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Natural Hazard Property Losses & Climate Change John McAneney Risk Frontiers Macquarie University Sydney NSW Overview • • • • • • • • Risk Frontiers Risk Assessment Climate Change North Atlantic Hurricane Windspeed Data Normalisation of ICA loss database Property losses from bushfires Policy implications Natural Hazard Risk Profiles Risk Frontiers An independent and local research capacity to help insurers better understand and price natural hazard risks in the Asia-Pacific Region by: • • • • Undertaking research in natural hazards Undertaking post-event reconnaissance Developing Probabilistic Catastrophe Loss models Increasing public awareness of natural perils Conceptual framework of risk assessment Mean intensity Vulnerability Exposure Annual Exceedance Probability Hazard Mean damage ratio (%) Risk = f (Hazard, Exposure, Vulnerability) Loss ($ Million) Risk Nicene Creed of Climate Change • Global mean and extreme temperatures are increasing • Heating is due to increasing atmospheric CO2 and other GH gases • Warming is occurring where models suggest it should under increasing CO2 • Sea level is rising • Take greenhouse gases out of the models, earth cooling slightly • If reject increasing GH gases as explanation, need to find some other hypothesis Uncertainties • GCMs are too complex to be fully understood and the climate system depends upon many ingredients that must be represented either empirically or through ad hoc treatments that differ between models • Arguments over the scale and speed of warming in the future • Models have little to say yet about regional implications • Models can’t resolve phenomena like droughts, floods, storms, cyclones, etc. • Attribution of individual weather events to climate change USD millions (2005 $) Costs of weather-related natural catastrophe losses are increasing: why? 2005: Hurricanes Katrina, Rita, Wilma, USD 65 bn 2004: Hurricanes Charley, Frances, Ivan, Jeanne, USD 29 bn 1992: Hurricane Andrew, USD 22 bn 1999: Storms Lothar/Martin, USD 10 bn Source: Swiss Re sigma Catastrophe database Distribution by hazard worldwide of the largest 40 insured losses 1970-2004 (Source: Swiss Re) Atlantic basin hurricane data – Wind speed Atlantic basin hurricane tracks (Category 1-5) during 1851-2006 Difference of wind speed distributions between the early historical period (18511946) and the recent six decades. - Early historical records significantly underestimate the frequency of Category 4-5 winds. - Wind speed distributions over the past two, four and six decades display little systematic changes. U.S. landfalling segments since 1947 Damage to property and other assets is linked to landfalling events. For the six decades since 1947, there are no sustained upward trends in - Average annual count of landfalling segments (blue curve) - Mean landfalling wind speeds (red curve) This contrasts with the dramatic increases in total economic and insured losses, suggesting the losses must be attributed to factors other than wind speed alone. Australian Property Losses over 20th Century Australian Losses - ICA Natural Disaster Event List • Insurance Council of Australia database of insured losses since 1967 • Estimate losses as if events took place in 2006 • Account for changes in – – – – Inflation Population Wealth Building Codes Original Annual Aggregate Losses (July 1 – June 30) Original annual aggregate insured losses (AUD$ million) for weather-related events in the ICA Disaster List for years beginning 1 July Methodology CL06 = Li × Ni,j × Di,k x Btc CL06 Li Ni,j Di,k jkBtc - Normalised (current) dollar loss (year 2006 value) Original dollar loss (year ‘i’) Dwelling number factor Dwelling value factor Urban Centre / Locality (UC/L) impacted by the event State or Territory that contains the impacted UC/L Building Code adjustment (= 1 for all hazards except tropical cyclones) Number of occupied dwellings in the Sydney UCL 1400000 Number of Occupied Dwellings 1300000 1200000 1100000 1000000 900000 800000 700000 600000 1966 1971 1976 1981 1986 Year 1991 1996 2001 2006 Average nominal new dwelling value (AUD$ thousands) for WA. 200 180 Average Nominal Value (AUD$ thousands) 160 140 120 100 80 60 40 20 0 1967 1972 1977 1982 1987 Year 1992 1997 2002 Building Code Adjustment • Estimate % of loss due to wind vis-à-vis flood • Proportions of pre- & post-1981 construction then and now • Use Central Pressure at landfall to determine characteristic gust speed for the cyclone • Calculate pre- & post-1981 loss ratios • Adjust normalised loss • Unique adjustment for each event Attributes • Uses publicly-available information • Based on dwellings rather than population – Number of dwellings ~ Population – Nominal value of new dwellings ~ Inflation & Wealth • Nominal dwelling value excludes land value – Assures reasonable alignment to insured losses • Easy to apply TC Tracy - 1974 • Original loss = $200M – Dwelling number factor ~ 3 – Nominal dwelling value factor ~13 • (1974: ~$18.5K; 2006: ~ $240K) – All losses attributed to wind or wind-driven rain – Current construction all post-1981 – Building code factor ~ 0.5 • Current loss ~ $3.6 billion • This may be slightly high as building code regulations were introduced in Darwin earlier than 1981 Normalised Annual Aggregate Losses (July 1 – June 30) TC Tracy Sydney Hail Brisbane Flood Sydney Flood & Brisbane Hail Sydney Hail Ash Wednesday Fires Top 10 Attribution of Loss Annual Australian Bushfire Losses Bushfire loss frequency Time Series Analysis to 2003 A “major” event is defined here as more than 25 homes destroyed within a 7 day period. Hurricane Damage if landfall in 2005 Billions Total Los s e s Pe r Ye ar from Atlantic Tropical Cyclone s w ith Pie lk e /Lands e a Norm alization & 11 yr Ce nte re d Ave rage $160 $140 Losses in 2005 USD $120 $100 $80 $60 $40 $20 $1900 1910 1920 1930 1940 1950 Year 1960 1970 1980 1990 2000 Population Changes Florida Coastal Population Population (Millions) 1900 to 1990 Year 15 million today! Implications for Insurers • Societal factors predominant drivers of increased natural disaster losses • No need to invoke global climate change for increasing losses – not yet • Expect this to be the case over the next few decades • Insurers need to worried about what might happen in the next twelve months or so Public Policy Implications • Efforts to reduce society’s vulnerability to current & future extremes • Improved wind standards best example • Bushfire: restrictions based on distance to forest • Flood: limit construction on floodplains • Risk reduction (adaptation) measures in addition to abatement of greenhouse gases Looking X years ahead • Ability to obtain insurance linked to the actual risk • Premium will be linked to actual risk – distance from bushland? – are you on a floodplain? – distance from the coast?