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Swiss Re sigma catastrophe database Lucia Bevere, Senior Catastrophe Data Analyst

Overview of the sigma catastrophe database

• International commercial database recording both natural and man-made disasters

• Global scale

• Over 10 000 entries

• Recording started in 1970

• Event-based

• Disasters are now geocoded at national (or state/province) level for GIS purposes

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Focus on insured losses

• Annual picture of global catastrophic activity

• Trends in insured losses

Disaster losses, USD billion (at 2013 prices)

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Source: Swiss Re sigma catastrophe database

Insured catastrophe losses - geographical distribution

2013 2012 10-y avgSource: Swiss Re sigma catastrophe database

42%

1%

5%

15%

4%

12%

11%

7%

34%

62%

84%

7%

0%

3%

3%

Storms account for the great majority of insured losses

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Source: Swiss Re sigma catastrophe database

Selected insured Nat Cat loss potentials compared to loss history

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Peak risks

Earthquake and windstorm ...

... in Industrialized countries ...

... with relatively high insurance density

Katrina 2005

Northridge 1994

23

80

8

Lothar 1999

Storm Europe

40

Hurricane US + Caribbean incl. NFIP, FHCF

Earthquake Japan incl. JER

85

55

Earthquake California

Historic insured loss (sigma, indexed to 2013)

Modelled 200 year insured loss

Insurance loss scenarios [USD bn]

FHCF: Florida Hurricane Catastrophe Fund

38

Tohoku 2011

JER: Japan Earthquake Reinsurance Scheme State-run schemes

NFIP: National Flood Insurance Program

200

Structure

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Classification of natural catastrophes

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Category Peril Group Peril

Natural catastrophe

Earthquake

Earthquake

Tsunami

Volcano eruption

Weather-related

Storm

Flood

Hail

Cold, frost

Drought, bush fires, heat waves

Other natural catastrophes

Examples of database entries

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Country Peril Date Number of victims Amount of damage

Event Source

Vietnam Storm 30.09.2007 95 dead 8 missing 90 injured 125 000 homeless USD 126m economic losses

Typhoon Lekima with winds up to 130 km/h, heavy rain, landslides; 9 500 houses destroyed, 115 000 ha of cropland (of which 30 000 ha of rice) flooded

Central Committee for Flood and Storm Control

US Storm 31.01.2011 36 dead USD 1 034m insured losses USD 2 000m economic losses

Groundhog Day Blizzard winter storm, heavy snowfall, freezing rain; damage to private, industrial and commercial buildings, damage to power houses, 20 000 flights cancelled

Various

Source: Swiss Re sigma catastrophe database

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Other natural catastrophe

Storm

Cold, frost

Storm

Drought, bush fire, heat wave

Other natural catastrophe

Weather related

Earthquake

Flood

Earthquake

Swiss Re current classification Swiss Re current classification IRDR-Data suggestion

Classification redesign

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Other natural catastrophe

Storm

Cold, frost

Storm

Drought, bush fire, heat wave

Other natural catastrophe

Weather related

Earthquake

Flood

Earthquake

Swiss Re current classification Swiss Re current classification IRDR-Data suggestion

Classification redesign

Variables: minimum selection thresholds for 2014

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• Insured losses and business interruption losses:

– marine USD 19.3 m

– aviation USD 38.6 m

– other property losses USD 48.0 m

• or Total losses (economic damage) USD 96.0 m

• or Casualties

– dead or missing 20

– injured 50

– homeless 2000

Each year the monetary thresholds are adjusted for inflation

Sources

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Swiss Re

• claims assessors

• underwriters

• National disaster authorities

• EU, UN, World Banks etc.

• Ad hoc scientific research

• etc.

Press National meteo/seismological

services

Industry

Governments, International

organisations, Science, NGOs etc.

How reliable? Global?

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Ocean Drive, FL, 1926. Ocean Drive, FL, 2000.

Population Growth Rates (1960-2000)

All US 57%

Florida 223%

Increasing values

concentration in exposed areas

Insurance penetration

Changing hazard

climate variability

climate change

Losses are not normalised for exposure

Assessment of social losses…

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NOAA Brunkart et al (2008)

Markwell et al (2010)

Deaths

Total

Louisiana

… straightforward?

Hurricane Katrina

1833

1577 1155 971

Economic losses subject to high degree of uncertainty

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• Data collection is not systematic • Lack of hazard-specific observation/monitoring • Official damage reports are often missing (particularly for

small/medium events) • No central repository • Data are often collected by different authorities using different

criteria and with different users in mind – ground losses – meteorological aspects

• No harmonization at supra-national level • Lack of damage details • Reporting on losses may be mixed with post-disaster expenditures

• Missing events

• Lack of any measure of cost

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Merci!

For further information contact: Lucia_Bevere@swissre.com

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