Housing Recovery

Recent efforts by the Federal government of Canada have devoted resources to mitigating disaster risk and identifying sustainable solutions for post-disaster recovery financing. In 2023, Canada committed funding to set up its National Flood Insurance Program. Once this program is operational, its foundations are expected to be carried over to other perils, such as earthquakes. In anticipation of the need to rethink post-earthquake recovery financing, the present study evaluates the feasibility of implementing three novel financing mechanisms in Canada, drawing inspiration from existing US and New Zealand programs. The proposed mechanisms include a grants program targeting low-to-moderate-income households, a low-interest loan program, and an affordable insurance program. Simulations of the impacts of M7 earthquake in the Strait of Georgia, British Columbia, are used to compare the post-earthquake uninsured losses in the status quo and if each new mechanism was in place before the event. Benefits are assessed through the reduction in uninsured losses, while opportunity losses measure the costs of each program. Results indicate that a loan program with an interest rate above 3.5% could offer benefits surpassing its opportunity cost, albeit with substantial initial expenses. Additionally, introducing an affordable insurance program and a disaster fund shows promise but requires robust capitalization in its initial years. A combination of affordable insurance and low-interest loans could alleviate long-term debt for homeowners, particularly for earthquakes causing moderate losses.

In the US, Presidential Major Disaster Declarations are a key factor in determining the availability of federal grants and low interest to support housing recovery. Without a Presidential Declaration, uninsured, lower-income, disaster-struck households may be forced into large amounts of debt to cover home repair costs. In the long term, paying off this debt reduces households' consumption, reducing well-being. While these facts have been documented in the literature on past disasters, this study provides a methodology to evaluate how Presidential Disaster Declarations may influence socioeconomic inequality in future disasters. The methodology also incorporates the effects of climate change as a multiplier that exacerbates these inequalities. We present a case study using the city of East Palo Alto in California and demonstrate how Presidential Disaster Declarations affect the disaster outcomes under multiple flood hazard scenarios. Results show that issuing a Presidential Disaster Declaration can reduce long-term consumption losses for lower-income households by more than 30%, and greatly reduce the disparity in consumption loss between low-income and high-income households. It is also shown that a 20-year return period flood without a Presidential Disaster Declaration may lead to greater long-term losses than a 100-year return period flood accompanied by a Presidential Disaster Declaration. The results of this study can help advocate for criteria for issuing Presidential Disaster Declarations that yield more equitable outcomes for the impacted communities.

In the United States, assistance from the Department of Housing and Urban Develop- ment (HUD) plays an essential role in supporting the postdisaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recov- ery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authori- ties argue that HUD’s simplified approach underestimates the state’s needs. This article presents a methodology to estimate postdisaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of dam- aged buildings are combined with models for expected repair costs. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast fund- ing provided by those agencies. With these forecasts, the unmet need to be funded by HUD is estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD’s methodology underestimated the state’s needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD’s funding appropriation without delays.  

Winner of 2021 EERI Best Graduate Paper Award.

Honorable mention at the 2022 EERI Best Graduate Paper Award.

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 reconstruction 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 article 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 M 6.5, M 7.2, and M 7.9 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.

Following a disaster, residents of a community may be displaced from their damaged homes, leading to expensive and lengthy disruption, with many choosing to move away permanently. Population losses may hinder recovery and exacerbate inequalities across neigh- borhoods. This study considered household place attachment and identified groups with low place attachment along with expensive and slow postdisaster recovery. We developed a framework to integrate place attachment considerations into housing recovery simulations. We used data from the American Housing Survey to develop housing and neighborhood satisfaction models and identify the neighborhoods with the least-attached residents. A computational simulation framework was used to simulate postearthquake housing recovery for a community and assess expected costs and time frames. We used the triad of low place attachment, high cost, and slow recovery to identify households prone to permanently moving away from their communities. A case study of housing recovery after a hypothetical earthquake near San Francisco dem- onstrated the application of the methodology. We found that about 10% of the population in some neighborhoods are prone to moving away after a large earthquake. Households with low income, renters, and those in older buildings are most likely to have low place attachment and ex- perience costly and slow recovery. Whereas existing approaches rely on heuristics, the approach and results in this paper provide quantitative means to assess potential population losses and inform efforts to reduce them. The framework to integrate place attachment into housing recovery simulations is versatile and employs publicly available information making it transferable to other communities.