If you want to win more loan business here is a tactic that experienced bankers know that many loan officers don’t. This should be part of every loan officers training (we work it in) and is handy to have in your back pocket for those rate-focused borrowers.
When rates rise there are two main offsetting effects on the value of bank’s loans. On one hand, rising rates usually mean an improvement of both the general economy and of a particular sector. Not only is demand improving, but some sectors, consumer products for example, are more sensitive to improving economic conditions. On the other hand, rising interest rates is also is a result of a rise in the price level of inputs such as labor, raw materials and rents. Taxes are another area that, depending on industry, can be positively correlated to a rise in interest rates.
For many community banks, a concentration in real estate lending may be an issue. This is especially concerning given the recent decrease in capitalization (cap) rates across many geographies and property classes. While community bankers have a difficult job trying to diversify their balance sheet away from real estate, we wanted to present a couple quantitative points that will make the job of risk management easier for banks.
When we tell banks they need to get to a 35% efficiency ratio to be competitive in the future they look at us like we are crazier than an outhouse rat. Let’s set the branch debate aside (the largest functional cost area) as that is basically a philosophical argument. Let’s just look at your next largest functional cost – loan processing. We get challenged all the time by banks telling us they are “already at full capacity” and “we are already lean.” We point out that there is a difference between being at full capacity and being at optimal productivity.
Is there an advantage to banks in diversifying loan portfolios by geography and category? As we learned in the downturn, geography can have a huge negative impact on banks. In fact, geography is the largest single factor driving profitability (largely due to credit performance) composing about 16% of a bank’s return.
The “Internet of Things” is well on its way of changing how we move around the physical world, and 2014 marks the first time banks are starting to capitalize off this trend. Manufacturers are embedding sensors, tracking devices and actuators in various devices and linking them via the Internet. These devices form a network that not only churn out a steady stream of raw and analyzed data, but are able to be communicated with. The result is a “dynamic information layer” that adds value to any activity touching this network, including bank financing.
We are often asked what is the single best measure of lending risk? Is it loan-to-value ratio? Is it interest-coverage ratio? Is it debt-service-coverage ratio? Is it liquidity ratio? While there is no single measure that can be used and each of these measures are important in various underwriting circumstances, if we were stranded on a deserted island and could only bring one tool to measure our underwriting risk, it would be debt-to-cash flow ratio (this is typically called the “leverage ratio”). The leverage ratio is the measure of the borrower’s debt divided by t
There is an argument to be made that a bank should underwrite every 7(a) SBA loan that comes its way. With a 75% guarantee and an average 10% premium net of cost and operational risk, that imputes a 30% cumulative probability of default. That is, as long as the bank thinks that there is a 70% chance of repayment, the bank should make that loan. This leaves a whole lot of room for error. Even if you underwrote nothing but one of the riskiest of major sectors, restaurant franchises, you would still be OK as their historic cumulative default rate is approximately 28%.
