Many community banks don’t underwrite with enough granularity to take into account major differences in credit between borrowers. This can hurt banks as credit spreads get tighter and banks add even more leverage to their balance sheets. When banks underwrite a particular borrower, the actual probability of default is usually within a defined range. Some industries, such as banking itself, have a very homogeneous set of companies. One bank, from a credit standpoint, is not substantially different from another bank. The risk of lending to a bank is not only small, but the difference between bank credit is small. However, this is not the same for companies that support rail operations, child care centers and others. In this article, we look at the risk of borrower selection and highlight industries where banks need to pay more, and less, attention to borrower underwriting.
The Riskiest Industries
For this analysis, we use PayNet’s probabilities of default and look at borrowers by four-digit NAICS code. We analyze one-year probabilities and then look at one standard deviation to answer the question – how accurate do you need to be during underwriting? For industries with wide probabilities of default, such as trucking companies, 68% of companies fall within a probability of default with 4.00% of each other. The probability that the average bank loan to a trucking company goes into default in the next year is about 2.16%. Thus, there is one standard deviation, or 68% probability that any randomly selected trucking company loan has a probability of default between 0.16% and 4.16%. This is a much wider spread compared to other industries.
In other words, when underwriting trucking companies, it pays for banks to spend extra resources ascertaining the true risk of their borrower. The expected loss or actual required reserves required for a quality, low-risk trucking company is five basis points, while a riskier trucking company and the other end of the one standard deviation spectrum would demand 1.25% in this current market. This is a 25x difference in risk. This means there is also a 16% probability that your randomly selected borrower could even be riskier and require closer to 2% reserve to cover the expected loss. The dispersion of credit performance in some industries like rail support, trucking, messenger/local delivery companies and child care are wide.
The Less Risky Industries
Conversely, if you are lending to a hog rancher, that industry not only has a low probability of default (0.70%) but like banks, also has a very small standard deviation (1.08%). Thus, if you get your underwriting wrong and you lend to a hog rancher that happens to be a little riskier, the odds of a default within a given year is only 1.24% requiring reserves of around 0.37%. This means that your most likely worst-case scenario is a loss severity of 0.53%.
To recap, for industries that have higher selection volatility such as trucking, your bank has a higher likelihood of being under-reserved. For lower risk industries, no matter who you underwrite you have a higher probability of being over-reserved.
Low and High Risk
While we are on this topic, we also want to point out for clarity that while risk and dispersion of credit performance often go hand and hand, it is not always the case. There a segment of industries that has a low median probability of default (all around 1%) but relatively highly dispersed as to the probabilities of default. For example, when lending to air-conditioning/heating equipment companies while the risk of lending is low, the risk of underwriting accurately is high since these firms have a wide range of performance levels. This is another form of credit risk that banks should take into account.
Below are ten popular industries that banks underwrite that have low default risk, but are volatile and thus present higher credit selection risk.
Putting This Into Action
In this market, it not only pays to know the probability of default on your borrower but the volatility around that probability of default. Industries with more selection variation should demand more in-depth underwriting in order to mitigate some of the risks. Banks should consider increasing pricing and/or reserves for those industries that have higher performance volatility since the rate of change can be acuter.
As underwriting becomes more quantitative, banks that understand the above concepts will have an edge both in winning the desired deals they want and in more successful portfolio management.
Submitted by Chris Nichols on June 19, 2017