CECL Methodologies Series: Remaining Life
This article is the fourth in our series of articles focusing on the different Current Expected Credit Loss (CECL) methodologies and their pros and cons. Previously, we have looked at the following methodologies:
- Cumulative loss rate (also known as “Snapshot”)
- Vintage loss rate
- Migration analysis
In this article, we will look at the remaining life method.
Overview
The remaining life methodology is a type of loss rate methodology that uses an average loss rate and applies it to future expected outstanding balances of the pool. This methodology may feel a lot like existing credit loss estimation models, but there are some critical differences management must consider before adopting this methodology.
How it Works
To complete a remaining life analysis, an institution must first calculate an average annual loss rate for the loan pool. (Does this sound familiar?) This is basically the same average annual loss rate that most institutions calculate today. The institution will consider the same factors it currently does to determine the lookback period (e.g., three years, five years, etc.) and the weighting it places on each year when it calculates the average annual loss rate.
The next step is to estimate the outstanding pool balance at each subsequent reporting period. This is going to be more challenging and will likely require new processes. Whatever system or process is used, an institution will need to consider the following:
- Balloon payments will need to be treated as payoffs and not as renewals.
- Prepayment estimates will need to be considered.
- Expected future loan originations will need to be excluded from the analysis.
The rest of the analysis is fairly straightforward—the institution multiplies the average annual loss rate by the current and each projected report balance of the loan pool and adds the results together to come up with the expected lifetime loss estimate of the pool.
For example, let’s assume an auto loan pool has an outstanding balance of $20 million at December 31, 2017. This pool is made up of three-year through five-year term loans. The institution determines that a three-year lookback period is appropriate to calculate an average loss rate and weights each year equally. Based on loss rates of 0.72%, 0.97%, and 0.68% for 2015, 2016, and 2017, respectively, the institution calculates an average annual loss rate of 0.79%.
The institution also determines the prepayment rate of the pool is approximately 4%. Using the prepayment factor and contractual payment schedules, it projects the following outstanding balances and multiplies each projected balance by the average annual loss rate of 0.79%:
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Table 1 |
|
|
|
|
|
Future Year-End |
Estimated Paydown (000s) |
Projected Balance (000s) |
Average Annual Loss Rate |
CECL Loss Estimate (000s) |
|
2017 |
|
$ 20,000 |
0.79% |
$ 158 |
|
2018 |
$ 9,471 |
10,529 |
0.79% |
83 |
|
2019 |
5,354 |
5,175 |
0.79% |
40 |
|
2020 |
3,206 |
1,969 |
0.79% |
16 |
|
2021 |
1,618 |
351 |
0.79% |
3 |
|
2022 |
351 |
0 |
0.79% |
0 |
|
|
|
|
|
$ 300 |
The CECL lifetime loss rate before any qualitative adjustments is $300 ÷ $20,000 = 1.50%.
This calculation only tells management what the expected future losses might be based on historical loss rates. Like existing incurred loss methodologies, additional analysis of qualitative (Q) factors will be needed to estimate the impact of current conditions as well as forecasted changes that could impact lifetime losses.
An institution will have some flexibility over how it applies these Q factors in the model. For example, the institution could apply the Q factors to the calculated loss amount, or it could adjust the average annual loss rate for each future reporting period based on the forecasted changes in expected losses. Either way, the institution will have to support and document the judgments it uses to determine the appropriate Q factors.
Pros and Cons
After reading how the remaining life analysis works, I’m sure a lot of CECL implementation teams will further consider this methodology because it is so similar to today’s analysis. It is true that institutions will be able to almost fully leverage existing processes to calculate the average annual loss rate for the pool, but management must carefully consider how much time, effort, and cost will be incurred to develop processes necessary to project future outstanding pool balances.
With some tweaking, institutions may be able to utilize their asset/liability management (ALM) system to project these balances—after all, that’s what ALM systems are designed to do! However, institutions may need to spend more time validating the calculations to make sure the system is using the correct inputs and assumptions and properly reporting estimated future balances. Other institutions may be able to purchase or develop a tool to help them with these calculations, but that will come with a cost, too.
The data needed to perform a remaining life analysis should be available to institutions without any additional work. Retrieving that data so it can be used in the analysis may be more challenging.
Q factor adjustments for changes between historical and current conditions and for future expected conditions will be very important. The analysis should result in a lower credit loss estimate than a cumulative loss rate (or snapshot) model, but other CECL methodologies could probably yield even better results.
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Pros |
Cons |
|
A relatively easy CECL methodology that could be prepared internally |
Will need to develop a process or system to project future outstanding balances |
|
Conceptually familiar and current processes can be significantly leveraged |
Will likely result in a higher CECL allowance for loan and lease losses (ALLL) balance than more precise methodologies |
|
More precise than the cumulative loss rate (or snapshot) methodology |
Supporting and documenting qualitative (Q) factors and related adjustments will be critical |
Concluding Thoughts
The remaining life model will look and feel familiar, and many institutions will investigate whether this would be a useful methodology; however, it will require some work to develop a means to estimate projected outstanding balances. Although this methodology is relatively easy to understand, CECL implementation teams will need to consider the cost of projecting future balances and the impact of higher ALLL estimates than some other methodologies might yield.
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
Investigating CECL Methodologies
CECL Methodologies Series: Cumulative Loss Rate
CECL Methodologies Series: Vintage Loss Rate
CECL Methodologies Series: Migration Analysis
