Upload
others
View
9
Download
0
Embed Size (px)
Citation preview
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
617-261-8172
2
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.
3
Pros & Cons
4
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
5
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
6
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 -
Example: Residential Real Estate (in 000’s)
– 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.
8
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
Example: Residential Real Estate (in 000’s)
• 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
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%
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
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% 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.
10
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.
11
Another Approach – Qualitative
Adjustments to Historical Losses
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.
12
Another Approach – Qualitative
Adjustments to Historical Losses
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
Another Approach – Qualitative
Adjustments to Historical Losses
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.
15
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
617-261-8172
17