Posted on: February 24, 2022
WI 894 | Real Estate Marketing Data


As technology evolves, people find various ways to earn a profit. It’s apparent everywhere, especially in real estate. The data is integrated to its fullest potential as businesses curate the actionable information presented by their constituents.

In today’s episode, the co-founder and CPO of Batchservice, Ivo Draginov, shares his take on the prospects of real estate marketing and how advanced the industry is, and the expectations they have as these innovations multiply.

Join us on this episode and discover what marketing has been successful throughout the years and how data has granulated into the ultimate puzzle for tailoring properties.

It’s the Data Stupid – Looking into the Future of Real Estate Marketing Data

Episode Transcription

I am here with Ivo Draginov, the owner of BatchLeads. How are you, Ivo?

Awesome. It’s great to be here.

We’re having a discussion about what are the best data sources, the best ways, and the best technologies to get the biggest and the best wholesale deals in 2022? This is going to be a free for all. Let’s have a conversation about this. In my world, you are the smartest guy when it comes to data. You are in this all day long. You have a whole team of people that are behind the scenes that are putting together the technologies that are advancing our business. What are we looking at in 2022? What exciting things are going to help us find better deals?

I’m glad to be here. To answer your question, technology as a whole is progressing a lot. Everything, from 2015 to 2017. When I first started wholesaling, I had to use 22 different systems at one point to be able to get a deal. It has changed up until today. Wholesalers probably cut that down to 6 or 7 different platforms. They use DocuSign, lead generation platforms, and CRMs.

What’s going to make a lot of difference going forward are platforms that are able to cut down on processes that people follow. It will be able to solve a lot of the manual processes and do a lot of the legwork. People may utilize virtual assistants. Package it up in a product that makes sense for the wholesaler, the use cases that the wholesalers have, and the way that work progresses from finding a deal to signing a contract, and then flipping that contract. The platforms that are able to cut down on the steps needed and the manual labor of these are going to shine going forward.

Is it like operations? Are we talking about the cleaning of Excel files? Is it signing documents? Is it pulling these lists and making sure that if they are already in your system, it will notify you? Tactically, if somebody is just getting into this business or just starting, we could start this business with a pad of paper. We could walk the streets, find ugly houses, write down the addresses, and probably get their phone numbers off of the internet, True People Search, White Pages or something like that, call them up and start finding some opportunities. Do you think that as you advance and build the business, technology is going to make it easier and you need fewer people?

In general, businesses are going to need fewer virtual assistants. As we started, we had X number of virtual assistants processing lists, doing a lot of manual data calculations, being able to triangulate the homeowners, and getting lists from the city. Converting PDF files into Excel was a mess. Imagine a platform where you log into it and it tells you, “These are the most likely properties to sell in the next 6 or 3 months. Here are their phone numbers. Pick up the phone and give them a call.”

It’ll be easier. You won’t have to have a virtual assistant go into that list, pull that list, and put it somewhere else to make sure that somebody calls it, texts it, mails it or whatever else. It will be a click of a button. Is that what you are seeing?

The ultimate goal is for that to happen. How many companies or platforms are able to achieve that is going to be different. The platforms that are going to advance where we are today from where we need to be in the future are going to be the companies that invest in machine learning and AI technologies. They will be able to predict what properties are most likely to sell based on historical characteristics or historical patterns. You can apply machine learning to a lot of skip tracing and finding people’s contact information. There are phone number databases that you can be able to put into a machine learning model.

WI 894 | Real Estate Marketing Data

Real Estate Marketing Data: What’s going to really make a lot of difference in going forward. Go to platforms that are able to cut down on processes that people follow, be able to solve a lot of the manual processes.


Let’s say I have ten phone numbers for an individual. Whether it’s 3 or 10 numbers, it doesn’t matter. I have all of these phone numbers and one of them is an invalid number. The other one changes carriers four times. One of them has been stagnant, so no changes are being made to that phone number, carrier, and things like that.

By that simple example, you can start having more understanding of which phone number is the right phone number for the contact. The more platforms that are able to automate that, the better. You’re in the process of wholesaling and trying to figure out what deals make sense for you to buy. You want to be able to try to avoid doing all the legwork to be able to come to that conclusion.

There are 400 other data points that you can look at that will show the characteristics. To boil this down in my simplified brain, machine learning is going to be able to quickly look at the characteristics of people that have sold their properties. They sold them to a wholesaler or investor at a price that’s much lower than the rest of the neighborhood or surrounding areas. It will look in and see what was the characteristics of this seller of this property. It combines it together so that you can get the most likely people that are going to sell at a discount. That’s bananas.

As a company, there are about 200 different fields or attributes on a per property basis. Everything as far as roofing, what the roof is built out of, grading even, besides normal information like bedrooms, bathrooms, square footage, and things like that.

Companies like Batch go in and you buy the data. You’re spending a ton on this data and then it all comes in. With the amount of data that you’re using, what percentage of that are you showing on the platform?

It’s about 50% to 55%, or maybe 60% on the high end.

You’ve got another 40% of characteristics once the machine learning comes in. Now we’re getting into a lot of data points. It’s tough to go and filter through too many data points. I’m sure everybody knows that if they’ve ever pulled a list, the more filters you put in, the smaller your list.

We haven’t exposed some of the filters because it doesn’t apply maybe to the wholesale industry. There’s no use case for some of the fields that we have. I will give you an example. From a consumer demographic standpoint, there are more than 400 different fields that we have as far as, are you a smoker? Are you a gambler? Do you own pets? What magazines do you subscribe to? What religion? All of these things are available.

You can apply machine learning to a lot of skip tracing and finding people’s contact information.

Imagine a machine that you feed all the data in, and you’re talking about 600 to 700 different data points. You say, “These are the twenty houses that were sold in this area. Can you try to predict the next five houses that are going to sell based on those twenty property characteristics?” You can combine that with consumer demographics.

Eventually, the machine algorithm is going to start picking up like, “There’s a mortgage refinance that happened. There is a line of credit that occurs at that point after a certain amount of time. That person is also a gambler. It’s a low-income type of family.” With that information, you can start predicting. Everything is based on the scoring model. You can have everything from 0% to 100% predictability of that event happening. In a nutshell, that’s machine learning that we are starting on working on heavily.

Ninety percent of the deals that we do in my company are tired landlords. They are landlords or they are vacant properties. How does that work with people that don’t live on that property? Is there a way to find out if the landlords are ready? The straw is going to break the camel’s back here soon and they’re getting ready. Is there anything besides driving around, looking at the property, seeing it, and being like, “That property looks pretty rough?” Is there any way to use that data for tired landlords? As you’re talking, I’m like, “That makes sense.” A lot of the properties that we buy are not owner-occupied. This isn’t their property. It’s a property that they have less emotion with.

It’s the question in the context of machine learning predictability like, what is that going to look like?

If the tired landlord is ready to sell.

It’s simple. How many properties does a person own when they buy them? All of the data comes into play. You can start looking at satellite photography. There are ways of detecting the wear and tear of the roofs, how filthy the backyards are, and empty pools. You can do all of those from satellite imagery. You can get granular and spend a lot of money doing that.

I feel like we’re going to be in a movie. There’s always something looking at the data.

Data is the most expensive asset in the world now. It’s only exponentially multiplying.

What happens to the data that you collect, put together, and then organize for the platform? Where does this data come from? Is it all over the place? Are there companies that go, “You know magazine subscriptions, why don’t you sell us your data?” “You understand credit scores, why don’t you sell us your data?” They then collect it all.

WI 894 | Real Estate Marketing Data

Real Estate Marketing Data: The way the work progresses from finding a deal to signing a contract and then flipping that contract, platforms that are able to really cut down on the steps needed.


We have an army of virtual assistants door-knocking. For the property data, for instance, there are two main data compilers, CoreLogic and Black Knight. You then get into the data aggregators, which are companies that license a lot of the data and they aggregate. You can think of a company that gets Black Knight or CoreLogic data, it gets both of those data sets, combines them, merges them, and is able to have a single record of all of those records that both companies provided. We aggregate data from multiple data sources and we’re trying to compile it to come to a single answer.

That’s what you guys are working on.

We’re working on machine learning. We already did data aggregation, even a lot of the skip tracing and property data. We combine multiple data sets. Maybe vendor A has data set A versus data set B, and there’s a 20% overlap. Now we have 180% more data because 20% there is an overlap, for instance. We’re able to do that. The large counties are usually pretty evenly distributed because they have more resources on how they process and distribute data.

There are some certain small counties. Let’s say CoreLogic is picking up or has better information in these counties and is able to get information quicker versus Black Knight. Maybe they are better on the West Coast. That’s not a fact but just an example. When you are able to aggregate this data, you can see the best of both worlds.

What does aggregate mean?

I’m getting two data sets. I’m merging them. I can see what the common attributes are and then I can have the best of both data sets.

This is an explosion in the last few years of being able to put this together and get a good idea of which properties we should spend time with and which ones we should go after. I’m old-school. The first twenty deals that I did were driving for dollars, calling them up, and putting together the deals. That’s great when you’re starting out. As you’re starting to expand and you start making this a business and hiring enough acquisition managers or salespeople, they need to constantly have new opportunities.

You’ve got to have different marketing channels. We do cold calling, texting, pay-per-click and referrals. In there, it’s going after the most likely people that need our services. We go in, we give them cash as-is. That’s what we do. There’s only a certain percentage of the real estate market, 6% to 10%, that would be open to something like that. You’re saying that we’re going to constantly be working to filter that out. The top of the funnel is 100%. The bottom is 6% to 10%. If we can focus on that, you’re going to get more out of it. You probably don’t need to have massive teams unless you want to go nationwide or multiple markets. The profitability is going to go up.

Everything is going to become more efficient. The top of the funnel is 155 million properties. If you narrow that down, what if I could give you that 5% of properties that are the most likely to sell? Instead of you even looking for building out your lists like tired landlords, vacancies or out of state owners, I can say, “Brent, here’s 1,000 records that are the most likely to sell. Focus on these 1,000 records.” You don’t have to send hundreds of thousands of even text messages monthly and have teams of ten people cold calling. You may have a single guy manually dialing and even texting from his cell phone. That’s what it might turn into.

My team hates this analogy because it’s silly. There’s a huge ocean. There are only certain parts that have fish. Fishermen figured this out, they go where the fish are. There are 155 million and there’s 5% that want to sell that we can go after, that truly are in distress, that truly needs some love, or they’re going through a tough time and they need to get this stress off their shoulders. We can focus our attention and be more profitable.

Data is the most expensive asset in the world today, and it’s only going to be exponentially multiplying.

You are going to be more profitable. Some of the companies that are investing in these things are going to succeed and the rest might be left behind. There’s a lot of change happening across not just our industry but in the world. Look at the whole text messaging situation in 2021. It has impacted everybody from Uber to everybody.

I want to switch tracks a little bit there. I’m excited about machine learning. I’m excited about getting that filter down. I’m excited that I don’t have to do the work on that. Non-disclosure states have been a hot topic. How do we comp in non-disclosure states? You’ve got twelve non-disclosure states, Alaska, Montana, Idaho, Wyoming, Utah, New Mexico, North Dakota, Kansas, Texas, Missouri, Mississippi and Louisiana. I pulled that from a Google search. The Carolinas are in there too but they didn’t pop up.

What’s going on with non-disclosure? The state voted that once you buy a piece of property, it’s not on the public record. Why would it be? What’s the point? Why would we disclose if somebody personally paid for something? We don’t disclose what we paid for our cars. Maybe it’s in some database somewhere. Maybe I can go to the county. I don’t even know. Every property owner knows Zillow, Redfin, and It’s on there. How do you comp in non-disclosure states? What’s the best data to pull so that you can get accurate? I have a follow-up question to that.

It’s going to be naturally more difficult to comp in non-disclosure states. Everything is a matter of public record except what the property is sold for. Why and when they did it, I don’t have the experience to talk about it. Nothing has changed. Those twelve states that you listed have always been non-disclosure. The way you comp properties is going to be more difficult.

What we do in our platform is triangulate the sale price, the active price, the pending price. We also have all the information from a recording standpoint when the title company records it. There is the loan type, the loan amount, the interest rate for the loan. It’s proprietary information for us but it’s not rocket science. You take the loan amount and the loan type, and you’re able to come up with a good price range of what the property is sold for. In those states, the sale price that you see in our platform would be an estimated price of what it’s sold for.

For FHA mortgages, the loan amount is 3.5%. With the loan type of FHA, you can triangulate back to the actual property. The loan amount that says $100,000 is $135,000. It is what the actual price was. It becomes a little bit more difficult comping what cash properties are sold for because the cash doesn’t have the loan amount. It’s going to be a lot more difficult comping cash properties. Also, commercial properties are going to be naturally more difficult to comp in those states.

Has the sold data ever been available to purchase in these non-disclosure states since you started buying data? It’s not off the MLS?

Nothing has changed in the past few years. There are MLS aggregators as well. We have several data providers that pull out MLS data.

WI 894 | Real Estate Marketing Data

Real Estate Marketing Data: There’s a lot of change happening across different countries.


What’s an aggregator?

They compile a database. An aggregator is like, “We’re going to go at least 50.” It’s actually more than 50 because each state has multiple MLS providers.

Is an aggregator somebody that scrapes the data from somewhere?

Not necessarily. You can scrape it. You can license the data. You compile it into a single delivery method. Companies like or Zillow would aggregate a lot of this property from local MLS and they will display what you see on their website. We do the same.

I coach a lot of students in these markets. Texas is the biggest one. You know when a deal is a deal. It’s when they’re tight when people are like, “I can’t find the comps.” Maybe it’s a little bit rural and there are not a lot of close active pendings and solds that have been compiled and triangulated on BatchLeads when you’re looking at it. A deal is a deal. You know when it’s close.

What I suggest to anybody is if you have a question, call an agent that has an active property or a pending property or has sold a property in that area. Even they don’t say what it’s sold for, you can call them up, “I’m buying this house at 1212 Banana Street. It needs a ton of work. Do you mind looking at this and see what you think the ARV would be?” It’s their expertise. In these non-disclosure states, you need to have an agent as a best friend. In this business, they should have somebody that can give them a second eye on if it’s a goofy one, but 90% of the time, you know it’s a deal.

If you’re in the bigger city and some of these non-disclosure states, the prices are going to be within a percent of. A percent of is not going to make that much of a difference when you’re calculating your maximum allowable offer.

Is there ever going to be a day where technology gets good at comping? There’s the estimate. You’re scraping and you’re pulling, and there’s probably something there. Is there any time that we can get an appraiser’s brain or an experienced wholesaler’s brain, plug it into the computer and say, “These are the characteristics. The closest ones that we want to look at in this area don’t cross this line,” and get true solid comps.

That’s back to AI and machine learning. What’s an ARV? Are you going to remodel the property fully? Are you going to change the carpets? What value are you looking for?

Everybody’s investing heavily into machine learning.

It’s tough because some properties are better off being rentals and cashflow. Some properties are better off getting fixed up and sold. Some of them are getting fixed up and rented. You’re right, there are different exit strategies. It would be great to have a filter for each one.

Even me and you, we’re both having comps to thousands of properties. You will get comps with a different value than I would comp in a lot of situations. I remember I’ve done some skip tracing in some South Texas locations. It was bizarre because I ran through twelve different vendors. The list of the city was on the border of Mexico. It was a bad hit rate, coming up with phone numbers. All of the vendors.

That’s life. Technology is going to allow us to keep more money, have smaller teams, be more efficient, go after the most likely properties, come up with better off-the-bat valuations so that we can look at it and we don’t have to get too nitty-gritty with it, but it will. This comes from machine learning. This comes from taking all of it, aggregating the data, spitting it out the other end, and putting together solid lists and values for the property. Is that what I can expect in 2022?

That’s what you can expect coming up from our platform. In general, in this industry, everybody is investing heavily in machine learning.

That’s what I love about you, you’re not the sales guy. You’re not like, “For everybody else, it’s bad.”

I’m trying to provide some value and things that I know. I want to say that we provide a solid service to everybody. There are a lot of different companies and different industries that we look up to as far as what they’ve done and the way they approach a problem. With a lot of these data and machine learning, some industries are a little bit ahead of others. Zillow, with the company size that they are, is investing heavily. They refactored their Zestimate model.

In the near future, you can expect like, “These are the properties that are most likely to sell. These are the accurate percentages that these properties are most likely to sell based on historical criteria.” Maybe we’ll automatically load them into the dialer for you and have somebody put on a headset and go through it.

That opens up the box. I’m sure people reading this are like, “These big companies have big budgets. They can spend a lot more on machine learning.” You’ve got Zillow that was buying properties and not anymore. What about the Opendoor? What about the Offerpad? What about these other companies that are going after that? They’re going after realtors. They’re going after the realtor market. We’re going after the properties that need a lot of work. We’re going after that 5% that is rough.

In general, not even wholesale specifically, some companies can do that. They probably have the budget because that’s their expertise. They might as well find properties somewhere else or utilize somebody else’s technology that the company specializes in machine learning. It becomes more of a business problem and specialization. If they should go into industry, can they?

WI 894 | Real Estate Marketing Data

Real Estate Marketing Data: Successful investors and wholesalers should just utilize multiple marketing channels.


This is when you sell BatchLeads to Zillow for $200 million. I see what’s going on here. You’re a smart guy. This is great.

$200 million is going to be enough.

A lot of people have an ungrounded fear of these big companies taking up market share or whatever else. Maybe they did to people that were going after cleaner properties. If people were selling for convenience, they hoovered all that up. For the rest of us, our business is built truly going after the most distressed property owners. I don’t see them going after that and looking at that market wanting to put in a ton of effort. The one thing blocking them is construction.

It’s localized. With the iBuyer, there’s nothing new. A few years ago, there’s a lot of hedge funds that were buying in Phoenix. I remember a couple of companies that were buying thousands of houses a month. They bought a lot from me. I sell the properties to them. That was a few years ago. It’s the same case today.

With the iBuyers, I’ve made a lot of money off of them. They’ve been fantastic. What else do you see from a marketing standpoint moving forward? I remember when text blasting came out and I was like, “What can you do?” There were barely any platforms. Everybody then figured it out. What do you see coming up from a lead generation standpoint? List is great. Reaching out to them, yes. Calling them, texting them, door knocking them, mailing them, DM-ing them or something like that are great. What do you see is the next marketing channel? What are you seeing out there?

I don’t know exactly what the next marketing channel is going to be.

There’s not like a text blasting thing coming?

Text blasting 2.0.

Like DM-ing, can you DM? I feel like that would be the next platform. That would be phenomenal. A lot of people are DM-ing more than they are texting.

Even Facebook has a spam box that I sometimes check. Every four months, I find messages that I should have seen in that spam box. Successful investors and wholesalers should utilize multiple marketing channels. Going back to texting, are you going to send out that 10,000 messages a day? I have before. Today, not so much.

For everybody reading this, if you’re getting started, focus on one marketing channel. Don’t listen to Ivo. Ivo is talking about companies with a lot of people. If you are starting, focus on one.

Finding the contact information for cash buyers in general is harder than finding the homeowners.

Fair point. You’ve got to get going. Otherwise, if you throw too many things, it’s too much, especially getting started. You’d be able to continue to text in a smaller capacity. Figure out how to filter down those leads. That’s where some of the platforms and how they aggregate data and how they do some machine learning initiatives will be able to point you in the right direction as far as what properties to market to. Instead of texting 1,000 properties, you can focus on the best 100.

It’s the same for cold calling. Cold calling is a little trickier to be able to spam detection rates and things like that because it’s conversational traffic. For Verizon, the big carriers can detect text messages because it’s written content. Those can be easily spammed and removed completely from even reaching the recipient. Cold calling is going to continue working. You can do all of the PPC stuff as well.

What about cash buyers? What advancements are going to be made for finding cash buyers, communicating with cash buyers, vetting cash buyers?

One of the things that we’re releasing is a cash buyer profile. You’d be able to view a lot of information when you click on a property about the cash buyer, how many properties he’s bought, all the equity that he has, potentially free equity that they have, buying power in the past 3, 6, 9, 12 months or whatever you’re filtering. Also, the types of properties that they buy. Additionally, you will be able to also search for those buyers. You’d be able to search buyers that have about three or more properties with some type of characteristics. They bought it with cash or finance them.

Finding buyers goes back again. I have a property, what are the most likely cash buyers to be able to buy that property based on buyers that have bought in the past twelve months? Which buyers have bought properties in this area in the past twelve months that are 3,200 or 1,600 square feet? Can you give me those cash buyers and provide the phone numbers for them?

When does that come out?

It will be a surprise. Cash buyers, in general, are harder to find as far as contact information. Especially with seasoned cash buyers, they will probably have an LLC set up, probably with the trust behind it. That’s not a matter of public record. All of that is private information. Finding the contact information for cash buyers, in general, is harder than finding the homeowners but it’s doable. What we do is we try to triangulate or aggregate the information that we need to. Let’s say you have a property that you own but maybe we’re going to look at your mailing address and see if Brent Daniels owns it or maybe it’s an LLC that owns it. We will be able to pierce through the information and the way you’ve allocated your assets.

WI 894 | Real Estate Marketing Data

Real Estate Marketing Data: It’s really going to be a lot of VR-AR type of a world in the future. So Web 3.0 properties that you can buy and the NFTs, land on the internet are only going to probably go up in value.


I’m going to throw a curveball at you. Maybe you have no knowledge of it, no research on it, no opinion on it. You have a lot of people in our space that are like, “Did you hear about Web 3.0 properties that you can buy and the NFTs for certain land on the internet?” Any thoughts there? Any idea? Is it crazy? Is it smart? Is it silly? Is it genius? What do you think? This is a personal opinion. You might not know anything about it. You might not be in that space. Every buzzword in the world is Web 3.0, the metaverse.

Mark Zuckerberg made it famous.

From a tech guy, from an engineering standpoint.

I’m not an engineer.

I know but you got that brain.

In my opinion, a lot of these things are going to keep going up. They’re going to keep getting more and more popular. It’s going to be a VR or AR-type world in the future. These things are only going to probably go up in value. I haven’t bought any digital real estate portfolio yet. It’s probably a matter of time. It’s a lot of speculation. These things could go up and could go down in the future, long term. There’s going to be a place for them. Which company would you bet on? It’s more of a speculation play. There’s The Sandbox and Decentraland. I know some of them but I haven’t dabbled into virtual real estate yet.

When you do, keep us posted. That would be interesting. I’m going to try to bring on somebody onto the channel to talk about it and that has some experience. I look at it and I don’t know if this is like a dot-com bubble type thing. It’s too early. People are going to get wrecked here. I listen to Gary Vee and he’s like, “99% of this is going to get wrecked.” I don’t know if that prediction is going to be right or not.

Nobody knows. It’s all speculation. There are going to be a lot of ups and downs before things get more stable. Is there a future for it? Probably.

Thanks for joining me, Ivo.

Thanks for having me.

That was awesome.

If you are interested in joining the most proactive group in real estate investing, it is the TTP program, go to Check out the hundreds of testimonials. Check out what the program is about. If it feels good in your gut, then sign up for a call. I look forward to working with you.


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About Brent Daniels

483Brent Daniels is a multi-million dollar wholesaler in Phoenix, Arizona… and the creator of “Talk To People” — a simple, low cost, and incredibly effective telephone marketing program…

Also known as “TTP”… it helps wholesalers do more, bigger, and more profitable deals by replacing traditional paid advertising (postcards, yellow letters, bandit signs, and PPC) with being proactive and taking action every single day!

Brent has personally coached over 1,000 wholesalers enrolled in his “Cold Calling Mastery” training, and helped 10,000’s of others who listen to him host the Wholesaling Inc. podcast, watch his YouTube channel, and attend his live events…

A natural leader, Brent combines his passion for helping others with his high energy, “don’t-wait-around-for-business” attitude to help you CRUSH your wholesaling goals as quickly and easily as possible!

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