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The Use of Machine Learning in Real Estate Research

Author

Listed:
  • Lennon H. T. Choy

    (Department of Real Estate and Construction, University of Hong Kong, Hong Kong SAR, China)

  • Winky K. O. Ho

    (Independent Researcher, Hong Kong SAR, China)

Abstract

This research seeks to demonstrate how machine learning, a branch of artificial intelligence, is able to deliver more accurate pricing predictions, using the real estate market as an example. Utilizing 24,936 housing transaction records, this paper employs Extra Trees (ET), k –Nearest Neighbors (KNN), and Random Forest (RF) to predict property prices and then compares their results with those of a hedonic price model. In particular, this paper uses a feature (property age x square footage) instead of property age in order to isolate the effect of land depreciation on property prices. Our results suggest that these three algorithms markedly outperform the traditional statistical techniques in terms of explanatory power and error minimization. Machine learning is expected to play an increasing role in shaping our future. However, it may raise questions about the privacy, fairness, and job displacement issues. It is therefore important to pay close attention to the ethical implications of machine learning and ensure that the technology is used responsibly and ethically. Researchers, legislators, and industry players must work together to create appropriate standards and legislation to govern the use of machine learning.

Suggested Citation

  • Lennon H. T. Choy & Winky K. O. Ho, 2023. "The Use of Machine Learning in Real Estate Research," Land, MDPI, vol. 12(4), pages 1-15, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:4:p:740-:d:1107160
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    References listed on IDEAS

    as
    1. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez, 2022. "Housing Price Prediction Using Machine Learning Algorithms in COVID-19 Times," Land, MDPI, vol. 11(11), pages 1-32, November.
    2. W. Erwin Diewert & Jan de Haan & Rens Hendriks, 2015. "Hedonic Regressions and the Decomposition of a House Price Index into Land and Structure Components," Econometric Reviews, Taylor & Francis Journals, vol. 34(1-2), pages 106-126, February.
    3. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    4. Trond-Arne Borgersen, 2022. "A Housing Market with Cournot Competition and a Third Housing Sector," International Journal of Economic Sciences, European Research Center, vol. 11(2), pages 13-27, November.
    5. Fumihiko Isada, 2022. "The impact of inter-organisational network structures on research outcomes for artificial intelligence technologies," International Journal of Economic Sciences, European Research Center, vol. 11(1), pages 1-18, April.
    6. Georgios Tsertekidis, 2022. "Migrating from Greece to Germany after 2010: a qualitative approach," International Journal of Social Sciences, European Research Center, vol. 11(1), pages 73-92, March.
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    Cited by:

    1. Hack, Lukas & Rostam-Afschar, Davud, 2025. "Which Macroeconomic News Matters for Price-Setting?," IZA Discussion Papers 17935, Institute of Labor Economics (IZA).

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