Future of Real Estate Data is Now in Real Time (LA 1352)

Future of Real Estate Data is Now in Real Time (LA 1352)

Transcript:

Steven Butala:
Steve and Jill here.

Jill DeWit:
Hello.

Steven Butala:
Welcome to the Land Academy show, entertaining land investment talk. I’m Steven Jack Butala.

Jill DeWit:
And I’m Jill DeWit broadcasting from sunny Southern California.

Steven Butala:
Today, Jill and I talk about the future, how the future of real estate data is now and it’s in real time. One of the roles that I’ve assigned my myself in life and in this partnership that Jill and I have is to be ahead of the game when it comes to technology and real estate. So I always kind of know what’s coming up before it actually is released so we can take advantage of it and I obviously can share it with the Land Academy group and on the show and the whole thing. So I’ve been taking a lot of, during these COVID times, a lot of webinars, both paid and free, continuing education type webinars about real estate data. And so we’re fortunate enough to have companies like CoreLogic, Black Knight and a couple other companies out there like ATTOM Data. There’s about five of them that consistently hold webinars that explain their products.
And so I’ve been listening and watching these webinars and they’re always held by these young geniuses. These kids are just amazingly intelligent and they explain their new products. So that’s what this whole show is about. I can see how this is going to go and how it’s going to go for us as land investors, which we will obviously share as it comes up with the Land Academy community. That’s what it’s all about.

Jill DeWit:
It’s funny how much we both consume of webinars. I love them. So, and it’s funny, my webinars, there’s a lot that we do together. And then there’s webinars that we each go off on our own separate way and get to be really proficient in. And that’s what this is all about. I love it. It’s good. There’s a lot of good content out there.

Steven Butala:
Before we get into it, let’s take a question posted by one of our members on the landinvestors.com online community. It’s free.

Jill DeWit:
Ian wrote, I want to buy a primary residence in San Antonio. I usually buy homes, retail through a realtor, but I figured I would get the direct mail route a shot. What’s the best way to send out a mailer to neighborhoods I’m interested in? I have ParcelFact, but I know Jill has talked about how NeighborScoop has a polygon function that I believe she said, we’ll put the ownership data into an Excel file. Should I use that method? I love it.

Steven Butala:
So I’m going to pose this question to you because I just want to see what the answer is. I know what the answer is, but if you want to buy a primary residence, let’s just use that example. I think Ian’s a house flipper. I know he is, but if you want to buy a primary residence and you want to send a mailer out, how would you do it, aside from just asking me to do it?

Jill DeWit:
You know what? It depends on if I really have my heart set on a geographic area.

Steven Butala:
Keep going, this is what I want. This is a great thought process.

Jill DeWit:
Right.

Steven Butala:
Go ahead.

Jill DeWit:
Or if I have my heart set on a school district kind of thing. So is it really the block and I like the subdivision? If it’s that detailed, I want waterfront in this subdivision, I want to be near the tennis courts. Who knows what it is that the community provides?

Steven Butala:
Maybe it’s on the water. You only want waterfront property?

Jill DeWit:
I’m going to use the polygon thing because I don’t want to send offers to any homes outside the geographic area. And then I could easily draw a polygon, like Ian says, download all the data. It gives me, the beautiful thing about NeighborScoop, it gives me every last thing, all the property details plus the phone number. I mean, that’s just priceless.

Steven Butala:
The owner’s phone number?

Jill DeWit:
The owner’s phone number. I love it. So that’s what I do. If I want to do the other way, do you want me to keep going?

Steven Butala:
Yeah.

Jill DeWit:
Okay. Say, I’m like, Hey, I just know San Antonio. What if it’s just San Antonio in general? I want to live in San Antonio. I actually don’t even know where. So I’m going to probably have some data things like I want to be in a neighborhood that has these type of community resources. Maybe I want to be in homes between 400 and $500,000, something like that. Then I’m going to come at it. I’m going to use you. We’re going to come at it that way. Do red, green, yellow. We’re going to pick the zips. First I have an idea, right, of these six zips. Let’s just say that all of them provide what I’m looking for. It’s the right demographics. It’s the right whatever. So then you are going to go through that and then you’re going to do red, yellow, green, those zip codes. Then those top two, three zip codes, they’re going to get an offer. And this is something that we’ve talked about as ourselves, as a family too, by the way, Ian, you’re not nuts.

Steven Butala:
We’ve done this.

Jill DeWit:
So, and I, and so then these, we’re going to send out the offers. I’m going to see what comes back. And this is what I would recommend. This is what I have asked Steven. So the offers come back in, Ian. You’re staring at 10, pick the three houses that meet your criteria money-wise, then let your wife pick from those three.

Steven Butala:
I think the wife’s going to say, okay, go ahead.

Jill DeWit:
No, this is what I said to you.

Steven Butala:
The wife’s going to say, this is the zip code I want to be in any way.

Jill DeWit:
Nope, we’re assuming you’re on the same page going into it.

Steven Butala:
Yeah.

Jill DeWit:
Give her at least three Ian that she can walk through and pick from and say, this one’s got the pool. This one faces the way I want. This one I hate the front door, the porch set up, it’s not big enough, whatever it is. Then let your wife step in and let her emotionally pick from the three that’s would be my advice. And that’s kind of how we go.

Steven Butala:
So I’ll take it a step further and I’ll describe the dataset. Jill’s first way, the special way. I don’t care how good you are. A drawing on a screen with your mouse, you’re going to lose some data. You’re going to just going to cut one lot in half and then all that. So an investor, that polygon thing works great. A great example of how to use the polygon thing is to go. Florida’s just packed full of waterfront communities, where you drive your boat in, it’s got docks. There’s 25 properties that are on the water where you can have a boat on a dock. And then there’s like three times as much that are one houses back or two houses back and you don’t want those. The polygon tool is amazing for that. It’s amazing. For neighborhood, it’s the second way that Jill described and she’s right.
The datasets, especially in these urban areas are so robust, it’s amazing. You can choose a census tract and just mail letters to that. You get all the data into a spreadsheet. You just do your research. I just want to be in this one census tract. You can choose a school system. That datasets in there. That’s a pretty popular one. So you download the zip code, all the zip code data, and then you extract that specific. This is for a primary residence so you shouldn’t be saying, “Oh my gosh, I don’t want to spend 15 cents. I can spend 30 cents.” And I’m saying that with a little bit of satire, because everybody doesn’t want to spend any money on data. It’s a crackup, especially if you’re… By the way, if you buy a primary house this way, you’re probably going to save, what, $100,000.

Jill DeWit:
That’s true. Exactly.

Steven Butala:
And that’s on like a $500,000 house. In the silly neighborhood we’re in, you can save half a million bucks easily. So dream it up is my point. That’s really what this topic is about today. It starts with a dream. I call it the dreaming phase of everything. Dream up the best place that you want to live and you’re going to sit there and say, I can’t afford it. Oh yeah, you can. Because you’re going to send a mailer out to that school system or whatever’s important to you and you’re going to knock off half the price and someone’s going to sign it and send it back.

Jill DeWit:
Awesome.

Steven Butala:
Today’s topic. The future of real estate data is now in real time. This is the meat of the show. I think of everything with how we do these real estate deals, because I’m a weirdo in terms of machining events. I have a very brief but meaningful background in manufacturing. So if you’re going to machine a part or whatever you’re making, it needs to be broken down or deconstructed into machining events. So very complicated aircraft type parts, there’s a lot of machining invents involved. And to make it worse for the manufacturer, the machining events have to be, the actual event itself, is very, very, very, it has to be very precise. So if it’s a millimeter off, this is not like the auto industry, but specifically with aeronautical parts, it has to be very specific. So you end up with a lot of parts that are a couple of millimeters off and you scrap them.
So there are whole companies out there that try to take making a part that has, let’s say, 15 machining events and make it 10 events because there’s a massive amount of savings in machinery and labor and all kinds of stuff. So the same thing happens with real estate. So here’s how we do a real estate deal and you can count the machining events and what happens. I manually go clicking around a website like Zillow or realtor.com and I take a look at what’s for sale. That’s the big dream. Okay? I want to find a place that there’s not a lot of properties for sale. The ratio of the sold properties to the active properties, just eyeballing it, seem acceptable to me. And again, Jill and I are in the process of doing very detailed education series, it’ll take the form of a webinar. I don’t know exactly what it’s going to be called because that’s Jill’s situation or how much it’s going to cost because Jill vetoes everything I like.

Jill DeWit:
I was just about to give you a nice compliment over here. I’m cuing up for beautiful compliment.

Steven Butala:
And I wrecked that.

Jill DeWit:
Okay.

Steven Butala:
That’s stage one. Okay. Let’s say I find a suburb of, we’ll pick on Texas still, Dallas, I don’t know. Maybe it’s less than a five-hour drive from Dallas, four hour drive and it looks good.

Jill DeWit:
Oklahoma.

Steven Butala:
Yeah. Oklahoma, that’s fair.

Jill DeWit:
Now you’re in Oklahoma.

Steven Butala:
Okay, great. So we’re in Oklahoma. There’s a couple of counties that look good just eyeballing it. Done. Check. Now I go in and get the data from a lot of different sources, like Redfin data, and we haven’t spent a dollar yet, by the way. And you don’t have to be a Land Academy member to do this at all. Then I’ll take a look at the zip code data through Redfin data and through a couple other sources, find out the universe of properties that are there, the universe of vacant land properties. I’ll look at what’s for sale. I’ll look at what’s sold and I’ll run ratios and percentages against it and I’ll take, let’s say that county has 10 zip codes. I’ll take all 10 zip codes. I’ll pit them against each other in what we call a red, green, yellow test. Now a half hour to 45 minutes has gone by.
I spend some time checking those counties and assigning a red, green, yellow. Invariably, there’s three counties that I should send mail to because they’re green, that passes all my tests. How many machining events is that? A lot. And there’s a little bit of variance that takes a little bit of skill. Number two. So then I move on. I pull the data, I go to one of our data providers, whether it’s DataTree, the licensed providers that Jill and I have, I pull the data up and I start to scrub. I scrub out things like the United States of America owns it. It’s got a Canadian mailing address or any type of foreign address. We don’t send mail out to anywhere outside of the United States. It’s too expensive and on and on and on and on. If you’re an experienced Land Academy member, you know what on and on and on and on means. In this environment, I can’t explain it.

Jill DeWit:
We’ll be here for all day.

Steven Butala:
Right. So at the end of that, I have a dataset to send, and then I price it, of course. And I send it to [O2O 00:12:01] and it gets in the mail. How many machining events is that? It’s a lot, and there’s a lot of room for error. So imagine this, and this is the future, and there are spreadsheets involved and five pieces of software and a bunch of things. The future is this. This is the dream part. So forget what I just said. Forget about real estate and just dream with me for a second. You have parameters. You set your own parameters for where you want to send mail and maybe one of your parameters is I have no geographic parameters. I realize the dataset for the entire country. There’s 150 million pieces of property in the country. And, but I don’t care about 150 million pieces of property. I only care about now we’re going to fill in the blank.
I only care about properties that are not houses or commercial real estate. I only care about land. Bang. Now we’re down to 80 million. I only care about properties where, in the zip code that it’s in, as a percentage, there’s only been 15 properties in that entire zip code that have sold in the last 12 months because I want to be there by myself. I don’t want any competition. And they’ve only sold for less than $1,200 an acre. The ones that are for sale right now, and there’s only a handful of those as a percentage, are listed for $3,500 an acre. So I know that if I get one for what’s been the sold price, I can triple my money. So this database is a live feed that’s coming in for all 150 million properties alive. It’s in a database and the database is waiting for you. It’s waiting for you to set parameters.
And it’s feeding this API out in real time. It’s feeding this data out and then it stops feeding half the data. Then it stops feeding a quarter of the data. Oh, it’s only feeding the data that you want and parameter after parameter after parameter you jam in there, it’s going to spit something out that says, “You know what, Steve? There’s this county in Maine that fits your parameters.” And so that all is happening in real time. And you might set it to wake you up in the middle of the night when this happens. This all happened and came about because of the mortgage industry and happened because of the COVID and lenders are deathly afraid that Congress is going to say something tomorrow like, “Well, you no longer can foreclose on property. We decided it’s in the best. I’m a lawyer. And I think it’s in the best interest, even though I’ve never done a real estate deal in my life, I’ve been a politician for 40 years, I know what’s good for you.
“You’re not going to foreclose. You’re a bank. You know stuff. You’ve done real estate deals. You’re way more wealthy than me, but I’m a lawyer and I’m in Washington and I’m going to decide what’s best for you.” So they make rules like you can’t foreclose on property anymore because everybody’s going to get the COVID.” So the banks got all got together, it’s a true story, recently and commissioned a data company to put together a dashboard like this, so they can see where properties are being foreclosed on, which properties they have lent on are being foreclosed and the thing updates every 15 minutes. So they put their parameters in. They can see what’s going to happen in the future. That is the future of real estate. Our job, my job, and your job is to figure out what those parameters are that makes sense for you.
Maybe you buy and sell houses. Maybe you buy and sell strip malls, and you want to see foreclosures rates. Maybe you don’t care about foreclosure rates. You just care about price per square foot and on and on and on. And Jill and I are going to do a presentation this Thursday on this. That’s the future of real estate data. So it takes those machining events to bring this full circle and removes like 80 or 90% of the machining events. So it tells you where to send mail right now. I’m going out there and searching for it.

Jill DeWit:
Can I ask some questions?

Steven Butala:
Yeah. And we’re very successful at this, obviously. Very, very successful, crazy successful. It’s going to get better.

Jill DeWit:
I know the answer to this, but I’m asking it for everyone else with me listening to this brilliant conversation and thank you for sharing all that.

Steven Butala:
Thank you, Jill.

Jill DeWit:
It’s brilliant.

Steven Butala:
I love this stuff.

Jill DeWit:
What is different about this versus the easy button? Or can you please explain to me how this is going to be created? Are we going to create this for other people, for us? Are you just sharing what’s possible? Because I know other people, we know other people personally that have made an easy button and it’s not good. That’s my point. I don’t want this to be seen as an easy button and you can just sit back and do nothing.

Steven Butala:
There are tools out there right now. Jill’s exactly right. There are tools out there right now that work sometimes. They’re very flawed and it’s like, let me put it this way, let’s say, this happened actually in the auto industry, in 1918 and 1925, the Model T got released and everybody overnight became a auto mechanic because it was always breaking down. Stuff was happening. And then in some crazy person in Arkansas in their barn figured out how to make a better carburetor for a Model T and got it to Detroit and eventually some version of that carburetor got incorporated into it, and the auto industry was built that way, between Daimler, Rolls Royce and Ford, they came up with, and they’re all pitting against each other, came up with little parts of a car that made the whole thing better. That’s what’s happening right now with data in real estate.
We’re in 1918 for that. And so there’s people out there that are developing these tools, albeit not perfect. They need to be. They’re part of a bigger system. Right? And so on a standalone basis, when you use one of those tools, something else is going to either not work, so we’re still in that. I’m not knocking these new tools. The structure of them is relevant, but the outcome is a false positive.

Jill DeWit:
Right.

Steven Butala:
Is that too technical?

Jill DeWit:
No. No, not at all. Just

Steven Butala:
She’s talking about pricing. Jill’s talking about pricing. There’s price. There’s ways to hit an easy button to price a mailer out there that are going to send you down some rabbit hole and nine times out of 10, it’s incomplete. And you’re going to, you’re going to say, “Shit. Shoot. This didn’t work. I wonder why.” Well, because you skipped 90 steps.

Jill DeWit:
Right. So there’s one in particular that we know that, you just said that, because we were going to work together on and we identified a bunch of flaws and kind of took a step back. I mean, true time, we took a step back and said, “Oh, we’re missing a bunch of things here.” And it’s almost like it’s missing an AI version of your head.

Steven Butala:
That’s what this is. Well said. That’s what the future of real estate data is. It’s, it’s accessing all the data at once instead of me clicking around and kind of manually finding it. And from there, there’ll be something else too. And so what’s beautiful about this is this. And so the questions that I’m going to get after the show airs are going to be like, “Well then, what do we do? Well then if that’s the case, then everyone’s going to go to that one county in Maine, if that’s it.” So here’s the beauty of this situation.

Jill DeWit:
And that will change the numbers then.

Steven Butala:
Here’s the flaw with the company that does this auto pricing. For this to work, you have to be able to set the parameters that make sense to you as a business person and an artist. Maybe you don’t like Texas. You only like Oklahoma. Maybe you hate five acre properties, but love 10 acre properties because you use them somehow. Maybe you only want a 4,000 square foot house, if you’re Jill, not a 2000 square foot house. And it can’t be in this state, this state, this state, this state, and this state, I only want these three states. You should be able to vary… There’s tools like Microsoft Power BI that allow you to very simply integrate tools you’re already using into a dashboard and then set alarms to have it go off. The stock market industry has been doing this forever. That’s where this is all going.
Real estate is going to go into a dashboard like on the stock market. And you set the parameters. That’s what stops everybody from buying one single stock, because everybody’s got different parameters. I like healthcare. I hate healthcare. I love manufacturing. I can’t stand manufacturing. I only like IPLs and on and on and on. That’s what makes this work.

Jill DeWit:
Exactly. Well, and the thing about too, you said, real time. I love that because you just used an example, everybody’s going to mail Maine. Well, you know what? That means Maine is going to be good today and it won’t tomorrow because it’s going to change the parameters. It’s going to wake up. I just think of my brother and his trading company and it was hours spent before the markets opened, tweaking things based on new information every single day. So, but you’re talking about, which is great, you’re going to have these machines, we’re calling them machines, your example, to do it for you with the parameters that you set.

Steven Butala:
Yeah. I’ve never talked to anyone in my life who’s regretted buying a waterfront property that’s buildable or usable.

Jill DeWit:
Good point.

Steven Butala:
So maybe that’s a parameter and that makes sense to you.

Jill DeWit:
That’s good.

Steven Butala:
I mean, there’s endless. The datasets.

Jill DeWit:
Yeah.

Steven Butala:
It’s hard to explain this in this audio video environment that we have on this podcast without showing you the data. If you’ve never been into the downloads for RealQuest or specifically Black Knight has some mortgage datasets that I don’t think Jill’s actually even seen yet, that are staggering. They can tell you if the payment was due yesterday and they defaulted on it and what their credit score is, if their credit cards are in the same situation, where they live, the only thing that’s missing is what the ages of their kids are and whether or not they’re arguing with their wife that day. I mean, the datasets-

Jill DeWit:
And their homework and their current grades. It could probably get that, too.

Steven Butala:
Black Knight’s bringing 25. Black Knight has a mortgage product like this. We’re bringing in 25 sources of API data sets for the backend of your dashboard.

Jill DeWit:
Right.

Steven Butala:
And so you can build models based on, oh my gosh, this one zip code in, I’m just using an example, in southern Illinois, everyone’s defaulting on their mortgage. That’s a great place to buy real estate if you’re into that, or it’s a great place to buy. It will deflate. You can predict with very great accuracy, that area in Illinois, the price of all houses because of this foreclosure thing, you’re like six months ahead of… You can see that these prices are going to deflate. Jill and I made a fortune doing this stuff manually with a lot of machining events during the last downturn in central Phoenix. And that’s because we were out there in a car watching it. This is a lot easier. That’s the future.

Jill DeWit:
Happy you could join us today. Five days a week, you can find us right here on the Land Academy Show.

Steven Butala:
Tomorrow, the episode on the Land Academy Show is called because, this was so brainy, Jill tomorrow is going to talk to us about land investing, Buddhist style. You are not alone in your real estate ambition. You didn’t know this, did you?

Jill DeWit:
I did not know this.

Steven Butala:
Well, you have a whole day to plan.

Jill DeWit:
Oh, great, I have five minutes.

Steven Butala:
Five minutes to plan.

Jill DeWit:
Awesome.

Steven Butala:
I need a glass of water after that.

Jill DeWit:
That was good. That was brainy. I have to ask you real quick in the after show here. You dropped a bomb here, something I don’t know about you.

Steven Butala:
Oh, no.

Jill DeWit:
No, wait.

Steven Butala:
Watch, she’s going to change her mind. Steve’s not for me.

Jill DeWit:
Aw, I wouldn’t do that. You know that. What’s this machine background you were talking about? What’s this job that you had?

Steven Butala:
Well, you know my high school friend. I never told you that story. Well, you know the high school friend that I’m talking about.

Jill DeWit:
Last name starts with an A.

Steven Butala:
Yeah. So he and I worked at his father’s company for three of the four summers in high school. And I met this guy in middle school because we had the same computer class and we were the two guys in the class, and this is middle school, that were going to the computer lab after class while everybody else was playing soccer and whatever.

Jill DeWit:
What did they call you back then?

Steven Butala:
Oh, you know what? I was pretty big kid for my age. So nobody called me anything.

Jill DeWit:
Okay, got it. I just kind of wondered did they attach a little nickname to you guys?

Steven Butala:
If they did, I didn’t know about it and didn’t care. And so we just were computer geeks.

Jill DeWit:
That was where I was going.

Steven Butala:
Since you asked, the computers that we had in middle school, I don’t know when this is, probably the 70s, middle 70s, were card readers. It was stamped out cards. And we would write these programs and do all this stuff. That’s how bill Gates learned.

Jill DeWit:
Right. Was it, there was the big-

Steven Butala:
The big rectangle cards you would run them through a machine and it would start to run a program.

Jill DeWit:
Okay.

Steven Butala:
And because his dad, we were just lucky as hell. His father built an amazing company in the 70s and 80s that-

Jill DeWit:
AKA they were loaded.

Steven Butala:
Very, very wealthy. So we had computers coming out of our ears and you know how fast computer technology changes. So he would always have the newest and greatest stuff. Talk about truly having an advantage.

Jill DeWit:
So what’s the machine background? Did you guys build some machines?

Steven Butala:
No. So he would buy, they owned a base of manufacturing companies, but they were always trying to improve them. And so it didn’t happen. They didn’t ever tell us go in and change the number of machining events that happened. By teaching everybody Lotus, believe it or not, which is just an early version of Excel.

Jill DeWit:
Right.

Steven Butala:
They started thinking, “Hey, this,” and these are people that are in their thirties and forties and fifties in the 70s, they didn’t even know what a computer was.

Jill DeWit:
It was Lotus 123. That’s what I remember.

Steven Butala:
And so there’s a lot of pushback in the beginning, but it’s the same now. You hand your phone to your kid and for whatever reason, they just know a lot more about it than you do. That’s what it was. We were just the kid. It was just computers.

Jill DeWit:
I would still argue that a lot of it came naturally to you because I know people exactly your age that can’t do a fraction, a small fraction of what you do. So I think it’s more than that for sure.

Steven Butala:
And I watch these webinars, 180 degrees on that. And I watch these kids are. I watched a webinar a couple of days ago or participated in one where the code that they were running, this is all in real-time and it’s live, wasn’t working. So we had a dashboard of 10 components and this one was feeding foreclosure rates. This was for real-time forbearance rates and then one module wasn’t working. So he said, “Oh, hold on a second.” And so bang, bang, bang, bang. He went in, troubleshot the code, fixed it, and then continued with the webinar. As fast as I just said that, that’s what they did. So that, that kid is me in 1978 explaining machining events, how they can be improved to people who are 30 years older than me.

Jill DeWit:
Yeah.

Steven Butala:
You got to keep an open mind to that. If your mind is closed about computers and learning new technology or apps or anything, you can hang it up now because this is here to stay.

Jill DeWit:
Love it. Thank you for joining us. We’re happy that you are here. If you love this, please like us, give us some love some way, Facebook, YouTube, whatever feels good for you. And by the way, we do read your comments. So if you take the time and write some comments on shows that you love or things you want to hear, know that they get to us. We are Steve and Jill.

Steven Butala:
Information.

Jill DeWit:
And inspiration.

Steven Butala:
To buy undervalued property.

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