Hurricane Loss Research Articles

Long term versus warm phase, part II: hurricane loss analysis

PurposeIn 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.Design/methodology/approachA 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.FindingsThe 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.Originality/valueThis 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.

The Unsustainable Trend of Natural Hazard Losses in the United States

In the United States, direct losses from natural hazards are on the rise with hurricanes, flooding, and severe storms contributing about three quarters of the total damages. While losses from severe storms have been stable over the past fifty years, hurricane and flood losses have tripled. Per capita losses are also increasing showing that impacts outpace population growth with high per capita losses occurring largely in the Southeast and Midwest. If the loss escalation of the past two decades continues into the future, then direct losses of $300 to $400 billion within a single decade are possible. In order to reverse this trend, sustainable development, vulnerability reduction, and hazard mitigation must become priorities and current loss reduction efforts need to be evaluated and re-assessed in terms of their effectiveness. These conclusions are drawn from the analysis of spatial and temporal trends in direct losses from natural hazards using SHELDUSTM data from 1960 through 2009. Loss data are adjusted for inflation, population, and wealth to capture both trends in total losses and per capita losses. The loss data are then compared to disaster-related federal government and private insurance expenditures.

Hurricane risk and coastal property owner choices

PurposeThe 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.Design/methodology/approachThe paper uses mitigation costs, hurricane probabilities, and insurance premiums to frame rational cost‐minimizing choices for the homeowner.FindingsFirst, 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.Originality/valueThe 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.

Damage Characterization: Application to Florida Public Hurricane Loss Model

Florida’s population growth in recent decades has produced a steady increase in the concentration of population and infrastructure along its coastline. During this growth period, the vulnerability of the Florida coastline to hurricane impact has been amply demonstrated. The aftermaths unveiled the underlying potential for dramatic economic loss, which is disruptive for the state economy and has created a crisis in the property insurance market. To aid in preparedness efforts, the state of Florida commissioned a group of experts to develop the Florida Public Hurricane Loss Model (FPHLM). This paper describes how hurricane damage prediction techniques were developed and implemented in the vulnerability module of the FPHLM. The module consists of an external damage simulation engine coupled with internal damage and contents damage simulation engines. These components are described in this paper.

HOW SENSITIVE ARE US HURRICANE DAMAGES TO CLIMATE? COMMENT ON A PAPER BY W. D. NORDHAUS

An article by William D. Nordhaus (2010) in this journal examined the economic impacts by hurricanes in the USA, and the potential impacts of climate change on future hurricane damages. His analyses show that hurricane damages normalized over time for changes in GDP have increased significantly since the year 1900, at a rate of about 3.1% per year. Moreover, the results of Nordhaus show that hurricane damages increase with the ninth power of maximum wind speed, which is considerably higher than findings of other studies. We perform similar statistical analyses with different data series of hurricane damage, which are more accurately corrected for changes in exposure of assets over time. Our results do not indicate an upward trend in hurricane losses since 1900, which is in line with earlier studies, and indicates that climate change has not increased hurricane damage in the past. Moreover, although the relation between damage and maximum wind speed appears to be considerably higher than assumed by other studies, this elasticity is more likely to be the eighth-power of maximum wind speed. This finding is relevant since it implies that future climate change impacts on hurricane damage may be considerably lower than Nordhaus indicates.

Hurricane Wind Damage Mitigation: Research and Outlook

The U.S. coast from Maine to Texas is vulnerable to hurricane impacts. Hurricane-induced economic losses have steadily increased in recent decades. At the same time, trillions of dollars have been invested in coastal development to accommodate the increasing population; therefore, the socioeconomic impacts of hurricane landfalls will also escalate. The state of Florida has sustained massive hurricane losses in recent years. Hurricane Wilma, barely a Category 2 hurricane in 2005, amounted to $16 billion in damages in South Florida. These tremendous losses have resulted in unprecedented insurance payouts. The answer to Florida’s hurricane crisis is to strengthen homes and businesses through retrofitting and improved new construction. This requires full-scale, destructive building testing to gain a better understanding of hurricane-induced, wind-rain effects on buildings and structures. Scientific understanding of hurricane effects will lead to innovative design technologies to mitigate hurricane damages. Th...

Climate and solar signals in property damage losses from hurricanes affecting the United States

The authors show that historical property damage losses from US hurricanes contain climate signals. The methodology is based on a statistical model that combines a specification for the number of loss events with a specification for the amount of loss per event. Separate models are developed for annual and extreme losses. A Markov chain Monte Carlo procedure is used to generate posterior samples from the models. Results indicate the chance of at least one loss event increases when the springtime north–south surface pressure gradient over the North Atlantic is weaker than normal, the Atlantic ocean is warmer than normal, El Nino is absent, and sunspots are few. However, given at least one loss event, the magnitude of the loss per annum is related only to ocean temperature. The 50-year return level for a loss event is largest under a scenario featuring a warm Atlantic Ocean, a weak North Atlantic surface pressure gradient, El Nino, and few sunspots. The work provides a framework for anticipating hurricane losses on seasonal and multi-year time scales.

Hurricane Damage and Loss Estimation Using an Integrated Vulnerability Model

An integrated vulnerability model is presented to estimate structural damage and related economic losses in clusters of residential buildings resulting from hurricane winds. The model accounts for the occurrence of a possible “chain reaction” of events involving wind-pressure damage and wind-borne debris damage, amplifying overall losses. Different approximate damage and loss prediction methods are compared, and three numerical examples are provided.

Discussion on “Public Hurricane Loss Evaluation Models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Discussion on “Public Hurricane Loss Evaluation Models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Discussion on “Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model” by S. Hamid et al.

Discussion on “Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model” by S. Hamid et al.

Discussion of “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Models” by S. Hamid, B.M.G. Kibria, S. Gulati, M. Powell, B. Annane, S. Cocke, J.-P. Pinelli and S.-C. Chen

Discussion of “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Models” by S. Hamid, B.M.G. Kibria, S. Gulati, M. Powell, B. Annane, S. Cocke, J.-P. Pinelli and S.-C. Chen

Discussion on “Public hurricane loss evaluation models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Discussion on “Public hurricane loss evaluation models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Discussion on “Public Hurricane Loss Evaluation Models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Discussion on “Public Hurricane Loss Evaluation Models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Discussion on “Public hurricane loss evaluation models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Discussion on “Public hurricane loss evaluation models: Predicting losses of residential structures in the state of Florida” by S. Hamid et al.

Authors’ responses to the discussion on “Predicting losses of residential structures in the state of Florida by the Public Hurricane Loss Evaluation Model”

Hurricanes threaten the Florida coast line every year and are capable of causing catastrophic losses. The Public Hurricane Loss Model was developed in response to the need for having an open transparent model to predict the above losses. The results were summarized in the paper “Predicting Losses of Residential Structures in the State of Florida by the Public Hurricane Loss Model” which was subsequently a subject of discussion by experts in Meteorology, Engineering, Actuarial Sciences and Statistics. The present paper presents a response to the discussions on the above mentioned article.

Social Vulnerability and Hurricane Impact Modeling

Numerical hurricane loss and impact prediction models have become widely used as tools to determine property damages. To yield loss estimates, models often combine building stock, economic data, and vulnerability functions in an effort to relate wind and surges to expected damages. Within this framework, vulnerability refers only to damage or economic loss to physical structures. Societal factors influencing building exposure and damage are disregarded although research shows that growing populations, increased wealth, and demographic shifts in high hazard areas are partially responsible for increased loss during the past 25 years. This paper addresses this missing societal component in hurricane loss modeling by examining two questions: (1) to what extent can a quantified measure of social vulnerability be incorporated into numerical hurricane impact modeling to improve loss prediction; and (2) which indicators of social vulnerability explain spatial differences in impacts from the wind and storm surge hazards associated with hurricanes? The social vulnerability index was coupled with a surface wind analysis, a storm surge inundation model, and a damage database along Mississippi's Gulf Coast in the aftermath of Hurricane Katrina. All components were input into a series of multiple regression analyses to explore the relationships between social vulnerability and hurricane impact. A relationship between social parameters, hurricane winds, storm surge inundation, and impact exists at both extensive and catastrophic levels of damage, and results reinforce the conclusions of earlier studies that illustrate the effects of social vulnerability on hurricane impacts.

Predicting losses of residential structures in the state of Florida by the public hurricane loss evaluation model

As an environmental phenomenon, hurricanes cause significant property damage and loss of life in coastal areas almost every year. Although a number of commercial loss projection models have been developed to predict the property losses, only a handful of studies are available in the public domain to predict damage for hurricane prone areas. The state of Florida has developed an open, public model for the purpose of probabilistic assessment of risk to insured residential property associated with wind damage from hurricanes. The model comprises three components; viz. the atmospheric science component, the engineering component and the actuarial science component. The atmospheric component includes modeling the track and intensity life cycle of each simulated hurricane within the Florida threat area. Based on historical hurricane statistics, thousands of storms are simulated allowing determination of the wind risk for all residential Zip Code locations in Florida. The wind risk information is then provided to the engineering and actuarial components to model damage and average annual loss, respectively. The actuarial team finds the county-wise loss and the total loss for the entire state of Florida. The computer team then compiles all information from atmospheric science, engineering and actuarial components, processes all hurricane related data and completes the project. The model was submitted to the Florida Commission on Hurricane Loss Projection Methodology for approval and went through a rigorous review and was revised as per the suggestions of the commission. The final model was approved for use by the insurance companies in Florida by the commission. At every stage of the process, statistical procedures were used to model various parameters and validate the model. This paper presents a brief summary of the main components of the model (meteorology, vulnerability and actuarial) and then focuses on the statistical validation of the same.

Windborne debris risk analysis - Part II. Application to structural vulnerability modeling

The 'chain reaction' effect of the interaction between wind pressure and windborne debris is likely to be a major cause of damage to residential buildings during severe wind events. The current paper (Part II) concerns the quantification of such pressure-debris interaction in an advanced vulnerability model that integrates the debris risk model developed in Part I and a component-based wind-pressure damage model. This vulnerability model may be applied to predict the cumulative wind damage during the passage of particular hurricanes, to estimate annual hurricane losses, or to conduct system reliability analysis for residential developments, with the effect of windborne debris fully considered.

풍하중 산정을 위한 한반도 단독주택의 대표유형 선정

As the damages due to natural disasters continue to increase, a growing interest is being witnessed in such studies that focus on preventive measures to reduce damages rather than on their recovery. As such, the U.S. has been actively conducting projects to develop new models that can forecast potential damages due to natural disasters and widely employing them in actual cases. With no specific models developed in Korea yet, this study aimed to introduce an overseas typhoon model as part of the advanced efforts and apply it the actual cases occurring across the nation. This model estimates wind loads by measuring the impact of a strong wind upon buildings, and measurements require a number of parameters. Those parameters should include the types and dimensions of buildings and the type of the roofs. As for the FPHLM(Florida Public Hurricane Loss Model), a precedent model for our study, we were able to take advantage of number of the statistics and detailed categorizations on the residential buildings in the U.S., which enabled us to select the representative building types and produce their wind loads. With no sufficient relevant statistics available for the nation, however, we may not be able to readily measure the wind loads on the nation's residential buildings. Therefore, this study tried to choose the representative types, heights and dimensions of the buildings for the measurement of wind loads. We consequently came up with a representative house having an area between 62.81 and <TEX>$95.56m^2$</TEX>, either a flat roof or hip roof, a height of 2.6 m, an side ratio of 1.5, and the width and length of the mean <TEX>$85m^2$</TEX> sized house being 11,300 mm and 7,530 mm, respectively.

Pricing index-based catastrophe bonds: Part 1: Formulation and discretization issues using a numerical PDE approach

This work is the first installment in a two-part series, and focuses on the development of a numerical PDE approach to price components of a Bermudan-style callable catastrophe (CAT) bond. The bond is based on two underlying stochastic variables; the PCS index which posts quarterly estimates of industry-wide hurricane losses as well as a single-factor CIR interest rate model for the three-month LIBOR. The aggregate PCS index is analogous to losses claimed under traditional reinsurance in that it is used to specify a reinsurance layer. The proposed CAT bond model contains a Bermudan-style call feature designed to allow the reinsurer to minimize their interest rate risk exposure on making substantial fixed coupon payments using capital from the reinsurance premium. Numerical PDE methods are the fundamental strategy for pricing early-exercise constraints, such as the Bermudan-style call feature, into contingent claim models. Therefore, the objective and unique contribution of this first installment in the two-part series is to develop a formulation and discretization strategy for the proposed CAT bond model utilizing a numerical PDE approach. Object-oriented code design is fundamental to the numerical methods used to aggregate the PCS index, and implement the call feature. Therefore, object-oriented design issues that relate specifically to the development of a numerical PDE approach for the component of the proposed CAT bond model that depends on the PCS index and LIBOR are described here. Formulation, numerical methods and code design issues that relate to aggregating the PCS index and introducing the call option are the subject of the companion paper.