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Assessing and Understanding Wildfire Risk

Assessing and Understanding Wildfire Risk

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    GDS International - Next - Generation - Insurance - Summit - US GDS International - Next - Generation - Insurance - Summit - US Document Transcript

    • White Paper 4 September 2011Assessing and UnderstandingYour Wildfire Risk
    • TABLE OF CONTENTSIntroduction................................................................................................................................................................................................... 2The Necessity of Accurate Brushfire Analysis..................................................................................................................................... 2 .The CoreLogic Brushfire Risk Model..................................................................................................................................................... 3 Fuel ..................................................................................................................................................................................................... 3 Aspect................................................................................................................................................................................................. 4 . Slope.................................................................................................................................................................................................... 4 Surface Composition..................................................................................................................................................................... 4 Final Brushfire Fuel Rank Data Model Composition.......................................................................................................... 5 . FIREbreak+ (WUI) Data Model.................................................................................................................................................. 5 . Wildfire Single Score Calculation............................................................................................................................................. 6 .Case Studies................................................................................................................................................................................................... 8 . Cedar Fire (San Diego County, CA 2003)............................................................................................................................... 8 Scripps Ranch and the Cedar Fire............................................................................................................................................ 9 Station Fire (Los Angeles County, CA 2009).......................................................................................................................10 .Conclusion ...................................................................................................................................................................................................12
    • White Paper Assessing and Understanding Your Wildfire Risk g CoreLogic® researches, creates, and validates spatial Wildfires in the U.S. number in the tens of thousands each models to estimate insurance risk. This white paper details year, with acreage totaling in the millions, as indicated the data and methodology underlying CoreLogic Brushfire in Figure 2. Additionally, recent data suggests that while Risk and FIREbreak+ Layers and also explains the factors the number of wildfire occurrences is declining, the and logic behind the Wildfire Single Score calculation. geographic area consumed by these fires is trending These three products offer a comprehensive evaluation of upward (Table 1). wildfire risk and provide a concise and accurate method of assessing risk for homes and businesses. Fig. 2 – Acreage consumed by wildfires (1960-2010) The CoreLogic Brushfire Risk data model was designed Wildfire Acreage to categorize the relative risk of brushfire for properties 12000000 across 15 states. The FIREbreak+ model utilizes structure 10000000 8000000 density to identify geographic areas where urban ACRES 6000000 development borders high fuel zones, commonly referred 4000000 to as the Wildland/Urban Interface (WUI). The Wildfire 2000000 Single Score calculation then combines the two factors 0 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 and adds several related variables to achieve a scaled YEAR numeric range that provides a comprehensive and precise evaluation of risk. table 1 The Necessity of Accurate Avg # of fires/yr Avg acres/yr Avg acres/fire 1983-1989 114,348 2,481,611 21.7 Brushfire Analysis 1990-1999 78,586 2,817,218 35.8 The threat of wildfire to residential and urban structures 2000-2010 77,951 6,612,363 84.8 is of concern for homeowners and insurance underwriters 2011 32,189 4,118,693 127.9 even in the best conditions. Recent weather cycles (through 6/12) and possible long-term climate change have created conditions that are contributing to increased wildfire Larger and more intense fires often hinder any efforts problems in the United States, and posted public to quickly contain the events, which results in a greater warnings (Figure 1) are becoming a common sight in likelihood of damage to structures in a fire’s path. Recent many areas. As with all hazards, large wildfire events that fire history indicates that individual wildfires greater than threaten densely populated areas receive the most media 100,000 acres in size may be occurring more frequently attention, but media coverage does not accurately portray than in the past. In 14 year period between 1997 and 2010, the entirety of risk presented by wildfire activity. there were 99 fires greater than 100,000 acres in size within the U.S. It is important to note that 68.7 percent Fig. 1 of these large fires occurred in just the last seven years. When a large fire moves through the WUI area, substantial property loss is often the result. Based on data through 2010, five of the six fires that have caused the largest losses in residential property occurred since 2000, and four of the five have had a total loss calculated at more than $1 billion (adjusted) dollars (Table 2). Given this apparent trend toward larger and more destructive fires, it has never been more important to understand and accurately assess the geography of wildfire risk and risk concentration.2
    • White Paper Assessing and Understanding Your Wildfire Risk gtable 2 Fig. 3Large loss wildfires Year Adjusted loss (billion)Oakland, CA 1991 2.3San Diego, CA 2007 1.9San Diego, CA 2003 1.2Los Alamos 2000 1.2San Bernardino, CA 2003 1.1Sacramento, CA 2008 0.8Increasing residential development and urban de-concentration, along with historic forest managementpolicies and extended drought conditions, have combinedto create devastating wildfire events affecting many homesin the WUI areas. Over the past 10 years, major eventssuch as the 2003 and 2007 fires around San Diego,which caused $1.2 billion and $1.9 billion in damage Dense stands of vegetation often contribute significantlyrespectively, as well as the Sacramento fires in 2008, which more risk than sparse or widely scattered patches ofcaused $0.8 billion in damage, exemplify the enormous vegetation due to the increased fuel volumes. However,property loss associated with WUI fires. The WUI transition it is not always necessary to have large stands of uniformzone that parallels the edges of urban developments vegetation to face a high level of brushfire risk. In fact,must be interpreted correctly for insurers to accurately natural vegetation, in contrast to agriculture, oftenidentify wildfire risk in this area of greatest potential loss. does not exist in large pure stands consisting of aCombined with the fact that approximately 38 percent single species. Mixed stands of vegetation create fuelof all new residential construction in the western US is combinations possessing a variety of ignition and burnoccurring adjacent to wildland, it becomes clear that any rate characteristics that may enable a fire to start andevaluation of wildfire risk must include the WUI. Long- then build in intensity as it progresses through theterm risk is ultimately the most important consideration various species. In order to incorporate this level of detail,for both homeowners and insurers so that they are better CoreLogic maps fuel data at a highly granular resolution.prepared for the hazard presented by wildfires. Detailed CoreLogic fuel data includes stands of mixed vegetation that are commonly found in transition areas.The CoreLogic Brushfire In mountainous regions, increasing elevation will causeRisk Model vegetation to transition from grass to grass/shrub mix to shrub/tree mix to dense stands of forest, and ultimatelyBrushfire risk is a function of four characteristics: (1) to sparse grass/tundra. Grass may burn with low intensity,fuel; (2) aspect; (3) slope; and (4) surface composition. but in an area of mixed grass/trees, the grass may serve to carry the fire to a larger fuel source such as trees that willFuel then burn with greater intensity.Fuel is the combustible natural vegetation that, after Accurate high-granularity vegetation data is extremelyignition, feeds the fire. All vegetation has the potential important in locating and identifying specific fuelto serve as fuel for a fire. The density of the fuel and the sources. The CoreLogic Brushfire Risk Model derivesvariability in the rate and intensity at which different vegetation data from Landsat satellite imagery atspecies of vegetation will burn determines the impact 30-meter spatial resolution. This high resolution dataof fuel on wildfire. Both vegetation type and density is a key element in accurately assessing risk associatedare included in the CoreLogic Brushfire Fuel Rank with transitional changes in vegetation that woulddata model and play an important role in determining otherwise be omitted with coarse (100 meter or larger)brushfire risk (Figure 3). resolution data. 3
    • White Paper Assessing and Understanding Your Wildfire Risk g Aspect with greater intensity as it migrates to higher elevations. The second variable in the CoreLogic Brushfire Risk data Depending on the terrain, slope may also intensify the model is referred to as aspect, and represents the compass spread of a fire by contributing to convection currents direction of the face of a slope. Aspect has an indirect that channel winds and flame upslope. This “chimney impact on fuel due to varying levels of solar radiation effect” funnels heat and flames from lower to higher on different directional slopes. A south-facing slope (in elevations. The CoreLogic Brushfire Risk data model the Northern Hemisphere) receives more solar energy calculates slope data from 30-meter USGS DEM data. and warms to higher daytime temperatures resulting in It is easy to see in Figure 5 that slopes can be highly drier fuel than a north-facing slope in the same region. variable and can change from flat to extremely steep As a result, aspect plays an important role in the ignition within a short distance. risk of the fuel. Both ignition risk and burn potential will Fig. 5 increase with rising temperatures and dryness of fuel. The CoreLogic Brushfire Risk Model derives aspect data from USGS 30 meter digital elevation (DEM) data. The 3-D scene of a DEM in Figure 4 reveals the multifaceted variation in aspect due to changing terrain. A “south- facing” slope may have a complex network of sub-slopes with aspects facing numerous directions. As with fuel, the accurate assessment of aspect and its impact on brushfire risk is strongly correlated with the granularity of the data. Fig. 4 Surface Composition The physical components of fuel, slope, and aspect combine to estimate “propensity-to-burn” with current terrain and fuel conditions. The CoreLogic Brushfire Risk Model includes a fourth factor that provides a unique set of data which incorporates characteristics used as a surrogate for unavailable or incomplete historic data. Lightning strikes, drought, wind, humidity and other physical factors contribute to wildfire risk, but accurate historic data does not exist for those events. Surface Slope composition is a proxy for this missing information The third variable in the CoreLogic Brushfire Risk Model by defining vegetation patterns that demonstrate an is slope, which is commonly described as the steepness association with cyclical historic fires. Even though of the terrain. Slope has minimal impact on ignition, these vegetation patterns are long-term and there but strongly influences the progression of wildfire. The is no fire perimeter data to corroborate them, their physics of fire dictates that in the absence of wind, fires existence indicates that throughout the historical record, tend to migrate vertically. Therefore, steeper slopes conditions have persisted that drive these patterns. enhance the vertical progression by providing the flame Surface Composition class is derived from USDA Forest with more surface-to-surface contact with vegetation Service data resampled to a 30-meter spatial resolution. growing upslope. Increasing the contact area of the flame with the fuel causes a fire to burn more quickly and4
    • White Paper Assessing and Understanding Your Wildfire Risk gFinal Brushfire Fuel Rank Fig. 7Data Model CompositionVegetation, slope, aspect, and surface composition classcombine to create the composite CoreLogic Brushfire RiskModel (Figure 6). In the process of analyzing wildfire risk,each element is first evaluated to determine its individualcontribution to fire risk and then assigned a risk factor.The individual risk factors are then weighted to reflecttheir individual contribution to overall fire risk beforebeing combined to identify the overall Brushfire Risk. Theresulting risk values are divided into four risk categories:(1) Low/None (including Urban and Agriculture); (2)Moderate; (3) High; and (4) Very High. The final BrushfireRisk data is stored as a set of GIS polygons. Fig. 8Fig. 6FIREbreak+ (WUI) Data ModelUnderstanding the geographic areas with the highestlikelihood of fire is the key to modeling risk. Insurancecompanies are most concerned with the threat of fire toresidential and urban structures, and wildfire that does notaffect residences and structures is a less important matter.Therefore it is only logical to pay attention to areas in Wildfire damage to residences is often linked to thewhich high population density and wildlands comingle. location of structures in or near the WUI (Figure 9, on page 6). Likewise, wildlands containing large contiguousThe area of highest brushfire risk to residential areas of natural vegetation adjacent to residential ordevelopment is the interface between built-up areas developed areas pose a significant brushfire risk. Byand wildlands. A wildland area adjacent to residential definition, wildlands are not solely limited to maturedevelopment often contains substantial amounts conifer forests, and are often associated with grasses,of fuel in the form of natural vegetation (Figure 7). shrubs, chaparral and/or deciduous forests in variousThe transitional region, or the WUI, is identified in combinations and plant densities. Many of these fuels poseFIREbreak+ as the area in which residences and wildland a significant risk to individual structures and urban areasvegetation meet and/or intermingle (Figure 8). The WUI due to their burn potential and proximity to the WUI.is an obvious area of concern due to the potential forfire to migrate from natural fuels in the wildland to thestructures in the more densely developed areas. 5
    • White Paper Assessing and Understanding Your Wildfire Risk g Fig. 9 (credit usfws) The CoreLogic FIREbreak+ Layer provides detailed information on the residential density surrounding an insured property that can be combined with a distance calculation to the nearest wildland region. All three properties in Figure 10 are in close proximity to the WUI. Together, the CoreLogic Brushfire Risk and FIREbreak+ data models are the basis for estimating brushfire risk with great accuracy, with the two data layers providing the visual context: ►► Identifying areas of elevated fire risk. ►► Determining if a residence or insured structure is in a high fire risk area. ►► Calculating the distance from a residence/structure to the nearest high risk area. ►► Estimating the distance to the Wildland/Urban The CoreLogic FIREbreak+ data layer consists of a Interface. high-resolution grid of 1/4 x 1/4 mile cells. The associated attribute table contains the number of residences per cell. ►► Verifying clusters of insured residences/structures Each grid cell is classified into one of nine categories, in high risk areas. based on the number of residences. The Wildland, Urban Non-Residential (airports, golf courses, industrial parks, Fig. 10 etc.), Agriculture and Water categories have no residential population density. Table 3 displays the FIREbreak+ residential density classes and an example map of the classes are shown in Figure 10. Interface areas which have a high residential density are of primary importance since it is these areas, in correlation with wildlands, which may have the highest potential for loss. table 3 — firebreak+ categories Description Houses Houses Acres per 1/4 mile cell per sq mile per house Wildland 0 0 0 Urban Non-Residential 0 0 0 Agriculture 0 0 0 Water 0 0 0 Scattered Wildfire Single Score Calculation Residential 1-2 1-32 20.1-40 Accurately evaluating the wildfire risk associated with Low Density Residential 3-8 33-128 5.1-20 any location necessitates the inclusion of the previous Medium Density two factors, Brushfire Risk category and the FIREbreak+ Residential 9-40 129-640 1.1-5 classification. Each of these criteria provides specific High Density information that can be used to evaluate the potential risk Residential 41-160 641-2560 0.25-1 of a property. To ease the risk analysis process, CoreLogic Urban 161+ 2561+ <0.25 has developed a methodology for combining both factors and incorporating two additional criteria to calculate a comprehensive risk score based on a 1 to 100 scale.6
    • White Paper Assessing and Understanding Your Wildfire Risk gThe wildfire risk within the boundary of a property Fig. 11 photo by frank franklin - APis correctly determined by the combination of fuel/aspect/slope/surface composition factors that causethe ignition and spread of a wildfire. But what happenswhen the interaction of these four factors results ina categorization of low risk for a property, while theadjacent or surrounding parcels display a high wildfirerisk? Susceptibility to wildfire is not limited to propertiesthat demonstrate a high risk solely within the boundaryof the property. Residences routinely succumb to fire dueto wind carried embers that are blown onto the property.Wind-driven embers can also easily cross a non-fuelexpanse, such as a multilane highway or bare ground.As a result, it is critical to evaluate the proximity of aproperty to the surrounding risk. The single score calculation, attached to parcel data inWildfire research, along with overwhelming historic Figure 12, reveals the importance of evaluating bothevidence, indicates that wind-blown embers (Figure 1 1) location and proximity. The map displays parcels with aare a significant ignition source. It is not necessary for color scheme from green to red identifying the scaledhigh-risk fuel to be in direct contact with a structure risk for the calculated single score. An additional benefitfor that structure to be at risk. In extreme cases, embers provided by the single score calculation is that thecan travel as far as 1/4 mile or more prior to setting fire resulting values can be categorized based on the end-to a structure. Given the right conditions, there is no user’s specific requirements. The example provided indoubt that a home can be ignited from a high-risk source Figure 13 (on page 8) shows the score divided into fourlocated well beyond the recommended 100-foot cleared decision categories. CoreLogic actuarial analysis of thedefensible space. single score values indicates that a score of 81 or higher is correlated to 81 percent of wildfire property loss. ByTo derive a single risk score, CoreLogic uses a calculation comparing the single score returns with specific lossto extract information from both the Brushfire Risk and history, a company may customize the thresholds toFIREbreak+ data and then combines that information create a write/do not write plan that works best for itswith two distance measurements to calculate a numeric book of business.score scaled from 1 to 100. The single score calculationincorporates the: The value of interpreting risk with these three tools, Brushfire Risk, FIREbreak+, and the Single Score ►► Brushfire Risk category calculation, lies in the ability to accurately assess the immediate, adjacent, and distant risk to any property. ►► FIREbreak+ class Fig. 12 ►► Distance calculation from the property to the nearest High or Very High Risk (if the property risk is less than H or VH) ►► Distance calculation to the nearest Wildland polygon (if the property is not already located in a Wildland polygon)These four factors are each assigned an individualrisk score, and when combined, result in a cumulativescore that ranges from 1 (lowest) to 100 (highest)possible risk. The resulting numeric score can thenbe subdivided into categories as dictated by the user’sspecific requirements. 7
    • White Paper Assessing and Understanding Your Wildfire Risk g Fig. 13 Virtually the entire burn zone (86.9 percent) had a FIREbreak+ classification of Wildland, with the remainder Risk Score Decision Type falling into one of the residential density classes 81 - 100 Do Not Write (Figure 15). Additionally, most of the residential density 61 - 80 Inspection Required class polygons were within a very close proximity to a 51 - 60 Refer to Underwriter Wildland polygon. Fig. 15 — cedar fire — firebreak+ classes 0 - 50 Acceptable Risk Case Studies Cedar Fire (San Diego County, CA 2003): Cedar Fire (San Diego County, CA 2003): The Cedar Fire, the single largest wildfire in California history, burned from October 25 to November 4, 2003 destroying 2,232 homes, 22 commercial buildings, and 566 outbuildings. State and local governments spent $32 million fighting the fire and total property losses topped $1.2 billion. The majority of the fire burned wildlands on chaparral Equipment and fire fighter accessibility is a critical and forest-covered slopes in largely undeveloped areas. element for rapid suppression of a wildfire. Road access Most personal property destruction occurred on the and the availability of fire hydrants found within residential fire’s edges along the Wildland/Urban Interface. Figure areas partly explain why fire perimeters like the Cedar Fire 14 shows the perimeter of the Cedar Fire. The Cedar usually closely coincide with the WUI. Figure 16 illustrates Fire burned 280,278 acres, of which a vast majority the alignment of the fire destruction region and the city’s (87.2 percent) were in areas classified by the CoreLogic edge. The majority of the personal property destruction Brushfire Fuel Rank data model with Very High (7.2 occurred along the WUI boundary where the fire spread precent), High (72.2 percent) or Moderate (7.8 percent) from the wildland into adjacent neighborhoods. brushfire risks. Fig. 16 Fig. 14 — cedar fire perimeter8
    • White Paper Assessing and Understanding Your Wildfire Risk gFigure 17 shows the Cedar Fire moving from the Fig. 18Wildland/Urban Interface into a built-up residentialarea where houses became fuel, increasing the spreadof the fire.Fig. 17 Photo by John Gibbins, San diego tribune Fig. 19Scripps Ranch and the Cedar Fire:The Scripps Ranch is a bedroom community in thenorthwestern section of San Diego north of MiramarMarine Air Station and east of I-15. Realtors describe thehousing development as tracts of contemporary homesoccupied by young professionals, surrounded with grovesof eucalyptus trees.On October 26, 2003, the Cedar Fire, powered bySanta Ana winds and fueled by chaparral and the groves ofmature eucalyptus trees, moved rapidly out of the wildlandzone and into the adjacent homes of Scripps Ranch.Before the fire was contained, 345 homes were destroyed.The Scripps Ranch residences burned in the CedarFire (Figure 18) provide an excellent illustration of whyBrushfire Risk data must be combined with FIREbreak+ Table 4 — Distribution of destroyed homesclasses to predict potential fire losses. by density classVirtually all homes destroyed in Scripps Ranch were FIREbreak+ Class Percentactually located in a low brushfire potential zone, but in Urban 7.8%very close proximity to a high brushfire fuel rank zone. High Density Residential 61.2%The average distance to a High or Very High zone was Medium Density Residential 29.5%1,382 feet. Given high wind speed, years of drought, and Low Density Residential 1.5%vegetation stress, this distance was largely meaningless.The FIREbreak+ classes shown below (Figure 19 andTable 4) illustrate the proximity of destroyed homes tothe wildland region. Burned homes averaged 1,971feet from the WUI. 9
    • White Paper Assessing and Understanding Your Wildfire Risk g Figure 20 shows what was left of the home at 12885 Fig. 22 Meadowdale Lane after the fire. Figures 21 and 22 give the location of the home relative to both a high brushfire zone (636 feet) and to the WUI (885 feet). Although the home was located in a low brushfire risk zone and a medium density residential zone, proximity to the WUI proved to be the most significant variable. Homes, once ignited, provided additional fuel that spread the fire. Fig. 20 Station Fire (Los Angeles County, CA 2009): The 2009 Station Fire in the Angeles National Forest just north of the city of Los Angeles is the largest wildfire to occur in Los Angeles County. Figure 23 displays the perimeter of the fire overlaid onto the CoreLogic Brushfire Risk Data. Fig. 23 Fig. 21 The arson-caused fire burned the chaparral and conifer- covered mountains on the edge of the city of Los Angeles for nearly two months before being fully contained on October 16th. During that time it consumed 161,189 acres (251 square miles) and affected 289 properties. Overall, more than 12,000 homes in a dozen different Los Angeles communities were threatened. A considerable fire-fighting effort with a total cost over $95 million prevented even more loss to residential properties along the WUI on the north side of the city (Figure 24).10
    • White Paper Assessing and Understanding Your Wildfire Risk gFig. 24 photo by jae c. hong - ap The spatial relationship between the Wildland and the Urban areas cannot be captured with categorical data. As previously mentioned, the location of wildland adjacent to the urban edge is a critical consideration in the analysis of wildfire risk for homes within an urban boundary. Table 6 identifies the single score calculations for all of the fire damaged properties in the Station Fire. There is no distinction between urban and non- urban, and the results of the calculation effectively and accurately assign risk to the urban properties. table 6 — single scores for properties damaged in the station fire Wildfire Single Score Count Average Value 81-100 168 $1,024,010The vast majority of the area inside the fire perimeter is 71-80 77 $943,376categorized as High and Very High Risk. However, most of 61-70 40 $685,255the property damage realized from the fire was focused on 51-60 4 $575,500residences located along the edge between the city and 50 and below 0 $0the wildland. The evaluation of the area in and aroundthe Station Fire using the CoreLogic Brushfire Risk Model Figure 25 clearly shows the clustered distribution (greenand the single score calculation confirm the accuracy of points) of the affected properties along the perimeterthese tools in effectively determining brushfire risk. of the fire, which coincides with the urban edge. TheThe categories defined by the CoreLogic Brushfire CoreLogic Brushfire Risk Model identifies risk for non-Risk Model accurately identify the risk in and around urban properties, while the single score calculationthe properties affected by the Station Fire. As shown accurately assesses the risk for all properties, includingin Table 5, a total of 148 of the 289 damaged or destroyed urban structures that are vulnerable to fires that occurproperties were categorized as Very High, High, or adjacent to the property.Moderate Risk. There were no fire damaged properties Fig. 25 photo by jae c. hong - apthat were categorized as Low Risk outside the urban area.table 5 — risk car for properties damagedin the station fireBrushfire Risk Properties Average ValueVery high 19 $508,640High 100 $703,756Moderate 29 $488,550Low 0 $0Urban 141 $1,157,540Total 289 $948,619The table clearly identifies the value of the Brushfire Riskdata as an accurate method of identifying risk in non-urban areas. More than 80 percent of the fire-affectedproperties were located in the two highest (Very High andHigh) Risk categories. 11
    • White Paper Assessing and Understanding Your Wildfire Risk g The final table (7) illustrates the importance of adding CONCLUSION the single score calculation when evaluating brushfire risk in and around urban areas. Nearly half the homes Fire risk is a function of many variables. No single variable affected by the Station Fire were located within an is sufficient to predict risk with an acceptable degree of urban boundary. It would be easy to assume that a accuracy. Insurance companies that underwrite policies brushfire is of little concern to a homeowner with based on potential risk have to consider all variables a manicured lawn and 100 feet of defensible space. that contribute to the probability of catastrophic fires However, Table 7 clearly demonstrates that through the that affect residences and structures. The CoreLogic addition of the single score calculation, homes located Brushfire Risk Model, FIREbreak+ and Wildfire Single adjacent to higher risk areas are at an elevated level of Score provides an accurate and comprehensive system for risk. Urban homes that were damaged in the fire and calculating brushfire susceptibility. Risk scores from these would not outwardly appear to have a high brushfire risk data layers and calculations can be assigned to every do indeed score well above the threshold for higher risk structure in a book of business. Even structures in Low as calculated by the single score. More than half Risk brushfire zones can be highly susceptible to fire if (55 percent) of the urban properties damaged by the they are in or near the WUI. Most of the homes destroyed Station fire had a score of 81 or above. All of the urban by the Cedar Fire, for example, were in Low to Medium homes damaged by the fire receive a single score that is brushfire risk zones, but were very close to the WUI and above the cautionary threshold of 50. would have an elevated single score as a result. As suburban expansion increases, the potential for table 7 — brushfire category and single score for urban properties damaged in the station fire insured losses increases proportionally. Since a single fire can mean millions of dollars in losses, underwriters Brushfire Wildfire Count Average must make sure that policy premiums and guidelines C ategory Single Score Value accurately reflect potential risk. Brushfire Risk and Urban 81-100 78 $1,363,718 FIREbreak+ scores, along with the single score calculation, Urban 71-80 30 $1,115,217 when combined with past actual loss locations, provide Urban 61-70 33 $708,688 insurance companies with an objective, quantitative and justifiable model for underwriting.12
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    • White Paper Assessing and Understanding Your Wildfire Risk gAbout CoreLogicCoreLogic (NYSE: CLGX) is a leading provider of consumer, financial and property information, analytics and servicesto business and government. The company combines public, contributory and proprietary data to develop predictivedecision analytics and provide business services that bring dynamic insight and transparency to the markets itserves. CoreLogic has built the largest and most comprehensive U.S. real estate, mortgage application, fraud, and loanperformance databases and is a recognized leading provider of mortgage and automotive credit reporting, property tax,valuation, flood determination, and geospatial analytics and services. More than one million users rely on CoreLogic toassess risk, support underwriting, investment and marketing decisions, prevent fraud, and improve business performancein their daily operations. The company, headquartered in Santa Ana, Calif., has more than 6,500 employees globally with2010 revenues of $1.6 billion. For more information visit www.corelogic.com.FOR MORE INFORMATION, PLEASE CALL 1.800.447.9959© 2011 CoreLogic, Inc.CORELOGIC and the CoreLogic logo are registered trademarks of CoreLogic, Inc. corelogic.comAll other trademarks are the property of their respective holders.Proprietary and confidential. This material may not be reproduced in any form without expressed written permission.WP_Spatial_Wildfire_1109_01