Green building certification and drivers of green premiums: a meta-regression analysis on global housing market
Year: 2025
Type: article
Study designs: Systematic Review, Meta-Analysis
Abstract: Purpose Climate change has become a critical challenge, drawing attention to the need for sustainable practices in the built environment. Despite the recognised environmental benefits of green buildings (GBs), research on the economic value of GBs, especially in the housing sector, remains limited, with mixed results documented. To better comprehend the economic value of green residential buildings, this study aims to examine the consistent effects of GB certifications (GBCs) on housing markets and identify factors contributing to the documented mixed results. Design/methodology/approach This study applies a meta-regression analysis supplemented by the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol to synthesise findings from past literature in testifying to the existence of green housing (GH) effects and identifying drivers for the varied effects. Findings The meta-regression evidence exhibits consistent green premiums in housing markets. In addition, four principal influential factors governing GH premiums are identified, including certification characteristics, data attributes, model specifications and external considerations. The implications of these findings and their practical applications are also discussed. Originality/value As the first study to examine price premiums and the reasons for heterogeneous effects related to GB certifications in housing markets, this meta-regression analysis extends the body of knowledge in this research domain. The findings of this study enhance the understanding of the economic value of GH and provide policymakers with some practical guidance aligning with markets.
Authors: Jianing Wang, Chyi Lin Lee, Hoon Han
Institution: UNSW Sydney
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410358395