Your Bank’s Credit Ratings Shouldn’t Be Like Toenails - Part I

Bank Credit Analytics

If truth be told, community bank credit ratings are like toenails. You see them a lot, you occasionally think about them, every now and then you maintain them, you make sure they look good when you know they are going to be seen, and you only have a vague awareness of their practical use. The underutilization of community bank credit ratings is about to change over the next five years as rates go up and more sophisticated models help bankers get more quantitative about how they classify credit, price risk and allocate capital.

 

Why Better Credit Classifications Matter

 

Your bank is paying approximately 9% to 22% for your capital depending on the characteristics of your bank and the composition of your shareholder base. If you think about it, that is one of the higher costs you have. Even more important, you are likely 8 to 10 times leveraged to that expensive capital. That leverage is more than most currently active hedge funds. The takeaway from these two facts is that your bank better be sure that you are allocating the right amount of capital to your loan portfolio. Allocating too much capital will hurt your competitiveness (both for loans and for future capital) and not allocating enough will spell disaster during the next down cycle.

 

It Matters Even More When Rates Move Up

 

As rates move up, credit becomes more expensive to borrowers and demand decreases. This means banks will be chasing fewer transactions and competition for loans will increase right at a time when banks want to manufacture more credit at higher rates. Liability cost will increase putting pressure on margins. While this business cycle is common for many industries, what makes banking unique is the dynamics of credit. All this will occur right when credit risk is increasing on all floating rate and adjustable loans. Thus, not only is credit risk increasing on all new production, but banking is one of the few industries on earth that carries with it so much legacy risk on a go forward basis. In other words, the credit decisions and pricing you make today not only has far-reaching consequences because of leverage, but those consequences carry forward far into the future.

 

Enter Credit Grades

 

Unfortunately, most banks work with a single dimension to their credit ratings. This lack of granularity forces many banks to use their limited scale to take into account both the probability of a credit going bad and the potential losses if that credit does go bad. These two factors are very different concepts and is the source of the bulk of the capital allocation confusion. Some characteristics just impact the probability of default, others impact only the loss given default, and many impact both.

 

Loss Given Default

 

Let’s take a look at probability of default (PD) first. Like toenails, most banks utilize a 10-grade system with the first five grades representing “pass” or approvable credits. Now if a borrower wants a 10-year loan for their multifamily project with 1.05x debt service coverage, that implies a very high probability of default. However, if the borrower wants to cash secure the loan, then it gets classified as a “1” rating, as while the probability of default is high, the loss given default (LGD) is low. As you can see, a good lender needs to understand how loan structure, pricing, rates, recourse, collateral and dozens of other variables impact either the PD or the LGD.

 

In case you have not seen what a 10Y variable rate loan looks like in the current environment, we have grabbed several hundred community bank commercial real estate (CRE) loans from across the United States and looked at the cumulative implied probabilities of default through the next credit cycle. As can be seen below, a rating of “1” means only a 0.19% chance of that loan going bad over its life, while a “5” rated loan has almost a 31% probability of the loan going bad.

 

Through cycle probability of default

 

Next Up – Loss Given Default

 

In our next blog post, we will add another layer of complexity and look at what potential losses could look like in the current environment broken down by pass credit grade. We will then tie the two factors back to expected loss and give your bank a comparison to calibrate its credit grading system. In addition, we will start to look into the future and see the impact of how having more granularity, more dynamism and more calibration in a community bank’s credit model will make for a more efficient bank. Until then, take a look at your current grading system and see if you come close to matching the above view, as it may shed light on if you are over or under reserved as we go into a new credit environment with higher rates and less room for error.