Alexander Potter : Great. Just one question from me, and it’s about the competitive landscape in auto loans. I know that historically, you had mentioned you were a little bit quicker to hike interest rates than some of your peers who had bigger balance sheets. Wondering if there’s been any rationalization in that regard or any other comments you’d be willing to make on the competitive landscape and auto lending would be helpful.
Ernie Garcia : Sure. Well, I think it’s been a dynamic environment. So I think the primary dynamic that we probably have spoken most about as it relates to our loans over the last year or so was kind of the spread between the 2-year Treasury and Fed funds, which the 2-year Treasury is a good proxy for our cost of funds when we sell our receivables because they have approximately a 2-year duration, and they’re generally sold into capital markets that use those sorts of rates as a reference point. And approximately half of the market for auto loans is provided by banks that oftentimes are using some combination of that and the Fed funds as their frame of reference. I think since the end of the first quarter when kind of the regional banking crisis hit or started, I think there’s also been some dramatic moves in spreads that I think are other impacts that are somewhat unique right now and I think are a little bit harder to forecast over the medium term.
I think probably there’s been some spread widening in different areas. I think undoubtedly, Carvana itself has seen spread widening over the last year. I think that’s too bad, but it shows up in our results. And so once you’ve got the results, you know what they are, I think that’s actually good news for the future. So I think there’s room for our spreads to come down over time as we approach the cost of funds of more mature issuers. So I think that, that’s a dynamic that will play out. And then I just think that from here to the next couple of years from now when things are normal again, I think there will probably, at some point, be a normalization in 2-year Treasury versus Fed funds, and that will — that should normalize. There will probably be something of a normalization in spreads.
And then there should be a normalization in Carvana spreads relative to other issuers. And I think that all of those things leave room, I think, to be optimistic, but the timing on all of them is also uncertain.
Operator: The next question is from Winnie Dong with Deutsche Bank.
Winnie Dong : I just have one. On the commentary that you expect similar SG&A expense on a quarter-over-quarter basis, can you maybe clarify whether this is on a per unit basis or absolute dollar? And then I know you’ve discussed all of the various buckets to sort of go after on a longer-term basis. But near term, what’s sort of driving that pause? And then when might that longer-term sort of reduction come back?
Mark Jenkins : Sure. Yes. Absolutely. So on the first part of the question, so we were talking about SG&A expense on a dollar basis. And I think the — to put that in context a little bit, I think last quarter, we outlined a goal to achieve approximately $420 million of non-GAAP SG&A expenses by Q2. We obviously did that a quarter early, and not only to be in a quarter early, we’d also beat that goal by more than $15 million. So I think we’re obviously feeling really great about the overall progress in moving SG&A expenses from the business and becoming more operationally efficient. So in Q2, our expectations similar SG&A expense to Q1, but I think that, in part, reflects just the very, very significant gains that we were able to make in Q1.
Looking forward beyond that, we certainly — we’ve made tremendous progress, but we certainly do not believe we’re done. I think we have significant opportunities across the business, as I alluded to earlier, to continue to become more efficient in our operations. And that happens through all of the technology projects that we have going on throughout the operational efficiency groups to automate manual tasks, to make our routing and scheduling more efficient, to, as we alluded to earlier, incentivize customers to choose the cars that are close to them or to incentivizing them to do — being pickups versus delivery, all kinds of things that help — that we still have in progress to help drive operational efficiencies we’re working on. And they’re not — we made great progress, but they’re not done yet.
So I think that’s a little bit more color on the opportunities that we see ahead.
Operator: The next question comes from Chris Bottiglieri with Exane BNP Paribas.
Chris Bottiglieri : I wanted to ask about — I guess, first about the financing market. I saw you said a nonprime deal, which is pretty impressive to get that price in this liquidity environment. But like conceptually, how do you think about owning that residual versus selling it off just given where discount rates and risk spreads are and all that? Like is it still, like, capital efficient to do get on sale for these nonprime type deals? Or would you ultimately look to sell the residual?
Ernie Garcia : I think in general, we’ve looked to sell the residual. I think you point to something that is correct, which is the yields that residual buyers today are getting are very high compared to the past. And those yield profiles are very robust. They can take very large multiples of expected losses and still do quite well. And so I think that dynamic is correct, and that makes those desirable assets. But I think in this environment with our current goals, we still intend to sell those residuals over time.
Chris Bottiglieri : Got you. Okay. And then one, I guess, conceptual question as well on inventory. So it sounds like you just make more money on retail GPU when you sell the cars under 90 days. And I get the world just shifted dramatically in a dime, like no one can move that quickly, but have you rethought how you priced cars to their algorithms? Like is there a way to get more proactive with taking markdowns and setting up through wholesale, just to, like, put a rule in place to sell cars above a certain number of days? Like how do you learn from this experience and realizing how much better the model runs with quicker inventory days and kind of prevent this from happening in the future?
