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We took a deep dive into our data and uncovered some expected and unexpected findings on loan time to close stats. George Neal and Jim Young sit down to discuss these findings and Neal explains how they can be applied to your lending processes.
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Time to Close Stats: Expected vs. Unexpected
Jim Young: Hi, and welcome to the Purposeful Banker. The podcast brought to by PrecisionLender, where we discuss the big topics on the minds of today's best bankers. I'm your host Jim Young, Director of Communications at PrecisionLender, and I'm joined today by PrecisionLender EVP of Analytics, George Neal. Today we're talking about the work George's department does here, and then we're going to get into some of their recent findings, which center on time to close data.
So George, first tell us a little bit about the analytics group at PrecisionLender. What you guys do all day, the type of data you analyze, and why?
George Neal: Our principle focus in the analytics team is to derive value for our clients from the data that we get in, so that's what we do all day. We take in data, we marry that data with the loan pricing information that we already have, and we try to turn that into value. So of course, we have a data ingress team, we have a data science team, and these teams work together to pull information of value for clients, out of the data that we possess.
Jim Young: And just to clarify for people that aren't PrecisionLender clients, the data we're pulling in is data from our clients using the application?
George Neal: Correct. So our client banks in many cases provide us with data feeds that represent their portfolios, so we have the ability to create views into these portfolios over time to see how they migrate, to see how they originate, to see what works, what doesn't, and to turn that into value for the collective community.
Jim Young: Right, and the collective community in this case is, we're basically talking about across an industry. It essentially gives us a view across an industry, which helps our clients, and also occasionally we then have the ability to share that as essentially industry data, which is what you did in the recent blog post about time to close data.
Now your findings fell into two categories, which pretty much there are only two, right? Expected and not expected. Let's start with expected. Tell us what you found regarding size of loan and time to close.
George Neal: So, when you talk about the category of expected, exactly what you would think would happen with time to close versus size of loans. We found that much as we expected to find, large loans take longer. And I don't think any banker, or anyone, even non-bankers, no one's going to be stunned by that. A $3 million transaction takes longer to process than a $100,000 transaction. So we found that to be true. We also found that there were some interesting leveling points in there that weren't particularly surprising. There tends to be a leveling point somewhere around the just larger than $1 million mark. That didn't really surprise anyone, but confirmation is always interesting as well.
Jim Young: And so on the flip side then, you've got lines of credit and time to close, right?
George Neal: So one of the other things that we discovered is that, in general banks are faster at what they do more of, and they tend to do a lot of lines of credit. Lines of credit are a fairly established mechanism in almost all of our client banks. And we see that they happen much faster, particularly when you weighed in renewals.
Jim Young: That makes sense. So I'll ask a little bit of a snarky question here. If this is expected information, does it still have value?
George Neal: Well, I don't think it's a particularly snarky question, I think there is some value to having someone say, "What you're experiencing is in fact what the industry experience is. It should not alarm you." Where you might be alarmed if you're a banker, and where this kind of information should add value, if your time to close on your lines of credit is as long as your commercial real estate, throw a flag, alert someone. Say, "We've got to be able to do this faster." Unless of course your CRE process is so fast, that it's your competitive advantage in the market, in which case go with that.
Jim Young: True, if you're closing CRE's as fast as you can close lines of credit, and your lines of credit are still fast, then yeah, you're in pretty good shape.
Actually and CRE's, speaking of which, it comes into our next area, which was the unexpected findings we had out of this time to close data. CRE's, I would expect commercial real estate deals to take a good chunk longer, but that's not what you found.
George Neal: So we were a little bit shocked by this finding too. They do take a little bit longer, but that little bit is 10 calendar days. So when you look at 10 calendar days, that's effectively a week. In our expectation, we thought that just the appraisal process alone, which you would consider perhaps the delineating factor, or the biggest time consideration in the difference between the two, we thought that would take much longer. And we did a little, I would say back of the napkin checking, we checked with a couple of our clients and said, "Hey, how long is your appraisal process?" The answers we were getting back were two weeks, three weeks, etc.
What this tends to tell me, is that there's a degree of parallelism that's helping in these banks. So we're going to sit and wait on the appraisal to come through, at the same time we're going to move everything forward as well as we can. Sounds like good practice. And what that does mean though, is that CRE's didn't take as much longer as we thought they might.
Jim Young: Okay, which actually answers ... you answered my next question, which is, is it possible that essentially they've refined the rest of the CRE process to essential offset the additional to-do's that you have in CRE, that you wouldn't have somewhere else?
George Neal: We believe that that is part of what happened. Again, you know you can poll a few of our clients, we can ask them. But what were finding is that on those banks that are relatively quick, they tend to be kind of quick across the board, and that CRE process is highly parallelized. On the flip side of that, if you're running a linear process and they're still only 10 days apart, that tends to tell me that your non-CRE loans have a lot of room for improvement, as well as your CRE loans.
Jim Young: Yeah, exactly. It's almost the flip side of what we just talked about with the LOC's. If your CRE's are not taking much longer, then maybe that your non-CRE's are taking too long.
So finally, let's look at what you found in terms of range of closing outcomes. What were you expecting here versus what you found?
George Neal: So we were expecting a couple of things. We were not expecting the full range of results that we saw. Again, I mentioned previously, we do see a leveling on these outcomes. Typically, that leveling comes just over $500,000 actually when you start looking at range of outcomes, distributions of how long it took something to close. Once you get about $1 million, we find that you've crossed typically the 40 day median mark, which I should stress, our time to close is from the time it enters the PrecisionLender application as a priced opportunity, to the time we see the data come back as booked.
So that's slightly different than a traditional definition that gets mentioned in the blog post. But for here, as an example, when we're talking about 41 days, understand that what we're saying is 41 days from when the opportunity gets priced in PrecisionLender, to the opportunity closing on the books. There's been conversations before that of course.
Jim Young: Okay. So basically ... All right, then our 41 days might be a little different, but in terms of comparison sake, it shouldn't be a big difference, right?
George Neal: So what you're going to see, is they're going to be slightly different, but the same difference is going to be consistent across all of the observations we made. So while the definition is slightly different, it is consistently different. And so the same trends, the same observations, the same conclusions all apply.
Now to go back to your question about kind of the range of things that we saw that we thought were very, very interesting. We saw a huge variance in time to close for very small loans that we weren't expecting. And by that I mean, I don't think that anyone's going to balk at spending 120 days to close a $10 million deal, that makes perfect sense. But when you see a large population of 120 plus day to close $100,000 deals, that doesn't make a lot of sense. So we saw that, we actually saw ranges of time to close for $100,000 and below deals that exceeded 260 days. That was shocking to us.
Jim Young: Wow.
George Neal: I'm actually surprised that any organization would choose to hold on and fight for a deal of that size for 260 days. Now I say that as a data person, I understand there's relationship reasons you would do that, there's all kinds of reasons that might happen, but it was kind of shocking to see it.
On the other hand, we see that these outliers actually normalize out at about $500,000. So the likelihood you're going to have an outlier, a really long drawn out time to close above $500,000, doesn't really get affected by the size of the deal. Above $500,000, if it's $1 million, if it's $2 million, if it's $5 million, the likelihood that you're going to be in that outlier space is above the same. So we thought that was kind of interesting too.
Jim Young: And then you kind of went from there to basically, could you make the argument then that banks that close fast, close more?
George Neal: We don't even have to make an argument, we can show that empirically. If you look at the distribution of number of loans closed to time to close, you will see that there are more faster closing loans. And if you are capable and you are doing those types of fast to close loans, the implication is clearly that you're doing more of them. We did go and back check, and it's not in the blog post, but we did go and look at those banks that are faster, and we found that in fact they're doing more per asset size. So what you'll find that for a comparable asset size bank, the bank that's faster to close is actually doing more loans, they're growing faster and that tends to say to us, it's a market proven competitive advantage.
Jim Young: Okay, well let me offer maybe a slight counterargument to that. Is it possible that they are simply doing a type of loan that is smaller and less profitable, and just doing more of it? Whereas, maybe another bank is saying, "You know what? Rather than closing a bunch of loans fast, we're going to take our time on a few loans that are large and very profitable."
George Neal: Certainly that's a possibility, right? I'm not saying that this is an absolutely certain, or that I can look into the market strategies of all the banks that are included in this. But when you look at the size and the type of data we're looking at, we're looking at $400 billion worth of price loans over a year, and we're looking multiple years through. So we're talking literally trillions of dollars of loans. And when we look at that, and we look at what we're seeing, I don't think that you're seeing a reflection of one or two banks market strategy to cherry pick a loan, or to cherry pick high priced loans. I think what you're seeing is an overall response of a market to different options.
There's an option to close fast, there's an option to close to slow. If I'm coming to an organization and I'm seeking money for expansion, for cash flow, for whatever reason, I didn't walk in the door and ask for the loan because I wanted it a year from now, right? So we see that those organizations that are able to provide that money more quickly, are able to do more transactions.
Jim Young: And that makes sense, and that goes with just about everything we've talked about, about the whole technology overhaul and the lender process that banks are going through, because they understand that as well.
Well listen, that'll do it for us today on this. George this is really interesting stuff, and I'm looking forward to talking to you in the future. I know you guys are continuing to mine through this data, and will be coming up with some more interesting findings that you guys will be able to share with us. But thanks for coming on.
George Neal: It's entirely my pleasure.
Jim Young: All right, if you'd like to learn more, visit our resource page at explore.PrecisionLender.com. If you like what you've been hearing, make sure to subscribe to the feed in iTunes, SoundCloud, Google Play or Stitcher. We love to get ratings and feedback on any of those platforms.
Thanks for listening. Until next time, this has been Jim Young with George Neal, and you've been listening to the Purposeful Banker.
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As a Content Manager here at PrecisionLender, Maria develops the messaging, stories and content pieces for prospects and current clients – showing them the value in PrecisionLender. Her passion for serving others is evident as she leads the volunteer program here at PrecisionLender. Maria’s ability to be organized and constructive, along with her ability to be practical makes her an exceptional addition to our team.
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