Forest Storm Damage and Insurance Losses

©2015 CoreLogic, Inc. All rights reserved. Proprietary and Confidential.

A Decision Support System

Forest Storm Damage and

Insurance Losses


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


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


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


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


Data That Powers Our Key Solutions

6

Underwriting

Advisory

Risk

Management

Compliance


RQE® v. 16.0 & European Winter Storm Model (EuroWind)

7


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


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


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



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

11

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



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


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




Three Risk Locations for the spatial distribution of produced wood

(in volume), and Forest Types

Forest Module

Model Components

14




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


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

©2015 CoreLogic, Inc. All rights reserved. Proprietary and Confidential. 17

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


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


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)


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


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


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


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


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


Use Cases

National Exposure Study

23

Total Damage to Forest Contributing part of Deciduous Forest Contributing part of Mixed Forest Contributing Part of Coniferous Forest


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.


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


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

http://www.corelogic.com/




http://www.corelogic.com/imgs/spatial/catastrophe-risk-management/eurowind-datasheet.pdf













Thank You

Q & A Session

27


[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

28





















http://www.skogsstyrelsen.se/en/AUTHORITY/Statistics/About-the-statistics/




http://www.metla.fi/metla/metla-esittelyaineistot-en.htm







http://shop.skogsstyrelsen.se/shop/9098/art72/4645972-02cb95-1556li.pdf






Forest Storm Damage and Insurance Losses

Science

Transcript of Forest Storm Damage and Insurance Losses