Predicting property prices with machine learning algorithms

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

68 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)48-70
Number of pages23
JournalJournal of Property Research
Issue number1
Publication statusPublished - Oct 2020


  • GBM
  • Machine Learning algorithms
  • property valuation
  • RF
  • SVM

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Urban Studies


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