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Recent major earthquake disasters have highlighted the effectiveness of financial soft policies (e.g., earthquake insurance) in transferring seismic risk away from those directly impacted and complementing `hard' disaster risk mitigation measures. However, the benefits of existing financial soft policies are often not guaranteed. This may be attributed to: (1) their low penetration rate (e.g., in the case of earthquake insurance); (2) the fact that they typically neglect the explicit needs of low-income sectors in modern societies, who are often disproportionately impacted by natural-hazard driven disasters; and/or (3) their failure to consider the time-dependent nature of urban exposure. We contribute towards addressing these shortcomings by proposing a flexible framework for designing and assessing bespoke, people-centred, household-level, compulsory financial soft policies (including conventional earthquake insurance, disaster relief fund schemes, income-based tax relief scheme, or a combination of those) across cities under rapid urban expansion. The proposed framework leverages the Tomorrow's Cities Decision Support Environment, which aims to facilitate pro-poor disaster-risk-informed urban planning and design in developing country contexts. The framework specifically enables decision makers to strategically design and then assess the pro-poorness of mandatory soft policies, using innovative financial impact metrics that discriminate losses on the basis of income. We showcase the framework using the hypothetical expanding city, ``Tomorrowville", successfully identifying pro-poor seismic-risk-related financial soft policies for different instances in the lifetime of the urban system.

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Residential damage from major disasters often displaces local residents out of their homes and into temporary housing. Out-of-town contractors assisting in post-disaster housing recovery also need housing, creating additional pressure on the local housing stock. Communities should thus prepare for a surge in temporary housing demand to minimize the impact on the local residents and to expedite housing recovery efforts. Computational models can support recovery planning. This paper introduces an agent-based simulation framework to estimate the workforce demand and the joint temporary housing needs of contractors and displaced households. The main agents are households seeking to repair their homes, local contractors, and out-of-town contractors. Out-of-town contractor agents come into the community if the labor and housing markets are favorable. The framework can be used to evaluate the resulting challenges and benefits of interventions aimed at attracting out-of-town contractors to expedite housing recovery. We present a case study on the housing recovery of the city of San Francisco after hypothetical M6.5, M7.2, and M7.9 earthquakes. A shortage of contractors is shown to bottleneck the reconstruction if no out-of-town contractors are recruited. Conversely, out-of-town contractors increase the likelihood of temporary housing shortages. These results highlight the need to plan for shortages of reconstruction labor and temporary housing during recovery.