Green building factor in machine learning based condominium price prediction

The negative impact of massive urban development promotes the inclusion of green building aspects in the real estate and property industries. Green building is generally defined as an environmentally friendly building, which rapidly emerged as a national priority in many countries. Acknowledging the...

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Main Authors: Masrom, S., Mohd, T., Rahman, A.S.A.
Format: Article
Published: Institute of Advanced Engineering and Science 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125877157&doi=10.11591%2fijai.v11.i1.pp291-299&partnerID=40&md5=59f8165d7d037d9a807fe7ba93904940
http://eprints.utp.edu.my/28806/
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spelling my.utp.eprints.288062022-03-17T02:44:38Z Green building factor in machine learning based condominium price prediction Masrom, S. Mohd, T. Rahman, A.S.A. The negative impact of massive urban development promotes the inclusion of green building aspects in the real estate and property industries. Green building is generally defined as an environmentally friendly building, which rapidly emerged as a national priority in many countries. Acknowledging the benefits of green building, Green Certificate and Green Building Index (GBI) has been used as one of the factors in housing prices valuation. To predict a housing price, a robust approach is crucial, which can be effectively gained from the machine learning technique. As research on green building with machine learning techniques is rarely reported in the literature, this paper presents the fundamental design and the comparison results of three machine learning algorithms namely deep learning (DL), decision tree (DT), and random forest (RF). Besides the performance comparisons, this paper presents the specific weight correlation in each of the machine learning models to describe the importance of the green building to the model. The results indicated that RF has been outperformed others while Green Certificate and GBI have only been slightly important in the DL model. © 2022, Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 2022 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125877157&doi=10.11591%2fijai.v11.i1.pp291-299&partnerID=40&md5=59f8165d7d037d9a807fe7ba93904940 Masrom, S. and Mohd, T. and Rahman, A.S.A. (2022) Green building factor in machine learning based condominium price prediction. IAES International Journal of Artificial Intelligence, 11 (1). pp. 291-299. http://eprints.utp.edu.my/28806/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The negative impact of massive urban development promotes the inclusion of green building aspects in the real estate and property industries. Green building is generally defined as an environmentally friendly building, which rapidly emerged as a national priority in many countries. Acknowledging the benefits of green building, Green Certificate and Green Building Index (GBI) has been used as one of the factors in housing prices valuation. To predict a housing price, a robust approach is crucial, which can be effectively gained from the machine learning technique. As research on green building with machine learning techniques is rarely reported in the literature, this paper presents the fundamental design and the comparison results of three machine learning algorithms namely deep learning (DL), decision tree (DT), and random forest (RF). Besides the performance comparisons, this paper presents the specific weight correlation in each of the machine learning models to describe the importance of the green building to the model. The results indicated that RF has been outperformed others while Green Certificate and GBI have only been slightly important in the DL model. © 2022, Institute of Advanced Engineering and Science. All rights reserved.
format Article
author Masrom, S.
Mohd, T.
Rahman, A.S.A.
spellingShingle Masrom, S.
Mohd, T.
Rahman, A.S.A.
Green building factor in machine learning based condominium price prediction
author_facet Masrom, S.
Mohd, T.
Rahman, A.S.A.
author_sort Masrom, S.
title Green building factor in machine learning based condominium price prediction
title_short Green building factor in machine learning based condominium price prediction
title_full Green building factor in machine learning based condominium price prediction
title_fullStr Green building factor in machine learning based condominium price prediction
title_full_unstemmed Green building factor in machine learning based condominium price prediction
title_sort green building factor in machine learning based condominium price prediction
publisher Institute of Advanced Engineering and Science
publishDate 2022
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125877157&doi=10.11591%2fijai.v11.i1.pp291-299&partnerID=40&md5=59f8165d7d037d9a807fe7ba93904940
http://eprints.utp.edu.my/28806/
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score 13.211869