Adaptability Consultant: A Support Tool to Evaluate Building Adaptability in the Housing Sector

In response to persistent global challenges in affordable housing sector—including low building quality, affordability, and sustainability—this research identifies building adaptability, or design for adaptability - DfA, as a potential strategy. An Excel-based assessment tool using a weighted-sum methodology is developed to support both the design and evaluation of adaptable buildings by quantifying their Adaptive Capacity, across new constructions, refurbishments, and existing buildings. The tool is refined and validated through expert elicitation, including 14 survey responses and 8 semi-structured interviews. The expert feedback contributed to improving the tool and also indicates a high degree of acceptance among scholars. Subsequently, three case studies of well-known adaptable residential buildings are analysed to assess the tool’s applicability and the consistency of its results. The case study results further reveal that buildings commonly perceived as adaptable do not necessarily exhibit adaptability in a comprehensive, multi-dimensional manner. The case study analysis also revealed substantial variation across adaptability types (e.g., from 0.19 to 0.67 for Adjustability), demonstrating the value of a multi-dimensional assessment. It also revealed how adaptability priorities vary following localized context analysis (e.g., the required threshold for Movability decreased from 0.50 to 0.10). The tool might support academics, designers, and technical professionals—during both early design and detailed design phases—in evaluating how design decisions influence a building’s capacity to accommodate change over its lifecycle. In addition, it provides guidance for policymakers and project owners in defining future-proof project requirements. Areas of future research include expanding the sample size of buildings to produce statistical data, tailoring the tool for different building typologies, statistically validating qualitative default values, and verifying design actions derived from academic literature and case studies.