CECL Methodologies Series: Migration Analysis

This article is the third in our series of articles focusing 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, which was the simplest methodology to use under the new standard, but it 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. The second, more complicated methodology we looked at was the vintage loss rate, one of the most discussed CECL models that could still be prepared internally and utilizes data institutions should already collect. In this article, we will explore migration analysis.

Overview

Migration analysis has been used for years by many different institutions to evaluate changes in the credit quality of a loan portfolio. The analysis tracks the changes in a credit quality factor (e.g., risk rating or credit score) of a pool of loans over a period of time to see whether the credit quality of the loan pool has improved or worsened. It also provides information about the ultimate credit losses realized and when they were realized. This information can help management make better decisions when managing the credit risk of the pool.

Under the new accounting standard, institutions can use the information obtained in a migration analysis to estimate expected loan losses in the loan pool. However, additional data may need to be collected by institutions to utilize this methodology.

How it Works

A migration analysis can be completed a number of different ways. Management may use the origination date and balance of a loan pool or the outstanding balance of a loan pool at a point in time. The analysis may track the loans through their maturity or through a cutoff date. The entire population of the pool may be used or just a subset. The complexity of the analysis can vary significantly based on these and other choices.

A fairly simple migration analysis could still provide a great deal of information for a CECL methodology. The simplest migration analysis would track the credit quality factor of a pool of loans from one date to a second date. This is very similar to the cumulative loss rate methodology except that the analysis disaggregates the loan pool by the credit quality factor.

For example, let’s assume a commercial real estate loan pool had an outstanding balance of $175 million at December 31, 2016. This pool is made up of three-year and five-year balloon notes.  Since the term of the loan pool is five years, the migration analysis will start on December 31, 2011.  As of December 31, 2011, the pool is $100 million, and management assigns risk ratings to each loan as summarized in Table 1.

Management then tracks all of the losses for this loan pool over the next five years and prepares a simple migration analysis summarized in Table 2.

The CECL loss rate at December 31, 2016, is only 0.49% ($855,000 ÷ $175 million) compared to the December 31, 2011, loss rate of 2.63%. The loss rate decreased because the credit quality of the loan pool improved over the five-year period. Since the analysis is disaggregated by the credit quality factor―in this case, risk rating―changes in the balance of each risk rating category are automatically incorporated into the migration analysis.

Like the previous CECL methodologies discussed, this calculation only tells management what the expected future losses may be based on historical loss rates. It does have the advantage of automatically updating the CECL loss rate for current credit quality conditions; however, additional analysis of Q factors will be needed to estimate the impact of other current and forecasted conditions.

Pros and Cons

Unlike the cumulative loss rate methodology and vintage analysis, migration analysis can provide information about changes in a loan pool’s credit quality, a critical factor when trying to estimate future expected credit losses. It can be a fairly simple analysis or a complex model depending on the precision management is looking for. 

To use migration analysis, though, management must track changes in the credit quality factor selected. Institutions that do not currently collect this data will have to implement brand new systems and processes to gather, store, update, and analyze the credit quality factor for each loan in the pool. Institutions that already collect this data may nevertheless need to implement certain internal controls to help ensure the data is accurate and updated in a timely manner. These institutions may also consider changes to existing data collection systems to make the analysis more efficient.

A migration analysis will provide information about changes in credit quality of the loan portfolio, but other changes in current or future expected conditions will need to be considered in the analysis through reasonable and supportable Q factor adjustments. Similar to a vintage analysis, generating a migration analysis will require the use of database modeling. The analysis generally results in a lower ALLL estimate than a cumulative loss rate or vintage loss rate model, yet other methodologies we discuss in future articles could reduce the CECL estimate even further.

Concluding Thoughts

A migration analysis can be a relatively simple model that provides more information about changes in credit quality of the loan pool; however, it may require new systems and internal controls to gather and track changes to the credit quality factor. Management teams must also determine whether they have people capable of utilizing the required database programs or functions necessary to complete a migration analysis.

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)

CECL Methodologies Series: Vintage Loss Rate (Sept 2017)