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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Vydané v:	Journal of property research Ročník 38; číslo 1; s. 48 - 70
Hlavní autori:	Ho, Winky K.O., Tang, Bo-Sin, Wong, Siu Wai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Abingdon Routledge 02.01.2021 Taylor & Francis Ltd
Predmet:	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
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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.
AbstractList	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. 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.
Author	Tang, Bo-Sin Ho, Winky K.O. Wong, Siu Wai
Author_xml	– sequence: 1 givenname: Winky K.O. surname: Ho fullname: Ho, Winky K.O. organization: The University of Hong Kong – sequence: 2 givenname: Bo-Sin surname: Tang fullname: Tang, Bo-Sin email: bsbstang@hku.hk organization: The University of Hong Kong – sequence: 3 givenname: Siu Wai surname: Wong fullname: Wong, Siu Wai organization: The Hong Kong Polytechnic University
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SubjectTerms	Algorithms GBM Learning algorithms Machine learning Machine Learning algorithms Performance measurement property valuation Root-mean-square errors Support vector machines SVM
Title	Predicting property prices with machine learning algorithms
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