Green retrofitting (GR) of existing residential buildings is widely acknowledged as a potential solution for addressing climate and energy challenges. However, the realization of GR potential depends on homeowners’ decision-making, as they play a pivotal role in initiating GR and are the primary users of retrofitted homes. Previous studies have explored homeowners’ GR decisions from various perspectives. The majority of these studies may have encountered limitations, such as the use of static analysis methods, homogeneity assumptions, and oversimplified decision processes. These limitations can potentially hinder a comprehensive understanding of homeowners’ GR adoption, indicating the need for further research. To address this research gap, this study develops a decision-making model for homeowners that incorporates multiple dimensions: heterogeneity (e.g., individual attributes and GR motivations), a multi-stage decision process (needs-utility-willingness-adoption), and dynamic adaptability (both at individual and overall networks). The proposed model is empirically validated through Python simulations based on a case study in China. The findings of this study reveal the critical roles of GR needs, perceived (actual) utility, and perceived behavioral control in homeowners’ adoption of GR. Moreover, this study highlights the significance of coordinating homeowners’ overall perceived utility with the specific sub-utility associated with their motivations. Furthermore, empirical evidence underscores the influence of social factors on GR adoption and emphasizes the existence of gaps across different stages of the decision-making process. Drawing from these research findings, policy implications are proposed for promoting housing GR. This research contributes to the advancement of decision models for building GR and provides valuable insights for policymakers to promote housing GR.