While most bankers are familiar with the probability of default for various lines of business, many do not have a feel for the volatility of the lending category nor the correlation to the US economy. While some of the data is obvious, much is less obvious and represents an opportunity for banks to generate above average risk-adjusted returns. When pricing loans, deciding on a lending mix, allocating capital and assigning monitoring resources, it is critical to understand the risk of each lending category in order to make the proper decisions and generate the highest “alpha,” or an excess return above a bank’s risk compared to the industry. What banks want is the highest pricing, the lowest credit risk, the lowest correlation to the rest of the portfolio, and the lowest credit volatility. This provides banks with the largest alpha or risk-adjusted return.
Going To The Data
We looked at all available loan data from call reports of every bank, going back to 1984, and mapped it against over 200 economic indicators. Unemployment was the one economic indicator that has the most accurate predictive power and is the easiest to obtain. We then looked at each loan categories performance to distill the two charts below. Since this is aggregate data, we caution about drawing too many conclusions as your individual underwriting might be different, but absent of individual data and analysis, we present the below to help get your bank in the right ballpark.
A couple of conclusions that can be drawn that can help community banks improve performance. On the obvious side, construction not only has high current defaults relative to other lending sectors and a high projected probability of default rate, but the sector (both residential and commercial) is the most correlated to the US economy of any sector and has the highest volatility. Of all the major lending categories, construction is by far the riskiest for banks and thus should garner the highest pricing, capital, and monitoring resources. In looking at the data history, banks have about 45 days from the first sign of problems to take action or suffer material losses because of the extreme correlation to the general economy.
Credit Card and Agriculture
In contrast, credit card debt is the best performing lending category (*) and ironically also has the best risk-adjusted pricing. This is an anomaly in the marketplace and represents an arbitrage opportunity for banks willing to invest in infrastructure to support credit card origination. The low volatility, low correlation to the general economy and low cross-correlation to other bank lending sectors makes this a performance driver and should be noted to bank managers and bank investors. It is no surprise that about 40% of the top 25 performing banks for the last five years derive their alpha or superior performance from their investment and management in credit card lending. When it comes to lending, this is the surest way to become a top performing bank.
Next to credit cards, agricultural production loans are the second best performing category on a risk-adjusted basis after taking into the various risk factors followed by leasing. Like credit cards, these two sectors stand out not only for their raw performance but also because of the wider relative pricing versus other sectors. These two lending categories generate significant alpha and have provided banks with superior performance over the last several decades.
C&I and Commercial Real Estate
In contrast to the above, C&I lending is also a superior performer, but the difference is that the lending category is fairly priced in the market and represents little alpha generation as a lending category itself. However, we will quickly add, and a topic for a future article, that C&I is one of the best customer lifetime value generators.
Other takeaways from the data include the underscoring of the risk of holding residential loans as they are both competitively priced and have risk on the upper end of bank lending categories. Comparatively, home equity generates superior risk-adjusted value than residential mortgages as credit spreads are wider and delinquency volatility much less and correlation lower. In a similar vein, multifamily, while an average risk for banks, is currently tightly priced in the marketplace and a potential destroyer of future alpha as risk could outweigh the returns.
The other interesting discussion that this data brings up is that commercial real estate pales in comparison to C&I lending as the current and projected default rates are higher, the sector is much more correlated to the economy and the volatility is about the same. While C&I is fairly priced in the marketplace, commercial real estate, like multifamily, is underpriced (but not as bad). Here, banks need to be very careful of structure and interest rate risk because if banks are not pricing these factors, the sector has the potential to perform worse than multifamily over the next 3 plus years.
While we are discussing real estate, it is interesting to point out that there is very little statistical difference between owner-occupied and investment commercial real estate. We run into banks all the time that have a preference for one over the other, but we question that assumption. Non-owner occupied real estate has better current performance and performs better in appreciating markets, but in downturns, owner-occupied is superior. Non-owner occupied is better priced (but by only 7bp on average), but has a greater correlation to the economy and greater volatility. From a statistical point of view, the differences are within the margin of error and so our conclusion is that these have about the same performance.
This graph shows the historic correlation of each lending sector to unemployment which represents US economic performance. A score of 100% means sector probabilities of defaults move exactly with the employment rate.
(*) There is evidence that auto lending may be the best performing sector, but data only goes back to 2007 and so could not be compared to other lending categories.
If you assume that we are late in the credit cycle, paying attention to pricing, correlations and volatilities can result in a huge difference under a stressed scenario.
Given bank competition, understanding the risk of each sector and how to generate superior performance can separate the top performing banks from the pack.
Submitted by Chris Nichols on April 08, 2019