Credit is always changing and we watch a variety of markers such as corporate bond credit spreads, vacancy rates, net effective rents and many others in order to help us understand credit. Three of those important credit metrics are the probability of default (POD) by industry, the rate of change of that POD and the volatility of credit of each industry. We just got fourth-quarter forward-looking, through cycle probabilities of default in driven by PayNet and have run our analytics.
Tag: Credit Analytics
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.
The goal of credit underwriting is to make prescient decisions. All credit has an outcome – either it pays as agreed or it does not. The role of the underwriter is to best predict that outcome which is why it is critical to limit the amount of bias inherent in any decision. While we have looked at overt bias in credit underwriting in the past (HERE for example), in this article we look at a particular bias inherent in all bank’s processes and why it matters.
A couple weeks ago we ran an article on how to price loans for default volatility (HERE). In it, we discussed how to price in the variability around default risk and showed multifamily and owner occupied commercial real estate examples. Many banks saw our data on ten-year loss rates and volatility around credit cards, commercial and residential constructions and patted themselves on the back that they have no or limited exposure to each of those sectors.
We always like to look back and see where underwriting and credit accuracy can be improved. Recently, we looked at almost 5,000 commercial real estate (CRE) loans from across the country that was underwritten in 2012. We looked at the property level cash flow projections to include revenue, expenses and net operating income (NOI) and then compared that to what has actually happened over the last 3 years. Our findings should give you some comfort to the conservative nature of your average underwriter.
A couple of weeks ago we discussed how quantitative banks can win more loan business by going after the two ends of the credit spectrum(usually Grade 2 and Grade 5 loans) that most community banks misprice (HERE). In that article, we show how banks that utilize a credit model have a distinct advantage against banks that don’t leverage a model particularly vulnerable in losing high-quality (Grade 2-type) borrowers.
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.
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.
Daniel Björkegren, an economist at Brown University in Providence, released research that shows that banks can predict how likely someone was to pay back a loan based on cell phone call metadata. After analyzing data from 3,000 borrowers from a bank in Haiti — the number of calls, the length, frequency and who was called, Björkegren found the bank can reduce consumer loan defaults by 43 percent.
Like Degas obsessed with dancers, bankers have been brought up to obsess over the composition of their loan portfolio. Egged on by our examiners, auditors and random pundits, we slice and dice our loan portfolios as if it means something and pat ourselves on the back when we can show a nice pie chart with lots of even looking slices. The reality is many of our sectors move together and offer little in the way of diversification. When the economy turns, tenants aren’t looking for new office space any more than they are looking for new industrial space.