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ImmoRisk:
Risikoabschätzung der zukünftigen Klimafolgen in der Immobilien- und Wohnungswirtschaft /
Évaluation des risques des évolutions climatiques futures pour l’économie immobilière
Research in Germany (DAAD): Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
Professor Dr. Sven Bienert MRICS REV
Geschäftsführer, IRE│BS International Real Estate Business School, Universität Regensburg
Increasingly, extreme weather events have
historical dimensions.
Overview of 2014/2015 extreme weather events (1/3)
Tokyo‘s heaviest
Snowfall in 45 years
(Japan, February 2014)
Midwest Coldwave affects 140
million Americans; ice load and
power blackouts cost billions
(U.S., January 2014)
49,3 °C in rural South Australia;
lightning strikes cause more than
250 forest fires
(Australia, January 2014)
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Increasingly, extreme weather events have
historical dimensions.
Overview of 2014 /2015 extreme weather events (2/3)
The wettest winter in 250 years
(United Kingdom, January 2014)
Flooding causes landslide; 300 to 500 dead,
4000 homeless (Afghanistan, May 2014)
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Increasingly, extreme weather events have
historical dimensions.
Overview of 2014 /2015 extreme weather events (3/3)
31 Tote nach Sturzfluten
(Oklahoma & Texas, Mai 2015)
Hitzewelle mit 2.500 Toten (Indien, Mai 2015)
Hitzewelle mit 2.000 Toten (Pakistan, Juni 2015)
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Downside Impacts of Climate Change
Zunehmende Extremwetterereignisse / Évènements climatiques extrêmes augmetnent
1
Intensität /
Intensité
2
Frequenz /
Fréquence
3
Schäden /
Dégâts
Quelle: Münchner Rückversicherungs-Gesellschaft, 2013
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Downside Impacts of Climate Change
(Indirekte) Auswirkungen am Beispiel Kalifornien 2015
 „California will run out of water within one year“ (NASA)
 Four-years drought and temperature records make California‘s water reserves drain
 New water saving regulation provides for annual cutbacks of up to 36 %.
 Fines of up to $10,000 for local water companies are legally enforced from July on
 Exemplary saving targets



Montecito / Beverly Hills
Palo Alto
Santa Barbara
minus 36 %
minus 28 %
minus 16 % …
 Properties are considered as a significant
driver of water consumption
 While agricultural production must be ensured,
especially real estate (existing and new buildings)
as well as their outdoor facilities (watering, pools)
are captured by the authorities.
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Downside Impacts of Climate Change
(Indirekte) Auswirkungen am Beispiel Tirol 2014
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Extreme weather events affecting property values
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Real Estate industry can address the risk
Elements for the derivation of expected losses
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Climate data is getting more reliable
Best practice - The ImmoRisk-Tool: Hazard data
Emergence of global climate model GCM:
Global vs. Regional climate models:
GCM
RCM
(REMO-CLM)
Source: www.southwestclimatechange.org (left), www.remo-rcm.de (right)
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Functional relationship of the variables
Best practice - The ImmoRisk-Tool: General Risk Approach
Risk = Hazard * Vulnerability * Value
Source: IRE|BS, 2013
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Functional relationship of the variables
Best practice - The ImmoRisk-Tool: General Risk Approach
Monetary
Loss
Risk
=
Median
Margin of error
Value
Risk
Hazard
*
Probability
Damage
Vulnerability
*
Hazard
Value
Vulnerability
I.e. wind velocity
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Functional relationship of the variables
Best practice - The ImmoRisk-Tool: General Risk Approach
Median
Risk
Margin of error
=
Hazard
*
Probability
Vulnerability
*
Hazard
Value
I.e. wind velocity
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Functional relationship of the variables
Best practice - The ImmoRisk-Tool: General Risk Approach
Median
Risk
Margin of error
=
Hazard
*
Probability
Damage
Vulnerability
*
Hazard
Value
Vulnerability
I.e. wind velocity
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Functional relationship of the variables
Best practice - The ImmoRisk-Tool: General Risk Approach
Monetary
Loss
Risk
Median
Margin of error
Value
=
Hazard
*
Probability
Damage
Vulnerability
*
Hazard
Value
Vulnerability
I.e. wind velocity
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Functional relationship of the variables
Best practice - The ImmoRisk-Tool: General Risk Approach
Monetary
Loss
Risk
=
Median
Margin of error
Value
Risk
Hazard
*
Probability
Damage
Vulnerability
*
Hazard
Value
Vulnerability
I.e. wind velocity
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Functional relationship of the variables
Best practice - The ImmoRisk-Tool: General Risk Approach
Risk
Median
Margin of error
=
Hazard
Monetary
Loss
Annual expected loss
=
Risk
*
Vulnerability
ML: Monetary Loss
*
P: Probability of occurence
Value
Probability
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
ImmoRisk-Tool: http://xrl.us/immorisk
Standort + Gebäude  Risiko / Site + Immeuble  Risque
Step 1:
Selection of location by
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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
Drag‘n‘Drop and

Address input
Prof. Dr. Sven Bienert MRICS REV
ImmoRisk-Tool: http://xrl.us/immorisk
Standort + Gebäude  Risiko / Site + Immeuble  Risque
Step 2:
Building characteristics
 Vulnerability
 Value
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
ImmoRisk-Tool: http://xrl.us/immorisk
Risikosteckbrief / Résumé de risque
Type
Hazard
Trend
Storm:
Flood:
Hail:
Heat:
Heavy Precipitation:
Annual Expected Loss* (Damage Ratio)
Forest Fire:
Storm
Present
2021-2050
Lightning Strike:
Excess Voltage:
Flood
Present
2050
Hazard:
Hail
Present (no prediction available yet)
*rounded to the nearest tens
Risk:
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
IRE|BS Competence Center of
Sustainable Real Estate
Contact
Prof. Dr. Sven Bienert MRICS
IRE|BS Department of Real Estate
Head of Department
Competence Center of Sustainable Real Estate
University of Regensburg
Universitätsstraße 31
D-93040 Regensburg
Tel.: +49 (0)941 943-6011
Fax: +49 (0) 941 943-816013
Mail: sven.bienert@irebs.de
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Real Estate industry can address the risk
New ULI Report: Extreme weather events and property values
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Extreme weather events affecting property values
Source: IPCC and others
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Real Estate industry can address the risk
Elements for the derivation of expected losses
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV
Real Estate industry can address the risk
Elements for the derivation of expected losses
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
25
Prof. Dr. Sven Bienert MRICS REV
Real Estate industry can address the risk
Elements for the derivation of expected losses
Stadt der Zukunft / La ville de demain, Paris, 19.11.2015
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Prof. Dr. Sven Bienert MRICS REV