John talks about the current state of the ever-changing conditions in real estate, the specific ways in which his team utilizes data to create in-depth analyses, and the importance of data in validating assumptions with empirical evidence.
This episode of The New World of Consumer Behavior features an interview with John McClelland, Vice President of Research at Coldwell Banker Commercial Premier, fulfilling real estate needs across all of the primary commercial sectors including office, industrial, retail, multifamily, and land.
As the source of Coldwell Banker Premier Realty's statistical, market data and reports, John provides in-depth analyses of current market conditions, past performance, and risk, which allows homeowners, corporations, and developers the ability to make the most educated decisions when selling, developing or purchasing real estate. Previously, John was the research manager for The Ryness Company's Southwest Division, an urban planner with the City of Las Vegas, and a regional economist with RCG Economics.
In this episode, John talks about the current state of the ever-changing conditions in real estate, the specific ways in which his team utilizes data to create in-depth analyses, and the importance of data in validating assumptions with empirical evidence.
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Key Quotes
“There's points in time where you need to take a snapshot of what's occurring at the time in a certain market. You need a good way to archive that and some redundancy because you never want to lose anything. A lot of that data, you cannot replicate it. Unless you can get a DeLorean with a flux capacitor or something it's not gonna happen and it's just gone forever. But it becomes extremely valuable particularly when you're trying to build time series and analyze things in the past and try to find, are there scenarios now that are developing that may look like something in the past.” - John McClelland
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Episode Timestamps
02:02 John’s current role
06:19 How John’s team uses data
12:44 The importance of data
15:53 How data has changed the game
18:57 Data use cases
25:19 Most asked questions in data
35:06 Data dos and don’ts
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Links
Kat Harwood: Hello and welcome to the New World of Consumer Behavior: Lessons From Data Pioneers. I'm your host, Kat Harwood, Director of Corporate Communications at Near. This episode features an interview with John McClelland, Vice President of Research at Coldwell Banker Commercial Premiere. John provides in-depth analysis of the housing market that helps homeowners, corporations, and developers make the most educated decisions in real estate.
In this episode, John talks about the current state of ever-changing market conditions, the specific ways in which his team utilizes data to create in-depth analysis, and the importance of data and validating assumptions with empirical evidence. But first, here's a quick word from our sponsor.
[00:00:52] Sponsor: Imagine what your company could do with one of the world's largest volts of intelligence on consumer behavior. The possibilities for business efficiency are endless across people, places, and products, across retail, restaurants, tech and tourism. Data is the key to unlocking insights and driving results for your business. Near is the global SAAS leader in privacy led data intelligence, with 1.6 billion data points worldwide. Go to near.com to learn how Near can help your business make better decisions.
[00:01:29] Kat Harwood: Now. Please enjoy this interview with John McClelland, Vice President of Research at Coldwell Banker Commercial Premiere. Thank you so much, John McClelland, for joining us today on our podcast. I'm super excited to talk with you, especially with you being in the real estate industry, which is a very, not contentious, but, um, you got a lot going on in your industry right now,
It's a very interesting time to be in real estate and we know you are with Coldwell Bankers, so we're very excited to learn a little bit about yourself. So if you could start off with telling us, you know, sort of who you are, what you do day to day at Coldwell Banker, that would be great.
[00:02:08] John McClelland: Sure. Thank you very much. Yeah, I agree. The industry is very dynamic and there's a lot of changes occurring, but it's, in my mind, it's intellectually stimulating. So there's a good mixture of, of a requirement for some academic knowledge as well as, you know, really in the field, on the ground kind of, uh, data gathering. In my role, I run research services for both our commercial company and then our sister company, which is residential.
Within commercial, we tend to touch nearly every asset class except for hospitality. That'd be really rare for us, but office, industrial, retail, land, we've been working. Mineral properties, so gold mines and things of that nature. And then on the residential side, it's high [00:03:00] rise and you know, town, home, ground up, single family development.
So our business practice, like in my division is it's a hybrid between doing the brokerage side and then advisory consult. We may not always participate on the, on the deal side, but we'll be on the consulting side or both. In my role it's, It's a lot of data collection, database management, writing, trying to build marketing materials along with our brokers that I help support.
And then we've got a great marketing team that will take sometimes. Highly in depth reporting and, you know, bite size and make it manageable because you know, at the same time, you want to be thorough. You've gotta respect that people have a finite amount of time in the day. We've seen a lot of movement towards infographics and videos that really take out the, you know, the highlights.
You wanna make sure that you have all the background data for that to support everything. [00:04:00] The way I approach it is to document everything. Kind of like you'd be writing a, an academic paper to submit to a journal. We wanna make sure that we've got attribution in there and you know, give credit where it's due.
And if somebody wants to go back and replicate something, if it's not proprietary data, then you know they can do.
[00:04:17] Kat Harwood: Wow. It sounds like you do a lot of interesting things across the spectrum. A
[00:04:22] John McClelland: lot of this stuff trades off market, you know, big expiration companies, they kind of know where all the players are, so we don't really fit in the space of, you know, some 8,000 acre mine.
We don't necessarily have to know where, you know, all the drill sites and things like that. Were, were a plan to be, but we've been kind of growing into that just because. There's a need for brokerage services for some of the, the smaller independent owners, and a lot of times it's people that inherit properties or they got it as part of collateral for something else that they were doing.
It's not something that I, I would. Say [00:05:00] that I'm an expert in yet, because you have to know so much about these properties. There's not one single type of person can know everything. Cause you're referring to geologists and in some cases the finance people. And it takes a lot to evaluate those kind of properties.
So we rely on a lot of, you know, other service providers to help us figure out what's going on. and then some of the, the legal context is, is different than just some of your, your standard type of deals, but that it could be sand and gravel, which is a little easier to understand. And then, you know, if it would be gold or recently I was examining where some lithium sites are and you know, just trying to help out somebody.
[00:05:44] Kat Harwood: Very cool. Sounds like a fun, fun part of your job. Can you tell me briefly how you and your team are currently using data in general, and then how are you using data to better understand the behavior of your consumers?
[00:05:59] John McClelland: [00:06:00] Since a lot of our data is, uh, transactional, much of it starts with that. So it, it could be drawing from assessor data, especially in rural counties.
Mm-hmm. , they may be the only source for what we call comparable sales. And we're trying to evaluate the, the potential selling price or what a buyer may reasonably expect to pay. Like in Clark County, Nevada, they've got a pretty good GIS system too, which you know, is helpful. Not every place has that. So that's a big part of it.
And then on the residential side, we use a lot of the multiple listing service data. Most people don't do the for sale by owner, most people go through realtors. And so a huge proportion of the sales data, you know, lands in, in those MLSs, I've worked in probably a dozen of them across the country. And some of them are more feature rich than others, so they'll have more fields and everything is different by region.
Some places don't have really any basements, so nobody really cares to put a field like that in there. Markets that are extremely dense, mostly Highrise, Midrise, they're gonna have a different, you know, context of the data there. And then back to the commercial side, CoStar is a dominant, you know, company for comparable sales, and they also own Lunet, which is.
A platform for listing, you know, commercial properties in land. They have some other resources as well for farmland and recreational properties, and so that's a vendor that we've used a lot. And then I'd say some other ones are well core logic. They run a lot of MLSs around the country and they're a good source of data for loan information and other real estate data attribute.
The other set of data that, that we often work with is some of the Esri products for, cuz GIS is such a huge component of our business because real estate all has a spatial context to it. [00:08:00] And so building the visualizations, cuz a lot of the data without a story is just kind of nebulous, right? You have to put it together in a, in a format that people can, you know, understand.
Hopefully something that has some intuitive appeal. So any GIS product is a big part of that. Census data is pretty common, although it's not as, as rich in providing some of the, you know, buyer segmentation. And how do we figure out, you know, who's who, that it, it doesn't work as well for that state of Nevada, you know, state of Texas.
They. You know, Arizona, they've all got some of their own state sources that we'll look at. You know, divisions of generals, things like that. Uh, Moody's Analytics, cre, they have a pretty good product, especially for the institutional, you know, commercial buyer res, et cetera. And then Hoovers, if we're trying to underwrite a tenant of like a triple net retail type property is a big one.
I guess because our [00:09:00] practice is kind of generalized, you know, some corporations will just do specifically an asset class, you'll. Somebody that only does, you know, multifamily or only does triple net retail. And we have teams within our organization that do specifically those things. But as an organization, we're looking at a lot of different things.
And since I oversee the data management and analytics for, for all of those folks, then I have to look at all these different sources and, and there's a lot of change. and how these data products are used. Uh, Krei, they're a listing service for commercial. They've been using, uh, like an AI to try to, if you, you have heard certain search parameters that you've put in the system before, their software will help find other properties like that, which they can email to you, put in the safe search, something like that.
Automated valuation models in residential, the big frontier. You know, everybody's probably seen the, the Zillow figures. Mm-hmm. , but there's, and Core Logic has a product for that. But that's [00:10:00] becoming even more interesting because some of the AVM automated valuation model providers are. Even using technology to like scan a picture, Like if you were to upload pictures to the mls, it will in a sense know if it's looks like a recent remodel.
Oh, that's interesting. Like a lot of more modern homes that you see, they've got the whites and grays and you know, all the, on the art services, they're training them to know that. And so you can assign a different value. Isn't that fascinating? It's really fascinating. And then the other frontier is, you know, human mobility data, which is what we've been, uh, recently using a lot because it tells a deeper, you know, richer story in the old days, really, if you're like evaluating a retail site, for example, you.
You drew, you know, one or three or five mile radius around something, found out how many homes were in there. You could maybe apply like a household that you, you know, borrowed from census or some other data [00:11:00] source. And that would tell you something about it. And if it fell in a certain census track, you could look up income, but it didn't really tell you the flow.
You could look up traffic counts, which is still relevant no matter what, but it's not telling you how people got there. And if there's any. Choke points in there and problems with egress ingress that didn't reveal themselves on a more cursory, you know, analysis or drive.
[00:11:25] Kat Harwood: Mm-hmm. such important information to have.
It's not
[00:11:28] John McClelland: practical to be there every, every hour of the day to measure how many people are coming, right? Mm-hmm. . So that's where the more advanced technology and larger data sets really come into play.
[00:11:38] Kat Harwood: Yeah. It sounds like it saves a lot of time
[00:11:39] John McClelland: too. Yeah, indeed. It would be impractical to. Collect that by hand.
Yeah, no, that's
[00:11:45] Kat Harwood: what I was thinking. But there's no way you could do that. We, we have had customers that used to, you know, go day to day or hour by hour and ask people where they live, where they came from, and it costs them so much money. And then you have something like, you know, [00:12:00] Human movement data and instantly you can get all the information you need at the, you know, reach of your fingertips.
So it's definitely a game changer for a lot of businesses. And it sounds like for you as well, you really use data day to day in your role. How important is it for you to have data at your fingertips?
[00:12:17] John McClelland: I don't know what I would do without it. If, if I was born maybe 20 years earlier, I, I may still be on the farm in Montana where I pick you up or something like that, because without the data, you're just kind of splashing around.
Not really. You can make assumptions and, and there's certain. Feedbacks that you'll get just from driving around to a place. And sometimes you can get kind of a visceral feel for a place, but that's not something that's easy to describe to somebody that, cause we're dealing with people that potentially all over the world that are looking at an asset.
And to try to relay what we're experiencing to them from afar is not the easiest thing to do. And so that's [00:13:00] where data, uh, analysis and presentation is key. If it was back in the computer punch card days, I don't know that I'd want to be in the industry at all. Because it would be onerous and, and kind of impractical in a sense.
[00:13:15] Kat Harwood: A lot more difficult. Yeah. Now that we know what we can do, it's table stakes.
[00:13:20] John McClelland: Yeah, exactly. And part of the, the reason that the data is so important is everybody's make, makes assumptions about things is, and, and sometimes they, they truly know and sometimes it's just conjecture. So we'll, we'll experience times where, you know, you'll present some data and then say, Well, we already know that, But they assumed that, but now they know that.
Right. So you validated it and then, you know, that helps the decision making. Yeah. And
[00:13:48] Kat Harwood: times change so quickly. People change, it's, it's hard to almost keep up with this rapid evolving that we have, especially since the pandemic. It's just you need data to keep up with it. [00:14:00]
[00:14:00] John McClelland: Especially with the pandemic and there's a tendency to, for humans to misremember things, which is why we need to validate our assumptions with some kind of a empirical analysis in, in many cases, I mean, there's obvious things like you could see if a building is pretty functionally obsolete, You know, you bring a tenant in there and like, Well, I'd have to figure this out and this and this and that.
There's those kind of things. But in many cases people assume like, Well, anything could work in this. That may not necessarily be true, and that's where you start to bring out the data and say, Well, this is actually what we've been finding, and it's something distinct from what the assumption was. Makes
[00:14:39] Kat Harwood: sense.
You can't just make choices on assumptions. You have to have that data and that to kind of give you the credibility to make the right actionable choices. Especially nowadays it's six are higher
[00:14:49] John McClelland: than ever, and in urban areas we often have that ability, you know? Part because of all the data sources we, we've kind of gone through in rural areas, [00:15:00] sometimes there is the, you know, you call it the gut check, like, does this feel right?
Or you have to go knock on some doors and talk to, you know, a rancher that's been there for 30 years to find out that that road washes out every year. Something like that. There's some distinctions there, but in urban areas, there's not too many excuses not to do some analysis of specific areas.
[00:15:20] Kat Harwood: How long have you been using data in your role and how has it maybe helped you solve problems or changed the way you work?
You know, like those gaps that you had. Now I have data. My life is different. Like how has it sort of changed your life?
[00:15:35] John McClelland: I mean, it certainly made it better because the data sets have gotten richer, you know, more expansive. A lot of the dui, the graphical user interface, you know, the, the way things are. Is better data, storage is better.
Almost everything I can think of and on the data, data management side and you know, illustration is better. So I started really working with [00:16:00] data heavily when, when I was an undergraduate in the early two thousands. So I studied economics and that science has general gone to the empiricists to really, really won, won that over.
And uh, a lot of it became more math based and statistical analysis and the real estate side. I started as we were leading, you know, into the big bubble in the early two. That must have been tricky. I was able to enter the market at probably one of the scarier times, but also more intellectually interesting times, I think.
Mm-hmm. . Mm-hmm. . And certainly we recognized that there was gaps. Like I was working at a economics consulting firm. We did regional economics and we were examining, uh, the housing markets in several areas and trying to really understand what was occurring there. We were looking at things. price to income, you know, ratios and price to rents, things like that.
But the one place that we didn't [00:17:00] have a lot of depth and insight into was just how bad the lending market was. So if there was any failure point that I could isolate during that whole time was we didn't know how horrible some of those mortgage backed securities. Most people didn't, which is part of the reason what happened happened in the, you know, the subsequent fall.
But these securities were, you know, on the face of it, it seemed for a lot of people, seemed to make sense. But it's kind of like, uh, that frozen food section, Hot Pockets, it's like you microwave it, it's cold on the outside and on the inside it's absolutely molten. Yeah. . And so there was this mol. Material on the no income, no job kind of loans, and then that affected ultimately all day and all these other different rating tranches.
Not many people had read a mortgage back security. I think Michael Brewery was probably one of the few people outside of the people that wrote them, that actually read one. You know, he was later made [00:18:00] famous by, um, you know, the movie The Big Short and the, and the book. Yeah. It's a good one when I look back on it and that's what always makes me think like today, you know, like, what are we missing?
And it gives you this ever increasing appetite for more data essent.
[00:18:18] Kat Harwood: It makes sense. Yeah. The more you have, probably the better off you are. Do you have any sort of recent use cases where you use data to, you know, overcome a challenge that you had?
[00:18:30] John McClelland: It seemed to be almost a, a weekly occurrence where there'll be.
Something that's unique. Keeps things interesting. Yeah. We'll have to go explore with different data sets. So sometimes we're asked to do, you know, projections and forecasts, which I'm not as big of a fan of doing forecasts as I am evaluating things from more of a risk management approach. And so we can look at what the Fed is saying and what [00:19:00] some of the presidents are, are talking about.
What Chairman Powell speaks. And then we, we can look at the big, you know, the headline numbers like CPI and the, um, consumer sentiment, you know, National Association of Home Builders puts out a lot of figures and, and we can kind of get a understanding of what's happening now, which is what you need is a starting point.
But to forecast the mortgage interest rates. Extremely difficult. Yeah. So sometimes we're asked to please give us some kind of a forecast and, and we can attempt to do that. But at the end of the day is trying to buy assets that you think will have more resiliency when things go go south, I think is, is a better place to be.
So that's the, the question that, that we try to answer. And a lot of that is has to do with site evaluation, like the site itself. We know that there's a lot of mean reversion that occurs and pricing. So boom, markets tend to [00:20:00] tend to get some mean reversion. Whether or not that means bust is is not necessarily the case.
But going back to more, you know, trend growth is a potential which, if you've, if you're using a lot of leverage, it's important to know what that may look like. And so using some data to try to say, Well, for example, we are recently evaluating, uh, Coffee location in Colorado, and we wanted to know how does this store perform without knowing any of the, uh, the point of sale data that would be proprietary to the, to the tenant.
Mm-hmm. , we wanted to know how does this perform relative to other stores in the area, and does the yield that's being advertised on this asset kind of make sense relative to. The area and what would happen if you lost that tenant. So even great areas can, you can have mismanagement on the part of the tenant.
It's [00:21:00] unlikely with, uh, a major operator. But what if they decided not to renew after, after their, uh, leases were up, they decided not to exercise options. You have to look at it in that what's the worst case scenario there? And in this particular store, in particularly, we're using. The near data for this is trying to understand is their traffic higher than the coffee place that's, uh, you know, within a couple miles of there.
And what do the road connections look like? What are the routes that people are taking? Because sometimes you'll have a place where the traffic counts that the Department of Transportation or, you know, some third party purveyor will show just the traffic count and say, Well, that looks good, but. They're building another road that makes maybe access a little more difficult to that particular site.
So when we examine this property, Most of the things that we're finding were, was in its favor. So the, it had more visitation, had longer visitation, had it certainly had [00:22:00] no issues with people arriving there. And you could see that the trade area was not what you would expect just from a, from estimating with three five mile, you know, ring radius, it, those don't tell us enough.
Sometimes you think that your trade area is bigger or smaller than it. and if you know all the, the routes and, and where people are coming from, like this data will reveal, you can build a true trade area for that location and that way it tells you maybe in you further up the road here, there's actually room for another location because we're probably not gonna cannibalize from that one such important data.
Exactly. Yeah. And all these data points they can talk together. So if we, if we identify that there. What the trade area is and, and that there won't be cannibalization. We can also look at what's the further growth up the road, and that's where we'll look at, you know, which builders are recording final [00:23:00] maps in that area, you know, where permits.
[00:23:02] Kat Harwood: So really using data for growth and expanding businesses and understanding areas a lot better and deeper. It sounds like a game changer.
[00:23:12] John McClelland: Yeah, very much so. Between, you know, three or four years ago even. And now that things are so much better, you know, and, and having dealt with a lot of, you know, these bizarre data sets and you know, you really get an appreciation for people that work with big data.
Cause a lot of it's unstructured. You're having to do data matching, the quality is not great. Yeah, yeah. They don't have a way of, you know, kicking out some outliers that could either be interesting or, or could be problematic for the, for the data set and, and even just basic data matching, like if, like street addresses, avenue, if it's AV versus av, it's not, it may not match up in a relational database.
Well, so people have to write the software to, to try. Join all these different things. And so you have to [00:24:00] really thank a lot of the, the startup people that, that have the courage to go start these kind of companies. And there's so many, so many work hours in into them that, you know, sometimes people will bock at pricing.
But then if you think about the infrastructure that went into that and, and the utility of the end product, you know, you can really understand that, you know, we've got some unique opportunities.
[00:24:24] Kat Harwood: Definitely. And I feel like once you start using data, there's no turning back. So you sort of arm yourself with so much more knowledge that you didn't have before.
I would think it would make you so much more competitive too.
[00:24:34] John McClelland: Yeah, more competitive. And people are, are asking for more. And that's in, in both the commercial real estate world and, and in residential.
[00:24:42] Kat Harwood: What are they asking for? What's sort of the, the highest ask question when it comes to data? What do people
[00:24:48] John McClelland: wanna.
In both residential and commercial, they'll wanting to know where the growth is is a big part of that. On the residential side, they want to know where, where the amenities gonna be, so where if somebody's gonna [00:25:00] live somewhere, where would they shop? That's a big part of it. There's also an element of just kind of wanting to know where the new homes will be, you know?
Cause they're thinking about their future. And traffic and things like that. On the commercial side, it's the more risk that you add to something so that as people wanna add leverage and try to, try to do more than trying to really understand is, is there. Yield hurdle appropriate for a certain area is one thing.
And we don't typically deal with like the big institutional players. It's only on occasion. So we, we tend to get more of the, our focus is more middle market and uh, we'll deal with anybody, you know, like the people call 'em in the industry, the mom and pops, right? This are, could be a high net worth individual.
They've got, you know, 10 31 money or they're just looking. Either dispossess themselves from a property or you know, buy another one in a different area. We see all kinds of different [00:26:00] motivations, but one of the main things that we're asked from these individual cuz their, their stake is so, You know, they may have a big proportion of their net worth tied up in one property.
So it's really important for them, like they want to feel like they're making a decision with the best available information, and they'll say, I want this kind of yield in a great area. You always get that, you know, and then you have to define what's a great area. So then you talk to them and they say, Well, I, I tend to like this area.
We used to live in California and I kind of want to replicate that somewhere else. Or maybe I don't wanna replicate, I want the opposite of that. It could be. So the best way to reveal that and, and really get a composite understanding of an areas through, not just photography. Drones help a lot to, to really visualize things.
But, you know, how, where are people going? Where, where do they live? What are the home prices in the, in the area around? Mm-hmm. , maybe a retail center. What do the incomes look? , those are [00:27:00] key attributes of that analysis. But one thing that we've found is that even in areas that you think would have, you know, superb, you know, regional demographics, we've seen turnover in shopping centers where like a grocery store would just fail over and over and there.
There's
[00:27:18] Kat Harwood: always those locations. You wonder, I've seen them before. They keep trying to reopen and it just never
[00:27:25] John McClelland: works. And you've probably seen it in what areas you'd think would be great for that, but it's not the whole story, right? Just the incomes in the area isn't the whole story. It could be access to that.
Or sometimes people just don't like taking a certain road. You may not really understand it if you're looking at it from an a. But once you see the movement patterns, you'll, you'll see that they prefer to take this. They're ignoring all this stuff on this side of the road. Uh, they don't wanna take a left turn there or something like that.
And then there's the mismatch, right between the retailer and the area. So [00:28:00] using, you know, segmentation and really affinity, you know, find what brands people have an affinity to. You can see like maybe in, in this area, or we've actually seen this in some of the Nevada one time where they would go a lot further to go to Trader Joe's.
Whole Foods. They didn't want to go to the prior grocery store that was there, which was the, you know, the large format. And so you saw that stay vacant for a long time. They just, they didn't want to go there. And you wouldn't really know that unless you either interviewed a lot of people, you lived there long enough to see, or you, in our case just, you know, look at the data and then we.
[00:28:38] Kat Harwood: That's really interesting. Yeah, I remember there was, I'm from San Francisco and there was always this one restaurant on a very busy street that would go outta business and then a new one would come in, it would go outta business. And I just, you know, you think, Oh, this area is jinx, and it's like, no, you have to look at the traffic patterns.
You have to understand, like you said, people don't wanna make a left hand turn there. It's a busy street. It's, it's just amazing how you [00:29:00] could make the wrong decision. With a gut instinct thinking, here's a busy area, I'll, I'll get people to come in. Not necessarily, you need to take a look at the data and see, you know, what the traffic patterns are like in brand affinity and stuff like that.
It's just mind blowing to me. The. Wrong choices you can make without the right data. I
[00:29:20] John McClelland: mean, people can mismanage a business, but when you see repeated turnover within a course of 10 years or something, it then you have to start looking for, Not everybody can be incompetent in in running the business.
There's gotta be some other variables that are in play there. We wouldn't have had that full story just a handful of years ago and, How things are changing, I think for, for the better. And I think the commercial real estate industry and residential is, I mean, you've had some pioneering firms like Zillow and the information space and, and residential.
But you know, up until, you know, a decade or so ago, I think the industry was still [00:30:00] lagging on the technological front. Why do you think that is? Maybe there's a, the incentive structure isn't quite there or the digital infrastructure. And I guess mature enough to adapt it to, So the, the one thing that characterizes the real estate industry is that the, the assets that are being sold can be very, Heterogeneous, Right?
So it's the opposite of, you know, homogenized milk, right? Where you can't really tell one from another, for the most part, right? So we may have, you know, buildings that were built in the, in the forties or, you know, if it's back in, you know, Boston or somewhere, it's gonna be even older. You may have some, you know, really old assets.
You know, there's buildings that still trade in Chicago that Al Capone probably went in at one. And then you've got like these desert states where like Phoenix and Las Vegas, a lot of the Southern California markets, they're frame and stock's popular. It's a little bit easier to [00:31:00] categorize in they in their newer housing stock.
So automated valuation models tend to work better in those areas than they do, and. Somewhere that's more remote, you know, semi rural or, Yeah. So it's, it's an industry where it takes a lot of due diligence. I mean, that's, that's the key word and or key phrase in our, our industry is due diligence. Cuz it's, there's a lot of different moving parts to a deal, especially development deals cuz you may be doing, um, You know, soils analysis, and you are hiring civil engineers and entitlement people.
You know, you're working with the, the local municipalities. You have town hall meetings with the neighbors, and so there's a lot of different, I guess you'd call 'em stakeholders. There's tensions there too. So if, if you're developing a master plan, cities may want certain things in that master plan that the developer may not want.
So there's kind of the back and forth that that goes along there and, but the best thing to do is inform all that discussion with the data. And [00:32:00] sometimes we're asked to do that on behalf of people in that are developing master plans or sometimes the municipality itself. But each one of those things is, you know, turning back to this technological lag.
I think because the industry is, the product is just, it's not widgets, right? It's mm-hmm. , everything is so unique. It was hard to adapt all those things to some of the platforms. There's a lot of talk, uh, you know, at least in the past 10 years, that term disruption. You hear that a lot. There's more on the horizon.
I think some of the technology that's, that's been manifesting from, you know, things like blockchain, I think will, will influence, you know, title and record keeping, you know, transparency. And it may not be even that the, you know, something like Bitcoin is, is necessarily the currency, the future, but there's technological components of that I think that, that are likely to be adapted, you know, deeper into our.
Probably hitting mostly, mostly title first, but other aspects of it. But [00:33:00] you know, I think the lag, there's bits and pieces that we knew we needed. It's a lot like. Like our parents and grandparents, when they went to the, the airport or the train station, they probably carried a, you know, luggage with the handle on it.
And they lifted it. Right. They walked around with it on their side, you know, to their side. Yeah. Wheels were invented. Who knows how long ago. Right. Mankind came up with a wheel millennia go. Right. Definitely has made it easier. Yeah. And then, but they didn't put wheels on luggage until about the nineties, I think maybe the late eighties.
And then polymers I think had a big role in that. So it was kind of like the, I guess sort of an analog where. You had the wheel, you had luggage and you didn't put the wheel on the luggage because, cuz maybe cuz you didn't have polymers or, you know, a way to make it cheaper and, and not tear up airport floors and stuff like that.
I think that's where we were as an industry is that we knew there's these bits and pieces out there and we just needed the, the timing [00:34:00] to where nothings were adapted and innovated over time to where we could put those. .
[00:34:05] Kat Harwood: Mm-hmm. . It's amazing. It's such a transformation over time. It's
[00:34:09] John McClelland: probably a long, long way to answer that,
[00:34:11] Kat Harwood: but no, it's very interesting the way that you've used data throughout your career and how it's sort of evolved with you with the times to make your job really easier and ensure you're making, you know, strategic business decisions.
So I can see how important it is in your role to have data. Um, I have one more question for you. What are your data dues and data don'ts? ,
[00:34:35] John McClelland: one of the dues is to not let anything disappear. So there's points in time where you need to take a snapshot of what's occurring at the time in, in a certain market.
Mm-hmm. , you need to, a good way to archive that and some redundancy, cuz you never wanna lose anything because a lot of that data you, you cannot replicate it. You know, Unless you can get a DeLorean [00:35:00] with a flux capacitor or something, it's not gonna happen and it's just gone forever. But it becomes extremely valuable, particularly when you're trying to build time series and analyze things in the past and try to find, uh, are there scenarios now that are developing that may look like something in the past?
So, archive things, and, uh, like a lot of large companies, they, they really have a good handle on this because particularly, you know, nine 11 caused a lot of firms to decide that they needed redundancy and where their data was located. So it has to be offsite. A lot of smaller companies, they often, because they're kind of trying to boots their themselves and things, they'll ignore, I think some of these, these important features or requirements.
So I guess that's part of the don't, is just don't ignore the data . Yeah. Yeah. Don't ignore the data. And also another, it's almost like a, there's an art to the. To the business too, even, even with data analytics and that you can get, we used to call it paralysis by analysis, so [00:36:00] you don't want to get so deep into it that you're convincing yourself not to do anything at all, which sometimes is the case.
People use data in different ways. Personalities matter a lot. So if, if we're dealing with somebody from an engineering background or accounting background, they're not gonna feel right about making a decision. Without looking at a lot of data, but also, you know, I, I, you know, at the same time, we're data driven.
I don't totally want to discount somebody's instincts too, because if, if somebody had lived through the, uh, resolution trust corporation, you know, days or through the, the housing bubble and, and subsequent decline, you don't want to discount some of their. Their own, I dunno if I wanna call it feelings, but sometimes there's an intuition there.
Uh, what we like to do is see if we can validate that intuition with the data. So we don't want to just discount somebody and say, [00:37:00] because we haven't looked at the data, we don't. Agree with you, right?
[00:37:03] Kat Harwood: Yeah. And those instincts inspire you to look at the data as well. It brings up things maybe
[00:37:09] John McClelland: you wouldn't think of, right?
It it does, and it, and it inspires us to look at the data. A lot of people have a valuable opinion on things. It's their money too, in many cases too, that we're, we're dealing with. So automatically we're not gonna discount what the. What their, their hopes and dreams are. If they're, if they're looking at bad information, then we, we wanna to find better information or maybe say improved information, something like that.
So, you know, hearsay is not the greatest thing in a court and is not the, the greatest thing when underwriting deals that could potentially influence not just the, the lives of that client, but even. Children, potentially. It's a big deal. It's something that we always have a certain amount of caution and to be careful and respectful of, of different opinions.
[00:38:00] Kat Harwood: Absolutely. This has been such a good conversation. I've really enjoyed talking to you about the industry and how you use data, and I feel like I'm inspired to use more data now.
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