Predicting property prices with machine learning algorithms

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 i...

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Bibliographic Details
Published in:Journal of property research Vol. 38; no. 1; pp. 48 - 70
Main Authors: Ho, Winky K.O., Tang, Bo-Sin, Wong, Siu Wai
Format: Journal Article
Language:English
Published: Abingdon Routledge 02.01.2021
Taylor & Francis Ltd
Subjects:
Algorithms
GBM
Learning algorithms
Machine learning
Machine Learning algorithms
Performance measurement
property valuation
Root-mean-square errors
Support vector machines
SVM
ISSN:0959-9916, 1466-4453
Online Access:Get full text
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Summary: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.
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ISSN:0959-9916
1466-4453
DOI:10.1080/09599916.2020.1832558