Do you know how your specific policy locations will be affected by a wildfire event? Are you able to operationalize historic and current wildfire data to answer these questions? Understanding how your policies may be affected due to wildfire activity can help you quickly review and price new policies while ensuring that exposure is adequately covered. The PreciselyID, Spectrum Global Geocoding, and Wildfire Risk provide access to industry-leading precision for home and commercial address locations when reviewing historical and real-time wildfire events. Join this on-demand webinar to learn how to mitigate costs and streamline business processes – in underwriting, risk selection, and claims processing.

1. Understand Wildfire Risk
In Underwriting, Pricing and Claims Processing
Daniel Tatro | Solution Architect, Insurance
Chris White | COO, Anchor Point

2. Understand Wildfire Risk
10
Million
Acres
60,000
Fires
50
States
2

3. Fuel
Weather
Topography
A Model to Predict Fire Behavior
3 Integrated Models
• Wildland – primarily wooded
areas
• Intermix – mixture of wooded and
developed land
• Interface – locations where urban
and suburban development
borders wildland/intermix
Understand Wildfire Risk
3

4. Wildfire Risk provides a scientific and up-to-date
wildfire hazard and risk assessment database for
the U.S.
Insurers and re-insurers can use Wildfire Risk to:
• Assess wildfire risk exposure, underwrite policies, and
calculate probable maximum loss (PML) with respect to
the geographic distribution of a book of business
• Create wildfire risk values for personal line limits, new
business processing, and for evaluating current assets
• Locate a residential property’s proximity to high threat
zones
Wildfire Risk creates fireshed
polygons with risk scores based on:
• Slope
• Aspect
• Vegetation type
• Burn frequency
• Distance to water
• Distance to fire stations
Wildfire Risk

5. Deployment Options
Cloud
API
• Given an address, lat/lon or
PreciselyID, returns fire shed
with all risk scores and the
distance to the nearest high risk
SaaS
• Risk Analyzer delivers at-a-
glance view of property, you
can assess risk and accurately
price premiums in a fraction of
the time.
Understand Wildfire Risk
5
Data
• Spatial data enables your
analytics and data science
initiatives
• “Roll your own” software
solutions
Custom
• Bespoke APIs
• UI Integration
• Professional Services
• Wildfire Consultation

6. Wildfire Risk Modeling
for Insurance
Fire Risk Pro A product of
Chris White
C.O.O., Anchor Point

7. Foundation of our Methodology
NFPA: FireWise Original Development Committee
FEMA: Wildfire Loss Avoidance Methodology /
Home Builder’s Guide / Wildfire Guidance
and Policy 2013
NFPA: National Wildfire Hazard Assessment Methodology
1141, 1143, 1144
IAFC: Leaders Guide to Community Wildfire Planning
ICC: Wildland Urban Interface Code / Training

8. California Projects
Recent Risk Assessment Projects
• Angeles National Forest
• Cleveland National Forest
• San Manual Indian Reservation
• City of Mammoth
• City of San Mateo
• City of Mill Valley
• Corty Millbrae
• Visit FD
• San Marcos FD
• Escondido FD
• Poway FD
• San Bernardino County
Custom No-HARM Risk Assessments
• Missoula county, Montana
• Ace Insurance, CO
• Taos County, New Mexico
• Chelan County, Washington
• Cowlitz County, WA
• Eagle County, CO
• City of Aspen / Aspen Fire Protection Dist.
• State of Nebraska
• Los Alamos National Laboratory
• Inyo County
• Mono County
• Placer County
Community of:
• Eldorado Hills
• Lexington Hills
• Forest Hill
• Safari Highland
• Arrowhead Resort
• San Antonio Heights
• Wrightwood

9. Data Use
• City & County Government / Land Use Planning
• Regulatory overlay zones
• Individual home assessments (No-HARM = Fire Context)
• Pre-Incident Attack Planning
• CWPP development and updates
• Grant Acquisition
• Municipal Bonds
• Utilities
• Insurance

10. 3 Integrated Models – Wildland, Intermix,
Interface (Ember Exposure Zone)
Intermix
Wildland
Interface

11. V4 Approach

12. Fire Behavior – 30m
custom analysis leads
to increased accuracy
and flexibility for
updating.
Hyper local weather
data from Athenium
Analytics & location
data from Precisely
enhances accuracy.
Rating transparency
in the form of full risk
description to
understand HOW
the risk was derived.
V4 Approach

13. Fuel – Weather and Topography
Initial Fuels Modeling – LandFire 2.0

14. Hand digitized features in CA & CO
Fuel Islands

15. Multiple diverse base weather models are
used to inform fire behavior
Hybrid Wind Zones with Historic Fire Perimeters

16. Fire Behavior
Analysis FlamMap
& Behave

17. Severity (Composite)

18. Firesheds
Traditional Wildfire Models Are Built For The Wildfire Professional Not Insurance Professionals
Fine-scale heterogeneity,
inherent in input data
When you zoom in it’s all Pixels Problematic for Decision Making
The Colony, FireWise community Bastrop, TX

19. SOLUTION:
ZONAL SUMMARIZATION BY “FireShed”
• North Aspect
• Same fuel type
• Same slope
• Same Fire Behavior

20. Spatial Integration
Each FireShed “looks” around itself and takes on some of the surrounding hazard and risk

21. V4 Frequency Enhancements
Frequency– Integration of
multiple new datasets
which provide observed,
simulated and modeled
probability at multi-scales.
Historic ignitions that created fires over 100 acres (FSim)

22. Frequency (Composite)
FSim Burn Probability
Perimeters > 100 Acres
Distance to Transmission lines

23. Probability Of Ignition (P.I.G.)
Agriculture
High Altitude

24. It Takes Both Severity And Frequency To
Create Significant Areas Of Concern
Grass
Timber
Timber
Grass

25. Mitigation Factors
Golf Courses
Agriculture –
Vineyards
Treatments in
CA, goat grazing
in yellow

26. Suppression Factors
Distance to
Dip and
Draft Water
Topographic
Position
Distance to
Fire Station

27. Damage Factors
Historic
Loss
Insect
and
Disease

28. Community Factors
(Intermix Areas)
Distance
to Veg
Distance
Between
Structures
Existing Vegetation
Cover

29. Interface Elements
• Fire behavior including:
• Max spotting distance
• Intensity
• Crown fire
• Wind speed
• Proximity to veg
• Smoke effects
• Distance between structures
• Topographic position
• Fuel islands
• Wind-aligned roads Max Spotting Distance
Wind Aligned Roads

30. Fire Behavior
Calculations
For Embering
For some fuel models – Embers
are sustained for long distances
Others degrade quickly

31. Interface / Ember Zone - Wildfire Risk is Calculated
Differently in Urban Areas than it is in Wildland Areas
Interface of urban
and wildland
Wildland

32. Parcel Ratings within a FireShed are Clear
and Data Granularity is Provided

33. Tubbs Fire
Santa Rosa, CA
PRECISION TO IDENTIFY HIGH RISK IN AREAS
NOT INTUITIVE TO FIRE THREAT

34. After
Tubbs Fire

35. Wildfire Class:
Interface
Risk 50 Score:
35 Risk
Description
High

36. Goodness of Fit Testing We examined 12,952 (structural) losses occurring in 16 fires across
1,287 No-HARM FireSheds in California and Colorado for the years 2013, 2017, and 2018.
Results for Wildland, Intermix and Interface FireShed types in California with Colorado
were excellent, being completely consistent with No-HARM.
Philip Turk, PhD; Western Data Analytics, Denver, CO

37. Chris White
303-665-FIRE (3473)
cwhite@AnchorPointGroup.com