Group Data for CECL Modelling - C&I and CRE Portfolios Eric Bao, - - PowerPoint PPT Presentation

Leveraging Bank Internal Data and Industry Group Data for CECL Modelling

• C&I and CRE Portfolios

Eric Bao, Yanping Pan, and Yashan Wang – ERS Research April 24, 2018

Leveraging Bank Internal Data and Industry Group Data for CECL Modelling, April 24, 2018 2

CECL Modeling Approach: Strategic and Tactical Considerations

Strategic Considerations Tactical Considerations

» Portfolio materiality » Data availability: historical and reporting-date data; internal vs. industry group » Development costs: short-term vs. long-term investments » Timing constraint, i.e., the remain time till effective date » Invest in data, measurement and system capabilities for both CECL and

• ther business applications

» Consider the impact of less granular quantification on competitiveness » Consider the impacts on lending and other business decisions » Coordination and alignment with other processes » Interactions with various internal and external stakeholders

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• 1. Loss Rate Modeling with Internal and Industry Data
• 2. Leveraging Bank Internal Ratings for CECL
• 3. Summary and Discussion

Agenda

1

Loss Rate Modeling

C&I Portfolios

1.a

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Leveraging Industry Data for Loss Rate Modelling

Moody’s Analytics Data Alliance

» MA Data Alliance has the world’s largest historical time series of private firm middle market loan data for C&I borrowers. There are 19 contributing banks in North America.

– Contains borrower financial statements, facility and loan information – Over 670,000 borrowers, 1.4 million facilities, 20 million entries – Facility information: origination date/amount, contractual maturity, unpaid balance, and net charge off (NCO) amounts in each quarter post default for defaulted loans – Borrower information: internal rating/PD, industry, geographical info, size, etc.

» The data allows us to track the default, charge off and recovery of each loan through its lifetime, calculating lifetime loss rate at loan, segment, and portfolio levels

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Data Alliance Contributing Banks

Historical Loss Rate of C&I Portfolio

» 7 million loan snapshots » Close to 1 million unique loans, 80% of the banks’ C&I portfolio » Quarterly observations from 2004Q3 to 2014Q4 » Segment and portfolio Loss Rates are calculated based on loan balance weights

0.0% 0.4% 0.8% 1.2% 1.6% 2.0% 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2 Lifetime Loss Rate Next 4-Quarter Loss Rate Quarterly NCO

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» Model lifetime loss rate or quarterly/annual loss rates as a function of loan/pool characteristics as well as macroeconomic scenarios 𝑀𝑝𝑡𝑡 𝑆𝑏𝑢𝑓 = 𝑔(𝑢𝑈𝑛, 𝐷𝑇𝐵𝑃, 𝑚𝑝𝑏𝑜𝑡𝑗𝑨𝑓, 𝑡𝑓𝑑𝑢𝑝𝑠, 𝑠𝑏𝑢𝑗𝑜𝑕, 𝐶𝑏𝑏 𝑍𝑗𝑓𝑚𝑒, 𝑉𝑜𝑓𝑛𝑞𝑚𝑝𝑧𝑛𝑓𝑜𝑢)

– Time to maturity (𝑢𝑈𝑛)= time between as-of date and contractual maturity date – Credit spread at origination (𝐷𝑇𝐵𝑃, vintage effect) = loan interest rate at origination – benchmark rate – Loan size = Log10(balance or commitment at origination) – Sector = {agriculture, health care, transportation…} – Reporting date credit state = internal or regulatory rating – US unemployment rate = change in unemployment rate in the next year – US Baa yield = average Baa yield in the next year

» May still consider Q-factors for additional adjustments for current and future environments that are not captured by the quantitative models

Loss Rate Modeling Based on Industry Group Data

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Incorporating Bank’s Loss Experience (I)

Example One

» Bank A only has segment level quarterly net charge off rate. Its 10-year average NCO rate is 45% higher than the Data Alliance contributing banks » A simple multiplier of 1.45 is applied to the model. Different look-back periods can be used to determine the multiplier

0.0% 0.4% 0.8% 1.2% 1.6% 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2 Quarterly C&I NCO Rate

Data Alliance Banks Bank A

0% 1% 2% 3% 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2 Modeled Lifetime Loss Rates

Modeled Loss Rate Pre-adjustment Modeled Loss Rate Post-adjustment

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Incorporating Bank’s Loss Experience (II)

Example Two

» Bank B has loan level historical data on payments and losses that are needed for lifetime loss rate calculation » Different level of calibration can be applied by examining loan portfolio loss history and characteristics, relative to industry data » An examination of Bank B’s portfolio shows that the loan size profile of the portfolio differs significantly from the industry peers » The following slide shows two approaches for adjustments. More granular adjustment could be further applied

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2 Lifetime Loss Rate Comparison

Data Alliance Banks Bank B

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Incorporating Bank’s Loss Experience (III)

Example Two (Continued)

Approach 1: Adjust model sensitivity to loan size

0% 1% 2% 3% 4% 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2 Modeled vs. Actual Lifetime Loss Rate

Modeled Loss Rate Pre-adjustment Modeled Loss Rate Post-adjustment Actual Loss Rate

Approach 2: Adjust the model sensitivity to both loan balance and economic variables.

0% 1% 2% 3% 2004Q2 2006Q2 2008Q2 2010Q2 2012Q2 2014Q2 Modeled vs. Actual Lifetime Loss Rate

Modeled Loss Rate Pre-adjustment Modeled Loss Rate Post-adjustment Actual Loss Rate

CRE Portfolios

1.b

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Fulfill CECL Requirements for CRE Loans

» Historical experience: Credit loss estimation based historically observed relationship between realized defaults/losses and CRE market cycles » Current conditions: Current conditions on market, property, and loan » Reasonable and supportable forecasts: A reasonable forward-looking view into the forecastable future, but no need to go overboard, e.g. 30-year forecast on CRE market condition is likely not supportable

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Historical CRE Loss Experience Is Correlated with Loan Characteristics

» CRE loan performance depends critically

• n origination vintage

» Origination LTV is a major risk driver for CRE loans

Based on CMM development dataset Based on CMM development dataset

0% 1% 2% 3% 4% 5% 1 2 3 4 5 6 7 8 9 10 Cumulative Loss Rate Year Overall LTV=50-60% LTV=70-80% 0% 1% 2% 3% 4% 5% 6% 1 2 3 4 5 6 7 8 9 10 Cumulative Loss Rate Year Overall 2007 2009

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CRE Loss Is Also Driven By Macroeconomic and Market Conditions

» Historical CRE loss is closely tied to historical macroeconomic and CRE market trends » A reliable CRE loss estimate depends on reasonable and supportable forecasts of future economic and CRE market conditions

50 100 150 200 250 300 0% 2% 4% 6% 8% 10% 12% CRE Price Index Unemployment Rate

Macroeconomic and CRE Market Trends (2007-2010)

Unemployment Rate CRE Price Index 50 100 150 200 250 0% 2% 4% 6% 8% 10% CRE Price Index Unemployment Rate

Macroeconomic and CRE Market Trends (2011-2014)

Unemployment Rate CRE Price Index

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0.0% 0.2% 0.4% 0.6% 0.8% 1.0% 1.2% 1.4% 1.6% 1.8% 2009 2010 2011 2012 2013 2014 2015 2016 2017

Historical CRE Annual Loss Rates

CRD Benchmark Individual Contributor 0.0% 0.5% 1.0% 1.5% 2.0% 2.5%

Historical CRE Annual Charge-Off Rates

All Banks Individual Bank

CRE Loss Rate Model Combines Industry Data with Bank Experience

» Model specification: 𝐹𝑀 = 𝑔 𝑀𝑝𝑏𝑜 𝐺𝑏𝑑𝑢𝑝𝑠𝑡, 𝑁𝑏𝑑𝑠𝑝 𝐺𝑏𝑑𝑢𝑝𝑠𝑡, 𝑁𝑏𝑠𝑙𝑓𝑢 𝐺𝑏𝑑𝑢𝑝𝑠𝑡

• Vintage
• Property Type
• Property Status
• GDP
• Unemployment
• Interest Rate
• CRE Price Index
• Market Vacancy
• Market Rent

» Final loss estimate can be calibrated to individual bank experience based on call reports

Multiplier = 0.82

» Alternatively, it can be calibrated to historical loss rate for banks with sufficient historical loss data

Multiplier = 0.85

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CRE Loss Rate Forecast: An Example

» Suppose that a bank always originates CRE loans at 50% or 60% LTV » Currently, 20% of its CRE loans were originated in 2014 and the rest were originated after 2014 » Historically, its CRE charge-off rate is 10% lower than that of its peers on average

Loss Rate Year LTV = 60% LTV = 50% Loss Rate Year LTV = 60% LTV = 50% Loss Rate Year Weighted Average Final Forecast

2014 Vintage Post-2014 Vintage

1.0% 0.9% 0.8% 0.6% 1.3% 0.9%

2

From Internal Rating to CECL Impairment

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What is the Rating to PD Convertor?

EDFs by Rating Country adjustment Sector adjustment

Point in Time PDs

• Use the public firm EDF database to estimate the

typical EDF given the rating

• Adjust for sector and country trends
• Use the EDF term structure to generate a Point-in-

Time PD term structure

• Can be applied to a financial institution’s internal

rating

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Ratings Converted into a “Point-in-Time 1-year PD” for a Country Sector Pair

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Scenario Conditioning Through GCorr Macro

U.S. Country Credit Factor Technology Sector Macro Factor Conditional PD, LGD

20% DJIA drop

GCorr Macro Correlations Conditional Credit Migration Input PD, US Tech Firm

Example – U.S. Tech firm

Multiple Scenarios

Allowance

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Scenarios for Macroeconomic Variables

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Scenarios for Macroeconomic Variables

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Example Results

» Loan extended to a US Furniture and Appliances firm

– 5.5 years maturity, Ba2 Rated – Moody’s ECCA Scenarios

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Consistent CRE Model Framework for Loss Rate and Rating-Based Allowance

» Model specification: 𝐹𝑀∗ = 𝑔 𝑀𝑝𝑏𝑜 𝐺𝑏𝑑𝑢𝑝𝑠𝑡, 𝑁𝑏𝑑𝑠𝑝 𝐺𝑏𝑑𝑢𝑝𝑠𝑡, 𝑁𝑏𝑠𝑙𝑓𝑢 𝐺𝑏𝑑𝑢𝑝𝑠𝑡

• Vintage
• Property Type
• Property Status
• GDP
• Unemployment
• Interest Rate
• CRE Price Index
• Market Vacancy
• Market Rent

Loan Rating Local Market Condition

* The dependent variable can also be PD or LGD.

3

Summary and Discussion

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Summary and Discussion

» Institutions often have limited data in loan payment history, default, charge off and recovery » Industry data has much richer and more granular coverage, and can be leveraged to capture the sensitivity of CECL impairments to various risk drivers » It is desirable to adapt models built from industry/peer group data to a bank’s own experience » We have discussed ideas and examples in incorporating both bank internal data and industry data for modeling CECL impairments of C&I and CRE portfolios