homeowners reserving it’s not as easy as it looks

Homeowners Reserving

It’s Not As Easy As It Looks

Casualty Loss Reserve Seminar

September 13, 2004

Presenters

Mark Allaben FCAS, MAAA– VP and Chief Actuary– Personal Lines– The Hartford

Betsy DePaolo FCAS, MAAA– VP and Actuary– Personal Lines Reserving and CW Pricing– Travelers

Homeowners Reserving

LDF's 1 to 2 2 to 3 3 to 4 4 to 5 5 to 61993 0.9910 0.9996 0.9998 0.9972 0.99921994 0.9917 0.9930 0.9955 0.9972 0.99781995 0.9996 0.9882 0.9932 0.9978 0.99881996 0.9982 0.9929 0.9960 0.9958 0.99981997 0.9788 0.9930 0.9914 0.9985 0.99991998 1.0050 1.0010 0.9993 1.00141999 0.9941 0.9988 1.00002000 1.0489 1.00742001 1.0376

3 year avg 1.0268 1.0024 0.9969 0.9986 0.99954 year avg 1.0214 1.0001 0.9967 0.9984 0.99915 year avg 1.0129 0.9986 0.9960 0.9982 0.99915 ex hi/lo 1.0122 0.9976 0.9961 0.9978 0.9992

2003 Industry Schedule P data

Homeowners Reserving

Short Tailed– Within 12 months

98% of ultimate claims have been reported 96% of reported claims have been closed 93% of ultimate dollars have been incurred 85% of ultimate dollars have been paid

Many considerations in first year of development

Topics of Discussion

Catastrophes Non Catastrophe Seasonality Coverage Expansion/Contraction Mix of Business Reinsurance Environmental Changes

Catastrophes

Seasonality of Occurrence Differences in Development Differences in Frequency Differences in Severity Other Issues Catastrophe Modeling

CatastropheDefinition

ISO definition: Any event with industry insured damage greater than $25 million

Not just Hurricanes and Earthquakes Can also include

– Hail Storms / Thunderstorms– Snowstorms/Blizzards/Ice storms– Wildfires– Winter Freeze

Catastrophe Seasonality

# of CatastropheCatastrophes $

1st Quarter 22.6% 33.0%2nd Quarter 37.8% 35.7%3rd Quarter 21.8% 16.6%4th Quarter 17.8% 14.6%Total 100.0% 100.0%

1991-2003 dataExcluding Hurricane Andrew (3Q1992)

CatastropheTornado Seasonality

050

100150200250300350400

Tornados

Jan March May July Sept Nov

Month

Three Year Average 2001 to 2003

Series1

Source: Storm Prediction Center Historical Data

CatastropheHurricane Seasonality

0

10

20

30

40

50

60

70

Jan March May July Sept Nov

Hurricanes from 1900 to 2000

Series1

Hurricanes by Month

CatastropheWildfire Seasonality

See Attached

Catastrophe Frequency and Severity

Differences in Frequency by quarter

Catastrophe Frequency Relativities(3 year averages)

0.200

0.700

1.200

1.700

2.200

15 mo 18 mo 21 mo

1st Q

2nd Q

3rd Q

4th Q

Catastrophe Frequency and Severity

Differences in Severity by quarter

Catastrophe Severity Relativities(3 year averages)

0.600

0.7000.800

0.900

1.0001.100

1.2001.300

1.400

15 mo 18 mo 21 mo

1st Q

2nd Q

3rd Q

4th Q

CatastropheSeasonality

Even the occurrence date within the quarter can have a significant impact on development

Examples:– Hurricane Isabel occurred on 9/18/03, near the

end of the 3rd quarter– California Wildfires occurred in October 2003, the

beginning of the 4th quarter

CatastropheDifferences in Development

Acc Qtr 3 Month LDF2002Q1 1.7992002Q2 1.5162002Q3 1.5132002Q4 1.4822003Q1 1.5492003Q2 1.3262003Q3 5.4312003Q4 1.0552004Q1 1.290

Hurricane Isabel 9/18/03

California Wildfires

October 2003

CatastropheReserving Models

Use of Catastrophe Models– Post Storm Simulation

Storm Track, Wind speed, Tides

– Exposure Based Projection Deductibles, construction, location, specialized

coverage

– Adjust for local conditions Demand Surge, Debris removal

One Tool in the Loss Reserving Tool Belt

CatastropheOther Issues

Large catastrophes may have extremely different claim patterns depending on circumstances– Difficulty in reaching claimants– Lack of Electricity, Phone service– Use of Additional Living Expense Coverage– Issues with Supply and Demand of building

materials

CatastropheWebsites

Hurricane – National Hurricane Center– www.nhc.noaa.gov

Tornado – Storm Prediction Center– www.spc.noaa.gov

Wildfires – USDA Forest Service– www.fs.fed.us/fire/news

Non Catastrophe Seasonality

Differences in Frequency and Severity for Catastrophes vs. Non-Catastrophes– Catastrophe frequency is much lower than non-

catastrophe frequency– Catastrophe severity is higher than non-

catastrophe in 2nd and 3rd quarters, lower in 1st and 4th quarters

PLIC Actual & Modeled Ex Cat Ex Mold Pure Premium 20 Accident Quarters

0

50

100

150

200

250

300

Actual062004 CWModeled062004 CWModeled122003

Coverage Expansion/Contraction

Mold Sinkhole Sewer Backup Extra Contractual Liability Automatic Increased Limits Guaranteed Replacement Cost Other?

Coverage Expansion/ContractionMold

Increases in frequency and severity of mold-related claims began to be seen in late 2000 / early 2001.

Majority of the claims were seen in the state of Texas.

Severity of claims much greater than average HO claim severity

Coverage Expansion/ContractionMold - Industry Reaction

Companies began implementing limits on mold coverage or excluding mold from coverage altogether

Typical mold limits are $5,000 or $10,000 Limits caused average severity to begin

leveling off

Coverage Expansion/ContractionReserving Issues with Mold

Mold claims tended to have longer development than normal HO claims

As exclusions and limits began to take effect, the development patterns seen during mold time period were no longer accurate predictors for development

Coverage Expansion/ContractionMold Development

6 Month Development Factors

1.000

1.020

1.040

1.060

1.080

1.100

1.120

Coverage Expansion/ContractionMold Development

9 Month Development Factors

0.990

1.000

1.010

1.020

1.030

1.040

1.050

Coverage Expansion/ContractionMold Development

AQ Incurred LDF's 3 6 9 12

Countrywide1998Q1 - 2000Q4 1.449 1.048 1.024 1.0202001Q1 - 2002Q2 1.475 1.080 1.038 1.029Change 1.8% 3.1% 1.4% 0.9%

Texas1998Q1 - 2000Q4 1.489 1.079 1.043 1.0302001Q1 - 2002Q2 1.781 1.192 1.103 1.059Change 19.6% 10.5% 5.8% 2.8%

Countrywide Excl Texas1998Q1 - 2000Q4 1.445 1.044 1.021 1.0182001Q1 - 2002Q2 1.432 1.060 1.025 1.023Change -0.9% 1.5% 0.4% 0.5%

Coverage Expansion/ContractionMold - One Reserving Method

Separate Mold from Other losses– Track separately

Create a Mold Prediction Model– Mold comes from Water Damage– Use Frequency and Severity Method

Number of water damage losses turn to mold Average value of mold loss Mold claims times average value equals losses

Coverage Expansion/ContractionMold Prediction Model

Claims Incurred

Water Claims Mold Average

Year Damage Mold Freq. Losses Severity

2000 3,267 784 24.0% $22,805,776 $29,089

2001 3,223 896 27.8% $30,918,272 $34,507

2002 2,576 801 31.1% $22,339,890 $27,890

2003 3,200 1,024 32.2% $15,360,000 $15,000

Note: 2003 includes a cap of $10,000 per mold claim.

Coverage Expansion/ContractionNew Mold Threat

Multiple events in a short Time Horizon– Damage from Hurricane Charley not repaired

before Hurricane Frances hit– Electricity not restored to properly dry out property

after a severe weather event– Tornados followed by severe thunderstorms

Coverage Expansion/ContractionExtra Contractual Liability (ECL)

“Bad Faith” Claim handling practices Payments in excess of coverage amounts

– Waiver of deductibles– Extension of additional living expenses– Negotiated losses/ settlements – coverage

disputes Increasing frequency Impact is to lengthen the development tail

Coverage Expansion/ContractionGuaranteed Replacement Cost

Historically, in event of total loss, Guaranteed Replacement Cost (GRC) coverage could be purchased.

Insurers paid to completely rebuild home, regardless of Coverage A amount.

Problems with underinsurance led insurers to set limit on GRC, typically 120% or 125% of Coverage A

Such a change in exposure could result in a change in development patterns in data

Coverage Expansion/ContractionAutomatic Increased Limits

Annual provision to increase Coverage A (or Coverage C for Condo/Tenant) to account for inflation

Intended to limit chance of underinsurance Does AIL change development patterns?

Coverage Expansion/ContractionOther???

We don’t know what the next “issue” may be Watch for changes in frequency, severity,

development patterns. Communicate with claim department

regarding any trends they may be seeing Implement detailed claim coding so the next

issue can be quickly identified and tracked

Mix of Business

Coverage Form State Deductible

Mix of Business Coverage Form

Dwelling vs. Condo vs. Tenant Coverage– Average Developed Severity

Dwelling: 4,866 Condo: 3,520 Tenant: 3,286

– Average Incurred Frequency (x100) Dwelling: 6.845 Condo: 3.739 Tenant: 1.807

Source: ISO HO Data cube, Accident Year 2002

Mix of BusinessState

Study performed on state-specific loss development patterns

Significant differences in 1st year of development Predominant cause of loss in state appeared to be

the primary factor Four states (NC, SC, AL, WA), which has a heavier

mix of fire claims, developed faster than other states (smaller LDF’s)

Mix of BusinessState

Incurred Claim Frequency (x 100)

Source: ISO HO Data cube, Accident Year 2002

Washington 4.447New Jersey 4.526Wisconsin 4.663Massachusetts 4.848Florida 4.991

Missouri 9.196Kentucky 10.781North Carolina 10.862Oklahoma 10.886Louisiana 14.360

All States 6.845

Mix of BusinessState

Developed Claim Severity

Source: ISO HO Data cube, Accident Year 2002

North Carolina 2,732Delaware 3,240Kansas 3,699Iowa 3,868Pennsylvania 3,899

Minnesota 6,136Florida 6,160Washington 6,713New Jersey 6,835California 7,446

All States 4,866

Mix of BusinessDeductible

Changes in deductible buying patterns could impact both frequency and severity

Historically, deductibles of $100 and $250 were common

Consumers are moving to $500, $1000 and even $2,500 deductibles as a means to decrease their Homeowners premium

Reinsurance

Facultative Catastrophe

– Layers, Aggregates, Reinstatements State Run Pools

– Florida Hurricane Fund– Citizens (Florida)– Wind Storm Pools– Fair Plans

Environmental Changes

Claim behaviors have shown a marked changed in last several years

Claim frequency has been steadily declining over the past five years

Environmental ChangesClaim Behavior

Incurred Claim Frequency (x 100)

Source: ISO HO Data cube

Dwelling All PolicyForms Forms

1998 9.525 8.5571999 8.697 7.7392000 8.195 7.4382001 7.920 7.2052002 6.845 6.232

Environmental ChangesClaim Behavior

Pattern has been continuing in 2003 and 2004 Drop in frequency most prominent at smaller claim

levels Some of the frequency drop may be explained by

changes in deductible selections But drop in frequency is also seen at claim sizes larger

than average deductible Consumers concerned about large rate increases

following a claim and/or being cancelled/non-renewed Consumers are effectively self-insuring Corresponding severities have exhibited an upward

trend

Conclusions

Homeowners reserving may be easier than most other lines but watch out for the pitfalls

Separate Catastrophe and Non-Catastrophe Claims Examine data by Accident Quarter Watch for signs of unexpected coverage expansion

or contraction which may impact patterns Watch for changes in mix of business (coverage

form, state, deductible) Consider the impact of reinsurance Watch for changes in consumer/claimant behavior

that may signal a turn