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Article: Predicting property prices with machine learning algorithms
Title | Predicting property prices with machine learning algorithms |
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Authors | |
Keywords | Machine Learning algorithms SVM RF GBM property valuation |
Issue Date | 2021 |
Publisher | Routledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/09599916.asp |
Citation | Journal of Property Research, 2021, v. 38 n. 1, p. 48-70 How to Cite? |
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. |
Description | Hybrid open access |
Persistent Identifier | http://hdl.handle.net/10722/294145 |
ISSN | 2023 Impact Factor: 2.1 2023 SCImago Journal Rankings: 0.364 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Ho, WKO | - |
dc.contributor.author | Tang, BS | - |
dc.contributor.author | Wong, SW | - |
dc.date.accessioned | 2020-11-23T08:27:00Z | - |
dc.date.available | 2020-11-23T08:27:00Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Journal of Property Research, 2021, v. 38 n. 1, p. 48-70 | - |
dc.identifier.issn | 0959-9916 | - |
dc.identifier.uri | http://hdl.handle.net/10722/294145 | - |
dc.description | Hybrid open access | - |
dc.description.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. | - |
dc.language | eng | - |
dc.publisher | Routledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/09599916.asp | - |
dc.relation.ispartof | Journal of Property Research | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Machine Learning algorithms | - |
dc.subject | SVM | - |
dc.subject | RF | - |
dc.subject | GBM | - |
dc.subject | property valuation | - |
dc.title | Predicting property prices with machine learning algorithms | - |
dc.type | Article | - |
dc.identifier.email | Ho, WKO: winkyh@HKUCC-COM.hku.hk | - |
dc.identifier.email | Tang, BS: bsbstang@hku.hk | - |
dc.identifier.authority | Tang, BS=rp01646 | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1080/09599916.2020.1832558 | - |
dc.identifier.scopus | eid_2-s2.0-85092761693 | - |
dc.identifier.hkuros | 318767 | - |
dc.identifier.volume | 38 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 48 | - |
dc.identifier.epage | 70 | - |
dc.identifier.isi | WOS:000586644700001 | - |
dc.publisher.place | United Kingdom | - |