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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Post-earthquake housing recovery monitoring is necessary, especially since the housing sector usually represents 50 percent of the total monetary disaster loss. However, very scarce recovery data, in addition to the complexities of the recovery process, make modeling housing recovery very difficult. Time-based stochastic models, which are commonly used in well-known frameworks such as the U.S. Federal Emergency Management Agency’s HAZARD-US (HAZUS), do not explicitly capture how the recovery process occurs in real life. In this article, we introduce a stochastic queuing model that considers the total number of damaged buildings, the damage distribution, resource constraints, and government-led reconstruction prioritization strategies. We applied our model to seven regions affected by the 2018 Lombok earthquakes, which destroyed over 226,000 residential buildings. In this study, we use publicly available daily data of the reconstruction progress obtained by local authorities for all damaged buildings. Using that dataset, we present recovery parameters for the Lombok region, including delay and reconstruction times. These parameters are an improvement over parameters currently available, which only apply to the U.S. region. Furthermore, we show that our model captures the observed recovery trajectory disaggregated by damage states, which provides insights into how the different building damage states recover individually. Results show that our queuing model reduces the root mean squared error (RMSE) of the recovery trajectory by 31.58% and is better able to represent the observed overall recovery trajectory compared to a time-based stochastic model.