Post on 21-Mar-2017
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A Decision Support System
Forest Storm Damage and
Insurance Losses
©2015 CoreLogic, Inc. All rights reserved. Proprietary and Confidential.
Agenda
Introduction – CoreLogic®
RQE® - EuroWind™
Insurance Industry & Forestry
Forest Module
Model Components
Validation
Scenario Single plot - monoculture
Single plot - mixed culture
Multiple plots
Market portfolio study
Conclusion
Q & A Session
2
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CoreLogic At A Glance
Leading Provider of Property Data, Analytics and
Data-Enabled Services
3
Market Cap:
$3.46 billion*
Operations:
8
countries
Employees:
4,800+
Principal Markets:
U.S. & Australia
Headquarters:
Irvine,
CA
* Market Capitalization
as of Oct 6, 2015
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600K+ Real Estate
Agents
500+ Lenders
2500+ Mortgage
Bankers
250+ Wall Street
Firms
17 Top MLS
25+ Federal Agencies
1M+ Users
Market Leadership
4
Data
Analytics
Workflow
Technology
Underwriting
Risk Management
Compliance
Advisory
Mortgage
Real Estate
Capital Markets
Multifamily
Public Sector
Insurance
Solution
Sets
Markets
Served Competencies
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Business Markets Served
Insights Powering the Real Estate Ecosystem
5
Underwriting, Risk Management, Compliance Advisory Solutions
Customer
Acquisition
Property Valuation
Consumer
Authentication and
Ability-to-Pay
Compliance
Management
Servicing
Portfolio
Management
Performance
Management
Loan Level
Mortgage Data
Predictive Analytics
Non Agency RMBS
Real Estate
Analytics
Real Estate data,
analytics &
technology
Realtor solutions
Valuation services
Property records
Historical
mortgages, home,
land & real estate
pricing & ownership
Lead generation
Robust national data
– single family,
multifamily and
commercial
Data management,
validation and
compliance solutions
Research,
benchmarking &
trending analysis
Regulatory, portfolio
and monitoring /
surveillance
solutions
Modeling and
analytics services
Dynamic property
insights
Geo-Spatial
solutions
Storm surge
modeling
Catastrophic risk
modeling
Natural hazard risks
(wildfire, hail, sink
hole, etc.)
MORTGAGE CAPITAL
MARKETS
REAL
ESTATE
PUBLIC
SECTOR
INSURANCE MULTI-
FAMILY
Resident screening
and scoring
Online leasing
workflow
Background data
records
Homeowner /
community
association financial
accounting
Renters insurance
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Data That Powers Our Key Solutions
6
Underwriting
Advisory
Risk
Management
Compliance
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RQE® v. 16.0 & European Winter Storm Model (EuroWind)
7
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After having a measure
of the damage, the
loss(es) are calculated
by applying financial
rules (e.g. limits and
deductibles).
Describes the relationship between the
severity of the event(s) and the probable
damage of the event(s).
In a probabilistic
model, the hazard will be
expressed as a stochastic set
of events, as a representation
of the risk in the real world.
A book of business, as a list of properties
with their characteristics and (market) value.
8
Components of a Probabilistic Model
Natural Catastrophe
Risk Modelling
Exposure
Database
Natural Hazard Model
Vulnerability
Financial Model
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Exposure
Insurance & Forest Industry
Photo: Areca (2009)
Windstorm damage is devastating to the forest
industry [1], because it causes:
• Disruption in the grow programs
• Additional clearing costs
• Unrecoverable production
Erwin Storm Facts:
• Most devastation storm in 65 years [2]
• 75 million m3 of production forest was lost [3]
• Total damage 2–3 billion Euro
9
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A probabilistic model
Covers 24 countries
A database of 361 historical
storms and the hazard
23,000 synthetic events
Built on the latest research
findings
Two additional components:
North Europe Offshore
Forest Damage
RQE & European Winter Storm Model (EuroWind)
What is EuroWind 2014?
North
Europe
Offshore
10
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RQE & European Winter Storm Model (EuroWind)
What are the components in EuroWind 2014 - Hybrid Hazard ?
WHY – Hybrid Hazard?
Historical Event Set
361 storms
1960-2013 period
Characteristics of
~25,000 simulated
storms from 800-2000
period
Ensemble of Historical “proxies”
Stochastic Event Set
~20,000 storms
Stochastic Event Set
~23,000 storms
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Gust measurements + interpolation
<- complemented with- > Measurements
Met. Station density
and accuracy “Ground Truth”
Historical, millennium year-long global
simulation
HYBRID HAZARD
Event
Set
Earth System
Model
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RQE & European Winter Storm Model (EuroWind)
EuroWind 2014 – Local Adjustments for wind speed
210 225 240
255 270 285 300 315 330
DEM (SRTM resampled to 250m)
LAND USE (Corine 2012, 100m)
GUSTINESS (10mins vs 3 sec)
DIRECTION (9 + Fetch)
12
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Based on the EuroWind 2014 Hazard
Six Vulnerability Curves for Forest
Forest Module
Model Components
Return Period
Dam
age
Other types are:
Coniferous – Low
Mixed – High/Low
Deciduous – High
Least vulnerable –
Deciduous - Low
Most vulnerable –
Coniferous - High
13
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Based on the EuroWind 2014 Hazard
Six Vulnerability Curves for Forest
Three Risk Locations for the spatial distribution of produced wood
(in volume), and Forest Types
Forest Module
Model Components
14
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Based on the EuroWind 2014 Hazard
Six Vulnerability Curves for Forest
Three Risk Locations for the spatial distribution of produced wood
(in volume), and Forest Types
Exposure database for Insurable Forest (in cubic meters), based on:
CORINE 2012 [4] digital land use database
Statistical data from: Skogsstyrelsen - The Swedish Forest Agency (SFA) [5]
Finnish Forest Research Institute – (METLA) [6]
Forest Module
Model Components
15
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Deciduous D>15
Deciduous D<15
Coniferous D>15
Coniferous D<15
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Deciduous (km2)
Mixed (km2)
Coniferous (km2)
Forest Module
Validation – Exposure Database for Standing Stock (in cubic meters)
The Swedish Forest Agency (SFA)
Corine Land Cover 2012 Distribution of Volume
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Mixed D>15
Mixed D<15
Deciduous D>15
Deciduous D<15
Coniferous D>15
Coniferous D<15
16
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Forest Module
Validation – Erwin [7,8]
0
10
20
30
40
50
60
70
80
90
100
0 50 100 150
Dam
ag
e (
m3)
Millio
ns
Return Period (years)
Winter Storm Erwin (2005)
Model Results
Damage -10%
Damage +10%
Erwin 2005 Reported
Erwin 2005 Modelled
Return Period 70-80y
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Scenario 1
Single Plot – Monoculture
18
-
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0 5 10 15 20 25 30 35 40
Pro
du
cti
on
(m
3/k
m2)
Millio
ns
Years
Wood Production
Wood Production
Wood production for Spruce on a 5km2 plot
Account Volume (m3)
Spruce 10 39,000
Spruce 15 109,800
Spruce 20 175,300
Spruce 25 228,500
Spruce 30 271,000
Spruce 35 302,800
Spruce 40 329,400
Adapted from: Forest Research Notes ,Volume 8, Number 2 ,Second Quarter, 2011
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Scenario 1 – Forest Growth and Damage
Single Plot – Monoculture Results
19
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
0 50 100 150 200 250
Dam
ag
e (
m3/k
m2)
Return Period (years)
Spruce Wind Damage
Growth 10y
Growth 15y
Growth 20y
Growth 25y
Growth 30y
Growth 35y
Growth 40y
Non-linear relationship
between forest growth
and damage
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Scenario 2 – Production Cycle Simulation
Single Plot – Mixed Culture on a Forest Plot
20
Height Time CNF DCD MXD
Low Year 00 25,000 60,000 40,000
Year 05 60,000 75,000 50,000
Year 10 10,000 90,000 60,000
Year 15 15,000 120,000 15,000
Year 20 22,500 30,000 20,000
High Year 00 200,000 40,000 225,000
Year 05 50,000 50,000 281,000
Year 10 75,000 60,000 350,000
Year 15 125,000 70,000 90,000
Year 20 187,500 20,000 115,000
Total Volume (m3)
0
5
10
15
20
25
0 50 100 150
Vo
lum
e (
m3)
Th
ou
san
ds
Return Period (years)
Year 00
Year 05
Year 10
Year 15
Year 20
DCD
CNF
MXD0
50
100
150
200
250
300
350
400
450
Vo
lum
e (
m3)
Th
ou
san
ds
DCD
CNF
MXD
Time Total Volume
(m3)
Year 00 590,000
Year 05 512,500
Year 10 645,000
Year 15 435,000
Year 20 395,000
Distribution of Volume (m3) by tree
types and height
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0%
1%
2%
3%
4%
5%
0 50 100 150 200 250
Dam
ag
e R
ati
o
Return Period
Wind Damage on Multiple Locations
Damage
Scenario 3
Multiple Plots – Single Culture – Spruce
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%loc: 1737
loc: 2161
loc: 2283
loc: 2284
loc: 2313loc: 2361
loc: 2482
loc: 2510
loc: 2523
RP: 50y
RP: 100y
RP: 250y
RP: 500y
Location Volume
(m3)
1737 21,000,000
2161 26,700,000
2283 23,700,000
2284 28,500,000
2313 24,800,000
2361 30,000,000
2482 21,300,000
2510 26,800,000
2523 25,100,000
21 21
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Scenario 4
Multiple Plots – Mixed Culture
22
0%
1%
2%
3%
4%
5%
0 50 100 150 200 250
Dam
ag
e R
ati
o
Return Period
Wind Damage on Multiple Locations
Damage Spruce only Damage Mixed Production
Tree Type % Production
CNF - Low 10%
CNF - High 37%
DCD - Low 9%
DCD - High 10%
MXD - Low 8%
MXD - High 25%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%loc: 1737
loc: 2161
loc: 2283
loc: 2284
loc: 2313loc: 2361
loc: 2482
loc: 2510
loc: 2523
RP: 50y
RP: 100y
RP: 250y
RP: 500y
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Use Cases
National Exposure Study
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Total Damage to Forest Contributing part of Deciduous Forest Contributing part of Mixed Forest Contributing Part of Coniferous Forest
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Exposure Study
Damage of a Storm like Erwin 2005
24
Damage Ratio based on CoreLogic Exposure
Database 2014
Difference due to:
- Exposure / inventory dates
- Modelled results vs. estimates on the
ground
Useful for:
- Quick indication of probable risks
- Assisting in guiding the relief work:
harvesting losses, repairing electricity
poles, emergency response, etc.
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Modelling forest damage is not a straight forward process, there are
many aspects involved: Measurements (e.g. wind speed observations / failing stations)
Statistical data (forest inventories)
Land use data
Climate change (increasing storm severity)
This information is not static, but highly dynamic, and can change
rapidly
Modelling forest damage from Winter Storm Damage is needed,
because the there are too many unknowns
Using a Decision Support System to model damages is one solution
to get an understanding on the damage and industry losses
Conclusion
25
©2015 CoreLogic, Inc. All rights reserved. Proprietary and Confidential.
CoreLogic.com
www.corelogic.com
RQE v.16.0
http://www.corelogic.com/products/rqe.aspx
EuroWind
http://www.corelogic.com/imgs/spatial/catastrophe-risk-
management/eurowind-datasheet.pdf
RiskMeter Online
http://www.corelogic.com/products/riskmeter.aspx
General Information
26
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Thank You
Q & A Session
27
©2015 CoreLogic, Inc. All rights reserved. Proprietary and Confidential.
[1] The Royal Swedish Academy of Agriculture and Forestry (KSLA), 2009, The Swedish
Forestry Model.
[2] Zanetti, A. (2006). Natural Catastrophes and man-made disasters 2005. Sigma. Zurich,
Swiss Re: 40.
[3] Windstorm Erwin/Gudrun - January 2005. Speciality Practice Briefing - An update from the
Property Speciality, Guy Carpenter & Company Ltd: 14.
[4] EEA, Version 16 (04/2012) - Raster data on land cover for the CLC2006 inventory,
http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-2#tab-gis-data
[accessed 29/10/2015]
[5] Swedish Forest Agency, Statistics, http://www.skogsstyrelsen.se/en/ [accessed 29/10/2015]
[6] (The) Finnish Forest Research Institute – Metla, MetINFO - Forest information services
http://www.metla.fi/metla/metla-esittelyaineistot-en.htm [accessed 29/10/2015]
[7] SFI (2006), Swedish Forest Industries Federation, Storm of the century –one year later
[8] Skogsstyrelsen (2006), MEDDELANDE I – 2006, Stormen 2005 - en skoglig analys,
http://shop.skogsstyrelsen.se/shop/9098/art72/4645972-02cb95-1556li.pdf [accessed
29/10/2015]
Reference
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