An Agent-based Financing Model for Post-Earhtquake Housing Recovery: Quantifying Recovery Inequalities Across Income Groups

Past disasters have consistently led to unequal housing recovery for different economic groups, in large part because of the difficulty to obtain funding for low-income groups. Current earthquake recovery models simplify the process of obtaining funding for homeowners to rebuild after earthquakes. Therefore, current models do not fully capture disparities in the recovery outcomes of economic groups. In this project, we develop an agent-based financing model for post-earthquake housing recovery, specifically for owner-occupied single-family homeowners. The model includes earthquake insurance, Federal Emergency Management Agency grants, Small Business Administration loans, Community Development Block Grants for Disaster Recovery, bank loans, Non-Governmental Organizations aid, 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.

The paper for this project is published in Earthquake Spectra. The article can be found here.

The code for this project can be found here.

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About Me

Stanford PhD graduate. Data Scientist @ Lacuna Technologies. Lecturer @ Stanford