Vargas-Calder'on & Jorge E. Camargo, 2020. "Towards robust and speculation-reduction real estate pricing models based on a data-driven strategy," Papers 2012.09115, arXiv.org.
    3. Sebastian Gnat, 2021. "Property Mass Valuation on Small Markets," Land, MDPI, vol. 10(4), pages 1-14, April.
    4. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    5. William Cheung & Lewen Guo & Yuichiro Kawaguchi, 2021. "Automated valuation model for residential rental markets: evidence from Japan," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-34, December.
    6. 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.
    7. My-Linh Thi Nguyen, 2020. "The Hedonic Pricing Model Applied to the Housing Market," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 416-428.
    8. Mahdieh Yazdani, 2021. "Machine Learning, Deep Learning, and Hedonic Methods for Real Estate Price Prediction," Papers 2110.07151, arXiv.org.
    9. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    10. Kristoffer B. Birkeland & Allan D. D'Silva & Roland Füss & Are Oust, 2021. "The Predictability of House Prices: "Human Against Machine"," International Real Estate Review, Global Social Science Institute, vol. 24(2), pages 139-183.
    11. 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.
    12. Núñez Tabales, Julia M. & Caridad y Ocerin, José María & Rey Carmona, Francisco J., 2013. "Artificial Neural Networks for Predicting Real Estate Prices || Redes neuronales artificiales para la predicción de precios inmobiliarios," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 29-44, June.
    13. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez & Pablo Marti & Juan-Carlos Perez-Sanchez, 2019. "Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression," Sustainability, MDPI, vol. 11(2), pages 1-33, January.
    14. Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023. "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations 112, Paderborn University, Faculty of Business Administration and Economics.
    15. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.
    16. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    17. Camilo Serrano & Martin Hoesli, 2010. "Are Securitized Real Estate Returns more Predictable than Stock Returns?," The Journal of Real Estate Finance and Economics, Springer, vol. 41(2), pages 170-192, August.
    18. Mahdieh Yazdani & Maziar Raissi, 2023. "Real Estate Property Valuation using Self-Supervised Vision Transformers," Papers 2302.00117, arXiv.org.
    19. Jungsun Kim & Jaewoong Won & Hyeongsoon Kim & Joonghyeok Heo, 2021. "Machine-Learning-Based Prediction of Land Prices in Seoul, South Korea," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    20. Çağlayan Ebru & Arikan Eban, 2011. "Determinants of house prices in Istanbul: a quantile regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(2), pages 305-317, February.

    More about this item

    Keywords

    real estate valuation; mass valuation; method of statistical analysis of the market; multiple regression models;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R52 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - Land Use and Other Regulations

    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:vrs:remava:v:27:y:2019:i:3:p:109-123:n:9. 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.

    If CitEc recognized a bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.sciendo.com .

    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.