Does Bank Proximity Still Matter?

Research shows physical distance constitutes and important determinant of the extent to which small firms access bank loans. To decide on a loan application, banks collect and assess information about the borrower, and borrower proximity is said to facilitate the collection of this information, especially for the so-called “soft information”.

This soft information refers to information that is hard to transmit and cannot be directly verified by anyone other than the agent who produces it (Stein, 2002). It comes in many shapes and forms. For instance, loan officers often incorporate soft information into their lending decisions by assessing the character of a firm’s manager. The officer may personally know the firm’s CEO and deem her an honest and reliable person. This constitutes information of great value for making credit decisions but at the same something intangible and hard to verify. Other examples of soft information include opinions, ideas, rumours, economic projections, statements of management’s future plans, and market commentary (Liberty and Petersen, 2018). These forms of information are usually collected in person which is why physical distance has been shown to affect lending decisions. 

As with many other industries, the internet has removed many physical barriers for trade and for the transition of information – including soft information. Online communication channels (e.g. video conferencing) have become almost as “good” as face-to-face ones. It would be reasonable then to expect physical distance not to be as important for lending decisions as reported in the past. 

Recent data released by the U.S. Treasury can serve to shed some light on this matter. The U.S. government, through the Small Business Administration (SBA), instituted a special loan program designed to provide financial assistance to small businesses impacted by the current Covid-19 crisis. This loan program, denominated Payment Protection Program (PPP), was intended to provide direct incentive for small businesses to keep their workers on payroll. To access these funds, small business would submit a loan application through any federally insured depository institution (mostly banks) under very favourable conditions (including loan forgiveness) provided the loans were used for eligible payroll costs. More than five million loans were generated according to the data released. This loan data is useful because it contains (uncommon) detailed information about each loan, including the borrower’s address and the name of the originating bank.

I gathered a random sample of more than 200k loan records and pinned down the exact geolocation of each borrower (firm) using Google’s geolocation API. Then, using the geographic location of each bank branch in the U.S., I calculated the geodesic distance between each borrower and the nearest branch of the lending bank. The following map shows the location of each PPP borrower colour-coded by distance to their corresponding lender.

Roughly 90% of the sampled borrowers obtain their loans from banks within a 100 km radius (in blue), whereas approximately 5% borrowed from lenders located between 100 km and 1,000 km away (in red). This is consistent with the notion that physical distance matters for lending, especially for small business loans whose approval relies heavily on soft information. Surprisingly, however, the remaining 5% corresponds to borrowers who obtained PPP funds from lenders more than 1,000 km away. In fact, the vast majority of those loans correspond to borrower-lender distances of more than 3,600 km, roughly the distance between the U.S. East and West coasts. 

The following histogram offers more insight into the distribution of the PPP borrower-lender distances. Because this distribution is heavily right-skewed, I plot the x-axis in a logarithmic scale. This means that moving a unit distance along the x-axis corresponds to multiplying the previous number (say 10 km) by ten. The histogram shows that most firms borrow from lenders located nearby. In fact, 75% of these borrowers purchased loans from banks less than 10 km away. However, it also shows that the distribution of distances has a right fat tail, which corresponds to firms whose lending bank’s nearest branch is located more than 3,000 km away.  Of these long-distance loans, the majority were originated by Cross River Bank. This is a relatively small bank with a single branch in Fort Lee, New Jersey that was featured in Fortune for originating $5.6 billion in PPP loans – No. 12 in the list of PPP loan originators – despite its small size. 

It turns out that Cross River Bank managed to accomplish this impressive amount of loan origination by partnering with FinTechs to automate its loan application process and serve businesses online. So, instead of relying on a broad branch network – which is how small businesses have traditionally accessed credit in the past – Cross River offered its loan services virtually, and quite successfully given the staggering amount of PPP loans supplied.

So, the PPP data suggests that more and more the so-called soft information can and is being transmitted online. This has important implications for small firms’ access to financial services. By removing the reliance on proximity for the acquisition of information, online lending allows for small businesses to have equal access to fairly priced loans, irrespective of their geographic location. Nevertheless, given the uniqueness of PPP loans (e.g.  no credit risk), it is unclear to what extent soft information was collected for loan approvals. Still, this may be an example of how the future of lending – and the acquisition of soft information – may look in the future.

References

  • Stein, J. C. (2002). Information production and capital allocation: Decentralized versus hierarchical firms. The Journal of Finance57(5), 1891-1921.
  • Liberti, J. M., & Petersen, M. A. (2019). Information: Hard and soft. Review of Corporate Finance Studies8(1), 1-41.

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