Forecasting Market Fundamentals to Estimate Expected Loss for Commercial Real Estate Portfolios - Moderator: Andy Condurache, PRMIA Presenters: ...
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Forecasting Market Fundamentals to
Estimate Expected Loss for Commercial
Real Estate Portfolios CO-SPONSORED BY:
Moderator: Andy Condurache, PRMIA
Presenters: Sumit Grover and Megha Watugala, Moody’s Analytics
June 2016
Speakers
Sumit Grover – Director, CRE Product Management
Moody's Analytics
Sumit Grover is a Director in Product Management within the Enterprise
Risk Solutions group at Moody’s Analytics. He manages CMM™
(Commercial Mortgage Metrics), a platform to assess credit risk in
commercial real estate (CRE) loans and portfolios. Sumit also supports
other CRE solutions at Moody’s Analytics, including spreading, scoring
and stress testing products. Sumit holds an undergraduate degree in
Information Systems and a MBA from Indiana University Bloomington.
Megha Watugala – Director, CRE Research
Moody’s Analytics
Megha Watugala joined Moody’s Analytics in 2012 and is currently engaged
in research and advisory work related to commercial real estate. In addition
to modeling various components of CMM, Megha has assisted many clients
with internal validation, customization and stress testing projects using CMM.
He has many years of experience in the research and consulting industry.
Megha obtained his Ph.D. in economics from Texas A&M University. His
Bachelor’s degrees in Computer Science and Economics are from California
Institute of Technology.
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 2
AGENDA
1. Understanding Challenges in CRE Risk Management
2. Moody’s Analytics Modelling Approach for CRE
3. Forecasting CRE Fundamentals for Canada
4. CMMTM (Commercial Mortgage Metrics) at a Glance
5. IFRS 9: Estimating Expected Loss for Your CRE Portfolio
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 3
1 Understanding Challenges in
CRE Risk Management
Challenges in CRE Risk Management
Updated Foresight into Default history Intuition vs Assessing the
Property Market and modeling quantitative impact of macro
Information Fundamentals expertise validation economy
Valued - $95M in 2007
Now?
• Data captured at • Sound forecast that • Default history over • Several qualitative • Different cities and
origination may not differentiates multiple credit factors can impact neighborhoods react
be complete for between property cycles and from the analysis and risk differently to an
data analysis. types and multiple sources is measures and economic recession
• Data management submarkets is important for sound integrating or expansion
is important for important modeling and CRE quantitative models
historical and data history is not with intuition can be
forward looking captured a challenge
analysis
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 5
2 Moody’s Analytics
Modelling Approach for CRE
Understanding why CRE credit events occur
Insufficient income (NOI) to make the mortgage payment
• Rising vacancy and loss of rental income during recessions
• Deterioration of certain neighborhoods or properties even during
boom years
Perceived inability to sell the property for the loan
amount
• LTV comes into play during times of cash-flow distress or at
refinancing
Lack of reserves and additional outside resources
available to cover cash flow shortfalls
• Both ability and willingness to pay shortfalls decrease during
recessions
• Size of shortfall likely to matter: $1 million >> $1000
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 7
Employing a modeling framework that reflects
business practices
Starting with collateral Modeling default Empirical Evidence
• Forecasting cash flow behavior • Inability to reach
under various scenarios • Option A: Continue consensus triggers
• Property value influences payment out of credit events
default decision mostly pocket, expecting • Borrowers are more
during cash flow stress market recovery likely to default in a
• Macro and local market • Option B: Default on recession than in an
condition matters loan economic
expansion
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 8Collateral performance and credit risk go hand-in-hand
Economic
shocks
Collateral Credit Risk
Model Model
Property-level NOI and Value Loan-level Characteristics
Systematic factors
Idiosyncratic factors PD and LGD Drivers
DSCR
Local CRE Market Info LTV
Volatility Market Vacancy
Current condition Market Price Index Change
Forward-looking views Loan seasoning
Property Type, MSA/submarket etc.
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 9Forecasting CRE 3 Fundamentals for Canada
Canadian CRE Market Followed Global Cycles, With
Some Variations
» Canadian CRE market experienced similar decline as the US in late 1980s through early
1990s, but its downturn in the late 2000s was limited
Source: IPD Canada (2000-2012) and Frank Russell Canada (1985-1999). Chart from IPD presentation.
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 11Cycles Can Differ Across Property Types
» Different property types can have different cycles due to their unique natures
12%
10%
8%
Vacancy
6%
4%
2%
0%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Retail Office Industrial All Properties
Source: MSCI/IPD
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 12Meaningful Difference Across Property Types and Location
» Office lagging in price appreciation, and Edmonton and Montreal saw price declines too.
Source: IPD. Data reflective of 2014 annual performance.
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 13Vacancy
Rent
15%
5%
10%
25%
0%
20%
30%
10
15
0
5
20
25
30
1981 1981
1981 1981
1982 1982
1983 1983
1984 1984
Calgary
1984 1984
1985 1985
1986 1986
1987 1987
1987 1987
1988 1988
Edmonton
1989 1989
1990 1990
1990 1990
1991 1991
1992 1992
Halifax
1993 1993
1993 1993
1994 1994
1995 1995
1996 1996
1996 1996
Montreal
1997 1997
1998 1998
1999 1999
1999 1999
2000 2000
2001 2001
Ottawa
2002 2002
2002 2002
2003 2003
2004 2004
2005 2005
Source: CB Richard Ellis Econometric Advisors (CBRE EA)
Toronto
2005 2005
2006 2006
2007 2007
2008 2008
2008 2008
2009 2009
2010
Vancouver
2010
2011 2011
2011 2011
2012 2012
2013 2013
2014 2014
2014 2014
Winnipeg
2015 2015
Location, Location, Location: Office Market Performance
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 14Submarkets Can Make A Big Difference Too:
Another Aspect of Location
Toronto Toronto - Downtown
25%
20%
Vacancy
15%
10%
5%
0%
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
25
20
15
Rent
10
5
0
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015
Source: CB Richard Ellis Econometric Advisors (CBRE EA)
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 15Never Underestimate the “Power” of Idiosyncratic Risk
» Individual properties can perform worse than the market average: idiosyncratic risks!
– In a generally healthy Toronto office market, some properties’ financial performance still got
distressed.
Property-level observations of normalized NOI for Toronto office properties
250
200
150
Normalized NOI
100
50
0
2006 2007 2008 2009 2010 2011 2012 2013 2014
Year
Source: Trepp
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 16Regional Economic Data/Forecast Offers Essential Context
Abbotsford Halifax Oshawa St. Catharines-Niagara
Alma Hamilton Ottawa St. John's
Barrie Joliette Owen Sound Stratford
» CMAs with macroeconomic Bathurst Kamloops Peterborough Swift Current
variables from Moody’s Calgary Kawartha Lakes Port Alberni Thetford Mines
Analytics Campbell River Kelowna Portage la Prairie Thompson
Cape Breton Kenora Prince George Thunder Bay
Chatham-Kent Kingston Quebec Tillsonburg
Collingwood Kitchener Red Deer Timmins
» Start date varies from 1971 Cornwall Lachute Regina Toronto
Cowansville Lethbridge Riviere-du-Loup Trois-Rivieres
to 1986 Dawson Creek London Roberval Truro
Drummondville Matane Saguenay Val-d'Or
Edmonton Medicine Hat Saint John Vancouver
Edmunston Midland Saint-Georges Vernon
» CMM Canada uses market
(property type and location) Elliot Lake Moncton Saint-Jean-sur-Richelieu Victoria
specific CRE forecasts from Estevan Montmagny Salaberry-de-Valleyfield Victoriaville
CBRE and Moody’s Fort St. John Montreal Sarnia Wetaskiwin
Fredericton Moose Jaw Saskatoon Williams Lake
Analytics in deriving
Gaspe Nanaimo Sault Ste. Marie Windsor
PD/LGD Gatineau New Glasgow Sept-iles Winnipeg
Granby North Battleford Shawinigan Wood Buffalo
Greater Sudbury North Bay Sherbrooke Woodstock
Source: Moody’s Analytics Guelph Orillia Sorel-Tracy
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 17Risk Assessment Is About Future, Not Past
» Moody’s Forecasts Regional Economic Performance: An example of Toronto CMA
14%
Unemployment Rate
12%
10%
8%
6%
4%
1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024
15%
10%
Real GDP
5%
0%
-5%
-10%
1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 18CRE Markets React to Macroeconomic Conditions in the
Supply Constraints of the Local Markets
25%
Calgary
20% Edmonton
Vacancy
Halifax
15%
Montreal
10% Ottawa
Toronto
5%
Vancouver
0% Winnipeg
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024
30
Calgary
25
Edmonton
20
Halifax
Rent
15 Montreal
10 Ottawa
Toronto
5
Vancouver
0
Winnipeg
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Source: CB Richard Ellis Econometric Advisors (CBRE EA)
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 194 CMM™ (Commercial Mortgage
Metrics) at a GlanceCMM™ (Commercial Mortgage Metrics)
capabilities at a glance
» Report risk measures at portfolio and loan level; also integrated with our spreading,
loan origination and stress testing solutions
» Supports back-testing by allowing historical analysis on a portfolio
» Supports regulatory review, by enabling you to generate risk measures under various
macroeconomic scenarios into CRE specific forecast and determine related losses on
your portfolio
» Provides flexible framework that is adjustable to your default experience
Save your CRE portfolio on the Combine your CRE portfolio and macro forecast and
Cloud and access from anywhere instantly see the impact on risk measures
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 21Default Drivers Can Be Identified and Quantified from
Empirical Data
» Financial ratios » Behavioral factors
– Debt-service-coverage ratio (DSCR) – Loan seasoning
– Loan-to-value ratio (LTV) – Guarantee
– Collateral size
» Property Type
» Market cycle factors – Core vs. non-core
– Space market (vacancy rate)
– Capital market (market price changes)
– Origination quality
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 22A Robust Default Risk Model Can Be Developed
» A very robust and comprehensive credit risk model can be built upon all the
datasets discussed: from CRE markets to loan credit performance history
CMM (Commercial Mortgage Metrics) Model quantifies risk by key ratios
Canada accuracy: 56%
Sensitivity of PD
10%
5%
1.3
0% 0.7
0
0.2
0.4 LTV
0.6
0.8
0.1
1
1.2
1.4
1.6
1.8
2
DSCR
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 23IFRS 9: Estimating Expected 5 Loss for Your CRE Portfolio
IFRS 9 – The Basics
» As part of the response to the last financial crisis, the International Accounting
Standards Board (IASB) recently issued IFRS 9, which moves from an “incurred loss”
model to an “expected loss model” , with a mandatory effective date of January 1, 2018.
» Canada’s Domestic Systemically Important Banks (D-SIBs) will early adopt IFRS 9 for
annual periods beginning November 1, 2017 to align with global peers.
» IFRS 9 provides a general “three-bucket” approach for regular financial instruments.
Increase in credit risk since initial recognition
Stage 1 Stage 2 Stage 3
Impairment recognition: a forward-looking “Expected Credit Loss” model
As soon as a financial instrument is Credit risk increases significantly Credit risk of a financial asset
originated or purchased. from when the entity originated or increases to the point that it is
was purchased and the resulting considered credit-impaired.
12-month expected credit loss is credit quality is not considered to
recognized in P&L and a loss be low credit risk; Financial assets in this stage will
allowance is established. generally be individually
lifetime expected credit loss is assessed.
Serves as a proxy for the initial recognized.
expectation of credit loss. lifetime expected credit loss is
recognized.
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 25IFRS 9 Requirements and Potential Solutions
IFRS 9 REQUIREMENTS SOLUTION/ IMPLEMENTATION
Apply Definition of Default » The standard default definition is 90 days or more past due.
1 consistent with internal credit
practices
» Align PD with an internal default definition that may be broader than the 90 days-
past-due definition.
Reflect a Probability- » Use multiple scenarios and weigh them to arrive at probability weighted outcome.
2 Weighted expected credit
loss
» Simulate different NOI and property value growth paths. The final PD and EL are
the probability-weighted average across all simulation paths.
Calculate both 12-Month » Term PD/EL to capture the risk of payment default during the loan term.
3 and Lifetime Expected
Credit Loss
» Maturity PD/EL to capture the refinance risk associated with a typically large
balloon payment at loan maturity.
Incorporate information from
4 Past Events » Use historical CRE loan and market data in model development
Incorporate Current Market » Update property Income & value using current CRE market conditions, and use
5 Condition the most recent loan attributes to estimate risk metrics.
» Incorporate forecasts of market conditions i.e. risk drivers such as DSC and LTV
Incorporate Forecast
6 Information should be driven based on market forecast
» Loan payments maybe driven by interest rate forecast.
Calculate Expected Credit » Use Loss models which incorporates cash flows from the sale of collateral and
Losses considering the from expenses incurred during the liquidation process.
7 collateral and other credit » Incorporate liquidation expense based on historical experience and internal
enhancements. policy (including discount rate).
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 26Using CMM for IFRS 9
Provides annual PD, LGD, EAD and EL loss measures for lifetime of
the CRE Loans
Calibrated to historical default and CRE market data
Market information updated quarterly capturing changes in market
conditions
Provides various forward-looking scenarios that move the loan
metrics such as DSC and LTV based on market conditions
CMM LGD incorporates cash flows from the sale of collateral and
from expenses incurred in the liquidation process
Consistent definition of default, along with ability to easily
customize the model
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 27Moody’s Analytics Credit Loss and Impairment Analysis Suite
Enterpirse
CREDIT IMPAIRMENT ANALYSIS SOFTWARE
Software
Scalable, Auditable, Cash Flow Engine, Workflow Management, Collateral Allocation
Credit Provision Stage Loss
Scenarios Credit Data
Modelling Calculations Allocation Forecasting
Advisory &
Modelling
Solutions
MA
MA Credit Impairment Calculation Solutions
Economic MA Data Model Credit Loss Benchmarks
Scenarios Solutions Suite
& Services Model Advisory/off-the-shelf models
Advisory Services
Forecasting Market Fundamentals to Estimate Expected Loss for CRE Portfolios | June 2016 | 28Q&A
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