CELC Workshop Historical Loss Rate Model

Transcription

1 CELC Workshop Historical Loss Rate Model Jim McGough, CPA, CGMA Michaela Bellefeuille, CPA MEMBER OF ALLINIAL GLOBAL, AN ASSOCIATION OF LEGALLY INDEPENDENT FIRMS 2017 Wolf & Company, P.C.

2 Introduction Jim McGough, CPA, CGMA Senior Audit Manager Michaela Bellefeuille, CPA Audit Manager 2

3 Loss Rate Approach Average Charge-off Method Most commonly used now. Historical loss is the starting point and base. Net charge-offs are measured as a percentage of the average balance of a loan segment over a look-back period. The historical loss factor is adjusted as necessary to reflect differences in qualitative and quantitative factors. Final factor is applied to current pool or individual loan balance to estimate loss. 3

4 Pros & Cons Pros Consistent with current approach used by many banks and works for all loan types Easy to apply at pool level with lower data needs Qualitative factor adjustment is similar to existing methods. Supported by memo with referenced quantitative support Cons Loan pool should be steady state (loan terms, prepayment, collateral, etc.) for this approach to be supportable More qualitative adjustments are necessary due to less data Qualitative adjustments are more subjective and difficult to validate Makes sense for small populations May require higher reserves 4

5 Example 1: Residential Real Estate 30 year term, first position lien, fixed rate residential real estate Assume CECL adopted at 12/31/16 for illustration: Balance at measurement date is $58.7 million. Utilized portfolio level data (call report) Separate calculations for variable versus fixed rate due to prepayments Steady state portfolio where underwriting standards and loan terms are consistent. Lifetime loss data is not available Annual loss rate based on available 15 years of data is 11 basis points. 5

6 Example 1: Residential Real Estate 6

7 Example 1: Residential Real Estate ASU Historical credit loss experience of financial assets with similar risk characteristics generally provides a basis for an entity s assessment of expected credit losses. Historical loss information can be internal or external historical loss information (or a combination of both). An entity shall consider adjustments to historical loss information for differences in current asset specific risk characteristics, such as differences in underwriting standards, portfolio mix, or asset term within a pool at the reporting date or when an entity s historical loss information is not reflective of the contractual term of the financial asset or group of financial assets. Is the loss data sufficient as is? 7

8 Example 1: Residential Real Estate Historical loss factor & adjustments Historical loss period is for last 15 years and is considered representative for the current portfolio and conservative for the purpose of establishing an annual loss rate. Loss rates have ranged from 0% to 0.39% with a median average of.05%*. The effect of the recession is reflected in the lookback period. The average annual loss rate is 11 basis points. No adjustment to historical data. * Based on Call Report data for MA insured savings banks > $1billion, 1-4 Family Residential Loans-1 st Lien 8

9 Current Conditions & Forecast Factors to be considered to adjust historical loss information for current conditions and reasonable and supportable forecasts: 9

10 Current Conditions & Forecast Factors to be considered to adjust historical loss information for current conditions and reasonable and supportable forecasts: 10

11 Example 1: Residential Real Estate Current conditions The below summary will be supported by qualitative memo with detail and 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 slowly decrease as lower loss years are added to the historical period. The Bank tracks average FICO and LTV to identify changes in credit risk. There is no adverse 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 are no indications that the average annual loss rate should be adjusted for credit quality concerns. Management will make a qualitative adjustment to increase 2 basis points for lack of precision with the pool method. 11

12 Example 1: Residential Real Estate Forecast Credit risk drivers are unemployment, local real estate values, and interest rates. Maximum forecast period is 2 years for this pool. Unemployment Trend for national unemployment rate is 4.8% Sept 2016 with long-term outlook 4.8% per Federal Reserve statistics. Regional unemployment is 3.6% at Nov 2016 showing continued improvement. The Bank's loan committee assesses employment factor as stable. No forecast adjustment is necessary for unemployment trends. 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

13 Example 1: Residential Real Estate Forecast (continued) Interest rates Fed increased rate 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. Based on bank s history and ALCO analysis, the bank's RE loans will prepay slower than mortgage backed securities. The prepayment speed assumption for this estimate has been adjusted to 150 % (or 7.9 yr 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 increases as management cannot forecast this factor. 13

14 Example 1: Residential Real Estate Weighted-average Life No Prepayments 14

15 Example 1: Residential Real Estate Weighted-average Life With Prepayments 15

16 Example 1: Residential Real Estate 16

17 Observations 1. Prepayment assumption is a critical input because it decreases amortized cost exposure over the contractual term of loan. 2. Note that management was conservative in decreasing the factor to reflect expectations for the bank s portfolio as compared to median PSA. 3. Vintage analysis requires a lot of data, but it will result in lower reserves for seasoned real estate loans. 17

18 Discussion Points 1. Is this adequate support? 2. How can this analysis be improved? 3. Must the forecast adjustments be quantitatively linked to prior or external loss experience? 4. Is industry data a useful alternative or addition? For example, published life of loan loss data for conforming residential real estate loans by LTV. 18

19 Example 2: Closed Pool Loss Rate Method Example in the ASU shows the cumulative loss rate approach as a method that might be used by a community bank. Applied to a segment of 10 year amortizing loans Solid state pool with consistent underwriting and terms during lookback period Historical loss rate already reflects prepayments and no change is expected 10 year history of losses is considered representative The method of calculating the cumulative loss rate and determining the qualitative factor adjustments is not shown by FASB. Following is one method of calculating the base loss factor using a closed pool analysis as presented by American Bankers Association and Crowe Horwath. 19

20 Example 2: Closed Pool Loss Rate Method 20

21 Example 2: Closed Pool Loss Rate Method 21

22 Example 2: Closed Pool Loss Rate Method 22

23 Example 2: Closed Pool Loss Rate Method Historical loss factor & adjustments Most recent 10 year history is a reasonable period for measurement Closed pool analysis Underwriting standards and loan terms are consistent during this period. Prepayments reflected in historical loss method The annual loss rate increased in the last two years which is not reflected in the closed pool. Adjustment to increase cumulative loss rate to reflect most recent historical loss experience which shows 3bp increase (12% increase to 150bp). 23

24 Example 2: Closed Pool Loss Rate Method Current conditions Refer to qualitative memo for detail and metrics. There are no indications that loss rate should be adjusted for current conditions. 24

25 Example 2: Closed Pool Loss Rate Method Forecasts Credit risk drivers are unemployment and local real estate values Forecast period is 2 years Real estate values have decreased in recent period and further decrease is expected over next one to two years and this will increase losses when loans default. Unemployment is expected to increase over the next one to two years increasing the risk of default. 25

26 Example 2: Closed Pool Loss Rate Method Forecasts (continued) Forecast is qualitative and directionally consistent. Losses will increase as a result of unemployment and real estate values. Management expects 30% increase in annual losses for 1.5 years related to unemployment and real estate values. (Most recent annual portfolio loss rate 30bp x 30% x 1.5 years = approximately 14bp and rounded up to 15b). Increase reserve 0.15% for forecast. 26

27 Observations 1. Cumulative loss for one vintage is a solid foundation 2. Provides loss curve information 27

28 Discussion Points 1. Is this adequate support? 2. How can this analysis be improved? 3. Must the forecast adjustments be quantitatively linked to prior or external loss experience? 4. Do you have many loan segments where a closed pool analysis can be performed? Start building history for pools where possible. 28

29 Questions

30 Thank you! Jim McGough, CPA, CGMA Senior Audit Manager Michaela Bellefeuille, CPA Audit Manager 30