Suchergebnisse

The floods caused by Hurricane Matthew in 2016 affected Lumberton, a socioeconomically diverse city in North Carolina with 729 public housing units. Public housing residents face unique challenges in accessing resources and post-disaster temporary accommodations, further delaying their recovery compared to other survivors. This paper investigates the obstacles to public housing recovery and the residents' recovery challenges using descriptive statistics, mapping, and qualitative analysis in Lumberton. Findings show the dependency of public housing units' recovery on assistance policies and decisions of various organizations, including local housing authorities. Multiple changes in recovery plans and limited, uncertain, delayed funding and bureaucratic obstacles to funding allocation slow the units' recovery and prolong the residents' displacement, adversely affecting their recovery. Hence, pre-disaster resilience initiatives should address these vulnerabilities and the recovery policy's limitations to support public housing units and residents' recovery. Moreover, affordable housing recovery must become a priority in national housing recovery policies.

AbstractHousing recovery is an unequal and complex process presumed to occur in four stages: emergency shelter, temporary shelter, temporary housing, and permanent housing. This work questions the four‐stage typology and examines how different types of shelter align with multiple housing recovery stages given different levels of social vulnerability. This article also presents a Markov chain model of the postdisaster housing recovery process that focuses on the experience of the household. The model predicts the sequence and timing of a household going through housing recovery, capturing households that end in either permanent housing or a fifth possible stage of failure. The probability of a household transitioning through the stages is computed using a transition probability matrix (TPM). The TPM is assembled using proposed transition probability models that vary with the social vulnerability of the household. Monte Carlo techniques are applied to demonstrate the range of sequences and timing that households experience going through the housing recovery process. A set of computational rules are established for sending a household to the fifth stage, representing a household languishing in unstable housing. This predictive model is exemplified on a virtual community, Centerville, where following a severe earthquake scenario, differences in housing recovery times exceed four years. The Centerville analysis results in nearly 5% of households languishing in unstable housing, thereby failing to reach housing recovery. These findings highlight the disparate trajectories experienced by households with different levels of social vulnerability. Recommendations are provided at the end for more equitable postdisaster recovery policies.

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