Is It Better For Banks To Diversify By Geography or Loan Type?

How to diversify credit

Most banks are concerned with their credit portfolio. As credit risk increases, the following question comes up:  is better to diversify by geography, by property type or by business type? This is to say that next year, do you focus your marketing dollars and pricing on particular counties, commuter zones, types of commercial real estate loans or certain C&I industries? The answers may not be so apparent and varies for each bank. In this article, we provide data and a framework for helping bank risk managers decide how to best deploy next year’s capital.

 

Looking At Geography

 

For the first part of the analysis, we look at geography both by county level and commuter zone performance (commuter zone analysis HERE). To do this, we will leverage PayNet data that supplies us with our probabilities of defaults. Some states, such as FL, TX, OK, NC, SC, WY, NM, NV and CA, have diverse enough economies where allocating capital to different areas makes sense. For example, Sarasota County has a 2.08% average annual probability of default (POD) on all credit. However, right next door is DeSoto County, with a POD of 2.49%, or 20% higher. More importantly, the correlation of PODs between the two counties is a relatively low 46%. Thus, if we adjust pricing and structure for risk, we would gain some portfolio diversification from lending to both counties.

 

In comparison, there are states such as WA, NY, PA, IL and WI that have fairly low differences in PODs and have high correlations across geographical areas. The state of Washington, for example, has more than half the standard deviation between counties and commuter zones when compared to Florida (graphic below). In addition, the cross-correlations between areas average in the 75% range. Thus, if you are a bank in these states, geographical diversification gains you little. 

 

Credit Diversification

 

Diversification By Loan Type

 

Next, we consider defaults and correlations by loan type. For simplicity, let’s focus just on commercial real estate. As can be seen below, commercial real estate is highly correlative. That is to say that the PODs tend to move in lockstep with each other. Whereas different geographies are 75% correlated across a state, commercial real estate is about 86% correlated. 

 

Cross-correlations by Loan Type

 

Given the data above, the preliminary conclusion is that if you are in a diverse state, it is better to diversify as to geography than it is to sub-loan type. That said, consider a couple items:

 

Targeted Sub-types: The above analysis assumes we are trying to diversify across all sub-types within a sector. However, if we target just a few, such as retail, even though it has higher PODs, the correlation to the rest of the portfolios are in the low 80%s. In certain areas, this might offer greater credit diversification than geography.

 

Contiguous Geographies:  CenterState has the ability to lend throughout the state of FL. Thus, the above analysis may be germane. However, if you are a smaller bank and you are constrained to a limited number of counties or commuter zones, then the opposite conclusion might be reached – diversification might be better achieved through loan type.

 

Asset Class: For the sake of simplicity and commonality, we did not include all asset classes such as consumer lines, leases and other lending alternatives. Lending lines such as credit cards, autos, student loans, municipal lending and equipment leases tend to have more diversification in PODs and have lower cross-correlations. In FL, across all bank asset class cross-correlations are approximately 65%.  Thus, to the extent we can leverage our retail bank, it would be better for us to maximize diversification across different asset classes.

 

Diversification By Industry

 

Finally, banks can diversify by type of industry they lend to. This means either through loans to a business or loans to a commercial real estate property that is leased to a single industry. This is the huge advantage that C&I loans have over other lending lines. They are often more profitable than consumer lines due to their large size and can achieve better diversification than almost any other bank alternative. As can be seen below, cross-correlations in the 20% range are common. The more geographically dispersed a business’ revenue stream is, the better diversification a bank can achieve. 

 

Credit Cross Correlations By Industry

 

Putting This Into Action

 

As a general statement, banks don’t have enough intent when designing their credit portfolio. The power to adjust pricing, structure and marketing resources to achieve greater diversification is often underappreciated and underutilized. Banks need to pay attention to geography, asset class, loan type and industry in order to achieve a balance sheet with the highest risk-adjusted return over a business cycle. 

 

If you are in a diversified state and you have access to multiple counties, then that is an easy place to start setting targets by county or commuter zone. Next, specialize in an industry such as homeowner’s associations (HOAs), alternative energy, education or consumer products and scale state or region-wide. After that, concentrate on asset class on your balance sheet and leverage lines of business such as agriculture, consumer lines, and specialty finance. After all that, fine tune each asset class as you add various loan sub-types.

 

By having intent when constructing your balance sheet, banks can make 1 + 1 =  >2.  This allows banks to achieve their current return taking on just a portion of their current risk. This will protect the bank as the economy changes or shocks hit certain sectors.