In our last newsletter, we began a series of articles that will focus on the different Current Expected Credit Loss (CECL) methodologies and their pros and cons. The first methodology we looked at was the cumulative loss rate, CECL Methodologies Series: Cumulative Loss Rate, which is the simplest methodology to use under the new standard, but will require a great deal of qualitative (Q) factor analysis and will likely result in a higher allowance for loan and lease losses (ALLL) balance relative to other available methodologies. In this article, we will explore the vintage loss rate methodology.
Overview
Even before the Financial Accounting Standards Board (FASB) finalized its new financial instruments credit impairment standard, it seemed the vintage loss rate methodology (“vintage analysis”) was one of the most talked about CECL models. The data needed for a simple vintage analysis is already collected by almost all financial institutions, though just because institutions may capture the required data at one point does not necessarily mean it can be easily analyzed using current systems (more on this in a bit).
Vintage analysis measures the amount of loan charge-offs net of recoveries (“loan losses”) recognized over the life of a pool of loans originated during a specific period of time—a vintage—and compares the loan losses incurred during future periods (“vintage loss periods”) to the original loan balance of the vintage. The vintage is identified as the actual period of time during which the loans were originated (e.g., “2017” or “Q3 2017”) and the vintage loss periods are relative to the vintage (e.g., “Year 3” or “Quarter 9”). A vintage loss rate is calculated for each vintage loss period, and the methodology then compares the vintage loss rates for all of the vintages in the pool of loans being evaluated.
How it Works
To complete a vintage analysis, management segregates loan originations for a loan pool into different vintages. For each vintage, management must determine when any loan losses occurred and assign them to the appropriate vintage loss period. The vintage loss rate is calculated as the ratio of period loan losses to the original vintage balance for each vintage loss period.
For example, let’s assume $10 million of 3-year consumer loans were originated in the first quarter of 2014 (Q1 2014). Management has identified all the loan losses for this vintage and calculated a vintage loss rate for each period as noted in Table 1.
| Table 1 | |||
| Quarter | Vintage Loss Period | Loan Losses ($) | Loss Rate (%) |
| Q2 2014 | Q1 | 0 | 0.00 |
| Q3 2014 | Q2 | 0 | 0.00 |
| Q4 2014 | Q3 | 10,000 | 0.10 |
| Q1 2015 | Q4 | 7,000 | 0.07 |
| Q2 2015 | Q5 | 15,000 | 0.15 |
| Q3 2015 | Q6 | 14,000 | 0.14 |
| Q4 2015 | Q7 | 23,000 | 0.23 |
| Q1 2016 | Q8 | 18,000 | 0.18 |
| Q2 2016 | Q9 | 4,000 | 0.04 |
| Q3 2016 | Q10 | 7,000 | 0.07 |
| Q4 2016 | Q11 | 0 | 0.00 |
| Q1 2017 | Q12 | 0 | 0.00 |
The institution would complete a similar analysis for all of the different vintages. After accumulating all of this data, management can begin analyzing trends and calculating expected vintage loss rates for future periods. Table 2 is an excerpt of what the final analysis might look like as of June 30, 2017.
| Table 2 | ||||||||||||||||||||||||
| Vintage Loss Rates (%) | Expected | |||||||||||||||||||||||
| Future | ||||||||||||||||||||||||
| Quarter | Q1 | Q2 | Q3 | Q4 | Q5 | Q6 | Q7 | Q8 | Q9 | Q10 | Q11 | Q12 | Losses | |||||||||||
| … | ||||||||||||||||||||||||
| Q4 2013 | 0.00 | 0.05 | 0.00 | 0.00 | 0.17 | 0.09 | 0.12 | 0.25 | 0.10 | 0.05 | 0.04 | 0.00 | 0.00 | |||||||||||
| Q1 2014 | 0.00 | 0.00 | 0.10 | 0.07 | 0.15 | 0.14 | 0.23 | 0.18 | 0.04 | 0.07 | 0.00 | 0.00 | 0.00 | |||||||||||
| Q2 2014 | 0.00 | 0.01 | 0.12 | 0.03 | 0.10 | 0.13 | 0.15 | 0.04 | 0.04 | 0.05 | 0.03 | 0.01 | 0.00 | |||||||||||
| Q3 2014 | 0.00 | 0.00 | 0.00 | 0.00 | 0.12 | 0.11 | 0.17 | 0.07 | 0.24 | 0.13 | 0.02 | 0.01 | 0.01 | |||||||||||
| … | ||||||||||||||||||||||||
| Q4 2016 | 0.00 | 0.00 | 0.06 | 0.05 | 0.14 | 0.15 | 0.17 | 0.14 | 0.11 | 0.08 | 0.03 | 0.01 | 0.94 | |||||||||||
| Q1 2017 | 0.00 | 0.01 | 0.06 | 0.05 | 0.14 | 0.15 | 0.17 | 0.14 | 0.11 | 0.08 | 0.03 | 0.01 | 0.95 | |||||||||||
| Q2 2017 | 0.00 | 0.01 | 0.06 | 0.05 | 0.14 | 0.15 | 0.17 | 0.14 | 0.11 | 0.08 | 0.03 | 0.01 | 0.95 | |||||||||||
The unshaded vintage loss rates in Table 2 represent actual loan loss rates calculated for historical vintage loss periods. The shaded vintage loss rates in Table 2 are estimated losses for future periods that are based on the historical loss rates adjusted for any trends or other qualitative information management believes would alter future loss rates.
Once the institution has calculated the expected future loss rates for each vintage, the estimated CECL ALLL is simply the originated principal balance for each vintage x the expected future loss rate. For example, assuming the originated balance of Q2 2017 loans was $17 million, the related allocation of the ALLL would be $17 million x 0.95% = $161,500.
Like the cumulative loss rate methodology, this calculation only tells management what the expected future losses might be based on historical loss rates. Additional analysis of Q factors will be needed, and adjustments will be made to the expected future vintage loss rates (e.g., the shaded loss rates in Table 2) and/or more broadly to the final estimated ALLL for the loan pool.
Pros and Cons
The vintage analysis has been discussed as a potential CECL methodology for several years because it is a relatively simple methodology that can provide information about when losses are historically incurred after the loans are originated. The analysis uses data already collected by most financial institutions in their loan trial balance systems and/or existing ALLL models, including:
- Loan origination date.
- Originated loan balance.
- The date and amount of loan losses (charge-offs net of recoveries).
- The related loan that incurred each loan loss.
Although the necessary data was collected at one point or another, current systems may not make it easy to gather the data for the vintage analysis. Loan origination dates and balances may need to be obtained from multiple fields (e.g., origination date or last renewal date). Loan charge-off information may be stored in a spreadsheet and will need to be merged with loan origination information. Employees may have to manually look up loan account numbers to match loan origination information to the loan losses. As a result, management teams will likely have to make several changes to current systems to effectively and efficiently gather the needed vintage analysis data.
A vintage analysis will provide more precise information about historical loan rates, but it is still heavily reliant on historical loan losses. Consequently, any changes in current or future expected conditions will need to be adjusted in the analysis through reasonable and supportable Q factor adjustments. In addition, generating a vintage analysis will require the use of database modeling, which may mean users will have to become familiar with new programs or database functions like pivot tables in spreadsheet programs. Many people are not familiar with these programs and/or functions, so employees will probably need additional training.
Finally, because the vintage analysis provides management with more precise information about historical loss rates when compared to a cumulative loss rate methodology, it will generally result in a lower ALLL estimate, but other methodologies we discuss in future articles could reduce the CECL estimate even further.
| Pros | Cons |
| Relatively easy initial CECL loss rate calculation | Will require database modeling techniques |
| Needed data should already be captured in existing systems | Analysis of qualitative (Q) factors will still be critical |
| Information may be used by public business entities when completing the required vintage footnote disclosures | Will likely result in a higher CECL allowance for loan and lease losses (ALLL) balance than more precise methodologies |
Concluding Thoughts
Vintage analysis is often discussed by institutions that are considering an internal CECL methodology because it is relatively easy to generate and maintain, it uses data that is already accumulated, and it provides some level of precision that can help institutions come up with a reasonable and supportable forecast of future expected losses. However, management teams should still consider the extent of the qualitative analysis that must accompany a vintage analysis and whether they have people capable of utilizing the required database programs or functions.
We will continue to look at other available CECL methodologies in future articles, but if you would like to discuss any or all of the available methodologies in more detail at any time, please contact Brett Schwantes or your Wipfli relationship executive, and we would be happy to set up an appointment with you!
For more information on CECL, please check out some of our recent articles:
- Measuring Credit Impairment of Financial Instruments (Sept 2016)
- Investigating CECL Methodologies (Nov 2016)
- CECL Governance (Jan 2017)
- CECL: Getting Started (Mar 2017)
- CECL Methodologies Series: Cumulative Loss Rate (July 2017)
