An official website of the United States government
Post-disaster housing recovery models increase our understanding of recovery dynamics, vulnerable populations, and how people are affected by the direct losses that disasters create. Past recovery models have focused on single-family owner-occupied housing, while empirical evidence shows that rental units and multi-family housing are disadvantaged in post-disaster recovery. To fill this gap, this article presents an agent-based housing recovery model that includes the four common type–tenure combinations of single- and multi-family owner- and renter-occupied housing. The proposed model accounts for the different recovery processes, emphasizing funding sources available to each type–tenure. The outputs of our model include the timing of financing and recovery at building resolution across a community. We demonstrate the model with a case study of Alameda, California, recovering from a simulated M7.0 earthquake on the Hayward fault. The processes in the model replicate higher non-recovery of multi-family housing than single-family housing, as observed in past disasters, and a heavy reliance of single-family renter-occupied units on Small Business Administration funding, which is expected due to low earthquake insurance penetration. The simulation results indicate that multi-family housing would have the highest portion of unmet need remaining; however, some buildings with unmet needs are anticipated to be able to obtain a large portion of their funding. The remaining portion may be filled using personal financing or may be overcome with downsizing or downgrades. Multi-family housing would also benefit the most from Community Development Block Grants for Disaster Recovery (CDBG-DR). This benefit is a result of modeling the financing sources, that CDBG-DR is available, and that many multi-family buildings do not qualify for other sources. Communities’ allocation of public funding is important for housing recovery. Our model can help inform and compare potential financing policies to allocate public funds.
more »
« less
Housing policies and energy efficiency spillovers in low and moderate income communities
Abstract Housing policies address the human dimensions of increasing urban density, but their energy and sustainability implications are hard to measure due to challenges with siloed civic data. This is especially critical when evaluating policies targeting low- and moderate-income (LMI) households. For example, a major challenge to achieving national energy efficiency goals has been participation by LMI households. Standalone energy efficiency policies, such as information-based programmes and weatherization assistance, tend to attract affluent, informed households or suffer from low participation rates. In this Article, we provide evidence that federal housing policies, specifically community development block grants, accelerate energy efficiency participation from LMI households, including renters and multifamily residents. We conduct record linkage on 5.9M observations of housing programme participation and utility consumption to quantify the hidden benefits of locally administered housing block grants in a typical entitlement community in the US Southeast. We provide long-run evidence across 16,680 properties that housing policies generate 5–11% energy savings as spillover benefits to economically burdened households not conventionally targeted for energy efficiency participation.
more »
« less
- Award ID(s):
- 1945332
- PAR ID:
- 10577950
- Editor(s):
- Contestabile, Monica
- Publisher / Repository:
- Nature Sustainability
- Date Published:
- Journal Name:
- Nature Sustainability
- Volume:
- 7
- Issue:
- 5
- ISSN:
- 2398-9629
- Page Range / eLocation ID:
- 590 to 601
- Subject(s) / Keyword(s):
- housing, energy efficiency, civic data science
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract ObjectiveThe study tests a community- and data-driven approach to homelessness prevention. Federal policies call for efficient and equitable local responses to homelessness. However, the overwhelming demand for limited homeless assistance is challenging without empirically supported decision-making tools and raises questions of whom to serve with scarce resources. Materials and MethodsSystem-wide administrative records capture the delivery of an array of homeless services (prevention, shelter, short-term housing, supportive housing) and whether households reenter the system within 2 years. Counterfactual machine learning identifies which service most likely prevents reentry for each household. Based on community input, predictions are aggregated for subpopulations of interest (race/ethnicity, gender, families, youth, and health conditions) to generate transparent prioritization rules for whom to serve first. Simulations of households entering the system during the study period evaluate whether reallocating services based on prioritization rules compared with services-as-usual. ResultsHomelessness prevention benefited households who could access it, while differential effects exist for homeless households that partially align with community interests. Households with comorbid health conditions avoid homelessness most when provided longer-term supportive housing, and families with children fare best in short-term rentals. No additional differential effects existed for intersectional subgroups. Prioritization rules reduce community-wide homelessness in simulations. Moreover, prioritization mitigated observed reentry disparities for female and unaccompanied youth without excluding Black and families with children. DiscussionLeveraging administrative records with machine learning supplements local decision-making and enables ongoing evaluation of data- and equity-driven homeless services. ConclusionsCommunity- and data-driven prioritization rules more equitably target scarce homeless resources.more » « less
-
Over the last three decades, the growth in housing costs relative to household incomes across cities in the United States has dramatically affected households' housing options. For this study, we apply a logit model to data from the American Housing Survey to provide evidence on how rising house costs affect female-headed households' decisions to move from the current home to another. Estimates reveal that total housing cost is a significant determinant of a female-headed household’s decision to move. We also found that lower-income female-headed households are more likely to move to a new location than higher-income female-headed households. These results support the idea that affordable housing programs should be maintained and expanded to offer some alleviation to the burden of rising housing costs on lower-income female-headed households and other vulnerable groupsmore » « less
-
Abstract This study analyzes household energy insecurity in the United States during the first year of the COVID-19 pandemic. Previous research is limited by mostly cross-sectional research designs that do not allow scholars to study the persistency of this specific type of material hardship. We fill this gap by analyzing data from an original, nationally-representative, panel survey of low-income households. We find high levels of energy insecurity during the first year of the COVID-19 pandemic, especially during the initial months when the economic dislocation was at its height, and that many low-income households experienced it on multiple occasions during this period. We also identify disparities: households with people of color, very low-income, children aged five years and younger, with someone who relies on an electronic medical device, and those living in deficient housing conditions were more likely to experience energy insecurity. Households with these characteristics were also more likely to suffer from energy insecurity on a persistent basis through the first year of the pandemic.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1038/s41893-024-01314-w
