AI-Powered Mortgage Innovation With Joe Tyrrell | May 2025 Data

Market Advantage Intro: 00:00:58

Welcome to Market Advantage, the monthly podcast from Optimal Blue. Tune in for valuable insights from the Market Advantage Mortgage Data report and in depth conversations with industry experts.

Stay competitive and optimize your advantage in the ever evolving mortgage landscape.

Olivia DeLancey: 00:01:18

Okay, welcome to the Market Advantage Podcast.

As always, I have Brennan o' Connell joining me and this is a very exciting month for the Market Advantage for several reasons and I'm so excited to tell you why.

t at Optimal blue since about: 2021

Now this is a completely free report where we essentially compile various data points that we have through our technology and make them available to the public to provide some deeper insight into what is happening with rate lock Activity. So hopefully you already subscribe. If you don't, check out the link in our show notes and get signed up again, it's totally free.

But what we've done starting this month is added a variety of new data points on both the origination side of the house, but also the secondary market side of the house.

The report has historically consisted primarily of rate lock data from the front side of the house, but now we've rounded it out to give you a more comprehensive look into capital markets activity across the full spectrum.

So some new data points that we've added are property type, debt to income ratio, first time homeowner status, and then as mentioned, we've added an entire new section focused on secondary market data. So think risk management strategies, loan sales statistics and mortgage servicing rights values.

And why this matters is because this is a very interconnected process throughout the capital markets life cycle.

So the profitability being realized on the back end of a mortgage origination shop directly impacts what is being offered to borrowers on the front end of the house. So by adding these new data points, we're giving you a more well rounded view into a origination activity and lender profitability.

So that being said, be sure to check out our report this month and subscribe so you get it on an ongoing basis so you can see what we've added. And we'd also love to hear your feedback. Brennan, anything you'd like to add?

Brennan O'Connell: 00:03:33

It was well said Olivia. As you kind of alluded to, we're well positioned to show folks how the sausage is made.

The way that borrowers receive financing and the interest rates that they are getting quoted from their origination partners.

There's a lot that goes into that and Optimal Blue and our associated software platforms are integral in sort of all of those steps that take place during that mortgage manufacturing process. And so what we've done here is take more of that data that are from different parts of that mortgage manufacturing process.

And we've, as you put it, dropped it into our free monthly report, which hopefully is valuable for market participants as well as the broader community that's interested in better understanding what's going on with mortgage. So really excited about that. Unfortunately less excited about the numbers. In May we did have a tough month again here to start the year.

So rates didn't help us. We had some, some headwinds. The 10 year treasury was up about 25 basis points.

30 year conforming interest rates are benchmark interest rate index from the OBMMI group was up 15 bips or 16 bips. So we picked up about 10 points on the spread at the treasury but we were still up, you know, just over an eighth in the rate department.

And so from an origination perspective, not surprisingly that had a pretty material impact on refinance volumes. Rate in terms were down about 40%. Cash outs were down 10%. The refi share dropped from 21% down to 16% of the total volume.

We saw in terms of rate locks, the more difficult part was that we also saw some of this rate headwinds and then maybe some general economic uncertainty impacting purchase volumes too in what normally would be sort of a seasonally inflated time of the year. So you kind of expect during the year you get spring and then into summer, you'd expect purchase volumes to continue to climb.

And we were basically flat from a volume perspective on purchase volume month over month. And if you look at it from a count, so purchase counts, which sort of negates the impact of the change in average loan amount.

On a year over year basis we're actually down 10%. So that's really like our bellwether metric to see where things are at. It was a disappointing month to say the least.

Obviously there's reasons that some of this could be just pent up demand. Folks were kind of waiting to see how things shook out from a rate and an economic perspective.

But we'll have to look to June to hopefully see some better numbers. The overall volume, not sure if I mentioned this yet, but the overall volume declined about 5.9%.

So a little bit below 6% in terms of a month over month decline from April to May. Just commenting a little bit on what we saw from a mix perspective. So there's a couple of odd dynamics.

Typically when you run into these more difficult months from a rate environment perspective, you would see folks shifting towards production options that are maybe a little bit more affordable or kind of like beyond the normal type of origination that folks would pursue. So like arms. So ARM share was up actually in April and was up to 10.3% but we we dropped so you didn't see a lot of ARM volume in May.

That could be a function of a lot of the ARMS or refinances on larger loans and we had less refis so that could explain it there. But we also saw a decline in FHA and va. So the gubby production went down again.

To me that just indicates that borrowers, likely home purchasers probably like first time home buyers who would normally be driving volume at this point in the year. And during this kind of like heavy spring buy buying season, schools just getting out, folks are out there and looking to get into homes.

It feels like a lot of folks sat on the sidelines in May.

So we'll just have to see if that is pent up demand that leads to higher numbers in June or are we in a state where we're still on kind of a lower trend on a year over year basis. Expanded guidelines volumes. This is a new one in the report.

Really excited about starting to produce more metrics around non QM or expanded guidelines which it's kind of an umbrella term for DSCR like investor loans, bank statement loans, extended DTI type origination. So that's a growing segment of the market for sure. We see just over 7% of our total production coming from expanded guidelines in May.

So that ticked up. That wasn't surprising. Tougher rate environment.

Folks are going to look to these these other non traditional products to finance both home purchases and refinances. A couple other odds and ends. I mentioned it before but first time home buyer is a new one that we've got in the data set.

Really excited to be able to report on that. It's certainly something that is relevant.

Policymakers are thinking about it that this first time home buyer community is really one of the areas that's been hurt the most by the rise in home prices and declining affordability driven by higher interest rates. And so this first time homebuyer metric is one that we're excited to start sharing. So we have in here the splits out by conforming FHA and VA.

So you can see that conforming first time homebuyer loans are about 42% of our total there. Within the FHA space it was just under 70% at 68% and then within the VA community was just under half, so 48%.

So slight decline in FHA and conforming first time homebuyer share in May suggests affordability. Headwinds are really weighing heavy on these entry level borrowers.

Finally, I just note that the DTI level, so we're going to be tracking DTI levels as well. This is related to the issues around affordability. How are borrowers being able to finance and handle larger home payments?

And so we're tracking DTI on a go forward basis. Similarly, we're going to break that out by conforming FHA and va and we were up slightly in May.

Nothing significant, but this will be an interesting one to watch certainly as we go forward and continue to deal with affordability concerns. But by and large not a great month for originations. I'm sure most of our listeners feel and know the same thing.

Just looking forward to June and hopefully get a little bit of a rebound.

Olivia DeLancey: 00:10:02

Thanks, Brennan. And as mentioned, you can read a lot more about what Brennan covered in the report itself, which is available in our show notes.

And don't forget to subscribe. It's time to welcome this month's Market Advantage guest. We have Optimal Blue CEO Joe Tyrrell joining us. Joe, welcome to the podcast.

Joe Tyrrell: 00:10:21

Well, thank you for having me.

Olivia DeLancey: 00:10:23

Yeah, I'm so glad you're joining us. AI is something Brennan and I have wanted to cover for quite a while now.

Last month we had Erin Wester join us, our chief Product officer, and she kind of gave us a look into our approach to product management at Optimal Blue. So it feels like the perfect time to do a deeper dive into AI specifically.

And I know this is an area expertise and passion for you, so maybe to kick us off you could tell us a little bit about your story and when and how AI became a focus area for you.

Joe Tyrrell: 00:10:55

Yeah, it really dates back to probably 10 years ago. And you know, there's lots of different variations and iterations in the evolution of AI.

If you go back to, you know, even 20 years ago there was artificial intelligence in the form of, you know, very primitive, but in the form of heuristics with a lot of if this then that sort of statements built into code.

I think for me I really got interested in it, in just struggling to deal with why we couldn't significantly reduce the amount of days it takes to originate a mortgage in the United States. You Know, there's two key metrics that I think our industry focuses on a lot.

One is the, you know, average number of days to close and the other is the cost to originate alone. And I really feel like those two metrics don't provide any value to the industry anymore.

I think what we found with the cost to originate alone is it pretty much is driven totally by the amount of volume because a lot of lenders have to maintain a lot of their fixed costs, especially as it relates to the expertise and in closers and funders and shippers and underwriters.

And then when you look at the average days to close, there's been so much technology that's been deployed in this industry and yet we've seen just incremental decreases, right? 60 days to 55 days, to 45 days to 40 days. But we're talking over a 20 year period of time. We should be seeing this routinely at four and five days.

And while I know some lenders are definitely able to achieve that, this should really be the standard in our industry.

And so I got very focused on how could we do more kind of serialized process, asynchronous processing, how do we allow things to just happen automatically without people needing to review and click buttons? Especially because our industry is so structured, right? So you think about structured data.

In our industry, everything is based upon rules, investor rules, compliance rules. So there's very clear guardrails.

So that is a perfect opportunity for artificial intelligence to consume those rules, understand how to navigate those rules and then just take action on behalf of the, of the lender.

Brennan O'Connell: 00:13:14

That makes a lot of sense, Joe.

And so just kind of continuing along the story here, obviously you spent a lot of time in the mortgage industry and then did have a stop at Medallia and was a very AI oriented enterprise as well.

Could you talk a little bit about some of the things that you saw in non mortgage related industries and maybe specifically how you think some of those applications of artificial intelligence could be and will be applied into the mortgage sector?

Joe Tyrrell: 00:13:44

Yeah, absolutely. I mean, I think one of the challenges in our industry is just the trust of automation because the stakes are so high for lenders.

If you get something wrong, you've got to live with it potentially for the life of the loan.

And I feel like a lot of lenders are worried about, everybody's looking for a reason not to acquire a loan or if something happens after the first or second payment, it's, it's as if whoever acquired is going to just go through that with a fine tooth comb and Anything that they could find as a reason to push that back from a repurchasing perspective, they're going to take advantage of it. And so for lenders, the concern about automation is, well, what if there is an unintended consequence?

What if something gets automated and a step gets skipped? And now I've got liability for that loan, so I understand the reasons why.

But leaving the industry and going to a company that really focused on, I mean, the primary solution for the company that I was leading when I left the industry was around the customer's experience. And you're talking about serving clients like Walmart, Meta, Apple, Mercedes Benz, Marriott.

And so it wasn't just a customer experience, it was a guest experience, it was the student's experience with, with all the healthcare companies that we supported in hospitals, it was the patient experience.

And it was amazing to look at the willingness of these global brands to really embrace the opportunity for artificial intelligence to dramatically improve the experience of the people that they serve. Now, the way that they did it was very interesting.

Instead of trying to apply AI in this kind of generic blanket sense, it was very specific use cases that tackled opportunities where there was a tremendous level of value with an incredibly low level of risk.

And what that really taught me was that the right way to deploy artificial intelligence, especially in our industry, where the stakes are so high, is to do it in a very use case specific way, applying that same sort of discipline of delivering a high level of value and a very low level of risk for our clients, which are lenders. And so I've really embraced that kind of methodology in deploying technology.

And I think the other reason for it, especially in our industry, is with a company like Optimal Blue, with the number of different products that we have, we serve thousands and thousands of types of different companies, so investors and then lenders, and then within lenders you've got credit unions and independent mortgage banks, regional banks, national banks, all of which have a different level of appetite for risk, not to mention all the mortgage brokers that we serve.

And so you always have to deliver technology across the spectrum where you're going to have some clients that are just can't wait for automation and want to run with headless processes where nobody touches anything. And then others are going to want to review absolutely everything.

They're still going to want people pushing buttons and you're going to have a lot, they're going to fall in the middle.

And so when you take a use case specific approach to generative AI especially, you really give people the opportunity to grow into the technology so that it's kind of there waiting for them as they're ready to really consume it and apply it. And so incredible learnings that I got by serving, you know, some of the biggest global brands in the world.

And I think it's incredibly applicable to what we're doing at Optimal Blue.

Brennan O'Connell: 00:17:23

Yeah, I think, and this is AI we're talking about, but this has probably been true across all new sort of like technological developments, these sort of general technologies that transform industries. The consumer facing application with a less regular or a lower regulatory burden is sort of the first one to start applying this. Right.

You mentioned some of these industries, more consumer oriented, tend to have less regulatory burden as say like institutions who are doing mortgage origination or you mentioned healthcare. Right.

Obviously, like I think of like how, how late or you know, how much later in the process were electronic health records were electronic tools within the mortgage space relative to, you know, the first time we were getting kind of like consumer facing applications with like the Internet, for example.

And so I think in a lot of ways it feels like, you know, you've been able to peer into the future and see the applications and then bring that into Optimal Blue and bring it back into the mortgage industry where we're going to be later in the development process in terms of like when we're going to adopt those types of technologies. But it's going to happen. I mean, it will be there.

So I think it's, as you've noted, it's something that we can put out there for our clients and then it will be there for them as they're ready to adopt it. So I think that's been great.

Can you talk a little bit about some of the applications that Optimal Blue, even just over the last six months has rolled out with the use of machine learning and AI that you think will be most impactful for lenders?

Joe Tyrrell: 00:19:03

Yeah, happy to. And kind of going back to what you were just talking about, Brennan.

What's interesting when you look at technology in general is there's always been a catalyst for people to do things differently. Now whether or not that catalyst sticks and actually creates a paradigm shift is how much easier it really made the experience for the user.

So if you think back to like music purchases, so the catalyst moment in the music industry was when the first ipod was introduced. So now all of a sudden you had itunes popping up, you had all of this mass consumption of music.

People were consuming music in ways that they had never done before. And then with the Internet, kind of the catalyst was really Smartphones.

But smartphones not only significantly increased the usage of the Internet for people, but it also changed the music industry because no longer were people purchasing music off of ipods, they were now streaming it off of all sorts of different streaming applications that were available. And so when you look at what's happened in the mortgage industry, there's been a couple of catalysts. Obviously.

One was for kind of, you know, e signing and E notes.

When the pandemic first hit, you know, I, I was on the origination side at that point and had just introduced a really simple, easy and intuitive eclosing solution. And so within, you know, six months, we saw a dramatic increase.

However, it was so different from what lenders were used to doing, that as the restrictions of COVID let up, people went right back to the process that they had been using prior to Covid.

And so when we've looked at, you know, generative AI specifically at Optimal Blue, it's always been through the lens of how do we create something that nobody would want to go back to the old ways because it's just so much easier. And again, how do you do it where you're tackling those specific use cases? High level of value, low level of risk.

So one of the generative AI solutions that I'm most excited about is called Originator Assistant. And you'll see a lot of the generative AI that's coming out from Optimal Blue is really under this suite of solutions we call assistants.

And the reason for that is again, we're delivering AI across a spectrum of customers.

And so we know that these early usages of AI for many of our lenders, as we've talked to them, they still want it to be where the expert that they've hired in that facet of the ecosystem or the process is still very much involved.

And so with Originator Assistant, what it does, it's actually AI that is helping to ensure that there is no anti human bias in the origination process. And so if you think about that for a second, everybody's worried about AI coming in and introducing this unintended bias.

One of our first iterations of AI that we're introducing is anti human bias. And here's how it works.

Like if you're a loan officer, if you're an originator, which is how I started my career in the industry, what typically happens, because it's not much different today than it was back when I was doing it, is you tend to learn a handful of products and you get really, really well versed on those handful of products.

Now, for the really fantastic originators, they know a lot of products, but for the average originator, you know, maybe six to eight products really well.

And so when you sit down across the kitchen table or from a Starbucks, or even on a zoom call or a teams call with a prospective home buyer, the first thing that happens is as you're having the conversation, you are mentally starting to qualify them. Even though you haven't taken any of the qualifying information, you're having a conversation, you get to know them.

But there's this thing in the back of your mind, you know, just the human in you starts to get ahead of yourself and start thinking about, well, what loan program could I put them in? If they start mentioning things like not having a lot of money down or things like that, then you naturally start going, okay, they're an FHA buyer.

And then what happens is, once you start the process, as soon as you get to yes, I can qualify you, then a lot of originators just stop. Now, the goal here is to help the consumer get into a home. So, you know, it's not like the originator's doing anything wrong.

They're getting to a yes and then they're stopping.

What Originator Assistant does is it ensures that there isn't any human bias in this process, that you're not sitting across the table automatically thinking someone's an FHA buyer because of, you know, where they might live or how they might look, or what they've initially told you about their income.

And so Originator Assistant goes out and looks at every loan program that that originator has available to them based upon the decisions that the lender that they work for has made in terms of the types of loan portfolio that's available. Not only does it come back with all of the loan programs that they would qualify for, but it also looks at any program that they just missed on.

So, for example, there might be a niche program that's offered by one of the GSEs that maybe the originator's not familiar with, but could provide a lower rate or better terms for that home buyer. So it surfaces those.

But in addition to that, if that home buyer just missed qualifying for a lower rate by, you know, 10 points from a credit score perspective or from a debt to income ratio perspective, if they closed one credit card and consolidated things, they'd now qualify. Originator Assistant also presents those items to the originator.

So now it's not just about I stop when I get to yes, I'm really trying to help you get into the very Best loan program for you, not only to get into the home, but to make sure that you've got the, you know, the best and most optimal cash flow, that you're, you've got the lowest possible payments or the best terms. And so when you look at something like Originator Assistant, it is exactly what it's named.

It is there to assist the originator to find the very best loan program for that consumer. And so that's the kind of ways that we're looking to deploy AI. We do something similar when we look at a profitability assistant.

So now look at the executives in the capital market space, and they're looking every day, okay, what was our loss? What was our gain? And why was our results what they were yesterday in the loans that we sold?

And so typically, the way that gets done for an incredibly seasoned financial professional who's got deep capital markets experience, it could still take them 45 minutes to an hour and a half because they've got to aggregate information and data from multiple systems, put it up on multiple screens, try to now, you know, stare at it, and compare and identify the differences. What Profitability Assistant will do is it summarizes all that for them using generative AI.

It looks at all of those data sources, aggregates, it summarizes and says, here's what your loss or gain was yesterday and here's the reasons why, and here's three things you could do to improve your results today.

Again, it's not making any decisions, but it's doing all of the analytical and administrative work so that that capital markets executive can be more effective and, and help that company be more profitable. And then the last one I'll mention because there's a lot I could talk about here.

We're doing a lot with AI, but I love this solution that we've introduced called Ask obi. So OBI is a generative AI solution that is based upon your data as a lender. And so you can ask it any question.

So think about, you're coming in and you're like, okay, you know, what does my lock position look like? What's the, what's the portfolio look like today? You know, what loans do I have that have not been locked but are active in my pipeline?

You can ask it literally any question about your business. So what would normally take hours running reports and having people do the work for you?

You ask it a question, not only will it summarize the answer and give it to you in a very just conversational way, but what it will also share with you is the charts so you can see it graphically.

If you want to click into that chart, it'll immediately take you into the data and allow you to be on this journey of now getting down to the very minute details that will help you understand what you need to do that day to impact the results of your business by the end of that day.

So where a lot of data in our industry tells you what happened, what we're doing with Askobe is giving leaders the ability to understand what needs to happen today in order to have a better, more profitable tomorrow.

Olivia DeLancey: 00:28:03

Joe, so you mentioned Optimal Blue's focus on creating these assistant type uses of AI.

And there's certainly no shortage of chatter in the industry and beyond about what AI will do to workforces, what kind of impact it will have, and you've made it pretty clear how Optimal Blue is approaching this philosophically.

But I'm just curious, do you have any advice for folks in our industry and even beyond of how they should be thinking about what AI will do to the workforce or embracing it?

Joe Tyrrell: 00:28:32

Yeah, I mean, I think the way that we've approached it with the assistance is, you know, our vision isn't that we're necessarily going to deploy all of this automation and now all these people are going to be out of jobs. It's really more about, especially right now in our industry, you know, volume is low and everybody's experiencing it.

So if you're using the time now to really adopt automation and artificial intelligence, what it's going to allow you to do is as interest rates come down, as volume comes back, you're going to be so much more profitable as a lender because you're not going to have to do what you've done in every cycle in the past when rates do go down and volume goes up, which is go out and try to hire people. And now you're not just hiring people, but you're paying premiums and signing bonuses because every lender's competing for the same resources.

By using this opportunity now really use case specific deployment of artificial intelligence, you're giving yourself the ability to scale your business in a way you've never had the opportunity to do so in the past. And so that's the window that lenders have right now. And it's not just lenders, it's anybody in our industry.

You know, we operate in such a cyclical and seasonal industry that, you know, right now is the moment where if you can, if you can see the opportunity to deploy some of the technology, even that we're just deploying you're going to be putting yourself in such a great position to scale your business without increasing your expenses and significantly increase the profitability in your bottom line.

Brennan O'Connell: 00:30:12

Sort of a dovetail off that one. Joe. For the most part, by and large, how the mortgage industry and how everybody is interacting with AI right now is more.

I kind of use the term like an oracle. It's sort of like I ask a question or it is providing me some knowledge.

And maybe the other end of the spectrum would be like fully autonomous or agentic, where you just give some sort of instruction and then the AI goes out and interacts with multiple systems. Maybe it interacts with the real world, whether that be humans or some through some other means.

I guess this is maybe more of a philosophical question. Just want to get your perspective. You look into your crystal ball and you peer out 3 years, 5 years, 10 years.

How do you see if we use that spectrum oracle to, you know, fully autonomous for this artificial intelligence that we're creating? Where do you see that landing?

And obviously this is a mortgage podcast, so if you have any specifics on how you think that will impact the mortgage industry would would be really interested in your perspective.

Joe Tyrrell: 00:31:16

Yeah.

So the way that we look at it at Optimal Blue, we think there's three obvious phases of kind of not just the evolution of AI, but more of the adoption of AI.

So that first phase is exactly what we've launched to the industry already, which is through our assistance, where you can say, these are the things that I'm interested in doing and it will come back and give you value, very specific actions to take in order to improve your results. The second phase is where the work is being done automatically and it's being pushed to you. Here are three things that you could do.

So if you think about even something as simple as ask Obie in the morning you could wake up, or two o' clock in the morning, you could wake up and you could start asking OB questions and it'll give you very specific, precise answers of exactly what you need to do.

But what if you just set it so that ask OB push notifications to you starting at 6am and it said, here's three actions that you can take that will increase the profitability of your company or the results for the day. So that kind of first is I'll interact with it. The way that I want is phase one.

The second phase is I want it to do things and then come and tell me with recommendations of things that I should do.

Not because I've interacted with it not because I've opened up a screen like Netflix and I can see recommendations, but because it's pushing to me things that it knows I should be doing today in order to get better results.

And then the third phase is it just does it, it doesn't need to push it to you to do it, it just does it on your behalf because it's learned over time and through interactions with you or with the data itself on what are the things that need to happen.

And by the way, we're able to do all three of those right now, but we're introducing the first because again we want our industry to be able to consume it and have confidence in it and get, you know, used to being able to interact with it so that they're ready to go to the second phase and then ultimately the third phase. But those are all capabilities that exist today at optimal Blue. If you think about some of the use cases. Let's just talk about that third phase.

One of the things that's interesting to me that I've always had as kind of a panacea is if you've originated a loan for someone four years ago and at the time you originated the loan, they got into a three bedroom home with their two children. And so now you fast forward five years later, we know the age of those children because it's listed on the application or on the tax returns.

And so why couldn't the technology five years from now go out and say you're probably ready to move up to a new home.

We've already pre qualified you for six homes that keep you in the same school district that are currently for sale that you could qualify for today and automatically shoot those notifications out to you. And so going through the steps of doing like in making it so that it's a really fully underwritten loan, there's so many data sources.

If they had opted into something that would allowed us or the lender to, to pull verification of income verification assets through their, through their bank statements.

There's ways that you could get that information, fully underwrite that loan, get those, that family pre qualified for their next, you know, move up if you will and be just looking and monitoring homes that will absolutely fit their need and only approve them for those loans and then only push those loans to them or to those, those homes to them then. And so that's, to me that's such a basic fundamental thing that technology should be doing.

And what it does is it, it moves the interaction with lenders from the point of sale to the Point of thought. And in fact, it allows the lender to be the one to introduce the thought.

I mean, now the lender could be introducing them to real estate agents versus the way it's historically been done in our industry or something that's even closer to home to us is, you know, why are we having people that wake up every morning and start looking at what interest rates are to determine if now's the time to, you know, counsel their borrower that they might want to lock the loan? Why couldn't, you know, and it can, we can do that today.

Generative AI could absolutely be looking at every attribute of that consumer and know based upon not only what's happened in the past, but what they need to qualify, but then also incorporating predictive analytics to determine should we lock today? Because what's the likelihood interest rates will be higher or lower tomorrow? And you can make that decision.

And that could change throughout the day as it's monitoring data that's happening real time across multiple markets that impact the mortgage industry. All of this is capable of being done right now.

And so we have the ability to do it optimal blue, but it's more of, you know, allowing people to kind of grow into the technology and take steps of adoption so that their confidence increases.

But there is unlimited potential in our industry, specifically if we take a use case specific approach and make sure that we're not introducing unintended consequences or unintended bias, but actually ensuring that that doesn't happen through the technology.

Olivia DeLancey: 00:36:44

I think maybe we get you a crystal ball, Joe, and put it on your shelf in the background with your sports memorabilia.

Joe Tyrrell: 00:36:51

Yeah, we just have to figure out who's going to sign it.

Olivia DeLancey: 00:36:55

So I think now would be a good moment to round out the interview with what we call our bonus question. So at the end of every episode, Brennan and I asked every guest this question.

So, Joe, in your opinion, what is one thing a lender should be doing in today's market to maximize profitability?

Joe Tyrrell: 00:37:11

Yeah, I think it's what we've touched about right now is that you've got this window where rates have not really come down significantly. We're starting obviously in June.

Now we're in kind of the normal high peak of the season, but it doesn't feel like a high peak compared to what's happened in the past.

So, you know, now is the time if you're a lender, to really look at ways that you can use technology to make your people more effective and more efficient, expand their capacity.

So as that volume returns, you're in a position where your folks are able to take on more without sacrificing quality or the, you know, work life balance. So that way you can grow your business and scale your business without having to do it through people.

And that falls directly to your bottom line as a lender. So that would be the number one thing I would stress right now.

Olivia DeLancey: 00:38:04

Excellent. Thank you so much. And thank you so much for joining us. It's been truly a pleasure to have you and look forward to welcoming you again in the future.

Joe Tyrrell: 00:38:11

Yeah, thanks for having me.

Olivia DeLancey: 00:38:21

Sam.