Office Rent Prediction based on the Influenced Features

Authors

  • Thuraiya Mohd Centre of Graduate Studies, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, 32610 Perak, MALAYSIA
  • Muhamad Harussani GreenSafe Cities (GreSAFE) Research Group, Department of Built Environment Studies and Technology, Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, 32610 Perak, MALAYSIA
  • Suraya Masrom Malaysia Machine Learning and Interactive Visualization (MaLIV) Research Group, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Perak Branch, Tapah Campus, 35400 Perak MALAYSIA
  • Noraini Johari Department of Built Environment Studies and Technology, Faculty of Architecture, Planning and Surveying, Universiti Teknologi MARA, Perak Branch, Seri Iskandar Campus, 32610 Perak, MALAYSIA
  • Lathifah Alfat Faculty of Technology and Design, Universitas Pembangunan Jaya, Indonesia

DOI:

https://doi.org/10.21834/ebpj.v7i19.3236

Keywords:

Office Rent, Machine Learning, Prediction

Abstract

This study applies a new approach in identifying the best Machine Learning model to predict office rent and determining the most significant factors influencing rental values. The Auto Model uses three (3) distinct types of Machine Learning algorithms, namely the Decision Tree, Random Forest, and Support Vector Machine. The Auto Model highlights that the Decision Tree outperformed Random Forest and Support Vector Machine for better prediction. The results of statistical analysis using Auto Model suggest that among the factors that influence office building rental, amenities, and in-house services show significant roles in the model.

Keywords: Office Rent, Machine Learning, Prediction

eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.

DOI:

Published

2022-03-31

How to Cite

Mohd, T., Harussani, M., Masrom, S., Johari, N., & Alfat , L. . (2022). Office Rent Prediction based on the Influenced Features. Environment-Behaviour Proceedings Journal, 7(19), 61-68. https://doi.org/10.21834/ebpj.v7i19.3236