Research library for resilient housing.

It is estimated that around 1/3rd of the US population lives in unincorporated areas that lie outside of municipal boundaries. Considering the substantial demographic segment and the increasing incidence of disasters, it is important to understand how unincorporated communities plan for, respond to, and recover from disasters; however, limited scholarly attention has addressed this topic with coverage focusing on singular forms of unincorporated communities, such as colonias and AIAN communities, and no coverage of unincorporated communities generally. A more comprehensive understanding of the vulnerability, exposure, risk, and resilience of unincorporated communities to disasters could allow addressing how these populations can better prepare for, respond to, and recover from disasters. This systematic review intends to explore the key problems, solutions, and challenges faced by these communities during different stages of disaster. The paper concludes with recommendations for how unincorporated communities can increase resilience and capacity when faced with disasters.

The Community Development Block Grant–Disaster Recovery (CDBG-DR) program is the primary federal mechanism for financing long-term housing recovery, yet expenditures often lag years behind events. This study examines how administrative processes influenced implementation following the 2017 disasters in Texas, Florida, and California. Drawing on state Action Plans and longitudinal data, the findings reveal that while repeated amendments allowed for adaptation, they contributed to delayed program rollout. The analysis shows that implementation efficiency varied significantly; Texas and Florida achieved closer obligation–expenditure alignment through incremental adjustments, whereas California faced persistent delays driven by prolonged front-end planning. The results suggest that delays in CDBG-DR assistance are systemic, highlighting a need for standardized early guidance and streamlined planning to expedite assistance to disaster-affected households.

The rise in global disasters has highlighted the need for innovative and resilient housing solutions that can withstand and recover from catastrophic events. Conventional construction methods are often time-consuming, labor-intensive, costly, and ineffective in providing adequate resilient housing able to withstand disasters and/or recover from them. 3D printing technology (3DPT) offers a promising solution for disaster-resilient housing. However, comprehensive knowledge of their utilization in this specific area is lacking. This systematic review explores the utilization of 3DPT specifically towards disaster-resilient housing, encompassing both pre-and post-disaster scenarios through retrofitting and recovery applications. In pre-and post-disaster applications, 3DPT offers promising solutions by streamlining construction processes, reducing waste, and enabling rapid customization of housing solutions. The review identifies critical barriers in pre and post-disaster housing and highlights the transformative potential of 3DPT in revolutionizing the construction industry. Through an analysis of the literature, it becomes evident that 3DPT presents opportunities to address significant challenges often faced by conventional construction methods. The review also conducts a t-distributed stochastic neighbor embedding (t-SNE) to help with visualizing the emerged clusters of the gathered reports using Gaussian Mixture Models, topic modeling using latent Dirichlet allocation, and a SWOT analysis, which reveals strengths such as customizability and time efficiency while acknowledging weaknesses like high initial investment and material restrictions. Recommendations for future research include standardization and code development, material innovation, community engagement, long-term performance evaluation, and policy and governance considerations. By addressing these research gaps, 3DPT can maximize their potential to provide sustainable, cost-effective, and resilient housing solutions for communities worldwide.

Reliable flood loss models can support rapid mitigation and recovery decisions, but their value depends on whether relationships learned from one disaster are useful in another. We evaluate tract-level National Flood Insurance Program (NFIP)-insured housing losses across 6065 census tracts affected by the 2016 Tax Day Flood, Hurricane Harvey, and Hurricane Irma. Mean normalized loss ratio (Mean_NLR) and the probability of observed NFIP-insured loss are modeled with a parsimonious hazard-exposure-vulnerability (HEV) specification driven by precipitation, Special Flood Hazard Area (SFHA) share, insurance penetration, population density, social vulnerability, building age, and Community Rating System discounts. We used nested grouped spatial cross-validation to tune Random Forest, XGBoost, and logistic classification models while retaining OLS and spatial lag models as benchmarks. Tuned regression performance is modest, with out-of-fold peaking at 0.34 for Harvey and lower values for Tax Day and Irma. Binary classification is stronger, with within-event AUC of 0.81–0.90 and Harvey PR-AUC up to 0.71. Transfer is high between the two Texas events, with XGBoost AUC of 0.92–0.95. Although population density and precipitation are consistently influential, AUC declines when Texas-trained models are applied to Irma because hazard mechanisms, exposure patterns, and predictor distributions differ across regions. Because the NFIP claims used to train these models capture insured and claimed building losses with capped payments, the models are appropriate for screening observed NFIP-insured losses. These models should not be used to estimate total physical damage or as universal flood loss transfer functions.

As climate change increases the frequency and severity of disasters, proactive planning for post-disaster housing recovery is essential to mitigate long-term social and economic disruption. Computational models can support this planning by simulating potential recovery trajectories, yet many existing approaches are limited by overwhelming data requirements or narrow applicability to past events. Here, we introduce RAAbIT (Recovery Assessment using Agent-based Tools), a novel agent-based model designed to simulate housing recovery using data available within weeks of a disaster. RAAbIT models individual households, insurers, and contractors as agents governed by empirical behavior rules, and incorporates modifiable system-level constraints, such as contractor availability, to reflect context-specific recovery dynamics. We demonstrate the model’s utility by hindcasting two California wildfires–the 2017 Tubbs Fire in Santa Rosa and the 2018 Camp Fire in Paradise–and capturing their divergent recovery trajectories. Despite similar hazards, the two communities experienced significantly different reconstruction outcomes, with Santa Rosa rebuilding 57% of destroyed homes and Paradise only 9% within five years. RAAbIT can reproduce temporal and spatial patterns of recovery observed in building permit and construction data. By balancing generalizability with data realism, RAAbIT provides a flexible and transferable tool for post-disaster recovery planning, supporting more effective decision-making under uncertainty and enhancing community resilience in the face of escalating climate risks.