The Use of Machine Learning in Real Estate Research

Lennon H. T. Choy and Winky K. O. Ho ()
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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

Land, 2023, vol. 12, issue 4, 1-15

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.

Keywords: Extra Trees; k –Nearest Neighbors; Random Forest; property prices (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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