@article{scholars17019,
            year = {2022},
       publisher = {Institute of Advanced Engineering and Science},
         journal = {IAES International Journal of Artificial Intelligence},
           pages = {291--299},
          number = {1},
          volume = {11},
            note = {cited By 1},
             doi = {10.11591/ijai.v11.i1.pp291-299},
           title = {Green building factor in machine learning based condominium price prediction},
            issn = {20894872},
          author = {Masrom, S. and Mohd, T. and Rahman, A. S. A.},
             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},
        abstract = {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. {\^A}{\copyright} 2022, Institute of Advanced Engineering and Science. All rights reserved.}
}