December 12, 2024 • 44 mins
You can do a lot of things with the click of a button nowadays. You can get insurance, open a bank account, or trade 347 different stocks all at once via an ETF. But one thing you definitely can't do via a single click, is refinance your mortgage. In fact, securing a mortgage still requires reams of paperwork -- a lot of which has to be physically mailed to all the different parties involved. So why is mortgage finance stuck in the stone age? In this episode we speak with Mike Yu, co-founder and CEO of Vesta, about why we don't have one-click mortgages refis. He describes how a mix of clunky legacy IT systems and regulation have combined to make mortgage finance a technological laggard.
Read More:
US Home-Purchase Applications Rise to Highest Since February
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Episode Transcript
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Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of the All Thoughts podcast.
I'm Tracy Alloway.
Speaker 3 (00:23):
And I'm Joe Wisenthal.
Speaker 2 (00:24):
Joe, do you remember a month or so ago we
recorded an episode all about why mortgage rates were going
up even though the FED has cut Yes.
Speaker 4 (00:35):
This was a big one at the time, and I
think it still is really important. Basically, there's this intuition
that people have that the FED effects policy by cutting rates,
and one thing that happens when rate cuts is that
borrowing costs go down. In one form of borrowing that's
very popular is mortgage rates. But we're in the middle
(00:55):
of a rate cut cycle. The FED cut fifty in September,
then kind of another twenty five minutes sobsequent meeting, But
mortgage rates have generally not moved down at all, and
in fact moved up after that fifty basis point cut.
By the way we're recording this November twenty fifth, we
haven't seen much improvement at all, and so there is
this I don't know if it's really a mystery, but
(01:16):
there is certainly a story about the FED in the
middle of a rate cut cycle, and yet it not
really feeding through to a lot of.
Speaker 3 (01:23):
Kinds of borrowing.
Speaker 2 (01:24):
I don't think it's a mystery. We did a whole
episode on it. You're explaining what's right.
Speaker 3 (01:27):
You're right, We explained it all. You're right.
Speaker 2 (01:29):
But what I was gonna say is, as part of
that conversation, you asked a really interesting question, which for once,
for once, which is why can't we have a one
click mortgage REFI do you remember that?
Speaker 5 (01:43):
Yes?
Speaker 4 (01:44):
So you know, like I've refiled a mortgage in my life.
It's kind of annoying. You know, if you're in the
right situation, you can save money and it's probably worth it,
but it involves a lot of paperwork, et cetera. And
you know, I think we're so used to one click
financial transaction, maybe two clicks or whatever.
Speaker 6 (02:02):
You know.
Speaker 4 (02:02):
One of the things that came up is that there
are often a lot of mortgages out there that theoretically
are sort of in the money where the borrower the
homeowner could take advantage of lower rates, but they don't
for whatever reason. Perhaps one reason is they don't know
that rates have getting down. Perhaps another reason is they
can't be bothered to do all the paperwork and stuff
(02:23):
and so there are these lag effects, and so I've
always sort of wondered, why can't you just have a
one click, one click refive well.
Speaker 3 (02:29):
As a new lower rate.
Speaker 2 (02:30):
As someone who lives in fear of paperwork, I think
this is an interesting question and we should talk about it.
And it turns out we actually have the perfect guest.
We're going to be speaking with someone who was involved
in a one click mortgage lender. Mike You, co founder
and CEO of Vesta. Mike, thank you so much for
coming on all thoughts.
Speaker 6 (02:52):
Yeah, thanks for having me.
Speaker 2 (02:53):
So why don't you give us a very quick career summary?
Why are we talking to you?
Speaker 6 (03:00):
Yeah, so I've spent my entire career in the mortgage
industry on the tech side purely. I like to joke
that everyone who ends up in mortgage origination stumbles into
it by accident. So I worked at Blend.
Speaker 4 (03:11):
Kids don't dream of like one day I'm going to
be a mortgage originator. You didn't dream of that when
you were in a elementary school. Anyway, keep going.
Speaker 3 (03:17):
Sorry.
Speaker 6 (03:18):
I actually when I'm recruiting engineers here at Vesta, I
tell them I'm like, you know, lots of founders will
give you some weird story about how they've dreamed about
doing this thing since they were twelve. I can tell
you the story like that, and you wouldn't believe me anyways,
So let's be honestly stumbled into it. I started at
a mortgage tech startup, Blend in twenty sixteen, so the
company was about fifty people back then, and we built
(03:40):
a ton of the bar we're facing experience for big banks,
big mortgage lenders, et cetera. But the goal really being
how you make the process more digital, like it was
all pay performs back then, even for the barer to
fill out, and then how do you know, eventually consolidate
that down into one click or one tap if you're
on mobile. That company went public in twenty one, but
I left in twenty twenty to go and build a
different startup, Vesta, where I think a lot of what
(04:02):
we struggled with that Blend was the core infrastructure in
the back end of the system made it really hard
to consolidate the mortgage process, accelerated, cut costs for lenders,
save time, and make it easier for borrowers, and so
kind of working on the back end system of record
now where I think a lot of the other technology
problems are and I think, as this is about one
click mortgages, I think there are of course some technology limitations,
(04:23):
which is why we started the whole company, But there's
also a fun variety of regulatory implications that I'm sure
we'll dive into today too. Great.
Speaker 4 (04:29):
You know what's funny is I think not only on
the podcast did we talk about why were there no
one click refise available? I think we specifically put out
a call. We were like, have you've ever been in
a startup, some y combinator thing which is attempted to
do one click? Reach out to us? And you were
one of the I don't know if Blend was ever
a y combinator thing, but that's not really that important.
(04:51):
But you answered the call literally and you heard it and.
Speaker 2 (04:53):
Sore the odd lots call.
Speaker 4 (04:55):
Answered the odd lots call to You're the perfect guest. Obviously,
we want to get into what you're doing it investa
and just how it all works. Talk to us a
little bit more. What did Blend do to attempt to
solve the problem of I don't know if it really
gets to one click, but simplifying or streamlining the mortgage
application process.
Speaker 6 (05:14):
Yeah, it's really funny because it's only been ten years,
but ten years ago, actually, if you wanted to get
a mortgage, you couldn't even go and apply on the internet.
I would actually say in the early days of Blend,
you know, twenty sixteen, seventeen, we'd talked to lenders and
they would be like, you don't understand people applying for
a mortgage. They don't want to do it online, which
just blew our mind. And there are a lot of
you know, old school loan officers who are like, I
(05:35):
call my borrower and I interview them, and I literally
take the paper it's called the ten oh three or
the uniform Residential Loan Application. I take this paper form
and I fill it out with a pencil. I pull
over to the side of the road. My bar was
talking to me on the phone. I take them through
my process and I fill it out with pen and pencil.
Then I give it to my assistant and they go
like type it into the back end system. And we
(05:55):
were getting this kind of pushback left, right and center
in the early days, really just around the idea that
people wouldn't want to apply online. And then rockets big
Super Bowl, the ad came out push button Get Mortgage,
and then all the banks are like, oh, well, if
Rocket's going to do it, we'd probably need something competitive
with this, And I would say that was really an
inflection point for that company. But so much of it
was like, if you want to get to a one
(06:15):
click mortgage, well, first of all, you need people applying
for the mortgage on the Internet, not via physical paper.
And then it really becomes about how do you start
to pull in the data from all these various sources.
So Blend was one of the first to work with
some of the GSS on getting asset data direct from
the banks and pulling that into the mortgage application so
you can get faster underwriting and not needing to upload
a whole bunch of paperwork and bank statements. Similar things
(06:36):
for like income data, and then people are making big
pushes around property data and AVMs. It's a whole variety
of data sources you've kind of got to stitch together
in order to really save the barrow from sending in
a whole bunch of paperwork. Because one thing that I
think is a little less obvious when you send the
lender your pay stub, you're not just proving that you
have the income. They actually take a bunch of the
numbers on that pay stub that they didn't ask you
to fill out anywhere, and fill that out in a
(06:58):
spreadsheet or something to calculate your income based on their
models and what the GSS tell them to do and
things like that, and so so much of it was
just if you can digitize the process and get that
data in a structured format from a whole bunch of sources.
At the beginning, the belief was that's really going to
drive you towards a faster and more efficient process and
eventually one quick So you.
Speaker 2 (07:16):
Mentioned the GSS a couple times there. I imagine if
you're doing a mortgage, at some point you're going to
have to get the guarantee from Fanny and Freddy, and
so you're going to have to go through them. What
are their systems, Like you mentioned you worked with them, Like,
how did you plug in to the GSE systems?
Speaker 6 (07:36):
Yeah, So, as you might expect from a couple of
large financial institutions that are also now under conservatorship, their
systems are definitely of varying degrees of maturity. One thing
that I will say that I actually really appreciate about
both Fanny and Freddy is they have invested a lot
in technology over the last decade. I think when you
get down to it, financial products are all The nice
(07:57):
thing is there's no physical commodity, right, It's all money
and numbers in a ledger, And so I think they've
definitely started to embrace their role more as needing to
provide technology to the ecosystem that is modern, that is effective,
but very honestly like the core piece of technology they
built that gives you guidance on whether your loan is
going to qualify or not. For sell to Fanil Freddy.
It's called Desktop Underwriter in the Fanny case and Loan
(08:19):
Product Advisor in the Freddie case. The original versions were
built in the nineties and so definitely some older systems
it's all still you know, XML system to system conversation
if you can integrate to them at all. Some of
the systems don't have any capability for system to system integration,
and so if you want to actually sell the loan
to Fanil Freddy, someone has to go to their existing
loan origination system, download an XML file, log into their
(08:41):
website and upload it. So there's definitely, I would say
varying degrees of modernization and capability across those technology systems.
And that is I would say, no more true at
Fany and Freddi than it is that most of the
major financial institutions you think about, even most of the
smaller lenders you think about, it's all kind of on
the spectrum of everything was built between one and thirty
(09:02):
years ago, and everyone's got to kind of move. Thirty
years is long left in technology. Everyone's got to kind
of move and rebuild a new version of this, new
version of that.
Speaker 2 (09:09):
Oh yeah, Joe, I remember you know those charts that
show like all the acquisitions that a Jpmore oh yeah,
or a Bank of America has done. It's sort of
like a flow chart. Yeah, every single one of those
probably has a different IT system, right, So I always
hear that one of the big difficulties in building a
giant bank is basically sorting out the.
Speaker 4 (09:31):
IT totally big institutions, you know, it's easy to sort
of assume that sort of CLOSEI government institutions are going
to be worse on so forth, and maybe sometimes that's
correct and sometimes that's not. But big gigantic institutions, particularly
ones that had all kinds of mergers and roll ups,
et cetera. They all have this, and this has been
(09:53):
something that's come up a little bit in the past.
We've done some episodes on bank software in general, and
so I'm not particularly surprised to hear that Fanny and
Freddy have a lot of still work to do, even
if they have invested.
Speaker 3 (10:07):
Why is it hard?
Speaker 4 (10:08):
Maybe from your perspective, from the perspective of either a
Fany or Freddy or just any other gigantic financial institution,
how would you describe why it's challenging to update these
systems so that they resemble the type of software we're
used to in twenty twenty four.
Speaker 6 (10:26):
Definitely, Maybe I'll start even way back, like fifty years ago.
Speaker 2 (10:30):
Are we going to talk about Kobyl? I hope, so
we can.
Speaker 6 (10:33):
Talk about Cobyl if you'd like. But I think it
actually when I was at Blend, I was fortunate enough
to work with tim Myoppolis, who was before I Blend,
the CEO at Fanny May and he has this line
which really stuck with me, which is that everyone says
that banks are like slow adopters of technology, but the
problem with banks actually is that they were very early
adopters of technology, right going back to everything really is
just a number inside a spreadsheet at a bank. There's
(10:55):
no corn that you're shipping or gold bars or whatnot.
Anctial services industry was really an early adoperative technology. What
that means is they installed a lot of technology very
very early on that then became harder and harder to
rip out. And one thing that we find, for example
at BESTA where we're replacing one of these core systems,
when I talk to other founders in the technology space,
(11:16):
it's much easier to install a new system to replace
a spreadsheet that just like so obviously doesn't work. The
enterprise security is terrible, the controls are terrible, et cetera.
Then to get someone to upgrade a system that you
know kind of works for them. It's clunky, it's inefficient,
it's slow, but it isn't a burning pain where they like, oh,
if I don't modernize, I'm going to lose the business
or lose my job or something. On the other hand,
(11:38):
I would say there's a very strong incentive in all
these big institutions if you try and do like a
huge modernization project of a big existing system of record
and it doesn't work, you're basically putting your job on
the line. And if it does work as a CIO
or a line level CIO at a bank, you're getting
a small promotion. I was just that the trade off
is pretty bad.
Speaker 4 (11:57):
I was just going to ask, like, how much is
it tech qua tech versus institutional inertia and incentives that
really create that problem of why it's harder to upgrade.
Speaker 6 (12:09):
I think it's mostly institutional inertial incentives. There certainly is
a lot of work that actually goes into it, right,
and so you've got to get budget, et cetera. Yeah,
but it's all very tractable. I will say in financial
services there are relatively few technology problems that are like
fundamentally hard technology problems, like we're not launching rockets over here.
They all tend to be people problems. Organizational problems are
problems that get in the way.
Speaker 2 (12:45):
Can you talk to us about the sort of life
cycle of a mortgage in terms of technology, so like
what's the first thing that happens, what system is it
put into, and then where does it go next?
Speaker 6 (12:57):
Sure, so we're talking one click refive today, So we'll
start with a refive in let's call it a relatively
idealized case. Let let's stop the real ideal, which is
like the bar is going to get an email from
their servicer right which says, hey, you're in the money, Like,
we service your loan, we know what you pay, we
know what rates are, we know you know roughly our
credit profile. We can tell you that you probably want
to refinance. So the consumer is going to click on
(13:19):
that link and they're normally going to go and fill
out an online application. Today they're going to go type
in a whole bunch of their data. Again, I think
you probably know your servicer has a ton of data
on you. Do you really need to type it in?
And this varies kind of depending on how tech forward
your servicer is, but often people are still typing in
their whole application. Again, they're uploading a whole bunch of documents.
And this is kind of sitting in the consumer facing system,
(13:39):
which today is you know, they call it a point
of sale. This is the space where really Blend is
the category leader now. And so the bar is going
to kind of type all that information in, They're going
to hit submit. That's going to push it to what's
called the loan origination system on the back end, and
you can think of that as both the system of
record and it's going to do all the compliance checks.
It's where the people are going to do all the
processing and the underwriting or any automated underwriting might happen.
(14:02):
And it's also the system that's going to be integrated
to like fifteen other systems. So one really annoying thing
about the mortgage ecosystem is to produce your loan from
front to back, you're probably hitting at least fifteen different
technology vendors.
Speaker 4 (14:14):
Can you run some what are some of these in?
What different parts of the stack are they serving? You
don't have to list all at fifteen, but give us
an example of like the various things that need to
be hit and who's doing them.
Speaker 6 (14:25):
Yeah, so I think of it as there's a whole
bunch of stuff around the property, right, so you've got
to go to a title company, you've got to go
to an appraisal management company kind of get the appraisal.
So there's a whole bunch of stuff around the property.
Someone's got to check what flood zone it's in. And
we'll get into why some of the rules are really
hard to change. But if you want to know what
flood zone a property is in. I mean you can
go on house Canary or Zillow and kind of figure
(14:45):
that out pretty quickly. If you want to sell a
mortgage to the GSS, you've actually got to hit one
of their four or five designated flood certificate providers for
an official flood certificate quote unquote, which is really just
you know, those are the providers that signed a deal
with the gs S where they get the FEMA maps
that everyone else gets and they produce a piece of
paper that's official enough and the GSS trust them. So
that's a provider you basically have to hit. And so
(15:07):
there's a whole variety of property you know, vendors you've
got to hit around those categories. There's a bunch of
bar er vendors you've got to hit around. The pulling
credit is the obvious one, but you're going to want
to verify their income. You're going to want to look
them up in fraud databases. So there's a whole set
of those, and then there's a bunch of compliance stuff
to do. So generally, you know, there are entire companies
that are dedicated to I have all the data in
the mortgage, and I'm going to prepare the disclosures for you.
(15:29):
Like the disclosures are so complicated, that's less a technology problem.
That's like that those companies have an army of lawyers
who basically read all the regulatory updates, all the updates
in each of the thirty eight hundred counties in the US,
any state updates, any investor updates around exactly what you
have to tell the consumer before they can kind of
sign a lian on their property, which, as you know,
like some states are very onerous about that. And so
(15:50):
between compliance and property and kind of checking the borrower,
there's just this whole constellation of stuff that has to
be done, a lot of data sources and a lot
of rules.
Speaker 2 (16:00):
Talk to us a bit more about the rules then,
Like I'm curious how these rules come into place, what
sort of factors they're being based on, and then how
often they actually change.
Speaker 6 (16:11):
Yeah, I kind of think of rules in two buckets. There.
Of course, the regulatory rules, so you can imagine a
ton of those regulatory rules were driven by seven eight
and a lot of the things that we saw during
the Great Financial Crisis or that kind of led up
to the Great financial crisis, so a lot of regulatory
stuff around what you disclose the consumers around you know,
you have to qualify their ability to repay in order
to have a compliant loan. So there's lots of regulatory stuff.
(16:34):
And then there's investor rules, which overwhelmingly you know, come
from Fanny er Freddy. There is a small private label
market and some other stuff. One thing that is true
about all of these rules, right the investor rules and
the regulatory rules, is they're both kind of set again
talking about organizational inertia by pseudo government institutions that have
really been burned by mortgages in the last two decades,
and so they're pretty you know, nervous about that, and
(16:57):
there's very little incentive to simplify the process to remove rules,
and so the rules change. I would say every month
or two you get a few new rules from the investors,
but they very rarely subtract rules, which tends to be
a reason that you end up with. I want to say,
the fan selling Guide is now twelve hundred pages basically
of rules that the loan has to satisfy. And these
rules can vary from you know, relatively straightforward things like
(17:18):
you can't refinance an FAJ loan within a certain amount
of time after the loan was originally originated, so there's
like a seasoning requirement. You know, there's a lot of
documentation rules, like if you're going to provide an income
to Fanny May, you normally have to have a pasteb
and in W two attached to it to kind of
verify that income. And you get into like really complex
and arcane rules as well, once you get into twelve
(17:38):
hundred pages, like are you allowed to have a ten
percent increase in income year over year and use that
new income, Well, you have to document that a certain way.
If it's about thirty percent, you have to document it
another way. So a whole kind of slew of rules,
which is why you have this huge body of people
basically that have to work on every single loan because
they have to learn all the rules.
Speaker 4 (17:57):
So one thing that would be really nice and gets
to the one clickness of what we're trying to get at,
is if it were really easy to pull in data
quickly from all these disparate providers. So I go to
your website and I want you to know my income,
and maybe I want you to know my assets that
I have, but I'm not sure if that's as important
(18:18):
in a mortgage. And I want you to know the
location of my property so that you can do various things,
including see what the floodplain looks like. The various providers
of this stuff, so one of the providers might be
my payroll provider. Another provider might be the bank that
I use, the bank online, et cetera. How forthcoming are
(18:39):
they in making these systems easy for a third party,
say yours or a Blend or some other fintech or
a rocket, et cetera, to just go access them such
that I don't have to download PDFs and then re
upload them somewhere else.
Speaker 6 (18:57):
Yeah. So I imagine you're asking because you know the
is going to be their very mixed results. So when
it comes to banks, for example, there have been a
whole advent of new kind of players that help you
connect your banking data. Claid is the big one exactly,
and then the banks, I would say, clearly had mixed
feelings about it. People are worried about the security of
people typing their bank password on something that's not the
(19:18):
bank's website. They're very notably over the last ten years
or so but a number of times in Chase just
shut off PLAIDS access, and so there certainly was some
complexity in that relationship. Earlier on with you know, some
of what's going on in open banking in the US.
I think the idea that you'll have access to your
own asset information is definitely a place the regulators are
pushing as well, and that gets easier and easier every year.
(19:40):
Payroll is a particularly interesting one. It's a very hot
topic in the mortgage world now because everyone basically uses
a product offered by appuifacts called the work number, which
you may have heard of. So the work number it
basically they have a partnership with ADP where ADP charges
them a very large amount of money actually to use
the borrow's social to look up the data. Work number
turns around and marks that up a whole bunch. So
(20:02):
they also charge a ton of money to the lender.
So I've heard of lender spending you know, like four
or five six hundred dollars a loon like to close
one loan to get that income and employment data in
this digitized way via the work number, which is I
think obviously ridiculous, And so there certainly is some struggle
going on with the data ecosystem. There are a bunch
of startups now trying to basically do a plaid did
(20:23):
where the barer can log into their payroll provider. One
problem you might imagine with that is like, do you
know your payroll password? Because I don't even know you
know who my provider is, no idea, and so there
certainly is some difficulty in getting the data together. I
would say that Ecosystem's made a lot of progress on
that in the last ten years, but payroll and income
tends to be a lot harder because it's much more fragmented.
And with banking and open banking and every bank you
(20:44):
know has some kind of electronic system of record, that's
a problem where I'd say they've made a lot more progress.
Speaker 2 (20:50):
How much does mortgage financing depend on just your sort
of basic mail. I want to be able to say
the housing market is powered by FedEx or something.
Speaker 6 (21:01):
So actually every closing I wouldn't say every closing package,
but the vast mandory of closing packages in this country,
like you go to your lender or a title office
and you sign the closing doc and the notes and
whatnot to actually the legally binding piece of paper that
says there is a lean on my property. Now, like
I have to pay this loan back, and that gets
FedEx back to the lender, and the lender then you know,
scans it, they upload it to their electronic system and
(21:23):
they turn around and they like FedEx that to a
doc custodian. And some lenders are more efficient with their
FedEx schemes than others on like it just goes straight
to the doc custodian and the DAK custodian scans and
sends it to them. But I would say that very
much all of the actual debt and recording and like
all of the legally binding stuff, probably ninety percent plus
is still physical pieces of paper that are getting mailed around.
(21:44):
There was a big trend, especially in twenty twenty, around
how do you digitize those notes? Hey do you e closing?
Then it's a matter off. You've got to get thirty
eight hundred counties to accept it, all the title companies
have to accept it, et cetera. So the big network
problem again, you're probably hearing a theme of it's just
like organizational inertia. Yeah, but you've very much can say
that home financing is still powered by FedEx because pretty
(22:04):
much everyone, I mean, we have a field in our
system where people are like, we need a FedEx tracking
number field for the note, like not even kidding. That
could be really cool if you could integrate that to
FedEx to like automate like looking at the tracking. And
it's like, of course you can do that technology wise,
but sometimes you ask yourself, like, what problem are we
really solving here? Guys, like we should just move it
to the cloud.
Speaker 4 (22:22):
So I think the last time I applied for a mortgage,
it's late twenty seventeen, and I just remember like documents
and documents and checkboxes, and I didn't read any of
those documents, had just signed the checkbox, and I assumed
it is all okay, Well were all those checkboxes I
was or signature boxes that I was putting a digital
signature into.
Speaker 6 (22:42):
A lot of those signature boxes are basically people disclosing
your rights, So very similar.
Speaker 3 (22:47):
To yeah, yea disclosure.
Speaker 6 (22:49):
Yeah, It's like you sign something that gives them authorization
to pull your credit in many cases, and then you
sign something that you know it's like, hey, here's your
credit score and here's how it was calculated. In the
state of California, you might something which is like if
you are getting an FAHA loan with lead paint, which
I hope you didn't, there's a disclosure that says, hey,
like we determined that the house has some old lead paint,
Like sign here to acknowledge that we disclosed that to you.
(23:11):
And then the other thing that happens is because each
of these disclosures are legally mandated and it's really hard
to make sure that you signed all of them in
the one go, they'll just say, hey, when you first
apply for the loan and we disclose through the terms,
we're going to stick all of those disclosures in there,
and then when you get to the closing table, we're
going to put them in there again, just so we've
belt and suspender that you know you've signed it. Like
you're at the closing table, you're not going to walk
(23:32):
away now, Like, let's just make you sign it one
more time. So, probably if I had the guests, you
got fifty or something disclosures, depending on the state you
were in, there's a whole bunch of you know, state
some states are more owners than others. We probably also
signed each one and average like two and a half times.
Speaker 4 (23:45):
I did just real quickly, how different was that experience
for me in twenty seventeen then it would have been
in two thousand and seven, before the mortgage crisis.
Speaker 6 (23:53):
Well, in two thousand and seven, you can imagine there
were way fewer disclosures. Actually, in twenty fifteen they passed
what's called TRID or tie the rest but integrated disclosures,
which is actually the main reason you can't have a
one click coage today. So TRID puts a minimum timeline
as well, where you have to give people, you know,
within three days of getting what's called a full application,
you have to provide them an estimate of all the
fees that like really clearly in a very standardized format,
(24:16):
discloses all the fees that comes with a bunch of disclosures.
And then you have to give the bar or seven
business days from giving them that loan estimate to close
the loan. And so we actually talk in mortgage now
a lot about the ten day mortgage, because you actually
can't have a one click mortgage purely by virtue of
the fact you need that seven day waiting period. There's
some other timelines and there even if you got rid
of that seven day waiting period, you'd still have to
(24:38):
remove a whole bunch of other regulatory timelines to really
get it down to one day. But in twenty fifteen
they released this new regulation which I would say made
it a lot more onerous, a lot more documents to sign,
and I mean it's good, right, Like pre twenty fifteen,
people were getting loans and they were getting bait and switched,
and you know, people were having new fees pop up
that they didn't know about. And now all that stuff
is really strictly regulated, but it definitely as to the
(24:59):
paperwork party.
Speaker 2 (25:15):
So how did Blend actually try to solve all of this?
Because when I listen to you talk about all these
sort of challenges in the mortgage market, it just sounds
like an unsolvable kind of spider web of requirements.
Speaker 6 (25:29):
Yeah, it certainly is very challenging. I wouldn't call it.
You know, nothing is really unsolvable except the regulatory timeline
is going to be what it is. But for a
lot of it was, hey, can you really get to
a one click and tell the bar or that they're
clear to close? And what that means is we've fully
underwritten everything. We know that you're going to close. The
only thing we're really waiting on is the compliance clock.
And so you kind of split it up into the
(25:51):
various things that get underwritten in the loan. So in property,
for example, the most clear thing you have to do
in order to say I can instantly underwrite your property
is they have to be able to get no appraisal
and get instant title. And today, title insurance is this
whole other thing that I'm sure you could do ten
episodes on. Some people have asked, which everyone, yes, I
would say every two years. Some Silicon Valley person tweets
(26:11):
that like, title insurance is a racket and someone should
go take it out, and I always get that tweet
texted to me like ten times. But there's this complexity around. Well,
the problem with title insurance is actually someone does have
to go to the county office still in a bunch
of counties, and go downstairs into the basement of the
courthouse and get the key and unlock it and go
like look up the records for that house or something
like that. So you have to figure out how you're
going to make title insurance instant, which there are a
(26:33):
whole bunch of startups that have worked on are working
on have had some success in digitizing that process. In
some counties, you've got to make the appraisal instant, which
basically means you have to get a message from Daniel
Freddy that for this particular property, they've written alone on
it recently enough that you don't have to appraise it again.
And so that's the property side. Those two things you
can imagine combined already, like you're taking one hundred percent
(26:55):
of properties in the US, and you're shrinking your hip
box to like twenty year or twenty five percent or
something like that, and then you've got to get the borrower.
And for Blend, a lot of the approach was, well,
we partner with a lot of big banks, and so
can you get the banking data directly from those banks
and use that to either figure out the income or
use something like the work number to instantly get income.
You can verify assets and income and the property, and
(27:15):
then you can of course, pulling credit is the easiest one,
because we've been able to pull credit digitally for decades
and decades in this country. If you can kind of
check all four of those boxes, then you're quite a
bit further towards a instant clear to close, you're still
not fully there. There's a bunch of stuff around the
margins you've got to go and sort out. But it
really is a matter of blocking and tackling, executing detail
by detail. And it's like, there are twelve hundred pages
(27:36):
of rules. I've probably read those twelve hundred pages of
Fanny rules three or four times, and you've just got
to systematically tick them off one by one.
Speaker 4 (27:42):
What are you doing now at VESTA that you weren't
doing and blend.
Speaker 6 (27:47):
Yeah, So a lot of the struggle that we had
up on was you really, because you own the front
end of the process, is you could get to fully automated.
You could pull it all in and be done. The
problem was, you know, you heard how I talked about
appraisal title. You kind of shrink the hit box for
what you can do fully automated. And so what we
found was that if you fully automated, like let's say
you really could fully automate one percent of lenders loans,
(28:10):
that would be great, but they're still you know, spending
a ton of manual dollars, a ton of you know,
operational people on ninety nine percent of their loans. And
the big problem was all the data that you got
at the front it was really hard to use that
to drive efficiencies at the back of the process or
for you know, any of the loans that did have
even one manual touch, like I ticked through all these rules.
You can imagine if only ten rules had to be
(28:31):
done by a human. Well, now it's got to go
through this manual process. And what it does today is
it goes through this old manual process where they basically
have to underwrite the whole loan manually because the system
doesn't have an understanding of what's already been done. It's
not you know, task or workflow oriented, and people basically
have muscle memory. So the underwriter is going to look
at everything, order the appraisal, whatnot, even if they don't
have to. And so a lot of what we realized
(28:53):
was the back end of the process was making it
really difficult to realize any efficiency from the good work
you're doing at the front end because the change management
and organizational inertia of you know, you've got three thousand
people on your mortgage manufacturing line so to speak, doing
exactly what they've always done, and the software isn't really
guiding them to do anything different, Like it's not it's
not a piece of software like you might be used
(29:14):
to working in today, like Slack gives you notifications for example.
It's really almost like a spreadsheet with a different UI
layer on top of it, and you've got to figure
out exactly what you're going to do. So a lot
of it was how do you change the way the
operation works so that people are doing a lot less.
And then the other big thing was with the existing
loan origination systems being so difficult to integrate to that
was one of the biggest hindrances and actually getting all
(29:35):
of the data and making that process one click. Was
that you couldn't actually do all of the jobs that
needed to be done by that old system, like the
old system that has all of the integrations I mentioned,
and they have hundreds of integrations to all these data providers,
like coordinating the appraisal. So you ended up having to
build around the old system instead of through the old
system to achieve a lot of this stuff, and that
(29:56):
just seemed like so clearly the wrong way to do it.
Now the downside is have to go and modernize the
old system. Which is a really hard problem. But by
kind of modernizing the old system, you unlock a the
operational efficiency that you actually get from all this data
and then be a much easier platform for everyone who
wants to build a front end to get that data
through your integrations and through your processes that the lender
(30:18):
already has that exists manually today of having to recreate
it on the side to try and automate it, if
that makes sense.
Speaker 2 (30:24):
I have a slightly random question, which is, given that
we're talking about technicalities, how easy is it to commit
some sort of mortgage fraud nowadays? Totally random, not out
of personal interest.
Speaker 6 (30:38):
Yeah, I did listen to your recent episode about government
Fraudry Joe was the one I think was very interested.
Speaker 3 (30:43):
Yeah, I was the one interring to start doing fraud.
Speaker 6 (30:46):
Yeah. Mortgage fraud, I think is actually quite difficult these days,
mostly because there are so many human eyeballs that look
at the loan and so let's take something really simple,
like you wanted to like doctor a document, like probably
the most straightforward thing because it's not like mascal fraud.
It's like somebody is like, I want a mortgage on
my primary residence. I can't afford it, and I'm just
(31:07):
going to doctor the documents to make my income look bigger. Well,
first you have to hope the lender doesn't check some
third party verified data source, or they don't reach out
to the employer, which they often do. And then you
have to hope that like your document makes it through
the processor are looking at it, and the underwriter looking
at it, and the closer looking at it, and the
underwriters especially they're looking for things that don't add up.
And so I would say mortgage fraud is probably really
pretty too, very difficult to actually accomplish today. It sounds
(31:29):
a lot harder to achieve than, like, you know, figuring
out how to get some Medicare dollars. Yeah, so it's
probably not worth squeeze. Now, we'll like generative AI make
it way easier to make fake profiles and all that stuff.
Maybe that's something that I think lots of people worry about,
But today I would say it's definitely the mortage industry
has done a pretty good job of doing that, and
I'd say the regulators have done a good job of
(31:50):
making it really hard, just given everything that happened two
decades ago.
Speaker 3 (31:53):
Back to the question of refise.
Speaker 4 (31:55):
So you mentioned that theoretically, if you're a homeowner and
you're in the money on your mortgage, that is to say,
where it would make economic sense for you to refi.
You might get an email or something it's like, hey,
you should refi and you can save this much. But it's,
as we're talking about, it's gonna be a lot of
paperwork and all this stuff. After our episode came out
(32:16):
several weeks ago, someone on Twitter, they said, why can't
we have a mortgage product that you pay a higher
premium upfront, but it's a floating rate mortgage that only
resets downward. In other words, basically, if rates drop lower,
your mortgage mechanically drops with it. And again, obviously, if
you're going to have that, you theoretically that's a more
(32:39):
valuable option and you pay some premium upfront, but then
in theory, you save all of this effort and time
and document checking and human hours that go into this.
In your mind, does that seem like a plausible financial
product that could exist.
Speaker 6 (32:52):
Seems like a totally reasonable financial product. I think that
there may even be somebody doing it. And like the
private label securities market, there is a market basically hedge
funds that will like underwrite non qms what they're called
mortgage products and offer those to lenders, and lenders can
originate them. One thing I will say is it seems
unlikely to come from the GSUS just because so much
of the gs's mission these days is affordability and democratizing
(33:15):
home ownership. And I can't really think of a marginal
person that that products would get into a home.
Speaker 4 (33:20):
Right, So even if it makes sense, that's just now
we'll move the dial.
Speaker 6 (33:24):
Yeah, I think it makes sense from a you know,
like single person financial instrument perspective, like I would love
to have one of those, for example, But I think
that from the kind of stated policy goals of the
biggest investors in the market, it's just not really something
that oligns with their policy goals, and so I can't
see that being a big area of where we're going
to see a bunch of those in a decade.
Speaker 2 (33:43):
So a lot of mortgages get bundled together into mortgage
backed securities. I'm curious, like how much of that granular
detail about pay and you know, lead paint in the
house and things like that gets ported over to the
securitization aspect of it.
Speaker 6 (34:02):
So it's really not a lot. I actually I have
capital markets people reaching out to me all the time,
being like, if you have a modern loan origination system,
you can solve my problem if I can't actually get
any of the data or much of the data that
you underlines these instruments when I am going and securitizing
them or trading them or whatnot. But generally comes out
as much banking technology still is today is you export
(34:24):
a big CSV of some of the data fields. You
take a bunch of docks and you send them off,
and then that CSV, which you know, they fancily call
it tape, but really it's a spreadsheet, just gets ingested.
And now you've taken a process that had three thousand
fields and hundreds of pages of docks, and you've boiled
that down into like fifty or one hundred fields that
describe the mortgage, which maybe is for the best, Like
I'm not really sure that people buying mbs should be
(34:46):
thinking about, you know, the specific credit profile of the
thousand different mortgages that are chopped in there and put
in and so it's nice that there's some standardization, but
it's definitely very lossy, and I will tell you it
is something that mortgage traders complain to me about a lot.
Speaker 4 (35:00):
So what's realistic. It doesn't sound like you'd ever get
like true one click, because it's you know, at a minimum,
you're probably going to have to tell the front end
who your.
Speaker 3 (35:10):
Payroll is and who your bank is, and a few
other things.
Speaker 4 (35:14):
What is a plausible version of if you know there's
continual coordination among different banks, if Fanny and Freddy continue
to update their technology so that you know, it's a
little easier, what could it look like if I were,
say you're say, in ten years.
Speaker 3 (35:29):
I'm applying for a mortgage again.
Speaker 6 (35:31):
I think it's very reasonable to strive for a world
where it is, you know, to your point, as close
to one click as possible on the very front end.
Maybe you're talking about like a ten minute application max,
where you connect some accounts once that's done. I think
that what you should get instantly is one of basically
three decisions. Hey, you are definitely clear to close. You're
(35:53):
going to get your disclosures, and then we're just going
to wait ten days and we'll close you. Option two
is hey, you as a barwer are definitely clear to close,
but we need some additional information on the property. So
you're going to wait ten to fourteen days, you're going
to pay for the appraisal, and we're going to close
you or three is basically unfortunately, you know, we're not
able to close you. Here some things you can do
to improve your stance. I think that property is probably
(36:14):
going to be the thing that even if you ask
me ten years from now, it's like it's really a
question of there will always be those corner cases. It
feels like where the gs are going to want to
see an appraisal unless they're the big credit profile change
or if they get privatized. You know, there's all sorts
of things that can happen in ten years. But I
think those three outcomes being ten minutes away fingertips wwise
from the bar when you close in ten days is
(36:35):
very much attainable, and it's really what everyone in the
industry is and ought to be working towards.
Speaker 2 (36:41):
Does blockchain solve this? And I mean that's somewhat serious.
Speaker 3 (36:45):
It's a good question.
Speaker 2 (36:46):
Because, like I've often thought like one of the few
real world applications of blockchain technology could be in the
mortgage assignation space where you have that sort of chain
of title moving around constantly. But also I'm thinking, like
from a wallet perspective, if you could have like a
personal profile that carried with it you know, your pay
(37:08):
and yeah, how much your worth and et cetera, you
could use that too.
Speaker 6 (37:14):
Yes, So those are two very interesting use cases on
the wallet perspective. The way that I think about blockchain
helping here is almost lets you build like a more
encompassing decentralized credit bureau. And decentralized is actually really important
because I don't think the banks are super excited about
the idea of helping build like a fourth credit bureau,
like they built three and now they pay the three
for their own data, which I think is a little
(37:36):
bit difficult for them. And then you know, the regulators
are not super excited about the centralized credit bureaus, et cetera.
And so I think the idea that each consumer could
have their own key that unlocks, you know, access to
all of their data on this decentralized credit bureau that
all the payroll providers and financial institutions, et cetera are
writing to, is very much an idea that has legs.
It's really hard to implement for you know, a lot
of similar organizational inertial reasons. But I do think that
(37:58):
is a real use case for blockchain, be it solves
the incentives problem where people are basically like, I don't
want there to be one middleman with every consumer in
America's financial data, and so blockchain, let's you kind of
like decentralize and split that up. So I think there's
some really interesting avenues there that people can go down,
and there are some companies I think actually looking at
that on the title front, what I usually tell people
on could title beyond blockchain? Absolutely? Is it an interesting
(38:19):
use case? Absolutely? The hard part of digitizing title is
getting thirty eight hundred counties to even move to like
putting capturing the records digitally and not in the courthouse basement.
Getting thirty hundred counties to move to blockchain seems further
away than that, not closer.
Speaker 4 (38:34):
I don't know if I've ever mentioned it on the
show before. I once had a gig right after college
in which there was some company out in California doing
some of his bestest lawsuits, and they needed names of
all these people who had been partied to some suit.
I don't they're maybe putting it together a database for lawyers,
and part of my job was to go to various
county courthouses all around rural Texas in central Texas and
(38:58):
go to the basement and just literally pull out files
and ask for names. I'm just going to ask one
last question since Tracy hit one tech buzzword, which is
blockchain generative AI. Whether it's in the field of scanning
documents or understanding documents quickly in your work right now,
is there a substantive use that you're getting out of
(39:18):
this technology.
Speaker 6 (39:19):
Yes, it is definitely to your point, it's scanning and
understanding documents, and so you can think of mortgage the
way that I think of it high level is it's
a whole bunch of data and it's a bunch of rules,
and the rules are well defined by investors, the government, etc.
And the data is just data, and so data and
rules to you know, a lot of this conversation should
be a one click experience, like we've known for decades
how to run rules on data. And a lot of
(39:40):
the problems come about because the rules are written in
the twelve hundred pah PDF and there's a little gray
and someone has to learn them, and the data in
a bunch of documents and a bunch of you know,
disparate places from the borrower, and so it's not structured.
And so if you can bring structure to the data
and structure to the rules, both of which generative AI
is really good at. Right. It can read the Fannia
Selling Guide and turn that into code rules. It can
read a document and turn that into data points. If
(40:02):
you can use generitive AI to structure those two things,
then you still have structured data, structured rules, and that
they're you know, whatever rules the GSE said, So you
don't have these compliance things with ohs AI underwriting below.
But we're seeing a lot of really promising results taking
the most cutting edge large language models and applying them
to these documents, both to write the rules for us
and to lift the data off the documents.
Speaker 2 (40:22):
All right, Mike, you thank you so much for coming
on all lots and explaining to us why we can't
have a nice thing.
Speaker 6 (40:30):
Yeah, thanks for having me.
Speaker 3 (40:31):
That was amazing.
Speaker 4 (40:32):
Mike, you're the perfect guest. Thank you so much for
coming on.
Speaker 6 (40:35):
Yeah, that was fun. Thanks for having me.
Speaker 2 (40:49):
Joe, that was really fun.
Speaker 3 (40:51):
That was really fun.
Speaker 4 (40:52):
I thought Mike was exceptionally clear at explaining how all
this works. And although it's still annoying the process of
getting more and lots of documents that I didn't read
and attached my signature to, Like, I guess I understand
a little bit more.
Speaker 2 (41:05):
Why now, do you remember after the financial crisis there
were all these problems with loan documentation and I remember,
like there's a big thing about assigning mortgages in blank
that all turned into like court cases. Yeah, I kind
of wonder, like.
Speaker 4 (41:21):
We did a great episode on that with David Yeah,
David Dian of the American Prospect, Like in twenty fifteen.
What was the name of his book, David chain of Title. Oh, yeah,
we did an episode with David Dan chain of Title.
At how crazy that was. It was just the state
of disarray in documentation after the mortgage crisis. But you
(41:42):
understand why it is when you still have, you know,
so much at the county level and the county level,
I guess for obvious reasons, not feeling any particular pressure
to update or digitize.
Speaker 3 (41:53):
Or modernize or coordinate all of their systems.
Speaker 2 (41:55):
Yeah, that's really it isn't It sort of like a
Hodgepodge of State and count Law. I feel like we're
going to be waiting a while for a solution to this.
Speaker 4 (42:05):
Yes, and I've sort of hinted at it before on
the podcast, but for very arcane reasons that I'm not
going to get into. I do have a loan that
will need to be refinanced at some point in the
next couple of years.
Speaker 2 (42:18):
You're very optimistic, Joe about interest rates.
Speaker 4 (42:21):
No, I'm very pessimistic and I'm very anxious about it.
But if I'm not optimistic about the path of interest rates,
maybe I'll be optimistic that in a couple of years
the process is at least a little bit better than
it was the last time I applied for a mortgage.
Speaker 2 (42:36):
Well, see, you'll have to tell me how many documents
like get mailed out and stuff, although I guess most
of that is on the sort of like lender and
servicers side.
Speaker 3 (42:45):
But I'm not looking forward to it.
Speaker 2 (42:47):
Yeah, all right, shall we leave it there.
Speaker 3 (42:48):
Let's leave it there.
Speaker 2 (42:49):
This has been another episode of the All Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 4 (42:55):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow Ugust Mike thank you, He's at Michael Underscore. You
follow our producers Kerman Rodriguez at Kerman Ermann, Dashel Bennett
at Deshbot and Kilbrooks at Kelbrooks. Thank you to our
producer Moses Ondem. For more Oddlogs content, go to Bloomberg
dot com slash odd Lots, where we have transcripts, a blog,
(43:16):
and a daily newsletter and you can chut them up
all of these topics twenty four to seven in our
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Speaker 2 (43:24):
And if you enjoy Oddlots, if you like it when
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(43:44):
find the Bloomberg channel on Apple Podcasts and follow the instructions.
Speaker 5 (43:48):
Thanks for listening, bend In
Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2 (00:18):
Hello and welcome to another episode of the All Thoughts podcast.
I'm Tracy Alloway.
Speaker 3 (00:23):
And I'm Joe Wisenthal.
Speaker 2 (00:24):
Joe, do you remember a month or so ago we
recorded an episode all about why mortgage rates were going
up even though the FED has cut Yes.
Speaker 4 (00:35):
This was a big one at the time, and I
think it still is really important. Basically, there's this intuition
that people have that the FED effects policy by cutting rates,
and one thing that happens when rate cuts is that
borrowing costs go down. In one form of borrowing that's
very popular is mortgage rates. But we're in the middle
(00:55):
of a rate cut cycle. The FED cut fifty in September,
then kind of another twenty five minutes sobsequent meeting, But
mortgage rates have generally not moved down at all, and
in fact moved up after that fifty basis point cut.
By the way we're recording this November twenty fifth, we
haven't seen much improvement at all, and so there is
this I don't know if it's really a mystery, but
(01:16):
there is certainly a story about the FED in the
middle of a rate cut cycle, and yet it not
really feeding through to a lot of.
Speaker 3 (01:23):
Kinds of borrowing.
Speaker 2 (01:24):
I don't think it's a mystery. We did a whole
episode on it. You're explaining what's right.
Speaker 3 (01:27):
You're right, We explained it all. You're right.
Speaker 2 (01:29):
But what I was gonna say is, as part of
that conversation, you asked a really interesting question, which for once,
for once, which is why can't we have a one
click mortgage REFI do you remember that?
Speaker 5 (01:43):
Yes?
Speaker 4 (01:44):
So you know, like I've refiled a mortgage in my life.
It's kind of annoying. You know, if you're in the
right situation, you can save money and it's probably worth it,
but it involves a lot of paperwork, et cetera. And
you know, I think we're so used to one click
financial transaction, maybe two clicks or whatever.
Speaker 6 (02:02):
You know.
Speaker 4 (02:02):
One of the things that came up is that there
are often a lot of mortgages out there that theoretically
are sort of in the money where the borrower the
homeowner could take advantage of lower rates, but they don't
for whatever reason. Perhaps one reason is they don't know
that rates have getting down. Perhaps another reason is they
can't be bothered to do all the paperwork and stuff
(02:23):
and so there are these lag effects, and so I've
always sort of wondered, why can't you just have a
one click, one click refive well.
Speaker 3 (02:29):
As a new lower rate.
Speaker 2 (02:30):
As someone who lives in fear of paperwork, I think
this is an interesting question and we should talk about it.
And it turns out we actually have the perfect guest.
We're going to be speaking with someone who was involved
in a one click mortgage lender. Mike You, co founder
and CEO of Vesta. Mike, thank you so much for
coming on all thoughts.
Speaker 6 (02:52):
Yeah, thanks for having me.
Speaker 2 (02:53):
So why don't you give us a very quick career summary?
Why are we talking to you?
Speaker 6 (03:00):
Yeah, so I've spent my entire career in the mortgage
industry on the tech side purely. I like to joke
that everyone who ends up in mortgage origination stumbles into
it by accident. So I worked at Blend.
Speaker 4 (03:11):
Kids don't dream of like one day I'm going to
be a mortgage originator. You didn't dream of that when
you were in a elementary school. Anyway, keep going.
Speaker 3 (03:17):
Sorry.
Speaker 6 (03:18):
I actually when I'm recruiting engineers here at Vesta, I
tell them I'm like, you know, lots of founders will
give you some weird story about how they've dreamed about
doing this thing since they were twelve. I can tell
you the story like that, and you wouldn't believe me anyways,
So let's be honestly stumbled into it. I started at
a mortgage tech startup, Blend in twenty sixteen, so the
company was about fifty people back then, and we built
(03:40):
a ton of the bar we're facing experience for big banks,
big mortgage lenders, et cetera. But the goal really being
how you make the process more digital, like it was
all pay performs back then, even for the barer to
fill out, and then how do you know, eventually consolidate
that down into one click or one tap if you're
on mobile. That company went public in twenty one, but
I left in twenty twenty to go and build a
different startup, Vesta, where I think a lot of what
(04:02):
we struggled with that Blend was the core infrastructure in
the back end of the system made it really hard
to consolidate the mortgage process, accelerated, cut costs for lenders,
save time, and make it easier for borrowers, and so
kind of working on the back end system of record
now where I think a lot of the other technology
problems are and I think, as this is about one
click mortgages, I think there are of course some technology limitations,
(04:23):
which is why we started the whole company, But there's
also a fun variety of regulatory implications that I'm sure
we'll dive into today too. Great.
Speaker 4 (04:29):
You know what's funny is I think not only on
the podcast did we talk about why were there no
one click refise available? I think we specifically put out
a call. We were like, have you've ever been in
a startup, some y combinator thing which is attempted to
do one click? Reach out to us? And you were
one of the I don't know if Blend was ever
a y combinator thing, but that's not really that important.
(04:51):
But you answered the call literally and you heard it and.
Speaker 2 (04:53):
Sore the odd lots call.
Speaker 4 (04:55):
Answered the odd lots call to You're the perfect guest. Obviously,
we want to get into what you're doing it investa
and just how it all works. Talk to us a
little bit more. What did Blend do to attempt to
solve the problem of I don't know if it really
gets to one click, but simplifying or streamlining the mortgage
application process.
Speaker 6 (05:14):
Yeah, it's really funny because it's only been ten years,
but ten years ago, actually, if you wanted to get
a mortgage, you couldn't even go and apply on the internet.
I would actually say in the early days of Blend,
you know, twenty sixteen, seventeen, we'd talked to lenders and
they would be like, you don't understand people applying for
a mortgage. They don't want to do it online, which
just blew our mind. And there are a lot of
you know, old school loan officers who are like, I
(05:35):
call my borrower and I interview them, and I literally
take the paper it's called the ten oh three or
the uniform Residential Loan Application. I take this paper form
and I fill it out with a pencil. I pull
over to the side of the road. My bar was
talking to me on the phone. I take them through
my process and I fill it out with pen and pencil.
Then I give it to my assistant and they go
like type it into the back end system. And we
(05:55):
were getting this kind of pushback left, right and center
in the early days, really just around the idea that
people wouldn't want to apply online. And then rockets big
Super Bowl, the ad came out push button Get Mortgage,
and then all the banks are like, oh, well, if
Rocket's going to do it, we'd probably need something competitive
with this, And I would say that was really an
inflection point for that company. But so much of it
was like, if you want to get to a one
(06:15):
click mortgage, well, first of all, you need people applying
for the mortgage on the Internet, not via physical paper.
And then it really becomes about how do you start
to pull in the data from all these various sources.
So Blend was one of the first to work with
some of the GSS on getting asset data direct from
the banks and pulling that into the mortgage application so
you can get faster underwriting and not needing to upload
a whole bunch of paperwork and bank statements. Similar things
(06:36):
for like income data, and then people are making big
pushes around property data and AVMs. It's a whole variety
of data sources you've kind of got to stitch together
in order to really save the barrow from sending in
a whole bunch of paperwork. Because one thing that I
think is a little less obvious when you send the
lender your pay stub, you're not just proving that you
have the income. They actually take a bunch of the
numbers on that pay stub that they didn't ask you
to fill out anywhere, and fill that out in a
(06:58):
spreadsheet or something to calculate your income based on their
models and what the GSS tell them to do and
things like that, and so so much of it was
just if you can digitize the process and get that
data in a structured format from a whole bunch of sources.
At the beginning, the belief was that's really going to
drive you towards a faster and more efficient process and
eventually one quick So you.
Speaker 2 (07:16):
Mentioned the GSS a couple times there. I imagine if
you're doing a mortgage, at some point you're going to
have to get the guarantee from Fanny and Freddy, and
so you're going to have to go through them. What
are their systems, Like you mentioned you worked with them, Like,
how did you plug in to the GSE systems?
Speaker 6 (07:36):
Yeah, So, as you might expect from a couple of
large financial institutions that are also now under conservatorship, their
systems are definitely of varying degrees of maturity. One thing
that I will say that I actually really appreciate about
both Fanny and Freddy is they have invested a lot
in technology over the last decade. I think when you
get down to it, financial products are all The nice
(07:57):
thing is there's no physical commodity, right, It's all money
and numbers in a ledger, And so I think they've
definitely started to embrace their role more as needing to
provide technology to the ecosystem that is modern, that is effective,
but very honestly like the core piece of technology they
built that gives you guidance on whether your loan is
going to qualify or not. For sell to Fanil Freddy.
It's called Desktop Underwriter in the Fanny case and Loan
(08:19):
Product Advisor in the Freddie case. The original versions were
built in the nineties and so definitely some older systems
it's all still you know, XML system to system conversation
if you can integrate to them at all. Some of
the systems don't have any capability for system to system integration,
and so if you want to actually sell the loan
to Fanil Freddy, someone has to go to their existing
loan origination system, download an XML file, log into their
(08:41):
website and upload it. So there's definitely, I would say
varying degrees of modernization and capability across those technology systems.
And that is I would say, no more true at
Fany and Freddi than it is that most of the
major financial institutions you think about, even most of the
smaller lenders you think about, it's all kind of on
the spectrum of everything was built between one and thirty
(09:02):
years ago, and everyone's got to kind of move. Thirty
years is long left in technology. Everyone's got to kind
of move and rebuild a new version of this, new
version of that.
Speaker 2 (09:09):
Oh yeah, Joe, I remember you know those charts that
show like all the acquisitions that a Jpmore oh yeah,
or a Bank of America has done. It's sort of
like a flow chart. Yeah, every single one of those
probably has a different IT system, right, So I always
hear that one of the big difficulties in building a
giant bank is basically sorting out the.
Speaker 4 (09:31):
IT totally big institutions, you know, it's easy to sort
of assume that sort of CLOSEI government institutions are going
to be worse on so forth, and maybe sometimes that's
correct and sometimes that's not. But big gigantic institutions, particularly
ones that had all kinds of mergers and roll ups,
et cetera. They all have this, and this has been
(09:53):
something that's come up a little bit in the past.
We've done some episodes on bank software in general, and
so I'm not particularly surprised to hear that Fanny and
Freddy have a lot of still work to do, even
if they have invested.
Speaker 3 (10:07):
Why is it hard?
Speaker 4 (10:08):
Maybe from your perspective, from the perspective of either a
Fany or Freddy or just any other gigantic financial institution,
how would you describe why it's challenging to update these
systems so that they resemble the type of software we're
used to in twenty twenty four.
Speaker 6 (10:26):
Definitely, Maybe I'll start even way back, like fifty years ago.
Speaker 2 (10:30):
Are we going to talk about Kobyl? I hope, so
we can.
Speaker 6 (10:33):
Talk about Cobyl if you'd like. But I think it
actually when I was at Blend, I was fortunate enough
to work with tim Myoppolis, who was before I Blend,
the CEO at Fanny May and he has this line
which really stuck with me, which is that everyone says
that banks are like slow adopters of technology, but the
problem with banks actually is that they were very early
adopters of technology, right going back to everything really is
just a number inside a spreadsheet at a bank. There's
(10:55):
no corn that you're shipping or gold bars or whatnot.
Anctial services industry was really an early adoperative technology. What
that means is they installed a lot of technology very
very early on that then became harder and harder to
rip out. And one thing that we find, for example
at BESTA where we're replacing one of these core systems,
when I talk to other founders in the technology space,
(11:16):
it's much easier to install a new system to replace
a spreadsheet that just like so obviously doesn't work. The
enterprise security is terrible, the controls are terrible, et cetera.
Then to get someone to upgrade a system that you
know kind of works for them. It's clunky, it's inefficient,
it's slow, but it isn't a burning pain where they like, oh,
if I don't modernize, I'm going to lose the business
or lose my job or something. On the other hand,
(11:38):
I would say there's a very strong incentive in all
these big institutions if you try and do like a
huge modernization project of a big existing system of record
and it doesn't work, you're basically putting your job on
the line. And if it does work as a CIO
or a line level CIO at a bank, you're getting
a small promotion. I was just that the trade off
is pretty bad.
Speaker 4 (11:57):
I was just going to ask, like, how much is
it tech qua tech versus institutional inertia and incentives that
really create that problem of why it's harder to upgrade.
Speaker 6 (12:09):
I think it's mostly institutional inertial incentives. There certainly is
a lot of work that actually goes into it, right,
and so you've got to get budget, et cetera. Yeah,
but it's all very tractable. I will say in financial
services there are relatively few technology problems that are like
fundamentally hard technology problems, like we're not launching rockets over here.
They all tend to be people problems. Organizational problems are
problems that get in the way.
Speaker 2 (12:45):
Can you talk to us about the sort of life
cycle of a mortgage in terms of technology, so like
what's the first thing that happens, what system is it
put into, and then where does it go next?
Speaker 6 (12:57):
Sure, so we're talking one click refive today, So we'll
start with a refive in let's call it a relatively
idealized case. Let let's stop the real ideal, which is
like the bar is going to get an email from
their servicer right which says, hey, you're in the money, Like,
we service your loan, we know what you pay, we
know what rates are, we know you know roughly our
credit profile. We can tell you that you probably want
to refinance. So the consumer is going to click on
(13:19):
that link and they're normally going to go and fill
out an online application. Today they're going to go type
in a whole bunch of their data. Again, I think
you probably know your servicer has a ton of data
on you. Do you really need to type it in?
And this varies kind of depending on how tech forward
your servicer is, but often people are still typing in
their whole application. Again, they're uploading a whole bunch of documents.
And this is kind of sitting in the consumer facing system,
(13:39):
which today is you know, they call it a point
of sale. This is the space where really Blend is
the category leader now. And so the bar is going
to kind of type all that information in, They're going
to hit submit. That's going to push it to what's
called the loan origination system on the back end, and
you can think of that as both the system of
record and it's going to do all the compliance checks.
It's where the people are going to do all the
processing and the underwriting or any automated underwriting might happen.
(14:02):
And it's also the system that's going to be integrated
to like fifteen other systems. So one really annoying thing
about the mortgage ecosystem is to produce your loan from
front to back, you're probably hitting at least fifteen different
technology vendors.
Speaker 4 (14:14):
Can you run some what are some of these in?
What different parts of the stack are they serving? You
don't have to list all at fifteen, but give us
an example of like the various things that need to
be hit and who's doing them.
Speaker 6 (14:25):
Yeah, so I think of it as there's a whole
bunch of stuff around the property, right, so you've got
to go to a title company, you've got to go
to an appraisal management company kind of get the appraisal.
So there's a whole bunch of stuff around the property.
Someone's got to check what flood zone it's in. And
we'll get into why some of the rules are really
hard to change. But if you want to know what
flood zone a property is in. I mean you can
go on house Canary or Zillow and kind of figure
(14:45):
that out pretty quickly. If you want to sell a
mortgage to the GSS, you've actually got to hit one
of their four or five designated flood certificate providers for
an official flood certificate quote unquote, which is really just
you know, those are the providers that signed a deal
with the gs S where they get the FEMA maps
that everyone else gets and they produce a piece of
paper that's official enough and the GSS trust them. So
that's a provider you basically have to hit. And so
(15:07):
there's a whole variety of property you know, vendors you've
got to hit around those categories. There's a bunch of
bar er vendors you've got to hit around. The pulling
credit is the obvious one, but you're going to want
to verify their income. You're going to want to look
them up in fraud databases. So there's a whole set
of those, and then there's a bunch of compliance stuff
to do. So generally, you know, there are entire companies
that are dedicated to I have all the data in
the mortgage, and I'm going to prepare the disclosures for you.
(15:29):
Like the disclosures are so complicated, that's less a technology problem.
That's like that those companies have an army of lawyers
who basically read all the regulatory updates, all the updates
in each of the thirty eight hundred counties in the US,
any state updates, any investor updates around exactly what you
have to tell the consumer before they can kind of
sign a lian on their property, which, as you know,
like some states are very onerous about that. And so
(15:50):
between compliance and property and kind of checking the borrower,
there's just this whole constellation of stuff that has to
be done, a lot of data sources and a lot
of rules.
Speaker 2 (16:00):
Talk to us a bit more about the rules then,
Like I'm curious how these rules come into place, what
sort of factors they're being based on, and then how
often they actually change.
Speaker 6 (16:11):
Yeah, I kind of think of rules in two buckets. There.
Of course, the regulatory rules, so you can imagine a
ton of those regulatory rules were driven by seven eight
and a lot of the things that we saw during
the Great Financial Crisis or that kind of led up
to the Great financial crisis, so a lot of regulatory
stuff around what you disclose the consumers around you know,
you have to qualify their ability to repay in order
to have a compliant loan. So there's lots of regulatory stuff.
(16:34):
And then there's investor rules, which overwhelmingly you know, come
from Fanny er Freddy. There is a small private label
market and some other stuff. One thing that is true
about all of these rules, right the investor rules and
the regulatory rules, is they're both kind of set again
talking about organizational inertia by pseudo government institutions that have
really been burned by mortgages in the last two decades,
and so they're pretty you know, nervous about that, and
(16:57):
there's very little incentive to simplify the process to remove rules,
and so the rules change. I would say every month
or two you get a few new rules from the investors,
but they very rarely subtract rules, which tends to be
a reason that you end up with. I want to say,
the fan selling Guide is now twelve hundred pages basically
of rules that the loan has to satisfy. And these
rules can vary from you know, relatively straightforward things like
(17:18):
you can't refinance an FAJ loan within a certain amount
of time after the loan was originally originated, so there's
like a seasoning requirement. You know, there's a lot of
documentation rules, like if you're going to provide an income
to Fanny May, you normally have to have a pasteb
and in W two attached to it to kind of
verify that income. And you get into like really complex
and arcane rules as well, once you get into twelve
(17:38):
hundred pages, like are you allowed to have a ten
percent increase in income year over year and use that
new income, Well, you have to document that a certain way.
If it's about thirty percent, you have to document it
another way. So a whole kind of slew of rules,
which is why you have this huge body of people
basically that have to work on every single loan because
they have to learn all the rules.
Speaker 4 (17:57):
So one thing that would be really nice and gets
to the one clickness of what we're trying to get at,
is if it were really easy to pull in data
quickly from all these disparate providers. So I go to
your website and I want you to know my income,
and maybe I want you to know my assets that
I have, but I'm not sure if that's as important
(18:18):
in a mortgage. And I want you to know the
location of my property so that you can do various things,
including see what the floodplain looks like. The various providers
of this stuff, so one of the providers might be
my payroll provider. Another provider might be the bank that
I use, the bank online, et cetera. How forthcoming are
(18:39):
they in making these systems easy for a third party,
say yours or a Blend or some other fintech or
a rocket, et cetera, to just go access them such
that I don't have to download PDFs and then re
upload them somewhere else.
Speaker 6 (18:57):
Yeah. So I imagine you're asking because you know the
is going to be their very mixed results. So when
it comes to banks, for example, there have been a
whole advent of new kind of players that help you
connect your banking data. Claid is the big one exactly,
and then the banks, I would say, clearly had mixed
feelings about it. People are worried about the security of
people typing their bank password on something that's not the
(19:18):
bank's website. They're very notably over the last ten years
or so but a number of times in Chase just
shut off PLAIDS access, and so there certainly was some
complexity in that relationship. Earlier on with you know, some
of what's going on in open banking in the US.
I think the idea that you'll have access to your
own asset information is definitely a place the regulators are
pushing as well, and that gets easier and easier every year.
(19:40):
Payroll is a particularly interesting one. It's a very hot
topic in the mortgage world now because everyone basically uses
a product offered by appuifacts called the work number, which
you may have heard of. So the work number it
basically they have a partnership with ADP where ADP charges
them a very large amount of money actually to use
the borrow's social to look up the data. Work number
turns around and marks that up a whole bunch. So
(20:02):
they also charge a ton of money to the lender.
So I've heard of lender spending you know, like four
or five six hundred dollars a loon like to close
one loan to get that income and employment data in
this digitized way via the work number, which is I
think obviously ridiculous, And so there certainly is some struggle
going on with the data ecosystem. There are a bunch
of startups now trying to basically do a plaid did
(20:23):
where the barer can log into their payroll provider. One
problem you might imagine with that is like, do you
know your payroll password? Because I don't even know you
know who my provider is, no idea, and so there
certainly is some difficulty in getting the data together. I
would say that Ecosystem's made a lot of progress on
that in the last ten years, but payroll and income
tends to be a lot harder because it's much more fragmented.
And with banking and open banking and every bank you
(20:44):
know has some kind of electronic system of record, that's
a problem where I'd say they've made a lot more progress.
Speaker 2 (20:50):
How much does mortgage financing depend on just your sort
of basic mail. I want to be able to say
the housing market is powered by FedEx or something.
Speaker 6 (21:01):
So actually every closing I wouldn't say every closing package,
but the vast mandory of closing packages in this country,
like you go to your lender or a title office
and you sign the closing doc and the notes and
whatnot to actually the legally binding piece of paper that
says there is a lean on my property. Now, like
I have to pay this loan back, and that gets
FedEx back to the lender, and the lender then you know,
scans it, they upload it to their electronic system and
(21:23):
they turn around and they like FedEx that to a
doc custodian. And some lenders are more efficient with their
FedEx schemes than others on like it just goes straight
to the doc custodian and the DAK custodian scans and
sends it to them. But I would say that very
much all of the actual debt and recording and like
all of the legally binding stuff, probably ninety percent plus
is still physical pieces of paper that are getting mailed around.
(21:44):
There was a big trend, especially in twenty twenty, around
how do you digitize those notes? Hey do you e closing?
Then it's a matter off. You've got to get thirty
eight hundred counties to accept it, all the title companies
have to accept it, et cetera. So the big network
problem again, you're probably hearing a theme of it's just
like organizational inertia. Yeah, but you've very much can say
that home financing is still powered by FedEx because pretty
(22:04):
much everyone, I mean, we have a field in our
system where people are like, we need a FedEx tracking
number field for the note, like not even kidding. That
could be really cool if you could integrate that to
FedEx to like automate like looking at the tracking. And
it's like, of course you can do that technology wise,
but sometimes you ask yourself, like, what problem are we
really solving here? Guys, like we should just move it
to the cloud.
Speaker 4 (22:22):
So I think the last time I applied for a mortgage,
it's late twenty seventeen, and I just remember like documents
and documents and checkboxes, and I didn't read any of
those documents, had just signed the checkbox, and I assumed
it is all okay, Well were all those checkboxes I
was or signature boxes that I was putting a digital
signature into.
Speaker 6 (22:42):
A lot of those signature boxes are basically people disclosing
your rights, So very similar.
Speaker 3 (22:47):
To yeah, yea disclosure.
Speaker 6 (22:49):
Yeah, It's like you sign something that gives them authorization
to pull your credit in many cases, and then you
sign something that you know it's like, hey, here's your
credit score and here's how it was calculated. In the
state of California, you might something which is like if
you are getting an FAHA loan with lead paint, which
I hope you didn't, there's a disclosure that says, hey,
like we determined that the house has some old lead paint,
Like sign here to acknowledge that we disclosed that to you.
(23:11):
And then the other thing that happens is because each
of these disclosures are legally mandated and it's really hard
to make sure that you signed all of them in
the one go, they'll just say, hey, when you first
apply for the loan and we disclose through the terms,
we're going to stick all of those disclosures in there,
and then when you get to the closing table, we're
going to put them in there again, just so we've
belt and suspender that you know you've signed it. Like
you're at the closing table, you're not going to walk
(23:32):
away now, Like, let's just make you sign it one
more time. So, probably if I had the guests, you
got fifty or something disclosures, depending on the state you
were in, there's a whole bunch of you know, state
some states are more owners than others. We probably also
signed each one and average like two and a half times.
Speaker 4 (23:45):
I did just real quickly, how different was that experience
for me in twenty seventeen then it would have been
in two thousand and seven, before the mortgage crisis.
Speaker 6 (23:53):
Well, in two thousand and seven, you can imagine there
were way fewer disclosures. Actually, in twenty fifteen they passed
what's called TRID or tie the rest but integrated disclosures,
which is actually the main reason you can't have a
one click coage today. So TRID puts a minimum timeline
as well, where you have to give people, you know,
within three days of getting what's called a full application,
you have to provide them an estimate of all the
fees that like really clearly in a very standardized format,
(24:16):
discloses all the fees that comes with a bunch of disclosures.
And then you have to give the bar or seven
business days from giving them that loan estimate to close
the loan. And so we actually talk in mortgage now
a lot about the ten day mortgage, because you actually
can't have a one click mortgage purely by virtue of
the fact you need that seven day waiting period. There's
some other timelines and there even if you got rid
of that seven day waiting period, you'd still have to
(24:38):
remove a whole bunch of other regulatory timelines to really
get it down to one day. But in twenty fifteen
they released this new regulation which I would say made
it a lot more onerous, a lot more documents to sign,
and I mean it's good, right, Like pre twenty fifteen,
people were getting loans and they were getting bait and switched,
and you know, people were having new fees pop up
that they didn't know about. And now all that stuff
is really strictly regulated, but it definitely as to the
(24:59):
paperwork party.
Speaker 2 (25:15):
So how did Blend actually try to solve all of this?
Because when I listen to you talk about all these
sort of challenges in the mortgage market, it just sounds
like an unsolvable kind of spider web of requirements.
Speaker 6 (25:29):
Yeah, it certainly is very challenging. I wouldn't call it.
You know, nothing is really unsolvable except the regulatory timeline
is going to be what it is. But for a
lot of it was, hey, can you really get to
a one click and tell the bar or that they're
clear to close? And what that means is we've fully
underwritten everything. We know that you're going to close. The
only thing we're really waiting on is the compliance clock.
And so you kind of split it up into the
(25:51):
various things that get underwritten in the loan. So in property,
for example, the most clear thing you have to do
in order to say I can instantly underwrite your property
is they have to be able to get no appraisal
and get instant title. And today, title insurance is this
whole other thing that I'm sure you could do ten
episodes on. Some people have asked, which everyone, yes, I
would say every two years. Some Silicon Valley person tweets
(26:11):
that like, title insurance is a racket and someone should
go take it out, and I always get that tweet
texted to me like ten times. But there's this complexity around. Well,
the problem with title insurance is actually someone does have
to go to the county office still in a bunch
of counties, and go downstairs into the basement of the
courthouse and get the key and unlock it and go
like look up the records for that house or something
like that. So you have to figure out how you're
going to make title insurance instant, which there are a
(26:33):
whole bunch of startups that have worked on are working
on have had some success in digitizing that process. In
some counties, you've got to make the appraisal instant, which
basically means you have to get a message from Daniel
Freddy that for this particular property, they've written alone on
it recently enough that you don't have to appraise it again.
And so that's the property side. Those two things you
can imagine combined already, like you're taking one hundred percent
(26:55):
of properties in the US, and you're shrinking your hip
box to like twenty year or twenty five percent or
something like that, and then you've got to get the borrower.
And for Blend, a lot of the approach was, well,
we partner with a lot of big banks, and so
can you get the banking data directly from those banks
and use that to either figure out the income or
use something like the work number to instantly get income.
You can verify assets and income and the property, and
(27:15):
then you can of course, pulling credit is the easiest one,
because we've been able to pull credit digitally for decades
and decades in this country. If you can kind of
check all four of those boxes, then you're quite a
bit further towards a instant clear to close, you're still
not fully there. There's a bunch of stuff around the
margins you've got to go and sort out. But it
really is a matter of blocking and tackling, executing detail
by detail. And it's like, there are twelve hundred pages
(27:36):
of rules. I've probably read those twelve hundred pages of
Fanny rules three or four times, and you've just got
to systematically tick them off one by one.
Speaker 4 (27:42):
What are you doing now at VESTA that you weren't
doing and blend.
Speaker 6 (27:47):
Yeah, So a lot of the struggle that we had
up on was you really, because you own the front
end of the process, is you could get to fully automated.
You could pull it all in and be done. The
problem was, you know, you heard how I talked about
appraisal title. You kind of shrink the hit box for
what you can do fully automated. And so what we
found was that if you fully automated, like let's say
you really could fully automate one percent of lenders loans,
(28:10):
that would be great, but they're still you know, spending
a ton of manual dollars, a ton of you know,
operational people on ninety nine percent of their loans. And
the big problem was all the data that you got
at the front it was really hard to use that
to drive efficiencies at the back of the process or
for you know, any of the loans that did have
even one manual touch, like I ticked through all these rules.
You can imagine if only ten rules had to be
(28:31):
done by a human. Well, now it's got to go
through this manual process. And what it does today is
it goes through this old manual process where they basically
have to underwrite the whole loan manually because the system
doesn't have an understanding of what's already been done. It's
not you know, task or workflow oriented, and people basically
have muscle memory. So the underwriter is going to look
at everything, order the appraisal, whatnot, even if they don't
have to. And so a lot of what we realized
(28:53):
was the back end of the process was making it
really difficult to realize any efficiency from the good work
you're doing at the front end because the change management
and organizational inertia of you know, you've got three thousand
people on your mortgage manufacturing line so to speak, doing
exactly what they've always done, and the software isn't really
guiding them to do anything different, Like it's not it's
not a piece of software like you might be used
(29:14):
to working in today, like Slack gives you notifications for example.
It's really almost like a spreadsheet with a different UI
layer on top of it, and you've got to figure
out exactly what you're going to do. So a lot
of it was how do you change the way the
operation works so that people are doing a lot less.
And then the other big thing was with the existing
loan origination systems being so difficult to integrate to that
was one of the biggest hindrances and actually getting all
(29:35):
of the data and making that process one click. Was
that you couldn't actually do all of the jobs that
needed to be done by that old system, like the
old system that has all of the integrations I mentioned,
and they have hundreds of integrations to all these data providers,
like coordinating the appraisal. So you ended up having to
build around the old system instead of through the old
system to achieve a lot of this stuff, and that
(29:56):
just seemed like so clearly the wrong way to do it.
Now the downside is have to go and modernize the
old system. Which is a really hard problem. But by
kind of modernizing the old system, you unlock a the
operational efficiency that you actually get from all this data
and then be a much easier platform for everyone who
wants to build a front end to get that data
through your integrations and through your processes that the lender
(30:18):
already has that exists manually today of having to recreate
it on the side to try and automate it, if
that makes sense.
Speaker 2 (30:24):
I have a slightly random question, which is, given that
we're talking about technicalities, how easy is it to commit
some sort of mortgage fraud nowadays? Totally random, not out
of personal interest.
Speaker 6 (30:38):
Yeah, I did listen to your recent episode about government
Fraudry Joe was the one I think was very interested.
Speaker 3 (30:43):
Yeah, I was the one interring to start doing fraud.
Speaker 6 (30:46):
Yeah. Mortgage fraud, I think is actually quite difficult these days,
mostly because there are so many human eyeballs that look
at the loan and so let's take something really simple,
like you wanted to like doctor a document, like probably
the most straightforward thing because it's not like mascal fraud.
It's like somebody is like, I want a mortgage on
my primary residence. I can't afford it, and I'm just
(31:07):
going to doctor the documents to make my income look bigger. Well,
first you have to hope the lender doesn't check some
third party verified data source, or they don't reach out
to the employer, which they often do. And then you
have to hope that like your document makes it through
the processor are looking at it, and the underwriter looking
at it, and the closer looking at it, and the
underwriters especially they're looking for things that don't add up.
And so I would say mortgage fraud is probably really
pretty too, very difficult to actually accomplish today. It sounds
(31:29):
a lot harder to achieve than, like, you know, figuring
out how to get some Medicare dollars. Yeah, so it's
probably not worth squeeze. Now, we'll like generative AI make
it way easier to make fake profiles and all that stuff.
Maybe that's something that I think lots of people worry about,
But today I would say it's definitely the mortage industry
has done a pretty good job of doing that, and
I'd say the regulators have done a good job of
(31:50):
making it really hard, just given everything that happened two
decades ago.
Speaker 3 (31:53):
Back to the question of refise.
Speaker 4 (31:55):
So you mentioned that theoretically, if you're a homeowner and
you're in the money on your mortgage, that is to say,
where it would make economic sense for you to refi.
You might get an email or something it's like, hey,
you should refi and you can save this much. But it's,
as we're talking about, it's gonna be a lot of
paperwork and all this stuff. After our episode came out
(32:16):
several weeks ago, someone on Twitter, they said, why can't
we have a mortgage product that you pay a higher
premium upfront, but it's a floating rate mortgage that only
resets downward. In other words, basically, if rates drop lower,
your mortgage mechanically drops with it. And again, obviously, if
you're going to have that, you theoretically that's a more
(32:39):
valuable option and you pay some premium upfront, but then
in theory, you save all of this effort and time
and document checking and human hours that go into this.
In your mind, does that seem like a plausible financial
product that could exist.
Speaker 6 (32:52):
Seems like a totally reasonable financial product. I think that
there may even be somebody doing it. And like the
private label securities market, there is a market basically hedge
funds that will like underwrite non qms what they're called
mortgage products and offer those to lenders, and lenders can
originate them. One thing I will say is it seems
unlikely to come from the GSUS just because so much
of the gs's mission these days is affordability and democratizing
(33:15):
home ownership. And I can't really think of a marginal
person that that products would get into a home.
Speaker 4 (33:20):
Right, So even if it makes sense, that's just now
we'll move the dial.
Speaker 6 (33:24):
Yeah, I think it makes sense from a you know,
like single person financial instrument perspective, like I would love
to have one of those, for example, But I think
that from the kind of stated policy goals of the
biggest investors in the market, it's just not really something
that oligns with their policy goals, and so I can't
see that being a big area of where we're going
to see a bunch of those in a decade.
Speaker 2 (33:43):
So a lot of mortgages get bundled together into mortgage
backed securities. I'm curious, like how much of that granular
detail about pay and you know, lead paint in the
house and things like that gets ported over to the
securitization aspect of it.
Speaker 6 (34:02):
So it's really not a lot. I actually I have
capital markets people reaching out to me all the time,
being like, if you have a modern loan origination system,
you can solve my problem if I can't actually get
any of the data or much of the data that
you underlines these instruments when I am going and securitizing
them or trading them or whatnot. But generally comes out
as much banking technology still is today is you export
(34:24):
a big CSV of some of the data fields. You
take a bunch of docks and you send them off,
and then that CSV, which you know, they fancily call
it tape, but really it's a spreadsheet, just gets ingested.
And now you've taken a process that had three thousand
fields and hundreds of pages of docks, and you've boiled
that down into like fifty or one hundred fields that
describe the mortgage, which maybe is for the best, Like
I'm not really sure that people buying mbs should be
(34:46):
thinking about, you know, the specific credit profile of the
thousand different mortgages that are chopped in there and put
in and so it's nice that there's some standardization, but
it's definitely very lossy, and I will tell you it
is something that mortgage traders complain to me about a lot.
Speaker 4 (35:00):
So what's realistic. It doesn't sound like you'd ever get
like true one click, because it's you know, at a minimum,
you're probably going to have to tell the front end
who your.
Speaker 3 (35:10):
Payroll is and who your bank is, and a few
other things.
Speaker 4 (35:14):
What is a plausible version of if you know there's
continual coordination among different banks, if Fanny and Freddy continue
to update their technology so that you know, it's a
little easier, what could it look like if I were,
say you're say, in ten years.
Speaker 3 (35:29):
I'm applying for a mortgage again.
Speaker 6 (35:31):
I think it's very reasonable to strive for a world
where it is, you know, to your point, as close
to one click as possible on the very front end.
Maybe you're talking about like a ten minute application max,
where you connect some accounts once that's done. I think
that what you should get instantly is one of basically
three decisions. Hey, you are definitely clear to close. You're
(35:53):
going to get your disclosures, and then we're just going
to wait ten days and we'll close you. Option two
is hey, you as a barwer are definitely clear to close,
but we need some additional information on the property. So
you're going to wait ten to fourteen days, you're going
to pay for the appraisal, and we're going to close
you or three is basically unfortunately, you know, we're not
able to close you. Here some things you can do
to improve your stance. I think that property is probably
(36:14):
going to be the thing that even if you ask
me ten years from now, it's like it's really a
question of there will always be those corner cases. It
feels like where the gs are going to want to
see an appraisal unless they're the big credit profile change
or if they get privatized. You know, there's all sorts
of things that can happen in ten years. But I
think those three outcomes being ten minutes away fingertips wwise
from the bar when you close in ten days is
(36:35):
very much attainable, and it's really what everyone in the
industry is and ought to be working towards.
Speaker 2 (36:41):
Does blockchain solve this? And I mean that's somewhat serious.
Speaker 3 (36:45):
It's a good question.
Speaker 2 (36:46):
Because, like I've often thought like one of the few
real world applications of blockchain technology could be in the
mortgage assignation space where you have that sort of chain
of title moving around constantly. But also I'm thinking, like
from a wallet perspective, if you could have like a
personal profile that carried with it you know, your pay
(37:08):
and yeah, how much your worth and et cetera, you
could use that too.
Speaker 6 (37:14):
Yes, So those are two very interesting use cases on
the wallet perspective. The way that I think about blockchain
helping here is almost lets you build like a more
encompassing decentralized credit bureau. And decentralized is actually really important
because I don't think the banks are super excited about
the idea of helping build like a fourth credit bureau,
like they built three and now they pay the three
for their own data, which I think is a little
(37:36):
bit difficult for them. And then you know, the regulators
are not super excited about the centralized credit bureaus, et cetera.
And so I think the idea that each consumer could
have their own key that unlocks, you know, access to
all of their data on this decentralized credit bureau that
all the payroll providers and financial institutions, et cetera are
writing to, is very much an idea that has legs.
It's really hard to implement for you know, a lot
of similar organizational inertial reasons. But I do think that
(37:58):
is a real use case for blockchain, be it solves
the incentives problem where people are basically like, I don't
want there to be one middleman with every consumer in
America's financial data, and so blockchain, let's you kind of
like decentralize and split that up. So I think there's
some really interesting avenues there that people can go down,
and there are some companies I think actually looking at
that on the title front, what I usually tell people
on could title beyond blockchain? Absolutely? Is it an interesting
(38:19):
use case? Absolutely? The hard part of digitizing title is
getting thirty eight hundred counties to even move to like
putting capturing the records digitally and not in the courthouse basement.
Getting thirty hundred counties to move to blockchain seems further
away than that, not closer.
Speaker 4 (38:34):
I don't know if I've ever mentioned it on the
show before. I once had a gig right after college
in which there was some company out in California doing
some of his bestest lawsuits, and they needed names of
all these people who had been partied to some suit.
I don't they're maybe putting it together a database for lawyers,
and part of my job was to go to various
county courthouses all around rural Texas in central Texas and
(38:58):
go to the basement and just literally pull out files
and ask for names. I'm just going to ask one
last question since Tracy hit one tech buzzword, which is
blockchain generative AI. Whether it's in the field of scanning
documents or understanding documents quickly in your work right now,
is there a substantive use that you're getting out of
(39:18):
this technology.
Speaker 6 (39:19):
Yes, it is definitely to your point, it's scanning and
understanding documents, and so you can think of mortgage the
way that I think of it high level is it's
a whole bunch of data and it's a bunch of rules,
and the rules are well defined by investors, the government, etc.
And the data is just data, and so data and
rules to you know, a lot of this conversation should
be a one click experience, like we've known for decades
how to run rules on data. And a lot of
(39:40):
the problems come about because the rules are written in
the twelve hundred pah PDF and there's a little gray
and someone has to learn them, and the data in
a bunch of documents and a bunch of you know,
disparate places from the borrower, and so it's not structured.
And so if you can bring structure to the data
and structure to the rules, both of which generative AI
is really good at. Right. It can read the Fannia
Selling Guide and turn that into code rules. It can
read a document and turn that into data points. If
(40:02):
you can use generitive AI to structure those two things,
then you still have structured data, structured rules, and that
they're you know, whatever rules the GSE said, So you
don't have these compliance things with ohs AI underwriting below.
But we're seeing a lot of really promising results taking
the most cutting edge large language models and applying them
to these documents, both to write the rules for us
and to lift the data off the documents.
Speaker 2 (40:22):
All right, Mike, you thank you so much for coming
on all lots and explaining to us why we can't
have a nice thing.
Speaker 6 (40:30):
Yeah, thanks for having me.
Speaker 3 (40:31):
That was amazing.
Speaker 4 (40:32):
Mike, you're the perfect guest. Thank you so much for
coming on.
Speaker 6 (40:35):
Yeah, that was fun. Thanks for having me.
Speaker 2 (40:49):
Joe, that was really fun.
Speaker 3 (40:51):
That was really fun.
Speaker 4 (40:52):
I thought Mike was exceptionally clear at explaining how all
this works. And although it's still annoying the process of
getting more and lots of documents that I didn't read
and attached my signature to, Like, I guess I understand
a little bit more.
Speaker 2 (41:05):
Why now, do you remember after the financial crisis there
were all these problems with loan documentation and I remember,
like there's a big thing about assigning mortgages in blank
that all turned into like court cases. Yeah, I kind
of wonder, like.
Speaker 4 (41:21):
We did a great episode on that with David Yeah,
David Dian of the American Prospect, Like in twenty fifteen.
What was the name of his book, David chain of Title. Oh, yeah,
we did an episode with David Dan chain of Title.
At how crazy that was. It was just the state
of disarray in documentation after the mortgage crisis. But you
(41:42):
understand why it is when you still have, you know,
so much at the county level and the county level,
I guess for obvious reasons, not feeling any particular pressure
to update or digitize.
Speaker 3 (41:53):
Or modernize or coordinate all of their systems.
Speaker 2 (41:55):
Yeah, that's really it isn't It sort of like a
Hodgepodge of State and count Law. I feel like we're
going to be waiting a while for a solution to this.
Speaker 4 (42:05):
Yes, and I've sort of hinted at it before on
the podcast, but for very arcane reasons that I'm not
going to get into. I do have a loan that
will need to be refinanced at some point in the
next couple of years.
Speaker 2 (42:18):
You're very optimistic, Joe about interest rates.
Speaker 4 (42:21):
No, I'm very pessimistic and I'm very anxious about it.
But if I'm not optimistic about the path of interest rates,
maybe I'll be optimistic that in a couple of years
the process is at least a little bit better than
it was the last time I applied for a mortgage.
Speaker 2 (42:36):
Well, see, you'll have to tell me how many documents
like get mailed out and stuff, although I guess most
of that is on the sort of like lender and
servicers side.
Speaker 3 (42:45):
But I'm not looking forward to it.
Speaker 2 (42:47):
Yeah, all right, shall we leave it there.
Speaker 3 (42:48):
Let's leave it there.
Speaker 2 (42:49):
This has been another episode of the All Thoughts podcast.
I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 4 (42:55):
And I'm Joe Wisenthal. You can follow me at the Stalwart.
Follow Ugust Mike thank you, He's at Michael Underscore. You
follow our producers Kerman Rodriguez at Kerman Ermann, Dashel Bennett
at Deshbot and Kilbrooks at Kelbrooks. Thank you to our
producer Moses Ondem. For more Oddlogs content, go to Bloomberg
dot com slash odd Lots, where we have transcripts, a blog,
(43:16):
and a daily newsletter and you can chut them up
all of these topics twenty four to seven in our
discord discord dot gg slash odlines.
Speaker 2 (43:24):
And if you enjoy Oddlots, if you like it when
we dive deep into the reasons we can't have one
click mortgage revise, then please leave us a positive review
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(43:44):
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Speaker 5 (43:48):
Thanks for listening, bend In
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