florida hurricanes and damage costs - tampa bay...hurricanes and damage costs 109 (hurricanes andrew...

southeastern geographer, 49(2) 2009: pp. 108–131

Florida Hurricanes and Damage Costs

JILL MALMSTADTFlorida State University

KELSEY SCHEITLINFlorida State University

JAMES ELSNERFlorida State University

Florida has been visited by some of the most de-structive and devastating hurricanes on record inthe United States causing well over $450 billion indamage since the early 20th century. The value ofinsured property in Florida against windstormdamage is the highest in the nation and on therise. The frequency and severity of hurricanes af-fecting Florida are examined from the best set ofavailable data and the damages are related tocharacteristics of the storms at landfall. Resultsshow that normalized losses are increasing overtime consistent with small increases in hurricaneintensity and hurricane size. The best predictorof potential losses is minimum central pressure.Hurricane size alone or in combination with hur-ricane intensity does not improve on the simplerrelationship. An estimate of potential losses fromhurricanes can be obtained using a formula in-volving only a forecast of the minimum pressureat landfall. The ability to estimate potential lossesin Florida will increase the ability to estimatelosses in other areas of the United States, and willalso allow policy makers and insurance com-panies to provide relevant information to the con-cerned public.

key words: Florida, hurricanes, landfall,insurance, losses, trends, correlation

introduction

The hurricane is an awesome, yet deadlyand destructive natural phenomenon ofthe Earth’s occasionally tumultuous atmo-sphere. A hurricane is powered by the heatand moisture of the tropical oceans ratherthan thermal contrasts across latitudes asis the case for the more common extra-tropical cyclone. The result is a powerfulstorm, causing unprecedented amounts ofdeaths and economic loss. Dollar lossesfrom hurricanes are at the top of the list ofcatastrophic events ahead of tornadoesand terrorism. Not surprisingly, because ofits location relative to the warm waters ofthe North Atlantic (including the Gulf ofMexico and the Caribbean Sea), Florida ismore likely to get hit by a hurricane thanany other state in the union. On average atleast one hurricane strikes Florida everytwo years and a strong hurricane hits Flor-ida on average once every four years (aver-ages come from available data during1900–2007). Eight of the 10 most expen-sive hurricanes ever to make landfall inU.S. history have had at least some affecton Florida, causing in excess of $60 billion(constant 2005 dollars) in insured losses

Hurricanes and Damage Costs 109

(hurricanes Andrew 1992, Charley, Fran-ces, Ivan and Jeanne in 2004, and Katrina,Rita, and Wilma in 2005). For this reason,as well as the devastating impact thesestorms have on human lives, scientists havebeen tackling the issue of hurricanes inorder to further understand their charac-teristics and better predict their impendingimpact on the coastline ahead. This paperwill focus on economic loss in the state ofFlorida, as Florida represents a unique casestudy for hurricane science.

Interest in economic loss from hur-ricanes is not new, as it was discussedthroughout the 1960s (Demsetz 1962;Sugg 1967), but interest has become ele-vated in recent times due to the destruc-tive Atlantic hurricane seasons of 2004and 2005.

The most damage-causing characteris-tics of a hurricane are the high winds,storm surge and large waves, as they eachhave potential for total destruction of prop-erty and livelihoods (Williams and Duedall1997). This potential for damage has re-cently increased due, in part, to a notablerise in global atmospheric temperatures.

The issue of climate change bringsalong a challenge to hurricane research-ers, as attempts are made to try to quan-tify the impact of climate change on thefuture of hurricanes. While many otherfactors play a role in hurricane develop-ment and intensity, the increasing sea sur-face temperatures associated with climatechange provide an obvious increase of fuelfor these storms and a heightened causefor alarm. Elsner et al. (2008) found anincreasing trend in the strength of thestrongest hurricanes, especially the 90th

percentile, meaning, in short, the strong-est storms are getting stronger. Emanuel(2005) uses the observed increase in sea

surface temperature to explain the in-crease in power dissipation within the av-erage North Atlantic hurricane and foundthat tropical cyclones have become moredestructive within the last 30 years. Thecombination of increasing storm strengthand coastal develop ment should yield in-creasing economic loss due to hurricanes.Changnon (2003) believes that coastal de-velopment is the main reason for recentincreasing economic loss, as the increasein losses throughout the 1990s occurredwhen hurricane frequencies decreased;the data showing no shift due to globalwarming. That is not to say that weatherextremes do not cause notable increases ineconomic loss, as the active weather of1991–1994 and associated hurricanes, se-vere storms, floods, tornadoes, etc. causedmore economic loss due to weather eventsthan any other four-year period. Yet still,the largest increases occurred in areas withthe greatest population growth (Chang-non 1997).

Florida, like most of the coastal UnitedStates, has seen a building boom, and theincreasing population and wealth is forc-ing insurers and re-insurers to rethinktheir exposures. According to the U.S.Census Bureau, Florida has the highestpopulation growth among states affectedby hurricanes and is expected to gainabout 13 million residents between 2000and 2030. The Citizens Property InsuranceCorporation in Florida (aka, Citizens), setup by the State of Florida in 2002 to be theproperty insurer of last resort, is now thelargest provider of property insurance inthe state. Florida homeowners can buycoverage from Citizens if the rates for acomparable policy from a private insurerexceed by 15 percent Citizens’ rates.

According to the Insurance Informa-

110 jill malmstadt, kelsey scheitlin, and james elsner

tion Institute (Hartwig 2008), the value ofinsured coastal property in Florida ranksfirst in the nation and, as of 2007, exceeds$2 trillion with about 60 percent in com-mercial exposure and the rest in residen-tial exposure (AIR 2005). Florida home-owner insurers’ underwriting losses in2004 ($9.3 billion) and 2005 ($3.8 bil-lion) resulted in a four-year cumulativeloss of $6.7 billion, even after includingthe profitable years of 2006 ($3 billion)and 2007 ($3.4 billion), when there wereno hurricanes (Hartwig 2008). Since 1992,Florida insurers have experienced a netloss of $6.2 billion (Hartwig 2008).

Citizens’ total exposure to loss is highand growing, increasing from $154.6 bil-lion in 2002 to $434.3 billion during thefirst quarter of 2007 (Insurance Informa-tion Institute 2007). The number of pol-icies written by Citizens is also on the risewith the total reaching 1.35 million as ofJuly 31, 2007. If losses by Citizens exceedits claim-paying capacity in a single sea-son, the state is required to impose an as-sessment on other lines of insurance, in-cluding policies not written by Citizens.Loss assessments (collected from all in-sured property owners from the entirestate), general revenue appropriations,and the reinsurance market can be aug-mented with the issuance of catastrophebonds. Catastrophe bonds help alleviatethe risk of a catastrophic event by transfer-ring some of that risk to investors. In Julyof 2007, Citizens floated a catastrophebond worth nearly $1 billion, and in Julyof 2008, Berkshire Hathaway, Inc. agreedto buy $4 billion in bonds if Citizens incursat least $25 billion in losses. The state hasestimated the probability of this level ofdamage occurring annually in the state ofFlorida to be about 3.1 percent per year. In

2008, in exchange for taking on this risk,Florida will pay Berkshire Hathaway, Inc.$224 million for a guarantee that the statewill receive up to $4 billion if the damagethreshold is reached (Kaczor 2008). Largeinvestors are becoming increasingly in-terested in catastrophe bonds and otherinsurance-linked securities because of theirlow correlation to traditional financialmarket performance providing a better di-versification of investment portfolios.

This paper provides a climatology ofhurricanes and hurricane losses in Florida.It is hypothesized that hurricane intensityis a predictor of total economic loss dueto hurricane landfalls. The purpose is tobuild a foundation for assessing the likeli-hood of future hurricane losses. The strat-egy is to graph and tabulate the historicalrecord of hurricane strikes and their asso-ciated damage costs, and examine how thestatistics of occurrence, intensity, and sizeare related to losses. Although others haveexamined damage losses from hurricanes(Katz 2002; Pielke et al. 2008; Jagger etal. 2008), this work is the first to lookat the problem focusing exclusively onFlorida.

The paper begins with a brief descrip-tion of the data sets followed by an exam-ination of Florida’s hurricane statisticsfrom the period 1900–2007. Inter-annual,seasonal, and intra-seasonal variability ofvarious hurricane characteristics are ex-amined first. Then the distributions andtemporal variations of the direct damagecosts associated with Florida hurricanesare considered. To bring together geophys-ical and economic issues, relationships be-tween losses and hurricane characteristicsare examined. It is found that historicallosses correlate best with minimum cen-tral pressure alone.


florida hurricaneand loss data

This study relies on two principal sourcesof data. For hurricanes affecting Florida,we use the list of historical hurricanes em-ployed to evaluate a risk model developedby the Florida Commission on HurricaneLoss Projection Methodology (FCHLPM)and supported by a research grant from theFlorida Office of Insurance Regulation(FOIR). A hurricane is a tropical cyclonewith maximum sustained wind speeds of atleast 33 m/s (64 kt/74 mph). The data setlargely conforms to the U.S. National Hur-ricane Center’s HURDAT storm archive(Ho et al. 1987; Landsea et al. 2004), but in-cludes storms only for the state of Florida.This dataset is available online at http://www.aoml.noaa.gov/hrd/lossmodel/AllFL.html.

The focus here is on hurricanes that di-rectly strike Florida. A direct strike (or hit)is one in which all or part of the hurri-cane’s eye wall reaches the coast. For thiswork, the Florida coast is defined as theboundary of the sea with the mainland,including all barrier islands surroundingFlorida. For the purposes of this study, hur-ricanes that approach Florida, but wherethe eye wall remains out at sea (e.g., Hur-ricane Elena in 1985) are not considereddirectly striking Florida. A direct strike in-cludes landfalling hurricanes and thosethat hit the islands making up the FloridaKeys.

A hurricane can make more than onedirect hit on the state. This occurs for in-stance when it first strikes the peninsulathen moves out over the eastern Gulf ofMexico before striking the panhandle re-gion (e.g., Storm #3 in 1903). Since a hur-ricane weakens over land, the intensity of

the hurricane at second landfall is typ-ically less than at first landfall. That said,most of the descriptive statistics presentedin this study are based on characteristics atthe time of first landfall, which for our pur-poses, is defined as the first direct striketo the mainland or a direct hit to the Flor-ida Keys only if the hurricane makes noother landfall in the state. If the hurri-cane makes landfall in the state more thanonce, the landfall characteristics at max-imum intensity are used because this com-parison is between loss data and hurricanecharacteristics, and the majority of lossescome from the strike of greatest intensity.

For losses incurred by hurricanes thatdirectly strike Florida, normalized damagedata are taken from the work of Pielke et al.(2008). There are two normalization pro-cedures presented in Pielke et al. (2008),both of which are estimates of the damagethat would have occurred if historic hur-ricanes struck in the year 2005. One pro-cedure allows for changes in inflation,wealth, and population, and the other pro-cedure allows for inflation, wealth, and anadditional factor that represents a changein the number of housing units that exceedpopulation growth between the year of theloss and 2005. The methodology producesa longitudinally consistent estimate of eco-nomic damage from past tropical cyclones.Losses caused by each storm event are ag-gregated from around the entire state anddo not necessarily cluster around the loca-tion of landfall. However, it is well knownthat the amount of damage experienced ishighly dependent upon where the stormmakes landfall in terms of buildings, in-frastructure, population, and so forth. Al-though this research focuses on aggre-gated loss values, it is useful because itprovides a general understanding of the


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Figure 1. Florida’s annual hurricane occurrence (1900–2007). (a) Time series of annual Floridahurricane counts. Only storms that made a direct strike on Florida with hurricane-force winds and

that have available economic loss values are included. (b) Distribution of annual Florida counts. Thereare a total of 67 known Florida hurricanes in the 108-year period.

economic losses experienced throughoutFlorida from 1900 to 2007.

hurricane statistics

The analysis begins with an examina-tion of the frequency of Florida hurricanes.Here the record starts with the 1900 sea-son and ends after the 2007 season. Notethat these economic loss data are referredto as losses and damage costs interchange-ably throughout this study. A Florida hur-ricane is a tropical cyclone that makes atleast one direct strike on the state as a hur-ricane. A hurricane that makes more thanone landfall in the state of Florida (e.g.Storm #3 in 1903) is considered andcounted as one, single Florida hurricane.

Figure 1 shows the time series and dis-tribution of annual Florida hurricanes overthe 108-year period. There are a total of 67Florida hurricanes. There are 62 yearswithout a Florida hurricane and one year(2004) with 4 different hurricanes affect-

ing the state. There are more years withoutFlorida hurricanes during the second halfof the 20th century (Elsner et al. 2004).Approximately 16 percent of the yearshave more than one hurricane event. Theaverage annual number of Florida hur-ricanes is 0.62 hur/yr with a variance of0.72 (hur/yr)2. Assuming that hurricaneoccurrence in Florida follows a Poisson dis-tribution similar to the climatological rec-ord for the rest of the United States af-fected by hurricanes (Elsner and Jagger2006), this translates into a 46 percentchance that Florida will be hit by at leastone hurricane each year.

Florida’s hurricane season runs fromthe beginning of June through the end ofNovember (even though storms occasion-ally occur outside this season), but themost active months are September fol-lowed by October (Figure 2). In fact, twiceas many hurricanes have hit Florida in Oc-tober than in August. Collectively themonths of June, July, and November ac-


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Figure 2. Florida’s intra-seasonal hurricane occurrence. (a) Monthly counts and (b) cumulativedistribution function (CDF) of Florida hurricanes.

count for about 16 percent of all Floridahurricanes while August, September, andOctober account for the remaining 84 per-cent. Yet the monthly distribution does nottell the entire story; another way to look atintra-seasonal activity is with the cumula-tive distribution function (CDF). The CDFsuggests a division of the season into fourdistinct periods. The periods are markedby a nearly straight line on the CDF indi-cating a constant probability of observinga hurricane during the period, but the pe-riods do not have equal lengths. The earlyperiod runs from 1 June through 31 July.The early mid period (with a steeper slopeon the CDF) runs from 1 August throughabout 5 September. The mid-season pe-riod, featuring the highest probability ofobserving a Florida hurricane, runs from 6September through 25 October, althoughthere is a slight break in activity duringlate September and October (representedby a small line just prior to Julian Day 300in Figure 2). The late period runs from26 October through the end of November.

The 1st quartile, median, and 3rd quar-tile dates are 242, 260, and 284, respec-tively. This implies that only 25 percent ofthe Florida hurricane season is typicallycomplete by 31 August, half the season iscomplete by 18 September, and 75 percentof the season is over by 12 October. The 1st

quartile date, 31 August, falls into the sec-ond period (early-mid) established by theCDF. As expected, the median date, 18September, falls during the mid-seasonperiod established by the CDF, which isslightly more than a week after the mediandate for all Atlantic hurricanes (Elsner andKara 1999). The 3rd quartile date, 12 Octo-ber, also falls into this mid-season period.None of these dates fall into the late periodestablished by the CDF showing that, forthis sample of Florida hurricanes, the ma-jority of the season is over by 12 October.

When comparing this seasonality ofFlorida hurricanes to the remainder of theUnited States coastline, Florida stands asunique. Coasts that are affected by Atlantichurricanes extend from Texas to Maine,


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Annual Sequence Number of a Florida Hurricane

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Figure 3. Florida’s occurrence of hurricanes by sequence number. The number refersto the sequence of tropical storms and hurricanes within a season, where 1 is the

first named tropical cyclone of the season.

and have been regionalized into four areasfor comparison purposes. The first regionextends from Texas to Alabama, and thesecond region consists of only Florida. Thethird and fourth regions consist of the en-tire eastern coast beginning with Georgiaand ending at Maine, and are separated atthe state of Virginia. It is found that for allregions, the month of highest hurricaneoccurrence is September. The main differ-ence between Florida and the other re-gions is that Florida experiences hurri-canes during the entire span of the Atlantichurricane season with a median date of 18September, while the other regions experi-ence hurricane seasonality of a differentscale. Region 1 experiences hurricanes fromJune through October with a median dateof 30 August. Region 3 is affected by stormsfrom July through November and has a me-dian date of 4 September, and the northernmost region, region 4, experiences stormsduring the shortest amount of time thanany other region, July through September

with a median date of 11 September. Flor-ida, therefore, is unique in its susceptibilityto hurricanes because it experiences amuch longer season than other regions,and it experiences more storms later in theyear than any other region as its medianlandfall date is later than anywhere else.

It is interesting also to consider whichtropical storm of the season is most likelyto strike Florida. Figure 3 shows the dis-tribution of storm numbers associatedwith Florida hurricanes. Storm number re-fers to the sequence of tropical storms andhurricanes within a season, with the firstnamed tropical cyclone being storm one.The plot shows that historically the mostlikely hurricane to affect Florida is the 5th

tropical cyclone of the season followed, inlikelihood, by the 2nd tropical cyclone.Florida has seen every sequence numberthrough 15. The highest sequence number(22) was Hurricane Wilma in 2005. Therewere a record 27 tropical storms and hur-ricanes during this remarkable season.


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Figure 4. Florida’s hurricane intensity at time of landfall (1900–2007). (a) Time series of minimumcentral pressure (hPa) and (b) distribution of minimum central pressure. (c) Time series of maximum

wind speed (kt) and (d) distribution of maximum wind speed.

The average intensity (as measured bythe minimum central pressure) of Floridahurricanes at the time of first landfall is 966hPa (millibars), and 90 kts as measured bythe maximum wind speed. The average in-tensity of Florida hurricanes at second land-fall is 981 hPa (75 kt). All five of the secondlandfalls (for storms striking Florida morethan once) occurred over the northwesternpart of the state. These five events have amean central pressure at first landfall of 970hPa showing that, for these five storms, theaverage difference between first and sec-ond landfall is +11 hPa. This increase in airpressure from first to second landfall in-dicates a decrease in intensity for thesestorms. The lowest pressure of any Floridahurricane is 892 hPa, which occurred withthe Labor Day hurricane of 1935 that de-molished the middle Florida Keys.

Figure 4 shows the time series and his-togram of minimum central pressures andwind speeds at landfall. If the hurricanemade more than one landfall (or Keyscrossing), the highest intensity (lowestpressure and highest wind speeds) is used.There appears to be no obvious long-termtrend in these variables for this sample ofFlorida hurricanes. The distributions areskewed (negatively for pressure and posi-tively for wind speed) as expected from aset of data representing a threshold pro-cess (only cyclones at hurricane intensityare considered).

Locations of hurricane landfalls areshown in Figure 5. The points delineatewhere the eye crossed the shore (or crossedthe Keys). Symbols signify intensity as de-termined by the maximum wind speedsand grouped by the Saffir/Simpson cate-


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°°WFigure 5. Florida hurricane landfalls 1900–2007. The locations indicate the landfall (or Key crossing)

locations, including second landfalls. The symbols denote hurricane intensity as measured by themaximum wind speed and categorized by the Saffir-Simpson hurricane damage potential scale. Anotable lack of hurricane strikes have occurred along the peninsula north of Cedar Key. All of thestrongest hurricanes (Category 4 & 5) have occurred south of a line from Sarasota to Vero Beach.

gories. Landfalls are more common overthe southern half of the peninsula includ-ing the Keys and along the panhandle.There is a notable lack of hurricane strikesalong the northeast coast and around thewestern peninsula north of Cedar Key. Allof the strongest hurricanes (categories 4and 5, having wind speeds of 114 kt or

greater) have occurred south of a line fromSarasota to Vero Beach.

The size of hurricanes directly affectingFlorida varies from storm to storm. Figure6 shows the time series and distribution ofthe radius of maximum wind (RMW) atlandfall as an indication of hurricane size.Five of the 67 Florida hurricanes do not


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Figure 6. Florida’s hurricane size at time of landfall (1900–2007). (a) Time series of radius ofmaximum wind (km) and (b) distribution of the radius.

have a value for RMW. The variation insize is quite large but there appears to bea modest trend toward larger hurricanes.The average size is 41 km (radius) with avariance of 434 km2. The distribution ispositively skewed with most hurricaneshaving an RMW between 20 and 60 km,and only a few greater than 80 km. Histor-ically, the smallest storm was HurricaneCharley (2004) at 8 km and the largestwas Hurricane Earl (1998) at 119 km.

damage statistics

The data on damage losses from hur-ricanes are taken from Pielke et al. (2008).The values represent the total estimatedeconomic damage amounts normalized to2005 dollars. The values are based on totaldamage estimates as opposed to insuredloss figures. Economic damage is the di-rect loss associated with a hurricane’s im-pact. It does not include losses due to busi-ness interruption or other macroeconomiceffects including increases in demand forconstruction materials and other house-

hold items. Total damage costs are twicethe estimated insured damage costs. De-tails and caveats of the normalization pro-cedure are provided in Pielke et al. (2008).The complete set of data used in this studyis provided as an Appendix. It should benoted that prior to 1940, 32 storms madelandfall somewhere on the United States’coastline with no reported damages, whereonly 8 such storms have occurred since1940 (Pielke et al. 2008). Some damagelikely occurred during all early 20th centurystorms but the lack of data probably resultsin an undercount of the overall economicloss from the storms affecting the UnitedStates prior to 1940, and, if at least onehurricane strikes Florida every two years,there is an undercount of overall damagein Florida prior to 1940 as well.

There are two sets of damage estimatesbased on slightly different normalizationprocedures provided in Pielke et al. (2008).The two approaches are the methodologyused by Pielke and Landsea (1998), whichadjusts for inflation, wealth, and popula-tion updated to 2005, and the methodol-


ogy used by Collins and Lowe (2001),which adjusts for inflation, wealth, andhousing units updated to 2005 (Pielke etal. 2008). Pielke et al. (2008) have takenthe methodologies given by Pielke andLandsea (1998) and Collins and Lowe(2001) and have slightly adjusted theirmethodologies to be appropriate for 2005dollars. Here we focus on the data set fromthe Collins and Lowe methodology, butnote that both data sets are quite similar. Inboth cases, researchers have estimated to-tal dollar value of damage that historicstorms would have caused had they oc-curred in 2005—given all the growth anddevelopment that has taken place sincethese historical storms occurred. The Col-lins and Lowe methodology produces atemporally consistent estimate of eco-nomic damage from past tropical cyclonesaffecting the U.S. Gulf and Atlantic coasts.Results presented in this study are not sen-sitive to the choice of data set. The Collinsand Lowe methodology is used for thisstudy, as opposed to that of Pielke andLandsea, because the housing unit variableincluded in Collins and Lowe is more rele-vant when dealing with economic loss thanpopulation statistics. The Collins and Lowe(2001) values, adjusted to 2005 dollars inPielke et al. (2008), are presented in ourAppendix under the Damage column.

Table 1 lists the top ten all-time hur-ricane loss events in Florida since 1900.The damage amount (loss) is in billions ofU.S. dollars. Fourteen of the 67 Floridahurricanes do not have an estimated lossvalue for unknown reasons. Topping thelist is the Great Miami Hurricane of 1926with an estimated total damage to Floridaof $129 billion. Again, this dollar figurerepresents an estimate of the total damageif the same cyclone were to have hit in

Table 1. Top ten loss events from Floridahurricanes (1900–2007). Damage amount is inbillions of U.S. dollars, normalized to the dollar

value of 2005. Loss values come from theadjustments made to Collins/Lowe (2001)

presented in Pielke et al. (2008).

Rank Storm Year RegionLoss

($bn)

1 Great Miami 1926 SE 129.02 Andrew 1992 SE 52.33 Storm # 11 1944 SW 35.64 Lake Okeechobee 1928 SE 31.85 Donna 1960 SW 28.96 Wilma 2005 SW 20.67 Charlie 2004 SW 16.38 Ivan 2004 NW 15.59 Storm # 2 1949 SE 13.5

10 Storm # 4 1947 SE 11.6

2005. Hurricane Andrew, which hit south-east Florida in 1992, comes in second witha damage tag of $52.3 billion if it wouldhave hit in 2005. Note that 3 of the top tencostliest Florida hurricanes occurred in2004 and 2005.

The total normalized losses for the 53Florida hurricanes (for which contempo-rary damage estimates are available) wouldbe $459 billion if these storms occurred in2005. Eighty percent of this total is from thetop 11 (21 percent) storm event losses. Themedian loss amount is $2.21 billion, but the95th percentile value is $33.3 billion. Figure7 shows the time series and histogram ofhurricane damage losses in the state of Flor-ida since 1900. The distribution is highlyskewed with many relatively small lossesand few very large losses. The Great MiamiHurricane of 1926 is clearly the worst lossevent (normalized) in Florida since 1900.There are two years with total losses of less


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Figure 7. Florida’s hurricane damage losses (1900–2007). (a) Time series and (b) distribution oflosses by event and (c) time series and (d) distribution of the logarithm (base 10) of losses. Some years

are without loss events.

than $15 million. The Insurance Service Of-fice (ISO), a private corporation that pro-vides information about risk assessment,defines a catastrophe as an event that causesmore than $25 million in insured ($50 mil-lion total) losses and causes a major disrup-tion (Insurance Information Institute2008). Of the Florida hurricanes that haveavailable economic loss values, 96 percentof the events were above this $50 millionthreshold. There are only two storms of the53 in this sample that do not have lossesexceeding this amount, and they are Flor-ence (1953) and Floyd (1987).

A good way to examine skewed distri-butions is to use logarithms. Figure 7cshows the time series and histogram aftertaking the logarithm (base 10) of each an-nual loss amount. In this figure, a value ofnine indicates a billion dollar loss, a value

of 10 indicates a $10 billion loss, and avalue of 11 indicates a $100 billion loss.Consistent with the modest increase in sizefor hurricanes affecting Florida seen in theprevious section, there appears to be aslightly increasing trend in the upper andlower quartile amounts of normalizeddamage since 1900, although theseslightly increasing trends are not statis-tically significant.

To estimate the annual probability ofyearly losses exceeding specified amountsthe normalized data are fit to a model. Themodel consists of the generalized Paretodistribution (GPD) to describe the behav-ior of extreme losses and the Poisson dis-tribution to specify the rate of loss yearsabove a given threshold level (Jagger andElsner 2006). Here the threshold value isset at $250 million as a compromise be-


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Figure 8. A model for Florida’s hurricane damage losses. The solid curve is based on using a generalizedPareto distribution for describing the magnitude of yearly total loss amount and a Poisson distribution

for the number of years exceeding a threshold loss amount of $250K. The dashed lines are the upperand lower 95 percent confidence limits on the loss model. The small boxes are empirical estimates ofthe loss amount and the large box corresponds to a total loss of $25 bn. The empirical estimates are

based on 1-e[-a*(rank-0.5)/M], where a is the number of years with losses (M) divided by the total numberof years (N), and rank is the order of losses, with a rank of 1 being the greatest loss.

tween being low enough to retain enoughyears to estimate the parameters of theGPD, but high enough so that the yearlyloss amount (exceedance) follows a GPD(Jagger and Elsner 2006). The model spe-cifies exceedance loss levels as a functionof annual probabilities.

Figure 8 shows the model as a curve ona semi-log plot; the higher the annual loss,the lower the probability of occurrence.The model indicates a 5 percent chance oflosses exceeding about $19.6 billion on anannual basis and a 10 percent chance oflosses exceeding $5.8 billion. Finally, Flor-ida can expect a storm to produce at least$1 billion in damage once every five years(a probability of 20 percent in any givenyear). According to the model, a loss of atleast $25 billion occurs with an annual

probability of about 2.1 percent, which is apercentage point below the state’s esti-mate of 3.1 percent mentioned in theIntroduction. Although this difference isnot statistically significant it shows thatthe state of Florida estimates their cata-strophic losses ($25 billion +) to occur abit more often than this model suggests.

trends and associations

As seen in the previous section, thereappears to be increasing trends in the sizeand intensity of Florida hurricanes and innormalized damage costs. To examine theseobservations in more detail, trends are com-puted and examined using ordinary leastsquares regression and quantile regression(Elsner et al. 2008). Ordinary regression is a


1900 1940 1980920930940950960970980990

Year

Min

. P (h

Pa)

a

1900 1940 1980

80

100

120

140

Year

Max

. Spe

ed (k

t)

b

1900 1940 1980

20406080

100120

Year

Rad

ius

of M

ax. W

ind

(km

)

c

1900 1940 1980

789

1011

Year

Log

Dam

age

(200

5 $U

S b

n)

d

Figure 9. Florida’s hurricane trends (1900–2007). (a) minimum central pressure, (b) maximum windspeed, (c) radius of maximum wind, and (d) logarithm of damage costs (losses). The thick line is the

trend in mean value. The upper, thin line is the trend in the upper quartile values for wind speed,RMW, and damage cost and the lower quartile for central pressure. The lower, thin line is the trend in

lower quartile values for wind speed, RMW, and damage cost, and the upper quartile for centralpressure. The trend values and standard errors are given in Table 2.

model for the conditional mean, where themean is conditional on the value of the ex-planatory variable. Quantile regression ex-tends ordinary least-squares regression toquantiles of the response variable. Quan-tiles are points taken at regular intervalsfrom the cumulative distribution function ofa random variable. The quantiles mark a setof ordered data into equal-sized data sub-sets. Thus, quantile regression is a model forthe conditional quantiles. For trend analysisthe explanatory variable is year. Relation-ships between hurricane characteristics andlosses are also examined.

Figure 9 shows the median and upperand lower quartile trends in hurricane in-tensity and size at landfall, and the corre-

sponding damage costs. Downward trendsare found in the mean and lower quartileof minimum central pressures, and up-ward trends are found in the mean andupper quartile of maximum wind speeds,both showing an increase in the strong-est storms over time. An upward trend isfound in the upper quartile of the size ofFlorida hurricanes. This indicates that onaverage and for the strongest cyclones,Florida hurricanes are getting more pow-erful over time. Trend values along withtheir associated standard errors are givenin Table 2. The relatively large standarderrors on the trends indicate the increasesin trend values shown in Table 2 are notstatistically significant against the null hy-


Table 2. Trend statistics and standard error(1900–2007). The lower quartile of the pressure

trend corresponds to the upper quartile ofdamage cost trend.

Minimum Central Pressure (hPa/yr)

Trend S.E.upper quartile 0.000 0.0367mean –0.064 0.0687lower quartile –0.083 0.0918

Maximum Wind Speed (hPa/yr)

Trend S.E.upper quartile +0.091 0.1403mean +0.094 0.0787lower quartile 0.000 0.1225

Radius to Maximum Wind (km/yr)

Trend S.E.upper quartile +0.318 0.1038mean +0.093 0.0865lower quartile –0.204 0.0614

Log Damage Costs (/yr)

Trend S.E.upper quartile +0.0042 0.00398mean +0.0018 0.00408lower quartile +0.0056 0.00356

pothesis of no trend. However, the upwardtrends in the 25th and 75th percentiles ofdamage costs might be associated with theupward trends in the power characteris-tics of hurricanes as seen in the previoussection.

Figure 10 shows a scatter plot matrixalong with regression lines of damage costsas a function of hurricane characteristics.It is clear that there is a statistically signifi-cant relationship between the intensity ofthe hurricane at landfall and the amount ofdamage. This strong relationship is seen

using either minimum central pressure ormaximum wind speed as the indicator ofhurricane intensity. However, the rela-tionship between damage amount andhurricane size is much less clear. In fact,the weak negative relationship is counter-intuitive as the larger hurricanes are asso-ciated with somewhat less damage. Thissomewhat puzzling observation can be ex-plained by the fact that hurricane intensityis inversely related to hurricane size for thisset of hurricanes. Thus the larger hur-ricanes tend to be weaker and thus causeless damage.

It has been suggested that estimationsof potential losses from hurricanes com-bine intensity and size characteristics(Kantha 2006). The Carvill Hurricane In-dex (CHI), discussed in Kantha (2006),determines a numerical measure of thepotential for damage from a particularhurricane event. On the Chicago Mercan-tile Exchange, the CHI is used as the basisfor trading hurricane futures and for trad-ing options about how best to mitigate thestorms, and captures this idea using thefollowing equation:

CHI = (v/vo)3 + 1.5 (r/ro) (v/vo)2 (1)

where v is the maximum wind speed (kt),vo is the threshold hurricane-wind speed(64 kt), r is the radius of threshold hurri-cane-wind speed or greater (km), and ro isthe threshold radius (97 km). To obtain r,a form of the Rankine vortex equation isused to obtain the radial decay of thewinds from their maximum value (Hol-land 1980). The equation is given by:

r = rmax (v/vo)1.5 (2)

The CHI is computed from the set ofFlorida landfalling characteristics. As ex-pected, the relationship between damage


920 940 960 980

789

1011

a

Minimum Central Pressure (hPa)

Log

Dam

age

(200

5 $U

S b

n)

80 100 120 140

789

1011

b

Maximum Wind Speed (kt)

Log

Dam

age

(200

5 $U

S b

n)

20 40 60 80 100 120

789

1011

c

Radius to Maximum Winds (km)

Log

Dam

age

(200

5 $U

S b

n)

5 10 15 20

789

1011

d

Carvill Hurricane Index

Log

Dam

age

(200

5 $U

S b

n)

Figure 10. Scatterplot matrix of damage costs and hurricane characteristics. Logarithm of damage costas a function of (a) minimum central pressure, (b) maximum wind speed, (c) radius to maximum

wind, and (d) Carvill Hurricane Index. The thick line is the trend in mean value, the thin lines are the95 percent confidence limits on the trend.

losses and the CHI is positive and signifi-cant. However the relationship does notappear to be stronger than either of theintensity estimates alone. The strong linkbetween hurricane intensity and damagecost coupled with the rather weak linkwith hurricane size indicates that theSaffir-Simpson hurricane scale, which isbased solely on wind speed, is, in largemeasure, an adequate measure of poten-tial damage amount, at least in Florida.Yet based on the somewhat better correla-tion of losses with minimum central pres-sure (see Table 3), we argue that centralpressure be used as a single variable forpotential loss estimation.

For instance, regressing the logarithm(base 10) of losses onto the minimum cen

tral pressure, an equation representing aloss index for Florida, called the Floridahurricane loss index (FHLI), is defined bythe following equation:

FHLI = 1040.912-0.0329pmin (3)

where pmin is the minimum central pres-sure in units of hPa forecast at landfall.Values of FHLI are damage estimates ex-pressed in dollar amounts. For multiplelandfalls the lowest minimum pressure isused. This model (which is not applicablefor hurricanes that hit only the FloridaKeys) explains only 40 percent of the vari-ability in the logarithm of Florida lossamounts but compares favorably with theCHI, which explains less than 28 percentof the losses. Table 4 shows resulting ex-


Table 3. Correlation of hurricane characteristicsat landfall with damage costs (losses) based on53 Florida hurricanes. Correlation coefficient r

and the associated 95 percent confidence intervalon that correlation under the null hypothesis of

zero correlation.

r 95% Confidence Interval

P min –0.59 (–0.74, –0.38)W max +0.52 (+0.29, +0.70)RMW –0.13 (–0.39, +0.14)CHI +0.53 (+0.30, +0.70)

pected economic loss from the FHLI basedon the pressure categorization associatedwith the Saffir-Simpson Scale (Kantha2006). The expected losses do not reflectfuture changes in wealth and inflation, northe expected increases in coastal develop-ment. It is important to note that these lossindex values will be highly dependentupon where the storm makes landfall andthe amount of development and popula-tion in the affected area.

Of course, the actual amount of dam-age a hurricane inflicts will also depend tosome extent on its forward speed and therate at which the wind subsides over land.Neither of these characteristics are consid-ered here, but have been analyzed else-where. Huang et al. (2001) considers eco-nomic loss as a function of the wind decayrate, and Watson and Johnson (2004)look at forward speed as one of the param-eters of their hurricane loss estimationmodels. These characteristics could be in-cluded in this model in a future study totry and increase its ability to explain thevariability in the logarithm of Florida lossamounts.

summary

More hurricanes strike Florida thananywhere else in the United States. Rec-ords of Florida hurricanes have recentlybeen updated and are reliable back to1900. This study examines various statis-tics of hurricanes affecting the state overthe period 1900–2007 and their associ-ated damage costs.

It is shown that the annual count ofFlorida hurricanes is consistent with a ran-dom Poisson process with a mean of 0.62hurricanes per year that translates to anannual probability of 46 percent for atleast one hurricane. Florida differs fromother regions of the United States in termsof hurricane seasonality because it is af-fected by storms throughout the entire At-lantic hurricane season, and it experiencesstorms later into the year than any otherarea of the United States’ coastline.

Although the variability in the amountof damage is quite large from one hurri-cane to the next, normalized losses are in-creasing over time, which is consistentwith the slight increases in hurricane in-tensity and hurricane size. The model pro-vided shows that on an annual basis, wecan expect a 10 percent chance of lossesexceeding $5.8 billion and a 5 percentchance of losses exceeding $19.6 billion.In addition, each year Florida has a 20 per-cent chance of experiencing at least $1 bil-lion in hurricane related losses; in otherwords, the State can plan on at least $1billion in losses once every five years.

Of the hurricane landfall characteris-tics examined here, the best predictor ofpotential losses is minimum central pres-sure. Hurricane size by itself or in com-bination with hurricane intensity does not


Table 4. Expected loss computed using the Florida Hurricane Loss Index based on categorical pressurevalues presented by Kantha (2006). Approximate exponent values are x where FLHI = 10x. Expected

loss is given in US dollars, normalized to 2005 dollar amounts. These approximate losses do not reflectfuture changes in wealth, inflation, and property, and are highly reliant on where the storm actually

makes landfall in terms of development and population.

CategoryPmin

ValuesApproximate

Exponent Values (x)Approximate Expected Loss

(Normalized 2005 $US)

1 989–980 8.40–8.69 $250 million–$499 million2 979–965 8.70–9.17 $500 million–$1.49 billion3 964–945 9.18–9.90 $1.50 billion–$7.99 billion4 944–920 9.91–10.69 $8.00 billion–$49.99 billion5 [920 ?10.70 ?$50.00 billion

improve on the simpler relationship. Anestimate of potential losses from hur-ricanes can be obtained by a formula in-volving only an estimate of the minimumpressure at landfall. Expected economicdamage costs are computed using theFHLI and categorized to provide a scalesimilar to the Saffir-Simpson for economicloss based on minimum central pressure.

In one sense Florida has been ratherfortunate. Although the 2004 and 2005seasons featured 7 Florida hurricanes,there are more years during the secondhalf of the record without a Florida hur-ricane. Moreover, despite some large lossessince 1950, Florida has not seen a repeat,in terms of losses, of the Great Miami Hur-ricane (of 1926).

Florida, along with other coastal states,is in a race to retrofit and harden its in-frastructure before another major storm oc-curs. Over the past 20 years alone, Hur-ricane Andrew (1992) almost made a directhit on downtown Miami, Hurricane Floyd(1987) made a last minute turn away fromthe state, and Hurricane Charley (2004)veered east and away from the Tampa Bay

area just hours before landfall. Had any ofthese storms made direct strikes on urbanareas, they could have caused losses largerthan anything Florida has experienced todate.

Recently, researchers have made im-provements in understanding and predict-ing hurricane intensity (Jones et. al 2006,Davis et. al 2008), hurricane tracks (Bar-ret et al. 2006), and seasonal hurricaneactivity (Elsner and Jagger 2006). In com-bination with this paper, better under-standing of hurricane activity and result-ing damage can better prepare coastalcommunities with what to expect witheach approaching season, allowing for in-formed decisions by their citizens, policymakers and insurance agencies about thefuture of Florida’s hurricane seasons andthe proper way to mitigate.

appe

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319

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1900

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Code

1960

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1992

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acknowledgmentsThanks go to King Burch for his contribution

of background material on Florida’s insurance.Thanks also go to Thomas Jagger for his assistancewith the loss model. All computations were com-pleted using R Statistical Package (R Develop-ment Core Team 2007). This work is supported bythe National Science Foundation (ATM-0738172)and by the Florida Catastrophic Storm Risk Man-agement Center. Finally, the lead author wouldlike to thank Chris Meindl for his encouragementand editorial guidance.

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evaluation of wind-speeds and expectedinsurance losses. Journal of Wind Engineeringand Industrial Aerodynamics 89:605–617.

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jill malmstadt is a Master’s student in theDepartment of Geography at the Florida StateUniversity, Tallahassee, FL 32306. Email:[email protected] scheitlin is a Ph.D. student in theDepartment of Geography at the Florida StateUniversity. Email: [email protected]. james elsner is a Professor in theDepartment of Geography at the Florida StateUniversity. Email: [email protected]. Hisresearch interests include hurricanes, climate,and spatial statistics.

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