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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to

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Speaker 1: another episode of The Odd Lads podcast. I'm Joe Wisenthal

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Speaker 1: and I'm Tracy Alloway. Crazy. I feel like I just

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Speaker 1: do not have any feel right now on like the

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Speaker 1: state of the consumer.

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Speaker 2: Really.

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Speaker 1: I mean, you hear k shaped economy, labor markets slowing down,

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Speaker 1: then it's like lowest layoffs in years. You go outside,

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Speaker 1: everything looks booming. Like, I just have no feel right.

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Speaker 1: I know, consumer sentiment is terrible, but consumer sentiment is

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Speaker 1: terrible for years, and people keep shopping. I have no

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Speaker 1: sense of it right.

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Speaker 3: Well, consumer sentiment actually came in higher than expected most recently,

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Speaker 3: big surprise, but I was gonna say, are you not

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Speaker 3: out shopping for Christmas present?

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Speaker 1: It's insane.

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Speaker 3: Yeah, there's a lot of people buying a.

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Speaker 1: Lot of stuff, buying a lot of stuff.

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Speaker 3: But I think this gets to the ke shaped economy point,

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Speaker 3: which is, if you have a cohort of wealthy people

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Speaker 3: who are buying more, it more than offsets the lower

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Speaker 3: income people who are buying less at lower prices. So

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Speaker 3: it's really hard to tell.

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Speaker 1: It's really hard to tell. One thing that definitely feels

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Speaker 1: different if you look at aggregate measures of household balance

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Speaker 1: sheets like this is something that is very different than

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Speaker 1: sort of like pre grade financial crisis. The general view

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Speaker 1: is that the American consumer or the American household has

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Speaker 1: a very big cushion. There is a lot of home

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Speaker 1: equity built up. There is not a thin layer. Obviously,

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Speaker 1: anyone with money and any sort of investment account has

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Speaker 1: done phenomenally well. We're according to this December twelfth, yesterday,

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Speaker 1: I think the s P five hundred hit yet a

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Speaker 1: new all time highest. If you have any sort of

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Speaker 1: home equity build up, if you have any sort of investments,

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Speaker 1: you are doing very well. On the other hand, of course,

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Speaker 1: people are stretched from years of inflation. We know that

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Speaker 1: hiring has slowed down.

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Speaker 2: We know that.

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Speaker 1: You know, we see these headlines delinquencies for cars have

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Speaker 1: like shot up. But I've been seeing these headlines for years.

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Speaker 1: I don't totally know what they mean or how apples

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Speaker 1: to apples they are with the past. I just don't know.

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Speaker 1: I just I'm very confused.

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Speaker 3: Yeah, you know what's really interesting to me just from

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Speaker 3: a financial perspective. Yeah, if you look at some of

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Speaker 3: the bonds that were actually built on consumer loans, the

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Speaker 3: weakest ones are now from the like twenty twenty to

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Speaker 3: twenty twenty two period.

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Speaker 1: Oh see, this is another interesting element of measures like delinquencies,

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Speaker 1: and why I sort of wonder like how comparable they

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Speaker 1: are because okay, partly a delinquency measure is a snapshot

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Speaker 1: of a moment in time, right, a snapshot of health,

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Speaker 1: but it also inherently reflects something in the past, because

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Speaker 1: it reflects, you know, what we're lending standards at the time,

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Speaker 1: right exactly, so you know, and now it's a period

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Speaker 1: of interest rate booming itself. Yea, give the money to

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Speaker 1: anyone anyway. We need to get a better picture of

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Speaker 1: exactly what's going on. How stressed is the consumer. How

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Speaker 1: much do these delinquencies just reflect the profligacy of lenders

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Speaker 1: during the boon times when rates were nothing, et cetera.

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Speaker 1: And yes, we need to figure this out, especially we're

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Speaker 1: in the middle of shopping season and all that stuff.

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Speaker 1: So I'm very excited to say, we really do have

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Speaker 1: the perfect guests. We're going to be speaking with Recard Bondebo.

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Speaker 1: He is the executive vice president, chief strategy officer, chief

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Speaker 1: economist Advantage Score, a credit scoring company. Recurd Thank you

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Speaker 1: so much for coming on the podcast.

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Speaker 4: Thank you for Havmius Snana.

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Speaker 1: What is Vantage Score a US credit scoring company? What

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Speaker 1: do you do there?

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Speaker 4: So, we're the largest credit scoring company in the United

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Speaker 4: States and we have founded almost twenty years ago by

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Speaker 4: the Free Credit bureas, TransUnion, ECOFAX and Experience, and we

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Speaker 4: were sort of created with a very specific mission in

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Speaker 4: mind to drive greater competition and credit scoring. Prior to us,

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Speaker 4: there really wasn't a lot of choice in space. We're

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Speaker 4: also there to drive more innovation and create the most

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Speaker 4: predictive scores, which was a big ass from the banks

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Speaker 4: at the time, and also to be able to expand

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Speaker 4: access to millions to enable everyone who really is credit

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Speaker 4: worthy to be able to get access to credit products.

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Speaker 3: You mentioned the banks just then, can you expound a

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Speaker 3: little bit more on your customer base.

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Speaker 4: Yes, so we used Obviously, the primary use case that

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Speaker 4: most people think about when it comes to credit scores

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Speaker 4: is for lending. Right when you know you're applying for

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Speaker 4: a loan and they want to evaluate whether or not

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Speaker 4: you're going to be able to perform on that loan,

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Speaker 4: they will often pull your credit score as part of

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Speaker 4: that process. But It's used in many other stages as well. So,

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Speaker 4: for instance, many people who are applying to rent in

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Speaker 4: a new apartment building may also get asked for it.

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Speaker 4: When you are trying to get a utility.

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Speaker 3: Bill in New York, you get asked for it.

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Speaker 4: Yes, you certainly do. And utility bills, telephones, anything that

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Speaker 4: involves a long term commitment on payments generally. Now you'll

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Speaker 4: often get asked to provide your credit score.

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Speaker 1: So just explain for the way you were started by

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Speaker 1: whom twenty years ago, we.

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Speaker 4: Were a joint venture and ECHOFACTX and TransUnion, the three

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Speaker 4: national credit reporting agencies.

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Speaker 1: So what is the difference between these major companies that

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Speaker 1: we've all heard of that provide a credit score, et cetera,

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Speaker 1: that they such founded? You like, what do you do

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Speaker 1: differently than them?

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Speaker 4: Well, so what they do is they're the ones who

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Speaker 4: collect all this data from lenders and others on your

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Speaker 4: credit performance. Right, So they call credit buros they collect that.

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Speaker 4: They're highly regulated. But then most lenders can't just make

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Speaker 4: sense of all of that data on its own. They

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Speaker 4: need some guidance to have to translate that into a

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Speaker 4: what does that mean? Right?

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Speaker 2: Okay?

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Speaker 4: And so that's where a scoring algorithm comes into effect. Right,

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Speaker 4: And so the scoring algorithm helps to take in all

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Speaker 4: these hundreds of different factors about you to try to

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Speaker 4: then determine what does that mean about your propensity to pay?

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Speaker 3: Okay, you mentioned predictive analysis as well, What exactly is

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Speaker 3: that and what's that based on?

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Speaker 4: Well, so when credit scores are created, right, the aim

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Speaker 4: the goal is to try to evaluate what is the

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Speaker 4: likelihood that somebody's going to default on a payment over

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Speaker 4: the next time twenty four months. So when you see

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Speaker 4: that score, the score is actually a translation of a

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Speaker 4: probability right or on odds right to evaluate what is

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Speaker 4: that risk?

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Speaker 3: And how did we end up with the system of

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Speaker 3: FICO scores in the US, because it has like an

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Speaker 3: interesting history.

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Speaker 4: Well, back in the day fair Isaac they created the

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Speaker 4: first sort of known credit score. They were the first

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Speaker 4: ones to realize that there was a.

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Speaker 1: Looking at fi and fair Isaac is that yes?

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Speaker 4: Okay, yes, And so let's go back a bit, right,

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Speaker 4: So in the old days, lending was not necessarily the

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Speaker 4: most fair system that there was, right, or want to

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Speaker 4: call up your previous employer, They may call your landlord.

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Speaker 4: They may just ask around and if they don't know

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Speaker 4: anything about you. You know, there was a lot of

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Speaker 4: judgment involved in.

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Speaker 3: Lending, a lot of racial discrimination.

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Speaker 4: Well that certainly was built into that system, right, And

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Speaker 4: so then there was a law created, the Fair Credit

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Speaker 4: Reporting Act, that said, like, you can't do that, you

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Speaker 4: need a better system that is fair and that is

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Speaker 4: a better quantitative ability to assess people's risk, right, And

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Speaker 4: that created then this need to be able to consolidate

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Speaker 4: all this quantitative information in a way that lenders could

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Speaker 4: easily use it. So Faiko was the first fair ISAAC

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Speaker 4: at the time was the first to create that and

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Speaker 4: they did very well doing so. But you know, then

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Speaker 4: there was a need for competition innovation, and there was

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Speaker 4: a lot of frustration around the time of twenty years

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Speaker 4: ago that there was only one game in town and

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Speaker 4: it didn't score about twenty percent of the US population.

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Speaker 4: It still doesn't, And then a lot of lenders were

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Speaker 4: felt frustrated, like if it doesn't work for twenty percent

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Speaker 4: of the population, there's a problem. We need something different.

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Speaker 4: And so then buwers took the unusual step of actually

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Speaker 4: coming together to create an alternative and that became Advantage School.

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Speaker 3: Interesting can I as a consumer go credit score shopping.

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Speaker 4: So first of all, there's different ways to use it, right,

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Speaker 4: So a lender will typically choose the credit score that

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Speaker 4: they're going to use for being able to underwrite alone

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Speaker 4: with you, and often they'll use many more factors than

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Speaker 4: just a simple credit score, right, particularly the more sophisticated ones. However,

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Speaker 4: when you're trying to understand what your situation is, you

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Speaker 4: can there are lots of different places you can go.

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Speaker 4: You can either go the credit bureaus, you can go

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Speaker 4: to the likes of credit Karma. There are many different

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Speaker 4: services out there.

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Speaker 3: But I can course the lender to look at a

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Speaker 3: specific Yeah, look at this one over here, it's great,

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Speaker 3: get a second opinion.

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Speaker 4: No, I'm afraid not. That's not how it works. It's

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Speaker 4: it's really you know, the lenders try to determine what

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Speaker 4: is the most appropriate score for their product. And there

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Speaker 4: are many many different schools out there. There's schools that

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Speaker 4: in some cases built specifically for types of products like

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Speaker 4: order loans, and the other schools that, like our schools,

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Speaker 4: that are generic that can be used for any type

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Speaker 4: of product.

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Speaker 1: So you collect more data. And you mentioned that there

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Speaker 1: is this wide swath of the population that wasn't being

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Speaker 1: captured by the credit bureaus? What do you do additionally

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Speaker 1: on top of them to expand the pool find potentially

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Speaker 1: credit worthy borrowers that they had been missing before.

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Speaker 4: So the thing is that the quality and the types

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Speaker 4: of data that's been collected by the credit bureaus has

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Speaker 4: improved significantly over time. OKA, And so when we started

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Speaker 4: creating our algorithms, we're in, you know, the current version

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Speaker 4: that's now being adopted for mortgages, the version four we're

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Speaker 4: releasing version five this year.

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Speaker 2: We actually go.

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Speaker 4: And rewrite the whole thing each time so that each

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Speaker 4: time we can come up with the most accurate way

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Speaker 4: based on the current data is available and our current

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Speaker 4: ability to understand how consumers are behaving, because that behavior

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Speaker 4: changes of a time. Other companies, what they've done is

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Speaker 4: they've built a model long time ago. They don't like

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Speaker 4: to necessarily revealed everybody how it works, the secret source. Right,

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Speaker 4: So when you're seeing there's a chief risk officer and

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Speaker 4: there's a new model coming along, either you want to

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Speaker 4: understand that it's going to pay very similarly to the

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Speaker 4: previous one to be okay with it, or you need

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Speaker 4: a lot of transparency and how it works, so you

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Speaker 4: can get comfort in this new model. So I think

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Speaker 4: there's the big divergence and strategy. We go back to

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Speaker 4: boots and redo everything from scratch each time, but in

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Speaker 4: the same time provide an awful lot of transparency and

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Speaker 4: a lot of tools so that lenders can get a

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Speaker 4: really good understanding of exactly how this is going to

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Speaker 4: work and how it's going to behave in different situations

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Speaker 4: and they can test it out right, whereas the other

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Speaker 4: one is still working with many limitations that have been

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Speaker 4: in place since the very first models. And because of

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Speaker 4: those limitations, that's a big difference in why we've scored

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Speaker 4: a lot more people. So I'll give you a very

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Speaker 4: concrete example. So, for instance, one of the limitations that

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Speaker 4: the others have is that if you haven't had any

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Speaker 4: credit activity for the past six months, that you're not

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Speaker 4: going to get a score. So you just imagine somebody

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Speaker 4: that works with the military has been deployed overseas or

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Speaker 4: anything else.

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Speaker 2: Right.

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Speaker 4: But the good thing is, starting about fifteen years ago,

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Speaker 4: the Bureau started click and storing data so we could

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Speaker 4: use time serious data because who'd have thought that time

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Speaker 4: serious data could be useful in prediction, right, completely strange idea.

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Speaker 2: Right.

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Speaker 4: So with finan Score four, we started using trended data

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Speaker 4: times series data, and with that, obviously we can see

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Speaker 4: back twenty four months. So yes, if there's a gap

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Speaker 4: in six months of history, it's important, but we're still

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Speaker 4: able to see what happened before then, right, And that

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Speaker 4: gets rid of tens of millions of people when you

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Speaker 4: have that constraint. There are the constraints in there too,

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Speaker 4: So people that are new to credit, so if they

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Speaker 4: haven't had a full six months of history again, they

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Speaker 4: won't get scored. Aren't any tradelines, they won't get scored, right,

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Speaker 4: And so what we've been able to do is to

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Speaker 4: deal with thos constraints in a different way by a

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Speaker 4: using time series data, b using some other data points.

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Speaker 4: So we were the first to use utility payments and rent.

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Speaker 4: I mean, who'd have thought that your ability to pay

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Speaker 4: your rent could somehow again be useful and trying to

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Speaker 4: assess your risk, right, And so including those new different

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Speaker 4: types of data, realizing that these constraints can be changed

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Speaker 4: now that you have time series data. But then also

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Speaker 4: guess what you know? Math has evolved as well, okay,

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Speaker 4: And so you know what we realized too was that

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Speaker 4: you can be really smart and use some new methods,

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Speaker 4: like you know, some AI methods for instance, like clustering,

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Speaker 4: to really understand, well, look, here's a group of people

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Speaker 4: and they behave in a certain way, and by doing

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Speaker 4: that in a better way, we can then figure out

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Speaker 4: what is the best way to measure this group of

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Speaker 4: people here versus this group of people here, and doing

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Speaker 4: that well enables you to build much more predictive scores.

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Speaker 4: And so that's an important yance too that not a

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Speaker 4: lot of people always realize is that it's not one

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Speaker 4: formula that's calculating everybody's score. People will get depending on

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Speaker 4: what their credit file looks like and their history looks like.

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Speaker 4: They'll get divided into different segments and then each segment

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Speaker 4: is scored according to that and that again increases the

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Speaker 4: ability to score people accurately and score more people, like

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Speaker 4: in our case it's thirty three million more people they're

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Speaker 4: able to score, so it's quite substantial.

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Speaker 3: How did the models deal with breaks in previous consumer patterns?

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Speaker 3: Because we have seen some major ones in recent years.

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Speaker 3: So after the pandemic, we had a phenomenally tight labor market,

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Speaker 3: and we saw a lot of wage growth for lower income,

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Speaker 3: a lot of spending that was sort of unprecedented in

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Speaker 3: many ways. How do models actually incorporate that sort of

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Speaker 3: big shit shift in the trend.

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Speaker 4: So I think that's something really important to try to understand,

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Speaker 4: and it's not easily understood by many. So give me

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Speaker 4: a second here, I'll try to break this down.

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Speaker 1: When people say give me a second, I have to

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Speaker 1: break this down. Please break it down.

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Speaker 3: You have a minute.

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Speaker 1: A minute.

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Speaker 4: Honestly, that's kind of what I'm enjoy most about listening

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Speaker 4: to your podcast. And so look, the first thing to

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Speaker 4: understand is that this credit score is not an absolute

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Speaker 4: measure of risk. It's a relative measure of risk. Let

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Speaker 4: me break that down for you, right, So what it

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Speaker 4: means is that you know a score. If somebody has

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Speaker 4: scored seven to twenty in one month, and then somebody

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Speaker 4: else has scored seven twenty three years later, the risk

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Speaker 4: will be different. And the reason for that is very deliberate.

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Speaker 4: When we are evaluating a person, right, because of the

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Speaker 4: laws of the Fair Credit Reporting Act, right, we're allowed

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Speaker 4: to look at the things that are about you. But

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Speaker 4: there are things that are going out on at the

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Speaker 4: same time in the economy that impact risk of the

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Speaker 4: population as a whole. Right, So that's that's why it's

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Speaker 4: so important when you're looking at things like credit scores

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Speaker 4: to understand when was that score pulled right, because the

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Speaker 4: score of seven twenty in twenty seventeen had a very

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Speaker 4: different characteristic of a score of seven twenty in twenty

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Speaker 4: twenty two. Okay, now, but the thing to bear in

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Speaker 4: mind is it's an excellent relative measure of risk. So

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Speaker 4: at any one given point in time, you know, somebody

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Speaker 4: with a seven twenty is going to be much better

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Speaker 4: performing than somebody at six thirty, And at the same time,

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Speaker 4: somebody eight forty is going to be much better than

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Speaker 4: both of them. Okay, And that holds consistently true, and

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Speaker 4: so that's why it's very important to include when you're

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Speaker 4: making lending decisions. But lenders have to be thoughtful, right,

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Speaker 4: they have to. As they're making decisions about, you know,

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Speaker 4: how many people they want to be able to underwrite,

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Speaker 4: and how to think about risk, they need to also

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Speaker 4: start thinking about these external factors as well, so that

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Speaker 4: they can then set their underwriting criteria to meet the

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Speaker 4: kind of level of risk that they're willing to take on.

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Speaker 1: I had never thought about this, but of course that

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Speaker 1: makes so much sense. So it's like I could have

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Speaker 1: an excellent credit history, I could have ex employment pay

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Speaker 1: on my rent, but for example, if the economy is

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Speaker 1: going down the tubes, I may still yet be a

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Speaker 1: risky credit because I may lose my job at some point.

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Speaker 1: And so this idea that it kind of has to

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Speaker 1: be relative because the underlying conditions that affect everyone through

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Speaker 1: outside of our control, but they are still important from

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Speaker 1: the perspective of the lender exactly.

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Speaker 4: And at the same time, if you get declined for

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Speaker 4: a credit card, you can't be told that the reason

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Speaker 4: you're getting declined is because unemployment has hit five percent. Okay, right,

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Speaker 4: that doesn't work. The laws are very specific. The reasons

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Speaker 4: for why you're not getting the top score have to

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Speaker 4: be explained, and they have to be based on attributes

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Speaker 4: and data obviously that are specific to you.

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Speaker 3: What's the most important external factor when it comes to

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Speaker 3: credit scoring, Because I've heard arguments for obviously the labor market,

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Speaker 3: the unemployment rate, but also wage income and therefore real

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Speaker 3: disposable income. How do you weight those different factors?

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Speaker 4: I mean, I think if your lender, it's going to

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Speaker 4: really depend upon what types of consumers you're lending to, right,

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Speaker 4: particularly now we're seeing such divergence across consumers in terms

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Speaker 4: of who's doing well.

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Speaker 2: And who isn't.

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Speaker 4: And so, for instance, if you are a lender focused

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Speaker 4: on people that are kind of below prime let's say

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Speaker 4: that not completely subprime, but that near prime group, and

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Speaker 4: you're focusing on ail loans, and you know you're in

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Speaker 4: regions like Texas or in certain areas, then obviously understanding

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Speaker 4: the economic conditions that are affecting those people, like a

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Speaker 4: lot of those people would be working in certain types

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Speaker 4: of industries. What is employment like in those types of industries, right?

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Speaker 4: Or is it people that are in the gig economy?

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Speaker 2: Right?

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Speaker 4: And so it is quite nuanced and it's not necessarily

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Speaker 4: one thing, and it's going to depend. Whereas on the

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Speaker 4: other extreme, you know, if you're handing out black cards

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Speaker 4: and your audience is incredibly affluent, then again it's less

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Speaker 4: about the risk because at that point your risk of

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Speaker 4: default is probably one end ten thousand. So they're just

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Speaker 4: trying to make you sure that it is absolute the

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Speaker 4: risk free.

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Speaker 3: Right. So someone in a highly cyclical industry, Like I

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Speaker 3: don't know, truck drivers in Texas or something that we're

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Speaker 3: taking out auto loans would probably be seen as riskier

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Speaker 3: or the labor market would depend more for them, whereas

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Speaker 3: if you're taking out a black AMEX card or something

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Speaker 3: like that, probably real disposable income.

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Speaker 2: Yeah, okay, so.

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Speaker 1: You mentioned different segmentation. People talk about this K shaped economy.

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Speaker 1: Is that real or is that immun.

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Speaker 4: I absolutely believe so, but I think that it's a

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Speaker 4: little bit more nuanced. And so, you know, one of

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Speaker 4: the things that we spotted late last year and we're

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Speaker 4: tracking into this year was that we want the first

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Speaker 4: to see that it was a K shaped economy. But

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Speaker 4: a lot of people were making the assumption that the

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Speaker 4: K shaped economy was being driven by income levels. But

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Speaker 4: when we were looking at the date at the time,

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Speaker 4: we were seeing that those that were in sort of

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Speaker 4: the higher income level in our case that's one hundred

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Speaker 4: and fifty thousand above. So that's not your you know,

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Speaker 4: people who are running hedge funds and sept and you know,

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Speaker 4: but still it's the relatively better to do cohort. They

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Speaker 4: were actually seeing the highest year of year increases in

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Speaker 4: delinquency rates at the time, so we knew that okay,

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Speaker 4: home in a second, it isn't as simple as this,

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Speaker 4: So you know, we spend a lot of time and

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Speaker 4: sort of a lot of banks and we kind of

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Speaker 4: collaborated with them to try to understand what's really then

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Speaker 4: the differentiator. And then what seemed to be really a

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Speaker 4: part of this is wealth. So you know, a lot

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Speaker 4: of people don't necessarily differentiate income and wealth, but they

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Speaker 4: are separate. And so you know, when you're looking then

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Speaker 4: at a high income cohort at the time to try

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Speaker 4: to see, like, okay, well, which ones are doing well,

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Speaker 4: which ones weren't. Home ownership was the biggest differentiator because

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Speaker 4: they had a bigger cushion, something they can rely on.

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Speaker 4: And then obviously other aspects as well, like stock ownership

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Speaker 4: and small business ownership, et cetera. But home ownership is

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Speaker 4: the one that has the bigger effect because there's more

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Speaker 4: people in the US economy that on a home then

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Speaker 4: let's say, has a stock portfolio.

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Speaker 3: Right, talk more about mortgage rates, because this feels pretty

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Speaker 3: key when you're talking about the K shaped economy, which

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Speaker 3: is if anyone, anyone who bought their house before twenty

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Speaker 3: twenty is probably a very lucky person and has locked

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Speaker 3: in a low mortgage rate. I think mortgage rates are

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Speaker 3: still even after the rate cut, we're at like six

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Speaker 3: percent or something versus I think at one point they

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Speaker 3: got down to like three percent.

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Speaker 1: Right after the filter.

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Speaker 2: Yeah.

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Speaker 1: Crazy, yeah.

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Speaker 3: And so if you bought a house, then you actually

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Speaker 3: have this like massive cushion as you put it, versus

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Speaker 3: someone who's buying a house now or in the past

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Speaker 3: couple of years.

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Speaker 4: In a way, I see there's a bit of a

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Speaker 4: silver lining when it comes to housing. Right We have

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Speaker 4: seen rates come down this week. Hopefully they will continue

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Speaker 4: to come down next year. There's a lot of debate, obviously,

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Speaker 4: even within the Federals to exactly the speed at which

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Speaker 4: that's going to be accomplished, and obviously there are many

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Speaker 4: other factors that can impact that. But the reason we

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Speaker 4: see a silver lining is for two reasons. Okay, as

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Speaker 4: interest rates comes down, Obviously, for people to own homes,

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Speaker 4: that's their biggest monthly payment right now, there's much point

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Speaker 4: for many to refinance given that where the interest rates are.

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Speaker 4: But if it comes down a bit more, it'll make

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Speaker 4: much more sense for a large tranch of homeowners that

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Speaker 4: have higher interest rates to be able to make that switch.

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Speaker 4: So we'd expect a bit of a refinancing boom as

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Speaker 4: it hits a certain level. But the other thing that's

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Speaker 4: really exciting about what's happening in the home ownership space

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Speaker 4: is that this year the FHFA changed the rules about

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Speaker 4: what credit scores can be used in mortgage. So historically

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Speaker 4: they've used a very old version that's from the nineties,

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Speaker 4: of the classic score in mortgage applications, and that was

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Speaker 4: actually not deliberately done, So it was just that we

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Speaker 4: got written into the rules and then since then as

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Speaker 4: a recommendation, but then it became kind of the de

442
00:20:36,040 --> 00:20:39,359
Speaker 4: facto and monopoly in that space. And the problem is

443
00:20:39,680 --> 00:20:41,960
Speaker 4: that's a model that went through the last crisis, and

444
00:20:42,040 --> 00:20:44,720
Speaker 4: the Federal Reservist and Lewis actually found that it didn't

445
00:20:44,760 --> 00:20:47,159
Speaker 4: work well at all in that situation. In fact, it

446
00:20:47,240 --> 00:20:50,680
Speaker 4: saw a bigger rate of increase in delinquencies amongst those

447
00:20:50,680 --> 00:20:52,880
Speaker 4: that were prime than it did amongst those that were subprime.

448
00:20:53,040 --> 00:20:54,879
Speaker 4: So the rate of increase, which is not how a

449
00:20:54,880 --> 00:20:58,920
Speaker 4: model is supposed to work, surprisingly right. And so what's

450
00:20:58,920 --> 00:21:02,120
Speaker 4: happening now is that they have allowed for varnish four

451
00:21:02,200 --> 00:21:04,080
Speaker 4: to be used. And the reason I say that is

452
00:21:04,080 --> 00:21:06,720
Speaker 4: that it's a because, as I mentioned before, a lot

453
00:21:06,760 --> 00:21:08,679
Speaker 4: more people will now have the ability to get access

454
00:21:08,680 --> 00:21:11,720
Speaker 4: to home ownership, so that will create a bit more demand, right,

455
00:21:11,920 --> 00:21:13,520
Speaker 4: which is great. And the other thing to think about

456
00:21:13,560 --> 00:21:17,000
Speaker 4: too is who are these people that get access to this, right,

457
00:21:17,040 --> 00:21:19,320
Speaker 4: It's a lot of people that are not necessarily in

458
00:21:19,400 --> 00:21:23,600
Speaker 4: the areas that have been so crazy with house price increases.

459
00:21:23,640 --> 00:21:23,720
Speaker 2: Right.

460
00:21:23,760 --> 00:21:25,479
Speaker 4: There are a lot of rural communities like so if

461
00:21:25,480 --> 00:21:27,919
Speaker 4: you look at the state where there's the biggest difference

462
00:21:27,920 --> 00:21:31,800
Speaker 4: between scoreable people with the new score, it's actually West Virginia.

463
00:21:32,200 --> 00:21:35,240
Speaker 4: And so those economies could certainly do really well from

464
00:21:35,320 --> 00:21:38,439
Speaker 4: a change to more people having home ownership. And then

465
00:21:38,480 --> 00:21:42,240
Speaker 4: the second thing too is obviously our MBS is really important.

466
00:21:42,840 --> 00:21:45,080
Speaker 4: Had some challenges back in two thousand and eight, two

467
00:21:45,080 --> 00:21:48,240
Speaker 4: thousand and nine, right, and so having a better.

468
00:21:48,000 --> 00:21:51,080
Speaker 3: Performing model a very smardest way about I.

469
00:21:52,600 --> 00:21:54,080
Speaker 1: Think they came up a couple of times.

470
00:21:54,160 --> 00:21:56,119
Speaker 4: I was educated in the UK. Pardon me, we have

471
00:21:56,160 --> 00:22:00,200
Speaker 4: a tendency to understate things. But so you know, having

472
00:22:00,320 --> 00:22:03,520
Speaker 4: a newer, more proven model, one that's you know, worked

473
00:22:03,520 --> 00:22:05,439
Speaker 4: so well in credit card and other things for the

474
00:22:05,440 --> 00:22:07,840
Speaker 4: past eight years, it's become the most used model in

475
00:22:08,400 --> 00:22:11,400
Speaker 4: many other segments. So having a proven model that newer

476
00:22:11,440 --> 00:22:13,880
Speaker 4: and more predictive is should help as well with reducing

477
00:22:13,880 --> 00:22:15,840
Speaker 4: the systemic risk in the R and BS market.

478
00:22:16,040 --> 00:22:18,320
Speaker 1: Just to go back very quickly, because I don't know

479
00:22:18,560 --> 00:22:21,719
Speaker 1: it sounded important, can you just clarify a little bit more?

480
00:22:21,760 --> 00:22:26,119
Speaker 1: What is this rule change such that could unlock additional

481
00:22:26,280 --> 00:22:27,360
Speaker 1: source of demand here?

482
00:22:27,520 --> 00:22:27,840
Speaker 2: Okay?

483
00:22:27,960 --> 00:22:31,439
Speaker 4: So when a bank before wanted to submit loans to

484
00:22:31,560 --> 00:22:34,679
Speaker 4: Fannie May and Freddy Mac, okay, they could only submit

485
00:22:34,720 --> 00:22:38,040
Speaker 4: those loans using the Phyco classic score. Okay, Okay, there's

486
00:22:38,040 --> 00:22:40,680
Speaker 4: actually two three different scores going too that another point.

487
00:22:40,720 --> 00:22:42,719
Speaker 4: But anyway, and there used to be like a cutoff

488
00:22:42,720 --> 00:22:44,480
Speaker 4: that if you didn't have six twenty then you couldn't

489
00:22:44,720 --> 00:22:46,679
Speaker 4: be able to submit it. You could go to an

490
00:22:46,760 --> 00:22:48,879
Speaker 4: FAH loan, but those are more expensive, right, but you

491
00:22:48,920 --> 00:22:51,199
Speaker 4: couldn't necessarily get a normal conforming loon the gost to

492
00:22:51,200 --> 00:22:53,159
Speaker 4: Fani Man and Freddy Mac. So that's not changed. The

493
00:22:53,160 --> 00:22:55,400
Speaker 4: first of all, that minimum limit of FIKO has been removed,

494
00:22:55,720 --> 00:22:58,679
Speaker 4: and now they are just updating all the pipes to

495
00:22:58,760 --> 00:23:02,159
Speaker 4: allow them to use varnished score as a choice. So

496
00:23:02,200 --> 00:23:04,399
Speaker 4: now there'll be a choice. Lenders can choose which score

497
00:23:04,440 --> 00:23:05,800
Speaker 4: they want to use, and they can make their own

498
00:23:05,840 --> 00:23:08,040
Speaker 4: evaluations about which one performs better.

499
00:23:08,280 --> 00:23:10,960
Speaker 1: Okay, so let's go back to starting at the end

500
00:23:10,960 --> 00:23:14,119
Speaker 1: of last year, and you say that increase in delinquencies

501
00:23:14,119 --> 00:23:18,040
Speaker 1: among people decent incomes, maybe they didn't have as much. Well,

502
00:23:18,400 --> 00:23:20,560
Speaker 1: talk to us about the numbers. How big were these numbers,

503
00:23:20,560 --> 00:23:22,879
Speaker 1: how much did they catch people by surprise? And what

504
00:23:23,040 --> 00:23:26,359
Speaker 1: is the story there about why there was this delinquency pressure.

505
00:23:26,680 --> 00:23:29,320
Speaker 4: Well, so the good thing is it's evolvedtle bit since

506
00:23:29,480 --> 00:23:31,280
Speaker 4: the end of last year. But you know, when we're

507
00:23:31,280 --> 00:23:35,320
Speaker 4: looking at this data, then again, look, high income earners,

508
00:23:35,520 --> 00:23:39,400
Speaker 4: not surprisingly have lower delinquency rates than middle income earners,

509
00:23:39,440 --> 00:23:43,440
Speaker 4: and that had themselves lower delinquency rates than the lower income. Right,

510
00:23:43,800 --> 00:23:45,399
Speaker 4: But not a lot of people were looking at that

511
00:23:45,480 --> 00:23:47,280
Speaker 4: kind of year of a year trend and the momentum. Right,

512
00:23:47,320 --> 00:23:49,159
Speaker 4: I'm always looking at momentum because I'm trying to get

513
00:23:49,160 --> 00:23:52,240
Speaker 4: an early read on kind of how things are developing,

514
00:23:52,680 --> 00:23:54,879
Speaker 4: and so at the time, Actually, let's go back a

515
00:23:54,920 --> 00:23:57,080
Speaker 4: little bit because I think it can explain a little

516
00:23:57,080 --> 00:23:58,879
Speaker 4: bit more about what's going on in the economy. Is

517
00:23:58,880 --> 00:24:03,240
Speaker 4: that a right, So when the pandemic happens, right, a

518
00:24:03,280 --> 00:24:06,160
Speaker 4: lot of stimulus comes in, a lot of forbearance programs

519
00:24:06,160 --> 00:24:08,679
Speaker 4: are put in place. As a result of that, so

520
00:24:08,760 --> 00:24:11,879
Speaker 4: many people's credit health and the way that they appear

521
00:24:11,880 --> 00:24:15,359
Speaker 4: on the credit files improved dramatically. They were paying down

522
00:24:15,400 --> 00:24:18,520
Speaker 4: their credit cards, they were building up their savings. It

523
00:24:18,560 --> 00:24:23,480
Speaker 4: was a good situation, temporary but good. But then twenty

524
00:24:23,600 --> 00:24:27,400
Speaker 4: one twenty two starts creeping in and we start seeing that, okay,

525
00:24:27,520 --> 00:24:31,640
Speaker 4: this is not a persistent situation. This was a one off, right,

526
00:24:31,640 --> 00:24:34,560
Speaker 4: and we started seeing delinquency rates starting to come up again.

527
00:24:34,680 --> 00:24:34,840
Speaker 2: Right.

528
00:24:35,520 --> 00:24:38,080
Speaker 4: What we saw, which shouldn't be too surprising, particularly given

529
00:24:38,080 --> 00:24:41,080
Speaker 4: that that was when inflation was kicking in in a

530
00:24:41,119 --> 00:24:44,239
Speaker 4: big way, was that those that were initially impacted and

531
00:24:44,240 --> 00:24:47,119
Speaker 4: who were sewing the biggest rises were the lower income households.

532
00:24:47,400 --> 00:24:47,560
Speaker 2: Right.

533
00:24:47,640 --> 00:24:49,760
Speaker 4: So for the first sort of you know, six months,

534
00:24:49,840 --> 00:24:51,800
Speaker 4: nine months, that was the group that was seeing the

535
00:24:51,840 --> 00:24:54,679
Speaker 4: biggest year of a year increases. But then what we

536
00:24:54,720 --> 00:24:58,159
Speaker 4: started seeing was that come twenty twenty three forward, we

537
00:24:58,200 --> 00:25:02,440
Speaker 4: actually started seeing that then the middle and hiring income

538
00:25:02,800 --> 00:25:06,360
Speaker 4: households were starting being impacted too, and that's probably related

539
00:25:06,400 --> 00:25:11,040
Speaker 4: to the fact that lower income households had less disposable income,

540
00:25:11,080 --> 00:25:13,399
Speaker 4: but they also had less savings put away, so that

541
00:25:13,440 --> 00:25:15,959
Speaker 4: you know, they're the first to feel the pain. But

542
00:25:16,000 --> 00:25:20,840
Speaker 4: then when there's this consistent imbalance between your inflows and

543
00:25:20,880 --> 00:25:24,440
Speaker 4: your outflows, right, even if you have you know, thirty

544
00:25:24,480 --> 00:25:27,000
Speaker 4: thousand or fifty thousand that's put away, that's going to

545
00:25:27,040 --> 00:25:30,359
Speaker 4: start depleting. And that's what we started seeing happening even

546
00:25:30,359 --> 00:25:32,920
Speaker 4: with these hiring income households because at the same time,

547
00:25:33,000 --> 00:25:34,760
Speaker 4: you know, they were hit by so many pressures. Right,

548
00:25:35,200 --> 00:25:38,120
Speaker 4: biggest rent increases I think we've ever seen came into

549
00:25:38,160 --> 00:25:41,760
Speaker 4: effect those few years after COVID. Right, we saw things,

550
00:25:42,000 --> 00:25:45,119
Speaker 4: you know, car prices, costs of auto financing going up

551
00:25:45,119 --> 00:25:47,760
Speaker 4: through the roof, and then various other costs also went

552
00:25:47,840 --> 00:25:51,840
Speaker 4: up substantially. And so it's not that hiring income people

553
00:25:51,880 --> 00:25:52,679
Speaker 4: were immune from this.

554
00:25:52,840 --> 00:25:53,000
Speaker 2: Right.

555
00:25:53,040 --> 00:25:57,080
Speaker 4: Also, as an economist, a lot of people always talk about, hey,

556
00:25:57,080 --> 00:26:01,159
Speaker 4: when inflation kicks in, it disproportionately impacts lower income households

557
00:26:01,200 --> 00:26:04,080
Speaker 4: because the cost of bread, the cost of milk, et cetera.

558
00:26:04,440 --> 00:26:06,960
Speaker 4: You know, it's not like high income people buy milk

559
00:26:07,000 --> 00:26:08,639
Speaker 4: that's one hundred times more expensive.

560
00:26:08,760 --> 00:26:08,960
Speaker 2: Right.

561
00:26:09,320 --> 00:26:11,119
Speaker 4: But the reality is is if you look at people's

562
00:26:11,119 --> 00:26:14,600
Speaker 4: big outlays, many of the hose actually scale with income.

563
00:26:14,760 --> 00:26:18,200
Speaker 4: Rent for instance, people that earn more tend to rent high.

564
00:26:18,520 --> 00:26:22,879
Speaker 4: Other big outlays such as childcare, education and other things,

565
00:26:23,000 --> 00:26:26,439
Speaker 4: they also have been scaling more with income. Now, obviously

566
00:26:26,520 --> 00:26:28,880
Speaker 4: there's a level of income that you know that does

567
00:26:28,880 --> 00:26:30,520
Speaker 4: not apply to but if we still talk about that

568
00:26:30,600 --> 00:26:33,480
Speaker 4: cohort one hundred and fifty to two hundred and fifty

569
00:26:33,560 --> 00:26:37,080
Speaker 4: or so of household income, they're definitely seeing that they

570
00:26:37,080 --> 00:26:39,280
Speaker 4: felt the pain too. It took them longer before it

571
00:26:39,320 --> 00:26:42,040
Speaker 4: started impacting their delinquencies, but they did start feeling the pain.

572
00:26:43,000 --> 00:26:47,000
Speaker 4: The good news though, is that as we started looking

573
00:26:47,000 --> 00:26:48,679
Speaker 4: at the second half of this year, right, so, we

574
00:26:48,720 --> 00:26:51,639
Speaker 4: still saw those delinquencies in high incomes rising very heavily

575
00:26:51,680 --> 00:26:53,040
Speaker 4: at the beginning the first half.

576
00:26:52,880 --> 00:26:53,400
Speaker 2: Of this year.

577
00:26:53,600 --> 00:26:56,919
Speaker 4: But since July, and I've got data from October, so

578
00:26:57,200 --> 00:27:00,440
Speaker 4: of the three of the four months since July, seen

579
00:27:00,520 --> 00:27:04,480
Speaker 4: that high income households came down. So that's a good sign.

580
00:27:04,800 --> 00:27:06,280
Speaker 4: And the reason I say that's a good sign is

581
00:27:06,280 --> 00:27:08,680
Speaker 4: not because I'm a fan of making the case shaped

582
00:27:08,680 --> 00:27:11,480
Speaker 4: economy even more so but the fact that so much

583
00:27:11,480 --> 00:27:13,520
Speaker 4: of the US economy is driven by spending. You mentioned

584
00:27:13,520 --> 00:27:17,719
Speaker 4: earlier that high income households disproportionately impact that, and so

585
00:27:18,160 --> 00:27:20,240
Speaker 4: if that dries up, that has a knock on effect

586
00:27:20,240 --> 00:27:21,920
Speaker 4: on the whole economy. So to the fact that we're

587
00:27:21,960 --> 00:27:25,560
Speaker 4: seeing that cohort that those delinquencies are starting to come down,

588
00:27:25,880 --> 00:27:28,280
Speaker 4: I think there's some light at the end of the tunnel.

589
00:27:44,240 --> 00:27:48,679
Speaker 3: I always wondered how useful are big shopping events like

590
00:27:48,760 --> 00:27:52,920
Speaker 3: Black Friday or Christmas in terms of gauging consumer sentiments.

591
00:27:52,960 --> 00:27:55,280
Speaker 3: So you always see the headlines. You certainly saw them

592
00:27:55,280 --> 00:27:59,000
Speaker 3: this year, you know, record Black Friday spending, But then

593
00:27:59,040 --> 00:28:02,240
Speaker 3: you also see people break down that spending and say, well, actually,

594
00:28:02,320 --> 00:28:05,760
Speaker 3: it's because everyone is so pressured they really need the

595
00:28:05,800 --> 00:28:08,800
Speaker 3: low prices, so they're buying everything. Now, how useful is

596
00:28:08,800 --> 00:28:10,199
Speaker 3: something like that to you?

597
00:28:10,200 --> 00:28:13,600
Speaker 4: You can always see trends, right, and so from one perspective,

598
00:28:13,600 --> 00:28:15,639
Speaker 4: it is always good to look at a number of

599
00:28:15,640 --> 00:28:18,960
Speaker 4: different things, such as spending on Black Friday, Cyber Monday,

600
00:28:18,960 --> 00:28:22,840
Speaker 4: et cetera, because there are nuances into how people have

601
00:28:22,840 --> 00:28:25,920
Speaker 4: been spending for those weekends over twenty years. But still,

602
00:28:25,960 --> 00:28:27,720
Speaker 4: if you look at the last three years, you can

603
00:28:27,760 --> 00:28:29,320
Speaker 4: start to see things that are happening.

604
00:28:29,640 --> 00:28:30,600
Speaker 2: But the thing that.

605
00:28:30,960 --> 00:28:33,520
Speaker 4: I haven't been able to get my head round is

606
00:28:33,960 --> 00:28:36,000
Speaker 4: how much of that year of year increase in spending

607
00:28:36,040 --> 00:28:38,480
Speaker 4: and the holidays is driven by the prices of the

608
00:28:38,520 --> 00:28:41,840
Speaker 4: goods going up versus people buying things that would have

609
00:28:41,880 --> 00:28:44,960
Speaker 4: traditionally been more expensive or splurging more. That for me

610
00:28:45,400 --> 00:28:48,800
Speaker 4: isn't obvious. And I think that again when trying to

611
00:28:48,880 --> 00:28:51,160
Speaker 4: understand how the economy is going, it's so important if

612
00:28:51,160 --> 00:28:53,920
Speaker 4: you're looking at from a spending perspective, to actually look

613
00:28:53,960 --> 00:28:58,080
Speaker 4: at the different merchants. Right, So, how's McDonald's doing, what

614
00:28:58,120 --> 00:29:00,520
Speaker 4: are the trends there, what's happening in higher and how

615
00:29:00,640 --> 00:29:05,080
Speaker 4: is LVMH doing versus Walmart, et cetera. Because again, you know,

616
00:29:05,120 --> 00:29:07,320
Speaker 4: even though I said that, you know there's a silver

617
00:29:07,400 --> 00:29:12,320
Speaker 4: lining and the high income consumers are seeing declines. Middle

618
00:29:12,360 --> 00:29:16,520
Speaker 4: income have come down, but they're still increasing, and low

619
00:29:16,600 --> 00:29:19,800
Speaker 4: income are persistently high around eight percent year every year

620
00:29:19,840 --> 00:29:24,200
Speaker 4: increases in their delinquency rates, and so we're probably next

621
00:29:24,280 --> 00:29:27,960
Speaker 4: year going to see more households struggling to make ends

622
00:29:27,960 --> 00:29:30,800
Speaker 4: meet than we saw this year. I still don't think

623
00:29:30,840 --> 00:29:33,400
Speaker 4: that there's just looking at the trend, it's going to

624
00:29:33,440 --> 00:29:36,080
Speaker 4: be any kind of major breakpoint. But the thing to

625
00:29:36,640 --> 00:29:40,120
Speaker 4: bear in mind with that, though, is that if you

626
00:29:40,160 --> 00:29:43,320
Speaker 4: look at here this situation, and you look at for instance,

627
00:29:43,360 --> 00:29:45,640
Speaker 4: JP Morgan published that, you know, the amount of cash

628
00:29:45,680 --> 00:29:49,160
Speaker 4: people have in their checking accounts is coming down. So

629
00:29:49,520 --> 00:29:53,760
Speaker 4: it just means that there's more of a challenge if

630
00:29:53,760 --> 00:29:55,680
Speaker 4: there's a big shock to the system at some point.

631
00:29:55,840 --> 00:29:58,040
Speaker 4: Not that I can foresee any shock to the system,

632
00:29:58,160 --> 00:29:59,960
Speaker 4: but it's always something to be a little bit wary

633
00:30:00,560 --> 00:30:01,280
Speaker 4: before going.

634
00:30:01,240 --> 00:30:04,760
Speaker 1: I want to go back to something you said very quickly.

635
00:30:04,880 --> 00:30:08,760
Speaker 1: You said, Okay, intuitively, people with higher incomes that are

636
00:30:08,800 --> 00:30:11,560
Speaker 1: going to have delinquencies at a lower rate than people

637
00:30:11,600 --> 00:30:14,240
Speaker 1: with middle incomes, and they're going to have delinquencies a

638
00:30:14,240 --> 00:30:16,640
Speaker 1: lower rate than people with lower incomes. That's not intuitive

639
00:30:16,720 --> 00:30:19,800
Speaker 1: to me, actually, because I would also imagine that underwriting

640
00:30:19,920 --> 00:30:22,560
Speaker 1: is very different, et cetera. It's not obvious to me

641
00:30:22,600 --> 00:30:27,040
Speaker 1: why higher income people default less than lower income people,

642
00:30:27,480 --> 00:30:30,200
Speaker 1: because I would imagine lenders know their income and they're

643
00:30:30,240 --> 00:30:33,240
Speaker 1: going to scrutinize the loan of a lower income person

644
00:30:33,400 --> 00:30:37,880
Speaker 1: much more intensely, et cetera. So why should this trend

645
00:30:38,120 --> 00:30:40,600
Speaker 1: exist given that they don't get the same loan terms,

646
00:30:40,600 --> 00:30:42,560
Speaker 1: their same loan availability, No.

647
00:30:42,680 --> 00:30:43,120
Speaker 2: They don't.

648
00:30:43,280 --> 00:30:45,880
Speaker 4: End You know, a high income household will buy typically

649
00:30:46,000 --> 00:30:48,880
Speaker 4: more expensive than a car than a low income household will,

650
00:30:49,280 --> 00:30:51,719
Speaker 4: but higher income households tend to have more of an

651
00:30:51,760 --> 00:30:55,240
Speaker 4: ability to squirrel some money away, or they tend to

652
00:30:55,320 --> 00:30:58,400
Speaker 4: have other assets that they knows that.

653
00:30:58,400 --> 00:31:01,239
Speaker 1: The lender knows that the higher income household is going

654
00:31:01,280 --> 00:31:04,360
Speaker 1: to have likely more savings, and the lender knows that

655
00:31:04,440 --> 00:31:08,320
Speaker 1: the householder's in a very tight income probably has very

656
00:31:08,360 --> 00:31:11,920
Speaker 1: little cushion in the form of what we call it wealth.

657
00:31:11,960 --> 00:31:15,600
Speaker 1: And so why doesn't that just get baked into the

658
00:31:15,720 --> 00:31:16,800
Speaker 1: underwriting standards.

659
00:31:16,880 --> 00:31:19,480
Speaker 4: Well, in ways it does, right, And so you know,

660
00:31:19,520 --> 00:31:22,600
Speaker 4: when you're underwriting a loan for let's say somebody who

661
00:31:22,680 --> 00:31:25,760
Speaker 4: is high income and has a good credit history, your

662
00:31:25,800 --> 00:31:28,960
Speaker 4: expectation of their default is going to be incredibly low, right,

663
00:31:29,000 --> 00:31:31,320
Speaker 4: So that's built into the pricing, and that's built into

664
00:31:31,680 --> 00:31:33,280
Speaker 4: and you obviously don't just look at a quiet score.

665
00:31:33,320 --> 00:31:35,400
Speaker 4: You look at what their income is and various other

666
00:31:35,720 --> 00:31:38,520
Speaker 4: important metrics to be able to determine the appropriate amount

667
00:31:38,560 --> 00:31:39,239
Speaker 4: that you will lend them.

668
00:31:39,280 --> 00:31:39,680
Speaker 2: Et cetera.

669
00:31:39,840 --> 00:31:43,440
Speaker 4: Right, but high income consumers may not need to take

670
00:31:43,480 --> 00:31:45,760
Speaker 4: on as much debt as a proportion to their income

671
00:31:45,920 --> 00:31:48,280
Speaker 4: as lower income households to get through what they need

672
00:31:48,320 --> 00:31:51,120
Speaker 4: to do. Right, And so if you look at, for instance,

673
00:31:51,200 --> 00:31:53,800
Speaker 4: a high income household, how much of their even though

674
00:31:53,840 --> 00:31:57,520
Speaker 4: for instance, probably housing and car costs are some of

675
00:31:57,520 --> 00:32:01,680
Speaker 4: the biggest outlays, they have proportion they're probably less than

676
00:32:01,800 --> 00:32:04,760
Speaker 4: for lower income households, right, And so you know, a

677
00:32:04,760 --> 00:32:07,040
Speaker 4: lot of it has to do with that proportionality. But

678
00:32:07,080 --> 00:32:09,640
Speaker 4: then also just that again, they will tend to have

679
00:32:09,840 --> 00:32:13,200
Speaker 4: a bit more reserves so that they can ride through situations.

680
00:32:13,360 --> 00:32:16,840
Speaker 1: Talk to us about autodelinquencies. Those have been rising and

681
00:32:17,040 --> 00:32:20,640
Speaker 1: obviously there's a lot of lending going on. Again, Tracy

682
00:32:20,720 --> 00:32:23,720
Speaker 1: mentioned the sort of twenty twenty to twenty twenty two

683
00:32:24,200 --> 00:32:27,400
Speaker 1: vintage car prices themselves are going up. So not only

684
00:32:27,400 --> 00:32:29,240
Speaker 1: have the raids gone up, but we've seen a tremendous

685
00:32:29,280 --> 00:32:32,920
Speaker 1: amount of auto inflation. So sort of stress at every level.

686
00:32:33,200 --> 00:32:35,400
Speaker 1: We see the numbers going up. What do those tell us?

687
00:32:35,720 --> 00:32:37,920
Speaker 4: It's a fascinating story. You know, there's been a lot

688
00:32:37,960 --> 00:32:40,640
Speaker 4: of interest that was paid attention to order loans because

689
00:32:40,720 --> 00:32:42,520
Speaker 4: you know, suddenly in twenty twenty two that we started

690
00:32:42,520 --> 00:32:45,840
Speaker 4: seeing these autolone delinquencies going up much faster than other

691
00:32:45,920 --> 00:32:48,880
Speaker 4: types of loan delinquencies, and it was having a profound

692
00:32:48,920 --> 00:32:52,000
Speaker 4: effect obviously on auto lenders and the economy as a whole.

693
00:32:52,560 --> 00:32:54,800
Speaker 4: And you know, everybody's trying to explain, well, you see,

694
00:32:54,800 --> 00:32:56,720
Speaker 4: we we've got a bit too loose during the period

695
00:32:56,800 --> 00:33:00,400
Speaker 4: of COVID and other things. And they started adjusting their

696
00:33:00,440 --> 00:33:03,280
Speaker 4: lending criteria, right, so they did adjut their lending criteria

697
00:33:03,560 --> 00:33:06,240
Speaker 4: around twenty twenty three for most of them, but then

698
00:33:06,240 --> 00:33:10,440
Speaker 4: we still saw that despite that, the delinquis rates kept

699
00:33:10,800 --> 00:33:13,560
Speaker 4: persistently increasing, and then when we looked at the data,

700
00:33:13,640 --> 00:33:15,880
Speaker 4: we actually saw that but they had actually had an

701
00:33:15,920 --> 00:33:19,000
Speaker 4: impact by adjusting their lending criteria. We saw that the

702
00:33:19,000 --> 00:33:23,040
Speaker 4: delinquiscy rates among subprime all the loans reduced quite dramatically

703
00:33:23,080 --> 00:33:25,920
Speaker 4: after that, so they did have that effect. But we

704
00:33:26,040 --> 00:33:29,120
Speaker 4: saw that the delinquency rates on near prime and prime

705
00:33:29,240 --> 00:33:32,040
Speaker 4: continued to go up, and that was what drove that increase,

706
00:33:32,560 --> 00:33:35,040
Speaker 4: and so we realized there's something more going on here,

707
00:33:35,040 --> 00:33:37,000
Speaker 4: and also why is it it's so different? So we

708
00:33:37,040 --> 00:33:39,400
Speaker 4: went back a long time. We went back to twenty

709
00:33:39,440 --> 00:33:41,880
Speaker 4: ten to try to understand kind of what's been going on,

710
00:33:42,120 --> 00:33:43,920
Speaker 4: because not many people look at it from that time scale.

711
00:33:43,960 --> 00:33:46,560
Speaker 4: But it's actually quite fascinating because back in twenty ten,

712
00:33:46,880 --> 00:33:49,120
Speaker 4: auto had the best performance of any loan product. At

713
00:33:49,120 --> 00:33:50,440
Speaker 4: the time, it was the least risky.

714
00:33:51,280 --> 00:33:54,080
Speaker 3: This was always the narrative that Americans will never give

715
00:33:54,160 --> 00:33:56,640
Speaker 3: up their cars, even if they lose their job. They

716
00:33:56,640 --> 00:33:58,480
Speaker 3: can sleep in their car and live in the car,

717
00:33:58,520 --> 00:34:02,040
Speaker 3: which is very dystopian, but that's I remember hearing that

718
00:34:02,120 --> 00:34:05,000
Speaker 3: story literally from a banker, a banker who was actually

719
00:34:05,000 --> 00:34:07,800
Speaker 3: working on bundling phone loans, and he was.

720
00:34:07,800 --> 00:34:13,120
Speaker 4: Like, so at that time it performed well, right, and

721
00:34:13,200 --> 00:34:15,880
Speaker 4: people did not default as much on that as on

722
00:34:15,920 --> 00:34:19,959
Speaker 4: other products. But then we've seen it has transitioned over

723
00:34:20,000 --> 00:34:22,560
Speaker 4: that fifteen year period to now in the first quarter

724
00:34:22,640 --> 00:34:25,840
Speaker 4: this year, it was the riskiest credit product out there,

725
00:34:26,200 --> 00:34:30,080
Speaker 4: and it then subsequently student loans started coming in, and

726
00:34:30,120 --> 00:34:33,520
Speaker 4: that's another story. Those delinquency rates are at historic levels.

727
00:34:33,719 --> 00:34:35,120
Speaker 4: But on the auto loans side, we then try to

728
00:34:35,160 --> 00:34:38,040
Speaker 4: understand what's causing this, right, and so what we're seeing

729
00:34:38,200 --> 00:34:40,239
Speaker 4: was that there's a number of factors, some of them

730
00:34:40,239 --> 00:34:42,160
Speaker 4: obvious some of them a little bit more subtle. Right,

731
00:34:42,840 --> 00:34:45,279
Speaker 4: the average cost of a car has gone up an

732
00:34:45,320 --> 00:34:47,880
Speaker 4: incredible amount, And what we're seeing is then the average

733
00:34:48,400 --> 00:34:51,839
Speaker 4: loan value for auto loans has increased more than any

734
00:34:51,840 --> 00:34:54,400
Speaker 4: other loan value. And that may sound like okay, but

735
00:34:54,480 --> 00:34:56,960
Speaker 4: if you think about it, mortgages tend to be the

736
00:34:57,000 --> 00:34:59,520
Speaker 4: one that grows the most because house prices have appreciated

737
00:34:59,520 --> 00:35:01,720
Speaker 4: so much of it. So the fact that the average

738
00:35:01,719 --> 00:35:04,200
Speaker 4: all alone has grown more than the average mortgage has

739
00:35:04,239 --> 00:35:06,000
Speaker 4: over that fifteen year period is telling.

740
00:35:06,160 --> 00:35:06,520
Speaker 2: Okay.

741
00:35:07,000 --> 00:35:09,879
Speaker 4: Secondly, obviously there is this double whammy. So not only

742
00:35:09,920 --> 00:35:12,279
Speaker 4: is the car more expensive, but then more recently interest

743
00:35:12,360 --> 00:35:14,640
Speaker 4: rates have been higher, right, and so you know then

744
00:35:14,719 --> 00:35:17,040
Speaker 4: I'm gonna have to pay more, not just for the

745
00:35:17,080 --> 00:35:19,520
Speaker 4: principle but also the interest. But I think one of

746
00:35:19,520 --> 00:35:23,960
Speaker 4: the things that has caught many consumers off guard is Okay,

747
00:35:23,960 --> 00:35:26,759
Speaker 4: so they're in the dealership, They're being shown some numbers.

748
00:35:27,120 --> 00:35:29,239
Speaker 4: Some people get it and they go like, okay, yes,

749
00:35:29,320 --> 00:35:30,919
Speaker 4: we can still do that, we can make it work.

750
00:35:30,960 --> 00:35:33,200
Speaker 4: We can just about max and stretch. Because also I

751
00:35:33,200 --> 00:35:35,799
Speaker 4: think people are trying to buy either the same that

752
00:35:35,840 --> 00:35:38,200
Speaker 4: they had before or slightly better. Right, not many people

753
00:35:38,400 --> 00:35:41,719
Speaker 4: like downgrading, okay, and so they think, well, it's the

754
00:35:41,760 --> 00:35:43,040
Speaker 4: same car, and yes, this is a little bit more,

755
00:35:43,080 --> 00:35:44,799
Speaker 4: but we can make and meet right by looking at

756
00:35:44,800 --> 00:35:47,120
Speaker 4: these numbers. But what they often forget about is that

757
00:35:47,200 --> 00:35:51,239
Speaker 4: insurance has gone up significantly, as have just the cost

758
00:35:51,239 --> 00:35:55,120
Speaker 4: of ownership. Repair costs have also gone up substantially, and

759
00:35:55,200 --> 00:35:58,080
Speaker 4: so when all those things then hit them, they can

760
00:35:58,120 --> 00:35:59,919
Speaker 4: be in a situation where we just can't make it work.

761
00:36:00,520 --> 00:36:03,520
Speaker 4: And so that's not good. And the key piece of

762
00:36:03,520 --> 00:36:05,600
Speaker 4: this too is, look, the good thing is, of all

763
00:36:05,640 --> 00:36:08,680
Speaker 4: the loan products, mortgages are performing pretty well. Okay, they

764
00:36:08,680 --> 00:36:11,400
Speaker 4: are increasing, but they're still much much lower than they

765
00:36:11,400 --> 00:36:13,319
Speaker 4: were back in twenty ten, or obviously much lower than

766
00:36:13,320 --> 00:36:15,960
Speaker 4: twenty eight twenty nine. So but if you default on

767
00:36:16,000 --> 00:36:20,520
Speaker 4: a mortgage, it takes some time before anything really happens. Right,

768
00:36:21,000 --> 00:36:23,160
Speaker 4: with an order loan, they will come and they will

769
00:36:23,239 --> 00:36:25,640
Speaker 4: take that car away from you. And given that, you

770
00:36:25,680 --> 00:36:28,520
Speaker 4: know so many people rely on that car to go

771
00:36:28,600 --> 00:36:31,000
Speaker 4: to their job, to make their income, to do other

772
00:36:31,040 --> 00:36:33,600
Speaker 4: tasks are important, like their shopping or taking their children

773
00:36:33,640 --> 00:36:35,759
Speaker 4: to the school or other things that they need to do.

774
00:36:36,280 --> 00:36:40,520
Speaker 4: People don't willingly just default on these auto loans, and

775
00:36:40,600 --> 00:36:43,360
Speaker 4: so I think it is a sign that correlates with

776
00:36:43,400 --> 00:36:46,320
Speaker 4: the fact that more households are struggling.

777
00:36:45,920 --> 00:36:46,560
Speaker 2: To make in meet.

778
00:36:47,480 --> 00:36:50,360
Speaker 3: How much insight do you have into leverage? And the

779
00:36:50,440 --> 00:36:53,560
Speaker 3: reason I ask is because we've seen an explosion in

780
00:36:53,600 --> 00:36:57,279
Speaker 3: buy now, pay later programs. Virtually every site you go

781
00:36:57,360 --> 00:37:00,960
Speaker 3: to now has three different options for getting alone for

782
00:37:01,040 --> 00:37:04,200
Speaker 3: a small amount, and only a few of those My

783
00:37:04,360 --> 00:37:09,040
Speaker 3: understanding is are actually reporting to the credit bureaus. So,

784
00:37:09,520 --> 00:37:11,680
Speaker 3: and I can also imagine if you're a lower income

785
00:37:11,719 --> 00:37:14,799
Speaker 3: person who is perhaps more pressured, you're probably going to

786
00:37:14,840 --> 00:37:17,640
Speaker 3: turn to a family member and say something like, hey,

787
00:37:17,640 --> 00:37:19,840
Speaker 3: can you loan me, I don't know, five hundred bucks

788
00:37:19,840 --> 00:37:21,239
Speaker 3: to make it to the end of the month. And

789
00:37:21,280 --> 00:37:23,920
Speaker 3: there's no way that credit scoring bureaus are going to

790
00:37:23,920 --> 00:37:26,440
Speaker 3: have insight into things like that informal loans.

791
00:37:27,080 --> 00:37:29,000
Speaker 4: The credit data that comes from the credit bureaus is

792
00:37:29,040 --> 00:37:32,360
Speaker 4: still incredibly predictive and useful, but it doesn't capture everything.

793
00:37:32,760 --> 00:37:35,200
Speaker 4: Look Alex was on, I think recently a firm has

794
00:37:35,280 --> 00:37:37,640
Speaker 4: done provision data to the credit file, which is great,

795
00:37:37,680 --> 00:37:41,279
Speaker 4: but not all of them are there, and so you know,

796
00:37:41,320 --> 00:37:43,400
Speaker 4: I think there is still a lot that's not visible,

797
00:37:43,440 --> 00:37:45,319
Speaker 4: and that is definitely a concern to lenders. We've been

798
00:37:45,360 --> 00:37:47,560
Speaker 4: hearing their concerns about stacking and.

799
00:37:47,480 --> 00:37:48,480
Speaker 2: Things of that nature.

800
00:37:48,840 --> 00:37:52,120
Speaker 4: Obviously, these companies haven't got to where they are without

801
00:37:52,200 --> 00:37:55,239
Speaker 4: having some understanding of risk themselves, right, So they're not

802
00:37:55,280 --> 00:37:58,240
Speaker 4: going to necessarily just let someone who's not performing alone

803
00:37:58,280 --> 00:38:01,360
Speaker 4: take out another five BNPL loan, right, And the ability

804
00:38:01,360 --> 00:38:04,360
Speaker 4: of a consumer to go to all different BNPL providers

805
00:38:04,360 --> 00:38:07,120
Speaker 4: and use that there's a level of effort and sophistication

806
00:38:07,160 --> 00:38:09,200
Speaker 4: required to do that that certainly, I'm sure they're going

807
00:38:09,239 --> 00:38:10,880
Speaker 4: to be some, but I don't know how many people

808
00:38:10,920 --> 00:38:15,520
Speaker 4: fall into that category. Nevertheless, it is a concern that

809
00:38:15,960 --> 00:38:18,439
Speaker 4: more is invisible, and that's why I think being able

810
00:38:18,480 --> 00:38:20,279
Speaker 4: to pull in more than credit file data, such as

811
00:38:20,280 --> 00:38:23,160
Speaker 4: cashlow data, becomes really important. And so what we're seeing

812
00:38:23,239 --> 00:38:25,759
Speaker 4: is that they're more and more that are looking to

813
00:38:25,840 --> 00:38:28,840
Speaker 4: incorporate cashlow into the process because then they can have

814
00:38:28,880 --> 00:38:31,440
Speaker 4: a better understanding of the ins and outs, right, is

815
00:38:31,480 --> 00:38:35,040
Speaker 4: there checking balance going up or declining over time? Is

816
00:38:35,040 --> 00:38:36,920
Speaker 4: there does their income look like it's stable, does it

817
00:38:36,960 --> 00:38:40,040
Speaker 4: look like it's more sporadic? And so we have started

818
00:38:40,239 --> 00:38:43,440
Speaker 4: building now credit scores that also incorporate that type of data,

819
00:38:43,680 --> 00:38:45,239
Speaker 4: and that I think is going to come even more

820
00:38:45,280 --> 00:38:48,080
Speaker 4: important as we go forward because there are going to

821
00:38:48,080 --> 00:38:50,200
Speaker 4: be more and more ways that consumers can borrow. So

822
00:38:50,760 --> 00:38:52,960
Speaker 4: that's probably the better way to get that holistic view.

823
00:38:53,280 --> 00:38:56,319
Speaker 1: Sure quickly, Auto delinguities, Are they at their highest level

824
00:38:56,360 --> 00:38:57,440
Speaker 1: ever right now? Or closed?

825
00:38:57,840 --> 00:38:58,040
Speaker 3: Yep?

826
00:38:58,200 --> 00:39:00,879
Speaker 4: I mean they are, yes, and they're continuing to go up.

827
00:39:01,160 --> 00:39:04,240
Speaker 4: So what we have seen though is that credit cards,

828
00:39:04,280 --> 00:39:06,600
Speaker 4: for instance, they went up a lot, so they went

829
00:39:06,719 --> 00:39:09,120
Speaker 4: up in good yeah delinquencies. Credit card delinquencies went up

830
00:39:09,280 --> 00:39:12,160
Speaker 4: a lot in twenty three twenty four, but they've started

831
00:39:12,160 --> 00:39:14,160
Speaker 4: coming down and we started seeing personal loans coming down

832
00:39:14,160 --> 00:39:15,680
Speaker 4: as well, so it's a very nuanced picture. So we've

833
00:39:15,719 --> 00:39:19,080
Speaker 4: seen the unsecuritized delinquencies that have started coming down this

834
00:39:19,160 --> 00:39:21,320
Speaker 4: year year of a year, but we're still seeing mortgage

835
00:39:21,320 --> 00:39:23,400
Speaker 4: and order loans continue to increase.

836
00:39:23,239 --> 00:39:25,680
Speaker 3: Which is pretty top seat turvy when you kind of

837
00:39:25,680 --> 00:39:28,200
Speaker 3: think about it. But anyway, so the other thing happening

838
00:39:28,239 --> 00:39:32,080
Speaker 3: now is insurance rates going up because of the I

839
00:39:32,080 --> 00:39:37,480
Speaker 3: guess non extension of previous subsidies. And one thing you're

840
00:39:37,520 --> 00:39:41,200
Speaker 3: seeing all over social media is people posting their new

841
00:39:41,280 --> 00:39:43,880
Speaker 3: insurance rates for twenty twenty six, and I've seen some

842
00:39:43,960 --> 00:39:46,800
Speaker 3: crazy ones, you know, something going from six hundred dollars

843
00:39:46,840 --> 00:39:50,920
Speaker 3: to like eighteen hundred dollars a month. How much pressure

844
00:39:50,920 --> 00:39:53,640
Speaker 3: would you expect something like that to exert on the

845
00:39:53,640 --> 00:39:54,919
Speaker 3: consumer for next year?

846
00:39:55,520 --> 00:39:57,200
Speaker 4: You know, for many it's going to be the straw

847
00:39:57,280 --> 00:39:58,680
Speaker 4: that could break the camels back.

848
00:39:59,200 --> 00:39:59,399
Speaker 3: Right.

849
00:39:59,480 --> 00:40:02,480
Speaker 4: It's just particularly if it's just like car insurance can

850
00:40:02,520 --> 00:40:06,080
Speaker 4: be a very significant outlay for many households. But you

851
00:40:06,080 --> 00:40:09,400
Speaker 4: know there's other insurance for homeowners, homeown insurance that's going up,

852
00:40:09,440 --> 00:40:12,720
Speaker 4: and particularly if they're in areas like California or Florida

853
00:40:12,840 --> 00:40:16,520
Speaker 4: where you know, natural disasters have led to an increase

854
00:40:16,520 --> 00:40:21,160
Speaker 4: in premiums above the national average. And so again I

855
00:40:21,200 --> 00:40:23,399
Speaker 4: don't see that there's any one thing that is going

856
00:40:23,440 --> 00:40:26,840
Speaker 4: to cause a house to fall down. But at the

857
00:40:26,880 --> 00:40:30,040
Speaker 4: same time, just there's more and more households where that

858
00:40:30,280 --> 00:40:34,000
Speaker 4: one unexpected increase puts them in a situation then makes

859
00:40:34,000 --> 00:40:36,600
Speaker 4: it impossible for them to make their payments that month

860
00:40:36,719 --> 00:40:37,960
Speaker 4: or for a number of months.

861
00:40:38,200 --> 00:40:41,120
Speaker 1: Talk to us about the resumption of student loan payments

862
00:40:41,160 --> 00:40:43,520
Speaker 1: after I mean, you mentioned the importance of doing is

863
00:40:43,560 --> 00:40:45,719
Speaker 1: not only the stimulus but all the sort of forbearance

864
00:40:45,760 --> 00:40:48,080
Speaker 1: and so all this stuff nice. The one thing that

865
00:40:48,239 --> 00:40:52,320
Speaker 1: just kept getting pushed forever was the resumption of student loans.

866
00:40:52,560 --> 00:40:54,960
Speaker 1: How much when those numbers turned back on or were

867
00:40:55,000 --> 00:40:58,160
Speaker 1: those payments turned back on? What kind of impact did

868
00:40:58,200 --> 00:41:00,839
Speaker 1: that have and what are we seeing with student loans delinquencies.

869
00:41:01,000 --> 00:41:04,120
Speaker 4: Yeah, it had a very big impact. And so you know,

870
00:41:04,120 --> 00:41:08,719
Speaker 4: if we look back before COVID, the average student condlinquency

871
00:41:08,800 --> 00:41:10,799
Speaker 4: rate was around that ten percent. It was sort of

872
00:41:10,960 --> 00:41:15,000
Speaker 4: wavering around between nine eleven percent in that sort of range.

873
00:41:15,239 --> 00:41:18,879
Speaker 4: And then obviously in this five year period of forbearances

874
00:41:18,880 --> 00:41:21,040
Speaker 4: and no reporting, because you had a period of a

875
00:41:21,120 --> 00:41:24,319
Speaker 4: year through twenty four where they were starting to need

876
00:41:24,360 --> 00:41:26,640
Speaker 4: to make payments, but they just weren't being reported. So

877
00:41:26,760 --> 00:41:29,759
Speaker 4: that's basically a long period of time where people just

878
00:41:29,760 --> 00:41:31,719
Speaker 4: got used to not having to make that payment. And

879
00:41:31,800 --> 00:41:35,359
Speaker 4: also a very substantial part of student luanbars who never

880
00:41:35,360 --> 00:41:37,440
Speaker 4: had ever made a payment because they, you know, they

881
00:41:37,480 --> 00:41:40,880
Speaker 4: finished their studies in a period when there was forbearance.

882
00:41:41,440 --> 00:41:44,640
Speaker 4: And so what then happened was they started trickling back

883
00:41:44,680 --> 00:41:48,160
Speaker 4: onto the credit file sort of mid February of this year,

884
00:41:48,520 --> 00:41:50,400
Speaker 4: and then I think you got the first batch really

885
00:41:50,520 --> 00:41:52,800
Speaker 4: kind of come in by May, and at that point

886
00:41:52,840 --> 00:41:55,719
Speaker 4: you saw the delinquency rates on the student loans that

887
00:41:55,840 --> 00:41:59,080
Speaker 4: were not in deferment was over twenty percent, so they

888
00:41:59,080 --> 00:42:01,520
Speaker 4: were over double what the historical norm.

889
00:42:01,440 --> 00:42:03,239
Speaker 2: Was since then.

890
00:42:03,400 --> 00:42:05,360
Speaker 4: Is that the highest level over It is the highest

891
00:42:05,440 --> 00:42:07,319
Speaker 4: level that we've seen going back a long long time.

892
00:42:07,440 --> 00:42:09,319
Speaker 4: So I remember there's been a lot of changes when

893
00:42:09,360 --> 00:42:11,680
Speaker 4: it was not federally mandated and privately owned, so I

894
00:42:11,719 --> 00:42:13,960
Speaker 4: don't have visibility going back as that far, but at

895
00:42:14,040 --> 00:42:17,360
Speaker 4: least in recent history, is absolutely the highest by a

896
00:42:17,520 --> 00:42:21,439
Speaker 4: very substantial amount. And that's not too surprising. But one

897
00:42:21,440 --> 00:42:23,520
Speaker 4: of the things that we've seen is that we expected

898
00:42:23,520 --> 00:42:25,600
Speaker 4: that there would be some people who go into delinquency

899
00:42:25,920 --> 00:42:28,120
Speaker 4: not because they intended to write, and so there were

900
00:42:28,560 --> 00:42:31,200
Speaker 4: some people who they moved and they didn't get their addresses,

901
00:42:31,280 --> 00:42:33,160
Speaker 4: or a lot of people that were confused because there's

902
00:42:33,160 --> 00:42:35,160
Speaker 4: so many mixed messages like we're going to be forgiven,

903
00:42:35,200 --> 00:42:37,320
Speaker 4: but that we're not and we're on a certain program,

904
00:42:37,360 --> 00:42:40,560
Speaker 4: but now that program doesn't exist anymore. So some people

905
00:42:40,840 --> 00:42:43,279
Speaker 4: have been able to then address that, and so it's

906
00:42:43,280 --> 00:42:45,000
Speaker 4: come down to this sort of seventeen seventeen and a

907
00:42:45,040 --> 00:42:48,760
Speaker 4: half percent, which is a good sign, and so that's improving.

908
00:42:48,800 --> 00:42:50,880
Speaker 4: But there still are people who are on programs that

909
00:42:50,920 --> 00:42:54,760
Speaker 4: have been killed, like the Safe program, and so they

910
00:42:55,000 --> 00:42:57,239
Speaker 4: are going to next year have to either get onto

911
00:42:57,280 --> 00:43:01,160
Speaker 4: another for Baron's program or start making payments, and so

912
00:43:01,600 --> 00:43:04,239
Speaker 4: maybe we haven't seen the full effect. Then basically of

913
00:43:04,280 --> 00:43:06,839
Speaker 4: the resumption those student loans, we've seen the biggest batch

914
00:43:06,880 --> 00:43:09,840
Speaker 4: come through, but there still are some more cohorts of

915
00:43:09,960 --> 00:43:14,120
Speaker 4: consumer borrowers that will either have their existing program expire

916
00:43:14,560 --> 00:43:17,560
Speaker 4: or that aren't being reported yet because the servicers are

917
00:43:17,560 --> 00:43:20,160
Speaker 4: trying to figure out exactly what's going on before they

918
00:43:20,160 --> 00:43:21,600
Speaker 4: report it to the credit bureau. So there still is

919
00:43:21,600 --> 00:43:23,920
Speaker 4: a little bit of lack of visibility there from on

920
00:43:23,920 --> 00:43:24,959
Speaker 4: the credit servicers side.

921
00:43:25,480 --> 00:43:27,480
Speaker 1: Kurk, thank you so much for coming on outlast.

922
00:43:27,520 --> 00:43:43,120
Speaker 2: There was great. Thank you. It's been a pleasure.

923
00:43:43,280 --> 00:43:45,680
Speaker 1: It always comes back to insurance, doesn't it. That's always

924
00:43:45,719 --> 00:43:48,160
Speaker 1: like the little fly in the ointment is you know,

925
00:43:48,200 --> 00:43:50,200
Speaker 1: you buy this car and just like, Okay, here's the

926
00:43:50,239 --> 00:43:52,840
Speaker 1: car and here's the interest payment. Oh, I think we

927
00:43:52,880 --> 00:43:55,120
Speaker 1: can make the math work. You can't control what that

928
00:43:55,239 --> 00:43:57,480
Speaker 1: insurance payment is going to be. You have no idea

929
00:43:57,520 --> 00:43:58,080
Speaker 1: what it's going to be.

930
00:43:58,200 --> 00:44:01,840
Speaker 3: This is my theory. Insure run the way, run the world,

931
00:44:01,880 --> 00:44:02,480
Speaker 3: they really do.

932
00:44:02,800 --> 00:44:03,000
Speaker 2: You know.

933
00:44:03,239 --> 00:44:05,160
Speaker 3: The other thing I was thinking, and we've written about

934
00:44:05,160 --> 00:44:08,239
Speaker 3: this in the newsletter, but one of the difficulties of

935
00:44:08,280 --> 00:44:12,120
Speaker 3: our current economic moment is there is so much division

936
00:44:12,400 --> 00:44:16,320
Speaker 3: and difference built into the aggregate. If you're just looking

937
00:44:16,320 --> 00:44:18,879
Speaker 3: at a single number a total, like if you've looked

938
00:44:18,920 --> 00:44:21,879
Speaker 3: at the average FIICO score of an American, it tells

939
00:44:21,920 --> 00:44:26,719
Speaker 3: you almost nothing now, because the individuals are so disparate.

940
00:44:27,080 --> 00:44:30,839
Speaker 1: Yeah, and it really does come down to, you know, wealth, right,

941
00:44:31,120 --> 00:44:33,600
Speaker 1: Wealth is just such an important factor in the economy.

942
00:44:33,600 --> 00:44:37,000
Speaker 1: We always talk about income and income inequality, and of

943
00:44:37,040 --> 00:44:40,360
Speaker 1: course that's a real phenomenon, but wealth is such an

944
00:44:40,400 --> 00:44:43,880
Speaker 1: important predictor driver of anything. And it also goes to

945
00:44:43,880 --> 00:44:47,800
Speaker 1: show like how important like financial markets and asset prices

946
00:44:47,840 --> 00:44:49,680
Speaker 1: are to the real economy. And it gives me once

947
00:44:49,719 --> 00:44:52,640
Speaker 1: again an opportunity to say the stock market is the

948
00:44:52,680 --> 00:44:56,320
Speaker 1: economy because we live in such a wealth driven economy.

949
00:44:56,560 --> 00:44:59,399
Speaker 3: You know, someone once wrote into me. I wrote something

950
00:44:59,480 --> 00:45:03,520
Speaker 3: aboutures on lower income people, and someone wrote into me saying, well,

951
00:45:03,520 --> 00:45:06,440
Speaker 3: why don't the lower income people own more assets. If

952
00:45:06,480 --> 00:45:09,560
Speaker 3: they did, they'd be in a better position. Have you

953
00:45:09,640 --> 00:45:10,719
Speaker 3: tried not being poor?

954
00:45:10,840 --> 00:45:13,040
Speaker 1: Why haven't you tried just being rich? Why haven't you

955
00:45:13,080 --> 00:45:15,600
Speaker 1: tried buying in video twenty years ago? Why haven't you

956
00:45:15,719 --> 00:45:18,560
Speaker 1: tried buying a house in California in two thousand and

957
00:45:18,640 --> 00:45:22,440
Speaker 1: nine after the bus? It's that simple. Stop being poor? Seriously.

958
00:45:22,440 --> 00:45:24,920
Speaker 3: The other thing I was thinking just on auto delinquencies.

959
00:45:25,040 --> 00:45:28,000
Speaker 3: I also think the trade down story is a big

960
00:45:28,040 --> 00:45:31,399
Speaker 3: piece here, which is I mean, a car from ten

961
00:45:31,480 --> 00:45:34,440
Speaker 3: years ago now is pretty decent. And I say that

962
00:45:34,480 --> 00:45:37,719
Speaker 3: as someone who owns. I think it's a Toyota rav

963
00:45:37,920 --> 00:45:40,719
Speaker 3: from like twenty eleven or something like that. Like it's

964
00:45:40,760 --> 00:45:43,920
Speaker 3: pretty dependable, and I don't really feel the need to

965
00:45:43,960 --> 00:45:47,040
Speaker 3: get like a fancy new car. And I imagine if

966
00:45:47,040 --> 00:45:50,319
Speaker 3: you're under pressure on your car loan, it's probably like

967
00:45:50,440 --> 00:45:54,200
Speaker 3: not that difficult necessarily to find an older car that

968
00:45:54,400 --> 00:45:55,759
Speaker 3: is somewhat reliable.

969
00:45:56,280 --> 00:45:56,960
Speaker 2: I don't know, you know.

970
00:45:57,000 --> 00:45:59,360
Speaker 1: The one thing though, So I have a car that

971
00:45:59,440 --> 00:46:04,280
Speaker 1: I bought twenty fifteen. It runs perfectly well. I would

972
00:46:04,280 --> 00:46:08,040
Speaker 1: not be surprised if it continued like no issues at all.

973
00:46:08,200 --> 00:46:09,960
Speaker 1: It doesn't have CarPlay integration.

974
00:46:10,160 --> 00:46:10,480
Speaker 3: Oh, yeah.

975
00:46:10,520 --> 00:46:14,040
Speaker 1: The one difference between older cars and newer cars is

976
00:46:14,080 --> 00:46:17,319
Speaker 1: that it's very nice, like having that no that that

977
00:46:17,440 --> 00:46:19,080
Speaker 1: interface where you have like a.

978
00:46:19,080 --> 00:46:20,960
Speaker 3: Nice little speaker or something.

979
00:46:21,080 --> 00:46:23,399
Speaker 1: Yeah, but what it doesn't have is that like really

980
00:46:23,520 --> 00:46:26,360
Speaker 1: nice interface with the map, like and I know that's minor,

981
00:46:26,440 --> 00:46:27,360
Speaker 1: but it's like, kind.

982
00:46:27,200 --> 00:46:28,200
Speaker 3: Of do you have a map?

983
00:46:28,680 --> 00:46:31,960
Speaker 1: It's a super U for those curious, and it's like,

984
00:46:32,239 --> 00:46:34,959
Speaker 1: you know, it's like they're in house. It's a crappy map.

985
00:46:35,239 --> 00:46:37,879
Speaker 1: It's not the really nice that Google Maps where it's

986
00:46:37,880 --> 00:46:40,000
Speaker 1: like you're really clear, and it doesn't have turn by

987
00:46:40,000 --> 00:46:42,640
Speaker 1: turn navigation. I know this sounds like kind of minor,

988
00:46:42,960 --> 00:46:45,520
Speaker 1: but it is very annoying. And like when I am

989
00:46:45,560 --> 00:46:48,120
Speaker 1: in a car that has like a modern off a

990
00:46:48,160 --> 00:46:50,759
Speaker 1: little tangent here, but I am in a car that

991
00:46:50,800 --> 00:46:54,000
Speaker 1: has like a really nice interface with a nice Google

992
00:46:54,040 --> 00:46:57,680
Speaker 1: Maps or Apple Maps and the Spotify integration. It's very nice.

993
00:46:57,719 --> 00:46:59,719
Speaker 1: And apparently we've taken it to the dealer they just

994
00:46:59,800 --> 00:47:03,440
Speaker 1: can it is un really Yeah, it's unupgradeable. There's no

995
00:47:03,600 --> 00:47:06,960
Speaker 1: for some reason, there's no way to put in a

996
00:47:07,040 --> 00:47:08,120
Speaker 1: new dash.

997
00:47:08,360 --> 00:47:11,680
Speaker 3: Oh sorry, I thought you meant unupgradeable in terms of trading,

998
00:47:11,719 --> 00:47:12,160
Speaker 3: it in.

999
00:47:12,120 --> 00:47:15,080
Speaker 1: For No, it's unbradable. It's like we cannot like this.

1000
00:47:15,320 --> 00:47:17,239
Speaker 1: We could never install car play or whatever.

1001
00:47:17,560 --> 00:47:20,640
Speaker 3: Yeah, car, you know, my husband and I rented one

1002
00:47:20,680 --> 00:47:24,320
Speaker 3: of those like big fancy trucks, pickup trucks, and I

1003
00:47:24,640 --> 00:47:28,560
Speaker 3: was amazed by the amenities that are actually including like

1004
00:47:28,640 --> 00:47:31,520
Speaker 3: the heated seats, I personalized heat what.

1005
00:47:31,960 --> 00:47:34,400
Speaker 1: Yeah, that's fancy. You know, it is very nice in

1006
00:47:34,480 --> 00:47:38,799
Speaker 1: the wintery heated seeds. We just talked about cars for yeah,

1007
00:47:38,880 --> 00:47:39,680
Speaker 1: maybe longer.

1008
00:47:39,880 --> 00:47:41,880
Speaker 3: But the thing is like, even for a car like that,

1009
00:47:42,080 --> 00:47:44,880
Speaker 3: I just I cannot imagine spending like one hundred thousand

1010
00:47:44,920 --> 00:47:49,160
Speaker 3: dollars plus whatever the interest rate actually is on something

1011
00:47:49,239 --> 00:47:49,400
Speaker 3: like that.

1012
00:47:49,680 --> 00:47:53,480
Speaker 1: I remember that meme from like twenty ten. It's like

1013
00:47:53,719 --> 00:47:55,759
Speaker 1: no one will ever do that was a big thing.

1014
00:47:55,880 --> 00:47:58,920
Speaker 1: And the phone, right, really two things that people always

1015
00:47:59,040 --> 00:48:01,759
Speaker 1: find a way to make a payment for the car

1016
00:48:02,520 --> 00:48:03,160
Speaker 1: and the phone.

1017
00:48:03,200 --> 00:48:04,680
Speaker 3: I forget, right, Which is why I think you have

1018
00:48:04,800 --> 00:48:08,440
Speaker 3: to look at something structural that's shifted. And I suspect

1019
00:48:08,600 --> 00:48:12,360
Speaker 3: maybe it's the availability of you know, lots of older cars.

1020
00:48:12,440 --> 00:48:15,680
Speaker 1: But just one last point, it's I thought it was

1021
00:48:15,840 --> 00:48:19,280
Speaker 1: very interesting. Ricardo is saying, it's like there's no obvious

1022
00:48:20,040 --> 00:48:24,440
Speaker 1: catalyst for cataclysm. There's not obvious like, oh, here is something.

1023
00:48:24,640 --> 00:48:27,520
Speaker 1: We are on the verge of consumer credit collapse. But

1024
00:48:27,680 --> 00:48:31,480
Speaker 1: it is a story of just like steadily building pressure

1025
00:48:32,040 --> 00:48:34,640
Speaker 1: such that if there is some sort of spark or something,

1026
00:48:34,960 --> 00:48:37,040
Speaker 1: there is a lot of stress not to you know,

1027
00:48:37,239 --> 00:48:40,040
Speaker 1: the resumption of student loans after five years, the fact

1028
00:48:40,080 --> 00:48:42,720
Speaker 1: that the total loan price of the car has gotten

1029
00:48:42,840 --> 00:48:46,200
Speaker 1: so high relative to people's income. All of these different things,

1030
00:48:46,640 --> 00:48:49,440
Speaker 1: so you like have all these upwards dresses on prices.

1031
00:48:49,680 --> 00:48:53,200
Speaker 1: You have all of this reliance obviously on accumulated wealth,

1032
00:48:53,320 --> 00:48:56,200
Speaker 1: most notably stock market and home equity. So you have

1033
00:48:56,280 --> 00:48:58,840
Speaker 1: a lot of things come together. They're not necessarily disaster

1034
00:48:58,960 --> 00:49:02,600
Speaker 1: or anything like that, but the alignment of pressures is

1035
00:49:02,760 --> 00:49:05,000
Speaker 1: there where things could potentially get back right.

1036
00:49:05,040 --> 00:49:08,200
Speaker 3: The consumer is much more fragile than they used.

1037
00:49:08,080 --> 00:49:09,200
Speaker 1: To they might have been a few years.

1038
00:49:09,239 --> 00:49:10,920
Speaker 3: Yeah, yeah, all right, shall we leave it there.

1039
00:49:11,040 --> 00:49:11,600
Speaker 1: Let's leave it there.

1040
00:49:11,640 --> 00:49:14,360
Speaker 3: Okay. This has been another episode of the aud Loots podcast.

1041
00:49:14,520 --> 00:49:17,760
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.

1042
00:49:17,560 --> 00:49:20,440
Speaker 1: And I'm Joe Wisenthal. You can follow me at The Stalwart.

1043
00:49:20,719 --> 00:49:24,280
Speaker 1: Follow our producers Carmen Rodriguez at Kerman Arman, Dashill, Bennett

1044
00:49:24,280 --> 00:49:27,760
Speaker 1: at Dashbot, and Kilbrooks at Kilbrooks. More odd Lots content,

1045
00:49:27,840 --> 00:49:30,000
Speaker 1: go to Bloomberg dot com slash odd Lots with the

1046
00:49:30,080 --> 00:49:32,600
Speaker 1: daily newsletter and all of our episodes, and you can

1047
00:49:32,680 --> 00:49:34,680
Speaker 1: chat about all of these topics twenty four to seven

1048
00:49:34,760 --> 00:49:37,920
Speaker 1: in our discord. Discord do Gg slash.

1049
00:49:37,680 --> 00:49:40,239
Speaker 3: Odd Lots and if you enjoy odd Lots, if you

1050
00:49:40,280 --> 00:49:43,040
Speaker 3: want me and Joe to just review cars in the future,

1051
00:49:43,120 --> 00:49:45,800
Speaker 3: then please leave us a positive review on your favorite

1052
00:49:45,800 --> 00:49:49,200
Speaker 3: podcast platform. And remember, if you are a Bloomberg subscriber,

1053
00:49:49,320 --> 00:49:52,359
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1054
00:49:52,760 --> 00:49:55,000
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1055
00:49:55,080 --> 00:49:58,360
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1056
00:49:58,520 --> 00:50:20,400
Speaker 3: listening it, Bend