Presentation by Lucia Bevere at the OECD Workshop on Improving the Evidence Base on the Costs of Disasters (21 November 2014). Find more information at http://www.oecd.org/governance/risk/workshoponimprovingtheevidencebaseonthecostsofdisasters.htm.

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

2. 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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3. Focus on insured losses
• Annual picture of global catastrophic activity
• Trends in insured losses
Disaster losses, USD billion (at 2013 prices)
3
Source: Swiss Re sigma catastrophe database

4. Insured catastrophe losses - geographical distribution
2013 2012 10-y avg
Source: Swiss Re sigma catastrophe database
42%
1%
5%
15%
4%
12%
11%
7%
34%
62%
84%
7%
0%
3%
3%

5. Storms account for the great majority of insured losses
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Source: Swiss Re sigma catastrophe database

6. Selected insured Nat Cat loss potentials compared
to loss history
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

7. Structure
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8. 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

9. 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

10. 10
Other natural catastrophe
Storm
Cold, frost
Storm
Drought, bush fire,
heat wave
Other natural catastrophe
Weather related
Earthquake
Flood
Earthquake
Swiss Re current classificationSwiss Re current classification IRDR-Data suggestion
Classification redesign

11. 11
Other natural catastrophe
Storm
Cold, frost
Storm
Drought, bush fire,
heat wave
Other natural catastrophe
Weather related
Earthquake
Flood
Earthquake
Swiss Re current classificationSwiss Re current classification IRDR-Data suggestion
Classification redesign

12. 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

13. Sources
13
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.

14. How reliable?
Global?
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15. 15
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

16. Assessment of social losses…
16
NOAA Brunkart et al
(2008)
Markwell et al
(2010)
Deaths
Total
Louisiana
… straightforward?
Hurricane Katrina
1833
1577 1155971

17. 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
=

18. Merci!
For further information contact:
Lucia_Bevere@swissre.com

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