Earlier in the week (HERE) we equated community bank credit ratings to toenails and substantively showed community banks the current probabilities of default for commercial real estate (CRE) by credit risk grade. We discussed how banking is becoming more quantitative and how when rates go up, it will be the bank that correctly classifies credit, allocates capital and correctly prices risk that will have a distinct advantage. Today, we continue on the analysis in order to lay the ground work for more quantified credit management.
Adding Loss Given Default To The Equation
We go back to our model (that is based on historical community bank loan performance) and show what 300+ 10Y, variable rate loans from across the United States looks like in the current environment. Where earlier we showed probabilities of default, today, we show what the loss given default looks like for each “pass” credit grade for the average community bank with a 10-grade credit rating system.
The output can be seen below, and shows that during the life of loan, a “3” graded credit usually loses 18.6% of principal that includes legal costs, lost interest, time and other resources. One interesting note is that since community banks invest a majority of resources in understanding the collateral, the sensitivity around loss given default is less by credit than it is by probability of default.
By multiplying the probability of default by the loss given default, you come up with the expected loss by credit rating which is essentially analogous to a bank’s reserves. We will focus on the blue column which is the current most probable case given where we are in the underwriting cycle, but we show the “Stressed” case as it is important to understate the variability of potential losses should we face another recession over the next 10 years. Below, as can be seen, the banks that rate their credits a “3” should be holding approximately 1.53% of the loan to cover expected credit losses in the portfolio.
How This Matters
We now have the stage set to look at how banks that have invested in credit models can outperform banks that are less quantitative. While the above data may not be accurate for your bank, the important point that you not only have a framework that accurately reflects your risk profile and underwriting, but that your bank has a methodology to continually improve your methodology.
Next week, we will look at two different strategies, using the data above, to either take business from your competition or leave them with an adversely selected portfolio that will make them ripe for failure at the next downturn. Of course, this information can also be used to protect your bank against larger competitors that want to drive you out of business. Until then, take a look at the above data and compare it to how you grade and allocate reserves. Knowing your positioning may help you not only understand where weaknesses lie, but where your strengths lie as well.
Submitted by Chris Nichols on November 12, 2015