Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to
Speaker 1: another episode of The Odd Lads podcast. I'm Joe Wisenthal
Speaker 1: and I'm Tracy Alloway. Crazy. I feel like I just
Speaker 1: do not have any feel right now on like the
Speaker 1: state of the consumer.
Speaker 2: Really.
Speaker 1: I mean, you hear k shaped economy, labor markets slowing down,
Speaker 1: then it's like lowest layoffs in years. You go outside,
Speaker 1: everything looks booming. Like, I just have no feel right.
Speaker 1: I know, consumer sentiment is terrible, but consumer sentiment is
Speaker 1: terrible for years, and people keep shopping. I have no
Speaker 1: sense of it right.
Speaker 3: Well, consumer sentiment actually came in higher than expected most recently,
Speaker 3: big surprise, but I was gonna say, are you not
Speaker 3: out shopping for Christmas present?
Speaker 1: It's insane.
Speaker 3: Yeah, there's a lot of people buying a.
Speaker 1: Lot of stuff, buying a lot of stuff.
Speaker 3: But I think this gets to the ke shaped economy point,
Speaker 3: which is, if you have a cohort of wealthy people
Speaker 3: who are buying more, it more than offsets the lower
Speaker 3: income people who are buying less at lower prices. So
Speaker 3: it's really hard to tell.
Speaker 1: It's really hard to tell. One thing that definitely feels
Speaker 1: different if you look at aggregate measures of household balance
Speaker 1: sheets like this is something that is very different than
Speaker 1: sort of like pre grade financial crisis. The general view
Speaker 1: is that the American consumer or the American household has
Speaker 1: a very big cushion. There is a lot of home
Speaker 1: equity built up. There is not a thin layer. Obviously,
Speaker 1: anyone with money and any sort of investment account has
Speaker 1: done phenomenally well. We're according to this December twelfth, yesterday,
Speaker 1: I think the s P five hundred hit yet a
Speaker 1: new all time highest. If you have any sort of
Speaker 1: home equity build up, if you have any sort of investments,
Speaker 1: you are doing very well. On the other hand, of course,
Speaker 1: people are stretched from years of inflation. We know that
Speaker 1: hiring has slowed down.
Speaker 2: We know that.
Speaker 1: You know, we see these headlines delinquencies for cars have
Speaker 1: like shot up. But I've been seeing these headlines for years.
Speaker 1: I don't totally know what they mean or how apples
Speaker 1: to apples they are with the past. I just don't know.
Speaker 1: I just I'm very confused.
Speaker 3: Yeah, you know what's really interesting to me just from
Speaker 3: a financial perspective. Yeah, if you look at some of
Speaker 3: the bonds that were actually built on consumer loans, the
Speaker 3: weakest ones are now from the like twenty twenty to
Speaker 3: twenty twenty two period.
Speaker 1: Oh see, this is another interesting element of measures like delinquencies,
Speaker 1: and why I sort of wonder like how comparable they
Speaker 1: are because okay, partly a delinquency measure is a snapshot
Speaker 1: of a moment in time, right, a snapshot of health,
Speaker 1: but it also inherently reflects something in the past, because
Speaker 1: it reflects, you know, what we're lending standards at the time,
Speaker 1: right exactly, so you know, and now it's a period
Speaker 1: of interest rate booming itself. Yea, give the money to
Speaker 1: anyone anyway. We need to get a better picture of
Speaker 1: exactly what's going on. How stressed is the consumer. How
Speaker 1: much do these delinquencies just reflect the profligacy of lenders
Speaker 1: during the boon times when rates were nothing, et cetera.
Speaker 1: And yes, we need to figure this out, especially we're
Speaker 1: in the middle of shopping season and all that stuff.
Speaker 1: So I'm very excited to say, we really do have
Speaker 1: the perfect guests. We're going to be speaking with Recard Bondebo.
Speaker 1: He is the executive vice president, chief strategy officer, chief
Speaker 1: economist Advantage Score, a credit scoring company. Recurd Thank you
Speaker 1: so much for coming on the podcast.
Speaker 4: Thank you for Havmius Snana.
Speaker 1: What is Vantage Score a US credit scoring company? What
Speaker 1: do you do there?
Speaker 4: So, we're the largest credit scoring company in the United
Speaker 4: States and we have founded almost twenty years ago by
Speaker 4: the Free Credit bureas, TransUnion, ECOFAX and Experience, and we
Speaker 4: were sort of created with a very specific mission in
Speaker 4: mind to drive greater competition and credit scoring. Prior to us,
Speaker 4: there really wasn't a lot of choice in space. We're
Speaker 4: also there to drive more innovation and create the most
Speaker 4: predictive scores, which was a big ass from the banks
Speaker 4: at the time, and also to be able to expand
Speaker 4: access to millions to enable everyone who really is credit
Speaker 4: worthy to be able to get access to credit products.
Speaker 3: You mentioned the banks just then, can you expound a
Speaker 3: little bit more on your customer base.
Speaker 4: Yes, so we used Obviously, the primary use case that
Speaker 4: most people think about when it comes to credit scores
Speaker 4: is for lending. Right when you know you're applying for
Speaker 4: a loan and they want to evaluate whether or not
Speaker 4: you're going to be able to perform on that loan,
Speaker 4: they will often pull your credit score as part of
Speaker 4: that process. But It's used in many other stages as well. So,
Speaker 4: for instance, many people who are applying to rent in
Speaker 4: a new apartment building may also get asked for it.
Speaker 4: When you are trying to get a utility.
Speaker 3: Bill in New York, you get asked for it.
Speaker 4: Yes, you certainly do. And utility bills, telephones, anything that
Speaker 4: involves a long term commitment on payments generally. Now you'll
Speaker 4: often get asked to provide your credit score.
Speaker 1: So just explain for the way you were started by
Speaker 1: whom twenty years ago, we.
Speaker 4: Were a joint venture and ECHOFACTX and TransUnion, the three
Speaker 4: national credit reporting agencies.
Speaker 1: So what is the difference between these major companies that
Speaker 1: we've all heard of that provide a credit score, et cetera,
Speaker 1: that they such founded? You like, what do you do
Speaker 1: differently than them?
Speaker 4: Well, so what they do is they're the ones who
Speaker 4: collect all this data from lenders and others on your
Speaker 4: credit performance. Right, So they call credit buros they collect that.
Speaker 4: They're highly regulated. But then most lenders can't just make
Speaker 4: sense of all of that data on its own. They
Speaker 4: need some guidance to have to translate that into a
Speaker 4: what does that mean? Right?
Speaker 2: Okay?
Speaker 4: And so that's where a scoring algorithm comes into effect. Right,
Speaker 4: And so the scoring algorithm helps to take in all
Speaker 4: these hundreds of different factors about you to try to
Speaker 4: then determine what does that mean about your propensity to pay?
Speaker 3: Okay, you mentioned predictive analysis as well, What exactly is
Speaker 3: that and what's that based on?
Speaker 4: Well, so when credit scores are created, right, the aim
Speaker 4: the goal is to try to evaluate what is the
Speaker 4: likelihood that somebody's going to default on a payment over
Speaker 4: the next time twenty four months. So when you see
Speaker 4: that score, the score is actually a translation of a
Speaker 4: probability right or on odds right to evaluate what is
Speaker 4: that risk?
Speaker 3: And how did we end up with the system of
Speaker 3: FICO scores in the US, because it has like an
Speaker 3: interesting history.
Speaker 4: Well, back in the day fair Isaac they created the
Speaker 4: first sort of known credit score. They were the first
Speaker 4: ones to realize that there was a.
Speaker 1: Looking at fi and fair Isaac is that yes?
Speaker 4: Okay, yes, And so let's go back a bit, right,
Speaker 4: So in the old days, lending was not necessarily the
Speaker 4: most fair system that there was, right, or want to
Speaker 4: call up your previous employer, They may call your landlord.
Speaker 4: They may just ask around and if they don't know
Speaker 4: anything about you. You know, there was a lot of
Speaker 4: judgment involved in.
Speaker 3: Lending, a lot of racial discrimination.
Speaker 4: Well that certainly was built into that system, right, And
Speaker 4: so then there was a law created, the Fair Credit
Speaker 4: Reporting Act, that said, like, you can't do that, you
Speaker 4: need a better system that is fair and that is
Speaker 4: a better quantitative ability to assess people's risk, right, And
Speaker 4: that created then this need to be able to consolidate
Speaker 4: all this quantitative information in a way that lenders could
Speaker 4: easily use it. So Faiko was the first fair ISAAC
Speaker 4: at the time was the first to create that and
Speaker 4: they did very well doing so. But you know, then
Speaker 4: there was a need for competition innovation, and there was
Speaker 4: a lot of frustration around the time of twenty years
Speaker 4: ago that there was only one game in town and
Speaker 4: it didn't score about twenty percent of the US population.
Speaker 4: It still doesn't, And then a lot of lenders were
Speaker 4: felt frustrated, like if it doesn't work for twenty percent
Speaker 4: of the population, there's a problem. We need something different.
Speaker 4: And so then buwers took the unusual step of actually
Speaker 4: coming together to create an alternative and that became Advantage School.
Speaker 3: Interesting can I as a consumer go credit score shopping.
Speaker 4: So first of all, there's different ways to use it, right,
Speaker 4: So a lender will typically choose the credit score that
Speaker 4: they're going to use for being able to underwrite alone
Speaker 4: with you, and often they'll use many more factors than
Speaker 4: just a simple credit score, right, particularly the more sophisticated ones. However,
Speaker 4: when you're trying to understand what your situation is, you
Speaker 4: can there are lots of different places you can go.
Speaker 4: You can either go the credit bureaus, you can go
Speaker 4: to the likes of credit Karma. There are many different
Speaker 4: services out there.
Speaker 3: But I can course the lender to look at a
Speaker 3: specific Yeah, look at this one over here, it's great,
Speaker 3: get a second opinion.
Speaker 4: No, I'm afraid not. That's not how it works. It's
Speaker 4: it's really you know, the lenders try to determine what
Speaker 4: is the most appropriate score for their product. And there
Speaker 4: are many many different schools out there. There's schools that
Speaker 4: in some cases built specifically for types of products like
Speaker 4: order loans, and the other schools that, like our schools,
Speaker 4: that are generic that can be used for any type
Speaker 4: of product.
Speaker 1: So you collect more data. And you mentioned that there
Speaker 1: is this wide swath of the population that wasn't being
Speaker 1: captured by the credit bureaus? What do you do additionally
Speaker 1: on top of them to expand the pool find potentially
Speaker 1: credit worthy borrowers that they had been missing before.
Speaker 4: So the thing is that the quality and the types
Speaker 4: of data that's been collected by the credit bureaus has
Speaker 4: improved significantly over time. OKA, And so when we started
Speaker 4: creating our algorithms, we're in, you know, the current version
Speaker 4: that's now being adopted for mortgages, the version four we're
Speaker 4: releasing version five this year.
Speaker 2: We actually go.
Speaker 4: And rewrite the whole thing each time so that each
Speaker 4: time we can come up with the most accurate way
Speaker 4: based on the current data is available and our current
Speaker 4: ability to understand how consumers are behaving, because that behavior
Speaker 4: changes of a time. Other companies, what they've done is
Speaker 4: they've built a model long time ago. They don't like
Speaker 4: to necessarily revealed everybody how it works, the secret source. Right,
Speaker 4: So when you're seeing there's a chief risk officer and
Speaker 4: there's a new model coming along, either you want to
Speaker 4: understand that it's going to pay very similarly to the
Speaker 4: previous one to be okay with it, or you need
Speaker 4: a lot of transparency and how it works, so you
Speaker 4: can get comfort in this new model. So I think
Speaker 4: there's the big divergence and strategy. We go back to
Speaker 4: boots and redo everything from scratch each time, but in
Speaker 4: the same time provide an awful lot of transparency and
Speaker 4: a lot of tools so that lenders can get a
Speaker 4: really good understanding of exactly how this is going to
Speaker 4: work and how it's going to behave in different situations
Speaker 4: and they can test it out right, whereas the other
Speaker 4: one is still working with many limitations that have been
Speaker 4: in place since the very first models. And because of
Speaker 4: those limitations, that's a big difference in why we've scored
Speaker 4: a lot more people. So I'll give you a very
Speaker 4: concrete example. So, for instance, one of the limitations that
Speaker 4: the others have is that if you haven't had any
Speaker 4: credit activity for the past six months, that you're not
Speaker 4: going to get a score. So you just imagine somebody
Speaker 4: that works with the military has been deployed overseas or
Speaker 4: anything else.
Speaker 2: Right.
Speaker 4: But the good thing is, starting about fifteen years ago,
Speaker 4: the Bureau started click and storing data so we could
Speaker 4: use time serious data because who'd have thought that time
Speaker 4: serious data could be useful in prediction, right, completely strange idea.
Speaker 2: Right.
Speaker 4: So with finan Score four, we started using trended data
Speaker 4: times series data, and with that, obviously we can see
Speaker 4: back twenty four months. So yes, if there's a gap
Speaker 4: in six months of history, it's important, but we're still
Speaker 4: able to see what happened before then, right, And that
Speaker 4: gets rid of tens of millions of people when you
Speaker 4: have that constraint. There are the constraints in there too,
Speaker 4: So people that are new to credit, so if they
Speaker 4: haven't had a full six months of history again, they
Speaker 4: won't get scored. Aren't any tradelines, they won't get scored, right,
Speaker 4: And so what we've been able to do is to
Speaker 4: deal with thos constraints in a different way by a
Speaker 4: using time series data, b using some other data points.
Speaker 4: So we were the first to use utility payments and rent.
Speaker 4: I mean, who'd have thought that your ability to pay
Speaker 4: your rent could somehow again be useful and trying to
Speaker 4: assess your risk, right, And so including those new different
Speaker 4: types of data, realizing that these constraints can be changed
Speaker 4: now that you have time series data. But then also
Speaker 4: guess what you know? Math has evolved as well, okay,
Speaker 4: And so you know what we realized too was that
Speaker 4: you can be really smart and use some new methods,
Speaker 4: like you know, some AI methods for instance, like clustering,
Speaker 4: to really understand, well, look, here's a group of people
Speaker 4: and they behave in a certain way, and by doing
Speaker 4: that in a better way, we can then figure out
Speaker 4: what is the best way to measure this group of
Speaker 4: people here versus this group of people here, and doing
Speaker 4: that well enables you to build much more predictive scores.
Speaker 4: And so that's an important yance too that not a
Speaker 4: lot of people always realize is that it's not one
Speaker 4: formula that's calculating everybody's score. People will get depending on
Speaker 4: what their credit file looks like and their history looks like.
Speaker 4: They'll get divided into different segments and then each segment
Speaker 4: is scored according to that and that again increases the
Speaker 4: ability to score people accurately and score more people, like
Speaker 4: in our case it's thirty three million more people they're
Speaker 4: able to score, so it's quite substantial.
Speaker 3: How did the models deal with breaks in previous consumer patterns?
Speaker 3: Because we have seen some major ones in recent years.
Speaker 3: So after the pandemic, we had a phenomenally tight labor market,
Speaker 3: and we saw a lot of wage growth for lower income,
Speaker 3: a lot of spending that was sort of unprecedented in
Speaker 3: many ways. How do models actually incorporate that sort of
Speaker 3: big shit shift in the trend.
Speaker 4: So I think that's something really important to try to understand,
Speaker 4: and it's not easily understood by many. So give me
Speaker 4: a second here, I'll try to break this down.
Speaker 1: When people say give me a second, I have to
Speaker 1: break this down. Please break it down.
Speaker 3: You have a minute.
Speaker 1: A minute.
Speaker 4: Honestly, that's kind of what I'm enjoy most about listening
Speaker 4: to your podcast. And so look, the first thing to
Speaker 4: understand is that this credit score is not an absolute
Speaker 4: measure of risk. It's a relative measure of risk. Let
Speaker 4: me break that down for you, right, So what it
Speaker 4: means is that you know a score. If somebody has
Speaker 4: scored seven to twenty in one month, and then somebody
Speaker 4: else has scored seven twenty three years later, the risk
Speaker 4: will be different. And the reason for that is very deliberate.
Speaker 4: When we are evaluating a person, right, because of the
Speaker 4: laws of the Fair Credit Reporting Act, right, we're allowed
Speaker 4: to look at the things that are about you. But
Speaker 4: there are things that are going out on at the
Speaker 4: same time in the economy that impact risk of the
Speaker 4: population as a whole. Right, So that's that's why it's
Speaker 4: so important when you're looking at things like credit scores
Speaker 4: to understand when was that score pulled right, because the
Speaker 4: score of seven twenty in twenty seventeen had a very
Speaker 4: different characteristic of a score of seven twenty in twenty
Speaker 4: twenty two. Okay, now, but the thing to bear in
Speaker 4: mind is it's an excellent relative measure of risk. So
Speaker 4: at any one given point in time, you know, somebody
Speaker 4: with a seven twenty is going to be much better
Speaker 4: performing than somebody at six thirty, And at the same time,
Speaker 4: somebody eight forty is going to be much better than
Speaker 4: both of them. Okay, And that holds consistently true, and
Speaker 4: so that's why it's very important to include when you're
Speaker 4: making lending decisions. But lenders have to be thoughtful, right,
Speaker 4: they have to. As they're making decisions about, you know,
Speaker 4: how many people they want to be able to underwrite,
Speaker 4: and how to think about risk, they need to also
Speaker 4: start thinking about these external factors as well, so that
Speaker 4: they can then set their underwriting criteria to meet the
Speaker 4: kind of level of risk that they're willing to take on.
Speaker 1: I had never thought about this, but of course that
Speaker 1: makes so much sense. So it's like I could have
Speaker 1: an excellent credit history, I could have ex employment pay
Speaker 1: on my rent, but for example, if the economy is
Speaker 1: going down the tubes, I may still yet be a
Speaker 1: risky credit because I may lose my job at some point.
Speaker 1: And so this idea that it kind of has to
Speaker 1: be relative because the underlying conditions that affect everyone through
Speaker 1: outside of our control, but they are still important from
Speaker 1: the perspective of the lender exactly.
Speaker 4: And at the same time, if you get declined for
Speaker 4: a credit card, you can't be told that the reason
Speaker 4: you're getting declined is because unemployment has hit five percent. Okay, right,
Speaker 4: that doesn't work. The laws are very specific. The reasons
Speaker 4: for why you're not getting the top score have to
Speaker 4: be explained, and they have to be based on attributes
Speaker 4: and data obviously that are specific to you.
Speaker 3: What's the most important external factor when it comes to
Speaker 3: credit scoring, Because I've heard arguments for obviously the labor market,
Speaker 3: the unemployment rate, but also wage income and therefore real
Speaker 3: disposable income. How do you weight those different factors?
Speaker 4: I mean, I think if your lender, it's going to
Speaker 4: really depend upon what types of consumers you're lending to, right,
Speaker 4: particularly now we're seeing such divergence across consumers in terms
Speaker 4: of who's doing well.
Speaker 2: And who isn't.
Speaker 4: And so, for instance, if you are a lender focused
Speaker 4: on people that are kind of below prime let's say
Speaker 4: that not completely subprime, but that near prime group, and
Speaker 4: you're focusing on ail loans, and you know you're in
Speaker 4: regions like Texas or in certain areas, then obviously understanding
Speaker 4: the economic conditions that are affecting those people, like a
Speaker 4: lot of those people would be working in certain types
Speaker 4: of industries. What is employment like in those types of industries, right?
Speaker 4: Or is it people that are in the gig economy?
Speaker 2: Right?
Speaker 4: And so it is quite nuanced and it's not necessarily
Speaker 4: one thing, and it's going to depend. Whereas on the
Speaker 4: other extreme, you know, if you're handing out black cards
Speaker 4: and your audience is incredibly affluent, then again it's less
Speaker 4: about the risk because at that point your risk of
Speaker 4: default is probably one end ten thousand. So they're just
Speaker 4: trying to make you sure that it is absolute the
Speaker 4: risk free.
Speaker 3: Right. So someone in a highly cyclical industry, Like I
Speaker 3: don't know, truck drivers in Texas or something that we're
Speaker 3: taking out auto loans would probably be seen as riskier
Speaker 3: or the labor market would depend more for them, whereas
Speaker 3: if you're taking out a black AMEX card or something
Speaker 3: like that, probably real disposable income.
Speaker 2: Yeah, okay, so.
Speaker 1: You mentioned different segmentation. People talk about this K shaped economy.
Speaker 1: Is that real or is that immun.
Speaker 4: I absolutely believe so, but I think that it's a
Speaker 4: little bit more nuanced. And so, you know, one of
Speaker 4: the things that we spotted late last year and we're
Speaker 4: tracking into this year was that we want the first
Speaker 4: to see that it was a K shaped economy. But
Speaker 4: a lot of people were making the assumption that the
Speaker 4: K shaped economy was being driven by income levels. But
Speaker 4: when we were looking at the date at the time,
Speaker 4: we were seeing that those that were in sort of
Speaker 4: the higher income level in our case that's one hundred
Speaker 4: and fifty thousand above. So that's not your you know,
Speaker 4: people who are running hedge funds and sept and you know,
Speaker 4: but still it's the relatively better to do cohort. They
Speaker 4: were actually seeing the highest year of year increases in
Speaker 4: delinquency rates at the time, so we knew that okay,
Speaker 4: home in a second, it isn't as simple as this,
Speaker 4: So you know, we spend a lot of time and
Speaker 4: sort of a lot of banks and we kind of
Speaker 4: collaborated with them to try to understand what's really then
Speaker 4: the differentiator. And then what seemed to be really a
Speaker 4: part of this is wealth. So you know, a lot
Speaker 4: of people don't necessarily differentiate income and wealth, but they
Speaker 4: are separate. And so you know, when you're looking then
Speaker 4: at a high income cohort at the time to try
Speaker 4: to see, like, okay, well, which ones are doing well,
Speaker 4: which ones weren't. Home ownership was the biggest differentiator because
Speaker 4: they had a bigger cushion, something they can rely on.
Speaker 4: And then obviously other aspects as well, like stock ownership
Speaker 4: and small business ownership, et cetera. But home ownership is
Speaker 4: the one that has the bigger effect because there's more
Speaker 4: people in the US economy that on a home then
Speaker 4: let's say, has a stock portfolio.
Speaker 3: Right, talk more about mortgage rates, because this feels pretty
Speaker 3: key when you're talking about the K shaped economy, which
Speaker 3: is if anyone, anyone who bought their house before twenty
Speaker 3: twenty is probably a very lucky person and has locked
Speaker 3: in a low mortgage rate. I think mortgage rates are
Speaker 3: still even after the rate cut, we're at like six
Speaker 3: percent or something versus I think at one point they
Speaker 3: got down to like three percent.
Speaker 1: Right after the filter.
Speaker 2: Yeah.
Speaker 1: Crazy, yeah.
Speaker 3: And so if you bought a house, then you actually
Speaker 3: have this like massive cushion as you put it, versus
Speaker 3: someone who's buying a house now or in the past
Speaker 3: couple of years.
Speaker 4: In a way, I see there's a bit of a
Speaker 4: silver lining when it comes to housing. Right We have
Speaker 4: seen rates come down this week. Hopefully they will continue
Speaker 4: to come down next year. There's a lot of debate, obviously,
Speaker 4: even within the Federals to exactly the speed at which
Speaker 4: that's going to be accomplished, and obviously there are many
Speaker 4: other factors that can impact that. But the reason we
Speaker 4: see a silver lining is for two reasons. Okay, as
Speaker 4: interest rates comes down, Obviously, for people to own homes,
Speaker 4: that's their biggest monthly payment right now, there's much point
Speaker 4: for many to refinance given that where the interest rates are.
Speaker 4: But if it comes down a bit more, it'll make
Speaker 4: much more sense for a large tranch of homeowners that
Speaker 4: have higher interest rates to be able to make that switch.
Speaker 4: So we'd expect a bit of a refinancing boom as
Speaker 4: it hits a certain level. But the other thing that's
Speaker 4: really exciting about what's happening in the home ownership space
Speaker 4: is that this year the FHFA changed the rules about
Speaker 4: what credit scores can be used in mortgage. So historically
Speaker 4: they've used a very old version that's from the nineties,
Speaker 4: of the classic score in mortgage applications, and that was
Speaker 4: actually not deliberately done, So it was just that we
Speaker 4: got written into the rules and then since then as
Speaker 4: a recommendation, but then it became kind of the de
Speaker 4: facto and monopoly in that space. And the problem is
Speaker 4: that's a model that went through the last crisis, and
Speaker 4: the Federal Reservist and Lewis actually found that it didn't
Speaker 4: work well at all in that situation. In fact, it
Speaker 4: saw a bigger rate of increase in delinquencies amongst those
Speaker 4: that were prime than it did amongst those that were subprime.
Speaker 4: So the rate of increase, which is not how a
Speaker 4: model is supposed to work, surprisingly right. And so what's
Speaker 4: happening now is that they have allowed for varnish four
Speaker 4: to be used. And the reason I say that is
Speaker 4: that it's a because, as I mentioned before, a lot
Speaker 4: more people will now have the ability to get access
Speaker 4: to home ownership, so that will create a bit more demand, right,
Speaker 4: which is great. And the other thing to think about
Speaker 4: too is who are these people that get access to this, right,
Speaker 4: It's a lot of people that are not necessarily in
Speaker 4: the areas that have been so crazy with house price increases.
Speaker 2: Right.
Speaker 4: There are a lot of rural communities like so if
Speaker 4: you look at the state where there's the biggest difference
Speaker 4: between scoreable people with the new score, it's actually West Virginia.
Speaker 4: And so those economies could certainly do really well from
Speaker 4: a change to more people having home ownership. And then
Speaker 4: the second thing too is obviously our MBS is really important.
Speaker 4: Had some challenges back in two thousand and eight, two
Speaker 4: thousand and nine, right, and so having a better.
Speaker 3: Performing model a very smardest way about I.
Speaker 1: Think they came up a couple of times.
Speaker 4: I was educated in the UK. Pardon me, we have
Speaker 4: a tendency to understate things. But so you know, having
Speaker 4: a newer, more proven model, one that's you know, worked
Speaker 4: so well in credit card and other things for the
Speaker 4: past eight years, it's become the most used model in
Speaker 4: many other segments. So having a proven model that newer
Speaker 4: and more predictive is should help as well with reducing
Speaker 4: the systemic risk in the R and BS market.
Speaker 1: Just to go back very quickly, because I don't know
Speaker 1: it sounded important, can you just clarify a little bit more?
Speaker 1: What is this rule change such that could unlock additional
Speaker 1: source of demand here?
Speaker 2: Okay?
Speaker 4: So when a bank before wanted to submit loans to
Speaker 4: Fannie May and Freddy Mac, okay, they could only submit
Speaker 4: those loans using the Phyco classic score. Okay, Okay, there's
Speaker 4: actually two three different scores going too that another point.
Speaker 4: But anyway, and there used to be like a cutoff
Speaker 4: that if you didn't have six twenty then you couldn't
Speaker 4: be able to submit it. You could go to an
Speaker 4: FAH loan, but those are more expensive, right, but you
Speaker 4: couldn't necessarily get a normal conforming loon the gost to
Speaker 4: Fani Man and Freddy Mac. So that's not changed. The
Speaker 4: first of all, that minimum limit of FIKO has been removed,
Speaker 4: and now they are just updating all the pipes to
Speaker 4: allow them to use varnished score as a choice. So
Speaker 4: now there'll be a choice. Lenders can choose which score
Speaker 4: they want to use, and they can make their own
Speaker 4: evaluations about which one performs better.
Speaker 1: Okay, so let's go back to starting at the end
Speaker 1: of last year, and you say that increase in delinquencies
Speaker 1: among people decent incomes, maybe they didn't have as much. Well,
Speaker 1: talk to us about the numbers. How big were these numbers,
Speaker 1: how much did they catch people by surprise? And what
Speaker 1: is the story there about why there was this delinquency pressure.
Speaker 4: Well, so the good thing is it's evolvedtle bit since
Speaker 4: the end of last year. But you know, when we're
Speaker 4: looking at this data, then again, look, high income earners,
Speaker 4: not surprisingly have lower delinquency rates than middle income earners,
Speaker 4: and that had themselves lower delinquency rates than the lower income. Right,
Speaker 4: But not a lot of people were looking at that
Speaker 4: kind of year of a year trend and the momentum. Right,
Speaker 4: I'm always looking at momentum because I'm trying to get
Speaker 4: an early read on kind of how things are developing,
Speaker 4: and so at the time, Actually, let's go back a
Speaker 4: little bit because I think it can explain a little
Speaker 4: bit more about what's going on in the economy. Is
Speaker 4: that a right, So when the pandemic happens, right, a
Speaker 4: lot of stimulus comes in, a lot of forbearance programs
Speaker 4: are put in place. As a result of that, so
Speaker 4: many people's credit health and the way that they appear
Speaker 4: on the credit files improved dramatically. They were paying down
Speaker 4: their credit cards, they were building up their savings. It
Speaker 4: was a good situation, temporary but good. But then twenty
Speaker 4: one twenty two starts creeping in and we start seeing that, okay,
Speaker 4: this is not a persistent situation. This was a one off, right,
Speaker 4: and we started seeing delinquency rates starting to come up again.
Speaker 2: Right.
Speaker 4: What we saw, which shouldn't be too surprising, particularly given
Speaker 4: that that was when inflation was kicking in in a
Speaker 4: big way, was that those that were initially impacted and
Speaker 4: who were sewing the biggest rises were the lower income households.
Speaker 2: Right.
Speaker 4: So for the first sort of you know, six months,
Speaker 4: nine months, that was the group that was seeing the
Speaker 4: biggest year of a year increases. But then what we
Speaker 4: started seeing was that come twenty twenty three forward, we
Speaker 4: actually started seeing that then the middle and hiring income
Speaker 4: households were starting being impacted too, and that's probably related
Speaker 4: to the fact that lower income households had less disposable income,
Speaker 4: but they also had less savings put away, so that
Speaker 4: you know, they're the first to feel the pain. But
Speaker 4: then when there's this consistent imbalance between your inflows and
Speaker 4: your outflows, right, even if you have you know, thirty
Speaker 4: thousand or fifty thousand that's put away, that's going to
Speaker 4: start depleting. And that's what we started seeing happening even
Speaker 4: with these hiring income households because at the same time,
Speaker 4: you know, they were hit by so many pressures. Right,
Speaker 4: biggest rent increases I think we've ever seen came into
Speaker 4: effect those few years after COVID. Right, we saw things,
Speaker 4: you know, car prices, costs of auto financing going up
Speaker 4: through the roof, and then various other costs also went
Speaker 4: up substantially. And so it's not that hiring income people
Speaker 4: were immune from this.
Speaker 2: Right.
Speaker 4: Also, as an economist, a lot of people always talk about, hey,
Speaker 4: when inflation kicks in, it disproportionately impacts lower income households
Speaker 4: because the cost of bread, the cost of milk, et cetera.
Speaker 4: You know, it's not like high income people buy milk
Speaker 4: that's one hundred times more expensive.
Speaker 2: Right.
Speaker 4: But the reality is is if you look at people's
Speaker 4: big outlays, many of the hose actually scale with income.
Speaker 4: Rent for instance, people that earn more tend to rent high.
Speaker 4: Other big outlays such as childcare, education and other things,
Speaker 4: they also have been scaling more with income. Now, obviously
Speaker 4: there's a level of income that you know that does
Speaker 4: not apply to but if we still talk about that
Speaker 4: cohort one hundred and fifty to two hundred and fifty
Speaker 4: or so of household income, they're definitely seeing that they
Speaker 4: felt the pain too. It took them longer before it
Speaker 4: started impacting their delinquencies, but they did start feeling the pain.
Speaker 4: The good news though, is that as we started looking
Speaker 4: at the second half of this year, right, so, we
Speaker 4: still saw those delinquencies in high incomes rising very heavily
Speaker 4: at the beginning the first half.
Speaker 2: Of this year.
Speaker 4: But since July, and I've got data from October, so
Speaker 4: of the three of the four months since July, seen
Speaker 4: that high income households came down. So that's a good sign.
Speaker 4: And the reason I say that's a good sign is
Speaker 4: not because I'm a fan of making the case shaped
Speaker 4: economy even more so but the fact that so much
Speaker 4: of the US economy is driven by spending. You mentioned
Speaker 4: earlier that high income households disproportionately impact that, and so
Speaker 4: if that dries up, that has a knock on effect
Speaker 4: on the whole economy. So to the fact that we're
Speaker 4: seeing that cohort that those delinquencies are starting to come down,
Speaker 4: I think there's some light at the end of the tunnel.
Speaker 3: I always wondered how useful are big shopping events like
Speaker 3: Black Friday or Christmas in terms of gauging consumer sentiments.
Speaker 3: So you always see the headlines. You certainly saw them
Speaker 3: this year, you know, record Black Friday spending, But then
Speaker 3: you also see people break down that spending and say, well, actually,
Speaker 3: it's because everyone is so pressured they really need the
Speaker 3: low prices, so they're buying everything. Now, how useful is
Speaker 3: something like that to you?
Speaker 4: You can always see trends, right, and so from one perspective,
Speaker 4: it is always good to look at a number of
Speaker 4: different things, such as spending on Black Friday, Cyber Monday,
Speaker 4: et cetera, because there are nuances into how people have
Speaker 4: been spending for those weekends over twenty years. But still,
Speaker 4: if you look at the last three years, you can
Speaker 4: start to see things that are happening.
Speaker 2: But the thing that.
Speaker 4: I haven't been able to get my head round is
Speaker 4: how much of that year of year increase in spending
Speaker 4: and the holidays is driven by the prices of the
Speaker 4: goods going up versus people buying things that would have
Speaker 4: traditionally been more expensive or splurging more. That for me
Speaker 4: isn't obvious. And I think that again when trying to
Speaker 4: understand how the economy is going, it's so important if
Speaker 4: you're looking at from a spending perspective, to actually look
Speaker 4: at the different merchants. Right, So, how's McDonald's doing, what
Speaker 4: are the trends there, what's happening in higher and how
Speaker 4: is LVMH doing versus Walmart, et cetera. Because again, you know,
Speaker 4: even though I said that, you know there's a silver
Speaker 4: lining and the high income consumers are seeing declines. Middle
Speaker 4: income have come down, but they're still increasing, and low
Speaker 4: income are persistently high around eight percent year every year
Speaker 4: increases in their delinquency rates, and so we're probably next
Speaker 4: year going to see more households struggling to make ends
Speaker 4: meet than we saw this year. I still don't think
Speaker 4: that there's just looking at the trend, it's going to
Speaker 4: be any kind of major breakpoint. But the thing to
Speaker 4: bear in mind with that, though, is that if you
Speaker 4: look at here this situation, and you look at for instance,
Speaker 4: JP Morgan published that, you know, the amount of cash
Speaker 4: people have in their checking accounts is coming down. So
Speaker 4: it just means that there's more of a challenge if
Speaker 4: there's a big shock to the system at some point.
Speaker 4: Not that I can foresee any shock to the system,
Speaker 4: but it's always something to be a little bit wary
Speaker 4: before going.
Speaker 1: I want to go back to something you said very quickly.
Speaker 1: You said, Okay, intuitively, people with higher incomes that are
Speaker 1: going to have delinquencies at a lower rate than people
Speaker 1: with middle incomes, and they're going to have delinquencies a
Speaker 1: lower rate than people with lower incomes. That's not intuitive
Speaker 1: to me, actually, because I would also imagine that underwriting
Speaker 1: is very different, et cetera. It's not obvious to me
Speaker 1: why higher income people default less than lower income people,
Speaker 1: because I would imagine lenders know their income and they're
Speaker 1: going to scrutinize the loan of a lower income person
Speaker 1: much more intensely, et cetera. So why should this trend
Speaker 1: exist given that they don't get the same loan terms,
Speaker 1: their same loan availability, No.
Speaker 2: They don't.
Speaker 4: End You know, a high income household will buy typically
Speaker 4: more expensive than a car than a low income household will,
Speaker 4: but higher income households tend to have more of an
Speaker 4: ability to squirrel some money away, or they tend to
Speaker 4: have other assets that they knows that.
Speaker 1: The lender knows that the higher income household is going
Speaker 1: to have likely more savings, and the lender knows that
Speaker 1: the householder's in a very tight income probably has very
Speaker 1: little cushion in the form of what we call it wealth.
Speaker 1: And so why doesn't that just get baked into the
Speaker 1: underwriting standards.
Speaker 4: Well, in ways it does, right, And so you know,
Speaker 4: when you're underwriting a loan for let's say somebody who
Speaker 4: is high income and has a good credit history, your
Speaker 4: expectation of their default is going to be incredibly low, right,
Speaker 4: So that's built into the pricing, and that's built into
Speaker 4: and you obviously don't just look at a quiet score.
Speaker 4: You look at what their income is and various other
Speaker 4: important metrics to be able to determine the appropriate amount
Speaker 4: that you will lend them.
Speaker 2: Et cetera.
Speaker 4: Right, but high income consumers may not need to take
Speaker 4: on as much debt as a proportion to their income
Speaker 4: as lower income households to get through what they need
Speaker 4: to do. Right, And so if you look at, for instance,
Speaker 4: a high income household, how much of their even though
Speaker 4: for instance, probably housing and car costs are some of
Speaker 4: the biggest outlays, they have proportion they're probably less than
Speaker 4: for lower income households, right, And so you know, a
Speaker 4: lot of it has to do with that proportionality. But
Speaker 4: then also just that again, they will tend to have
Speaker 4: a bit more reserves so that they can ride through situations.
Speaker 1: Talk to us about autodelinquencies. Those have been rising and
Speaker 1: obviously there's a lot of lending going on. Again, Tracy
Speaker 1: mentioned the sort of twenty twenty to twenty twenty two
Speaker 1: vintage car prices themselves are going up. So not only
Speaker 1: have the raids gone up, but we've seen a tremendous
Speaker 1: amount of auto inflation. So sort of stress at every level.
Speaker 1: We see the numbers going up. What do those tell us?
Speaker 4: It's a fascinating story. You know, there's been a lot
Speaker 4: of interest that was paid attention to order loans because
Speaker 4: you know, suddenly in twenty twenty two that we started
Speaker 4: seeing these autolone delinquencies going up much faster than other
Speaker 4: types of loan delinquencies, and it was having a profound
Speaker 4: effect obviously on auto lenders and the economy as a whole.
Speaker 4: And you know, everybody's trying to explain, well, you see,
Speaker 4: we we've got a bit too loose during the period
Speaker 4: of COVID and other things. And they started adjusting their
Speaker 4: lending criteria, right, so they did adjut their lending criteria
Speaker 4: around twenty twenty three for most of them, but then
Speaker 4: we still saw that despite that, the delinquis rates kept
Speaker 4: persistently increasing, and then when we looked at the data,
Speaker 4: we actually saw that but they had actually had an
Speaker 4: impact by adjusting their lending criteria. We saw that the
Speaker 4: delinquiscy rates among subprime all the loans reduced quite dramatically
Speaker 4: after that, so they did have that effect. But we
Speaker 4: saw that the delinquency rates on near prime and prime
Speaker 4: continued to go up, and that was what drove that increase,
Speaker 4: and so we realized there's something more going on here,
Speaker 4: and also why is it it's so different? So we
Speaker 4: went back a long time. We went back to twenty
Speaker 4: ten to try to understand kind of what's been going on,
Speaker 4: because not many people look at it from that time scale.
Speaker 4: But it's actually quite fascinating because back in twenty ten,
Speaker 4: auto had the best performance of any loan product. At
Speaker 4: the time, it was the least risky.
Speaker 3: This was always the narrative that Americans will never give
Speaker 3: up their cars, even if they lose their job. They
Speaker 3: can sleep in their car and live in the car,
Speaker 3: which is very dystopian, but that's I remember hearing that
Speaker 3: story literally from a banker, a banker who was actually
Speaker 3: working on bundling phone loans, and he was.
Speaker 4: Like, so at that time it performed well, right, and
Speaker 4: people did not default as much on that as on
Speaker 4: other products. But then we've seen it has transitioned over
Speaker 4: that fifteen year period to now in the first quarter
Speaker 4: this year, it was the riskiest credit product out there,
Speaker 4: and it then subsequently student loans started coming in, and
Speaker 4: that's another story. Those delinquency rates are at historic levels.
Speaker 4: But on the auto loans side, we then try to
Speaker 4: understand what's causing this, right, and so what we're seeing
Speaker 4: was that there's a number of factors, some of them
Speaker 4: obvious some of them a little bit more subtle. Right,
Speaker 4: the average cost of a car has gone up an
Speaker 4: incredible amount, And what we're seeing is then the average
Speaker 4: loan value for auto loans has increased more than any
Speaker 4: other loan value. And that may sound like okay, but
Speaker 4: if you think about it, mortgages tend to be the
Speaker 4: one that grows the most because house prices have appreciated
Speaker 4: so much of it. So the fact that the average
Speaker 4: all alone has grown more than the average mortgage has
Speaker 4: over that fifteen year period is telling.
Speaker 2: Okay.
Speaker 4: Secondly, obviously there is this double whammy. So not only
Speaker 4: is the car more expensive, but then more recently interest
Speaker 4: rates have been higher, right, and so you know then
Speaker 4: I'm gonna have to pay more, not just for the
Speaker 4: principle but also the interest. But I think one of
Speaker 4: the things that has caught many consumers off guard is Okay,
Speaker 4: so they're in the dealership, They're being shown some numbers.
Speaker 4: Some people get it and they go like, okay, yes,
Speaker 4: we can still do that, we can make it work.
Speaker 4: We can just about max and stretch. Because also I
Speaker 4: think people are trying to buy either the same that
Speaker 4: they had before or slightly better. Right, not many people
Speaker 4: like downgrading, okay, and so they think, well, it's the
Speaker 4: same car, and yes, this is a little bit more,
Speaker 4: but we can make and meet right by looking at
Speaker 4: these numbers. But what they often forget about is that
Speaker 4: insurance has gone up significantly, as have just the cost
Speaker 4: of ownership. Repair costs have also gone up substantially, and
Speaker 4: so when all those things then hit them, they can
Speaker 4: be in a situation where we just can't make it work.
Speaker 4: And so that's not good. And the key piece of
Speaker 4: this too is, look, the good thing is, of all
Speaker 4: the loan products, mortgages are performing pretty well. Okay, they
Speaker 4: are increasing, but they're still much much lower than they
Speaker 4: were back in twenty ten, or obviously much lower than
Speaker 4: twenty eight twenty nine. So but if you default on
Speaker 4: a mortgage, it takes some time before anything really happens. Right,
Speaker 4: with an order loan, they will come and they will
Speaker 4: take that car away from you. And given that, you
Speaker 4: know so many people rely on that car to go
Speaker 4: to their job, to make their income, to do other
Speaker 4: tasks are important, like their shopping or taking their children
Speaker 4: to the school or other things that they need to do.
Speaker 4: People don't willingly just default on these auto loans, and
Speaker 4: so I think it is a sign that correlates with
Speaker 4: the fact that more households are struggling.
Speaker 2: To make in meet.
Speaker 3: How much insight do you have into leverage? And the
Speaker 3: reason I ask is because we've seen an explosion in
Speaker 3: buy now, pay later programs. Virtually every site you go
Speaker 3: to now has three different options for getting alone for
Speaker 3: a small amount, and only a few of those My
Speaker 3: understanding is are actually reporting to the credit bureaus. So,
Speaker 3: and I can also imagine if you're a lower income
Speaker 3: person who is perhaps more pressured, you're probably going to
Speaker 3: turn to a family member and say something like, hey,
Speaker 3: can you loan me, I don't know, five hundred bucks
Speaker 3: to make it to the end of the month. And
Speaker 3: there's no way that credit scoring bureaus are going to
Speaker 3: have insight into things like that informal loans.
Speaker 4: The credit data that comes from the credit bureaus is
Speaker 4: still incredibly predictive and useful, but it doesn't capture everything.
Speaker 4: Look Alex was on, I think recently a firm has
Speaker 4: done provision data to the credit file, which is great,
Speaker 4: but not all of them are there, and so you know,
Speaker 4: I think there is still a lot that's not visible,
Speaker 4: and that is definitely a concern to lenders. We've been
Speaker 4: hearing their concerns about stacking and.
Speaker 2: Things of that nature.
Speaker 4: Obviously, these companies haven't got to where they are without
Speaker 4: having some understanding of risk themselves, right, So they're not
Speaker 4: going to necessarily just let someone who's not performing alone
Speaker 4: take out another five BNPL loan, right, And the ability
Speaker 4: of a consumer to go to all different BNPL providers
Speaker 4: and use that there's a level of effort and sophistication
Speaker 4: required to do that that certainly, I'm sure they're going
Speaker 4: to be some, but I don't know how many people
Speaker 4: fall into that category. Nevertheless, it is a concern that
Speaker 4: more is invisible, and that's why I think being able
Speaker 4: to pull in more than credit file data, such as
Speaker 4: cashlow data, becomes really important. And so what we're seeing
Speaker 4: is that they're more and more that are looking to
Speaker 4: incorporate cashlow into the process because then they can have
Speaker 4: a better understanding of the ins and outs, right, is
Speaker 4: there checking balance going up or declining over time? Is
Speaker 4: there does their income look like it's stable, does it
Speaker 4: look like it's more sporadic? And so we have started
Speaker 4: building now credit scores that also incorporate that type of data,
Speaker 4: and that I think is going to come even more
Speaker 4: important as we go forward because there are going to
Speaker 4: be more and more ways that consumers can borrow. So
Speaker 4: that's probably the better way to get that holistic view.
Speaker 1: Sure quickly, Auto delinguities, Are they at their highest level
Speaker 1: ever right now? Or closed?
Speaker 3: Yep?
Speaker 4: I mean they are, yes, and they're continuing to go up.
Speaker 4: So what we have seen though is that credit cards,
Speaker 4: for instance, they went up a lot, so they went
Speaker 4: up in good yeah delinquencies. Credit card delinquencies went up
Speaker 4: a lot in twenty three twenty four, but they've started
Speaker 4: coming down and we started seeing personal loans coming down
Speaker 4: as well, so it's a very nuanced picture. So we've
Speaker 4: seen the unsecuritized delinquencies that have started coming down this
Speaker 4: year year of a year, but we're still seeing mortgage
Speaker 4: and order loans continue to increase.
Speaker 3: Which is pretty top seat turvy when you kind of
Speaker 3: think about it. But anyway, so the other thing happening
Speaker 3: now is insurance rates going up because of the I
Speaker 3: guess non extension of previous subsidies. And one thing you're
Speaker 3: seeing all over social media is people posting their new
Speaker 3: insurance rates for twenty twenty six, and I've seen some
Speaker 3: crazy ones, you know, something going from six hundred dollars
Speaker 3: to like eighteen hundred dollars a month. How much pressure
Speaker 3: would you expect something like that to exert on the
Speaker 3: consumer for next year?
Speaker 4: You know, for many it's going to be the straw
Speaker 4: that could break the camels back.
Speaker 3: Right.
Speaker 4: It's just particularly if it's just like car insurance can
Speaker 4: be a very significant outlay for many households. But you
Speaker 4: know there's other insurance for homeowners, homeown insurance that's going up,
Speaker 4: and particularly if they're in areas like California or Florida
Speaker 4: where you know, natural disasters have led to an increase
Speaker 4: in premiums above the national average. And so again I
Speaker 4: don't see that there's any one thing that is going
Speaker 4: to cause a house to fall down. But at the
Speaker 4: same time, just there's more and more households where that
Speaker 4: one unexpected increase puts them in a situation then makes
Speaker 4: it impossible for them to make their payments that month
Speaker 4: or for a number of months.
Speaker 1: Talk to us about the resumption of student loan payments
Speaker 1: after I mean, you mentioned the importance of doing is
Speaker 1: not only the stimulus but all the sort of forbearance
Speaker 1: and so all this stuff nice. The one thing that
Speaker 1: just kept getting pushed forever was the resumption of student loans.
Speaker 1: How much when those numbers turned back on or were
Speaker 1: those payments turned back on? What kind of impact did
Speaker 1: that have and what are we seeing with student loans delinquencies.
Speaker 4: Yeah, it had a very big impact. And so you know,
Speaker 4: if we look back before COVID, the average student condlinquency
Speaker 4: rate was around that ten percent. It was sort of
Speaker 4: wavering around between nine eleven percent in that sort of range.
Speaker 4: And then obviously in this five year period of forbearances
Speaker 4: and no reporting, because you had a period of a
Speaker 4: year through twenty four where they were starting to need
Speaker 4: to make payments, but they just weren't being reported. So
Speaker 4: that's basically a long period of time where people just
Speaker 4: got used to not having to make that payment. And
Speaker 4: also a very substantial part of student luanbars who never
Speaker 4: had ever made a payment because they, you know, they
Speaker 4: finished their studies in a period when there was forbearance.
Speaker 4: And so what then happened was they started trickling back
Speaker 4: onto the credit file sort of mid February of this year,
Speaker 4: and then I think you got the first batch really
Speaker 4: kind of come in by May, and at that point
Speaker 4: you saw the delinquency rates on the student loans that
Speaker 4: were not in deferment was over twenty percent, so they
Speaker 4: were over double what the historical norm.
Speaker 2: Was since then.
Speaker 4: Is that the highest level over It is the highest
Speaker 4: level that we've seen going back a long long time.
Speaker 4: So I remember there's been a lot of changes when
Speaker 4: it was not federally mandated and privately owned, so I
Speaker 4: don't have visibility going back as that far, but at
Speaker 4: least in recent history, is absolutely the highest by a
Speaker 4: very substantial amount. And that's not too surprising. But one
Speaker 4: of the things that we've seen is that we expected
Speaker 4: that there would be some people who go into delinquency
Speaker 4: not because they intended to write, and so there were
Speaker 4: some people who they moved and they didn't get their addresses,
Speaker 4: or a lot of people that were confused because there's
Speaker 4: so many mixed messages like we're going to be forgiven,
Speaker 4: but that we're not and we're on a certain program,
Speaker 4: but now that program doesn't exist anymore. So some people
Speaker 4: have been able to then address that, and so it's
Speaker 4: come down to this sort of seventeen seventeen and a
Speaker 4: half percent, which is a good sign, and so that's improving.
Speaker 4: But there still are people who are on programs that
Speaker 4: have been killed, like the Safe program, and so they
Speaker 4: are going to next year have to either get onto
Speaker 4: another for Baron's program or start making payments, and so
Speaker 4: maybe we haven't seen the full effect. Then basically of
Speaker 4: the resumption those student loans, we've seen the biggest batch
Speaker 4: come through, but there still are some more cohorts of
Speaker 4: consumer borrowers that will either have their existing program expire
Speaker 4: or that aren't being reported yet because the servicers are
Speaker 4: trying to figure out exactly what's going on before they
Speaker 4: report it to the credit bureau. So there still is
Speaker 4: a little bit of lack of visibility there from on
Speaker 4: the credit servicers side.
Speaker 1: Kurk, thank you so much for coming on outlast.
Speaker 2: There was great. Thank you. It's been a pleasure.
Speaker 1: It always comes back to insurance, doesn't it. That's always
Speaker 1: like the little fly in the ointment is you know,
Speaker 1: you buy this car and just like, Okay, here's the
Speaker 1: car and here's the interest payment. Oh, I think we
Speaker 1: can make the math work. You can't control what that
Speaker 1: insurance payment is going to be. You have no idea
Speaker 1: what it's going to be.
Speaker 3: This is my theory. Insure run the way, run the world,
Speaker 3: they really do.
Speaker 2: You know.
Speaker 3: The other thing I was thinking, and we've written about
Speaker 3: this in the newsletter, but one of the difficulties of
Speaker 3: our current economic moment is there is so much division
Speaker 3: and difference built into the aggregate. If you're just looking
Speaker 3: at a single number a total, like if you've looked
Speaker 3: at the average FIICO score of an American, it tells
Speaker 3: you almost nothing now, because the individuals are so disparate.
Speaker 1: Yeah, and it really does come down to, you know, wealth, right,
Speaker 1: Wealth is just such an important factor in the economy.
Speaker 1: We always talk about income and income inequality, and of
Speaker 1: course that's a real phenomenon, but wealth is such an
Speaker 1: important predictor driver of anything. And it also goes to
Speaker 1: show like how important like financial markets and asset prices
Speaker 1: are to the real economy. And it gives me once
Speaker 1: again an opportunity to say the stock market is the
Speaker 1: economy because we live in such a wealth driven economy.
Speaker 3: You know, someone once wrote into me. I wrote something
Speaker 3: aboutures on lower income people, and someone wrote into me saying, well,
Speaker 3: why don't the lower income people own more assets. If
Speaker 3: they did, they'd be in a better position. Have you
Speaker 3: tried not being poor?
Speaker 1: Why haven't you tried just being rich? Why haven't you
Speaker 1: tried buying in video twenty years ago? Why haven't you
Speaker 1: tried buying a house in California in two thousand and
Speaker 1: nine after the bus? It's that simple. Stop being poor? Seriously.
Speaker 3: The other thing I was thinking just on auto delinquencies.
Speaker 3: I also think the trade down story is a big
Speaker 3: piece here, which is I mean, a car from ten
Speaker 3: years ago now is pretty decent. And I say that
Speaker 3: as someone who owns. I think it's a Toyota rav
Speaker 3: from like twenty eleven or something like that. Like it's
Speaker 3: pretty dependable, and I don't really feel the need to
Speaker 3: get like a fancy new car. And I imagine if
Speaker 3: you're under pressure on your car loan, it's probably like
Speaker 3: not that difficult necessarily to find an older car that
Speaker 3: is somewhat reliable.
Speaker 2: I don't know, you know.
Speaker 1: The one thing though, So I have a car that
Speaker 1: I bought twenty fifteen. It runs perfectly well. I would
Speaker 1: not be surprised if it continued like no issues at all.
Speaker 1: It doesn't have CarPlay integration.
Speaker 3: Oh, yeah.
Speaker 1: The one difference between older cars and newer cars is
Speaker 1: that it's very nice, like having that no that that
Speaker 1: interface where you have like a.
Speaker 3: Nice little speaker or something.
Speaker 1: Yeah, but what it doesn't have is that like really
Speaker 1: nice interface with the map, like and I know that's minor,
Speaker 1: but it's like, kind.
Speaker 3: Of do you have a map?
Speaker 1: It's a super U for those curious, and it's like,
Speaker 1: you know, it's like they're in house. It's a crappy map.
Speaker 1: It's not the really nice that Google Maps where it's
Speaker 1: like you're really clear, and it doesn't have turn by
Speaker 1: turn navigation. I know this sounds like kind of minor,
Speaker 1: but it is very annoying. And like when I am
Speaker 1: in a car that has like a modern off a
Speaker 1: little tangent here, but I am in a car that
Speaker 1: has like a really nice interface with a nice Google
Speaker 1: Maps or Apple Maps and the Spotify integration. It's very nice.
Speaker 1: And apparently we've taken it to the dealer they just
Speaker 1: can it is un really Yeah, it's unupgradeable. There's no
Speaker 1: for some reason, there's no way to put in a
Speaker 1: new dash.
Speaker 3: Oh sorry, I thought you meant unupgradeable in terms of trading,
Speaker 3: it in.
Speaker 1: For No, it's unbradable. It's like we cannot like this.
Speaker 1: We could never install car play or whatever.
Speaker 3: Yeah, car, you know, my husband and I rented one
Speaker 3: of those like big fancy trucks, pickup trucks, and I
Speaker 3: was amazed by the amenities that are actually including like
Speaker 3: the heated seats, I personalized heat what.
Speaker 1: Yeah, that's fancy. You know, it is very nice in
Speaker 1: the wintery heated seeds. We just talked about cars for yeah,
Speaker 1: maybe longer.
Speaker 3: But the thing is like, even for a car like that,
Speaker 3: I just I cannot imagine spending like one hundred thousand
Speaker 3: dollars plus whatever the interest rate actually is on something
Speaker 3: like that.
Speaker 1: I remember that meme from like twenty ten. It's like
Speaker 1: no one will ever do that was a big thing.
Speaker 1: And the phone, right, really two things that people always
Speaker 1: find a way to make a payment for the car
Speaker 1: and the phone.
Speaker 3: I forget, right, Which is why I think you have
Speaker 3: to look at something structural that's shifted. And I suspect
Speaker 3: maybe it's the availability of you know, lots of older cars.
Speaker 1: But just one last point, it's I thought it was
Speaker 1: very interesting. Ricardo is saying, it's like there's no obvious
Speaker 1: catalyst for cataclysm. There's not obvious like, oh, here is something.
Speaker 1: We are on the verge of consumer credit collapse. But
Speaker 1: it is a story of just like steadily building pressure
Speaker 1: such that if there is some sort of spark or something,
Speaker 1: there is a lot of stress not to you know,
Speaker 1: the resumption of student loans after five years, the fact
Speaker 1: that the total loan price of the car has gotten
Speaker 1: so high relative to people's income. All of these different things,
Speaker 1: so you like have all these upwards dresses on prices.
Speaker 1: You have all of this reliance obviously on accumulated wealth,
Speaker 1: most notably stock market and home equity. So you have
Speaker 1: a lot of things come together. They're not necessarily disaster
Speaker 1: or anything like that, but the alignment of pressures is
Speaker 1: there where things could potentially get back right.
Speaker 3: The consumer is much more fragile than they used.
Speaker 1: To they might have been a few years.
Speaker 3: Yeah, yeah, all right, shall we leave it there.
Speaker 1: Let's leave it there.
Speaker 3: Okay. This has been another episode of the aud Loots podcast.
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 1: And I'm Joe Wisenthal. You can follow me at The Stalwart.
Speaker 1: Follow our producers Carmen Rodriguez at Kerman Arman, Dashill, Bennett
Speaker 1: at Dashbot, and Kilbrooks at Kilbrooks. More odd Lots content,
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