An Integrated Assessment of Risks in Post-Disaster Temporary Housing: Evidence from Türkiye Using Fuzzy Synthetic Evaluation

Post-disaster temporary housing (PDTH) plays a central role in bridging emergency response and long-term recovery, yet its implementation is affected by a wide range of institutional, economic, site-related, technical, and environmental risks. Existing studies commonly examine only one or a limited number of these risk categories, making it difficult to compare their relative importance within a common analytical framework. This study addresses this limitation by developing and empirically evaluating an integrated risk framework for PDTH. A systematic literature review based on the PRISMA protocol was conducted to identify relevant risks, followed by a questionnaire survey of construction professionals. Principal component and confirmatory factor analyses were employed to identify the underlying risk dimensions and assess the fit and measurement properties of the resulting structure. Meanwhile, fuzzy synthetic evaluation was utilized to determine the relative importance of these dimensions. Following significance-index screening and cross-loading assessment, 29 risks were retained and grouped into four dimensions: institutional and governance risks; economic and lifecycle risks; site planning and infrastructure risks; and design and environmental performance risks. The normalized coefficients were closely clustered, ranging from 0.247 to 0.255. Institutional and governance risks had the largest numerical coefficient (0.255), followed by economic and lifecycle risks (0.250), site planning and infrastructure risks (0.248), and design and environmental performance risks (0.247). The narrow spread indicates that respondents assigned broadly comparable importance to all four dimensions, rather than identifying a single dominant risk area. The findings, therefore, point to the need for a balanced approach to PDTH risk management across governance, economic, site-related, and design-related concerns. The study contributes an empirically supported classification and prioritization framework that brings previously fragmented risk categories into a single assessment structure. The analysis does not establish causal relationships or dynamic interactions among the identified risks; rather, it provides a basis for their systematic comparison and for more detailed investigation of risk interdependencies in future research.