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

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

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.

Data Quality - Components

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.

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

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

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

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

 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.

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.

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

 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.

Data Quality – Modeling Process

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.

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

 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.

 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.

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

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

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

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

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

Data Quality – Impact on Loss Results

CharacteristicInformationAAL% Change ZipUnknown Street AddressUnknown ParcelUnknown OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown — CharacteristicInformationAAL% Change Zip32901 Street AddressUnknown ParcelUnknown OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL ParcelUnknown OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel OccupancyUnknown ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel % OccupancySingle Family ConstructionUnknown # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel % OccupancySingle Family % ConstructionWood Frame # StoriesUnknown Year BuiltUnknown AreaUnknown SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel % OccupancySingle Family % ConstructionWood Frame % # Stories1 Year BuiltUnknown AreaUnknown SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel % OccupancySingle Family % ConstructionWood Frame % # Stories % Year Built1987 AreaUnknown SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel % OccupancySingle Family % ConstructionWood Frame % # Stories % Year Built % Area1440 SecondaryUnknown % CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel % OccupancySingle Family % ConstructionWood Frame % # Stories % Year Built % Area % SecondaryGable roof, unknown pitch CharacteristicInformationAAL% Change Zip — Street Address621 Burr Street, Melbourne, FL % Parcel % OccupancySingle Family % ConstructionWood Frame % # Stories % Year Built % Area % SecondaryGable roof, unknown pitch % Overall % © 2014 JLT Re. All rights reserved.

(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.”

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….

Data Quality – Q & A

Data Quality Quiz

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

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

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

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

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

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

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

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

Data Quality – Case Study

 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.

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

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