On today’s sponsored episode, Editor in Chief Sarah Wheeler talks with Chris McEntee, VP of corporate development at ICE Mortgage Technology, about data, change management and what really moves the needle for lenders in this environment.
Related to this episode:
ICE Mortgage Technology
https://mortgagetech.ice.com/index
Chris McEntee
https://www.linkedin.com/in/mcentee/
Enjoy the episode!
The HousingWire Daily podcast brings the full picture of the most compelling stories in the housing market reported across HousingWire. Each morning, listen to editor in chief Sarah Wheeler talk to leading industry voices and get a deeper look behind the scenes of the top mortgage and real estate stories. Hosted and produced by the HousingWire Content Studio.
Related to this episode:
ICE Mortgage Technology
https://mortgagetech.ice.com/index
Chris McEntee
https://www.linkedin.com/in/mcentee/
Enjoy the episode!
The HousingWire Daily podcast brings the full picture of the most compelling stories in the housing market reported across HousingWire. Each morning, listen to editor in chief Sarah Wheeler talk to leading industry voices and get a deeper look behind the scenes of the top mortgage and real estate stories. Hosted and produced by the HousingWire Content Studio.
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NewsTranscript
00:00Welcome, everyone. My guest today is Chris McEntee, Vice President of Corporate Development
00:11at ICE Mortgage Technology, to talk about data, change management, and what really moves the
00:16needle for lenders in this environment. Chris, welcome back to the podcast. Great to see you.
00:21Yeah, great to see you too, Sarah. Always a pleasure.
00:25I love this. And I love checking in with you because things are changing so fast.
00:30And I know you guys really have your finger on the pulse of what's happening in the industry
00:35with technology. So let's start there. Kind of tell me as a sort of a check-in, what's going on
00:41with you right now? Well, you know, there's quite a bit going on. I think, you know, step back for a
00:47minute and think about where we are, you know, in context of history. I mean, I reflect a little bit
00:53on NBA, you know, in 24, right? And there was this, and even 23, I think, at the end of 23,
01:03the rate cycle being where it was and coming off the boom, people said, oh, survive to 25, right?
01:08There was that was that slogan that was kind of on everybody's lips related to, well, you know,
01:13we thought we'd see a rate cycle. And obviously, with the election, some changes in policy, I think
01:18the belief that there would be a pronounced uptick potentially in refis didn't really emerge.
01:25And as a result, now we're three, three and a half years into a higher rate cycle. We know a lot of
01:31the loans still are at low loan balances, but also loan rates. So I think it's really prompted a lot
01:38of the lenders to rethink how they're approaching the business. And my, you know, my feedback that
01:47I've got, and we've done a lot of outreach with customers has been that they've almost gone through
01:52two cycles. So if you follow me, what we've heard from customers was, you know, we had the boom,
01:58obviously during pandemic and rates and everybody, we're really, we had huge volumes. And then there
02:05was a lot of retooling as the rates dropped down, right? Obviously people reduced headcount. We saw
02:11the numbers, certainly Bureau of Labor Statistics, some headcounts come down. And then there was some,
02:16you know, kind of analysis around, well, are we hitting where given rate environment and production
02:23is the labor capacity about right? And I think we got probably about 15 months ago thinking, okay,
02:29we're kind of hitting this inflection point where there's some supply demand equivalency there.
02:35What we heard from lenders was though, some of that was that we just didn't see the uptick.
02:40So we retooled some infrastructure, we brought in some new staff, we trained them,
02:45and then we didn't see the production increase. And that created some attrition in those organizations.
02:51And then they said, look, I had to bring in some new people into my organization. And really,
02:56that's a long way of describing what's been probably generally frustrating, obviously,
03:00about the industry that was up to, you know, 24 month up and down rate cycle, being able to see
03:05different types of refinancing too. So it wasn't just cash out refinance, it was other people say,
03:11moving out of say, government programs into GSE programs or non-QM into more agency. So that
03:19business has gone away. So what it's really forced a lot of lenders is to come up with new strategies,
03:24really look critically at their loan product mix, their investor mix, their vendor mix,
03:31and make a lot more just strategic decisions than it would have been like, hey, look,
03:36if we had a lot more volume, and we're making money. Now, the good news is, and you've seen this from
03:40the NBA numbers, there has been some rung out enough of the excesses where you're starting to see
03:45some profitability, which is good for everyone, right, the health of the industry. But I still think
03:50we're in a really interesting time really for the mortgage market and really US economy more broadly.
03:57I think that interesting is the right word there, right? It's been a wild ride, especially since the
04:04inauguration, then maybe especially since the Liberation Day tariffs were announced. You know,
04:09we're trying to make sense of it on our side. And, you know, where bond yields have gone, where mortgage
04:14rates are going, which, you know, we're so sensitive to. But I think your point about the right
04:19sizing in the industry is so interesting and important. And really, part of what our discussion
04:25is today is like, how we can use technology, how lenders can use technology to help some of those
04:32things happen, right? And so maybe the first place to go is talking about your role at ICE,
04:38which is corporate development, and how you see this overall picture in that role.
04:43Well, you know, that's really a good point, Sarah. And I think, I think, you know, really,
04:48my role has evolved as the industry's evolved, right? So I think people are familiar with the
04:53ICE story, we had a number of high profile acquisitions, the earlier ones, which is MERS and
04:59Mortgage Electronic Registration System, and then Simplifile, I was very active in we went through
05:04with the Ellie Mae acquisition, which was really the, the kind of more attention grabbing one around
05:10the origination system, obviously, Ellie Mae, the kind of flagship, and really largest market share
05:16and best in class in that tool. Then we went and we looked at Black Knight. And so when we acquired
05:21Black Knight, we brought in the integration, my role of change clearly, as we worked in kind of M&A
05:28transaction, it's more how do we get the integrations, right? So I've spent a lot of my effort
05:32now working with the teams. And the integrations really are two pieces of a similar exercise.
05:38On the one hand, it's how do you get the synergies that you promise to investors. So clearly looking
05:43at, you know, duplicative licensing, staffing levels, things like that. And that's just, you
05:49know, the typical M&A playbook. So but it also involves a lot of, you know, grinding work and
05:55just kind of learning capabilities of different systems, what systems and tech stacks play nice,
06:00certainly in the corporate environment with with ICE more broadly. And then the second piece is more
06:06cultural, right, is how do you bring in these different cultures. And when you acquire a large
06:10company has multiple, you know, 1000s of headcount, you know, they have a way of doing things. I mean,
06:15culture in a nutshell is how do you get things done, right? How does how does the organization get
06:20things done? And there's historical legacies. And we have to change that. So we have to kind of bring
06:24in the the ice culture, which you know, is renowned. And you know, we were at 100 billion mark market cap
06:31when I joined ice 15 years ago, I think we were at 10 million $10 billion market cap. So we've grown
06:38significantly. And I've grown with the organization. But what that means from for the customer and the
06:43product side is really not losing sight of that right at the end of the day, we succeed by listening
06:50to our customers, and trying to find out the product mix they want, the type of features and
06:55functions that they want, the economics they want, if they say, Look, I want more products from from one
07:00vendor, I think for the first time in years, I'm starting to hear more and more from customers,
07:06where there was a best of breed strategy, historically, and certainly we had the heydays of FinTech
07:11roll ups and startup startups, you know, call it 2016 to 2020, where a lot of funding went into
07:20the space, there was a lot of experimentation, a lot of, you know, kind of proofs of concept.
07:24Increasingly, what I'm hearing now is, I really want to concentrate around a few large players that
07:31are dedicated to the to the business, understand the domain expertise will be here long term,
07:39and really understand how we know the business too, and how we want to take the business strategically
07:44in alignment with them. So what you're starting to see is a lot more crisper decision making,
07:49where somebody may have said, Look, I was okay, carrying, you know, 100 plus vendors, historically,
07:56that's not the case anymore. They're saying, Look, I have to rationalize my vendor decisions,
08:01I have to really look very hard at the economics. So yeah, ice, we would like to do more with you. But
08:07how do I get some better synergies across these different type types of technology? And then I think
08:12that's the other piece of it, right? We want to be aligned with their strategy. So we're trying to figure out,
08:18Hey, is the trade off that you're going to do fewer products and more scale? Or is there certain QC
08:24tools that we have that we have to adapt them so that they're representative of your product mix? So
08:30really, all this shift in the macro environment has required us to be more agile.
08:36And even as we're integrating these large acquisitions,
08:40I think the interesting part of that is that as you go, you know, as someone is is figuring out their
08:46who they want to use, what their tech stack looks like, it's an opportunity for them to improve in other ways to
08:52beyond just like, here's what I'm doing with my tech. But some of the questions you're asking is like,
08:57gets at the root of your, of somebody's business, how they're going to make money.
09:01Well, Sarah, that's a great observation. And I think it's, you know, business process reengineering,
09:07I mean, I've been able to learn at ICE and have the fortune of being able to see a lot of different
09:13business models, right, evolve, certainly as FinTech has come in. And as the banking industries change,
09:20as global trading has changed. And I think you really raise the question of that, on the one hand,
09:26the technology is what it is, and the data is what it is, too, right? Like, I've said this at
09:31experience when I was out there, like, data is, is, you know, as a data element, it's a static element,
09:37it's what I do with that data, right? How I integrate it into my workflows, how I help it on my
09:43outputs in terms of my reporting, how I visualize it so that other people can see the views that
09:48we're trying to see. So the key piece of that is the change management side of the business.
09:54So if you think about it, the conversations we've had with with lenders, and you've looked at the
09:58historical model, right, it says, I have a giant funnel, I take in x number of applications, some of
10:04those applications fall out, some go to other lenders. And then I kind of come down to this bottom
10:10of the funnel where those are the loans that I actually close, I sell into the secondary and I
10:14make profitable. What I think has changed in this environment is the fixed cost associated with a
10:22kind of giant funnel, call it kind of a pray and spray approach has not, has got to evolve. And I
10:30think lenders are thinking about it, if I can connect with a consumer, and I can bring them into my
10:35pipeline, what do I need to do to ensure I close that loan. So where it used to be some ratio and
10:41say, you know, I cast the net out x amount of times, I'm going to retrieve this many fish, you know, in
10:46some cases, I think now they're thinking every fish I get into my net, I have to bring in the haul,
10:51right, I've really got to have that I've got to, again, you know, skin it, gut it, eat it, put it in
10:56the frying pan and move on. And so but to use the extend the metaphor, but the main point is, is
11:02that has challenged lenders to think about, again, the vendor question, then what's my product mix,
11:08how am I targeting my market, right, and then and then the emergence of larger marketing budgets
11:14versus more targeted marketing, lead generation, more specifically trying to say how with all the
11:21changes that are happening in the realtor world, how is that going to help advance my business, the
11:26bigger challenge too, and I've heard this from some lenders, increasingly, a large number of
11:32customers who have not the traditional credit profile, right, they might be gig economy players,
11:37they might be first generation. So how do I make for underwriting? And how do I change things?
11:42So all these variables have really required the leaders to think differently about the business
11:48model, and how to execute successfully in this environment. Well, I love that. That's such a great
11:54explanation of the way that things have changed, I think, for every business, but especially in mortgage,
11:58like you said that, you know, we have a funnel, this is, you know, here are the predictable things
12:02we can do. And I just think a lot of those things have, are not so certain now. Yeah, well, they're
12:09not as certain. And I think, I think the old playbooks are being, you know, rearchitected almost
12:15entirely, and go back to your point, the change management, this is the time for leadership, right? I think
12:21where the emergent leaders are saying, yes, there's things are uncertain, and there's global uncertainty,
12:28maybe macroeconomically, but I can't put my head in the sand, I have to step up to the play,
12:35and focus on the controllables that I can influence, right, my team, my leadership capabilities. I think
12:43where historically, where people would say, hey, to the extent that I may have nonproductive assets,
12:49I have to remove those, I can't carry that cost any longer. And then I think more importantly,
12:54where is the strategy taking this, right? I think nothing better than looking at all the changes
13:01that are going in in Washington, right? This industry is one of the largest economic
13:07generators in the country, and the importance of being able to say, how do we keep that engine of
13:13house production going, and lending, and finance? I mean, if you go around, and I had the benefit a
13:19number of years ago of talking to housing professionals globally, this is professionals
13:24in Latin America, and in Asian Pacific countries, and we are the envy in the sense that we can,
13:33you know, that these capital markets are so fluid, and they're so liquid, and the ability for a lender
13:39to be able to go, look, I can close a loan today, sell it on the secondary market, and having a security
13:45almost immediately is really critical. But with some of the changes that are jeopardizing at least
13:51kind of that certainty, I think people are trying to go, okay, well, I might not be able to control
13:55that, but what can I control at my macro level, or micro level, and, you know, guide through the macro
14:02environment and those uncertainties. But more importantly, it's how do we continue to serve the
14:08customer and consumer, right, who wants to try and get into a home, certainly in a large purchase
14:13market. And then importantly, for us, for ISIS, business to business, how are we really helping
14:19our customers move through this with a lot more alacrity, because I think they have to pivot a
14:24little bit more. So it's a little bit more fleet of foot, we're trying to bring in some of our principles
14:30of, okay, listen closely to the customer, what is it that they want? And great, you might want all
14:35these feature sets, I can't do all that tomorrow. So give us a rank priority, tell us where your
14:40business is going. So I think, in this time, you need really attentive leaders, you need a lot of
14:47intestinal fortitude, as they would say, right, so guts and strength, and ability to kind of drive
14:52straight through those circumstances, as they would impact really, both your team, your business model,
15:00and your counterparties. I love that. Let's, let's talk about data, a little bit and the role of data,
15:08as you're helping these companies, you know, how lenders are using data to remain competitive,
15:13even if they're not that big, right, like, like the role of data and making them successful.
15:19How do you guys help there? Well, yeah, and I think if you, you know, that the famous saying is,
15:23you know, if you don't know where you're going, any map will take you there, right. And I think that's
15:28really key is to think through, okay, so how, how do I get both reporting within my organization,
15:36fine tuned enough that I really understand all the dimensionality, right? Those things I can control,
15:42those things I can't control. And then if you look at it, like some of the tools that we have that would,
15:48I mean, we had the benefit on some of our business intelligence tools, you walk into a customer,
15:53and in their, in their coffee area, they actually have a screen with our screens on it, right,
15:58and are showing, okay, it's a league table. So it can maybe gamifying production,
16:03in some cases, right, with some of our tools to say, hey, look, these will be incentivized,
16:07and we're super transparent. But I think the main point is, is that that's all driven by core data,
16:13the data that we have from our customers that we can put out real time, right, with a high degree of
16:18fidelity, high quality, real time, and then they can, you know, respond to it, understand,
16:26you know, share it with colleagues, I know that some of our tools, you know, we have some customers
16:30that have decentralized models, they'll say, Look, I'll leave that up to a branch decision. But can you
16:36give me the ability to be able to securely push these reports out, or do set and forget. So I think
16:41there's the mechanisms by which the data gets out to the end user. And then there's the quality of the
16:46data. But I think part of this is the future state, right, everybody talks about data, data,
16:51data, but really, the data is contingent upon, what is my business need? What's my use case?
16:57And then it then you start to go, Okay, well, where do I look on it? Do I look on it on my dashboards,
17:02again, at an enterprise level? Am I looking at more as customers, my data? How do I prioritize a long
17:08list of potential refi opportunities, right? And how do I fine grain that we have the tools,
17:14capably to be able to put out campaigns. So you know, you'd be able to sit there and say,
17:19Look, I can customize a campaign where I can tell somebody, Hey, here's my loan to value. And here's
17:24what I might be available for a home equity line of credit, and it's, it's actionable. So I think I
17:30think the data really the benefits of our you have the right data, you have the right partner who can
17:35deliver the right data, and then it's actionable.
17:37So how are you seeing lenders rethink the tech stack so that really the data is is the focus
17:45as opposed to just like the LOS or the CRM systems? Is there a way they're configuring that?
17:50Yeah, there's, there's, that's a really good question, Sarah, because I think I think you've
17:54seen this evolution of how people stage their data, if you would, right, there was always the belief of,
18:00Hey, look, this data is real important. I want it on prem, I kind of hold it closely, it's very closely
18:07guarded, it's, you know, kind of Fort Knox and security, then you saw the emergence of the
18:12cloud data providers, your data breaks, your your snowflakes, and those vendors who were able to say,
18:19Look, we can stage your data, we can have a lot of functionality for you don't have to build this.
18:24And you see some customers going to that model, then you have people who are hybrid, right, who are
18:28saying, I have a lot of enterprise data, I wanted to be able to interact with a lot of third party data.
18:34Some of that analysis might happen within the enterprise, some of it might happen in the cloud,
18:38some of it some combination of it coming back and forth. I think some of the benefits that we have
18:43is that, and this is at ICE, is that we say, Look, we're kind of agnostic as to how you ingest it. If you
18:49want it through an API, you want it to stage it somewhere, you want it in some of our analytical tools,
18:56you know, those are all different methods by which you can ingest it and consume the end data.
19:01I think we always like to be able to say, at the end state, ours isn't solving your business needs,
19:07more importantly, right. And by that, even the curation, right. So I use as an example would be,
19:14you know, if a vendor comes to us, or at least a lender comes to us and says, Hey, look, I want
19:19these data sets, these are the vendors, some of them might be our proprietary data or derived data,
19:25things like HP eyes, right, where I'm trying to calculate where some some loan balances might be.
19:30But then more importantly, can you go out and pull some of this data in which we do through some of
19:34our network partners. So really, offering that flexibility for people who say, either one, I want
19:42something out of the box, I don't have the team to maintain a lot of data tools, and tooling, I,
19:47it's a specialty that's, you know, some of the more agile data operators would say, I don't want
19:53the overhead of having these people, but I do want the tools. And so what you're seeing increasingly is
19:59a tool set that has a lot of capability at a relatively low cost price point that allows people
20:05to have a lot more flexibility with how they manage it. And more importantly, like I said, does the data
20:10serve is an application is a pre populating application, is it looking at my loan roles to
20:16be able to in my servicing book to see if there's some challenges around, say, a disaster, I want to
20:21almost immediately know what kind of risk I've got there. Is it loan modifications that I have to look
20:26at those programs are always evolving, and they have capital implications. So I think, more importantly,
20:32it's having the right data at the right place at the right time, in the method by which people find it usable.
20:38I think that evolution is really interesting in our, in our industry to see, it's sort of like
20:43the sophistication of how people are using tech products. Like I said, it's not just like a check,
20:48a checkbox anymore. I've got this, I've got this, I've got this, but like, what are those things doing
20:53for you? How are you utilizing them to do for you? What do you actually need? And what don't you need
20:57on that list? Yeah, I think that's a good observation, because I think people in this environment have had to be
21:05more critical about what they, what they look at, right? And I think there's, there's, in some ways,
21:12the bigger effort is, what is data that's actionable? What are things I can really,
21:18you know, react to or evolve? And so the example might be, hey, in this environment, I'm seeing
21:23uptake of a particular product, do we step in the accelerator around that product? Or I'm seeing
21:28investors come into the market with different products, how do we ignite something like that,
21:32or activated through our campaigns. But I think more importantly, but what has happened has been
21:38more of a maturity scheme, we have a graphic where we talk about people on a maturity scheme. And what
21:44I mean by that is, you have your basic data, which is like, hey, it's reports, it's sometimes static,
21:48it's something we can look at. And then it's okay, I'm going to go move up that I'm going to have more
21:52analytics, I'm going to see more dimensionality to my data, then I might have predictive models,
21:57right? And predictive models are not anything new. But they're, they're more kind of saying,
22:01if I have a forecast, I can project that forecast out, I can say high, middle, low,
22:06I think people would argue we're in very, you know, almost uncharted territory from economic times.
22:12So past is not prologue. But if I have the right data, the right data set, I can shock it,
22:18I can look at different ways of how I would see and perform in different environments. And then you
22:23have the future state where you're saying, Look, how do I automate my processes? And how do I sit there
22:29and say, Okay, can my machine code, and this is kind of, you know, echoing where we're going,
22:34and where I think the industry probably is going around some of the machine learning and the AI
22:39tools is, my data is actually driving activity, okay, independent of human interaction, because
22:47there's a lot of causal logic that is setting up because of the way the data interacts with my codes,
22:53to actually initiate transactions, do certain activities that are automated, right. And so I
22:59think, you know, if you look at it, it's the evolution both of computing, computing capabilities,
23:04but also just coding, I mean, now, now, you know, we've all kind of seen some of the Navidian and some
23:09of the other processes and some of the even POCs. And obviously, with some of the AI, where some of these
23:15are just super novel applications, they're very energy hungry, right. So I think we've all recognized
23:22that, but you always see tools coming out that. But now you even see, you know, machines generating
23:28its own code in some capacity. So that's like a new frontier. But I look at mortgage, you know, my
23:35and you and I've said this to you before, and I'll say it again, like, Hey, can can we before we get
23:40flying cars? Can we get off a paper? You know, like, we still have paper, right, we still have
23:45these large stacks of paper and people signing. And then, you know, the extraction of the data from
23:49the paper and the normalization. So I think on that continuum, people are still trying to see,
23:55honestly, facing themselves saying, Where do I fit on the continuum? What is the tool sets that I need
24:00to move up the continuum? And then really, how does it serve my business purpose? So I think it's,
24:06that's, that's, that's the way I try to frame out the data discussion.
24:11I like that. And yes, you know, before the flying cars, we have a few other things to solve
24:16first, doesn't seem too much to ask. When it comes to AI, and you're talking to lenders,
24:23is it like they have to do ABC before they get to that, you know, if AI is like starting at M or N or O
24:29repeat, do they do you have to start at the first step? Or can you sort of jump into it? If you
24:36haven't followed all these other things? Like, does AI just kind of come in and like, obliterate
24:41whatever you were doing before? You know, that that's, that's, again, a great observation,
24:45Sarah, it's always a pleasure to talk with you, because you think about these things. And I think
24:49it's, it's, but I mean, think about the framing of these things. Because I had the benefit, I sit in
24:54some of peer groups, and I was in Atlanta last week, and I with a bunch of kind of big thinkers,
25:01and they had this very different view, because I think, or not different view, but it was a little
25:04contrarian view is, yes, the promise of the tools are amazing, for all the reasons we know, we see
25:10this, these emergent tools that some of the capabilities and, and, and looking at it and
25:15going, wow, this is this was edited, it looks like it was, you know, this was a professional editing
25:20job of some of the context of the reading. But a lot of the precursor steps is getting the systems
25:26and processes in place. So this harkens back to what we're talking about earlier, change management,
25:31hey, look, we're going to do this thing differently, because one, we have, we need higher quality data,
25:36or our data might be locked in legacy systems that we've got to extrapolate from, and we've got to
25:41normalize or put a taxonomy around it. So I use a good example, because we've talked about it before,
25:47with the AI engine that we rolled out with our all regs product, right. And that was Ask Reggie. And,
25:53you know, it was really interesting, because as we rolled that out, we were out of experience,
25:57and we were having conversation with people. It was really very robust dialogue, because we had a lot
26:01of customers come to us and say, Hey, I'm getting different results, I experimented in my sandbox.
26:06And you know, this question wasn't answered right, or this context wasn't right. And, you know,
26:11a lot of that is driven by the library, the context on this. So I had to look at this and go,
26:16okay, your tool was maybe trained off internet wide. So it may be a broader language model. But
26:24our model is based off a million documents that are mortgage specific. And we're really trying to
26:30get granular levels of detail. And we're curating out legacy documents that are not relevant to get
26:34results. So our product evolved very rapidly in that space to generate very cogent results with
26:42cross references and citing, to the extent that a compliance officer really wanted to double click
26:47into that data and go, Look, I can trust, but I need to verify. So I think that the that there was
26:53a sense of some of the experimentation was yielding really good results. But the other piece of it was
27:00going, there's a maintenance to this, right. And we're constantly working on the quality of that
27:04documentation. None of these models are static, they're all super organic, they're in some cases,
27:11creating their own content. So they're self referential. And I think the main point is, is that
27:17understanding the human in the loop, as we've all talked about some sanity check around some of the
27:21results, where you're saying, Well, why did my result get this? And, and literally, we came down to,
27:27you know, thesauruses and synonyms and how the different weightings of words, and,
27:31I mean, I find it immensely fascinating, just from an intellectual curiosity standpoint,
27:36and where it's going, I think we've all kind of gotten on this continuum that we know these things
27:40are going to be, there's going to be a place where they're going to take us. So I think, you know,
27:45we encourage obviously, all of our customers who are experimenting with the technology,
27:49I think more importantly, go back to your main point is, look at your infrastructure, is it going
27:55to serve that future state, right? And is the tech stack there? And is my data cleansed and curated
28:00impossible, right? Yes, the tools are very powerful, and they can kind of pick up on some
28:05of these anomalies. But what I go back to the point where I was in the peer group, a lot of these
28:09discussions were around, what foundational changes can I make today, that will prepare me for these
28:16tools that how they evolve, and you know, you always think about it as like, I go back to my days
28:22when the first, you know, dot com era, right, I'm old enough to be able to talk about that. And,
28:27you know, the rush to the internet, and everybody was like, Oh, you know, what's going to happen?
28:31And I said, Look, you just need to get in there and start to experiment and understand. But the
28:37main thing, and I think we're coming back to is, what does change management look like? And what
28:42does my team look like? Right? I might need a staff of people who are more capable of adapting to new
28:49tools, then people have a domain expertise in this niche area that I can solve for, which maybe
28:55historically people said, Look, I need a bunch of SMEs in these various areas. And now what you're
29:00saying is, I need people who can adapt almost as rapidly as the technology, which is a very different
29:06profile from what you would have historically. That is a really different profile. Actually,
29:11I love that observation. Because when I think you mentioned all regs, I remember when I started
29:17covering mortgage, and you know, was was learning about all regs, and what that looked like. And just
29:22the fact that that was such a innovation in and of itself that it could, it could at least consolidate
29:28things and help you find things and the difference between that 13 years ago, and where we are with
29:33that now is really it's like the whole industries like that. Yeah, yeah, and I really do. And I mean,
29:39look, the GPT general purpose technology, I think people that might not know even that's what the
29:45acronym stands for. But it's horizontal, right? It's not vertical, it's going to it's going to influence
29:50all sorts of industries. And so in that general view, you have to kind of experiment. So I think
29:56those those are all good things. But today, where the customer is, we have to meet them on their
30:02journey. And that's where I think just being more attentive. And then also understanding that there's
30:08some, you know, shared investment of equity of time and resources and say, if you tell us what problems
30:13you want us to solve, we can meet you along that continuum, right? And we both can be successful,
30:19because, you know, the health of the US housing financing, you know, market is really important to
30:24us, right? We try to be influential, influential where we can in policy, work closely with the NBA
30:30and the trades to try and go where are we going in direction, you see obviously very big changes in
30:35terms of policy, maybe some vacuums in some areas, we're certainly commercial operators saying,
30:41look, here's the things that we can do to help guide people on a good path, right from both a
30:46compliance standpoint, more importantly, for consumer education. And then to us, the idea is,
30:52look, you know, there's there's interest from a policy perspective and economic getting somebody
30:57into a new home. But also the question is, is with the right risk adjustment with the right product,
31:02with the right understanding of what that responsibility comes with. So I think,
31:06you know, I'd like to be a contrarian, I'd like to think that, okay, we're at a point where things
31:12might be uncertain, but you drive through that uncertainty, certainly as a leader, you try to
31:18understand where can I help my team adapt, right? There's a time where I think a lot of people can
31:23get very anxious about all these events, the headline risk and all these things. I mean, I have this with
31:29my kids, when I talk to them, I'm going, can you control it? Is it within your domain? Can you fix this?
31:33And they'll be like, no, then I'm like, why are you worrying about it? I mean, you, you, by nature,
31:39being anxious about something you can't control is kind of not productive, right? So why don't you
31:45look at those things you can do, right? I can go to sleep early, I can eat a better diet,
31:49I can take care of myself, like, okay, those things are going to those things you can influence.
31:52So I think you look at it more broadly against how or more specifically how it impacts the industry,
31:58you have seen that with lenders. And it goes back to what we talked about very early in the
32:01interview where people are saying, I almost have to look at staffing models differently,
32:07I have to look at how I train my employees differently, I have to look at what the expectation
32:14is there from compensation differently. So all those things are on the table in this environment.
32:20Wow, you know, thinking about all of that opportunity, thinking about all of the things that could exist,
32:26what excites you the most when you think about the direction ICE is headed as far as its data and
32:31analytics strategy, say over the next year or 24 months? Well, I mean, first of all, one super
32:37excited about the opportunity, because I think, you know, we as you step back, always you look at
32:43addressable markets, right? If you're an investor, or you're an operator, and this is the largest one,
32:48certainly one of the largest ones in the in the world, right, the global housing, US housing,
32:53finance and really, broadly housing construction, and everything associated with it and housing
33:00finance, specifically around mortgage and lending. And so I think the way that we see this is it's
33:06still very robust market. I mean, how many markets when you say it's a down year, we have $2 trillion
33:11in production, right? Like, oh, that that's so it's a, you know, go back to the total addressable market,
33:17I think our ability to be able to help our customers cross this strategic transom is this
33:24is where you make your metal, this is where you show your value, because they're looking to you to,
33:30they're going to say, I have a host of problems, these I can solve those I want you to solve. And
33:35that's where you create value, right, both from an economic standpoint, and then as a strategic
33:40partner. And that's where we see ourselves, I think, go back to where people in the models say,
33:45look, I want to breast to breed, I can select through various entities. And again, think about
33:49the breast and breed, a lot of this was because you had startups that were VC funded, they could
33:53give the product away, the support may not have been there, the long term strategy, the dependability,
33:59but hey, I'll experiment a little bit, I think people are coming back to what are the tried and
34:04trues? What really works? Who can I look at across from the table says is a genuine partnership. So I
34:09think we have that in, in great depth and breadth. I think more importantly, it's how can we help
34:16people go through these difficult times and transition to, you know, healthier economic
34:22environment to the extent that we want to see that higher production levels, maybe more diversity of
34:27products, better liquidity sources, these are all the areas that we wake up every day thinking about
34:31this is ice more globally. And then I think more importantly, it's just trying to say, how can we
34:37continue to keep the flows of capital, which have come in through the GSEs and through other financing
34:42mechanisms coming into the US housing finance market to lower that cost of funding, right, and
34:48to make the affordability for our future state of homeowners in the US. Chris, I have enjoyed this
34:55conversation so much, as always, love talking to you and finding out what is the latest that you're
35:01working on that ice is working on. Thanks for sitting down and talking today. Well, look, it's always a
35:05pleasure, Sarah, don't, you know, don't hesitate to reach back out. And I think, you know, it's always
35:12I enjoy housing wire and all the team there. So thank you. We will talk to you again soon. Thanks so much.
35:19All right. Bye bye.