Unveiling vulnerabilities: Integrating socioeconomic and physical dimensions in residential exposure modelling

Natural hazard exposure modelling involves compiling a comprehensive database with the elements at risk such as people, buildings, and infrastructure within a specified area of interest. Residential building (physical) exposure, in particular, is a critical component of disaster risk modelling and management. However, it cannot be examined in isolation from the socioeconomic characteristics of its residents, especially when estimating advanced, people-centred disaster impact metrics (e.g., population displacement) or disaggregating traditional metrics (e.g., financial losses) to account for the unequal impact of hazards on various social segments. Household income has great potential to shape decisions made by homeowners or renters regarding the structural vulnerability of their dwellings. Key decisions -such as home maintenance, the selection and quality of construction materials, compliance with building design codes, and the adoption of private insurance- are profoundly influenced by income levels. These factors, although frequently overlooked in conventional building exposure assessments, can significantly impact risk estimates and recovery planning. The main objective of this study is to develop a residential building exposure model that integrates both physical (i.e., building types) and socioeconomic dimensions (i.e., household income levels) in Saint Lucia, in the eastern Caribbean Sea. The model aims to test the hypothesis that income-poor households are disproportionately likely to inhabit physically vulnerable dwellings. To this end, the study introduces SimLucia , a residential building exposure model to hurricane hazards. Employing spatial microsimulation methodologies, SimLucia combines geographically aggregated data from the 2010 National Population and Household Survey with a-spatial microdata from the 2016 Living Conditions and Household Budgets Survey to result in virtual household populations with their structural and economic attributes at the district level. Outcomes from the SimLucia model reveal that low-income households are over twice as likely to reside in more physically vulnerable building typologies, thereby confirming the initial hypothesis. SimLucia model can serve as a decision-support tool, enabling targeted, human-focused, and pro-poor policy interventions (e.g., housing recovery financing, income-generating instruments). • Physical vulnerability of dwellings and household income are intertwined, thus making the socioeconomic dimension of residential exposure essential in disaster risk modelling and reduction. • We propose a new approach to residential exposure modelling based on the IPF spatial microsimulation method (SimLucia model), which captures key structural and social dimensions of vulnerability. • The outcome of the SimLucia model provides the residential building exposure by structural type and household income level at the district level in Saint Lucia. • The proposed model confirms the initial hypothesis that poor households are more likely to reside in structurally vulnerable dwellings, making the case for the introduction of new policies for stakeholders and policy-makers. • The inclusion of household income in residential exposure development reshapes priorities in risk-reduction policy design, ultimately fostering equity in disaster risk prevention and recovery planning.