Episode 33 | Eli Fisher – Property Data & Predictive Modeling with Audantic

Episode Summary: 

In this episode, Craig Fuhr & Jack BeVier interview Eli Fisher from Audantic about the importance of data and predictive modeling in real estate investing. They discuss the challenges of finding motivated sellers and the limitations of traditional data sources. Eli explains how Audantic uses predictive modeling to identify the individuals most likely to sell off-market to investors. He emphasizes the need for operational efficiency and consistent marketing to effectively leverage data. The episode concludes with an invitation to the next episode, where they will dive deeper into the topic.

*The following transcript is auto-generated.

Craig Fuhr (00:18.638)

All right, well, welcome everyone to Real Investor Radio. I’m back with Jack Bevere. Jack, how are you today?


Jack BeVier (00:25.245)

Doing great, man. Good morning, great to see you.


Craig Fuhr (00:27.57)

I’m Craig Fuhr and we’re gonna introduce our guest in just one second. Jack, we were talking earlier in January, I believe it was, about software that the Dominion Group has used to scale over time. And I’m so excited to talk today with our guests that’s coming on because we’re gonna dive deep into data and… But…


But talk, I would just say to folks who are listening to the show, haven’t had a chance yet to check out some of all the episodes, I would go back to the episode where Jack and I were talking about various types of software that Dominion has used over the years, Jack, to grow the business, to get better, to scale. So yeah, speak to that for a second.


Jack BeVier (01:14.521)

Yeah, sure. Um, so we’ve been, you know, I always have tried to like dive really deep into whatever, you know, and anything that we’re doing from a data point of view, from a software point of view, always trying to use the best tools and products that were available. So I’ve always kind of, you know, I added myself to every newsletter and clicked on every housing wire link to hear about the new product announcement. And, um, and I think that’s been a really big help for us. So early in


One of the things that I got really into, uh, as was probably in like the early 2010s, uh, the national assessor and recorder data for public records. Everything started to go online, right? Like before that you could go to your, you may, maybe your, your state had like a website where you could look up public records data, but they started to scan in everything, uh, where you could actually search documents in most states. And then.


It’s just every year been incrementally improved. And out of that kind of, uh, out of that period of time, uh, there was really three companies that, uh, it was black night core logic and, um, well now it’s Adam that has the third license or that they’re really the three data providers for the not the assessor and recorder data nationally, and they’ve got this oligopoly, right, that they are those three companies of the source data. If you want to get


Craig Fuhr (02:35.921)



Jack BeVier (02:41.301)

all of the real estate data put together. And as data science became more and more popular, uh, those more and more companies kind of started to realize the power that having the access to all of that data, you know, could, uh, could, could offer. And that idea really, uh, attracted my attention from an, from an early stage. So one of the first things we did was I was like, Hey,


I, you know, we, we wanted to lend money to fix and flip investors. And so I want to see every time that there’s a flip and I called up the data providers and those two was dated. It was black knight and core logic at the time, and they, they didn’t know it. You know, they, they thought I was, I was speaking Greek as far as they were.


Craig Fuhr (03:29.162)

I was gonna say that I’m sure they were excited to hear from you.


Jack BeVier (03:32.069)

Yeah, they, yeah. I was, I was tiny, right? Like I wasn’t a major mortgage company. They didn’t do small licenses. They certainly didn’t do custom queries. If they did, it was like, Hey, we’ll charge you hundreds of thousands of dollars to even like touch our database. So that wasn’t going to fly. And I found this company land vision, which I’m hoping to get on as a guest. They’re it’s a really cool data science company and they were kind of just small enough that they’d write a query for me. So I said, Hey, I want to, I want to.


Eli Fisher (03:32.439)

Thank you.


Eli Fisher (03:40.27)

Thank you.


Jack BeVier (04:00.901)

I want to, um, I want you to tell me every time that an entity buys a property and then resells it. I just chose an arbitrary timeframe of within 12 month period. And the difference between the purchase price and the sale price of that property is at least is at least $60,000. Because I wanted to draw the distinction between wholesales and flips, which even today, the, uh, the Adam data doesn’t draw that distinction when they give the flipper report.


they’re including all the wholesaler data in there. They just say like if a property was sold, I’m going to bought and then resold, even if it was for $10,000 more, they call that a flip. I’m like, well, that’s a completely, it’s not the same thing.


Craig Fuhr (04:38.694)

Yeah. And so you had to like, so you had to like Frankenstein together a list based on was it purchased in January and then sold in March? Did it was it purchased for $100,000 and then sold for 250. And from that you were able to surmise. Yeah, that was probably a flip.


Jack BeVier (04:57.477)

Yeah. Like I was, I was compiling it manually myself back in 2012, 13, and I was spending a lot of time doing it, but it was a super valuable list, right? Like it’s, it’s everyone I wanted to talk to. It’s everyone that I, you know, that I thought was, you know, really interesting, that was cool. Right. Um, and so I got land vision to cut me that query. And, uh, and then, so we started sending that list back in 2014. And that’s like, frankly, like the, one of the main ways that we grew 10 years ago.


was through that specific use of unique data, giving you a very long preamble, but I promise there’s a point here somewhere. Um, so it was like that, that specific use of, of like, you know, targeted data became to me, incredibly low cost of acquisition of a customer, right? Like I was then able to send direct mail to flippers before anyone was. And we had a super low cost of acquisition of a customer doing that. And, and


Craig Fuhr (05:44.778)

Oh yeah.


Craig Fuhr (05:52.85)

Well, that was when you’re as when you were still working an hourly wage to Jack that was pretty low. So you know, like you’re


Jack BeVier (05:56.681)

That’s true. I still am. Yeah. And so I just fell in love with that experiment. Like I was like, it was an idea. And then I went and found the company to write the query for us. It wasn’t cheap, but it was, oh my God, so worth it. That like, I just fell in love with the idea of how, like, you know, applying ideas to this data set could produce incredibly high returns on investment.


Craig Fuhr (06:11.99)

so valuable.


Jack BeVier (06:24.577)

And really kind of like, you know, ramp up, you know, make your marketing like, highly, highly effective, highly profitable, right. And so


Craig Fuhr (06:35.126)

Jack, at the time, were you still looking for something that was very local? Was this, or?


Jack BeVier (06:41.065)

I start, well, so I was doing it myself in Maryland, but then when we, when, when we, as a lending company started to lend outside of Maryland, some states didn’t have data that was quite as good as Maryland. I didn’t want to learn, have to learn all these new databases. I was just frankly, spending a ton of energy and time, um, just cutting these lists on nights and weekends myself, cause they were worth it, but they were like very labor intensive to do that. So I tried to find, I started, I started like trying to explore the data science space and figure out like which companies were, uh,


Craig Fuhr (07:00.724)



Jack BeVier (07:10.405)

you know, really kind of early movers in that space. And so I went to a mastermind and, uh, I went to a mastermind and, you know, it was, you know, would mention some of these ideas and I met, I met Chris, Chris Richter, who is the, one of the founders of Audantic works with Eli. And he was the first guy who I started, like, when I started talking to him at the bar about data stuff, like listening to them talk, I’m like, this guy freaking gets it like.


Craig Fuhr (07:12.064)



Jack BeVier (07:39.633)

Like we were two nerds in a pod, right? And we just talked about data science and like, he was way more technically, he was way ahead of me from a, from a technical point of view. And I thought it was just the coolest thing ever, but I was like the. Yeah. Oh yeah. Way ahead of his time. And, but I was like the business guy who he was just like, yeah, exactly. I don’t understand why all these knuckleheads don’t get it. Like, yes, like clearly, you know, there’s clearly a use case here, right? Like, yeah, tell me I’m not taking crazy pills. And, uh, so we really hit it off.


Craig Fuhr (07:50.962)

Way ahead of his time as well. Way ahead of his time.


Jack BeVier (08:07.777)

And so we’ve been, we’ve been working with Audantic that super long preamble is to tell you how, you know, why, why I think this is so cool. Why I think this is such a high value ad for our listeners is that we’ve been working with Audantic since then and, uh, following the company, watching their development of now a lot of different products, it’s almost become, they’ve also gotten out of the kind of a software platform to help you really, uh, you know, a really business set intelligence tool. Um, which I think is just incredible.


And so we’ll dig into all that stuff, but wanted to give the preamble as to why we thought, you know, why we, I was so excited to get Eli from Audantic on the, uh, the podcast today. So thanks.


Craig Fuhr (08:47.514)

Well, Eli, welcome to the show. That was a long preamble. Indeed. I you know, I was a Jack knows. Just having some fun here, Jack. Eli, welcome to the show. Eli Fisher, go ahead and introduce yourself. And tell us a little bit about you and a little bit about the company and we’ll jump in.


Eli Fisher (09:06.134)

Yeah, absolutely guys. Well, thank you, Craig and Jack. Appreciate you guys. And thank you for the kind words, Jack. I’m Eli Fisher. I oversee licensing here at Audantic. A little bit about me. I’ve been in the tech space 20 years. A lot of startup battles here. And yeah, it’s just kind of what I do. My original background, I come from cattle ranching family. So, you know,


Growing up in Montana, I learned a quick lesson here during calving season. If you work around cows, you end up smelling like cow. And so I did not want to be a rancher. So I wanted to do anything other than that. And so, um, I started out my career in the medical industry, uh, you know, basically, uh, going in, working with orthopedic surgeons, going in, covering case and the ironic part about it is, you know, I look at this industry, a lot of fix and flip guys we work with. Um, the surgeons that I worked with basically called it.


glorified carpentry because, you know, a lot of the tools that we used to build a house, we used to fix people. There’s alls, nails. It’s just a different application, but it’s the same, you know, mechanical concepts. And so, um, after I left the medical industry, I was part of a startup in Kansas city, and then I moved back out here to Washington. I’ve known Chris for over 20 years. And you know, at the time I moved back out here, Chris had said, Hey,


You know, I’ve built this company right here. I’m looking to probably scale out the sales function. You’ve been in sales your whole life. You love data. Why don’t you come be a part of it? And I joined the company a little over five years ago. And then, so when we look at what Audantic does today, you know, we try and make data actionable for people. You know, there’s a lot of data that’s out there. And


It’s highly commoditized in that I like to let investors know that everybody pretty much has access to the same data. You know, if you ever see a guy on YouTube talking about, you know, the secret data, the special data, generally that lives in the corner with the leprechaun and unicorn, it doesn’t exist, you know, and so if everybody really has access to the same data, that does not mean you know how to make that data actionable.


Craig Fuhr (11:03.859)



Eli Fisher (11:17.258)

And that’s where we really come in and fill the gap of saying, look, you know, in any given market, there are only so many properties that are actually going to trade off market to an investor. Here’s how you focus on who those best people are. So we really look at it from a revenue optimization standpoint for not only the marketing aspect, but also the internal revenue funnel. Because one of the curious things that we see


is that a majority of loss that occurs within any customer’s revenue funnel generally happens within the follow-up stages. And we can actually quantify that so that they can start converting at a higher level without even spending any more money. It’s just asking a better question relative to the data that they have.


Craig Fuhr (11:55.73)

Yeah, you know, I can’t wait to talk more about the approach and how you guys target and use predictive modeling. But one of the things that every investor at Jack either, you know, young old experience, not experience, obviously, our podcast is geared towards advanced investors, but we all need data. And there’s no lack of it. No lack of providers out there now, Jack, you know, it’s


I remember when I used to go out to the register of will site in Maryland, Jack, and it’s just a big long list that comes out one. Actually, the list is updated, I think, daily, if I’m not if I’m not mistaken. But there’s no way to download that data. And so I went out and I found a really smart VA, I think, in India. And I paid him to write me a macro. I tell you this, Jack. I paid him to pay them to write me this killer macro.


Jack BeVier (12:33.425)



Jack BeVier (12:45.894)

Yeah, yeah, I love this.


Craig Fuhr (12:50.77)

that would log into the register of will site, go to Baltimore County, let’s say, download all of that data, Eli, and it would put it perfectly formatted into a spreadsheet for me that I could then go send to like a go big printing or something like that to print off my letters for me. And it was seamless. I mean, it was perfect. The problem with it was, Eli, is that, so maybe I would do several counties in Maryland.


include and then include Baltimore City as well. And so, you know, per month, there might be I don’t know how many people die, you know, so it was it was probably like 700, you know, say it’s five to 700 letters a month that I would send out. And as you know, it’s a very profitable list for many people probate marketing, everyone does it now. Right? Back then, not so many. However, what I learned along the way was, geez, there’s just a lot of people that


there’s just a lot of folks who there’s no house as part of the estate. And then a lot of these people just aren’t gonna sell to me. And I’m all for shotgun marketing and throwing as much stuff up against the wall as I can. And back then it was very inexpensive to do. But yeah, I would love to talk about a much more targeted approach for better investors. And I’m sure that’s what Audantic is all about.


Eli Fisher (14:17.126)

Yeah, Craig, you bring up a lot of great points. And so, if we think about kind of the life cycle of how most investors approach data, typically what I will see is people will start off with niche lists. And by niche, what I mean is they’ll go and they’ll pull, like to your point, the probate list or affidavit of death. There’s a cute term out there now, pre-probate. That’s the one thing I do see in this industry is there’s a lot of made up terms. I don’t know what…


Craig Fuhr (14:43.122)

Is that like I’m about to die, but I’m not sure I’m dying yet? I’m like, yeah.


Eli Fisher (14:47.97)

I think the intimation there is that it’s basically, it’s like, you know, before the probate process is started, but it’s a made up term. It’s like, I don’t know what that means. Affidavit of death is actually a defined thing. And so, you know, you have these made up terms, but when we look at a lot of these niche categories, whether it be pre-foreclosure, what have you, that data is…


Craig Fuhr (14:55.687)

Yeah, of course.


Eli Fisher (15:09.346)

Pretty good for the most part because you know the inherent motivation of the individual. You know, if you see somebody that’s in a pre-foreclosure situation, the government says you’re moving, you are probably moving. Now, the downside of that is, is when you actually look at a distribution of product that trades off market to investors, those niche categories only make up a very, very small percentage because to your point, there’s not necessarily a lot of it. Okay. And then that challenge is then compounded by the fact of that.


everybody and their dog, the second something gets published to the public notice stage, it’s the equivalent of shooting a flare gun off in the sky and say, hey, investor, come talk to me. And so usually it’s the first person to engage that’s going to win that deal. And so if you think about it, it becomes very, very hard to have a scalable, repeatable model only relying on that data. And so then you kind of look at kind of the next step, the evolution that we would see people do. And this is where people started stacking data.


Craig Fuhr (16:02.352)



Eli Fisher (16:05.074)

And on the surface, it makes sense. It’s not a bad approach either. So you will see people go to, you know, the batches, the prop streams, the atoms of the world, and it’s pretty similar as to what people pull. They’ll pull the high equity list. They’ll stack it against absentee, and then they’ll layer on a couple of other things. And that’s not a bad approach either. You will certainly get deals that way, but it becomes problematic because you’re making some universal assumptions. You’re assuming that because this individual has


equity and because this individual also is an absentee owner that is a good target. And sure in some cases that person will sell to an investor but the vast majority of those individuals will never ever sell to an investor. And so when you consider that there’s this inherent waste of all these investors are targeting the same people because they’re using essentially the same stack and they’re just basically throwing money after it over and over again to people that will never sell to them and it’s highly inefficient.


Craig Fuhr (16:40.342)



Jack BeVier (16:55.838)



Craig Fuhr (17:03.862)

Sure, absolutely.


Eli Fisher (17:03.87)

And that’s where we really step in to try and solve that problem by using predictive models to indicate who has the highest likelihood to transact off market to an investor, but also the class of investor as well.


Craig Fuhr (17:17.414)

Yeah, I think that’s you mentioned class, I truly think that’s the class of your product. And so maybe you can just dive in about predictive modeling at this point exactly what it is and how you all use it.


Eli Fisher (17:30.806)

Yeah. So, you know, when you talk about what do we do, you know, everybody and their dog, ever since they learned to play a chat GPT as an AI expert, we’ve been doing predictive modeling for the last eight years. And if you really want to boil it down as to what it is we do, we just do math at scale. We do large scale statistical analysis. And so what is our predictive modeling, which is a type of AI, what does it actually do?


Well, every day we monitor, ingest, clean, and standardize and house every single real estate transaction across the country. And really, what does the algorithm do? Well, it looks at, say, in a specific area, here’s all the people that traded on market. Here’s all the people that traded to an investor. And then the data, basically, the algorithm is looking and saying, OK, those people that have historically sold.


to an investor, what do they look like from a persona standpoint? What does the product specifically look like? And then through large scale statistical analysis, or often what we refer to as back testing, we basically are able to say, okay, these are the individuals based on the historic performance in any given market that have the highest likelihood based on the person in the product to sell off market at a discount to a specific class of investor. And when I talk about, yeah, go ahead, Jack.


Jack BeVier (18:49.753)

Hey, you like, can I, can I, let me interrupt you real quick. Cause I want to use you get, that was a, there’s a ton right in that, in that 30 seconds right there. And, and I want to really want to, uh, labor, labor the point a bit because it’s where people sometimes miss that. This is so interesting. We’re all normal, like, you know, just normal, right? You know, normal investors. We’re all coming. We generally come to the space with this hypothesis that.


Craig Fuhr (18:55.61)



Jack BeVier (19:20.637)

people who are going to sell to investors at a discount or who have solute, you know, who have problems that we have an opportunity to solve, right? A reason for us to get a deal right on the property itself, that there’s some thesis behind that distress. And then we go look for the indicators of that distress and that’s smart, right? Like that’s better than just, you know, knocking on door, you know, knocking on random doors or like blasting an entire zip code. Like it’s not a bad place to start.


Craig Fuhr (19:44.804)

I was about to say the same exact thing.


Eli Fisher (19:45.486)

Thanks for watching!


Jack BeVier (19:50.545)

But the problem is that now everyone’s doing it, right? Like back in 2013, 14, that was good enough. But by 2017, 18, that was no longer good enough, right? Like everyone’s doing that. And now the market’s gotten super saturated from that point where, as you pointed out, there’s no special list, there’s no special like corner of public records that people can’t find. So there’s no such thing as a.


as the market is more efficient than it ever has been before. There’s no such thing as getting a, a single look at a deal, right? Like you’re one of four appointments, best case scenario, generally, even when you’re sending direct mail, um, uh, particularly on these, on these lists, um, on the, on these lists where people are looking for indicators of distress. So we’re always looking for the indicators of distress and then hoping that there’s a deal that’s somewhere in there.


What you guys are doing is starting from the other end. You’re saying, let’s go look at everyone who sold to an investor last month. And so these are deals that we’re not going to go get, right? Like generally this is stuff that I’m not even paying attention to. Normal investors not even paying attention to. Let me see everybody who sold to an investor and that the price that we, and we think that based off of an AVM, an automated valuation model, that price looks cheap.


Craig Fuhr (21:01.349)



Jack BeVier (21:14.853)

Right? Like looks low, looks lower than what that, you know, it looks like that house needs some work. Probably, you know, it’s a hundred, a hundred thousand dollars sale in a 225 neighborhood. And so rather than, and let’s go find out what the people who were selling to investors, where they are, who they are. And so you guys are adding all these other lists, all these other, um, indicators that are non-distressed necessarily indicators, right? They’re, they’re just,


Craig Fuhr (21:34.1)



Jack BeVier (21:44.617)

They’re just demographics, psychographic, you know, uh, you know, lists, you know, th there’s, you know, there’s the, the whole, the data industry is a, is a, is a whole thing, right? Like, um, you can get lists of people, magazines that people buy, uh, you know, all kinds of stuff. And so you guys are laying on those kinds of things to look for, to look for other markers in the probable sellers category and then saying, okay, here’s what different categories of probable sellers are.


Let’s now apply those same filters to everyone who still owns property. And let’s go try to find people who may not be on a pre foreclosure list. No one’s, you know, there’s no affidavit of death. There’s no like a state open. There’s no recent non-consideration transfer. And, but those people get put on your list so that hopefully we as an investor are now spending our marketing dollars on a


a warmer list that is hopefully also less competitive, because it doesn’t necessarily have those classic distress indicators attached to it. And so you’re then more likely, the thesis is, we are then more likely to get a less competitive situation, to get a less competitive situation, and that we are honing our marketing dollars in on a smaller subset.


so that we can hit that list harder. And that was something that really took me a second to like wrap my brain around is like, the point is to fill, you know, there’s, you know, you’ve got a million people in a particular Metro. Maybe there’s, you know, maybe there’s a hundred thousand that, you know, that are high equity and you know, that would fall into like, you know, some classic list criteria, but you guys are gonna narrow it down to 15 or 20,000 and we should hit that three times as hard.


Craig Fuhr (23:19.259)



Jack BeVier (23:37.081)

Right. Uh, and you will, as a result, have a higher conversion rate on our marketing dollar, um, because we’re, we’ve narrowed the list down. We think we’re doing something in T we think that there’s something intelligent about our list that other people are missing and we’re spending more money on those less competitive, uh, but still eligible leads. Uh, and to me, and, and that’s, and that’s the thesis is that, you know, even after, uh, even after paying for this service,


that our return on our marketing dollar is going to be better than if we had just cut a list on batch, cut a list on prop stream. Fair? Did I summarize that okay?


Eli Fisher (24:10.718)

Yeah, I mean, yeah, I think it.


Craig Fuhr (24:13.35)

Yeah, one thing I would just say to that is everyone has access, Eli, to, you know, Jack was just saying back in 2012, you could easily find the access of sales data, you know, this house should be worth this, but it sold for this. And that’s the easy stuff that this there’s a there’s some sort of indicator of distress. I think what we what I’m most interested in is


How do you guys go back and look at sort of all of that data and then layer on additional variables that show motivation?


Eli Fisher (24:52.162)

So I think when you look specifically at what the algorithm is doing, and I’ll give you a case study here. So there’s a property that was in Richmond, Virginia, and you go back to 2021. And essentially the algorithm identifies this target and says, hey, this guy here, there’s something going on in his life. He’s gonna sell to an investor. Now at that snapshot in time, there’s no D transfers, the property is owner occupied, there’s no liens against him.


there’s no pre-foreclosure file, there is nothing on the surface other than the fact that this property has equity that would tell an investor to go after it. Yet the algorithm says, hey, go after this guy and classify it as the highest projected ROI target in that area. So how does it do that? Well, when we look at


what the algorithm is doing, it’s going in and it’s looking at all the people that have historically sold to an investor. And to Jack’s point, we’re looking at the demographics and socioeconomic standards of these individuals. Because as much as I would love to tell you that, people wake up and say, oh, you know what? Yeah, Craig Jack, today I thought I would be a pre-foreclosure, I just arbitrarily made that decision. Unfortunately, that’s not the case, right?


If you think about it, that is the end state that this person has wound up in, meaning they had to go through a lot of things in their life to get to that point. And so really what the algorithm is doing is it’s looking for those underlying markers that are going on in that person’s life that says, hey, this individual has a high likelihood to sell to an investor based on the historic precedent of these other individuals that have gone through similar life scenarios that ultimately sell to an investor.


Craig Fuhr (26:13.553)



Eli Fisher (26:32.598)

So if you really want to look at it from an overly simplistic standpoint, let’s just say you have two individuals that own a property and let’s talk about these individuals. Okay. So you got individual A. Well, what does this person do for a living? Well, individual A is a CPA. You know, what does his Friday night look like? Well, he typically comes home on a Friday night. He’s opening up a bottle of Woodford Reserve double oak. He’s turning on CNBC.


Craig Fuhr (26:54.302)

Hold on.


Eli Fisher (26:56.358)

And he’s watching Kramer scream about the market on Mad Money. That’s individual A who owns a house, right? Now compare that to individual B here. What does this guy do for a living? Well, this guy hangs sheetrock for 14 hours a day.


What does his Friday night look like? Well, his habits are basically he stops off at 7-Eleven, picking up a couple of tall boys, pack of smokes. He doesn’t have a 401k or multiple IRS like Mr. CPA over here. So what does his retirement planning look like? Well, he’s buying scratch-off tickets and Powerball because let’s be honest, man, somebody’s got to win, right? And then his evening looks like this. He goes home and he’s watching TMZ and pro wrestling. One of those individuals stands a significantly higher chance of selling off-market at a discount.


to an investor. And so when we talk about the demographics of socioeconomic data that we look at, that is the type of data that we ingest. Sometimes it’s referred to as the creepy data. But the reality of it is, is that type of data is just a commodity these days. Anybody can go out and buy that as scary as it sounds. The key here is gleaning statistical significance from that data, because you can have all of that data.


and you can make some very, very bad assumptions. And that’s why what the algorithm is doing is it is running simulations at scale to say, okay, here’s all these different factors. Here are the people that have historically sold. We’re gonna look at those factors and then almost build a lookalike audience in Baltimore and say, okay, based on the historic precedent, here are all the individuals that meet that criteria.


that are not in those stages that we look at historically from like a pre foreclosure and affidavit of debt. We may only see that they have high equity, but we know that they will eventually wind up at that point. And so the competitive advantage is we can give to our clients and say, hey, look, based upon the data, you really need to focus on this specific group of targets. So Jack’s point, maybe it’s 20,000, but it’s the 20,000 best.


Eli Fisher (28:54.53)

that have the highest likelihood to sell off market at discount. So as we bring that full circle, I shared with you that case study from Virginia, where the algorithm identified that individual had no markers, except for the fact that had high equity, yet the algorithm knew. Well, if you play that guy’s story out, what happened is eventually he did go into pre foreclosure and then we saw an investor pick up the property at 50 cents on the dollar, slaps it on the MLS after doing a little paint and carpet.


and ripped 137K in under two months. So that is what the goal of the algorithm is doing, is it’s identifying those people before they’ve hit those known distressor stages that everybody and their dog looks for, but it can also quantify, and we call this in statistics, front loading of a data set, meaning we put the very, very best people that have the highest potential yield in the front of the data set, so that not only can the client focus on the best available target,


Jack BeVier (29:27.177)



Eli Fisher (29:51.67)

but also the people that will yield the most of a return.


Craig Fuhr (29:56.626)

Brilliant. So with the availability of data these days, we can just go off on this tangent for a little bit. With the availability of data, and I assume it’s coming down in price for you guys to aggregate all this data, my wonder is when will more competitors pop up to what you’re doing?


Obviously, it feels to me like you guys have been doing it a while, you’re doing it well, but I have to imagine that there’s a lot of other companies out there who are saying, oh, we know the kind of the standard sets that everybody’s used, how do we layer on what Audantic is doing to provide a lower cost solution. Everything becomes commoditized over time.


Eli Fisher (30:46.786)

Yeah, I think when you look specifically as to what we do, there’s a level of technical sophistication that is required to truly accomplish building predictive models. And so the reality of it is, is to actually hire data scientists and data engineers, just to manage and be able to build these types of models that are truly effective do back testing. That is a very, very special skill set.


And so you look at the companies that actually service and have the ability to do that, they look at basically, let’s call it what we’re in, a niche vertical. And the total addressable market for them is tiny. So for people that have that skill set and ability, this isn’t really on their radar because there are a lot of better fishing waters for them to be fishing in.


Craig Fuhr (31:39.685)



Eli Fisher (31:39.774)

So from a competitive standpoint, I think that we’ve done a nice job of going into a niche vertical where the people that can truly do that, they’re not looking here because it just doesn’t make enough sense financially for them to go and do that. Not to say that they could not do that, but from an opportunity standpoint, they look at and say, well, that doesn’t really make a lot of sense for us. And


It’s really important to understand when I talk about the technical ability of being able to do what it is we do. We’ve been very fortunate, you know, in our industry. And again, I go back to ever since anybody learned to play a chat, GTP, everybody is an AI expert. You know, I’ll give you an example. There was an organization about a year and a half ago.


that basically said, oh yeah, we do data scientists. It’s like, okay, well, what are your technical benefits? You go on a LinkedIn and their lead data scientist has a marketing degree from Southern Utah University. I’m sorry, you are not a data scientist. Just because you type that online and say you are, you are not a data scientist. That is a defined thing that requires a very specific background from an educational standpoint, training standpoint. And so, well, yeah, exactly.


Craig Fuhr (32:53.45)

Shout out to Southern Utah University, Jack. There goes that sponsorship.


Eli Fisher (32:58.982)

Yeah, so you know…


Jack BeVier (33:00.893)

Hey, so Eli, what’s, um, what, what’s the, uh, you meant you mentioned in terms of like, you know, from a, the addressable market point of view, right? So if you’re a, you know, given the cost of your guys services, there’s a certain threshold, right? Where it doesn’t make sense. Like if you’re trying to buy three houses this year, five houses this year, yeah, they’re not, they’re not your target audience.


Eli Fisher (33:08.078)



Jack BeVier (33:21.585)

What’s the minimum like transaction volume from like from for wholesalers, flippers that, you know, for, for people looking to buy properties that where, where you see your, you know, what’s, what’s the men on purchases per year where you guys see that’s where that’s where our sweet spot is from a customer point of view.


Eli Fisher (33:37.834)

You know, typically 50 deals and up. You know, part of it, what it comes down to is, and this may make me the most hated man in data, is there’s this misnomer of motivated seller, okay? When you think about true motivation, there’s only a handful of scenarios where somebody’s highly, highly motivated to, you know, give their property away.


Okay. And so with that, people are always looking for like this magic bullet, this magic answer. We don’t sell magic here. I’m sorry. We don’t do that. So with that, to be able to


act upon the data, you have to be consistent within your marketing. But on top of that, the reason that I say 50 and up is there’s a lot that goes into it from a systems and operations standpoint of have you scaled your business to the point where you can actually take best in class data and make it actionable? You know, one of the things that we do is that


we can empirically prove how well our models work because we’re the only people in the industry that publish attribution, meaning Craig, I generate a predictive data set for you in this market. After one quarter, I can show you how many deals came out of the data set. So it’s not a question of, Hey, does the data work? No, we publish the results. It really becomes a question of, okay, is the organization operationally efficient where they can actually act upon the data, engage it on a consistent basis, and then measure their performance.


and why I harped. Oh, go ahead, Jack.


Jack BeVier (35:04.637)

Well, yeah, by the way, I feel like that’s an incredibly useful and interesting feature, right? Because like, we were always, investors are always sending mail into the ether, right? We have no idea. We get certain calls. We don’t get other calls. We don’t know why. We don’t know what happened with those other calls. We don’t know if we got all the deals in the market or none of the deals in the market. Like, are we any good at this? Like, and what would you guys have is this, as you mentioned, this attribution, uh, module, which


for the list that you sent out, they’ll say, hey, here’s the metro area that we used to cut your list. And then we sent you this list and then you mailed it. And then here’s all the deals that happened in that metro area. And of the deals that happened, here’s the deals that were on your list and here’s the ones that we missed.


So the model will take those into consideration and further refine it and get better, should get better each quarter, right? Because there’s a constant feedback loop of the deals that are happening to make the algorithm better and better. Market changes over time, right? Like maybe it’s a gentrifying area, so all of a sudden, like this particular neighborhood is selling to investors because everything’s getting renovated over there. The…


Craig Fuhr (36:24.364)



Jack BeVier (36:25.809)

The algorithm doesn’t know that, but the algorithm will take that into consideration and you will see more deals in that neighborhood on your list the next quarter because that’s where transactions are happening to investors. So anyway, so the, so you actually do see in the attribution, you’ll see all the deals that happened, all the ones that were on your list, all the ones that weren’t and of the ones that were on your list, you’ll see which ones you got and which ones everybody else got. And you’ll see the name of the guy who beat you, right? Like you’ll, you’ll.


Craig Fuhr (36:53.181)

Oh wow.


Jack BeVier (36:55.765)

You mailed that person and then you go look into your CRM and say, all right, did we get a call from this person? Right. Like, did they not like our piece or was our piece not frequent? You know, like, is this, is it something that like, was it that we didn’t get, you can then start to track within your system. And as long as, and to Eli’s point, you have to have good systems set up to use this data properly, because otherwise you can’t tell where along the way.


it broke or like where along the way you didn’t win. But if you do have your system set up, you can tell exactly that because you’ll be able to look into your CRM and say, did I get a call from this person? Did I get an appointment from this person? Did I attend an appointment for this person? Did I make an offer on this property was and was my property was my offer accepted or not? And then you’ll be able to and then and then you’ll be able to see Oh, look, I offered 120 and it sold to this other guy for


135. I got outbid. Wait, I made an offer on this property for 120. And it’s sold to this other guy for one Oh, for one Oh five. Like, what, what the hell? Like my sales guy is not doing follow up. Like he got, he made a better offer and it sold somewhere else. And that happens. And that happens all the freaking time. Uh, and it’s, and it’s in, you know, generally that, that is your like indicator of lack of follow up, right? Like the other guy just kept following up with him or was better at building rapport, right? That’s a sales guy issue.


Craig Fuhr (38:10.095)

Oh, absolutely.


Jack BeVier (38:21.209)

versus an appointment setting issue versus a piece issue, versus the direct mail issue. And that attribution is super powerful because you’re always getting that availability of data. And frankly, it’s great from a motivation point of view because most investors are competitive people, I imagine. And indeed, it gets my blood up when I see myself losing to these other folks, especially when they’re like,


Eli Fisher (38:45.879)



Jack BeVier (38:49.805)

not all, you know, if, if it’s not just because they outbid me, um, you know, the outbid me, you know, ah, fine. You know, somebody to chase the money and they closed, you know, good for them. Um, you know, did I offer the right price, you know, like maybe we underbid it, you know, like you can’t go in there and low ball, you know, you’re, you’re still a competitive market, you know? So it helps you, it helps you constantly refine your machine, um, to have this level of, uh, well, of attribution.


And so that’s like, I thought when you guys added that feature, I don’t know what it was four or five years ago, three, four years ago, that was like a game changer from investors ability to really get good at this thing and not just be sending mail into the ether and hope that, you know, hope that your response rate is good and hope that your sales guy is talented enough, which is what a lot of guys are just operating off of, right, just winging it.


Eli Fisher (39:43.082)

Yeah, it allows the investor to ask a better question. Okay, by publishing the results and saying, okay, we got in front of this many deals. You know, one of the things that I ask investors when we’re doing a consult with them before they’re client, I’ll just ask them a real simple question. I’ll say, hey, in the last 90 days, how many properties did you market to that sold to a competitor that never contacted you? Do you know?


And in the time I’ve been doing this, not a single person has been able to answer that question. And then generally what they’ll respond with is, Oh, well, we converted this. I’m like, really? Well, how do you know that number is accurate? Because really what, if you think about what they’re measuring is it’s just their in-house conversion, what came in that they’re seeing, they’re not actually measuring what happened against the market. And so, yeah. Yeah. And so if you think about it, yeah, exactly.


Jack BeVier (40:31.549)

in the world, right? How do you know that’s good? Like, oh yeah, you bought some houses. Is that good? Like, you know.


Craig Fuhr (40:31.666)

Yeah, right.


Eli Fisher (40:38.87)

Yeah, that’d be like if Jack and I were stock brokers. Let’s just say this last month, we got a 10% return. Jack and I are like, man, we’re good, we’re good. But without knowing it, the market actually went up 30%. All of a sudden Jack and I, you’re not so good. Ignorance is bliss, unless it’s your own money. And so the point of the attribution is just, you know.


Jack BeVier (40:45.893)



Jack BeVier (40:53.929)

It looks so good.


Eli Fisher (41:02.834)

It helps you ask a better question because you can say, look, I got in front of, you know, 1200 opportunities in this quarter. How many of them actually did we engage with? Cause then you start to understand, okay, well, that’s how good my marketing is actually converting. But it also, it starts to, if you think about there becomes a layer of accountability because if you know how many deals you actually got in front with your marketing.


Irregardless of if marketing closed or not, your marketing guy can no longer come back to you and say, well, Craig, you know, the list was no good. The list doesn’t work, it’s a bad list. It’s like, really? Well, there’s 1200 opportunities here and only two people called us off that. Maybe you’re not so good. And then we look at the downstream effects of that. You know, I think, you know, people always say, well, where’s the largest opportunity? And generally where I see a significant amount of loss is actually in the followup stages. So think about this, you know, marketing did their job, right? They got the person in.


Jack BeVier (41:34.994)



Craig Fuhr (41:41.866)



Eli Fisher (41:57.43)

But all of a sudden, this deal that you’re still marketing to sold a month and a half ago, and it actually sold for less than what you offered happens all the time. So you start to see how much actually fell out at the acquisition stage. And then it allows the operator to go and


Jack BeVier (42:12.145)

Especially like appointment appointments from like eight months ago. You’ll see appointments from eight months ago that all of a sudden transferred last month and you’re like, what the heck? And it’s like, oh yeah. You look in the system and sales guy hasn’t followed up in the past four months. Right. He, he followed up for 90 days, right? He gave, he gave it the old college try and then he stopped and it transferred four months after that. And that’s like a thing. Like it’s a thing in the industry. Yeah.


Eli Fisher (42:16.386)



Craig Fuhr (42:34.866)

happens all the time.


Eli Fisher (42:37.438)

And you look at the notes and the acquisition guys said, well, you know, Tammy’s crazy and has 17 cats and says she wants $1.2 million for a $98,000 house. It’s like, okay, Tammy probably is crazy and does have 17 cats, but she also sold it to her property for 45,000, you know, a month and a half ago to a competitor of yours. So let’s, let’s get down.


Jack BeVier (42:58.481)

Also, also it’s great from like, you know, you, you look at the competitors, you see the other, you know, you see the other active people in your market. And you, it also helps you to understand like where, how are they beating you? Right? Like, you know, all right, I got beat, but it helps you trace how you got beat. Right? Like, did we, you know, did we get the appointment there? Is there marketing better than us or, you know, in that, that they got, they got the appointment and we didn’t get the appointment. So like, maybe we need to be doing more.


on the outreach side. Um, and then I mean, you know, markets are small, right? Like everyone knows like what their competitors are doing from like, who’s doing direct mail versus television versus texting versus cold calling, right? We’re getting the damn texts ourselves. So you, you can start to see like which mixes, which marketing mixes are, you know, if you’re, if you’re, you’ve got one competitor who’s just kicking your butt.


just rip off what they’re doing, right? And rebrand it yourself. Like don’t re, you know, just reinvent the wheel. Like just go do what they’re doing to give yourself a shot. But without that, you’re just like, I don’t know, they’re doing something over there. We, you know, I don’t know, but they’re doing something. So this really gives you the visibility.


Craig Fuhr (44:07.738)

Eli, can you hang out with us for a second episode? I’m sure there’s a lot more stuff I want to dive into as well as Jack. It’s been a great conversation thus far. I would encourage people to check out the next episode. We dive into some more stuff with Eli Fisher from Audantic. Thanks for joining us on this one. It’s Craig Fuhr with Real Investor Radio. We’ll catch you on the next episode.


Eli Fisher (44:14.114)

Yeah, yeah, certainly.

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