Predictive analysis on the influence of green certifications for rent performance in Kuala Lumpur

Authors

  • Harussani Salam Centre of Graduate Studies, Universiti Teknologi MARA, Shah Alam Campus, 40450 Selangor, Malaysia
  • Thuraiya Mohd GreenSafe Cities (GreSAFE) Research Group, Department of Built Environment Studies and Technology, University Teknologi MARA, Perak Branch, Seri Iskandar Campus, 32610 Perak, Malaysia

DOI:

https://doi.org/10.37134/kupasseni.vol14.sp.16.2026

Keywords:

Green Certification, Green Building Index, Rental Performance , Office Market, SDG

Abstract

Green building has been widely promoted globally as a pathway to sustainability, aligning with the United Nations Sustainable Development Goal (SDG) 11 on sustainable cities and communities as well as Malaysia’s commitment to achieving a low-carbon nation under the National Green Technology Policy. Certified buildings, such as those recognized under the Green Building Index (GBI) and Leadership in Energy and Environmental Design (LEED), typically integrate energy-efficient systems, sustainable materials, and environmentally friendly designs, which are often assumed to justify higher rental values. The objective of this study is to test the rent performance among office buildings in Kuala Lumpur with different certification status, while accounting for other building attributes such as size and location. Transaction data were collected from the Valuation and Property Services Department (VPSD) covering the period 2023–2025. Machine learning predictive analytics was employed to test the R2 among three categories of office buildings: non-green buildings, GBI-certified buildings, and LEED-certified buildings. The findings reveal a relatively higher Coefficient of determination R2 for LEED suggests that internationally recognized certifications are beginning to be reflected in rent. Overall, the predictive results demonstrate that rental performance in Kuala Lumpur office market is primarily driven by conventional physical attributes, particularly sizes and locations, which consistently achieved the highest values across all machine learning algorithms. The significance of this study lies in its contribution to the understanding of the evolving dynamics of green-certified office buildings in Malaysia, where the market has yet to consistently recognize certification as a premium value driver. By highlighting the gap between global sustainability aspirations and local market realities, this study provides important insights for investors, developers, and policymakers in shaping strategies that support Malaysia’s transition towards a greener and more sustainable built environment in line with both national and global agendas.

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Published

2026-04-27

How to Cite

Salam, H., & Mohd, T. (2026). Predictive analysis on the influence of green certifications for rent performance in Kuala Lumpur. KUPAS SENI: Jurnal Seni Dan Pendidikan Seni, 14(Isu Khas), 177-188. https://doi.org/10.37134/kupasseni.vol14.sp.16.2026