TY - JOUR
T1 - LASSO Model of Postdisaster Housing Recovery
T2 - Case Study of Hurricane Sandy
AU - Nejat, Ali
AU - Ghosh, Souparno
N1 - Funding Information:
The authors express their appreciation to the National Science Foundation (NSF) for their support of this research under grant number CMMI 1313946.
Publisher Copyright:
© 2016 American Society of Civil Engineers.
PY - 2016/8/1
Y1 - 2016/8/1
N2 - Understanding the recovery phase of the disaster cycle is still in its infancy. A thorough grasp of recovery can lead to effective postdisaster planning, which is essential for enabling communities to recover quickly from natural and human-made catastrophes. Recent major disasters such as Hurricanes Sandy and Katrina have revealed the inability of existing policies and planning to promptly restore infrastructure, residential properties, and commercial activities in affected communities. While many studies examined the macroeconomic effects of certain policies on overall recovery, very few, if any, have focused on modeling the housing recovery decisions of households and their contributions to the community's recovery. The reestablishment of housing is a crucial parameter in understanding recovery processes because it has a ripple effect on the overall timing of recovery. The objective of this study is to create a model capable of predicting postdisaster housing recovery decisions founded on the data that was collected in the aftermath of Hurricane Sandy. The collected data encompassed a wide range of internal and external attributes, including demographic, socioeconomic, exposure parameters, and the effect of external signals such as public policies and spatial activities that can affect household's housing recovery decisions. Based on these variables, 23 predictors were chosen. A statistical analysis was performed using least absolute shrinkage and selection operator (LASSO), and the results highlighted the significance of insurance reimbursements, tenure, and financial assistance from federal, state, and local organizations. Furthermore, the analysis revealed the marginal significance of three other predictors including habitability of residence posthurricane, accessibility to families, and marital status on housing recovery decisions to either rebuild, repair, or relocate. Finally, the scalability of the model to predict housing recovery decisions is presented through a case study of the Midland Beach and New Dorp Beach neighborhoods.
AB - Understanding the recovery phase of the disaster cycle is still in its infancy. A thorough grasp of recovery can lead to effective postdisaster planning, which is essential for enabling communities to recover quickly from natural and human-made catastrophes. Recent major disasters such as Hurricanes Sandy and Katrina have revealed the inability of existing policies and planning to promptly restore infrastructure, residential properties, and commercial activities in affected communities. While many studies examined the macroeconomic effects of certain policies on overall recovery, very few, if any, have focused on modeling the housing recovery decisions of households and their contributions to the community's recovery. The reestablishment of housing is a crucial parameter in understanding recovery processes because it has a ripple effect on the overall timing of recovery. The objective of this study is to create a model capable of predicting postdisaster housing recovery decisions founded on the data that was collected in the aftermath of Hurricane Sandy. The collected data encompassed a wide range of internal and external attributes, including demographic, socioeconomic, exposure parameters, and the effect of external signals such as public policies and spatial activities that can affect household's housing recovery decisions. Based on these variables, 23 predictors were chosen. A statistical analysis was performed using least absolute shrinkage and selection operator (LASSO), and the results highlighted the significance of insurance reimbursements, tenure, and financial assistance from federal, state, and local organizations. Furthermore, the analysis revealed the marginal significance of three other predictors including habitability of residence posthurricane, accessibility to families, and marital status on housing recovery decisions to either rebuild, repair, or relocate. Finally, the scalability of the model to predict housing recovery decisions is presented through a case study of the Midland Beach and New Dorp Beach neighborhoods.
KW - Housing recovery
KW - Hurricane Sandy
KW - Least absolute shrinkage and selection operator (LASSO) method
UR - http://www.scopus.com/inward/record.url?scp=84978771917&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)NH.1527-6996.0000223
DO - 10.1061/(ASCE)NH.1527-6996.0000223
M3 - Article
AN - SCOPUS:84978771917
VL - 17
JO - Natural Hazards Review
JF - Natural Hazards Review
SN - 1527-6988
IS - 3
M1 - 4016007
ER -