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By reviewing recent literature, it is noticeable that considerable attention has been given to the relationship between all Atlantic hurricanes and those that make landfall in the USA. However, less research has been done regarding landfall frequency and identifying spatial areas that are statistically more likely to produce landfalling hurricanes. The purpose of this paper is to provide a better prediction method for US landfalling hurricanes.

This work is based on the hypothesis that landfall variations along the US coast can be better explained in terms of hurricane origination points over more susceptible areas on the North Atlantic Ocean. Simulation techniques are used to spatially quantify the landfall probability.

Results indicate the existence of a landfall corridor in the Atlantic Basin, which explains some of the variances observed in the landfall process. Two different hypotheses of climate are examined. A long‐term assumption is based on the historical data from 1940 to 2010. The second assumption is based on the Atlantic Multidecadal Oscillation. Since 1995, we are in a warm phase and we assume that sea surface temperatures remain warmer than the long‐term average over the next several years. Results indicate that the average increase on landfall frequency is about 13 per cent.

This paper is the first paper that introduces the concept of landfall origination corridor. It spatially identifies the differences between long term and warm phase of the atmosphere in terms of US landfall occurrence using hurricane origination points.

The cone of uncertainty (COU) warning graphic has created confusion for people trying to make evacuation and safety decisions. The purpose of this research was to create several alternative tropical cyclone graphics and present them to the public and college students via face‐to‐face surveys and polling.

Surveys depicting hypothetical landfall scenarios were administered in Pensacola and Jacksonville, FL. Respondents ranked five graphics in order of preference, and were encouraged to discuss their rankings. Following this initial field research, the most popular graphic of these five was compared to a graphic resembling the one used by The Australian Bureau of Meteorology. Comments were recorded for respondents favoring or disliking the Australian graphic in two separate analyses. A final graphic emphasizing post‐landfall hazards was also created as a suggestion for future research and evaluated directly against the most popular graphics from field research.

A graphic called the color‐probability‐cone was the most popular graphic in field research. There were subtle differences in graphic preference resulting from age and gender influences, with only one significant result. Comments from subsequent analyses reveal that the Australian graphic causes mixed reactions. A final analysis with a larger sample of college students revealed that the color‐probability‐cone was the most popular choice; however, comments reveal that many respondents who had used hurricane graphics before liked the specificity presented by the Australian graphic and the hazards graphic.

This research represents a possible initial step in the process of establishing a tropical cyclone warning graphic that is informative, visually appealing, and effective.

In general, the insurance industry accepts large risks due to the frequency and severity of extreme events. Because of the short record on hazard data for such events, a large amount of uncertainty has to be dealt with. Given this large uncertainty it is important to better quantify the hazard parameters that are defined as inputs to the catastrophe models. The purpose of this paper is to evaluate the hurricane risk from loss point of view in the USA for both long‐term and warm phase conditions using a simulation‐based stochastic model.

A Poisson process is used to simulate the occurrence of events for both conditions. The generated event‐sets were used along with vulnerability and cost models to estimate the loss to an insurance industry portfolio. The paper discusses the statistics of events categorized by the Saffir‐Simpson Hurricane Wind Scale, annualized and return period losses and compares the results for both assumed long‐term and warm phase climate states.

The analysis shows that the population of landfall data for the two climate conditions is not statistically different. However, if we accept that a difference in the frequency of landfall occurrence between the two assumptions exists, the increase in average annual loss is about 17 per cent.

This paper provides insights to the difference between the two states of atmosphere from the point of view of insured losses for hurricanes and is one of the first papers that offers conclusion on the uncertainty associated with the warm phase data.

This is a selective annotated bibliography of the literature on Christopher Columbus from 1970 to 1989. The subject is particularly relevant considering the approach of the Quincentenary of the “discovery” of America in 1992. For that same reason, there has been an outpouring of literature on the subject since 1990, a significant subset of which contributes to are interpretation of Columbus the man, his voyages, and their impact on the new world. It is hoped that this more recent literature will be part of a subsequent annotated bibliography.

The purpose of this study is to examine how the lodging market and the state economy affected by Hurricane Sandy have recovered from the damages sustained. Specifically, this study examines and predicts the influence of revenue management key performance indicators (KPIs) on recovery and lodging revenue in the affected states and the states’ economies. These KPIs include average daily rate (ADR), occupancy and revenue per available room (RevPAR).

Secondary financial data were collected for the states most damaged by Hurricane Sandy. Subsequently, pooled Ordinary Least Square (OLS) regression was conducted combining time and non-time dependent variables based on the states and radius from the landfall.

The results indicate that although the lodging market and the state economies have recovered since the onslaught of Hurricane Sandy, certain KPIs still need to improve.

Managerial implications are suggested in terms of dynamic pricing, market-based recovery, the KPIs, federal aid and facility management.

Despite its importance, research on the effects of climate change in the hospitality context has not actively progressed after Hurricane Katrina. Time and non-time dependent variables are combined in this analysis to gain a richer understanding of the impacts and recovery of KPIs on the revenue in the lodging market and the revenue on states’ economies. Additional analysis based on the radius from the landfall of the hurricane was performed to examine the impact and recovery based on geographical proximity.

Purpose — A new method of collecting hurricane evacuation data using time-dependent stated choice is developed and evaluated in this study.

Methodology/approach — Hypothetical storms are presented in a video in a sequence of scenarios showing prevailing conditions at discrete points in time as each storm approaches land. Respondents are exposed to nine hypothetical storms representing a range of hurricane characteristics. One of the hypothetical storms is secretly the same as an actual storm the respondents experienced in the past and for which they are required to report their behaviour in a revealed preference survey.

Findings — Stated and actual behaviour was compared and general agreement was found between what people say they would do and what they did. The revealed preference (RP) data was supplemented with time-dependent data from official sources and hurricane evacuation demand models estimated on this enhanced RP data, as well as on a combination of the enhanced RP and time-dependent stated choice (SC) data. When the models were applied to a different data set than the ones on which the models were calibrated, the combined time-dependent RP/SC model performed slightly better than the enhanced RP model. Detailed accounting revealed that time-dependent SC data is 25 percent more expensive to collect than enhanced RP data, although some of this cost may be due to the first-time collection of this type of data.

This study aims to examine the linkage between bankruptcy filings and hurricane events. Several independent variables related to local district court bankruptcy filings are examined. The primary question posed is whether Category 3,4 and 5 hurricanes result in personal bankruptcy filings due to the real property and other damage that ensures.

Landfall hurricanes in Florida from 2001 through 2018 were examined by using the fully modified least square regression model. Descriptive statistics include elasticity measures that show statistics prior and post the passage of the Bankruptcy Abuse and Prevent and Consumer Protection Act of 2005 (BAPCPA).

The elasticity of housing prices was a useful statistic in explaining bankruptcy filings. Regression results indicate that bankruptcy filing occur within one year of a serious hurricane. The regression model found hurricane events and housing price trends were significant variable when predicting district court bankruptcy filings.

BAPCPA targets fraud under Chapter 7 bankruptcy filings. Unfortunately, this also had the unintended consequence of discouraging legitimated filings due to the lowering of the marginal benefit associated with filing when the “means test” is applied.

Lack of flood insurance coverage and stagnant real estate prices could limit the desirability of filing under Chapter 13 resulting in an inventory of damaged properties being foreclosed.

Prior researchers relied on a descriptive approach by using percentage rates to quantify the association between hurricane damage and bankruptcy filings. By using the fully modified regression-based approach, the study herein establishes that filings occur approximately a year after the household experiences the real property loss and identifies other casual factors that influence the decision to file.

The purpose of this paper is to contrast the behavior of a US homeowner exposed to hurricane risk with government policies designed to limit hurricane losses. Owners limit these losses by selecting structural improvements or mitigation and wind and flood insurance.

The paper uses mitigation costs, hurricane probabilities, and insurance premiums to frame rational cost‐minimizing choices for the homeowner.

First, even though nationwide hurricane damage costs are large, the cost‐minimizing response for an individual property owner may be to buy no mitigation or structural improvements, no flood insurance and minimal wind insurance, as probabilities of strong hurricanes striking particular locations are extremely low. Second, additional insurance is a less costly defense than structural improvement, even under much higher insurance premiums and hurricane strike probabilities. Third, federally subsidized flood insurance may reduce the effectiveness of government programs encouraging structural mitigation.

The last few years were underscored by the catastrophic damages of Hurricanes‐Katrina, Ike and Wilma. Enormous costs suffered by the public and private sectors could have been avoided with greater mitigation by homeowners. This paper examines the financial incentives for such mitigation. Those incentives are examined in a previously untested framework.

Changing climate dynamics have resulted in a confluence of disaster events to which Louisiana government leaders and emergency managers have never before had to respond simultaneously: a global pandemic and an “epidemic” of landfalling hurricanes during the 2020 season (eight cones over Louisiana) with challenging, unusual characteristics: (1) two hurricanes passing over the same location within 36 hours, a fujiwhara – Hurricanes Marco and Laura, (2) 150 mile-per-hour winds inadequately forecasted and of an almost unprecedented speed, (3) a difficult to forecast surge magnitude that led to incorrect immediate response, (4) delayed long-term recovery efforts from responders outside of the area because of initial reporting errors regarding surge heights and wind speed, and (5) a storm, Zeta, that passed directly over a densely populated area that would have been hard hit by rain if the storm had slowed. In addition, the number and closeness in dates of storm occurrences led to lengthy coastal high-water levels. To these co-occurring threats forecasters, state and local officials and residents responded with expertise and commitment, adhering to close collaboration, modifying evacuations and undertaking protective measures, all contributing to a low death rate from storms and a modest death rate from COVID. More just outcomes were supported by the general capacity of the responders, commitment to keep the residents informed about both risks and appropriate responses to them and the provision of special services, calculated for the new situation of the pandemic and the storm epidemic, for those without the means to respond adequately to both.

The simulated scenarios can be used to compute risk premiums per risk class in the portfolio. These can then be used to adjust the policy premiums by accounting for storm risk.

A complete model to analyse and characterise future losses of the property portfolio of an insurance company due to hurricanes is proposed. The model is calibrated by using the loss data of the Fidelidade insurance company property portfolio resulting from Hurricane Leslie, which hit the centre of continental Portugal in October, 2018.

Several scenarios are simulated and risk maps are constructed. The risk map of the company depends on its portfolio, especially its exposure, and provides a Hurricane risk management tool for the insurance company.

A statistical model is considered, in which weather data is not required. The authors reconstruct the behaviour of storms through the registered claims and respective losses.