Maybe not everywhere, but what most people are experiencing when they go out looking for a HELOC. But at the same time, we’re not even a shadow of the way where we’re going to be down the road. But it’s working. It’s good. We’re launching more states. And each week or two of development is typically a big step forward for us because there’s just so many obvious things that we’re improving as we get started.
Operator: We’ll take our next question from Peter Christiansen with Citi.
Peter Christiansen: I was just curious, with loan size being kind of flattish sequentially, just curious if we should think of average loan size as a function of available capital or capital supply or, I guess, more or less an issue of what’s going on in consumer credit right now. And I guess as a follow-up, just curious if we should think of loan size impacting the contribution profit margin.
Sanjay Datta: It’s Sanjay. I think your question is about loan size, not loan volume, correct?
Peter Christiansen: Correct, correct. Yes.
Dave Girouard: Loan size, let’s see, there’s a couple of different impacts. I would say the biggest one is borrower mix. So all else equal, the sort of more low risk borrowers, you can think of maybe in a traditional sense, the primary borrowers that you have will tend to be able to be approved for larger loan sizes and riskier borrowers, in contrast, lower loan sizes. So, if you have, for example, a macro event such as we’ve had where there’s a lot of people being nudged outside of the approval box at the riskier side and then, consequently, have a primer borrower mix than before, you would expect to have larger loan size and vice versa. So, borrower mix is one. Overall sort of risk and approvability is another, all else equal, as loss rates get higher.
And the macro UMI gets higher, we would generally sort of, on the margin, be approving folks for smaller loans than in a very constructive environment. So I would say it’s really down to – I wouldn’t necessarily view loan size as a sort of inherent or fundamental metric for our business in any sort of significant way. Although it is true, just answering your last question, it is true that all else equal, a larger loan size will lead to a healthier contribution margin.
Peter Christiansen: Just as a quick follow-up, I guess, how should we think about what borrowers are requesting versus what they’re being approved for? Is there a meaningful difference there in this environment?
Sanjay Datta: I would say that, in relative terms, they’ve gone in very much in the opposite directions. I think the request for credit, the underlying fundamental demand for credit is as high as we’ve seen it in quite a while. And there’s a lot of underlying demand from borrowers. Conversely, our ability to approve them is as limited as it’s been in our company’s history as a reflection of the UMI. And so you sort of have them at cross purposes right now.
Operator: We will take our next question from James Faucette with Morgan Stanley.
James Faucette: I want to follow-up on that last question. Clearly, you guys had to tighten things up. And given the low savings rates, that demand for credit seems understandable. But what are you seeing in terms of actual conversion, that is offers for loans that are actually converted into loans and what that origination looks like? Are you seeing any movement in that metric?
Sanjay Datta: So you’re asking about the conversion rates?
James Faucette: Yeah.
Sanjay Datta: Yeah, I think our metric for this quarter was around 8.5%. So, meaning, of all applicants who fill in an application and submit, about 8.5% of them become funded loans. I think at our peak, that number was closer to 24%.
James Faucette: Can I just ask for a clarification, what about – that’s of all applicants. What about approved loans that actually ultimately convert any sense what that looks like?
Sanjay Datta: From loans that are approved and offered to funded loans, I believe, is on the order of about a third.
James Faucette: About a third, okay. That’s helpful. And then I wanted to go back to the committed capital partners, just any additional color that you can provide in terms of the things that they are looking for, whether it be specific metrics or catalysts to get them moving forward. And you feel like for them, maybe it’s done the same thing as we’re talking about for borrowers that we kind of would like to – we would like to see underlying rates come down and then, for your own metrics, UMI come down, just wondering if your potential committed capital partners are kind of waiting for a similar relief across the market or if there are other issues that they’re watching.
Dave Girouard: Let’s see. Well, certainly, for counterparties that are, shall we say, newer to unsecured consumer credit, there’s a lot of investigation with the asset class itself. With respect to Upstart, I don’t think it’s as much about whether rates are going to move or UMI is going to move. It’s more about developing a confidence in their diligence that we are going to hit the targets that we claim, meaning when we predict loss estimates, they are accurate. And so, they care a lot about our accuracy, rather than the absolute direction of racing the economy right now. And then, there’s clearly a macro component as well. All of these counterparties have credit committees and macro committees that take point of view on broad asset allocation and what the right timing is for these investments.
And so, many of them are actively engaged with us in asking what we’re seeing in our data with respect to macro trends and loss rates in the different segments of the borrower base. So that’s sort of, I think, at the highest level, how do we think about the framework of investing with us. And then, with respect to specific deals, as we said before, you could sort of broadly class counterparties into some that are interested in maybe earning a premium on the return right now and they’re trying to understand where that can come from, certainly in a high rate environment like today’s, if they can lock something in, it can look very good down the road a year from now, if the economy changes. And then, there are others who are less interested in return premiums, they’re interested in predictability.
And that’s where, as we’ve said in the past, in some instance, because we have a very precise understanding of our model calibration, we can sort of co-invest with them, put some skin in the game, give them some level of comfort on that dimension.
Operator: We’ll take our next question from Ramsey El-Assal with Barclays.
John Coffey: This is John Coffey on for Ramsey. My first question is on slide 21. And you may have already answered this and I missed it. When I look at the cadence of the auto secured loans, it looks like that halved from Q2 of – well, last quarter to this quarter. I was wondering if you could just talk a little bit about the drivers there of what caused that?
Sanjay Datta: Auto lending, I would say, is currently subject to all of the same sort of dynamics and forces as our core business, meaning rates themselves have risen and default trends look very similar to what they look like in the unsecured world. Maybe there’s some slight timing differences. But in terms of the sort of relative level of increase in default rates, they very much mirror what we’ve seen in our core business. And just as our core business has had to retrench, as we’ve had to recalibrate our models, as a lot of people, as Dave described, have fallen above the 36% line and out of the approval box. A lot of those dynamics are very similar in auto as well, with the difference that auto being a more nascent sort of product for us we’ve been calibrating a newer model in real time in that, whereas with personal lending, I think we’ve got a very sophisticated model that we just kind of make sure that it stays calibrated to the macroenvironment.
