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Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM)
This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568682/ http://dx.doi.org/10.1007/s11146-021-09861-1 |
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author | Sing, Tien Foo Yang, Jesse Jingye Yu, Shi Ming |
author_facet | Sing, Tien Foo Yang, Jesse Jingye Yu, Shi Ming |
author_sort | Sing, Tien Foo |
collection | PubMed |
description | This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%. |
format | Online Article Text |
id | pubmed-8568682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-85686822021-11-05 Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM) Sing, Tien Foo Yang, Jesse Jingye Yu, Shi Ming J Real Estate Finan Econ Article This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public housing transactions in Singapore for the period from 1995 to 2017 in the training of the AI-AVM models. The boosting model is the best predictive model that produce the most robust and accurate predictions for housing prices compared to the decision tree and multiple regression analysis (MRA) models. The boosting AI-AVM models explain 91.33% and 94.28% of the price variances, and keep the mean absolute percentage errors at 8.55% and 5.34% for the public housing market and the private housing market, respectively. When subject the AI-AVM to the out-of-sample forecasting using the 2018 housing sale samples, the prediction errors remain within a narrow range of between 5% and 9%. Springer US 2021-11-05 2022 /pmc/articles/PMC8568682/ http://dx.doi.org/10.1007/s11146-021-09861-1 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Sing, Tien Foo Yang, Jesse Jingye Yu, Shi Ming Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM) |
title | Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM) |
title_full | Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM) |
title_fullStr | Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM) |
title_full_unstemmed | Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM) |
title_short | Boosted Tree Ensembles for Artificial Intelligence Based Automated Valuation Models (AI-AVM) |
title_sort | boosted tree ensembles for artificial intelligence based automated valuation models (ai-avm) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568682/ http://dx.doi.org/10.1007/s11146-021-09861-1 |
work_keys_str_mv | AT singtienfoo boostedtreeensemblesforartificialintelligencebasedautomatedvaluationmodelsaiavm AT yangjessejingye boostedtreeensemblesforartificialintelligencebasedautomatedvaluationmodelsaiavm AT yushiming boostedtreeensemblesforartificialintelligencebasedautomatedvaluationmodelsaiavm |