Gulf property damage, housing price trends and US bankruptcy filings

Purpose This paper aims to examine potential causes of bankruptcy as they relate to hurricane damage. Investigate whether hurricanes result in personal bankruptcy filings due to real property damages. Strengthen existing descriptive results by using fully modified ordinary least squares (FMOLS). Design/methodology/approach Lagged FMOLS model is used with data from states that suffered hurricane damage between 2000 through 2020. FMOLS controls for various financial distresses that can cause bankruptcy filings. Findings Bankruptcy is usually filed for within one year of a hurricane. Changes in house prices and hurricane severity were significant indicators of bankruptcy filings. However, the divorce rate, commonly thought of as a primary reason for bankruptcy, is insignificant. Research limitations/implications Data was available on a state level for the independent variables. Hurricane damage needed to be financially significant enough for inland flooding to be measurable and influential. Practical implications Establishes that financial distress comes from several sources, not just home damage. Financial distress is highly correlated with whether a home was insured. Divorce does not cause bankruptcy filings. Social implications Federal flood insurance programs should be reexamined. Having a broader all-risk homeowner policy could reduce the number of households that file for bankruptcy after a hurricane. Originality/value Existing research uses descriptive statistics and obtains mixed findings regarding the association between hurricane damage and bankruptcy filings. The FMOLS approach provides clarity about this association.

A Comprehensive Analysis of Hurricane Damage across the U.S. Gulf and Atlantic Coasts Using Geospatial Big Data

(1) Background: Hurricane events are expected to increase as a consequence of climate change, increasing their intensity and severity. Destructive hurricane activities pose the greatest threat to coastal communities along the U.S. Gulf of Mexico and Atlantic Coasts in the conterminous United States. This study investigated the historical extent of hurricane-related damage, identifying the most at-risk areas of hurricanes using geospatial big data. As a supplement to analysis, this study further examined the overall population trend within the hurricane at-risk zones. (2) Methods: The Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model and the HURRECON model were used to estimate the geographical extent of the storm surge inundation and wind damage of historical hurricanes from 1950 to 2018. The modeled results from every hurricane were then aggregated to a single unified spatial surface to examine the generalized hurricane patterns across the affected coastal counties. Based on this singular spatial boundary coupled with demographic datasets, zonal analysis was applied to explore the historical population at risk. (3) Results: A total of 777 counties were found to comprise the “hurricane-prone coastal counties” that have experienced at least one instance of hurricane damage over the study period. The overall demographic trends within the hurricane-prone coastal counties revealed that the coastal populations are growing at a faster pace than the national average, and this growth puts more people at greater risk of hurricane hazards. (4) Conclusions: This study is the first comprehensive investigation of hurricane vulnerability encompassing the Atlantic and Gulf Coasts stretching from Texas to Maine over a long span of time. The findings from this study can serve as a basis for understanding the exposure of at-risk populations to hurricane-related damage within the coastal counties at a national scale.

Convolutional Neural Network based Hurricane Damage Detection using Satellite Images

Huge swirling storms known as hurricanes are tropical storms appearing in the North Atlantic Ocean and Northeast Pacific that result in winds of 120 km/hour and higher. The winds occurring during hurricanes are catastrophic resulting in immense damage to human life and property. Rapid assessment of damage caused by hurricanes is extremely important for the first responders. But this process is usually slow, expensive, labor intensive and prone to errors. The advancements in remote sensing and computer vision help in observing Earth at a different scale. In this paper, a Convolutional Neural Network model has been designed that assesses the damage caused to buildings of post hurricane satellite images. The images have been classified as Damaged and Undamaged. The model is composed of five convolutional layers, five pooling layers, one flattening layer, one dropout layer and two dense layers. Hurricane Harvey dataset consisting of 23000 images of size 128 X 128 pixels has been used in this paper. The proposed model performed best at learning rate of 0.00001 and 30 epochs with the Adam optimizer obtaining an accuracy of 0.95, precision of 0.97, recall of 0.96 and F1-score of 0.96. It also achieved the best accuracy and minimum loss.

Tree damage, growth and phenology after a hurricane in a tropical dry forest in Veracruz

As a major disturbance, hurricanes affect growth and phenology of trees. Tree diameters were annually measured for three years, and the phenology of 16 tree species monthly recorded in a seasonally dry tropical forest in Veracruz, Mexico, when on September 2010, Hurricane Karl struck the region. One month later, tree damage was recorded and phenological observations resumed for 12 more months, and diameter measurement for two more years. Tree damage due to the hurricane was high: 10 % were uprooted, 7 % broken and 2 % bent. All trees uprooted died, but some broken or bent trunk trees resprouted (15 % of tagged trees died). Overall, mean diameter growth of trees that survive the hurricane (0.79 cm yr-1) was greater than pre-hurricane growth rate (0.68 cm yr-1). For all the studied species together, leaf fall, leafing, and flowering phenology did not differ between pre- and post-hurricane whereas fruiting was lower for the post-hurricane year. At species level, most species displayed differences in reproductive phenology between pre-hurricane and post-hurricane years. Most species did not flower, lower flowering and fruiting (Calyptranthes schiedeana), or did not fruit (Luehea candida, Maclura tinctoria, Tabebuia chrysantha) the year following the hurricane. In conclusion, due to hurricane damage, tree mortality was high but many trees recovered from damage, and that the hurricane negatively influenced intensity in reproductive phenology, and in turn may alter forest structure, shift species composition, and affect the trophic relationships and functioning of the whole forest community.