1. Garbage In = Garbage Out? How Data Characteristics and Details Drive the Results Lizzie Edelstein & Brandon Katz October 9, 2014 THIS AREA IS FREE FOR GRAPHICS/IMAGES OR SOLID COLOUR OR MIXTURE

2.  Introduction  Data Requirements  Modeling Process  Impact on Loss Results  Q & A  Case Study Set-up Agenda © 2014 JLT Re. All rights reserved.

3. Framework of Catastrophe Modelling Calculate Damage Quantify Financial Loss Assess Hazard Generate Stoch. Events Policy Conditions DATA QUALITY Portfolio $$ $ $$$ Apply Vulnerability Flood Height % Damage 2 nd floor 1 st floor Geo-locating Building Characteristics © 2014 JLT Re. All rights reserved.

4. Data Quality - Components

5. Key Data to Capture Portfolio Exposure Value by Location Coverage Specific Limits vs. Replacement Costs Geographic Information Primary & Secondary Building Characteristics Policy Structure Limits/Deductibles/Excess Layers Wind/Flood/Quake Endorsements Facultative Reinsurance Model Output Required Settings Output Detail $ 6 © 2014 JLT Re. All rights reserved.

6. Portfolio Exposure Value by Location (Coverage Specific Replacement Value) © 2014 JLT Re. All rights reserved. Building (BLDG) Contents (CNTS) Business Interruption (BI) Typically Provided Sometimes Provided Often assumed as a percentage of BLDG Sometimes Provided Often assumed as a percentage of BLDG

7. 621 Burr St., Melbourne, FL 32901 Geographic Information Actual Building Interpolated (RMS) 621 © 2014 JLT Re. All rights reserved. 630 610 Location with just street address Location provided with Lat/Long

8. Exposure Data – Primary Building Characteristics © 2014 JLT Re. All rights reserved.  Occupancy  Construction  Number of Stories  Year Built  Square Footage

9. Example of ISO Fire mapping to RMS construction classes  Some other construction classes include: –Manufactured/Mobile Home (With or Without Tie-downs) –Automobiles (Personal or Dealers) –Boats (Various options for length and power/sail) –Inland Marine (Bridges, Towers, Cranes, Pipelines, etc.) Construction – Examples © 2014 JLT Re. All rights reserved. ISO Fire ClassRMS Class 1 - Frame1 - Wood 2 - Joisted Masonry2 - Masonry 3 - Non-Combustible4A - Steel Frame (3 - Reinforced Concrete for HU) 4 - Masonry Non-Combustible3 - Reinforced Concrete 5 - Modified Fire Resistive4A - Steel Frame (3 - Reinforced Concrete for HU) 6 - Fire Resistive3 - Reinforced Concrete 7 - Heavy Timber Joisted Masonry2 - Masonry 8 - Superior Non-Combustible4A - Steel Frame (3 - Reinforced Concrete for HU) 9 - Superior Masonry Non-Combustible3 - Reinforced Concrete

10.  Dwelling  Apartment/Condo  Office  Warehouse  Bowling Alley  Retail store  Restaurant  Parking Garage  Green House  School  Health Care  Etc. Occupancy – Examples © 2014 JLT Re. All rights reserved.

11. Exposure Data – Secondary Building Characteristics Varies by Peril  Hurricane/SCS –Roof Shape –Construction Quality –Cladding Type –Window Protection –Roof Age –Roof Anchorage System  Earthquake –Building Foundation –Building Shape –Construction Quality © 2014 JLT Re. All rights reserved.

12. Model Output © 2014 JLT Re. All rights reserved. Earthquake Severe Convective Storm Terrorism Hurricane Flood

13.  Account Number or Name  Policy Number  Line of Business  Inception Date  Expiration Date  Blanket/Policy Limit  Blanket/Policy Deductible  Wind Excluded (Y/N)  Earthquake Endorsed (Y/N)  Premium (for portfolio management)  Site Number (ID)  Building Number (ID)  State  ZIP Code (5- or 9-Digit)  County  City  Street Address  Latitude  Longitude  Construction  Occupancy  Number of Stories  Year Built  Square Footage (residential risks)  Building Limit/Value  Appurtenant Structures Limit/Value  Contents Limit/Value  Time Element Limit/Value  Site Blanket Limit  Site Blanket Deductible Data Requirements © 2014 JLT Re. All rights reserved.

14. Data Quality – Modeling Process

15. Cat Modelling Process – the Flow of Information Aggregated and Scrubbed Data Raw Data from Clients Review/Reformat Data Run Analyses Provide and Discuss Results with Client © 2014 JLT Re. All rights reserved.

16.  Usually sent as a.txt or.csv file Raw Data © 2014 JLT Re. All rights reserved.

17.  Data is Cleaned and Scanned –Garbage In, Garbage Out  Geocoding Resolution –County –City –Zip Code –Street Address –Lat/Long  Exposure Summaries are Compiled –Make sure submitted data makes sense Raw Data © 2014 JLT Re. All rights reserved.

18.  Deeper dive into the data?  Do they only write in certain states/counties/tiers?  Are all lines of business captured?  Do all risks have a city/state?  Does the zip code match with supplied city?  Do limits make sense for each risk?  Any exceptions for peril deductibles?  Occ/Const/Num Stories/Year Built? Data Review Checklist © 2014 JLT Re. All rights reserved.

19. Review/Reformated Data © 2014 JLT Re. All rights reserved.

20.  Prepare Data for Import  Final Data Checks/Comparisons  Maps  Import Run Analyses © 2014 JLT Re. All rights reserved.

21.  Select Appropriate Perils  Apply Treaties Correctly  Selecting Appropriate Options (Near/Long Term, Demand Surge, Storm Surge) Run Analyses © 2014 JLT Re. All rights reserved.

22. Run Analyses (Example: NA Hurricane) © 2014 JLT Re. All rights reserved.

23. Run Analyses (Example: NA Hurricane) © 2014 JLT Re. All rights reserved.

24. Data Quality – Impact on Loss Results

25. CharacteristicInformationAAL% Change ZipUnknown Street AddressUnknown ParcelUnknown OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown 220.96— CharacteristicInformationAAL% Change Zip32901 Street AddressUnknown ParcelUnknown OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown 232.525.2% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL ParcelUnknown OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown 240.833.6% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726 OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown 222.34-7.7% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726240.833.6% OccupancySingle Family ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown 245.9910.6% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726240.833.6% OccupancySingle Family222.34-7.7% ConstructionWood Frame # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown 235.89-4.1% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726240.833.6% OccupancySingle Family222.34-7.7% ConstructionWood Frame245.9910.6% # Stories1 Year BuiltUnknown AreaUnknown SecondaryUnknown 282.0419.6% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726240.833.6% OccupancySingle Family222.34-7.7% ConstructionWood Frame245.9910.6% # Stories1235.89-4.1% Year Built1987 AreaUnknown SecondaryUnknown 317.4312.5% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726240.833.6% OccupancySingle Family222.34-7.7% ConstructionWood Frame245.9910.6% # Stories1235.89-4.1% Year Built1987282.0419.6% Area1440 SecondaryUnknown 354.3611.6% CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726240.833.6% OccupancySingle Family222.34-7.7% ConstructionWood Frame245.9910.6% # Stories1235.89-4.1% Year Built1987282.0419.6% Area1440317.4312.5% SecondaryGable roof, unknown pitch CharacteristicInformationAAL% Change Zip32901220.96— Street Address621 Burr Street, Melbourne, FL232.525.2% Parcel28.069065 -80.609726240.833.6% OccupancySingle Family222.34-7.7% ConstructionWood Frame245.9910.6% # Stories1235.89-4.1% Year Built1987282.0419.6% Area1440317.4312.5% SecondaryGable roof, unknown pitch354.3611.6% Overall354.3660.4% © 2014 JLT Re. All rights reserved.

26. (Building Located in Palm Beach, FL – An Illustrative Example) Data Input Sensitivity: RMS Case Study Base Case: Industry Default SettingsUnknown Defaults to Industry Average Source: RMS Legal disclaimer note from RMS: “The results quoted in this case study are for illustrative purposes only. Do not assume that they represent actual loss estimates for Palm Beach, FL or any other location.”

27. Example: RMS v11 Storm Surge  New York, NY  TIV $66.3M  Distance to Coast ~150 ft  Multi-Family Dwelling (Apartment/Condo)  AAL without Storm Surge $4,981  AAL with Storm Surge $119,830 Data Quality – Impact on Loss Results (Peril Options) © 2014 JLT Re. All rights reserved. What if you didn’t have the correct address of the location….

28. Data Quality – Q & A

29. Data Quality Quiz

30. Which Occupancy type is better in a windstorm? © 2014 JLT Re. All rights reserved. Mobile Home Single Family Dwelling

31. Which Construction is better in a windstorm? © 2014 JLT Re. All rights reserved. Masonry Wood Frame

32. Which Construction is better in an earthquake? © 2014 JLT Re. All rights reserved. Masonry Wood Frame

33. Which # of Stories is better in a windstorm? © 2014 JLT Re. All rights reserved. Low rise High Rise

34. Which # of Stories is better in an earthquake? © 2014 JLT Re. All rights reserved. Low rise High Rise

35. Which Roof Shape is better in a windstorm? © 2014 JLT Re. All rights reserved.

36. Which Roof Anchors are better in a windstorm? © 2014 JLT Re. All rights reserved. Clips Double Wraps

37. Which Roof Coverings are better in a windstorm? © 2014 JLT Re. All rights reserved. Wood Shakes Clay Tiles

38. Data Quality – Case Study

39.  Groups will receive one of three datasets –Northeast Super Regional writing hurricane –Midwest Mutual writing tornado hail –National writing earthquake  Look through the data set and see if you can find questionable data entries –Highlight the errors or possible errors  Results will be shown modeled as received and as corrected as a comparison Case Study Introduction © 2014 JLT Re. All rights reserved.

40. Case Study Introduction – Exposure Concentrations © 2014 JLT Re. All rights reserved.

41. Case Study – Results RMS RiskLink v11 © 2014 JLT Re. All rights reserved.