Who’s a better credit risk for a $15K loan? A person making $80K a year or a person making $35K? Most of us would prefer the $80K person – makes enough to cover the interest payments … probably facing unanticipated expenses … wedding? … medical? But wait! What if that 35K person worked in a factory in TN and the 80K person worked for an oil rig in LA? To misappropriate Yogi Berra, in this case, 80K ain’t worth 35K. Why? Because as of April 2016, the future prospects for income for a factory worker in TN (booming segment) are very different from those of an oil rig worker in LA (deteriorating segment).
Most consumer risk assessment models pay careful attention to a borrower’s past behavior and credit utilization. They consider debt level and income. But most fail to consider the future prospects of that individual’s income. To compound the problem, it is easier for an individual to control credit utilization (spend less) or payment behavior (budget responsibly; don’t delay paying bills) but harder for most to influence future incomes. Especially those who might need to borrow the occasional $15K.
Could lenders systematically incorporate information concerning a borrower’s source of income when making credit decisions? Where the borrower works (geography) and the industry that the borrower works in (not always the same as occupation) would be two important factors. Knowing these factors would allow lenders to act on information such as “Construction is booming in most of NC and weak in large parts of CA”. It would allow understanding the difference in the income prospects of a software developer based on the industry they worked in — Healthcare sector versus Financial services.
Local Economic Vitality
WAIN Street has developed a Local Economic Vitality gauge that provides lenders a quantitative perspective they can incorporate into their credit decisioning. We leveraged a sub-index of the WAIN Street Business Default Index concerning sole proprietors (BDX-Solo) to calculate an area’s economic vitality based on the forecasted business default rate and default gradient. The default gradient captures the direction and magnitude of change in default rate and the forecasted default rate includes labor market conditions and other macro data as explanatory variables of future business performance. We have previously found a strong causal relationship between the BDX-Solo and consumer behavior. In another analysis, we demonstrated the value of using local economic vitality metrics in tuning the performance of Lending Club loan portfolios.
The chart below shows all 917 US Core Based Statistical Areas (CBSA) clustered by their economic vitality. CBSAs are a more inclusive extension of Metropolitan Statistical Areas (MSA) and identify large clusters of population that are meaningfully connected. A CBSA can span counties and states. The size of each bubble represents the area’s economic impact to the US. The two axes use a standardized scale centered at 100 with a standard deviation of 15. On both axes, higher values correspond to weakness. Five clusters are identified with an additional one for capturing areas that represent “Special situations” or outliers. The clusters are colored based on their distance from the origin with the closest ones being stronger. The results are intuitive. Looking down the purple, light green, and dark green clusters, we observe areas where businesses will achieve similar default rates but from different directions. The dark greens consist of businesses that are forecasted to improve whereas businesses in the purple cluster are forecasted to deteriorate. Similarly, looking across the light green, steel blue, and magenta clusters, we find areas where businesses are not forecasted to change much in their default rates.
Local Economic Vitality – April 2016
We commonly identify geographic areas with certain industries. San Francisco and High Tech; Hartford and Insurance; Las Vegas and Entertainment. It is not that these areas don’t have other industries. In fact, the largest industry in Las Vegs is ‘Retail trade’ and in Hartford it is Manufacturing. What we identify is the area’s specialization—the degree to which that specific area has a greater impact from an industry compared to the industry’s impact on the entire US.
The map below shows all the US Core Based Statistical Areas (CBSA) identified in the chart above with each area’s industry specialization. With this map, lenders have a granular way of incorporating industry preferences when understanding consumer risk. If the lender has the Mining sector on a negative watch, this map alerts the lender when a consumer is from a geography that specializes in Mining and hence, might alter the consumer’s riskiness.
Local Economic Vitality Map – April 2016
Using Local Economic Vitality
WAIN Street’s Local Economic Vitality gauge is a tool for consumer lenders to systematically incorporate geography and industry preferences into their credit decisioning. And when data availability allows directly linking a consumer to an industry, lenders can further improve consumer risk assessment in much the same way auto insurance underwriters use occupation to vary rates.
- When underwriting a new facility, a lender might be able to offer more competitive pricing to a borrower from a booming locality.
- When renewing or extending a credit facility, a lender might want to check for any changes in the local economic vitality for a possible closer look.
- When presented with a macro view on industry sectors, a lender can easily identify geographic areas that specialize in specific industries and adjust their decision criteria.
- Knowledge about the interaction between a borrower and the borrower’s industry/geography profile can help lenders manage emergent credit performance challenges in their portfolio.