Predicting property prices with machine learning algorithms

Winky K.O. Ho, Bo-Sin Tang and Siu Wai Wong

Journal of Property Research, 2021, vol. 38, issue 1, 48-70

Abstract: This study uses three machine learning algorithms including, support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM) in the appraisal of property prices. It applies these methods to examine a data sample of about 40,000 housing transactions in a period of over 18 years in Hong Kong, and then compares the results of these algorithms. In terms of predictive power, RF and GBM have achieved better performance when compared to SVM. The three performance metrics including mean squared error (MSE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) associated with these two algorithms also unambiguously outperform those of SVM. However, our study has found that SVM is still a useful algorithm in data fitting because it can produce reasonably accurate predictions within a tight time constraint. Our conclusion is that machine learning offers a promising, alternative technique in property valuation and appraisal research especially in relation to property price prediction.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://hdl.handle.net/10.1080/09599916.2020.1832558 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jpropr:v:38:y:2021:i:1:p:48-70

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RJPR20

DOI: 10.1080/09599916.2020.1832558

Access Statistics for this article

Journal of Property Research is currently edited by Bryan MacGregor

More articles in Journal of Property Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().