We thank Gary Kerney at Property Claim Services for providing loss data and NOAA’s H*Wind project for providing wind data. We thank three anonymous reviewers for their comments. This work was partially supported by the Willis Research Network and the Research Partnership to Secure Energy for America and has benefitted from discussions with Zurich Insurance Group.
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Murnane and Elsner (2012) found an exponential relationship between maximum wind speed and loss is a better fit than a power law.
Irene total damages at approximately $15.8 billion place it in the top 10 costliest hurricanes on record through 2011 (http://www.wunderground.com/hurricane/damage.asp?MR=1), and loss estimates for Sandy of approximately $65.7 billion (http://www.ncdc.noaa.gov/billions/events) place it second behind Katrina in 2005.
Smith and Katz (2013) point out the need for local-scale analysis to avoid potential bias in loss estimation.
The National Hurricane Center lists county-by-county hurricane strikes (direct and indirect) from each hurricane from 1900 to 2009 (http://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html). Strikes are constrained to the 175 counties listed, of which 168 (96%) are coastal counties: that is, some portion of their county boundary directly abuts the Atlantic Ocean, an associated inlet, or the Gulf of Mexico.
The National Hurricane Center (NHC) deadliest and costliest (Blake et al. 2007) includes adjusted NFIP flood damage amounts.
Hurricanes can affect the weather far outside the region of hurricane force winds resulting in other wind-associated damaging weather such as severe thunderstorms and tornadoes hundreds of kilometers from the hurricane track. Hurricane Ivan in particular was a prolific producer of tornadoes with 117 reported across eight states from the Gulf Coast to Pennsylvania. It is likely the losses in states that did not experience hurricane force winds can be partly attributed to these hurricane spawned tornadoes.
The Pielke et al. normalized data are adjusted for wealth increases which are defined as “current-cost net stock of fixed assets and consumer durable goods” (Pielke et al. 2008, p. 9). However, this adjustment is done at a national level, not necessarily specific to the impacted areas. Further, fixed assets are defined more broadly than just commercial fixed assets including private residential fixed assets as well as all types of government fixed assets.
Wind speed data are described in the next section.
Since we are joining the H*Wind data at the relatively granular census tract level, it is necessary to regrid the gridded H*Wind data to ensure no spatial gaps exist in the gridded data at this geographic level. We also incorporated nearest values of wind speed within 1-mile distance to ensure no data gaps with the regridded data.
Nor do these counties appear in the NHC affected county list.
Although both storms are classified as category-3 hurricanes at landfall, sustained hurricane force wind values in the impacted census tracts are well below this amount and are a consequence of both area averaging and the abrupt reduction of wind speeds at the coast in the H*Wind analysis.
Exposure and vulnerability data are sourced from HAZUS-Multi-Hazard (MH) 2.1. Their general building stock data are sourced from year 2000 U.S. Census Bureau data and year 2002 Dun and Bradstreet data, prior to the occurrence of both Ivan and Dennis (http://www.fema.gov/media-library-data/20130726-1820-25045-8522/hzmh2_1_hr_um.pdf).
While it is certainly plausible to think that some of this larger percentage for Dennis is due to the homes destroyed by Ivan and thus newly constructed for Dennis, it is not likely much of a contributing factor because the source of these data is ascribed to Dun and Bradstreet (2002) data.
While the duration result is in agreement with Powell et al. (1995), the directional uniformity result contradicts Powell et al.’s findings and is explored later in this section.
Results reported in these tables are robust to the hazard variables employed in a linear continuous fashion in models 1–3 as well as to other high and low cutoff points based upon the median distribution of the continuous variables. Given the discretization of our hazard variables, we are not estimating a log–log functional form.
Likelihood ratio (LR) tests are calculated using nonrobust standard errors.
The spatial weighting matrix required for the estimation is generated by the shp2dta and spmat commands in StataCorp (2011) 12.1. The elements of a spatial weighting matrix are binary indicators that identify observations within a neighborhood: πi,j = 1 when observations I and j are neighbors and πi,j = 0 otherwise. By convention, the diagonal elements of the weighting matrix are set to zero and row elements are standardized such that they sum to one or are interpreted as an average of neighborhood values. We define our neighborhood by contiguity, where census tracts are considered neighbors if they share a common border and utilized a spectral-normalized weighting matrix. Each tract on average had six identified neighbors. The spreg and spatreg commands were utilized for the estimation, with Lagrange multiplier (LM) tests conducted using spadiag.