An Approach to Pricing Natural Perils

Transcription

1 17th An Approach to Pricing Natural Perils Tim Andrews David McNab Ada Lui Finity Consulting Pty Ltd 2010

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3 Why is everyone talking about the weather?

4 The Melbourne and Perth storms were further evidence that severe weather is becoming more frequent and severe, according to Mike Wilkins, CEO of IAG. His actuaries calculated the likelihood of two $1 billion events occurring in quick succession to have a 1 in 1400 year probability (Sep 2010) "Climate change, we believe, is a fact. It triggers natural disasters and the number of these natural disasters has more than doubled since the 1980s Ernst Rauch, Munich Re (Jul 2010) We ve seen more losses in areas in terms of threat and severity that we haven t seen before Robert de Souza, Aon Benfield (Nov 2010)

5 Historical Estimated Underwriting Ratio Home 20% 10% 0% % -20% -30% -40% Source: ISC and APRA to 2001, ISA from 2002 onwards

6 Breakdown of Weather and Attritional Losses - Home 140% 120% 100% Loss Ratio 80% 60% 40% 20% 0% Mar-02 Sep-02 Mar-03 Sep-03 Mar-04 Sep-04 Mar-05 Sep-05 Mar-06 Sep-06 Mar-07 Sep-07 Mar-08 Sep-08 Mar-09 Sep-09 Mar-10 Attritional Weather & Events Source: ISA, Insurance Council Disaster List

7 Our Objective Use examples of bushfire, storm and cyclone to illustrate approaches that may be used to estimate cost of natural perils Focused on Home

8 Roadmap Background What do natural perils cost? Growing availability of data What overall cost to allow for Examples: Bushfire, Storm, Cyclone

9 Background

10 What do natural perils cost? On an annual basis, estimate of industry ground up average cost for Home: Peril AAL ($m) Storm $800m to $1,000m Bushfire $100m to $150m Cyclone $150m to $250m Earthquake $50m to $100m Total $1,100m to $1,500m Average annual cost over last 5 years: Approximately $3.0 billion (all Home costs)

11 Available data is expanding all the time Insurance Council Catastrophe listing Flood database Geographic data Some of this already compiled by third parties Bureau of Meteorology wind and rain data Insurer s own experience, or pooled industry data (eg Insurance Statistics Australia) Catastrophe modelling results Competitor rates.but there are still a lot of gaps

12 What overall cost should we allow for?

13 Natural Perils Cost Build Up Earthquake RI Premium Cyclone Bushfire Loss Ratio (%) Average Cost of Retention Loss Ratio (%) Flood (nonriverine) Hail Attritional Events Storm

14 Example 1: Bushfire

15 Drivers of Bushfire Risk Broad geographic factors Humidity Temperature Factors within region and potentially address-specific Site specific landscape (eg slope of land and nearby topography) Proximity to fuel Relatively simple approaches can probably give significant gains for many insurers

16 Bushfire Average Annual Loss 1,400 Inflated Insured Cost (2010$) 1,200 1, Hobart Adelaide Hills Ash Wednesday Canberra Black Saturday Source: Insurance Council disaster list, adjusted for population growth and price inflation Historic average annual loss of around $85 million Allowance for some events not already in data Is climate change impacting? Could assume around $100 million in average annual loss

17 Risk Index Benefits of having a risk index Compare for an identical risk on a zone by zone basis Assists with technical costing Can compare indices based on different sources Can be useful for checking from an intuitive point of view

18 Variations by State 1,400 Inflated Insured Cost (2010$) 1,200 1, State Insurance Council PerilAus Other Selected ACT ?????? NSW ?????? NT ?????? QLD ?????? SA ?????? TAS ?????? VIC ?????? WA ?????? Note: PerilAus index based on analysis of PerilAus scores by postcode ACT NSW NT QLD SA TAS VIC WA Source: Insurance Council disaster list, adjusted for population growth and price inflation

19 Zone 34 Dandenong Ranges

20 Bushfire Risk - Kinglake VIC 2009 Bushfires Around 900 residences Most are fairly close to bushland

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22 17th 7 10 November 2010 Sheraton Mirage, Gold Coast Bushfire Risk Boronia Around 12,000 addresses Only a small number are close to bushland

23 Bushfire Top Down Approach VIC $40m (12%) Zone Rel: 2.0 Zone Rel: <1 ICA Zone 34 (Dandenong ranges) $5m (24%) P/C Rel: 2.5 P/C Rel: Kinglake $0.1m (60%) Premium loadings up to 100% State Rel: 2.0 Australia $100m (5% of gross building prem) 3155 Boronia Other Zones $0.01m (4%) Other states $35m Premium loadings from 0% up Note that these numbers are purely illustrative $60m State Rel: <1 State relativities States with substantial risk are VIC, NSW, SA, TAS Mapping of number of houses ISA data PerilAus ICA relativities Combination of State and Postcode approaches Postcode relativities Index based on number of houses proximity to bushland

24 Example 2: Storms

25 Key Challenges for Storm What is the true annual cost given the historical experience is so volatile? How should this cost be allocated within the portfolio?

26 Average Annual Loss Has it been more or less stormy than average? 200 National Storm Index 180 National Storm Index Year Average Annual Score Note: Indices based on a subset of Bureau of Meteorology rain and wind data to 30 June Index set to a base of 100 at June 2010 Storm scores could be used to assess whether last 3, 5, 10 years of actual data is reflective of the longer term

27 Storm Risk Index Top down approach to allocation What can be used to establish geographic relativities? Historical data BoM rainfall and wind storm scores Elevation data Other interesting risk measures PerilAus, NFID Index (NSW Average set at 100) Storm Score Index (8 Year Rolling Average) ACT NSW NT QLD SA TAS VIC WA Note: Indices based on a subset of Bureau of Meteorology rain and wind data to 30 June Index set to a base of 100 for the average of NSW.

28 Example 3: Cyclone

29 Cyclone 4,000 3,500 Inflated Insured Cost (2010$) 3,000 2,500 2,000 1,500 1, Source: Insurance Council disaster list, adjusted for population growth and price inflation Sparse losses in historical data More appropriate to use a bottom up approach

30 Cyclone Bottom up approach Reliance on catastrophe modelling Need to specify the right risks to input Different use of reinsurance models Not PML, rather cost per risk

31 Cyclone Risk

32 Summary of conclusions

33 Summary Build up of natural perils cost Top down/bottom up approach Use as much data as possible Construction of a risk index

34 Questions?