Real Estate Blog & Podcast

Episode 305: Stefan Tsvetkov from RealtyQuant

brrrr method david dodge discount property investor michael slane podcast real estate 101 real estate coaching real estate investing real estate investor real estate tips wholesaling wholesaling real estate Sep 23, 2022

Show Notes

In today’s episode of Discount Property Investor Podcast, David Dodge has a special guest Stefan Tsvetkov is the Founder of RealtyQuant.

Things that will cover in this episode:

  • Walks about what is
  • Who is Stefan Tsvetkov
  • Talking about buying and deals and how people find deals

Service Mentioned:

Transcript Episode:

Welcome back to the Discount Property Investor podcast. Our mission is to share what we have learned from our experience and the experience of others to help you make more money investing like a pro. We want to teach you how to create wealth by investing in real estate, the discount property investor way. To jumpstart your real estate investing career, visit, the most complete free course on wholesaling real estate ever. Thanks for tuning in.

David: Alright guys, welcome back to the discount property investor podcast. This is your host David Dodge and today I got a special guest Stefan Tsvetkov, did I say that right?

Stefan: That's good, that's great actually.

David: Stefan Tsvetkov, I think I'm saying it right, from Realty Quant but Stefan's an investor too and before we started this episode I was telling Stefan you know about the show and about how you know, this is the discount property investor podcast. Let's talk about buying deals and how people can find deals and what kind of marketing it takes to get into deals because as we know you make your money when you buy, you get paid when you sell, and if you get a deal, you have a lot of exits. So I'm just excited to have Stefan on today to tell us about data-driven you to know methods. Stefan, welcome to the show.

Stefan: A pleasure to be here and thanks for having me.

David: Man I'm happy you're here, I'm really happy you're here. Tell us about Realty Quant.

Stefan: Yeah so Realty Quant is really kind of my mission for like data-driven investing industry so uh we published like different market data, market analytics, and also it's an investment company, it's also my investment company at the same time so it's really like using in-house data models to find basically discounted properties like that's the- that's really what everybody wants to find right? Inefficiency is in the real estate market. So what are some ways? So for example, so when I started like a few years ago as you know, exclusively residential investor and I've been transitioning into the commercial space but what I would do is so I'm in New York City so I would go to just like for the benefit of your audience, so I would go to you know, various online sources and I would pull let's say about 6000 multifamily like small multifamily properties in the New York City area so that- when I say pull it would mean you know, at the time it would mean like web scraping various resources and that's on market and then there's for the off-market component that is very important but really on market scraping like thousands and thousands of listings. Now most investors would say okay that's- you know there's no inefficiency once it's on market, you know it's not a discount, it's already on market you know agents have priced it in. I have found like amazing deals actually like that because you have like 6000 multifamily and you're looking at the whole distribution and you take the zero-point- top 0.1% you know, you're gonna find something good and especially in like some parts like in markets that are more inefficient, for example in my area that happens to be upstate New York so I did like different strategies, I did condo conversions by New York City so that's something I still do now, so condominium converter so that's taking a multi-family splitting it in condominiums and kind of- so the multi-family is not discounted but there is this kind of value at that if you just legally separate it, the value- the sum of the components is bigger than the whole you know, this kind of perspective, so that's one strategy. With data-driven investing, I discovered deals such as you know a four-unit that happens to have a fourth auxiliary unit.

David: Okay so here, I gotta stop you for a second. You got a lot of knowledge and I'm following you but I don't think that the audience is per se so let's just- let's slow down a little but I can tell you have a wealth of knowledge and I'm happy that you're here today 'cause we're all gonna learn something. This is great. So you say data-driven, let's talk about that. What does that mean to anybody that doesn't know anything about real estate or data?

Stefan: Data-driven means sort of investing- means sort of like trading properties in a way like similar to the stock market. So you would kind of- you would pull, you would write scripts perhaps or have like people on Fiverr or whatever write scripts for you. They would pull all the property's data, you would have them fully underwritten for you, have all your cap rates, cash on cash return, perhaps IRR that you're gonna have, you know, what returns are you gonna have on that property potentially and you would do this at scale-

David: Okay that's my next question. So you're doing this at scale and you're building a list, right? Then what are you guys- so then are you stacking different levels of motivation by using the data to basically say hey you're gonna have a higher likelihood of buying this property here or these properties here versus just trying to mail the entire list?

Stefan: That is- your speaking of market kind of direct mail like lists and kind of like your point like motivated finding, motivated sellers.

David: Yup yup.

Stefan: That is a viable strategy as well.

David: But you're doing it on market with this?

Stefan: That part is its own market.

David: Oh my goodness okay okay hey I'm learning something new. I don't typically talk to people that do- that have a bunch of on market strategies so I'm very interested to hear more about this. So by you doing- pulling all this data and doing it at scale, how does that help you market? It doesn't, does it? Or does it?

Stefan: I mean so for so let's split your point like on-market and off-market. So on market, you don't need to like send direct mail right? Cause there are already properties on the market, presumably it's not discounted, right?

David: True, I guess you don't have to spend money on marketing to generate the lead if it's online- if it's online already. Okay got it, so then you gotta reach out to the agents to make offers and start the line of communication.

Stefan: Exactly so that's like- I mean it's like the straightforward way I agree. It's just concept there, what gives you the advantages by doing it data-driven, we can actually discover like the top .01% let's say something like that so that's kind of the way you do on market because to your point if you don't market the standard way, you just open Zillow and do whatever, you're not gonna find any deals but if you pull like 6000 properties from Zillow then it's going to be a different approach right? So it's different you know, it's a different mechanics there and so this is yeah I mean that's- this is one approach. For off-market, I've done it in the commercial space, one can do things such as I'm not sure if you have any commercial investors in the audience or you want to speak to the residential only, I just wanted to make sure.

David: You know, we do, we have all the above.

Stefan: Sure yeah, so for commercials what is the data-driven off-market method for example there? So one can pull rental listings data from places such as and model for thousands again the properties which ones have a bigger value add, that's an example of a data-driven method. So then you kind of- then you're going to target your mailing list not based on what you just put a single county and you mail that county every single property there, you're actually gonna send mail to only properties that have shown potential to improve their NY to raise the value of that property and you're going to do it perhaps across geographies. You would also do like sort of a market analysis where it can do like in a data-driven way, you can run like appreciation forecast with various like forecasting models for example and again like to some of your audience that you're going to say okay they're not gonna do like- they're not gonna do this well they can use like different vendors and kind of like putting pieces together, you know be it through my company or be it you know higher like a contract- you know contract labour online and so forth and so on but this is really what I do, I mean this is really what I did at the time and I found many deals like that and many different strategies as well. And like some of the flexibility of that is you can actually tune it to let's say five different strategies and you can kind of have all your properties underwritten and ready to go you know, you just have it in a very you know kind of streamlined, easy manner. And so in the commercial space, like in that example, so that's much better- if you have something like prospect now but if you do this deeper analysis of actually modelling the value out of properties, well that's you know, that's like a better list, right? It's kind of similar like to your point if you're looking for motivated sellers, it's just more focused on the NY perspective rather than the seller's motivation in this case. It's more like focused actually, if you're sending direct mail to a building that has all its rent at market perhaps it's not particularly useful so-

David: Makes sense.

Stefan: Yeah so that's like this is- these are some examples. There are various other things such as using value estimates and again just speaking like I'm a finance guy, I'm financial, so speaking as a-, for example, things like estimates we take, now that's an automated valuation. So those are using like various- one can subscribe to vendors using various tools for automated valuation can foster finding discounted properties. Now I have done this like upstate, so upstate I would run my own in-house Realty Quant automated valuation and I would pull like let's say 1000 properties upstate and because that market is illiquid so they could be on market but because the market is illiquid, you wanna go on the market they're discounted sometimes.

David: Oh yeah absolutely.

Stefan: 10, 20% discount in some cases relative to where they should be. It's just the agent price incorrectly or something like this. But again like to be able to do this on markets, again in that discussion you have to do it data-driven. There's no other way to do on the market because your point it's- I mean like you don't expect to particularly find many deals right? And it's really the way is to put like the whole distribution of properties and kind of look at you know at the tail of the distribution at the very best ones and so that is like- that is another perspective. Then there are various other kinds of technology pieces assisting this and you know there are things like how do you evaluate the conditional properties? Now if you wanna do it in a data-driven way to discover deals, now it's very important to condition and so there's like various artificial intelligence machine learning tools one can use to kind of score condition, a company in New York called Foxy AI, for example, does it and again this is just perspective of where you know to some of the techier parts of your audience, they may want to okay we're just gonna do our own Python scripts or something. I know personally know a few people who could do it, they would just write their own Python scripts and kind of try to find deals you know, try to find deals.

David: Yeah most of my listeners don't know about Python scripts because they're looking for yeah they're just trying to get into deals. You know this stuff like the back of your hand because this is what you do and I love it. So hey let's take a shift here and have some fun, you have mastered data-driven, you're talking awesome stuff that is kind of going over my head but I'm glad because that means that you know it, you got this stuff, you have a passion man, you are on a mission to add value through education investment technology and analytics man. It's awesome. So what- how has this data-driven approach helped you? Tell me about your real estate investing career and portfolio.

Stefan: Yeah so my career- so I do like short term equity gain so flips basically in the New York City area, uh so I do-

David: Are you doing multifamily ones or single families or commercial? What kind of properties are you targeting?

Stefan: More multifamily so like three to five units typically is where I'd go.

David: Okay sure.

Stefan: So let's say my current projects just to give an example, so I have a four-unit condominium conversion in downtown Jersey City so it's like 1.65 million. It's a 4 unit but it's kind of expensive right? Cuz in New York City and so that's- that one I discovered it again like with data and it's just the way I discovered it is I try to you know, I pull the data for what condo prices are and then pull like what the multifamily price and try to find the discrepancy and condominium conversion again to your audience is really just legally separating the property, the multifamily, into condominiums and uh it's a viable strategy in like primary markets, you know places like New York City and in this case downtown Jersey City and so forth but it happens this deal happens to have a really nice 4 bed 2 bath single-family house in the back property and so we are actually hiring kind of a high-end contractor to make it like super nice with like custom finishes make like an amazing single-family house in this primary market and we're targeting probably at 1.2 million for the back house alone on a 1.65 purchase for the whole property. And so that's like a deal I discovered through data. Another condominium conversion in New Jersey Weehawken so just 3 family 3 bedroom apartments again like make them in to- make it into condos. I've done deals like where I mentioned I purchased a 4-plex upstate and they made it- essentially did zero renovation, I just added you know just 5 units were not made legal with the town. So it happened 5th studio apartments so essentially the valuation method changed from residential to commercial, the property value kind of doubled through that so that's like another example. I've done a few like just rental properties that I still hold in New Jersey like Hudson County New Jersey so it's really like small multifamily basically, it's what they've been doing and I've been transitioning to the commercial space and so I was bidding on a 48 unit in Iowa more recently with like one of the bigger syndicators from New York City as a partner. So that was a pretty good deal like IRR seems to be very high but again I have like some uncertainty around the valuation there, I felt it's a little bit priced high and so I kind of- I think I'm gonna pull back from that.

David: I like it, man. Well, you're- you got your hands in multiple projects, you're making moves, you're doing deals and you're doing this all in a data-driven approach man. I think it's awesome, it's very cool. If somebody wanted to connect with you or to learn more about Realty Quant, is that something that you offer as a service or is it like a product that somebody could use to help themselves or what is it?

Stefan: Yes so Realty Quants, we offer market analytics currently so we offer appreciation forecasts and downside forecasts. It's actually very relevant to the current time so with the potential prospects of recession it essentially has estimates for how over-under fairly valued every- basically every county in the country so we have data for about 2700 US counties so it's really gonna tell you okay is your market you know super overheated you know or is it perhaps underheated you know, and that's kind of- that's what it does. So we focus on the market side as far as what's published out there for people to use but uh house I do property data driven stuff and analytics as well.

David: Awesome, very cool. How would- how would somebody connect with you, Stefan?

Stefan: Yes so, that's the best way.

David: Awesome, very cool man. Well, I'm super excited that you were able to make this podcast episode with me. What would be you know one tip that you would give to a new investor that's you know looking to get their first deal?

Stefan: Working to get their first deal, I mean I guess house hacking is something probably other people say as well. I did that, my first deal was house hacking a 4-plex in New Jersey. I was pretty happy, I was living rent-free so I think that's a great- that's a great way to get in and get like the best mortgage, the best lender terms, you know, and purchase multifamily this way.

David: Hey Stefan that was my- the first deal I ever did was a house hacking deal. The first three actually so I agree 100%, great tip man. Guys listen to this guy, he knows what he's talking about. Stefan thank you so much for coming on. Guys check out to learn more about Stefan's site data-driven, he's got information on trends in every county. Sounds awesome, I'm gonna check it out myself. Stefan, any parting words for the audience today?

Stefan: No, so you have a great channel so just make sure to check out all your episodes and learn as much as they can, that's all.

David: I love it, man, thank you so much for coming on. Guys there'll be a link in the show notes as well, thanks for listening and with that we are signing off.

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