Property Reinsurance Ratemaking

Sean DevlinReinsurance Boot Camp on Pricing Techniques July 29, 2005

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Agenda Background ELR determination Primary “Price” Experience Rating Exposure Rating Weighting of Methods Catastrophe Loads and Issues Conversion of Loss Cost to Pricing Summary and Questions

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Background My past experience, particularly

AmRe: 3 years of leading Finite, National and Specialty business pricing GE: 3 years leading Global Property Product Pricing

What I have seen Common mistakes, Emerging exposures Worst and best of the market The most complex treaties Management of a global portfolio and its effect on strategy and pricing

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Exposure Rating

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ELR DeterminationFoundation of Exposure Rating

Which ELR to use? Must match your curve in exposure rating Preference: Eliminate cat as much as possible Options for ELR:

• Full LR• No cat whatsoever• Exclude certain cats

Methodology Equivalent to primary ratemaking, except Need for factors to back out certain cats to match exposure curve, if the match isn’t already made

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ELR Calculation - Per Risk/Pro Rata

Determining your ELR Breakout components

Basic LR – very stable small, non-cat events Risk LR – losses subject to a per risk Layer

• Breakout into layers, like per risk rating• Appropriate blend of experience & exposure

Small Cat LR(s) – experience rate vs. model Modeled Cats

More reasons for breakout? Inuring reinsurance or contract features Understand the drivers of the ELR Appropriate targets for quoting business

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ELR DeterminationTrend Parameters

Cost of contracting labor Size of homes increasing Deductible impacts on frequency and severity Data – shifts in and out of E&S market Excess business Non-standard classes Demand surge

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Note on Primary “Price” Price Monitoring Reports

Typically created to measure price lift circa 2000 Know what is (isn’t) captured

Filed rate changes Schedule modification factors Experience modification factors SIR/Limit Terms and conditions New business

Test for bias Trend or shift in adjusted loss ratios Discuss with client changes More important for high capacity eaters

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Note on Primary “Price” Effect of missing uncaptured price

Typically underestimated the magnitude of change Softening Cycle:

Underestimating decreased rates Underestimating reserves Calendar year results lag true results Delays recognition of results Softening prolonged– damage is slowly realized

Hardening Cycle: Underestimating increased rates Overestimating reserves Calendar year results lag true results Delays recognition of results Hardening prolonged– success is slowly realized

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Primary “Price” (cont’d)

Rate Adequacy Over Time

Time

Ind

ex

Regional

Specialty

National

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Primary “Price” (cont’d)

True Price vs Captured Price

Time

Ind

ex

Price Monitor

Actual Price

“Uncaptured” Rate change

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Primary “Price” (cont’d)Price Assumption Effects on Cal Yr Results

Time/Year

Lo

ss R

ati

o

Plan

Actual

Cal Yr

Calendar Year results understated during soft market

Actual peak of soft market

Should be hardening here

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Exposure and Experience Rating

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Experience Rating Premium Side

Same as pro rata, mostly Splitting up business into exposed and not exposed In split business, parameters may be different Exiting class? Reflect all premium affected if excl.

Loss Side Capping at policy limits – TIV and loss both trend Losses should be on same basis as exposure rating Reflective of per risk definition – READ the slip Two methods to calculate burning cost

Empirical - weighted Fit distribution

Split quoted layers into sub layers to add credibility

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Exposure Rating – Loss Curves (cont’d)

General Considerations ELR must reflect the data underlying loss curve Understanding of the data and assumptions is key

Assumptions of the loss curves Data in exposure profiles

What curves to use PSOLD Lloyds curves Salzmann curves Ludwig curves Curves created by reinsurers

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Exposure Rating – Loss Curves (cont’d)

PSOLD Becoming a standard Most recent data Only one that varies my AOI Has the most variables More on this later

Lloyds curves Reversals exist A premium calculator for facultative Source unknown

Curves created by reinsurers Old data Source unknown in some cases

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Exposure Rating – Loss Curves (cont’d)

Salzmann curves1960 Cov A Fire Losses Only Varied by protection & construction classes Not recommended by Salzmann herself Use was to describe first loss scales

Ludwig curves1984-88 data to update the Salzmann paper Based on Hartford Insurance Co. data HO - all coverages, all perils HO - varies by protection/construction CP - small commercial data CP - varies by occupancy class

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Exposure Rating (cont’d)

What is in the companies profile? Limits – don’t assume, ask if unsure

Business interruption and/or contents included? Policy limit Location limit PML MFL Key location Limits or values for layered business ITV issues

Other coverages Excess policies Subscription business

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Exposure Rating (cont’d)

What is in the companies profile (cont’d)? Any perils excluded? Homeowners

Form (HO-2,3,4,5,6) Coverage A only or all coverages

Farmowners Multiple diverse buildings on a farm One TIV

Smell test for reasonability, especially: Order of magnitude of some TIV Premium allocation

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Exposure Rating - PSOLD 2004 PSOLD

Data from 1992-2002 Can separate business by

Occupancy – 22 groups, diff. strongest btw.• Manufacturing• Non-manufacturing• HPR• Little differences within these groups

State – just distribution of business in a state Gross or Net of Deductible Include/Exclude Cats >$100M industry loss Coverage – BGI, BGII, special, all Include/Exclude WTC Include/Exclude Business Interruption

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Exposure Rating (cont’d)

Issues With PSOLD Not all segments represented evenly by PSOLD Loss history is thin for some groups Based on 1.8M occurrences, after scrubbing Losses above $5M in the database are thin

# of losses > $5M is 421 # of losses > $10M is 243

Refer to a list of large industry losses for more input Blanket policies small amount of database US business only – applicable abroad?

HO – US homes are built out of “cardboard” Factory in US similar to one in UK? Main street business in US same as France?

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Exposure Rating (cont’d)

Application of PSOLD Occupancy classes

22 groups, diff. strongest btw.• Manufacturing• Non-manufacturing• HPR• Little differences within these groups

May need to enter TIV profile by class• HPR business is usually higher in limit• BOP type bussiness usually smaller

Excess Policies Subscription business

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Exposure Rating (cont’d)

Subscription and Excess Policies Participation on a single layer policy

Insured writes 20% of a policy of 5M Reinsurance layer is 500K xs 500K Layer is really 25% of the loss 2.5M xs 2.5M Losses above the 5M limit is not relevant to layer

Pure Excess Policies SIRs are important Limit – TIV or a hard cap Blanket policies are common – allocation issues 10M indivisible premium on 10 locations

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Exposure Rating (cont’d)

Subscription MarketLayers of 50x50 and 50x100500M

25% of250x250

250M

100M50% of 150x100

100M SIR

50x50 reinsurance layer:25M from 25% of 100xs25025M from 50% of 50x200

100x50 reinsurance layer:37.5M from 25% of 150xs350

12.5M unexposed if hard cap of 500M

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Exposure Rating (cont’d)

Don’t Trust the Black Box Check the output for reasonability Contract Match:

Definition of risk• One building (possibly less)• Multiple buildings at one location• Entire policy• Company has sole determination

Exposure profiles Loss curve Dual trigger contracts – cat and risk combined Scope of coverage

READ THE SLIP

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Weighting of MethodsGeneral Considerations

Actual vs. Expected counts to layer (significant) Actual – Needs to be adjusted for volume Severity differences – may need to subdivide layer Make sure that both methods reflect the same risk No loss = no weight to experience? Not necessarily Deficiencies in exposure data or curves Past experience indicative of future Do not be afraid of splitting quoted layer into parts

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Catastrophe PerilPer RiskPro RataCat XL

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Vendor Models –What to Use? Major modeling firms

AIR EQE RMS Other models, including proprietary

Options in using the models Use one model exclusively Use one model by “territory” Use multiple models for each account

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Vendor Models –What to Use? (Cont’d)

Use One Model Exclusively Benefits

Simplify process for each deal Consistency of rating Lower cost of license Accumulation easier Running one model for each deal involves less time

Drawbacks Can’t see differences by deal and in general Conversion of data to your model format

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Vendor Models –What to Use? (Cont’d)

Use One Model By “Territory” Detailed review of each model by “territory” Territory examples (EU wind, CA EQ, FL wind) Select adjustment factors for the chosen model Benefits

Simplify process for each deal Consistency of rating Accumulation easier Running one model involves less time

Drawbacks Can’t see differences by deal Conversion of data to your model format

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Vendor Models –What to Use? (Cont’d)

Use One Model By “Territory” – An Example Weights

Zone CT RMS EQECA EQ 70% 0% 30%Japan EQ 50% 0% 50%FL WS 0% 100% 0%Euro Wind 20% 40% 40%

Factors

Zone CT RMS EQECA EQ 70% 150% 130%Japan EQ 80% 120% 120%FL WS 90% 100% 75%Euro Wind 150% 85% 75%

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Vendor Models –What to Use? (Cont’d)

Use Multiple ModelsBenefits

Can see differences by deal and in generalDrawbacks

Consistency of rating? Conversion of data to each model format Simplify process for each deal High cost of licenses Accumulation difficult Running one model for each deal is time consuming

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Model Inputs Garbage In => Garbage Out

TIV checks/ aggregates

“As-if” past events Scope of data (e.g. RMS – WS, EQ, TO datasets) Which “territories” modeled and not modeled Type of country considered for exposures abroad Clash between separate zones (US – Caribbean)

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“Unmodeled” Perils Winter storm

Not insignificant peril in some areas, esp. low layers 1993: 1.75B – 14th largest 1994: 100M, 175M, 800M, 105M 1996: 600M, 110M, 90M, 395M 2003: 1.6B # of occurrences in a cluster????? Possible Understatement of PCS data

Methodology Degree considered in models Evaluate past event return period(s) Adjust loss for today’s exposure Fit curve to events

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“Unmodeled” Perils (cont’d) Flood

Less frequent Development of land should increase frequency Methodology

Degree considered in models Evaluate past event return period(s),if possible No loss history – not necessarily no exposure

Terrorism Modeled by vendor model? Scope? Adjustments needed

Take-up rate – current/future Future of TRIA – exposure in 2006 Other – depends on data

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“Unmodeled” Perils (cont’d) Wildfire

Not just CA Oakland Fires: 1.7B – 15th largest Development of land should increase freq/severity Two main loss drivers

Brush clearance – mandated by code Roof type (wood shake vs. tiled)

Methodology Degree considered in models Evaluate past event return period(s), if possible Incorporate Risk management, esp. changes No loss history – not necessarily no exposure

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“Unmodeled” Perils (cont’d) Fire Following

No EQ coverage = No loss potential? NO!!!!! Model reflective of FF exposure on EQ policies? Severity adjustment of event needed, if

Some policies are EQ, some are FF only Only EQ was modeled

Methodology Degree considered in models Compare to peer companies for FF only Default Loadings for unmodeled FF Multiplicative Loadings on EQ runs

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“Unmodeled” Perils (cont’d) Extratropical wind

National writers tend not to include TO exposures Models are improving, but not quite there yet Significant exposure

Frequency: TX Severity: May 2003 event of 10B – 9th largest

Methodology Experience and exposure Rate Compare to peer companies with more data Compare experience data to ISO wind history Weight methods

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“Unmodeled” Perils (cont’d) No Data

Typically for per risk contracts without detailed data Typically not a loss driver on per risk treaties However, exceptions exist Methodology

Experience and exposure Rate Compare to peer companies with modeling Develop default loads by layer/location

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“Unmodeled” Perils (cont’d)Other Perils

Expected the unexpected – Dave Spiegler article Examples: Blackout caused unexpected losses Methodology

Blanket load Exclusions, Named Perils in contract Develop default loads/methodology for an complete list of perils

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Using the Output Don’t Trust the Black Box

Data, Data, Data Contract Match:

Definition of risk Definition occurrence Dual trigger contracts Scope of coverage

Modeling of past exposures Need to convert to prospective period TIV inflation Change in exposures

Know what assumptions were used by modeler

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Experience Rating – AdjustmentsTrended Volume

Year Exposure Loss Adj Loss1990 1,000 500 2,0891991 1,100 0 01992 1,210 2,000 6,9051993 1,331 0 01994 1,464 0 01995 1,611 0 01996 1,772 5,000 11,7901997 1,949 0 01998 2,144 0 01999 2,358 0 02000 2,594 2,000 3,2212001 2,853 0 02002 3,138 0 02003 3,452 900 1,0892004 3,797 0 02005 4,177 0 0

Average 650 1,568

Industry 30 yr 80,000Industry (90-04) 100,000

Adjusted 1,255

Reduce 80% for more credible long term experience

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Loadings to final ELConsiderations in final indicated “price”

% of loss? % of ? Combination of above? Target LR, TR, CR? Reflect red zone capacity constraints? “Unused” capacity loads

EL for Layer 100M x 100M is 5M EL for Layer 200M x 100M is 5.1M Loading for 100M x 200M??????

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Capacity Charge - Simplistic

5,000

5,000

0

3,000

3,000

0

600

600

800

250

250

3,500

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

PremiumComponents$000s

50x50 100x100 100x200 200x300Layer

$M

Indicated vs. Capacity

Capacity

Load

EL

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Conversion to PricingGeneral Considerations

Create loss distribution – even if “not needed” Adjust for treaty features – AAD, swing rate, etc. Understand upside and downside of deal “Unpriced” capacity – blown limit, cat on tail of curve Is the rate on line appropriate “Red Zone” catastrophe utilization Treaty correlation to book

Layered/Subscription business Catastrophes

Soft Factors – Don’t be biased, though Check yourself for naive capital – cheap cat cover

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Finishing The Job

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Pro Rata ExampleDetermining your Target Loss Ratio

Loss Ratio Loading Total Example/Comments30.0% 2.5% 32.5% First 100K per risk10.0% 2.0% 12.0% unl xs 100k per risk10.0% 2.0% 12.0% Thunderstorm/Tordano/Hail2.0% 0.5% 2.5% Winterstorm/Wildfire5.0% 5.0% 10.0% Hurricane/EQ

30.0% 0.0% 30.0% Could be negative load for slide87.0% 12.0% 99.0% Total Should be less than 100%

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Key Takeaways

Understand the data inputs Understand your models and parameters Understand strength and weakness of the models Proper match to treaty terms – READ THE SLIP Reflect true primary price Rate for everything Include the untested and unmodeled exposure Work with your underwriter Question everything – Assume nothing at face value

THINK - Don’t Just Go Through The Motions