Modeling post-disaster permanent housing reconstruction outcomes in the U.S. using resourcing factors
Date
2020
Authors
Pradhan, Srijesh, author
Arneson, Erin, advisor
Vasquez, Rodolfo Valdes, advisor
Mahmoud, Hussam N., committee member
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Abstract
The residential housing stock in the U.S. is vulnerable to the rising frequency of weather-related hazards, exemplified by economic losses and social disruptions caused by recent billion-dollar events. Reconstruction of damaged residential housing is essential for the swift recovery and long-term resilience of communities. However, recovery is often delayed, and the outcomes are not uniform across disaster-affected regions of the U.S. which may be attributable to unequal access to reconstruction resources. Permanent housing reconstruction in the U.S. adopts a market-driven resourcing approach which is dependent on the availability of construction and capital resources. The availability of construction resources is determined by the capacity of the regional construction market to supply labor and material resources while the availability of capital resources is determined by the socioeconomic characteristics of households and the availability of federal grants for home repairs. Under a market-driven model, the socioeconomic characteristics of households, construction industry, and the federal government constitute three core resourcing forces, composed of various resourcing factors, that influence the availability and accessibility of capital and construction resources. Although the availability of resources is crucial for reconstruction, very few studies have quantitatively examined the influence of resourcing factors on residential reconstruction outcomes at a regional scale. As geographic regions of the U.S. vary in their socioeconomic conditions and construction capacity to supply resources, the influence of resourcing factors on reconstruction outcomes may also show regional variation. However, very few studies have explored the spatially varying influence of resourcing factors on reconstruction outcomes across disaster-affected regions. Using both aspatial and spatial statistical approaches, this study performs a quantitative analysis of post-disaster permanent housing reconstruction outcomes from the lens of resource availability and accessibility. Using Ordinary Least Square regression (OLS) and Geographically Weighted Regression (GWR) models, this study seeks to: (1) quantify the global relationships between socioeconomic, construction industry, and federal government resourcing factors and post-disaster permanent housing reconstruction outcomes at a regional scale in the U.S.; and (2) explore the spatially varying local relationships between resourcing factors and reconstruction outcomes. Over 600 counties hit by federally declared weather-related hazards, with substantial residential losses, between 2007-2015 are analyzed to establish the global relationships between resourcing factors and reconstruction outcomes. The Northeast Census Region of the U.S., hit by catastrophic weather-related hazards between 2011-2012 with unprecedented residential losses, is used as a case study region to explore the spatial heterogeneity in the relationships between resourcing factors and reconstruction outcomes. Findings from the OLS model reveal that availability of construction and capital resources, measured through socioeconomic and construction industry resourcing factors, significantly influence reconstruction outcomes in disaster-hit counties across the U.S. Findings from the case study of the Northeast Census Region, analyzed through the GWR model, reveal that the relationships between resourcing factors and reconstruction outcomes showed regional variation as a result of region-specific resourcing context. The findings of this study will help emergency planners, policymakers, contractors, homeowners, and reconstruction stakeholders in resource planning, policymaking, and decision-making through the identification of critical resourcing bottlenecks and their spatially varying influence across geographical boundaries.
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Subject
geographically weighted regression
post-disaster reconstruction
resource availability
market-driven reconstruction
disasters
residential housing