Green building factor in machine learning based condominium price prediction

Masrom, Suraya; Mohd, Thuraiya; Abd Rahman, Abdullah Sani

doi:10.11591/ijai.v11.i1.pp291-299

Akses terbuka Jurnal SINTA 1

Green building factor in machine learning based condominium price prediction

IAES International Journal of Artificial Intelligence · Vol. 11 No. 1 (2022) · hlm. 291–299

DOI: 10.11591/ijai.v11.i1.pp291-299
ISSN: 2252-8938, 2089-4872
Terbit: 2022-03-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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Universiti Teknologi MARA
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Tampilkan sebagai tabel

Rekan penulis	Organisasi	Artikel bersama
Norhayati Baharun	Afiliasi tidak diketahui	7
Masurah Mohamad	Universiti Teknologi MARA	4
Rahayu Abdul Rahman	Afiliasi tidak diketahui	2
Syafiqah Jamil	Afiliasi tidak diketahui	2
Nasiroh Omar	Afiliasi tidak diketahui	2
Shahirah Mohamed Hatim	Afiliasi tidak diketahui	2
Teoh Sian Hoon	Afiliasi tidak diketahui	2
Nadirah Othman	Afiliasi tidak diketahui	2
Nor Aslily Sarkam	Afiliasi tidak diketahui	2
Nor Faezah Mohamad Razi	Afiliasi tidak diketahui	2
Anis Zulaikha Mohd Zukri	Afiliasi tidak diketahui	1
Siti Rasidah Md Sakip	Afiliasi tidak diketahui	1
Maheran Zakaria	Afiliasi tidak diketahui	1
Rahayu Abdul Rahman	Afiliasi tidak diketahui	1
Normah Omar	Afiliasi tidak diketahui	1
Nor Faezah Mohamad Razi	Afiliasi tidak diketahui	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Green building factor in machine learning based condominium price prediction</dc:title>
	<dc:creator>Masrom, Suraya</dc:creator>
	<dc:creator>Mohd, Thuraiya</dc:creator>
	<dc:creator>Abd Rahman, Abdullah Sani</dc:creator>
	<dc:subject xml:lang="en-US">Machine learning</dc:subject>
	<dc:subject xml:lang="en-US">Decision tree; Deep learning; Green building; Price prediction; Random forest</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Ministry of Education, Malaysia</dc:contributor>
	<dc:date>2022-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/20910</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i1.pp291-299</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 1: March 2022; 291-299</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/20910/13312</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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