December 22, 2025 • 50 mins
By and large, American households are in a healthy economic position. Yes, unemployment has been rising, but it's still at fairly low levels. Consumer spending has held up well despite terrible sentiment. And many households are sitting on huge stock market gains and have a big home equity cushion. And yet, there are signs of trouble. Most notably, auto loan delinquencies have been surging to their highest level in history. It's the same with student loans, where delinquencies are far higher than normal. So what's going on? On this episode, we speak with Rikard Bandebo, the chief economist at VantageScore, which offers a consumer credit score that's different from the traditional FICO measures. He explains how surging prices, rising interests, and -- crucially -- rising insurance costs have created an auto squeeze. We also discuss what this means for broader consumer health and whether this auto delinquency phenomenon signals something broader about consumer stress.
Read more:
Rise of the ‘Zombie’ Loans
First Brands Asks Lenders for Fresh Cash of Up to $800 Million
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to
another episode of The Odd Lads podcast. I'm Joe Wisenthal
(00:23):
and I'm Tracy Alloway. Crazy. I feel like I just
do not have any feel right now on like the
state of the consumer.
Speaker 2 (00:31):
Really.
Speaker 1 (00:32):
I mean, you hear k shaped economy, labor markets slowing down,
then it's like lowest layoffs in years. You go outside,
everything looks booming. Like, I just have no feel right.
I know, consumer sentiment is terrible, but consumer sentiment is
terrible for years, and people keep shopping. I have no
sense of it right.
Speaker 3 (00:49):
Well, consumer sentiment actually came in higher than expected most recently,
big surprise, but I was gonna say, are you not
out shopping for Christmas present?
Speaker 1 (00:59):
It's insane.
Speaker 3 (00:59):
Yeah, there's a lot of people buying a.
Speaker 1 (01:01):
Lot of stuff, buying a lot of stuff.
Speaker 3 (01:03):
But I think this gets to the ke shaped economy point,
which is, if you have a cohort of wealthy people
who are buying more, it more than offsets the lower
income people who are buying less at lower prices. So
it's really hard to tell.
Speaker 1 (01:19):
It's really hard to tell. One thing that definitely feels
different if you look at aggregate measures of household balance
sheets like this is something that is very different than
sort of like pre grade financial crisis. The general view
is that the American consumer or the American household has
a very big cushion. There is a lot of home
(01:40):
equity built up. There is not a thin layer. Obviously,
anyone with money and any sort of investment account has
done phenomenally well. We're according to this December twelfth, yesterday,
I think the s P five hundred hit yet a
new all time highest. If you have any sort of
home equity build up, if you have any sort of investments,
you are doing very well. On the other hand, of course,
(02:01):
people are stretched from years of inflation. We know that
hiring has slowed down.
Speaker 2 (02:06):
We know that.
Speaker 1 (02:07):
You know, we see these headlines delinquencies for cars have
like shot up. But I've been seeing these headlines for years.
I don't totally know what they mean or how apples
to apples they are with the past. I just don't know.
I just I'm very confused.
Speaker 3 (02:19):
Yeah, you know what's really interesting to me just from
a financial perspective. Yeah, if you look at some of
the bonds that were actually built on consumer loans, the
weakest ones are now from the like twenty twenty to
twenty twenty two period.
Speaker 1 (02:35):
Oh see, this is another interesting element of measures like delinquencies,
and why I sort of wonder like how comparable they
are because okay, partly a delinquency measure is a snapshot
of a moment in time, right, a snapshot of health,
but it also inherently reflects something in the past, because
it reflects, you know, what we're lending standards at the time,
(02:56):
right exactly, so you know, and now it's a period
of interest rate booming itself. Yea, give the money to
anyone anyway. We need to get a better picture of
exactly what's going on. How stressed is the consumer. How
much do these delinquencies just reflect the profligacy of lenders
during the boon times when rates were nothing, et cetera.
(03:19):
And yes, we need to figure this out, especially we're
in the middle of shopping season and all that stuff.
So I'm very excited to say, we really do have
the perfect guests. We're going to be speaking with Recard Bondebo.
He is the executive vice president, chief strategy officer, chief
economist Advantage Score, a credit scoring company. Recurd Thank you
so much for coming on the podcast.
Speaker 4 (03:37):
Thank you for Havmius Snana.
Speaker 1 (03:38):
What is Vantage Score a US credit scoring company? What
do you do there?
Speaker 4 (03:43):
So, we're the largest credit scoring company in the United
States and we have founded almost twenty years ago by
the Free Credit bureas, TransUnion, ECOFAX and Experience, and we
were sort of created with a very specific mission in
mind to drive greater competition and credit scoring. Prior to us,
there really wasn't a lot of choice in space. We're
also there to drive more innovation and create the most
predictive scores, which was a big ass from the banks
(04:06):
at the time, and also to be able to expand
access to millions to enable everyone who really is credit
worthy to be able to get access to credit products.
Speaker 3 (04:14):
You mentioned the banks just then, can you expound a
little bit more on your customer base.
Speaker 4 (04:18):
Yes, so we used Obviously, the primary use case that
most people think about when it comes to credit scores
is for lending. Right when you know you're applying for
a loan and they want to evaluate whether or not
you're going to be able to perform on that loan,
they will often pull your credit score as part of
that process. But It's used in many other stages as well. So,
for instance, many people who are applying to rent in
a new apartment building may also get asked for it.
(04:40):
When you are trying to get a utility.
Speaker 3 (04:42):
Bill in New York, you get asked for it.
Speaker 4 (04:44):
Yes, you certainly do. And utility bills, telephones, anything that
involves a long term commitment on payments generally. Now you'll
often get asked to provide your credit score.
Speaker 1 (04:54):
So just explain for the way you were started by
whom twenty years ago, we.
Speaker 4 (04:58):
Were a joint venture and ECHOFACTX and TransUnion, the three
national credit reporting agencies.
Speaker 1 (05:03):
So what is the difference between these major companies that
we've all heard of that provide a credit score, et cetera,
that they such founded? You like, what do you do
differently than them?
Speaker 4 (05:14):
Well, so what they do is they're the ones who
collect all this data from lenders and others on your
credit performance. Right, So they call credit buros they collect that.
They're highly regulated. But then most lenders can't just make
sense of all of that data on its own. They
need some guidance to have to translate that into a
what does that mean? Right?
Speaker 2 (05:33):
Okay?
Speaker 4 (05:33):
And so that's where a scoring algorithm comes into effect. Right,
And so the scoring algorithm helps to take in all
these hundreds of different factors about you to try to
then determine what does that mean about your propensity to pay?
Speaker 3 (05:45):
Okay, you mentioned predictive analysis as well, What exactly is
that and what's that based on?
Speaker 4 (05:51):
Well, so when credit scores are created, right, the aim
the goal is to try to evaluate what is the
likelihood that somebody's going to default on a payment over
the next time twenty four months. So when you see
that score, the score is actually a translation of a
probability right or on odds right to evaluate what is
that risk?
Speaker 3 (06:10):
And how did we end up with the system of
FICO scores in the US, because it has like an
interesting history.
Speaker 4 (06:17):
Well, back in the day fair Isaac they created the
first sort of known credit score. They were the first
ones to realize that there was a.
Speaker 1 (06:24):
Looking at fi and fair Isaac is that yes?
Speaker 4 (06:29):
Okay, yes, And so let's go back a bit, right,
So in the old days, lending was not necessarily the
most fair system that there was, right, or want to
call up your previous employer, They may call your landlord.
They may just ask around and if they don't know
anything about you. You know, there was a lot of
judgment involved in.
Speaker 3 (06:46):
Lending, a lot of racial discrimination.
Speaker 4 (06:48):
Well that certainly was built into that system, right, And
so then there was a law created, the Fair Credit
Reporting Act, that said, like, you can't do that, you
need a better system that is fair and that is
a better quantitative ability to assess people's risk, right, And
that created then this need to be able to consolidate
all this quantitative information in a way that lenders could
easily use it. So Faiko was the first fair ISAAC
(07:10):
at the time was the first to create that and
they did very well doing so. But you know, then
there was a need for competition innovation, and there was
a lot of frustration around the time of twenty years
ago that there was only one game in town and
it didn't score about twenty percent of the US population.
It still doesn't, And then a lot of lenders were
felt frustrated, like if it doesn't work for twenty percent
of the population, there's a problem. We need something different.
(07:33):
And so then buwers took the unusual step of actually
coming together to create an alternative and that became Advantage School.
Speaker 3 (07:39):
Interesting can I as a consumer go credit score shopping.
Speaker 4 (07:44):
So first of all, there's different ways to use it, right,
So a lender will typically choose the credit score that
they're going to use for being able to underwrite alone
with you, and often they'll use many more factors than
just a simple credit score, right, particularly the more sophisticated ones. However,
when you're trying to understand what your situation is, you
can there are lots of different places you can go.
You can either go the credit bureaus, you can go
(08:05):
to the likes of credit Karma. There are many different
services out there.
Speaker 3 (08:08):
But I can course the lender to look at a
specific Yeah, look at this one over here, it's great,
get a second opinion.
Speaker 4 (08:15):
No, I'm afraid not. That's not how it works. It's
it's really you know, the lenders try to determine what
is the most appropriate score for their product. And there
are many many different schools out there. There's schools that
in some cases built specifically for types of products like
order loans, and the other schools that, like our schools,
that are generic that can be used for any type
of product.
Speaker 1 (08:31):
So you collect more data. And you mentioned that there
is this wide swath of the population that wasn't being
captured by the credit bureaus? What do you do additionally
on top of them to expand the pool find potentially
credit worthy borrowers that they had been missing before.
Speaker 4 (08:49):
So the thing is that the quality and the types
of data that's been collected by the credit bureaus has
improved significantly over time. OKA, And so when we started
creating our algorithms, we're in, you know, the current version
that's now being adopted for mortgages, the version four we're
releasing version five this year.
Speaker 2 (09:05):
We actually go.
Speaker 4 (09:06):
And rewrite the whole thing each time so that each
time we can come up with the most accurate way
based on the current data is available and our current
ability to understand how consumers are behaving, because that behavior
changes of a time. Other companies, what they've done is
they've built a model long time ago. They don't like
to necessarily revealed everybody how it works, the secret source. Right,
(09:26):
So when you're seeing there's a chief risk officer and
there's a new model coming along, either you want to
understand that it's going to pay very similarly to the
previous one to be okay with it, or you need
a lot of transparency and how it works, so you
can get comfort in this new model. So I think
there's the big divergence and strategy. We go back to
boots and redo everything from scratch each time, but in
the same time provide an awful lot of transparency and
(09:47):
a lot of tools so that lenders can get a
really good understanding of exactly how this is going to
work and how it's going to behave in different situations
and they can test it out right, whereas the other
one is still working with many limitations that have been
in place since the very first models. And because of
those limitations, that's a big difference in why we've scored
a lot more people. So I'll give you a very
concrete example. So, for instance, one of the limitations that
(10:10):
the others have is that if you haven't had any
credit activity for the past six months, that you're not
going to get a score. So you just imagine somebody
that works with the military has been deployed overseas or
anything else.
Speaker 2 (10:22):
Right.
Speaker 4 (10:22):
But the good thing is, starting about fifteen years ago,
the Bureau started click and storing data so we could
use time serious data because who'd have thought that time
serious data could be useful in prediction, right, completely strange idea.
Speaker 2 (10:35):
Right.
Speaker 4 (10:36):
So with finan Score four, we started using trended data
times series data, and with that, obviously we can see
back twenty four months. So yes, if there's a gap
in six months of history, it's important, but we're still
able to see what happened before then, right, And that
gets rid of tens of millions of people when you
have that constraint. There are the constraints in there too,
So people that are new to credit, so if they
(10:56):
haven't had a full six months of history again, they
won't get scored. Aren't any tradelines, they won't get scored, right,
And so what we've been able to do is to
deal with thos constraints in a different way by a
using time series data, b using some other data points.
So we were the first to use utility payments and rent.
I mean, who'd have thought that your ability to pay
your rent could somehow again be useful and trying to
(11:19):
assess your risk, right, And so including those new different
types of data, realizing that these constraints can be changed
now that you have time series data. But then also
guess what you know? Math has evolved as well, okay,
And so you know what we realized too was that
you can be really smart and use some new methods,
like you know, some AI methods for instance, like clustering,
(11:39):
to really understand, well, look, here's a group of people
and they behave in a certain way, and by doing
that in a better way, we can then figure out
what is the best way to measure this group of
people here versus this group of people here, and doing
that well enables you to build much more predictive scores.
And so that's an important yance too that not a
lot of people always realize is that it's not one
formula that's calculating everybody's score. People will get depending on
(12:04):
what their credit file looks like and their history looks like.
They'll get divided into different segments and then each segment
is scored according to that and that again increases the
ability to score people accurately and score more people, like
in our case it's thirty three million more people they're
able to score, so it's quite substantial.
Speaker 3 (12:36):
How did the models deal with breaks in previous consumer patterns?
Because we have seen some major ones in recent years.
So after the pandemic, we had a phenomenally tight labor market,
and we saw a lot of wage growth for lower income,
a lot of spending that was sort of unprecedented in
many ways. How do models actually incorporate that sort of
(12:59):
big shit shift in the trend.
Speaker 4 (13:01):
So I think that's something really important to try to understand,
and it's not easily understood by many. So give me
a second here, I'll try to break this down.
Speaker 1 (13:10):
When people say give me a second, I have to
break this down. Please break it down.
Speaker 3 (13:14):
You have a minute.
Speaker 1 (13:15):
A minute.
Speaker 4 (13:16):
Honestly, that's kind of what I'm enjoy most about listening
to your podcast. And so look, the first thing to
understand is that this credit score is not an absolute
measure of risk. It's a relative measure of risk. Let
me break that down for you, right, So what it
means is that you know a score. If somebody has
scored seven to twenty in one month, and then somebody
else has scored seven twenty three years later, the risk
(13:38):
will be different. And the reason for that is very deliberate.
When we are evaluating a person, right, because of the
laws of the Fair Credit Reporting Act, right, we're allowed
to look at the things that are about you. But
there are things that are going out on at the
same time in the economy that impact risk of the
population as a whole. Right, So that's that's why it's
(14:00):
so important when you're looking at things like credit scores
to understand when was that score pulled right, because the
score of seven twenty in twenty seventeen had a very
different characteristic of a score of seven twenty in twenty
twenty two. Okay, now, but the thing to bear in
mind is it's an excellent relative measure of risk. So
at any one given point in time, you know, somebody
(14:22):
with a seven twenty is going to be much better
performing than somebody at six thirty, And at the same time,
somebody eight forty is going to be much better than
both of them. Okay, And that holds consistently true, and
so that's why it's very important to include when you're
making lending decisions. But lenders have to be thoughtful, right,
they have to. As they're making decisions about, you know,
how many people they want to be able to underwrite,
(14:42):
and how to think about risk, they need to also
start thinking about these external factors as well, so that
they can then set their underwriting criteria to meet the
kind of level of risk that they're willing to take on.
Speaker 1 (14:55):
I had never thought about this, but of course that
makes so much sense. So it's like I could have
an excellent credit history, I could have ex employment pay
on my rent, but for example, if the economy is
going down the tubes, I may still yet be a
risky credit because I may lose my job at some point.
And so this idea that it kind of has to
be relative because the underlying conditions that affect everyone through
(15:19):
outside of our control, but they are still important from
the perspective of the lender exactly.
Speaker 4 (15:24):
And at the same time, if you get declined for
a credit card, you can't be told that the reason
you're getting declined is because unemployment has hit five percent. Okay, right,
that doesn't work. The laws are very specific. The reasons
for why you're not getting the top score have to
be explained, and they have to be based on attributes
and data obviously that are specific to you.
Speaker 3 (15:44):
What's the most important external factor when it comes to
credit scoring, Because I've heard arguments for obviously the labor market,
the unemployment rate, but also wage income and therefore real
disposable income. How do you weight those different factors?
Speaker 4 (15:59):
I mean, I think if your lender, it's going to
really depend upon what types of consumers you're lending to, right,
particularly now we're seeing such divergence across consumers in terms
of who's doing well.
Speaker 2 (16:12):
And who isn't.
Speaker 4 (16:13):
And so, for instance, if you are a lender focused
on people that are kind of below prime let's say
that not completely subprime, but that near prime group, and
you're focusing on ail loans, and you know you're in
regions like Texas or in certain areas, then obviously understanding
the economic conditions that are affecting those people, like a
(16:34):
lot of those people would be working in certain types
of industries. What is employment like in those types of industries, right?
Or is it people that are in the gig economy?
Speaker 2 (16:41):
Right?
Speaker 4 (16:42):
And so it is quite nuanced and it's not necessarily
one thing, and it's going to depend. Whereas on the
other extreme, you know, if you're handing out black cards
and your audience is incredibly affluent, then again it's less
about the risk because at that point your risk of
default is probably one end ten thousand. So they're just
trying to make you sure that it is absolute the
risk free.
Speaker 3 (17:01):
Right. So someone in a highly cyclical industry, Like I
don't know, truck drivers in Texas or something that we're
taking out auto loans would probably be seen as riskier
or the labor market would depend more for them, whereas
if you're taking out a black AMEX card or something
like that, probably real disposable income.
Speaker 2 (17:20):
Yeah, okay, so.
Speaker 1 (17:22):
You mentioned different segmentation. People talk about this K shaped economy.
Is that real or is that immun.
Speaker 4 (17:27):
I absolutely believe so, but I think that it's a
little bit more nuanced. And so, you know, one of
the things that we spotted late last year and we're
tracking into this year was that we want the first
to see that it was a K shaped economy. But
a lot of people were making the assumption that the
K shaped economy was being driven by income levels. But
when we were looking at the date at the time,
(17:48):
we were seeing that those that were in sort of
the higher income level in our case that's one hundred
and fifty thousand above. So that's not your you know,
people who are running hedge funds and sept and you know,
but still it's the relatively better to do cohort. They
were actually seeing the highest year of year increases in
delinquency rates at the time, so we knew that okay,
home in a second, it isn't as simple as this,
(18:09):
So you know, we spend a lot of time and
sort of a lot of banks and we kind of
collaborated with them to try to understand what's really then
the differentiator. And then what seemed to be really a
part of this is wealth. So you know, a lot
of people don't necessarily differentiate income and wealth, but they
are separate. And so you know, when you're looking then
at a high income cohort at the time to try
(18:29):
to see, like, okay, well, which ones are doing well,
which ones weren't. Home ownership was the biggest differentiator because
they had a bigger cushion, something they can rely on.
And then obviously other aspects as well, like stock ownership
and small business ownership, et cetera. But home ownership is
the one that has the bigger effect because there's more
people in the US economy that on a home then
let's say, has a stock portfolio.
Speaker 3 (18:51):
Right, talk more about mortgage rates, because this feels pretty
key when you're talking about the K shaped economy, which
is if anyone, anyone who bought their house before twenty
twenty is probably a very lucky person and has locked
in a low mortgage rate. I think mortgage rates are
still even after the rate cut, we're at like six
percent or something versus I think at one point they
(19:12):
got down to like three percent.
Speaker 1 (19:14):
Right after the filter.
Speaker 2 (19:16):
Yeah.
Speaker 1 (19:17):
Crazy, yeah.
Speaker 3 (19:18):
And so if you bought a house, then you actually
have this like massive cushion as you put it, versus
someone who's buying a house now or in the past
couple of years.
Speaker 4 (19:28):
In a way, I see there's a bit of a
silver lining when it comes to housing. Right We have
seen rates come down this week. Hopefully they will continue
to come down next year. There's a lot of debate, obviously,
even within the Federals to exactly the speed at which
that's going to be accomplished, and obviously there are many
other factors that can impact that. But the reason we
see a silver lining is for two reasons. Okay, as
(19:49):
interest rates comes down, Obviously, for people to own homes,
that's their biggest monthly payment right now, there's much point
for many to refinance given that where the interest rates are.
But if it comes down a bit more, it'll make
much more sense for a large tranch of homeowners that
have higher interest rates to be able to make that switch.
So we'd expect a bit of a refinancing boom as
(20:10):
it hits a certain level. But the other thing that's
really exciting about what's happening in the home ownership space
is that this year the FHFA changed the rules about
what credit scores can be used in mortgage. So historically
they've used a very old version that's from the nineties,
of the classic score in mortgage applications, and that was
actually not deliberately done, So it was just that we
(20:31):
got written into the rules and then since then as
a recommendation, but then it became kind of the de
facto and monopoly in that space. And the problem is
that's a model that went through the last crisis, and
the Federal Reservist and Lewis actually found that it didn't
work well at all in that situation. In fact, it
saw a bigger rate of increase in delinquencies amongst those
that were prime than it did amongst those that were subprime.
(20:53):
So the rate of increase, which is not how a
model is supposed to work, surprisingly right. And so what's
happening now is that they have allowed for varnish four
to be used. And the reason I say that is
that it's a because, as I mentioned before, a lot
more people will now have the ability to get access
to home ownership, so that will create a bit more demand, right,
which is great. And the other thing to think about
(21:13):
too is who are these people that get access to this, right,
It's a lot of people that are not necessarily in
the areas that have been so crazy with house price increases.
Speaker 2 (21:23):
Right.
Speaker 4 (21:23):
There are a lot of rural communities like so if
you look at the state where there's the biggest difference
between scoreable people with the new score, it's actually West Virginia.
And so those economies could certainly do really well from
a change to more people having home ownership. And then
the second thing too is obviously our MBS is really important.
Had some challenges back in two thousand and eight, two
(21:45):
thousand and nine, right, and so having a better.
Speaker 3 (21:48):
Performing model a very smardest way about I.
Speaker 1 (21:52):
Think they came up a couple of times.
Speaker 4 (21:54):
I was educated in the UK. Pardon me, we have
a tendency to understate things. But so you know, having
a newer, more proven model, one that's you know, worked
so well in credit card and other things for the
past eight years, it's become the most used model in
many other segments. So having a proven model that newer
and more predictive is should help as well with reducing
the systemic risk in the R and BS market.
Speaker 1 (22:16):
Just to go back very quickly, because I don't know
it sounded important, can you just clarify a little bit more?
What is this rule change such that could unlock additional
source of demand here?
Speaker 2 (22:27):
Okay?
Speaker 4 (22:27):
So when a bank before wanted to submit loans to
Fannie May and Freddy Mac, okay, they could only submit
those loans using the Phyco classic score. Okay, Okay, there's
actually two three different scores going too that another point.
But anyway, and there used to be like a cutoff
that if you didn't have six twenty then you couldn't
be able to submit it. You could go to an
FAH loan, but those are more expensive, right, but you
(22:48):
couldn't necessarily get a normal conforming loon the gost to
Fani Man and Freddy Mac. So that's not changed. The
first of all, that minimum limit of FIKO has been removed,
and now they are just updating all the pipes to
allow them to use varnished score as a choice. So
now there'll be a choice. Lenders can choose which score
they want to use, and they can make their own
evaluations about which one performs better.
Speaker 1 (23:08):
Okay, so let's go back to starting at the end
of last year, and you say that increase in delinquencies
among people decent incomes, maybe they didn't have as much. Well,
talk to us about the numbers. How big were these numbers,
how much did they catch people by surprise? And what
is the story there about why there was this delinquency pressure.
Speaker 4 (23:26):
Well, so the good thing is it's evolvedtle bit since
the end of last year. But you know, when we're
looking at this data, then again, look, high income earners,
not surprisingly have lower delinquency rates than middle income earners,
and that had themselves lower delinquency rates than the lower income. Right,
But not a lot of people were looking at that
kind of year of a year trend and the momentum. Right,
(23:47):
I'm always looking at momentum because I'm trying to get
an early read on kind of how things are developing,
and so at the time, Actually, let's go back a
little bit because I think it can explain a little
bit more about what's going on in the economy. Is
that a right, So when the pandemic happens, right, a
lot of stimulus comes in, a lot of forbearance programs
are put in place. As a result of that, so
(24:08):
many people's credit health and the way that they appear
on the credit files improved dramatically. They were paying down
their credit cards, they were building up their savings. It
was a good situation, temporary but good. But then twenty
one twenty two starts creeping in and we start seeing that, okay,
this is not a persistent situation. This was a one off, right,
(24:31):
and we started seeing delinquency rates starting to come up again.
Speaker 2 (24:34):
Right.
Speaker 4 (24:35):
What we saw, which shouldn't be too surprising, particularly given
that that was when inflation was kicking in in a
big way, was that those that were initially impacted and
who were sewing the biggest rises were the lower income households.
Speaker 2 (24:47):
Right.
Speaker 4 (24:47):
So for the first sort of you know, six months,
nine months, that was the group that was seeing the
biggest year of a year increases. But then what we
started seeing was that come twenty twenty three forward, we
actually started seeing that then the middle and hiring income
households were starting being impacted too, and that's probably related
to the fact that lower income households had less disposable income,
(25:11):
but they also had less savings put away, so that
you know, they're the first to feel the pain. But
then when there's this consistent imbalance between your inflows and
your outflows, right, even if you have you know, thirty
thousand or fifty thousand that's put away, that's going to
start depleting. And that's what we started seeing happening even
with these hiring income households because at the same time,
(25:33):
you know, they were hit by so many pressures. Right,
biggest rent increases I think we've ever seen came into
effect those few years after COVID. Right, we saw things,
you know, car prices, costs of auto financing going up
through the roof, and then various other costs also went
up substantially. And so it's not that hiring income people
were immune from this.
Speaker 2 (25:52):
Right.
Speaker 4 (25:53):
Also, as an economist, a lot of people always talk about, hey,
when inflation kicks in, it disproportionately impacts lower income households
because the cost of bread, the cost of milk, et cetera.
You know, it's not like high income people buy milk
that's one hundred times more expensive.
Speaker 2 (26:08):
Right.
Speaker 4 (26:09):
But the reality is is if you look at people's
big outlays, many of the hose actually scale with income.
Rent for instance, people that earn more tend to rent high.
Other big outlays such as childcare, education and other things,
they also have been scaling more with income. Now, obviously
there's a level of income that you know that does
not apply to but if we still talk about that
(26:30):
cohort one hundred and fifty to two hundred and fifty
or so of household income, they're definitely seeing that they
felt the pain too. It took them longer before it
started impacting their delinquencies, but they did start feeling the pain.
The good news though, is that as we started looking
at the second half of this year, right, so, we
still saw those delinquencies in high incomes rising very heavily
(26:51):
at the beginning the first half.
Speaker 2 (26:52):
Of this year.
Speaker 4 (26:53):
But since July, and I've got data from October, so
of the three of the four months since July, seen
that high income households came down. So that's a good sign.
And the reason I say that's a good sign is
not because I'm a fan of making the case shaped
economy even more so but the fact that so much
of the US economy is driven by spending. You mentioned
earlier that high income households disproportionately impact that, and so
(27:18):
if that dries up, that has a knock on effect
on the whole economy. So to the fact that we're
seeing that cohort that those delinquencies are starting to come down,
I think there's some light at the end of the tunnel.
Speaker 3 (27:44):
I always wondered how useful are big shopping events like
Black Friday or Christmas in terms of gauging consumer sentiments.
So you always see the headlines. You certainly saw them
this year, you know, record Black Friday spending, But then
you also see people break down that spending and say, well, actually,
it's because everyone is so pressured they really need the
(28:05):
low prices, so they're buying everything. Now, how useful is
something like that to you?
Speaker 4 (28:10):
You can always see trends, right, and so from one perspective,
it is always good to look at a number of
different things, such as spending on Black Friday, Cyber Monday,
et cetera, because there are nuances into how people have
been spending for those weekends over twenty years. But still,
if you look at the last three years, you can
start to see things that are happening.
Speaker 2 (28:29):
But the thing that.
Speaker 4 (28:30):
I haven't been able to get my head round is
how much of that year of year increase in spending
and the holidays is driven by the prices of the
goods going up versus people buying things that would have
traditionally been more expensive or splurging more. That for me
isn't obvious. And I think that again when trying to
understand how the economy is going, it's so important if
(28:51):
you're looking at from a spending perspective, to actually look
at the different merchants. Right, So, how's McDonald's doing, what
are the trends there, what's happening in higher and how
is LVMH doing versus Walmart, et cetera. Because again, you know,
even though I said that, you know there's a silver
lining and the high income consumers are seeing declines. Middle
(29:12):
income have come down, but they're still increasing, and low
income are persistently high around eight percent year every year
increases in their delinquency rates, and so we're probably next
year going to see more households struggling to make ends
meet than we saw this year. I still don't think
that there's just looking at the trend, it's going to
(29:33):
be any kind of major breakpoint. But the thing to
bear in mind with that, though, is that if you
look at here this situation, and you look at for instance,
JP Morgan published that, you know, the amount of cash
people have in their checking accounts is coming down. So
it just means that there's more of a challenge if
(29:53):
there's a big shock to the system at some point.
Not that I can foresee any shock to the system,
but it's always something to be a little bit wary
before going.
Speaker 1 (30:01):
I want to go back to something you said very quickly.
You said, Okay, intuitively, people with higher incomes that are
going to have delinquencies at a lower rate than people
with middle incomes, and they're going to have delinquencies a
lower rate than people with lower incomes. That's not intuitive
to me, actually, because I would also imagine that underwriting
is very different, et cetera. It's not obvious to me
(30:22):
why higher income people default less than lower income people,
because I would imagine lenders know their income and they're
going to scrutinize the loan of a lower income person
much more intensely, et cetera. So why should this trend
exist given that they don't get the same loan terms,
their same loan availability, No.
Speaker 2 (30:42):
They don't.
Speaker 4 (30:43):
End You know, a high income household will buy typically
more expensive than a car than a low income household will,
but higher income households tend to have more of an
ability to squirrel some money away, or they tend to
have other assets that they knows that.
Speaker 1 (30:58):
The lender knows that the higher income household is going
to have likely more savings, and the lender knows that
the householder's in a very tight income probably has very
little cushion in the form of what we call it wealth.
And so why doesn't that just get baked into the
underwriting standards.
Speaker 4 (31:16):
Well, in ways it does, right, And so you know,
when you're underwriting a loan for let's say somebody who
is high income and has a good credit history, your
expectation of their default is going to be incredibly low, right,
So that's built into the pricing, and that's built into
and you obviously don't just look at a quiet score.
You look at what their income is and various other
important metrics to be able to determine the appropriate amount
(31:38):
that you will lend them.
Speaker 2 (31:39):
Et cetera.
Speaker 4 (31:39):
Right, but high income consumers may not need to take
on as much debt as a proportion to their income
as lower income households to get through what they need
to do. Right, And so if you look at, for instance,
a high income household, how much of their even though
for instance, probably housing and car costs are some of
the biggest outlays, they have proportion they're probably less than
(32:01):
for lower income households, right, And so you know, a
lot of it has to do with that proportionality. But
then also just that again, they will tend to have
a bit more reserves so that they can ride through situations.
Speaker 1 (32:13):
Talk to us about autodelinquencies. Those have been rising and
obviously there's a lot of lending going on. Again, Tracy
mentioned the sort of twenty twenty to twenty twenty two
vintage car prices themselves are going up. So not only
have the raids gone up, but we've seen a tremendous
amount of auto inflation. So sort of stress at every level.
We see the numbers going up. What do those tell us?
Speaker 4 (32:35):
It's a fascinating story. You know, there's been a lot
of interest that was paid attention to order loans because
you know, suddenly in twenty twenty two that we started
seeing these autolone delinquencies going up much faster than other
types of loan delinquencies, and it was having a profound
effect obviously on auto lenders and the economy as a whole.
And you know, everybody's trying to explain, well, you see,
we we've got a bit too loose during the period
(32:56):
of COVID and other things. And they started adjusting their
lending criteria, right, so they did adjut their lending criteria
around twenty twenty three for most of them, but then
we still saw that despite that, the delinquis rates kept
persistently increasing, and then when we looked at the data,
we actually saw that but they had actually had an
impact by adjusting their lending criteria. We saw that the
(33:19):
delinquiscy rates among subprime all the loans reduced quite dramatically
after that, so they did have that effect. But we
saw that the delinquency rates on near prime and prime
continued to go up, and that was what drove that increase,
and so we realized there's something more going on here,
and also why is it it's so different? So we
went back a long time. We went back to twenty
(33:39):
ten to try to understand kind of what's been going on,
because not many people look at it from that time scale.
But it's actually quite fascinating because back in twenty ten,
auto had the best performance of any loan product. At
the time, it was the least risky.
Speaker 3 (33:51):
This was always the narrative that Americans will never give
up their cars, even if they lose their job. They
can sleep in their car and live in the car,
which is very dystopian, but that's I remember hearing that
story literally from a banker, a banker who was actually
working on bundling phone loans, and he was.
Speaker 4 (34:07):
Like, so at that time it performed well, right, and
people did not default as much on that as on
other products. But then we've seen it has transitioned over
that fifteen year period to now in the first quarter
this year, it was the riskiest credit product out there,
and it then subsequently student loans started coming in, and
(34:30):
that's another story. Those delinquency rates are at historic levels.
But on the auto loans side, we then try to
understand what's causing this, right, and so what we're seeing
was that there's a number of factors, some of them
obvious some of them a little bit more subtle. Right,
the average cost of a car has gone up an
incredible amount, And what we're seeing is then the average
loan value for auto loans has increased more than any
(34:51):
other loan value. And that may sound like okay, but
if you think about it, mortgages tend to be the
one that grows the most because house prices have appreciated
so much of it. So the fact that the average
all alone has grown more than the average mortgage has
over that fifteen year period is telling.
Speaker 2 (35:06):
Okay.
Speaker 4 (35:07):
Secondly, obviously there is this double whammy. So not only
is the car more expensive, but then more recently interest
rates have been higher, right, and so you know then
I'm gonna have to pay more, not just for the
principle but also the interest. But I think one of
the things that has caught many consumers off guard is Okay,
so they're in the dealership, They're being shown some numbers.
(35:27):
Some people get it and they go like, okay, yes,
we can still do that, we can make it work.
We can just about max and stretch. Because also I
think people are trying to buy either the same that
they had before or slightly better. Right, not many people
like downgrading, okay, and so they think, well, it's the
same car, and yes, this is a little bit more,
but we can make and meet right by looking at
these numbers. But what they often forget about is that
(35:47):
insurance has gone up significantly, as have just the cost
of ownership. Repair costs have also gone up substantially, and
so when all those things then hit them, they can
be in a situation where we just can't make it work.
And so that's not good. And the key piece of
this too is, look, the good thing is, of all
the loan products, mortgages are performing pretty well. Okay, they
(36:08):
are increasing, but they're still much much lower than they
were back in twenty ten, or obviously much lower than
twenty eight twenty nine. So but if you default on
a mortgage, it takes some time before anything really happens. Right,
with an order loan, they will come and they will
take that car away from you. And given that, you
know so many people rely on that car to go
to their job, to make their income, to do other
(36:31):
tasks are important, like their shopping or taking their children
to the school or other things that they need to do.
People don't willingly just default on these auto loans, and
so I think it is a sign that correlates with
the fact that more households are struggling.
Speaker 2 (36:45):
To make in meet.
Speaker 3 (36:47):
How much insight do you have into leverage? And the
reason I ask is because we've seen an explosion in
buy now, pay later programs. Virtually every site you go
to now has three different options for getting alone for
a small amount, and only a few of those My
understanding is are actually reporting to the credit bureaus. So,
(37:09):
and I can also imagine if you're a lower income
person who is perhaps more pressured, you're probably going to
turn to a family member and say something like, hey,
can you loan me, I don't know, five hundred bucks
to make it to the end of the month. And
there's no way that credit scoring bureaus are going to
have insight into things like that informal loans.
Speaker 4 (37:27):
The credit data that comes from the credit bureaus is
still incredibly predictive and useful, but it doesn't capture everything.
Look Alex was on, I think recently a firm has
done provision data to the credit file, which is great,
but not all of them are there, and so you know,
I think there is still a lot that's not visible,
and that is definitely a concern to lenders. We've been
hearing their concerns about stacking and.
Speaker 2 (37:47):
Things of that nature.
Speaker 4 (37:48):
Obviously, these companies haven't got to where they are without
having some understanding of risk themselves, right, So they're not
going to necessarily just let someone who's not performing alone
take out another five BNPL loan, right, And the ability
of a consumer to go to all different BNPL providers
and use that there's a level of effort and sophistication
required to do that that certainly, I'm sure they're going
(38:09):
to be some, but I don't know how many people
fall into that category. Nevertheless, it is a concern that
more is invisible, and that's why I think being able
to pull in more than credit file data, such as
cashlow data, becomes really important. And so what we're seeing
is that they're more and more that are looking to
incorporate cashlow into the process because then they can have
a better understanding of the ins and outs, right, is
(38:31):
there checking balance going up or declining over time? Is
there does their income look like it's stable, does it
look like it's more sporadic? And so we have started
building now credit scores that also incorporate that type of data,
and that I think is going to come even more
important as we go forward because there are going to
be more and more ways that consumers can borrow. So
that's probably the better way to get that holistic view.
Speaker 1 (38:53):
Sure quickly, Auto delinguities, Are they at their highest level
ever right now? Or closed?
Speaker 3 (38:57):
Yep?
Speaker 4 (38:58):
I mean they are, yes, and they're continuing to go up.
So what we have seen though is that credit cards,
for instance, they went up a lot, so they went
up in good yeah delinquencies. Credit card delinquencies went up
a lot in twenty three twenty four, but they've started
coming down and we started seeing personal loans coming down
as well, so it's a very nuanced picture. So we've
seen the unsecuritized delinquencies that have started coming down this
(39:19):
year year of a year, but we're still seeing mortgage
and order loans continue to increase.
Speaker 3 (39:23):
Which is pretty top seat turvy when you kind of
think about it. But anyway, so the other thing happening
now is insurance rates going up because of the I
guess non extension of previous subsidies. And one thing you're
seeing all over social media is people posting their new
insurance rates for twenty twenty six, and I've seen some
(39:43):
crazy ones, you know, something going from six hundred dollars
to like eighteen hundred dollars a month. How much pressure
would you expect something like that to exert on the
consumer for next year?
Speaker 4 (39:55):
You know, for many it's going to be the straw
that could break the camels back.
Speaker 3 (39:59):
Right.
Speaker 4 (39:59):
It's just particularly if it's just like car insurance can
be a very significant outlay for many households. But you
know there's other insurance for homeowners, homeown insurance that's going up,
and particularly if they're in areas like California or Florida
where you know, natural disasters have led to an increase
in premiums above the national average. And so again I
(40:21):
don't see that there's any one thing that is going
to cause a house to fall down. But at the
same time, just there's more and more households where that
one unexpected increase puts them in a situation then makes
it impossible for them to make their payments that month
or for a number of months.
Speaker 1 (40:38):
Talk to us about the resumption of student loan payments
after I mean, you mentioned the importance of doing is
not only the stimulus but all the sort of forbearance
and so all this stuff nice. The one thing that
just kept getting pushed forever was the resumption of student loans.
How much when those numbers turned back on or were
those payments turned back on? What kind of impact did
that have and what are we seeing with student loans delinquencies.
Speaker 4 (41:01):
Yeah, it had a very big impact. And so you know,
if we look back before COVID, the average student condlinquency
rate was around that ten percent. It was sort of
wavering around between nine eleven percent in that sort of range.
And then obviously in this five year period of forbearances
and no reporting, because you had a period of a
(41:21):
year through twenty four where they were starting to need
to make payments, but they just weren't being reported. So
that's basically a long period of time where people just
got used to not having to make that payment. And
also a very substantial part of student luanbars who never
had ever made a payment because they, you know, they
finished their studies in a period when there was forbearance.
(41:41):
And so what then happened was they started trickling back
onto the credit file sort of mid February of this year,
and then I think you got the first batch really
kind of come in by May, and at that point
you saw the delinquency rates on the student loans that
were not in deferment was over twenty percent, so they
were over double what the historical norm.
Speaker 2 (42:01):
Was since then.
Speaker 4 (42:03):
Is that the highest level over It is the highest
level that we've seen going back a long long time.
So I remember there's been a lot of changes when
it was not federally mandated and privately owned, so I
don't have visibility going back as that far, but at
least in recent history, is absolutely the highest by a
very substantial amount. And that's not too surprising. But one
of the things that we've seen is that we expected
(42:23):
that there would be some people who go into delinquency
not because they intended to write, and so there were
some people who they moved and they didn't get their addresses,
or a lot of people that were confused because there's
so many mixed messages like we're going to be forgiven,
but that we're not and we're on a certain program,
but now that program doesn't exist anymore. So some people
have been able to then address that, and so it's
come down to this sort of seventeen seventeen and a
(42:45):
half percent, which is a good sign, and so that's improving.
But there still are people who are on programs that
have been killed, like the Safe program, and so they
are going to next year have to either get onto
another for Baron's program or start making payments, and so
maybe we haven't seen the full effect. Then basically of
the resumption those student loans, we've seen the biggest batch
(43:06):
come through, but there still are some more cohorts of
consumer borrowers that will either have their existing program expire
or that aren't being reported yet because the servicers are
trying to figure out exactly what's going on before they
report it to the credit bureau. So there still is
a little bit of lack of visibility there from on
the credit servicers side.
Speaker 1 (43:25):
Kurk, thank you so much for coming on outlast.
Speaker 2 (43:27):
There was great. Thank you. It's been a pleasure.
Speaker 1 (43:43):
It always comes back to insurance, doesn't it. That's always
like the little fly in the ointment is you know,
you buy this car and just like, Okay, here's the
car and here's the interest payment. Oh, I think we
can make the math work. You can't control what that
insurance payment is going to be. You have no idea
what it's going to be.
Speaker 3 (43:58):
This is my theory. Insure run the way, run the world,
they really do.
Speaker 2 (44:02):
You know.
Speaker 3 (44:03):
The other thing I was thinking, and we've written about
this in the newsletter, but one of the difficulties of
our current economic moment is there is so much division
and difference built into the aggregate. If you're just looking
at a single number a total, like if you've looked
at the average FIICO score of an American, it tells
you almost nothing now, because the individuals are so disparate.
Speaker 1 (44:27):
Yeah, and it really does come down to, you know, wealth, right,
Wealth is just such an important factor in the economy.
We always talk about income and income inequality, and of
course that's a real phenomenon, but wealth is such an
important predictor driver of anything. And it also goes to
show like how important like financial markets and asset prices
(44:47):
are to the real economy. And it gives me once
again an opportunity to say the stock market is the
economy because we live in such a wealth driven economy.
Speaker 3 (44:56):
You know, someone once wrote into me. I wrote something
aboutures on lower income people, and someone wrote into me saying, well,
why don't the lower income people own more assets. If
they did, they'd be in a better position. Have you
tried not being poor?
Speaker 1 (45:10):
Why haven't you tried just being rich? Why haven't you
tried buying in video twenty years ago? Why haven't you
tried buying a house in California in two thousand and
nine after the bus? It's that simple. Stop being poor? Seriously.
Speaker 3 (45:22):
The other thing I was thinking just on auto delinquencies.
I also think the trade down story is a big
piece here, which is I mean, a car from ten
years ago now is pretty decent. And I say that
as someone who owns. I think it's a Toyota rav
from like twenty eleven or something like that. Like it's
pretty dependable, and I don't really feel the need to
(45:43):
get like a fancy new car. And I imagine if
you're under pressure on your car loan, it's probably like
not that difficult necessarily to find an older car that
is somewhat reliable.
Speaker 2 (45:56):
I don't know, you know.
Speaker 1 (45:57):
The one thing though, So I have a car that
I bought twenty fifteen. It runs perfectly well. I would
not be surprised if it continued like no issues at all.
It doesn't have CarPlay integration.
Speaker 3 (46:10):
Oh, yeah.
Speaker 1 (46:10):
The one difference between older cars and newer cars is
that it's very nice, like having that no that that
interface where you have like a.
Speaker 3 (46:19):
Nice little speaker or something.
Speaker 1 (46:21):
Yeah, but what it doesn't have is that like really
nice interface with the map, like and I know that's minor,
but it's like, kind.
Speaker 3 (46:27):
Of do you have a map?
Speaker 1 (46:28):
It's a super U for those curious, and it's like,
you know, it's like they're in house. It's a crappy map.
It's not the really nice that Google Maps where it's
like you're really clear, and it doesn't have turn by
turn navigation. I know this sounds like kind of minor,
but it is very annoying. And like when I am
in a car that has like a modern off a
little tangent here, but I am in a car that
(46:50):
has like a really nice interface with a nice Google
Maps or Apple Maps and the Spotify integration. It's very nice.
And apparently we've taken it to the dealer they just
can it is un really Yeah, it's unupgradeable. There's no
for some reason, there's no way to put in a
new dash.
Speaker 3 (47:08):
Oh sorry, I thought you meant unupgradeable in terms of trading,
it in.
Speaker 1 (47:12):
For No, it's unbradable. It's like we cannot like this.
We could never install car play or whatever.
Speaker 3 (47:17):
Yeah, car, you know, my husband and I rented one
of those like big fancy trucks, pickup trucks, and I
was amazed by the amenities that are actually including like
the heated seats, I personalized heat what.
Speaker 1 (47:31):
Yeah, that's fancy. You know, it is very nice in
the wintery heated seeds. We just talked about cars for yeah,
maybe longer.
Speaker 3 (47:39):
But the thing is like, even for a car like that,
I just I cannot imagine spending like one hundred thousand
dollars plus whatever the interest rate actually is on something
like that.
Speaker 1 (47:49):
I remember that meme from like twenty ten. It's like
no one will ever do that was a big thing.
And the phone, right, really two things that people always
find a way to make a payment for the car
and the phone.
Speaker 3 (48:03):
I forget, right, Which is why I think you have
to look at something structural that's shifted. And I suspect
maybe it's the availability of you know, lots of older cars.
Speaker 1 (48:12):
But just one last point, it's I thought it was
very interesting. Ricardo is saying, it's like there's no obvious
catalyst for cataclysm. There's not obvious like, oh, here is something.
We are on the verge of consumer credit collapse. But
it is a story of just like steadily building pressure
such that if there is some sort of spark or something,
(48:34):
there is a lot of stress not to you know,
the resumption of student loans after five years, the fact
that the total loan price of the car has gotten
so high relative to people's income. All of these different things,
so you like have all these upwards dresses on prices.
You have all of this reliance obviously on accumulated wealth,
most notably stock market and home equity. So you have
(48:56):
a lot of things come together. They're not necessarily disaster
or anything like that, but the alignment of pressures is
there where things could potentially get back right.
Speaker 3 (49:05):
The consumer is much more fragile than they used.
Speaker 1 (49:08):
To they might have been a few years.
Speaker 3 (49:09):
Yeah, yeah, all right, shall we leave it there.
Speaker 1 (49:11):
Let's leave it there.
Speaker 3 (49:11):
Okay. This has been another episode of the aud Loots podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 1 (49:17):
And I'm Joe Wisenthal. You can follow me at The Stalwart.
Follow our producers Carmen Rodriguez at Kerman Arman, Dashill, Bennett
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(49:58):
listening it, Bend
Bloomberg Audio Studios, Podcasts, Radio News. Hello and welcome to
another episode of The Odd Lads podcast. I'm Joe Wisenthal
(00:23):
and I'm Tracy Alloway. Crazy. I feel like I just
do not have any feel right now on like the
state of the consumer.
Speaker 2 (00:31):
Really.
Speaker 1 (00:32):
I mean, you hear k shaped economy, labor markets slowing down,
then it's like lowest layoffs in years. You go outside,
everything looks booming. Like, I just have no feel right.
I know, consumer sentiment is terrible, but consumer sentiment is
terrible for years, and people keep shopping. I have no
sense of it right.
Speaker 3 (00:49):
Well, consumer sentiment actually came in higher than expected most recently,
big surprise, but I was gonna say, are you not
out shopping for Christmas present?
Speaker 1 (00:59):
It's insane.
Speaker 3 (00:59):
Yeah, there's a lot of people buying a.
Speaker 1 (01:01):
Lot of stuff, buying a lot of stuff.
Speaker 3 (01:03):
But I think this gets to the ke shaped economy point,
which is, if you have a cohort of wealthy people
who are buying more, it more than offsets the lower
income people who are buying less at lower prices. So
it's really hard to tell.
Speaker 1 (01:19):
It's really hard to tell. One thing that definitely feels
different if you look at aggregate measures of household balance
sheets like this is something that is very different than
sort of like pre grade financial crisis. The general view
is that the American consumer or the American household has
a very big cushion. There is a lot of home
(01:40):
equity built up. There is not a thin layer. Obviously,
anyone with money and any sort of investment account has
done phenomenally well. We're according to this December twelfth, yesterday,
I think the s P five hundred hit yet a
new all time highest. If you have any sort of
home equity build up, if you have any sort of investments,
you are doing very well. On the other hand, of course,
(02:01):
people are stretched from years of inflation. We know that
hiring has slowed down.
Speaker 2 (02:06):
We know that.
Speaker 1 (02:07):
You know, we see these headlines delinquencies for cars have
like shot up. But I've been seeing these headlines for years.
I don't totally know what they mean or how apples
to apples they are with the past. I just don't know.
I just I'm very confused.
Speaker 3 (02:19):
Yeah, you know what's really interesting to me just from
a financial perspective. Yeah, if you look at some of
the bonds that were actually built on consumer loans, the
weakest ones are now from the like twenty twenty to
twenty twenty two period.
Speaker 1 (02:35):
Oh see, this is another interesting element of measures like delinquencies,
and why I sort of wonder like how comparable they
are because okay, partly a delinquency measure is a snapshot
of a moment in time, right, a snapshot of health,
but it also inherently reflects something in the past, because
it reflects, you know, what we're lending standards at the time,
(02:56):
right exactly, so you know, and now it's a period
of interest rate booming itself. Yea, give the money to
anyone anyway. We need to get a better picture of
exactly what's going on. How stressed is the consumer. How
much do these delinquencies just reflect the profligacy of lenders
during the boon times when rates were nothing, et cetera.
(03:19):
And yes, we need to figure this out, especially we're
in the middle of shopping season and all that stuff.
So I'm very excited to say, we really do have
the perfect guests. We're going to be speaking with Recard Bondebo.
He is the executive vice president, chief strategy officer, chief
economist Advantage Score, a credit scoring company. Recurd Thank you
so much for coming on the podcast.
Speaker 4 (03:37):
Thank you for Havmius Snana.
Speaker 1 (03:38):
What is Vantage Score a US credit scoring company? What
do you do there?
Speaker 4 (03:43):
So, we're the largest credit scoring company in the United
States and we have founded almost twenty years ago by
the Free Credit bureas, TransUnion, ECOFAX and Experience, and we
were sort of created with a very specific mission in
mind to drive greater competition and credit scoring. Prior to us,
there really wasn't a lot of choice in space. We're
also there to drive more innovation and create the most
predictive scores, which was a big ass from the banks
(04:06):
at the time, and also to be able to expand
access to millions to enable everyone who really is credit
worthy to be able to get access to credit products.
Speaker 3 (04:14):
You mentioned the banks just then, can you expound a
little bit more on your customer base.
Speaker 4 (04:18):
Yes, so we used Obviously, the primary use case that
most people think about when it comes to credit scores
is for lending. Right when you know you're applying for
a loan and they want to evaluate whether or not
you're going to be able to perform on that loan,
they will often pull your credit score as part of
that process. But It's used in many other stages as well. So,
for instance, many people who are applying to rent in
a new apartment building may also get asked for it.
(04:40):
When you are trying to get a utility.
Speaker 3 (04:42):
Bill in New York, you get asked for it.
Speaker 4 (04:44):
Yes, you certainly do. And utility bills, telephones, anything that
involves a long term commitment on payments generally. Now you'll
often get asked to provide your credit score.
Speaker 1 (04:54):
So just explain for the way you were started by
whom twenty years ago, we.
Speaker 4 (04:58):
Were a joint venture and ECHOFACTX and TransUnion, the three
national credit reporting agencies.
Speaker 1 (05:03):
So what is the difference between these major companies that
we've all heard of that provide a credit score, et cetera,
that they such founded? You like, what do you do
differently than them?
Speaker 4 (05:14):
Well, so what they do is they're the ones who
collect all this data from lenders and others on your
credit performance. Right, So they call credit buros they collect that.
They're highly regulated. But then most lenders can't just make
sense of all of that data on its own. They
need some guidance to have to translate that into a
what does that mean? Right?
Speaker 2 (05:33):
Okay?
Speaker 4 (05:33):
And so that's where a scoring algorithm comes into effect. Right,
And so the scoring algorithm helps to take in all
these hundreds of different factors about you to try to
then determine what does that mean about your propensity to pay?
Speaker 3 (05:45):
Okay, you mentioned predictive analysis as well, What exactly is
that and what's that based on?
Speaker 4 (05:51):
Well, so when credit scores are created, right, the aim
the goal is to try to evaluate what is the
likelihood that somebody's going to default on a payment over
the next time twenty four months. So when you see
that score, the score is actually a translation of a
probability right or on odds right to evaluate what is
that risk?
Speaker 3 (06:10):
And how did we end up with the system of
FICO scores in the US, because it has like an
interesting history.
Speaker 4 (06:17):
Well, back in the day fair Isaac they created the
first sort of known credit score. They were the first
ones to realize that there was a.
Speaker 1 (06:24):
Looking at fi and fair Isaac is that yes?
Speaker 4 (06:29):
Okay, yes, And so let's go back a bit, right,
So in the old days, lending was not necessarily the
most fair system that there was, right, or want to
call up your previous employer, They may call your landlord.
They may just ask around and if they don't know
anything about you. You know, there was a lot of
judgment involved in.
Speaker 3 (06:46):
Lending, a lot of racial discrimination.
Speaker 4 (06:48):
Well that certainly was built into that system, right, And
so then there was a law created, the Fair Credit
Reporting Act, that said, like, you can't do that, you
need a better system that is fair and that is
a better quantitative ability to assess people's risk, right, And
that created then this need to be able to consolidate
all this quantitative information in a way that lenders could
easily use it. So Faiko was the first fair ISAAC
(07:10):
at the time was the first to create that and
they did very well doing so. But you know, then
there was a need for competition innovation, and there was
a lot of frustration around the time of twenty years
ago that there was only one game in town and
it didn't score about twenty percent of the US population.
It still doesn't, And then a lot of lenders were
felt frustrated, like if it doesn't work for twenty percent
of the population, there's a problem. We need something different.
(07:33):
And so then buwers took the unusual step of actually
coming together to create an alternative and that became Advantage School.
Speaker 3 (07:39):
Interesting can I as a consumer go credit score shopping.
Speaker 4 (07:44):
So first of all, there's different ways to use it, right,
So a lender will typically choose the credit score that
they're going to use for being able to underwrite alone
with you, and often they'll use many more factors than
just a simple credit score, right, particularly the more sophisticated ones. However,
when you're trying to understand what your situation is, you
can there are lots of different places you can go.
You can either go the credit bureaus, you can go
(08:05):
to the likes of credit Karma. There are many different
services out there.
Speaker 3 (08:08):
But I can course the lender to look at a
specific Yeah, look at this one over here, it's great,
get a second opinion.
Speaker 4 (08:15):
No, I'm afraid not. That's not how it works. It's
it's really you know, the lenders try to determine what
is the most appropriate score for their product. And there
are many many different schools out there. There's schools that
in some cases built specifically for types of products like
order loans, and the other schools that, like our schools,
that are generic that can be used for any type
of product.
Speaker 1 (08:31):
So you collect more data. And you mentioned that there
is this wide swath of the population that wasn't being
captured by the credit bureaus? What do you do additionally
on top of them to expand the pool find potentially
credit worthy borrowers that they had been missing before.
Speaker 4 (08:49):
So the thing is that the quality and the types
of data that's been collected by the credit bureaus has
improved significantly over time. OKA, And so when we started
creating our algorithms, we're in, you know, the current version
that's now being adopted for mortgages, the version four we're
releasing version five this year.
Speaker 2 (09:05):
We actually go.
Speaker 4 (09:06):
And rewrite the whole thing each time so that each
time we can come up with the most accurate way
based on the current data is available and our current
ability to understand how consumers are behaving, because that behavior
changes of a time. Other companies, what they've done is
they've built a model long time ago. They don't like
to necessarily revealed everybody how it works, the secret source. Right,
(09:26):
So when you're seeing there's a chief risk officer and
there's a new model coming along, either you want to
understand that it's going to pay very similarly to the
previous one to be okay with it, or you need
a lot of transparency and how it works, so you
can get comfort in this new model. So I think
there's the big divergence and strategy. We go back to
boots and redo everything from scratch each time, but in
the same time provide an awful lot of transparency and
(09:47):
a lot of tools so that lenders can get a
really good understanding of exactly how this is going to
work and how it's going to behave in different situations
and they can test it out right, whereas the other
one is still working with many limitations that have been
in place since the very first models. And because of
those limitations, that's a big difference in why we've scored
a lot more people. So I'll give you a very
concrete example. So, for instance, one of the limitations that
(10:10):
the others have is that if you haven't had any
credit activity for the past six months, that you're not
going to get a score. So you just imagine somebody
that works with the military has been deployed overseas or
anything else.
Speaker 2 (10:22):
Right.
Speaker 4 (10:22):
But the good thing is, starting about fifteen years ago,
the Bureau started click and storing data so we could
use time serious data because who'd have thought that time
serious data could be useful in prediction, right, completely strange idea.
Speaker 2 (10:35):
Right.
Speaker 4 (10:36):
So with finan Score four, we started using trended data
times series data, and with that, obviously we can see
back twenty four months. So yes, if there's a gap
in six months of history, it's important, but we're still
able to see what happened before then, right, And that
gets rid of tens of millions of people when you
have that constraint. There are the constraints in there too,
So people that are new to credit, so if they
(10:56):
haven't had a full six months of history again, they
won't get scored. Aren't any tradelines, they won't get scored, right,
And so what we've been able to do is to
deal with thos constraints in a different way by a
using time series data, b using some other data points.
So we were the first to use utility payments and rent.
I mean, who'd have thought that your ability to pay
your rent could somehow again be useful and trying to
(11:19):
assess your risk, right, And so including those new different
types of data, realizing that these constraints can be changed
now that you have time series data. But then also
guess what you know? Math has evolved as well, okay,
And so you know what we realized too was that
you can be really smart and use some new methods,
like you know, some AI methods for instance, like clustering,
(11:39):
to really understand, well, look, here's a group of people
and they behave in a certain way, and by doing
that in a better way, we can then figure out
what is the best way to measure this group of
people here versus this group of people here, and doing
that well enables you to build much more predictive scores.
And so that's an important yance too that not a
lot of people always realize is that it's not one
formula that's calculating everybody's score. People will get depending on
(12:04):
what their credit file looks like and their history looks like.
They'll get divided into different segments and then each segment
is scored according to that and that again increases the
ability to score people accurately and score more people, like
in our case it's thirty three million more people they're
able to score, so it's quite substantial.
Speaker 3 (12:36):
How did the models deal with breaks in previous consumer patterns?
Because we have seen some major ones in recent years.
So after the pandemic, we had a phenomenally tight labor market,
and we saw a lot of wage growth for lower income,
a lot of spending that was sort of unprecedented in
many ways. How do models actually incorporate that sort of
(12:59):
big shit shift in the trend.
Speaker 4 (13:01):
So I think that's something really important to try to understand,
and it's not easily understood by many. So give me
a second here, I'll try to break this down.
Speaker 1 (13:10):
When people say give me a second, I have to
break this down. Please break it down.
Speaker 3 (13:14):
You have a minute.
Speaker 1 (13:15):
A minute.
Speaker 4 (13:16):
Honestly, that's kind of what I'm enjoy most about listening
to your podcast. And so look, the first thing to
understand is that this credit score is not an absolute
measure of risk. It's a relative measure of risk. Let
me break that down for you, right, So what it
means is that you know a score. If somebody has
scored seven to twenty in one month, and then somebody
else has scored seven twenty three years later, the risk
(13:38):
will be different. And the reason for that is very deliberate.
When we are evaluating a person, right, because of the
laws of the Fair Credit Reporting Act, right, we're allowed
to look at the things that are about you. But
there are things that are going out on at the
same time in the economy that impact risk of the
population as a whole. Right, So that's that's why it's
(14:00):
so important when you're looking at things like credit scores
to understand when was that score pulled right, because the
score of seven twenty in twenty seventeen had a very
different characteristic of a score of seven twenty in twenty
twenty two. Okay, now, but the thing to bear in
mind is it's an excellent relative measure of risk. So
at any one given point in time, you know, somebody
(14:22):
with a seven twenty is going to be much better
performing than somebody at six thirty, And at the same time,
somebody eight forty is going to be much better than
both of them. Okay, And that holds consistently true, and
so that's why it's very important to include when you're
making lending decisions. But lenders have to be thoughtful, right,
they have to. As they're making decisions about, you know,
how many people they want to be able to underwrite,
(14:42):
and how to think about risk, they need to also
start thinking about these external factors as well, so that
they can then set their underwriting criteria to meet the
kind of level of risk that they're willing to take on.
Speaker 1 (14:55):
I had never thought about this, but of course that
makes so much sense. So it's like I could have
an excellent credit history, I could have ex employment pay
on my rent, but for example, if the economy is
going down the tubes, I may still yet be a
risky credit because I may lose my job at some point.
And so this idea that it kind of has to
be relative because the underlying conditions that affect everyone through
(15:19):
outside of our control, but they are still important from
the perspective of the lender exactly.
Speaker 4 (15:24):
And at the same time, if you get declined for
a credit card, you can't be told that the reason
you're getting declined is because unemployment has hit five percent. Okay, right,
that doesn't work. The laws are very specific. The reasons
for why you're not getting the top score have to
be explained, and they have to be based on attributes
and data obviously that are specific to you.
Speaker 3 (15:44):
What's the most important external factor when it comes to
credit scoring, Because I've heard arguments for obviously the labor market,
the unemployment rate, but also wage income and therefore real
disposable income. How do you weight those different factors?
Speaker 4 (15:59):
I mean, I think if your lender, it's going to
really depend upon what types of consumers you're lending to, right,
particularly now we're seeing such divergence across consumers in terms
of who's doing well.
Speaker 2 (16:12):
And who isn't.
Speaker 4 (16:13):
And so, for instance, if you are a lender focused
on people that are kind of below prime let's say
that not completely subprime, but that near prime group, and
you're focusing on ail loans, and you know you're in
regions like Texas or in certain areas, then obviously understanding
the economic conditions that are affecting those people, like a
(16:34):
lot of those people would be working in certain types
of industries. What is employment like in those types of industries, right?
Or is it people that are in the gig economy?
Speaker 2 (16:41):
Right?
Speaker 4 (16:42):
And so it is quite nuanced and it's not necessarily
one thing, and it's going to depend. Whereas on the
other extreme, you know, if you're handing out black cards
and your audience is incredibly affluent, then again it's less
about the risk because at that point your risk of
default is probably one end ten thousand. So they're just
trying to make you sure that it is absolute the
risk free.
Speaker 3 (17:01):
Right. So someone in a highly cyclical industry, Like I
don't know, truck drivers in Texas or something that we're
taking out auto loans would probably be seen as riskier
or the labor market would depend more for them, whereas
if you're taking out a black AMEX card or something
like that, probably real disposable income.
Speaker 2 (17:20):
Yeah, okay, so.
Speaker 1 (17:22):
You mentioned different segmentation. People talk about this K shaped economy.
Is that real or is that immun.
Speaker 4 (17:27):
I absolutely believe so, but I think that it's a
little bit more nuanced. And so, you know, one of
the things that we spotted late last year and we're
tracking into this year was that we want the first
to see that it was a K shaped economy. But
a lot of people were making the assumption that the
K shaped economy was being driven by income levels. But
when we were looking at the date at the time,
(17:48):
we were seeing that those that were in sort of
the higher income level in our case that's one hundred
and fifty thousand above. So that's not your you know,
people who are running hedge funds and sept and you know,
but still it's the relatively better to do cohort. They
were actually seeing the highest year of year increases in
delinquency rates at the time, so we knew that okay,
home in a second, it isn't as simple as this,
(18:09):
So you know, we spend a lot of time and
sort of a lot of banks and we kind of
collaborated with them to try to understand what's really then
the differentiator. And then what seemed to be really a
part of this is wealth. So you know, a lot
of people don't necessarily differentiate income and wealth, but they
are separate. And so you know, when you're looking then
at a high income cohort at the time to try
(18:29):
to see, like, okay, well, which ones are doing well,
which ones weren't. Home ownership was the biggest differentiator because
they had a bigger cushion, something they can rely on.
And then obviously other aspects as well, like stock ownership
and small business ownership, et cetera. But home ownership is
the one that has the bigger effect because there's more
people in the US economy that on a home then
let's say, has a stock portfolio.
Speaker 3 (18:51):
Right, talk more about mortgage rates, because this feels pretty
key when you're talking about the K shaped economy, which
is if anyone, anyone who bought their house before twenty
twenty is probably a very lucky person and has locked
in a low mortgage rate. I think mortgage rates are
still even after the rate cut, we're at like six
percent or something versus I think at one point they
(19:12):
got down to like three percent.
Speaker 1 (19:14):
Right after the filter.
Speaker 2 (19:16):
Yeah.
Speaker 1 (19:17):
Crazy, yeah.
Speaker 3 (19:18):
And so if you bought a house, then you actually
have this like massive cushion as you put it, versus
someone who's buying a house now or in the past
couple of years.
Speaker 4 (19:28):
In a way, I see there's a bit of a
silver lining when it comes to housing. Right We have
seen rates come down this week. Hopefully they will continue
to come down next year. There's a lot of debate, obviously,
even within the Federals to exactly the speed at which
that's going to be accomplished, and obviously there are many
other factors that can impact that. But the reason we
see a silver lining is for two reasons. Okay, as
(19:49):
interest rates comes down, Obviously, for people to own homes,
that's their biggest monthly payment right now, there's much point
for many to refinance given that where the interest rates are.
But if it comes down a bit more, it'll make
much more sense for a large tranch of homeowners that
have higher interest rates to be able to make that switch.
So we'd expect a bit of a refinancing boom as
(20:10):
it hits a certain level. But the other thing that's
really exciting about what's happening in the home ownership space
is that this year the FHFA changed the rules about
what credit scores can be used in mortgage. So historically
they've used a very old version that's from the nineties,
of the classic score in mortgage applications, and that was
actually not deliberately done, So it was just that we
(20:31):
got written into the rules and then since then as
a recommendation, but then it became kind of the de
facto and monopoly in that space. And the problem is
that's a model that went through the last crisis, and
the Federal Reservist and Lewis actually found that it didn't
work well at all in that situation. In fact, it
saw a bigger rate of increase in delinquencies amongst those
that were prime than it did amongst those that were subprime.
(20:53):
So the rate of increase, which is not how a
model is supposed to work, surprisingly right. And so what's
happening now is that they have allowed for varnish four
to be used. And the reason I say that is
that it's a because, as I mentioned before, a lot
more people will now have the ability to get access
to home ownership, so that will create a bit more demand, right,
which is great. And the other thing to think about
(21:13):
too is who are these people that get access to this, right,
It's a lot of people that are not necessarily in
the areas that have been so crazy with house price increases.
Speaker 2 (21:23):
Right.
Speaker 4 (21:23):
There are a lot of rural communities like so if
you look at the state where there's the biggest difference
between scoreable people with the new score, it's actually West Virginia.
And so those economies could certainly do really well from
a change to more people having home ownership. And then
the second thing too is obviously our MBS is really important.
Had some challenges back in two thousand and eight, two
(21:45):
thousand and nine, right, and so having a better.
Speaker 3 (21:48):
Performing model a very smardest way about I.
Speaker 1 (21:52):
Think they came up a couple of times.
Speaker 4 (21:54):
I was educated in the UK. Pardon me, we have
a tendency to understate things. But so you know, having
a newer, more proven model, one that's you know, worked
so well in credit card and other things for the
past eight years, it's become the most used model in
many other segments. So having a proven model that newer
and more predictive is should help as well with reducing
the systemic risk in the R and BS market.
Speaker 1 (22:16):
Just to go back very quickly, because I don't know
it sounded important, can you just clarify a little bit more?
What is this rule change such that could unlock additional
source of demand here?
Speaker 2 (22:27):
Okay?
Speaker 4 (22:27):
So when a bank before wanted to submit loans to
Fannie May and Freddy Mac, okay, they could only submit
those loans using the Phyco classic score. Okay, Okay, there's
actually two three different scores going too that another point.
But anyway, and there used to be like a cutoff
that if you didn't have six twenty then you couldn't
be able to submit it. You could go to an
FAH loan, but those are more expensive, right, but you
(22:48):
couldn't necessarily get a normal conforming loon the gost to
Fani Man and Freddy Mac. So that's not changed. The
first of all, that minimum limit of FIKO has been removed,
and now they are just updating all the pipes to
allow them to use varnished score as a choice. So
now there'll be a choice. Lenders can choose which score
they want to use, and they can make their own
evaluations about which one performs better.
Speaker 1 (23:08):
Okay, so let's go back to starting at the end
of last year, and you say that increase in delinquencies
among people decent incomes, maybe they didn't have as much. Well,
talk to us about the numbers. How big were these numbers,
how much did they catch people by surprise? And what
is the story there about why there was this delinquency pressure.
Speaker 4 (23:26):
Well, so the good thing is it's evolvedtle bit since
the end of last year. But you know, when we're
looking at this data, then again, look, high income earners,
not surprisingly have lower delinquency rates than middle income earners,
and that had themselves lower delinquency rates than the lower income. Right,
But not a lot of people were looking at that
kind of year of a year trend and the momentum. Right,
(23:47):
I'm always looking at momentum because I'm trying to get
an early read on kind of how things are developing,
and so at the time, Actually, let's go back a
little bit because I think it can explain a little
bit more about what's going on in the economy. Is
that a right, So when the pandemic happens, right, a
lot of stimulus comes in, a lot of forbearance programs
are put in place. As a result of that, so
(24:08):
many people's credit health and the way that they appear
on the credit files improved dramatically. They were paying down
their credit cards, they were building up their savings. It
was a good situation, temporary but good. But then twenty
one twenty two starts creeping in and we start seeing that, okay,
this is not a persistent situation. This was a one off, right,
(24:31):
and we started seeing delinquency rates starting to come up again.
Speaker 2 (24:34):
Right.
Speaker 4 (24:35):
What we saw, which shouldn't be too surprising, particularly given
that that was when inflation was kicking in in a
big way, was that those that were initially impacted and
who were sewing the biggest rises were the lower income households.
Speaker 2 (24:47):
Right.
Speaker 4 (24:47):
So for the first sort of you know, six months,
nine months, that was the group that was seeing the
biggest year of a year increases. But then what we
started seeing was that come twenty twenty three forward, we
actually started seeing that then the middle and hiring income
households were starting being impacted too, and that's probably related
to the fact that lower income households had less disposable income,
(25:11):
but they also had less savings put away, so that
you know, they're the first to feel the pain. But
then when there's this consistent imbalance between your inflows and
your outflows, right, even if you have you know, thirty
thousand or fifty thousand that's put away, that's going to
start depleting. And that's what we started seeing happening even
with these hiring income households because at the same time,
(25:33):
you know, they were hit by so many pressures. Right,
biggest rent increases I think we've ever seen came into
effect those few years after COVID. Right, we saw things,
you know, car prices, costs of auto financing going up
through the roof, and then various other costs also went
up substantially. And so it's not that hiring income people
were immune from this.
Speaker 2 (25:52):
Right.
Speaker 4 (25:53):
Also, as an economist, a lot of people always talk about, hey,
when inflation kicks in, it disproportionately impacts lower income households
because the cost of bread, the cost of milk, et cetera.
You know, it's not like high income people buy milk
that's one hundred times more expensive.
Speaker 2 (26:08):
Right.
Speaker 4 (26:09):
But the reality is is if you look at people's
big outlays, many of the hose actually scale with income.
Rent for instance, people that earn more tend to rent high.
Other big outlays such as childcare, education and other things,
they also have been scaling more with income. Now, obviously
there's a level of income that you know that does
not apply to but if we still talk about that
(26:30):
cohort one hundred and fifty to two hundred and fifty
or so of household income, they're definitely seeing that they
felt the pain too. It took them longer before it
started impacting their delinquencies, but they did start feeling the pain.
The good news though, is that as we started looking
at the second half of this year, right, so, we
still saw those delinquencies in high incomes rising very heavily
(26:51):
at the beginning the first half.
Speaker 2 (26:52):
Of this year.
Speaker 4 (26:53):
But since July, and I've got data from October, so
of the three of the four months since July, seen
that high income households came down. So that's a good sign.
And the reason I say that's a good sign is
not because I'm a fan of making the case shaped
economy even more so but the fact that so much
of the US economy is driven by spending. You mentioned
earlier that high income households disproportionately impact that, and so
(27:18):
if that dries up, that has a knock on effect
on the whole economy. So to the fact that we're
seeing that cohort that those delinquencies are starting to come down,
I think there's some light at the end of the tunnel.
Speaker 3 (27:44):
I always wondered how useful are big shopping events like
Black Friday or Christmas in terms of gauging consumer sentiments.
So you always see the headlines. You certainly saw them
this year, you know, record Black Friday spending, But then
you also see people break down that spending and say, well, actually,
it's because everyone is so pressured they really need the
(28:05):
low prices, so they're buying everything. Now, how useful is
something like that to you?
Speaker 4 (28:10):
You can always see trends, right, and so from one perspective,
it is always good to look at a number of
different things, such as spending on Black Friday, Cyber Monday,
et cetera, because there are nuances into how people have
been spending for those weekends over twenty years. But still,
if you look at the last three years, you can
start to see things that are happening.
Speaker 2 (28:29):
But the thing that.
Speaker 4 (28:30):
I haven't been able to get my head round is
how much of that year of year increase in spending
and the holidays is driven by the prices of the
goods going up versus people buying things that would have
traditionally been more expensive or splurging more. That for me
isn't obvious. And I think that again when trying to
understand how the economy is going, it's so important if
(28:51):
you're looking at from a spending perspective, to actually look
at the different merchants. Right, So, how's McDonald's doing, what
are the trends there, what's happening in higher and how
is LVMH doing versus Walmart, et cetera. Because again, you know,
even though I said that, you know there's a silver
lining and the high income consumers are seeing declines. Middle
(29:12):
income have come down, but they're still increasing, and low
income are persistently high around eight percent year every year
increases in their delinquency rates, and so we're probably next
year going to see more households struggling to make ends
meet than we saw this year. I still don't think
that there's just looking at the trend, it's going to
(29:33):
be any kind of major breakpoint. But the thing to
bear in mind with that, though, is that if you
look at here this situation, and you look at for instance,
JP Morgan published that, you know, the amount of cash
people have in their checking accounts is coming down. So
it just means that there's more of a challenge if
(29:53):
there's a big shock to the system at some point.
Not that I can foresee any shock to the system,
but it's always something to be a little bit wary
before going.
Speaker 1 (30:01):
I want to go back to something you said very quickly.
You said, Okay, intuitively, people with higher incomes that are
going to have delinquencies at a lower rate than people
with middle incomes, and they're going to have delinquencies a
lower rate than people with lower incomes. That's not intuitive
to me, actually, because I would also imagine that underwriting
is very different, et cetera. It's not obvious to me
(30:22):
why higher income people default less than lower income people,
because I would imagine lenders know their income and they're
going to scrutinize the loan of a lower income person
much more intensely, et cetera. So why should this trend
exist given that they don't get the same loan terms,
their same loan availability, No.
Speaker 2 (30:42):
They don't.
Speaker 4 (30:43):
End You know, a high income household will buy typically
more expensive than a car than a low income household will,
but higher income households tend to have more of an
ability to squirrel some money away, or they tend to
have other assets that they knows that.
Speaker 1 (30:58):
The lender knows that the higher income household is going
to have likely more savings, and the lender knows that
the householder's in a very tight income probably has very
little cushion in the form of what we call it wealth.
And so why doesn't that just get baked into the
underwriting standards.
Speaker 4 (31:16):
Well, in ways it does, right, And so you know,
when you're underwriting a loan for let's say somebody who
is high income and has a good credit history, your
expectation of their default is going to be incredibly low, right,
So that's built into the pricing, and that's built into
and you obviously don't just look at a quiet score.
You look at what their income is and various other
important metrics to be able to determine the appropriate amount
(31:38):
that you will lend them.
Speaker 2 (31:39):
Et cetera.
Speaker 4 (31:39):
Right, but high income consumers may not need to take
on as much debt as a proportion to their income
as lower income households to get through what they need
to do. Right, And so if you look at, for instance,
a high income household, how much of their even though
for instance, probably housing and car costs are some of
the biggest outlays, they have proportion they're probably less than
(32:01):
for lower income households, right, And so you know, a
lot of it has to do with that proportionality. But
then also just that again, they will tend to have
a bit more reserves so that they can ride through situations.
Speaker 1 (32:13):
Talk to us about autodelinquencies. Those have been rising and
obviously there's a lot of lending going on. Again, Tracy
mentioned the sort of twenty twenty to twenty twenty two
vintage car prices themselves are going up. So not only
have the raids gone up, but we've seen a tremendous
amount of auto inflation. So sort of stress at every level.
We see the numbers going up. What do those tell us?
Speaker 4 (32:35):
It's a fascinating story. You know, there's been a lot
of interest that was paid attention to order loans because
you know, suddenly in twenty twenty two that we started
seeing these autolone delinquencies going up much faster than other
types of loan delinquencies, and it was having a profound
effect obviously on auto lenders and the economy as a whole.
And you know, everybody's trying to explain, well, you see,
we we've got a bit too loose during the period
(32:56):
of COVID and other things. And they started adjusting their
lending criteria, right, so they did adjut their lending criteria
around twenty twenty three for most of them, but then
we still saw that despite that, the delinquis rates kept
persistently increasing, and then when we looked at the data,
we actually saw that but they had actually had an
impact by adjusting their lending criteria. We saw that the
(33:19):
delinquiscy rates among subprime all the loans reduced quite dramatically
after that, so they did have that effect. But we
saw that the delinquency rates on near prime and prime
continued to go up, and that was what drove that increase,
and so we realized there's something more going on here,
and also why is it it's so different? So we
went back a long time. We went back to twenty
(33:39):
ten to try to understand kind of what's been going on,
because not many people look at it from that time scale.
But it's actually quite fascinating because back in twenty ten,
auto had the best performance of any loan product. At
the time, it was the least risky.
Speaker 3 (33:51):
This was always the narrative that Americans will never give
up their cars, even if they lose their job. They
can sleep in their car and live in the car,
which is very dystopian, but that's I remember hearing that
story literally from a banker, a banker who was actually
working on bundling phone loans, and he was.
Speaker 4 (34:07):
Like, so at that time it performed well, right, and
people did not default as much on that as on
other products. But then we've seen it has transitioned over
that fifteen year period to now in the first quarter
this year, it was the riskiest credit product out there,
and it then subsequently student loans started coming in, and
(34:30):
that's another story. Those delinquency rates are at historic levels.
But on the auto loans side, we then try to
understand what's causing this, right, and so what we're seeing
was that there's a number of factors, some of them
obvious some of them a little bit more subtle. Right,
the average cost of a car has gone up an
incredible amount, And what we're seeing is then the average
loan value for auto loans has increased more than any
(34:51):
other loan value. And that may sound like okay, but
if you think about it, mortgages tend to be the
one that grows the most because house prices have appreciated
so much of it. So the fact that the average
all alone has grown more than the average mortgage has
over that fifteen year period is telling.
Speaker 2 (35:06):
Okay.
Speaker 4 (35:07):
Secondly, obviously there is this double whammy. So not only
is the car more expensive, but then more recently interest
rates have been higher, right, and so you know then
I'm gonna have to pay more, not just for the
principle but also the interest. But I think one of
the things that has caught many consumers off guard is Okay,
so they're in the dealership, They're being shown some numbers.
(35:27):
Some people get it and they go like, okay, yes,
we can still do that, we can make it work.
We can just about max and stretch. Because also I
think people are trying to buy either the same that
they had before or slightly better. Right, not many people
like downgrading, okay, and so they think, well, it's the
same car, and yes, this is a little bit more,
but we can make and meet right by looking at
these numbers. But what they often forget about is that
(35:47):
insurance has gone up significantly, as have just the cost
of ownership. Repair costs have also gone up substantially, and
so when all those things then hit them, they can
be in a situation where we just can't make it work.
And so that's not good. And the key piece of
this too is, look, the good thing is, of all
the loan products, mortgages are performing pretty well. Okay, they
(36:08):
are increasing, but they're still much much lower than they
were back in twenty ten, or obviously much lower than
twenty eight twenty nine. So but if you default on
a mortgage, it takes some time before anything really happens. Right,
with an order loan, they will come and they will
take that car away from you. And given that, you
know so many people rely on that car to go
to their job, to make their income, to do other
(36:31):
tasks are important, like their shopping or taking their children
to the school or other things that they need to do.
People don't willingly just default on these auto loans, and
so I think it is a sign that correlates with
the fact that more households are struggling.
Speaker 2 (36:45):
To make in meet.
Speaker 3 (36:47):
How much insight do you have into leverage? And the
reason I ask is because we've seen an explosion in
buy now, pay later programs. Virtually every site you go
to now has three different options for getting alone for
a small amount, and only a few of those My
understanding is are actually reporting to the credit bureaus. So,
(37:09):
and I can also imagine if you're a lower income
person who is perhaps more pressured, you're probably going to
turn to a family member and say something like, hey,
can you loan me, I don't know, five hundred bucks
to make it to the end of the month. And
there's no way that credit scoring bureaus are going to
have insight into things like that informal loans.
Speaker 4 (37:27):
The credit data that comes from the credit bureaus is
still incredibly predictive and useful, but it doesn't capture everything.
Look Alex was on, I think recently a firm has
done provision data to the credit file, which is great,
but not all of them are there, and so you know,
I think there is still a lot that's not visible,
and that is definitely a concern to lenders. We've been
hearing their concerns about stacking and.
Speaker 2 (37:47):
Things of that nature.
Speaker 4 (37:48):
Obviously, these companies haven't got to where they are without
having some understanding of risk themselves, right, So they're not
going to necessarily just let someone who's not performing alone
take out another five BNPL loan, right, And the ability
of a consumer to go to all different BNPL providers
and use that there's a level of effort and sophistication
required to do that that certainly, I'm sure they're going
(38:09):
to be some, but I don't know how many people
fall into that category. Nevertheless, it is a concern that
more is invisible, and that's why I think being able
to pull in more than credit file data, such as
cashlow data, becomes really important. And so what we're seeing
is that they're more and more that are looking to
incorporate cashlow into the process because then they can have
a better understanding of the ins and outs, right, is
(38:31):
there checking balance going up or declining over time? Is
there does their income look like it's stable, does it
look like it's more sporadic? And so we have started
building now credit scores that also incorporate that type of data,
and that I think is going to come even more
important as we go forward because there are going to
be more and more ways that consumers can borrow. So
that's probably the better way to get that holistic view.
Speaker 1 (38:53):
Sure quickly, Auto delinguities, Are they at their highest level
ever right now? Or closed?
Speaker 3 (38:57):
Yep?
Speaker 4 (38:58):
I mean they are, yes, and they're continuing to go up.
So what we have seen though is that credit cards,
for instance, they went up a lot, so they went
up in good yeah delinquencies. Credit card delinquencies went up
a lot in twenty three twenty four, but they've started
coming down and we started seeing personal loans coming down
as well, so it's a very nuanced picture. So we've
seen the unsecuritized delinquencies that have started coming down this
(39:19):
year year of a year, but we're still seeing mortgage
and order loans continue to increase.
Speaker 3 (39:23):
Which is pretty top seat turvy when you kind of
think about it. But anyway, so the other thing happening
now is insurance rates going up because of the I
guess non extension of previous subsidies. And one thing you're
seeing all over social media is people posting their new
insurance rates for twenty twenty six, and I've seen some
(39:43):
crazy ones, you know, something going from six hundred dollars
to like eighteen hundred dollars a month. How much pressure
would you expect something like that to exert on the
consumer for next year?
Speaker 4 (39:55):
You know, for many it's going to be the straw
that could break the camels back.
Speaker 3 (39:59):
Right.
Speaker 4 (39:59):
It's just particularly if it's just like car insurance can
be a very significant outlay for many households. But you
know there's other insurance for homeowners, homeown insurance that's going up,
and particularly if they're in areas like California or Florida
where you know, natural disasters have led to an increase
in premiums above the national average. And so again I
(40:21):
don't see that there's any one thing that is going
to cause a house to fall down. But at the
same time, just there's more and more households where that
one unexpected increase puts them in a situation then makes
it impossible for them to make their payments that month
or for a number of months.
Speaker 1 (40:38):
Talk to us about the resumption of student loan payments
after I mean, you mentioned the importance of doing is
not only the stimulus but all the sort of forbearance
and so all this stuff nice. The one thing that
just kept getting pushed forever was the resumption of student loans.
How much when those numbers turned back on or were
those payments turned back on? What kind of impact did
that have and what are we seeing with student loans delinquencies.
Speaker 4 (41:01):
Yeah, it had a very big impact. And so you know,
if we look back before COVID, the average student condlinquency
rate was around that ten percent. It was sort of
wavering around between nine eleven percent in that sort of range.
And then obviously in this five year period of forbearances
and no reporting, because you had a period of a
(41:21):
year through twenty four where they were starting to need
to make payments, but they just weren't being reported. So
that's basically a long period of time where people just
got used to not having to make that payment. And
also a very substantial part of student luanbars who never
had ever made a payment because they, you know, they
finished their studies in a period when there was forbearance.
(41:41):
And so what then happened was they started trickling back
onto the credit file sort of mid February of this year,
and then I think you got the first batch really
kind of come in by May, and at that point
you saw the delinquency rates on the student loans that
were not in deferment was over twenty percent, so they
were over double what the historical norm.
Speaker 2 (42:01):
Was since then.
Speaker 4 (42:03):
Is that the highest level over It is the highest
level that we've seen going back a long long time.
So I remember there's been a lot of changes when
it was not federally mandated and privately owned, so I
don't have visibility going back as that far, but at
least in recent history, is absolutely the highest by a
very substantial amount. And that's not too surprising. But one
of the things that we've seen is that we expected
(42:23):
that there would be some people who go into delinquency
not because they intended to write, and so there were
some people who they moved and they didn't get their addresses,
or a lot of people that were confused because there's
so many mixed messages like we're going to be forgiven,
but that we're not and we're on a certain program,
but now that program doesn't exist anymore. So some people
have been able to then address that, and so it's
come down to this sort of seventeen seventeen and a
(42:45):
half percent, which is a good sign, and so that's improving.
But there still are people who are on programs that
have been killed, like the Safe program, and so they
are going to next year have to either get onto
another for Baron's program or start making payments, and so
maybe we haven't seen the full effect. Then basically of
the resumption those student loans, we've seen the biggest batch
(43:06):
come through, but there still are some more cohorts of
consumer borrowers that will either have their existing program expire
or that aren't being reported yet because the servicers are
trying to figure out exactly what's going on before they
report it to the credit bureau. So there still is
a little bit of lack of visibility there from on
the credit servicers side.
Speaker 1 (43:25):
Kurk, thank you so much for coming on outlast.
Speaker 2 (43:27):
There was great. Thank you. It's been a pleasure.
Speaker 1 (43:43):
It always comes back to insurance, doesn't it. That's always
like the little fly in the ointment is you know,
you buy this car and just like, Okay, here's the
car and here's the interest payment. Oh, I think we
can make the math work. You can't control what that
insurance payment is going to be. You have no idea
what it's going to be.
Speaker 3 (43:58):
This is my theory. Insure run the way, run the world,
they really do.
Speaker 2 (44:02):
You know.
Speaker 3 (44:03):
The other thing I was thinking, and we've written about
this in the newsletter, but one of the difficulties of
our current economic moment is there is so much division
and difference built into the aggregate. If you're just looking
at a single number a total, like if you've looked
at the average FIICO score of an American, it tells
you almost nothing now, because the individuals are so disparate.
Speaker 1 (44:27):
Yeah, and it really does come down to, you know, wealth, right,
Wealth is just such an important factor in the economy.
We always talk about income and income inequality, and of
course that's a real phenomenon, but wealth is such an
important predictor driver of anything. And it also goes to
show like how important like financial markets and asset prices
(44:47):
are to the real economy. And it gives me once
again an opportunity to say the stock market is the
economy because we live in such a wealth driven economy.
Speaker 3 (44:56):
You know, someone once wrote into me. I wrote something
aboutures on lower income people, and someone wrote into me saying, well,
why don't the lower income people own more assets. If
they did, they'd be in a better position. Have you
tried not being poor?
Speaker 1 (45:10):
Why haven't you tried just being rich? Why haven't you
tried buying in video twenty years ago? Why haven't you
tried buying a house in California in two thousand and
nine after the bus? It's that simple. Stop being poor? Seriously.
Speaker 3 (45:22):
The other thing I was thinking just on auto delinquencies.
I also think the trade down story is a big
piece here, which is I mean, a car from ten
years ago now is pretty decent. And I say that
as someone who owns. I think it's a Toyota rav
from like twenty eleven or something like that. Like it's
pretty dependable, and I don't really feel the need to
(45:43):
get like a fancy new car. And I imagine if
you're under pressure on your car loan, it's probably like
not that difficult necessarily to find an older car that
is somewhat reliable.
Speaker 2 (45:56):
I don't know, you know.
Speaker 1 (45:57):
The one thing though, So I have a car that
I bought twenty fifteen. It runs perfectly well. I would
not be surprised if it continued like no issues at all.
It doesn't have CarPlay integration.
Speaker 3 (46:10):
Oh, yeah.
Speaker 1 (46:10):
The one difference between older cars and newer cars is
that it's very nice, like having that no that that
interface where you have like a.
Speaker 3 (46:19):
Nice little speaker or something.
Speaker 1 (46:21):
Yeah, but what it doesn't have is that like really
nice interface with the map, like and I know that's minor,
but it's like, kind.
Speaker 3 (46:27):
Of do you have a map?
Speaker 1 (46:28):
It's a super U for those curious, and it's like,
you know, it's like they're in house. It's a crappy map.
It's not the really nice that Google Maps where it's
like you're really clear, and it doesn't have turn by
turn navigation. I know this sounds like kind of minor,
but it is very annoying. And like when I am
in a car that has like a modern off a
little tangent here, but I am in a car that
(46:50):
has like a really nice interface with a nice Google
Maps or Apple Maps and the Spotify integration. It's very nice.
And apparently we've taken it to the dealer they just
can it is un really Yeah, it's unupgradeable. There's no
for some reason, there's no way to put in a
new dash.
Speaker 3 (47:08):
Oh sorry, I thought you meant unupgradeable in terms of trading,
it in.
Speaker 1 (47:12):
For No, it's unbradable. It's like we cannot like this.
We could never install car play or whatever.
Speaker 3 (47:17):
Yeah, car, you know, my husband and I rented one
of those like big fancy trucks, pickup trucks, and I
was amazed by the amenities that are actually including like
the heated seats, I personalized heat what.
Speaker 1 (47:31):
Yeah, that's fancy. You know, it is very nice in
the wintery heated seeds. We just talked about cars for yeah,
maybe longer.
Speaker 3 (47:39):
But the thing is like, even for a car like that,
I just I cannot imagine spending like one hundred thousand
dollars plus whatever the interest rate actually is on something
like that.
Speaker 1 (47:49):
I remember that meme from like twenty ten. It's like
no one will ever do that was a big thing.
And the phone, right, really two things that people always
find a way to make a payment for the car
and the phone.
Speaker 3 (48:03):
I forget, right, Which is why I think you have
to look at something structural that's shifted. And I suspect
maybe it's the availability of you know, lots of older cars.
Speaker 1 (48:12):
But just one last point, it's I thought it was
very interesting. Ricardo is saying, it's like there's no obvious
catalyst for cataclysm. There's not obvious like, oh, here is something.
We are on the verge of consumer credit collapse. But
it is a story of just like steadily building pressure
such that if there is some sort of spark or something,
(48:34):
there is a lot of stress not to you know,
the resumption of student loans after five years, the fact
that the total loan price of the car has gotten
so high relative to people's income. All of these different things,
so you like have all these upwards dresses on prices.
You have all of this reliance obviously on accumulated wealth,
most notably stock market and home equity. So you have
(48:56):
a lot of things come together. They're not necessarily disaster
or anything like that, but the alignment of pressures is
there where things could potentially get back right.
Speaker 3 (49:05):
The consumer is much more fragile than they used.
Speaker 1 (49:08):
To they might have been a few years.
Speaker 3 (49:09):
Yeah, yeah, all right, shall we leave it there.
Speaker 1 (49:11):
Let's leave it there.
Speaker 3 (49:11):
Okay. This has been another episode of the aud Loots podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 1 (49:17):
And I'm Joe Wisenthal. You can follow me at The Stalwart.
Follow our producers Carmen Rodriguez at Kerman Arman, Dashill, Bennett
at Dashbot, and Kilbrooks at Kilbrooks. More odd Lots content,
go to Bloomberg dot com slash odd Lots with the
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Speaker 3 (49:37):
Odd Lots and if you enjoy odd Lots, if you
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(49:58):
listening it, Bend
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Joe Weisenthal
Tracy Alloway
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