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MEMBER OF ALLINIAL GLOBAL, AN ASSOCIATION OF LEGALLY INDEPENDENT FIRMS © 2017 Wolf & Company, P.C.

CECL Workshop

Vintage Method

John J. Doherty, CPA

Introduction

John J. Doherty

Member of the Firm

[email protected]

617-261-8172

2

mailto:[email protected]

Overview

• Vintage analysis measures losses based on the origination date and the

historical performances of loans with similar risk characteristics.

• Vintage methodology works well with loans that follow patterns that are

similar and predicative for subsequent generations of loans

(homogeneous).

• Vintage analysis requires segmentation and stratification of the loan

portfolio, with the additional requirement that loans be stratified by

origination period.

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Pros & Cons

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Pros Cons

Forecasting ability can improve as more

data is collected, allowing more precise

qualitative and quantitative adjustments

to be made at the vintage level

Data mining can be extensive based on

the level of disaggregation…does your

loan system provide enough data to

efficiently pull together the required data?

Adequately segmented data eliminates

qualitative changes in portfolio growth/mix

Monitoring of prepayments is required to

ensure that baseline data that drives the

calculation is reasonable.

Can be used to isolate changes in

economic environment, collateral value

and underwriting to a given year

Doesn’t work well with revolvers or loans

subject to frequent renewal (i.e.

commercial)

Easier to understand

Consistent with disclosure requirements

and expectations of life of loan estimate

Flexible to add new information for new

loans

Example: Residential Real Estate

30 year, first position lien, fixed rate residential real

estate

• Consider separate calculations for variable versus fixed rate due

to prepayment speeds

• Generally should apply vintage to a homogeneous portfolio

where underwriting standards and loan terms and behavior are

generally consistent

• Loans conforming to secondary market standards are likely

homogeneous with respect to underwriting standards

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Example: Residential Real Estate (in 000’s)

(Note: shaded regions are future estimates)

• Data through 12/31/16 is known

• Estimated life of loan is 6 years, but will vary based on rate environment and prepayment

speeds

• Loans stratified by year of origination and type of loan

• Above is fairly linear, results will vary significantly by rate environment

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Vintage Principal Collections

Year Originations 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

2011 40,000 6,667 6,125 6,548 6,978 7,152 6,530 - - - - -

2012 42,400 - 7,067 6,941 7,397 7,581 6,922 6,493 - - - -

2013 44,944 - - 7,491 7,840 8,036 7,337 6,882 7,357 - - -

2014 47,641 - - - 7,940 8,518 7,778 7,295 7,799 8,311 - -

2015 50,499 - - - - 8,417 8,244 7,733 8,267 8,810 9,029 -

2016 53,529 - - - - - 8,922 8,197 8,763 9,338 9,571 8,739

Totals 6,667 13,192 20,980 30,155 39,704 45,734 36,599 32,186 26,459 18,600 8,739

Period End Loan Balances 33,333 62,542 86,506 103,991 114,787 122,582 85,983 53,798 27,339 8,739 -


– Loss rates here correspond to actual charge-off history and loan balance data as

previously presented, for 2011-2016.

– Blue highlights represent expected credit losses over the life of the loan vintages,

qualitatively adjusted for reasonable supportable forecasted items (illustrated on next

slide). 7

Origination Losses by Vintage By Year

year Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Total

2011 0.13% 0.21% 0.28% 0.31% 0.16% 0.09% 1.18%

2012 0.12% 0.20% 0.27% 0.30% 0.15% 0.09% 1.13%

2013 0.11% 0.19% 0.26% 0.29% 0.13% 0.09% 1.07%

2014 0.10% 0.18% 0.25% 0.23% 0.13% 0.09% 0.97%

2015 0.09% 0.17% 0.18% 0.22% 0.13% 0.09% 0.88%

2016 0.08% 0.12% 0.18% 0.23% 0.14% 0.11% 0.84%

Origination Charge-offs by Origination Year ($)

Year 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Total

2011 52 84 112 124 64 36 - - - - - - 472 1.18%

2012 - 51 85 114 127 64 38 - - - - - 479 1.13%

2013 - - 49 85 117 130 59 39 - - - - 480 1.07%

2014 - - - 48 86 119 107 61 42 - - - 463 0.97%

2015 - - - - 45 86 90 112 66 47 - - 446 0.88%

2016 - - - - - 43 63 94 120 73 56 - 450 0.84%

Totals 52 135 246 372 439 478 357 306 228 120 56 - 2,789

Example: Residential Real Estate

Estimate of expected losses: Calculating the correlation between loss factor and

qualitative (Q) factor (MA unemployment).

• The correlation is established by measuring historical losses and tying to the Q

Factor. The idea being that you can predict the Q factor and use this to adjust future

loss ratios.

• Above: the average loss for Y2 is 0.19% which is 3.74% of the Q factor.

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Origination Y1 Y2 Y3 Y4 Y5 Y6 Origination Y1 Y2 Y3 Y4 Y5 Y6

2011 0.13% 0.21% 0.28% 0.31% 0.16% 0.09% 2011 6.70% 6.70% 6.10% 5.10% 4.30% 3.20%

2012 0.12% 0.20% 0.27% 0.30% 0.15% 2012 6.70% 6.10% 5.10% 4.30% 3.20%

2013 0.11% 0.19% 0.26% 0.29% 2013 6.10% 5.10% 4.30% 3.20%

2014 0.10% 0.18% 0.25% 2014 5.10% 4.30% 3.20%

2015 0.09% 0.17% 2015 4.30% 3.20%

2016 0.08% 2016 3.20%

2017 2017

Average 0.11% 0.19% 0.27% 0.30% 0.16% 0.09% Average 5.35% 5.08% 4.68% 4.20% 3.75% 3.20%

Loss/Q factor 1.96% 3.74% 5.67% 7.14% 4.13% 2.81%

Loss Rates by Vintage Q Factor by Vintage - Eg. MA Unemployment


• Arrive at reasonable supportable forecast for Q factor (future unemployment)

• Multiply the average loss factor times Q ratio for each period to arrive at an estimated

future loss9

Loss Rates by Vintage Q Factor by Vintage - Eg. MA Unemployment


2011 0.13% 0.21% 0.28% 0.31% 0.16% 0.09% 2011 6.70% 6.70% 6.10% 5.10% 4.30% 3.20%

2012 0.12% 0.20% 0.27% 0.30% 0.15% 2012 6.70% 6.10% 5.10% 4.30% 3.20%

2013 0.11% 0.19% 0.26% 0.29% 2013 6.10% 5.10% 4.30% 3.20%

2014 0.10% 0.18% 0.25% 2014 5.10% 4.30% 3.20%2015 0.09% 0.17% 2015 4.30% 3.20%

2016 0.08% 2016 3.20%

Average 0.11% 0.19% 0.27% 0.30% 0.16% 0.09% Average 5.35% 5.08% 4.68% 4.20% 3.75% 3.20%

Loss/Q factor 1.96% 3.74% 5.67% 7.14% 4.13% 2.81%

Loss Rates by Vintage Reasonable Supportable Forecast


2011 0.13% 0.21% 0.28% 0.31% 0.16% 0.09% 2011 6.70% 6.70% 6.10% 5.10% 4.30% 3.20%

2012 0.12% 0.20% 0.27% 0.30% 0.15% 0.09% 2012 6.70% 6.10% 5.10% 4.30% 3.20% 3.15%

2013 0.11% 0.19% 0.26% 0.29% 0.13% 0.09% 2013 6.10% 5.10% 4.30% 3.20% 3.15% 3.10%

2014 0.10% 0.18% 0.25% 0.23% 0.13% 0.09% 2014 5.10% 4.30% 3.20% 3.15% 3.10% 3.15%

2015 0.09% 0.17% 0.18% 0.22% 0.13% 0.09% 2015 4.30% 3.20% 3.15% 3.10% 3.15% 3.30%

2016 0.08% 0.12% 0.18% 0.23% 0.14% 0.11% 2016 3.20% 3.15% 3.10% 3.15% 3.30% 3.75%

Average 0.11% 0.18% 0.24% 0.26% 0.14% 0.09% Average 5.35% 4.76% 4.16% 3.67% 3.37% 3.28%

Loss/Q factor 1.96% 3.74% 5.67% 7.14% 4.13% 2.81%

Additional Considerations

• Vintage analysis can also be applied without tying to a Q factor. The average loss

by vintage is useful in itself and can be qualitatively adjusted for new information.

• Qualitative adjustments can be evaluated in a similar fashion to how they are

arrived at now, with the inclusion of reasonable supportable forecasts.

• In our previous example, one specific Q factor (unemployment), was tied directly to

loss rates used as the starting point for calculating expected losses. Additional

qualitative adjustments can be made based on the economic current environment

(delinquency, management etc.) and other forecasted items (Schiller index,

foreclosure rates, interest rates, LTV, other economic data).

• Qualitative adjustments can be made by vintage or evaluated at the pool level.

• Vintage analysis identifies the loss emergence period (LEP), which may be

relevant information for other methods. For example, a discontinued loan

segment that is seasoned would require less reserves if the LEP is known.

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Another Approach – Qualitative

Adjustments to Historical Losses

Current conditions - 2016 vintageRefer to qualitative memo for detail analysis of metrics.

Delinquency ratio is consistent year to year but higher than custom peer group. Net losses were

relatively elevated during the years 2009 to 2012 and have since decreased and are favorable to

peer. The recent trend is positive and the annual loss rate will decrease as lower loss years are

added to the historical period.

The Bank tracks average FICO and LTV to identify changes in credit risk and there is no change

in portfolio metrics. Based on current real estate valuations the average LTV of the portfolio

should be improving and providing more collateral support.

There have been no significant changes in lending policies, underwriting or management during

2016.

There are no indications that the average annual loss rate for 2016 should be adjusted for credit

quality concerns. Management will make a qualitative adjustment to increase the historical loss

3 basis points for lack of historical loss consistency across the different vintages.

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Forecast – 2016 vintageCredit risk drivers are unemployment, local real estate values, and interest rates.

(General consensus centers around a 2 year forecast).

Unemployment

Trend for state unemployment rate is positive at 3.2%, decreasing from 4.3% in 2015. Regional

unemployment is 3.6% at December 2016. Fed outlook over the next 6 years is marginal

declines over the next two years, followed by increases through 2022 up to 4%. The Bank's

loan committee assesses employment factor as stable. An adjustment of 5 basis points

will be made based on expected future increases in unemployment.

Real estate

Per Sept 2016, FRB Boston's quarterly publication NEPPC: Home prices continued to grow

both nationally and regionally, with national growth rates continuing to exceed regional rates. All

six New England states reported positive house price growth year-over-year, but these gains all

trailed the national rate. The Bank's loan committee assesses this factor as stable. No

forecast adjustment is necessary for real estate values.

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Forecast (continued)

Interest rates

Fed increased rates during 2016 and effect is reflected in year end prepayment

speed assumption. Bloomberg median factor for 30 year FNMA MBS with same

terms is 239% (or 5.75 yr life) at 12/31/16.

Generally the bank’s prepayment speeds lag secondary market speeds. The

prepayment speed assumption for this estimate has been adjusted to a 6 year life

based on historical performance and already reflects extension due to the 2016 rate

hike.

Management is conservative in the determination of prepayment risk and no

adjustment has been made for future rate hikes as management cannot

forecast this factor.

13



14

Vintage loss factor and Adjustments - 2016

Vintage average loss (previous 5 year expected vintage loss plus 2016 actual) 1.03%

Adjustments

Current Conditions 0.03%

Forecasts 0.05%

Total 2016 Expected Loss Factor - Vintage 1.11%

Qualitative Factors

Schedule out economic factors by vintage and analyze for

trends that should warrant additional consideration.

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Economic Factor Summary by Vintage

Year

2012 2013 2014 2015 2016

Micro Data:

Delinquencies 0.76% 0.93% 1.05% 1.09% 0.00%

Non-accrual rates 0.82% 0.78% 0.82% 0.71% 0.69%

Underwriting stable stable stable stable stable

Added 5 resi. lendersManagement stable stable stable stable

Macro Data:

Unemployment 6.70% 6.10% 5.10% 4.30% 3.20%

Interest Rates

Shiller Price Index 145.53 161.11 168.28 176.98 186.54

Change 6.50% 10.71% 4.45% 5.17% 5.40%

Others to consider if available

Average LTV

Average FICO scores

Observations

1. Data mining by segment and vintage is critical

2. Vintage analysis requires a lot of data, but may result in a more

precise estimate of expected credit losses. Loss rates

decrease over time as borrower obtains equity in collateral, but

are also impacted by qualitative considerations, including

reasonable supportable forecasts.

3. Different approaches to applying qualitative adjustments

16

Questions?

John J. Doherty

Member of the Firm

[email protected]

617-261-8172

17

mailto:[email protected]

cecl workshop vintage method - wolf & company, p.c....cecl workshop vintage method john j....

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