First Quarter COVID-19 Credit Loss Estimates: Where No Man Has Gone Before

By Josh Stein

Whether institutions are using CECL or incurred loss methodology, estimating credit losses in today’s pandemic-stressed economic environment is challenging, to say the least.

With government-sanctioned lockdowns and resulting job losses being countered by extraordinary loan forbearance requirements and congressional stimulus measures, bankers are forced to play part-epidemiologist, part-Keynesian forecaster and part-fortune teller. Those looking to traditional credit metrics, such as the levels of past-due loans and troubled debt restructurings for example, have been frustrated this first quarter, as the suddenness of lockdowns and the size of governmental programs have essentially muted these trends. Bankers know, however, that credit losses are out there somewhere, and that they could be big.

How big?

Based on CECL forecasts, energy credit loss provisions spiked, with coverage ratios up to 20 percent. With the exception of targeted provisions for hospitality and retail, however, the remaining first quarter commercial loan loss provisions were relatively muted, as forecasted collateral prices remain firm: while increasing substantially from 2019 levels, they are generally between 100 and 200 basis points.

In credit card lending, coverage ratios approached 10-12.7 percent for some portfolios. Increasing provisions in residential mortgage portfolios were similar to those in commercial lending: while on the rise, coverage ratios remain rather low (under 100 bps) as home prices remain solid. All this said, it is significant that CECL forecasts were made in a quickly deteriorating economy—most banks assumed unemployment rates approximating 10 percent—far lower than the 14.7 percent April unemployment levels announced by the Labor Department.

With unemployment during the 2007-2010 recession having reached 10 percent, it is difficult to see how April’s unemployment rate of 14.7 percent could be well-integrated into current credit loss estimation models. And taking into account Federal Reserve Chairman Jerome Powell’s comment that the unemployment rate could reach 20-25 percent in May or June, the loss estimate then often becomes an exercise in “Q-factors,” as qualitative adjustments from models appear to be ruling the day. In fact, many publicly held banks announcing first quarter earnings have emphasized significant qualitative adjustments from the model-derived results.

In light of this, the importance of qualitative adjustments has never been higher, especially since newly passed auditing standards are focusing on assessing “management bias” in these estimates. In response to the new auditing standard, the AICPA’s Practice Aid on Credit Losses refers to auditing various aspects of management bias—availability, anchoring, confirmation and familiarity bias—all of which require structured governance responses and internal controls to make sense of the reasonableness of the resulting Q-factor adjustments.

Josh Stein is VP for accounting and financial management at ABA.