Speaker 1:
From the New York Stock Exchange at the corner of Wall and Broad Streets in New York City, welcome Inside the ICE House. Our podcast from Intercontinental Exchange is your go-to for the latest on markets, leadership, vision, and business. For over 230 years, the NYSE has been the beating heart of global growth. Each week, we bring you inspiring stories of innovators, job creators, and the movers and shakers of capitalism here at the NYSE and ICE's exchanges around the world. Now let's go Inside the ICE House.
Varun Pawar:
Data and analytics has evolved to become a central driver of business strategy, helping companies make data-driven decisions. Once focusing on basic reporting and trend analysis, advancements in technology have enabled real-time data processing and predictive modeling. As businesses increasingly rely on large data sets. Data analytics now support everything from consumer insights to operational efficiencies and risk management. Joining me, Varun Pawar, chief product officer at ICE Data Services is Julian Grey, senior director of mortgage data and analytics at ICE. Her leadership in combining technology, data science and privacy expertise, as well as her decades of experience have helped position the firm as a leader in the space setting it apart in the industry. Julian, thanks for joining us Inside the ICE House. It's a pleasure to have you here today.
Julian Grey:
It's really good to be here.
Varun Pawar:
So just taking a step back, at multiple town halls, multiple investor calls, Jeff, our CEO, speaks about ICE as an all-weather company, and I think this is pretty evident if you look at businesses across the exchanges, across the data services, and now a big, big play within the mortgage technology space. Talk to me a little bit about the mortgage business and the data business within IMT. Give me your perspective on it.
Julian Grey:
Data and analytics is a really interesting part of the mortgage ecosystem and what we've done in the last couple of years is we've brought together data that comes out of the servicing system. So that's the mortgage performance data, our property record data, and the origination data. So those three things, along with probably 40 or 50 other ancillary data sets means that we've got this really full view of the mortgage ecosystem. And that supports everyone from lenders to servicers to investors across that ecosystem. And data and analytics is really only interesting when markets are moving. If there's nothing going on, then you actually don't need any fresh information.
Varun Pawar:
[inaudible 00:03:00]
Julian Grey:
Yeah. So at the end of the day, what happens is that when markets are volatile, our clients need information in order to navigate them. And so we actually, through the process, we're helping our clients be all-weather. For example, lenders right now are really worried about retaining their portfolios and gaining new clients and following home prices and default rates and prepayment rates. All of those things, because markets are particularly volatile right now, are things that we help them with. So at the end of the day, what that means is that we're helping our clients build all-weather businesses. And when they do that, then we also add to that vision within ICE.
Varun Pawar:
Yeah. You've been in the mortgage data and analytics space for a while now. I mean, that's your craft. That's why you spent decades perfecting, and ever since, say 2008, since the mortgage crisis, the space has been in constant evolution of making sure that the data is accurate and we've got better data and broader data sets to make assessments and judgments, but there's also a markets element to it, right? I mean, if you look what happened after COVID, interest rates fell down, mortgage applications went up, and then all of a sudden interest rates started going up, mortgage applications are going down. Just talk to me about that whole journey ever since the 2008 crisis to the COVID crisis and how things have evolved.
Julian Grey:
It's pretty fascinating. If you look back to 2007 through 2012, markets came to a standstill. And the reality is, in this space, I mean you were in fixed income, so I think that the maturity of the mortgage space wasn't quite there yet in terms of the depth of data and the complexity of analytics. Although they were there, it's just not everyone was using them. The problems that were being solved in 2007 were pretty basic, which is, "What is my collateral risk?" "What's happening with home prices?" "What is the impact on my non-agency securities?" And that stuff was really, really important. Come 2012, obviously people are thinking about regulatory reporting and so on, and we can look at every single time period, including the COVID time period, which you just mentioned, where forbearance was the name of the game or a couple of years later where folks are very focused on the fact that interest rates are so low, we're seeing this massive refi boom.
And the common theme in all of this is that there is not a common theme. It's that markets drive very much which data and analytics are important for the moment. So for us at ICE, the way that we really think about that is by assembling the most accurate comprehensive data set so that we can build analytics, whether they're predictive or descriptive at the moment for the moment, because that's the only way our clients can compete really at the end of the day. And that's really evolved. In some ways data and analytics failed the industry back in 2007. In some ways. In other ways, the industry failed itself because they didn't leverage all of the data and analytics that were there at the time. But the thing about this space is that even though it's mortgage and mortgage tends to be a little slower moving than some of the other asset classes you work on, you have to be nimble. You have to constantly be nimble in thinking about what's next.
Varun Pawar:
Well, speaking about what's next, you've worked at a few other firms before coming to ICE through the Black Knight acquisition. Can you talk to me a little bit about how ICE distinguishes itself relative to the other firms that are within this space trying to offer clients data and analytics that are very similar to what we have to offer?
Julian Grey:
It's actually kind of fun, fascinating culture now being part of ICE because if you think about it, ICE has had this deep experience in data and analytics for a long time in a really intense, urgent way. I mean, they own exchanges, they have the fixed income business, we've got clearing and sustainable finance and energy. And in each one of these spaces, you need to have, not only comprehensive data analytics, but you have to have it at the highest level of quality and accuracy as well as with urgency and pace. And so that's fascinating in its own way. So joining this organization means that not only are we really thinking about how we bring together all the data assets that we have in a really comprehensive way, and I would definitely say the ICE strategy has been really to make sure that we have all of the legs of the stool, meaning everything about the property, everything about the mortgage, everything about the groups of mortgages, meaning the mortgage-backed securities, whether they're agency, non-agency CMOs, and then all of the ancillary information that it can impact the performance and value of those assets.
So that's the first piece that I noticed that's really unique at ICE, which is that we're looking at things in a really holistic, comprehensive way, that the idea of having deep expertise is commonplace here. The expectation is that the data will be at the highest quality, at the fastest frequency and as complete as possible. But moreover, what's really unique is the fact that there's a lot of ambition and curiosity around where we go next. Meaning how do we bring together data sets so that we have even more powerful prepayment indicators? How do we bring together data sets so that our models are comprehensive for every zip code in the United States with zero-day lags when we're measuring things like home prices? And that's something you only see here at ICE, hands down.
Varun Pawar:
When Jeff speaks about the mortgage industry, you can see that he's extremely passionate about modernizing the whole process. I think if you've gone down this road with applying for a mortgage, the amount of paperwork, the amount of documentation, it's quite stressful. It's not a seamless process. More importantly, there's a lot of time that's been built into it, like when people receive that information, they need to go and do something with it and assess it and things like that. So the turnaround could be relatively lengthy with that whole process, but ICE is committed to modernizing the whole mortgage experience. And as a result, we've made some of these strategic investments over the last few years within the space itself. Which part of that whole process excites you the most?
Julian Grey:
Well, if you think about it, the biggest single asset class in the US is residential real estate. It's not futures, it's not derivatives, it's not fixed income, it's residential real estate. And I mean, that's kind of mind-boggling to think about. And so if you think about the size of that and what happens if that space, that mortgage and real estate space, which touches the consumer, is modernized in a way that we've seen other markets and other asset classes. What that means is that information is more readily available for investors, which brings more cashflow into the industry, which brings more opportunity for people to become homeowners and realize the American Dream. That's a really big deal.
Varun Pawar:
It is.
Julian Grey:
Data and analytics plays a piece of that. It's not the whole thing. My colleagues in ICE Mortgage Technology, they're tackling that from a technology standpoint under Tim Bowler and they're doing really a beautiful job. But data and analytics does play an important piece, which is, and I guess I'd say kind of threefold. One is doing the obvious stuff. Let's make sure the right data is in the right place at the right time in the technology. Let's make sure that the same data is at the beginning of the mortgage process that's seen by investors at the end of the mortgage process. That sounds incredibly obvious, but it doesn't happen today. Most consumers, they have access to what I would call proxy data in the industry, stuff that's freely available on websites. And for the most part, that's not decision quality data, to be honest. Either it's old and antiquated or it's very lagged, or it leverages questionable data sources. So data in an obvious way, we can bring it inside these mortgage applications to create seamless information, less duplication, and it becomes more efficient. That's number one.
Number two, which I really think is quite important, which is that the consumers begin to have a relationship with decision quality data. And this gives us a chance to let consumers be smart, let consumers ask questions, let consumers be skeptical about the information, about the value of their home, about the interest rates they're paying, or why they're paying mortgages or what their fees are. All of those things, data and analytics has a really important role as we connect these platforms. I think it allows consumers to be smarter and make better decisions for themselves and again, end up in homes. So I think that piece I'm excited about. And then last, but definitely not least is when we think about emerging technologies and leveraging bigger, more complete data sets, we can optimize outcomes for everyone, which is leveraging analytics that comes from our own data and exhaust to speed up the mortgage process to help decisioning for consumers in terms of what's best for them.
So I think there's definitely a lot here. And then ultimately, I think the thing that's always near and dear to my heart is just the idea that if we put enough information in the right hands, we have more liquidity, more homeownership, more diversity of product type in the market.
Varun Pawar:
More transparency as well.
Julian Grey:
More transparency. Absolutely.
Varun Pawar:
So let's talk a little bit about the forward. How do you see this space evolving over the next five to 10 years? Obviously, things are becoming more digitized, and if we're successful in our mission that we had stated out before getting into the space, it should be relatively easy to get an application and a mortgage through in the next five to 10 years. But within the data and analytics space, what does that forward look like? What do you think the landscape will look like in the next five to 10 years?
Julian Grey:
I think that there's a whole group of standard pieces of information in mortgage and real estate and data and analytics that have been there for a long time. So you might imagine things like prepayment default models, it's bread and butter in this space. Economic models, home price indexes, rate indices, all of those kinds of things, they've been around for some time, they're really essential to the market. They're essential to decision making. First and foremost, the standards around those analytics is going to be higher. It's going to be higher than it ever was, and to some degree, in part because of what we're doing at ICE, to be honest, which is that we're absolutely raising the bar.
A quick silly story, and I'm using this as an example because I think it's kind of easy to follow. Most of the home price indexes in the market that are readily available and freely out there, most of them are leveraging very stale data sets that are either six months lagged, they don't cover certain areas, and even though the methodology can be solid, that information is not all weather during volatile markets. And some of the things that we're doing is saying, "Okay, wait a minute. Is there a world where we can bring in the best-in-class modeling, the most complete data sets and eliminate all of these things we've all lived with, which is, 'Oh yeah, no, there's always going to be lags in real estate-related data'?" Well, no, there doesn't have to be.
Same thing with prepayment default models. Many, many prepayment default models out there have had a number of different kinds of limitations because of restrictions in data and everything else. We've been working closely with some of our internal innovation partners to bring together data sets that previously were really difficult to connect because of compliance and other things to create prepayment signals that really will allow folks to make better decisions, quicker. The simplest things. So we're going to see a lot more of that, which is taking the old and making it really valuable and new. The second thing I think that we will see is we're going to see a maturing of use in data science in our industry. It's very bifurcated today. And what I mean by that is 10, 20 years ago, the only folks who were working with data and analytics were actually people like you and your teams, and they had quants and the quants were very deep in the data and they were very excited about it, but it was gibberish to everyone else.
And with really new emerging technologies and data science across the mortgage industry, we've seen a lot more leveraging of data and analytics, but it is not quite mature yet. Sometimes I joke and I say, "Well, it's data and analytics. I'd like to get me some of that." And people will invest, but they're not really sure where they want to be with it. I think that both availability of complete data sets, like what we have at ICE and real data science where people are using some of the rigor of our previous quants and some of the innovation of data science will mean that we'll start seeing shops leverage all of this technology on a daily basis. They're not doing it today. Some folks are, but there's still a lot of folks out there who are using hunches and proxy data in order to place bets, and that is a really dangerous territory. So that's number two.
And then number three, which is incredibly boring, but I am excited about it, is the fact that with emerging technologies, people are going to get faster at computational agents. Things that we really struggle with now, running certain kinds of models or asking and answering a question that's going to be nothing. It's just going to be, "What are the default rates in zip code 12345?" "What are the cohorts that are both driving those default rates?" Instead of that taking 10 minutes, it'll take seconds and we'll have all of that information at our fingertips.
Varun Pawar:
No, that's great. It's really exciting. Talk to us a little bit about the data that you and your team work with. We've got data right from the origination side all the way to the servicing side, all the way to how the underlying asset is performing. Talk to us a little bit about that.
Julian Grey:
It's pretty incredible. So I think about the asset itself as the property, so that's the home, and at ICE we have everything you might want to know about just about every home in the United States. So bedrooms, bath, what it was sold for yesterday, what it was sold for 25 years ago, where it was bought and sold, what were the particulars, all of that detail. And that's of course model rich information because it's telling you about the actual physical asset. So of course, we have that data-
Varun Pawar:
Well, added to that we have a lot of data across climate and climate risk as well, which we're now applying to the actual physical asset.
Julian Grey:
Absolutely great point. Which is anything that could happen to that asset and physically happen to that asset, whether it's a hurricane or whether it's future risk factors. Absolutely. Or will it be listed and has it been listed and what's happening on the for sale market? So again, if you think just about anything surrounding that physical asset, we've put all that data together. How cool is that? And then there's the mortgage. So the mortgage itself is, Varun, you have a mortgage, you pay X dollars-
Varun Pawar:
I pay on time.
Julian Grey:
Do you pay on time? Right. No, you forget because you travel too much and you forgot to automate it. But probably not. But are you paying on time or do you refi every five minutes or did you pay it off so you could buy a second home? All of those behavioral statistics we capture at an anonymized loan level so that we can understand the behavior in and around mortgages. And why is this important? Because there's a lot of nuance in terms of who does what when, and that is super important to the capital markets so that they can create a diverse set of product types and lending types that really put something for everyone out in the space. And that actually creates an entire ecosystem that is safer for everyone. It has less risk because there's risk-based pricing and risk-based product types.
So we have the asset, we have just about everything with the mortgages and just as you said, with sustainable finance with regards to that mortgage data. We've been bringing in other data sets using some very specialized hash keys so that we can connect anonymized data in a very accurate but safe and sane way, which is incredibly exciting to some of us nerdy people. And then in and around that mortgage, we're also collecting all of the origination data and that origination data, again, I'm going to take you as an example. You just went out and applied to buy a home and you locked at a certain percentage rate. You locked over a certain amount of time. The primary and secondary markets paid a certain price for that data. All of your details were captured at that moment, and eventually what we'll be doing is connecting those data sets. And of course we'll do it in a compliant and safe and sane way, but how cool is it to be able to tell the behavioral story of a mortgage from the time it begins to the time it prepays and dies?
Varun Pawar:
It's a fascinating space. Spend a little bit of time on the proprietary or other, the privileged nature of some of this information. Obviously, there's a ton of personal information that's in there, and we make sure that we take the compliance of personal information very seriously. Talk to us a little bit about how that information is anonymized or aggregated and as a result loses some of that personal information.
Julian Grey:
I love that you asked that question because you could tell I was being very careful around it. At the end of the day, we're talking about... It's why this asset class is so fascinating. This asset class is directly related to American citizens, to people, to people who own homes and have families and go to work and whose privacy is incredibly important. And so public record data, so that's the data that comes in and around properties,. That data has some personally identifiable information in it. And if you want, tomorrow you could probably go to your county recorder office if you live in a disclosure state and you could look up your home and you'll see your full name and you may see details on what you purchased it for, and that will all be there. And that's a relatively common knowledge in that public record data. That's true usually for listing data and some other data assets in and around the property.
But mortgages are quite private and that's considered private information and we very much respect it. It's one of the reasons that the relationship being acquired as Black Knight by ICE is so fantastic because ICE has a long history of dealing with sensitive information, especially with the exchanges. So there's no learning curve there. And the idea for us with these data sets is that we keep them completely anonymized so that you cannot reverse engineer back to borrower name or social security or any of those pieces of information. But what can be done is that we can create predictive and descriptive and optimized analytics in order to help people make decisions. So even though, yes, your mortgage, Varun, is probably in our database, as is mine and as is anyone who's listening to this, the likelihood is it's there, but it will be so anonymized that there's no way to ever trace it back.
The cool thing is that the level of sophistication on our teams... You talked about what's changed over a number of years, the level of sophistication in terms of putting data sets together without violating any privacy or without exposing users in any way has gotten quite good. And so now we can bring all of those data sets together, sometimes in real time and keep them fully informative and anonymized, and that's something that we're really serious about. Our whole team takes it seriously, and it's one of the reasons that ICE has been such a good fit because ICE does too.
Varun Pawar:
Right. Yeah. We're very interested in the macro picture as opposed to the individual line items within that. Take a step back, take a look at what's trending broadly as opposed to what any single, individual or any group of individuals are looking to do. Julian, thank you so much. It's been a pleasure having you Inside the ICE House, and it was a great chat, very informative. Again, thank you very much.
Julian Grey:
It's just lovely to be here. Thank you.
Speaker 1:
That's our conversation for this week. Remember to rate, review, and subscribe wherever you listen and follow us on X at ICEHousePodcast. From the New York Stock Exchange, we'll talk to you again next week Inside the ICE House.
Information contained in this podcast was obtained in part from publicly available sources and not independently verified. Neither ICE nor its affiliates make any representations or warranties, expressed or implied, as to the accuracy or completeness of the information, and do not sponsor, approve or endorse any of the content herein, all of which is presented solely for informational and educational purposes. Nothing herein constitutes an offer to sell, a solicitation of an offer to buy any security, or a recommendation of any security or trading practice. Some portions of the preceding conversation may have been edited for the purpose of length or clarity.
