A spatially explicit model of postdisaster housing recovery

Abstract

Although post-disaster housing recovery is an important player in community recovery, its modeling is still in its infancy. This research aims to provide a spatial regression model for predicting households' recovery decisions based on publicly available data. For this purpose, a hierarchical Bayesian geostatistical model with random spatial effects was developed. To calibrate the model, households' data that were collected from Staten Island, New York in the aftermath of Hurricane Sandy were used. The model revealed that on the scale of census tract, residents with higher income or larger household size were significantly less likely to reconstruct. In contrast, odds of reconstruction rose with increase of long-term residents. The model outputs were also employed to develop a reconstruction propensity score for each census tract. The score predicts probability of reconstruction/repair in each tract versus others. The model was validated through comparison of the propensity scores with the distribution of Community Development Block Grant Disaster Recovery (CDBG-DR) assistance and its resultant reconstruction. The validation indicated capability of the model to predict the potential hotspots of reconstruction. Accordingly, the propensity score can serve as a decision-support tool to tailor recovery policies.

Computational models of community resilience

“…Risk analysis simulations to advance the execution and efficiency decision making before and after disastrous events (Salgado et al, 2016), fragility curves in quantifying structural damage after a natural disaster (Kammouh et al, 2018a), and the implementation of performance-based earthquake engineering frameworks to model the post-earthquake recovery of a community of residential houses (Burton et al, 2015) have all contributed into finding a solution for bettering our communities' resilience. Nejat et al (2019) used a spatial regression model to predict households' recovery decisions from data available of the aftermath of Hurricane Sandy.…”

Perceived neighborhood: Preferences versus actualities

“…Housing recovery is affected by many drivers and requires a collective effort to be understood [36]. These drivers can be broadly categorized into three groups: internal, interactive, and external drivers [37]. Internal drivers are individual and household-level factors that influence recovery, such as demographic and socioeconomic attributes, experience of past disasters, and level of damage.…”

Empirical study of housing recovery and property abandonment following valley fire in California: insights from neighborhood characteristics and building attributes

“…The study also suggested that resource scarcity should be accounted in simulations to reflect the real situations during housing recovery. Nejat et al (2020) developed a regression model focusing on the factors affecting household' recovery decisions. Authors divided drivers of housing recovery into three categories of internal (i.e.…”