AI-driven approach for accelerating sustainability in homeowner associations (HOAs)

Extended abstract 7-274-26

Background
In the Netherlands, approximately 18% of the housing stock is managed by homeowner associations: HOA or Vereniging van Eigenaren  in Dutch (Goedhuys, ten Kate & Wegstapel, 2023). Many of these buildings face significant energy-efficiency challenges. Only about ten percent of HOAs are pursuing sustainability measures (Hilhorst et al., 2023). The ‘cluster approach’ (contingentenaanpak in Dutch) is an AI-based clustering method using over 125 building attributes to predict suitable retrofit solutions (Mulder et al., 2021; TNO, 2024). Its potential for HOAs - where decisions, procurement, and renovations occur collectively - remains unexplored. Understanding whether and how this method can support HOAs is crucial for accelerating large-scale energy retrofits. Thus the central aim of this study is to explore how this cluster approach can deliver added value to sustainability retrofits of HOAs.

Methods
A qualitative study using grounded theory was conducted. Semi-structured interviews with ten experts explored the applicability of the cluster approach for HOAs. Data were thematically analyzed to identify opportunities, barriers, and functional requirements of the application of the cluster approach for HOAs.

Results
The approach is most valuable for well-organized HOAs with sound financial planning and buildings of uniform typology. Key stakeholders benefiting include HOA members, boards, advisors, construction firms, and local governments. Opportunities lie in providing reliable, scenario-based information early in the decision-making process. Barriers include high variability among HOAs and limited activation of smaller associations.

Conclusions
The cluster approach and other large scale sustainability efforts can accelerate by supporting early decision-making. Future work should focus on co-designing user-friendly interventions with functionalities for cost-benefit analyses of retrofit measures and group scenario building. This research highlights the potential of AI-driven clustering to streamline collective retrofitting in multi-owner housing contexts.

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