A framework of agent-based models for housing recovery is presented and used to investigate post-earthquake recovery in the City of Vancouver, Canada. Housing recovery is modeled for a portfolio of buildings, contrasting with the practice of assessing the reconstruction of buildings in isolation. Thus, the presented approach better captures the effect of competition for resources, infrastructure disruptions, and socioeconomic factors on recovery. The analyses include models for damage, inspection, financing, power infrastructure, and labor/materials for repairs. The presented approach is applied to simulate the recovery of 114,832 residential buildings in 22 neighborhoods in Vancouver. Results indicate that recovery after a strong earthquake will take more than three years. The density of old and rented buildings, and the income and immigration status of the homeowners are shown to be good predictors of the speed of recovery for a neighborhood. Mitigation measures are compared and it is shown that retrofitting the most physically vulnerable buildings or doubling the available workforce are effective at reducing housing recovery times. It is demonstrated that the equity in recovery between low and high socioeconomic status homeowners is improved if mitigation measures are implemented. The results presented in this article can inform disaster recovery plans and mitigation actions in Vancouver and similar communities.

Past disasters have consistently led to unequal housing recovery for different economic groups, in large part, because of the difficulty of obtaining funding for low-income groups. Current earthquake recovery models simplify the financing process for homeowners to rebuild after earthquakes, and in consequence, cannot fully capture disparities in the recovery outcomes of economic groups. In this article, we develop an agent-based financing model for post-earthquake housing recovery. We focus on single-family, owner-occupied homes. The model includes funding from earthquake insurance, the Federal Emergency Management Agency, the Small Business Administration, the Department of Housing and Urban Development, private banks, Non-Governmental Organizations, and personal savings. We present a case study investigating the housing recovery financing in the economically diverse city of San Jose, California, following a hypothetical 7.0 Mw earthquake. By including the financial model in housing recovery simulations, we quantify inequalities in recovery time and total reconstruction completion between income groups. We complement the case study by evaluating several strategies to reduce these disparities and show that a combination of income-targeted funding and redistribution of construction crews can reduce inequalities in regional housing recovery.

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