Cornhusker Economics May 25, 2022
Complying with CECL – Applying the SCALE Method to Nebraska Community Banks

Current Expected Credit Losses (CECL) is a new expected credit loss accounting standard that was first introduced by the Financial Accounting Standards Board (FASB) in 2016. This new method is meant to replace the current Allowance for Loan and Lease Losses (ALLL) standard. CECL requires the estimation of expected losses over the life of a loan, while ALLL is based on historic, or incurred, losses. This change largely affects banks, but other financial institutions that hold or trade securities can also be subject to the rule. Although CECL was introduced in 2016, the implementation of the rule has had a slow rollout and faced further delays due to economic policy in response to COVID-19. Many larger banks have already adopted CECL, and as of January 2023, all insured depository institutions will be required to maintain the CECL standard.

Moving from incurred losses (ALLL) to lifetime losses (CECL) can be a challenge for many banks. To estimate expected future losses, banks need to forecast economic changes and the impacts on their portfolios. Many banks may face limitations in their ability to generate proper forecasts or to capture the quality of data needed to make such estimates. These challenges can be the greatest for smaller community banks which may lack the appropriate resources to develop this type of approach. In response to the difficulties associated with CECL implementation, the Federal Reserve issued the Scaled CECL Allowance for Losses Estimator (SCALE) in July of 2021. Meant for institutions under $1B in assets, SCALE is based on the utilization of loss rates from larger banks that follow CECL requirements. Rather than deriving their own expected loss rate, banks using SCALE can calculate an average lifetime loss rate, coming from a group of larger peers, as a proxy for their own. If SCALE can provide appropriate estimates, it can be a suitable alternative to the development of a full CECL model, saving community banks time and resources.

Leveraging publicly available Call Report data, we place 140 qualifying Nebraska banks into three groups based on total assets – Small, Medium, and Large (Table 1). With these groups in place, we use the SCALE method to estimate an expected allowance for credit loss (ACL) rate for the first quarter of 2022 and compare the ACL estimates to bank-reported ALLL rates for the same period. The results are shown in terms of simple and weighted averages for each group of banks, where weights are based on each bank’s total loan volume with respect to their group (Table 2).

As shown in Table 2, the SCALE estimated average ACL for each group of banks is lower than the actual average ALLL for each group of banks. Further, the smallest Nebraska community banks appear to have, on average, higher ACL and ALLL than their larger contemporaries. This is due, at least in part, to the fact that small Nebraska community banks have a higher percentage of nonperforming loans. Nonperforming loans can be an indicator that losses may occur, consequently requiring a greater level of reserves to be held against them (Table 1).

Comparing SCALE ACL Estimates to Bank-Reported ALLL

In theory, the lifetime of a loan should reflect greater risk and therefore yield a higher loss rate. Contrary to this idea, results show that average expected loss rates for all groups are lower than those reported under ALLL. There are a few reasons why this could be the case. First, it could simply be the data leveraged from the Call Reports demonstrate a lower level of losses across the sample period. If the data does not show a greater number of losses, the SCALE method will not estimate a higher expected loss rate.

Second, banks might be making qualitative adjustments to their ALLL. The SCALE method allows for banks to consider whether a qualitative adjustment to the loss rate is necessary to reflect any changes in economic and business conditions that may not be reflected in the final estimate. Since the SCALE method relies on a proxy expected lifetime loss rate from peer banks based on information from the previous reporting period, a lag exists between the proxy data and the reporting date. This makes qualitative adjustments especially important during periods when the economic environment is rapidly changing (CECL Resource Center). For this analysis, we are unable to make reasonable assumptions for any qualitative adjustments without a thorough understanding of local economic conditions and bank exposure to impacted markets. Therefore, the disconnect between the two could be due to the lack of qualitative adjustments to SCALE estimates and/or the presence of qualitative adjustments to ALLL.

Finally, in conjunction with the first reason stated above, Nebraska banks in this sample may not be experiencing substantial losses due to increased collateral values. Being under $1B in total assets, most banks have a presence in rural communities and are actively involved in the agricultural industry. Small, Medium and Large banks in the sample show a large portion of their loans servicing agricultural real estate – 23.37%, 23.53%, and 20.20% respectively. Survey results show that the market value of agricultural land in Nebraska increased by 16% in 2021, over the prior year (Jansen & Stokes, 2022). The farm economy in the state has been strong, but if banks were to experience defaults in the agriculture sector, higher land values can help mitigate losses.

Overall, results from this analysis may be helpful to institutions considering the use of SCALE. The shift from incurred to expected lifetime losses can present challenges and maintaining a system that is consistent and accessible may be difficult. If SCALE accurately depicts a bank’s credit risk, it can be an effective approach to comply with the upcoming deadline for adopting the CECL standard.

References

CECL Resource Center, available at https://www.supervisionoutreach.org/cecl, accessed in May 2022

J. Jansen & J. Stokes, (2022) Nebraska Farm Real Estate Report, available at https://cap.unl.edu/realestate, accessed May 2022

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Jeffrey Stokes
Professor
Department of Agricultural Economics
University of Nebraska-Lincoln
jeffrey.stokes@unl.edu
(402) 472-2127

Jared Stauffer
Graduate Student
Department of Agricultural Economics
University of Nebraska-Lincoln
jared.stauffer@huskers.unl.edu