Many of the best lending opportunities have arisen because a group of borrowers becomes underserved by capital providers. These borrowers have scarce access to capital, so are therefore willing to pay high rates, despite being good borrowers who on a risk-adjusted basis should likely be offered more attractive financial products.
In most cases, these opportunities last for some period of time, until word gets out that a lender is earning a good return, and other capital starts to compete.
At that point, the cost of originating loans goes up, (because acquiring customers becomes more competitive), and rates come down, (because borrowers now have more options from lenders who are competing with each other on price).
In rare cases, yields stay high due to a cost structure that makes it unaffordable to drop cost of capital. Payday lending is one of those spaces:
If a payday lender offers a $500 loan, over a 10 day period, and charges state usury (let’s say usury is 25%), they can only earn $3.42 of total revenue on the transaction.
However, if it takes the person sitting behind the counter 15 minutes to service the borrower, and the person behind the counter makes $10/hr, it costs $3.33 of labor to originate the loan. That’s before the cost printing the paper the loan is written on, the real estate of the payday lending store, the regulatory costs of operating a payday lender, the cost of marketing to borrowers, etc.
I am not defending payday lending, it’s an expensive business that needs to find a solution, but in that case, all lenders have had to keep their yields high, because no amount of competition has (to date), made those fixed costs go away.
But in some spaces, yields can be kept high, not because of fixed costs that force us to charge draconian rates to borrowers, but rather via defensible competitive advantages.
These are our “Unicorns.” Below are some fictitious examples, that are illustrative of the type of stuff we like:
(1) Switching Costs | Some lenders have built technology, that integrates with their “Point of Origination.” This gives them the defensibility via “switching costs.”
Example, Imagine a lender who provides Point of Sales software to a jewelry store. The PoS software handles orders, cash, inventory, etc. But even better than that, the PoS also offers loans to the customers of the jewelry store. These are people who would not be able to purchase the jewelry in one lump sum, but could purchase the jewelry if they were offered a payment plan.
This lender who is providing the PoS software, and originating the loans through it, has a defensible moat. It is much easier for this lender to get access to its target borrower than another competitor not integrated in the sales process. And if the lender starts offering 18% rates to the jewelry store’s customers, it’ll likely be able to maintain that yield, even if others attempt to compete.
If a new a competitor tells the jewelry store it’ll build PoS software too, and offer 16% rates to its customers, the jewelry store likely will not switch — because the cost of learning a whole new PoS system is greater than the burden of offering a 2% higher rate to jewelry store’s customers.
If a competitor offers 15%, the answer would probably be the same, and the same at 14%. (At some point there is a level that is just too great, just like eventually there will be a much better PoS system, but this example is more defensible than traditional lending).
(2) A data point others cannot observe.
In some cases, there are lenders who have built businesses where they can see horizontally across an entire industry. They therefore know what is going to happen, before its reported, based on the first party data they’ve been able to collect.
Companies that can observe borrowers across multiple platforms are powerful. For example, the type of lender who can observe a user’s propensity to cancel Uber rides last minute, is likely to be able to observe the user’s likelihood of cancelling Lyft rides last minute.
There are lenders who can observe analogous behavior on one platform to predict behavior on another platform, and underwrite against this. This gives those lenders data to use, to offer a loan other lenders wouldn’t be able to underwrite.
(3) Ability to affect outcome.
Lenders who can actually help create a repaying loan, are some of our favorites. For example — imagine of Amazon made a loan to one of its vendors who was about to default. Amazon could then take that vendor, put them on the company’s home page, drive a lot of traffic to their site so the defaulting borrower could do more revenue, and become current again.
More practically, marketplaces could lend to the suppliers on their website. If the suppliers start to fall behind, they could promote those suppliers to the buyers on the platform.
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These are just a handful of examples of “sustainable moats” that can be built by originators. And these lending opportunities are a lot more exciting than temporary scarcities of capital within a given asset class/region/demographic.
The lenders who find these defensible moats are the types of companies we like the back most.
