Podcast Details

Episode 7

Ariel Herrera

Join us as we embark on a journey through the multifaceted world of real estate investing, exploring how extreme ownership and data analytics play pivotal roles in forging success. Our guest, Ariel Herrera, a seasoned investor and data scientist, shares her transition from traditional analysis to a tech-driven approach, leveraging tools like the US Census and Zillow Data Center to uncover hidden gems in the property market. She underscores the significance of demographic and economic data in pinpointing areas with high growth potential. You'll be inspired to hear about Ariel's methods for improving tenant screening and property listings post-pandemic, demonstrating that challenges can become opportunities for enhancement.

Listen in as we discuss the strategic expansion of a diverse real estate portfolio, ranging from house hacking in New Jersey to acquiring international properties in Playa del Carmen, Mexico. We tackle the benefits and complexities of investing sight unseen, the shifts towards favorable tax regions, and the essential practice of working with trusted partners abroad. Moreover, we delve into the operational nuances of managing multiple properties across various regions, the increasing reliance on property managers, and the upcoming integration of innovative software solutions aimed at simplifying investor workflows.

In our final chapter, we examine the power of a buy-and-hold-forever strategy, reduced operational risks, and the advantages of leveraging property for borrowing. I share insights into using cutting-edge tools like Python, Tableau, and GPT for data analysis, making it accessible to those without a coding background. We also look at how technologies such as Rouse AI can automate data collection, and introduce Coffee Closers, an app that aids investors in scouting deals in up-and-coming areas. Whether you're a seasoned investor or just starting, you'll learn the importance of data-driven decision-making and discover resources to help you stay ahead in today's competitive real estate landscape.

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Key Takeaways

  1. Data-Driven Real Estate Success: Ariel Herrera emphasizes the power of leveraging data analytics in real estate investment. She showcases how employing sophisticated tools like Python, Tableau, and GPT for data analysis can uncover lucrative investment opportunities, optimize tenant screening, and streamline property management. Her approach illustrates that even investors without a tech background can make informed, data-driven decisions to succeed in the competitive real estate market.
  2. The Value of Global Portfolio Diversification: The episode highlights the strategic advantage of expanding one's real estate portfolio globally, as demonstrated by Ariel's own investments ranging from New Jersey to Playa del Carmen. This approach requires thorough research, understanding of local markets, and a willingness to invest sight unseen, but it can lead to diversification and potential tax benefits. The use of technology and reliable property managers is crucial for managing such a geographically diverse portfolio efficiently.
  3. Long-Term Buy-and-Hold Strategy: Ariel and the podcast hosts discuss the long-term benefits of the buy-and-hold-forever real estate strategy, noting its ability to reduce operational risks and offer opportunities for leveraging properties in smart borrowing strategies. This approach is contrasted with the idea of flipping or short-term investing, showing that holding properties over a longer period can lead to substantial wealth accumulation and more stable investment returns.


00:00 - Vikas Gupta (Co-host)
This is the Hacking Real Estate podcast, season two, episode seven.

00:05 - Ariel Herrera (Guest)
As an individual, as a leader, you should always take full responsibility of whatever occurs, no matter the circumstance. An example that I have here of how I applied it was after March 2020, I had tenants that were unable to pay the rent, so I had to cover that extra amount since they had lost their jobs. And in that process I was so angry. I was like, oh, why is this happening to me? But then I thought back like how can I make this better? So there was a couple of things that I was able to improve upon by taking extreme ownership. So I learned how to screen better, how to more appropriately look at credit scores and types of jobs relative to the area, better times to list a property, which could really matter the day of the week as well as the season, and then also having more appealing photos. So, even though I was able to rent it out in the past with an iPhone, take some pics I realized that if I could have more appropriately staged photos that are better quality, I can attract better tenants as well.

01:09 - Brandon Hall (Co-host)
Welcome to the Hacking Real Estate podcast, where we dive into the stories of seasoned, hands-on and tech savvy real estate investors. We'll learn the strategies and tools they use to maximize returns and minimize hassle, all while navigating the rapidly changing real estate market. I'm your co-host, brandon Hall, and managing partner of Hall CPA, and I'm sitting alongside my co-host, vikas Gupta, ceo of Azibo. With our combined 15 years of experience in real estate investing and entrepreneurship, we're here to help you up your real estate game. Let's get hacking.

01:41 - Vikas Gupta (Co-host)
Hi everyone. Welcome to today's episode of the Hacking Real Estate podcast. Our guest today is Ariel Herrera. She is a professional data scientist and seasoned buy-and-hold real estate investor. She previously led a data science and analytics team in operations and automation at Appfolio and developed AI and ML models at other companies such as Deutsche Bank, accenture and Calimby. Her passion for data analytics in real estate started when she tried to evaluate her first property in 2019. She soon discovered that information was limited, so she started her own YouTube channel, tech in Real Estate, to share her journey on bridging the gap between real estate and data analytics with Python. Now she is helping real estate investors to find cash-flowing deals in up-and-coming areas with coffee closers. Welcome to the show. Thanks for being here.

02:36 - Ariel Herrera (Guest)
Thank you so much, vikas, so excited to be here.

02:39 - Vikas Gupta (Co-host)
So, in your own words, can you tell us a little bit more about your real estate journey?

02:45 - Ariel Herrera (Guest)
Sure, my name is Ariel. I'm a data scientist based out in Tampa, florida. I started investing about five years ago. At that time I was working in New York City, actually doing a two-hour commute each way from New Jersey, which is not as untypical in the tri-state area. But at that moment I realized, even though I've accomplished what my parents always wanted, which was to go to school, get a great job and then maybe stay in it for 30 years, I realized that just wasn't going to be my path. I wanted to make a bigger impact and ultimately achieve financial freedom.

So, as I was staring at the New York City skyline traveling into work for that day, I decided to research what are ways to achieve financial freedom, and the one that spoke to me the most was real estate and reason being. As I read about many different people over hundreds of years who have pursued real estate with less resources, less money than myself, and I thought if they could do it, why can't I? So, after about nine months of reading books, listening to podcasts and watching YouTube videos, I decided to take the leap of analyzing my own deals and trying to purchase my first property in New Jersey. As I was doing so, I realized oh my God, the data is everywhere. It's super manual to analyze a deal. It's difficult to understand what areas are appreciating or up and coming so with. That is why I decided to create the YouTube channel to help document my process, so that others in the future could learn how to leverage data for their analysis.

04:26 - Vikas Gupta (Co-host)
What data were you looking at for your first deal?

04:29 - Ariel Herrera (Guest)
Yeah, for my first deal I started looking at data from Zillow, just looking at property information. I was looking for a property that I could potentially add value to at a bedroom. So I wanted to find properties where, say, there was only two bedrooms, about 1500 square feet, because in my head I think there's likely some extra space a dining room, maybe a nook that can be converted to a third bedroom. But analyzing these deals one by one in Zillow was just not really efficient. So I had a friend who was my agent and he helped to download data for a particular zip code and there I just kind of put it into Excel, put my little formula of square footage, bedrooms, what I was looking for, and from there had like a targeted list of what properties I could potentially add value for adding a bedroom.

05:19 - Vikas Gupta (Co-host)
Got it Fast forwarding to today. How has your data analytics gotten more complex, or what data sources are you pulling in, or how have you evolved since you did your first deal?

05:32 - Ariel Herrera (Guest)
Yeah, Since then, it's a lot more data sources and a lot of them being free actually. When it comes to location-based data the US Census, which is a government site you can acquire a lot of information like the demographics, what the median household income is, employment. These are all things that should be important when you're looking to invest An area that's growing in population. People have a diverse set of jobs and, overall, are starting to make from there. I also try to acquire more information about prices, days on market rental information, and that is all actually freely available through Zillow data center, as well as Realtor and Redfin too, all the way down to the zip code level.

06:18 - Vikas Gupta (Co-host)
That's quite a bit of data. Do you have a machine in the background that's just constantly churning through this data and spitting out, sending you a text? Hey, take a look at this deal, or how is it actually working?

06:32 - Ariel Herrera (Guest)
Yeah, I do have a Python script that goes on in the background, but this could actually be done without Python by using tools like Zapier. Initially, for one of my deals that I did one of my house hacks in Florida I was trying to find a deal. It was a hot market around 2021. Every time a good deal came up, there was always like 10 offers. I was sitting in meetings half my day of being a data scientist manager so I didn't have the time to really analyze these deals right away.

What I did was I set up Zillow alerts for properties that were in my buy box. Those would then get fed into my email and every time a new email came through, zapier would be alerted. Hey, there's a new property that could fit your match. From there, I automatically calculated cashflow by leveraging some external data sources and had a little last check of if the cashflow met my criteria, which I think was that time $300 a month I was looking for. It would send me a text. Now I was able to do that whole deal analysis automatically, alert myself automatically and then pretty much just forward that straight to the agent so that I could get ahead of some of these offers.

07:47 - Vikas Gupta (Co-host)
Then, after you forward that to the agent, what happens next? Are you doing an additional level, or are you, as an agent, doing an additional level of diligence by looking at pictures and stuff like that? Or are you just saying, hey, go put it in offer and then we'll figure it out on inspection?

08:03 - Ariel Herrera (Guest)
The latter, Putting an offer and figuring it out after for inspection. Ideally with now, with GPTs and stuff, I would have loved to have the availability of scanning the property images, understanding what we need to be fixed up, what are some things to look out for. But at that time it was just straight to inspection.

08:23 - Vikas Gupta (Co-host)
So have you ever had to then back out of a deal on inspection?

08:26 - Ariel Herrera (Guest)
I have had to yes.

08:28 - Vikas Gupta (Co-host)
What did you? What caused you to do that?

08:30 - Ariel Herrera (Guest)
I've had a sinkhole issues. That's a thing in Florida where you'll see a vertical crack in a wall and there's properties that have had sinkhole repair due to it, but it's a risk that that could happen in the future. So that's been one thing, as well as foundational issues, when I was looking at older properties in New Jersey so looking at properties that were built in 1910, 1920, and some of them you could roll a ball down the dining room and just see that ball go in different directions because the floors were uneven.

09:04 - Vikas Gupta (Co-host)
So have you had to walk away from earnest money deposits?

09:08 - Ariel Herrera (Guest)
Luckily, no, I've been able to always keep earnest money deposits.

09:13 - Vikas Gupta (Co-host)
Okay, yeah. So that's a great strategy, right? Beat the rush, put in the offer and then you sort of like, once you know you may get the house, then go do all the diligence, as opposed to spending all your time diligence see things where your offers may not get accepted.

09:28 - Ariel Herrera (Guest)
Exactly, and I think that's one of the biggest things that say, like newbie investors may not realize at first is the thought of having to get everything right just before offering on a property. But really, like you said, you could do diligence once your offer is accepted and you're in that phase.

09:46 - Vikas Gupta (Co-host)
So how did you get comfortable with that?

09:48 - Ariel Herrera (Guest)
I think just listening to a lot of podcasts, reading a lot of books one particular long distance real estate investing by David Green and he specified about like building a team, trusting people in your team. So being able to kind of vet out my agents inspectors ahead of time and looking at reviews gave me that assurance to trust the people within my team.

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10:59 - Vikas Gupta (Co-host)
So you had a friend who was an agent who helped you in New Jersey. How did you go about assembling your team in Florida?

11:08 - Ariel Herrera (Guest)
Yeah, it was a little bit more challenging and this time around I tried to lean on an investor or an agent that was investor friendly. So I use bigger pockets at the time to research agents there, call them up and ask what is your experience with investing? I didn't need to be someone who invested before, but if they've done property management or assisted in flips in some way, that would help. So I went forward with one agent and he used to do flips so he was able to spot hey, by the AC unit this looks a little bit like dirty. It might be kind of old. I have some issues, the blinds or just he would just be able to nitpick and find potential issues. So that, as I was long distance investing, looking to move to Florida but still living in New Jersey, I was able to trust him and his insights.

11:56 - Vikas Gupta (Co-host)
Did you buy that first property in Florida? Site unseen.

12:00 - Ariel Herrera (Guest)
Yes, I've done that three times now.

12:03 - Vikas Gupta (Co-host)
Was that first Florida one, your second investment then, or your third?

12:08 - Ariel Herrera (Guest)
It was my third. So when I was living in New Jersey, in my first house hack I invested in a duplex in Detroit which I never saw and still have not seen. It's an outside but never the inside in person.

12:23 - Vikas Gupta (Co-host)
Wow, so site unseen is your way of going about doing things.

12:26 - Ariel Herrera (Guest)
Yeah, if I could completely trust the people around me, ask the right questions, bet them out, screen them, then I feel confident in their abilities.

12:34 - Vikas Gupta (Co-host)
So you've, in a few of your previous answers you've mentioned house hacking. Tell us a little bit more about that strategy and how it. Let you sort of assemble your portfolio.

12:46 - Ariel Herrera (Guest)
Sure. So as I was learning about real estate and wanting to invest in a property, I realized most people get traditional financing. So you put 20% down on an investment property and New Jersey being an expensive market, in my head I was like where am I going to come up with? You know, 60 to $100,000 for my first investment property. But by living in a property for at least one year, you could put as little as 3.5% down, which is a huge difference and really helps to get started. So from there, I decided I was going to live in the property for a year, tried to run out the extra bedroom that I had to save more money and then, once that year was up, look to move into the next place. I thought I would be moving locally in New Jersey, but once March 2020 happened, I realized that areas of low income or to no income tax were going to boost, and that's why I'd made my second house hack in Florida.

13:44 - Vikas Gupta (Co-host)
And when you moved out of that first one, did you continue doing rentals or did you rent out the whole place to someone?

13:50 - Ariel Herrera (Guest)
I rented out the whole place. Looking back, I probably could have started that as like a median term rental, but I feel like I wasn't as educated in the space, so I just went straight to what I read about and was comfortable with, which was long term.

14:05 - Vikas Gupta (Co-host)
Got it, so what does your portfolio look like today?

14:08 - Ariel Herrera (Guest)
Today it's the property single family in New Jersey. I have two single families in Tampa, florida, duplex, detroit and, soon to be completed, mexico condo out in Playa del Carmen. How did that one?

14:24 - Vikas Gupta (Co-host)

14:26 - Ariel Herrera (Guest)
It happened as I was trying to make another investment while I was doing my house hack and I was getting to be out on offers left and right in Florida, as a lot of people were coming from like the tri-state area with all cash offers. So I started to look at cheaper markets. But then I thought, well, if I could live, if this remote thing continues to happen, people are going to think, well, why don't I live in a really cheap area that's bright, sunny, and be able to maximize my income? So from there I thought, well, it's the next best place Mexico, because it's super cheap, it's connected to North America and it's easy to get to. So I started to look at different cities within Mexico and I was looking for growth. So I noticed that Tulum was going to have an airport built within the next two years as well. Playa del Carmen had been growing and has a lot of native Mexicans that live there too, so not just a tourism spot. And from those two factors is what got me interested to learn more about the area.

15:33 - Vikas Gupta (Co-host)
Got it and then did you do a similar data science exercise that you described for us in Florida and New Jersey in Mexico, or did you have to approach it differently once you narrowed it down to those two areas?

15:47 - Ariel Herrera (Guest)
Yeah, I had to approach it a little bit differently because there wasn't as free data out there. So now it's more so digging through Facebook posts and seeing what other investors who were investing within communities like Playa del Carmen to loom, what they said about their experiences and what the cash flow looked like. And it also took meeting with a lot of different agents. So I did go out there and I saw the property management team as well as where the building was going to be built. I've seen the building actually be built in progress.

I know it's real, but one of the considerations if you're looking to invest in Mexico is going towards a development group that has has been proven to do many buildings. So there are some development groups that may only do have done like one building or so and their success or ability to finish out the building is really tied to how many investors they can get to commit to these units. So if only 70% commit, they might just literally stop the building and just stop developing it and just kind of walk away. So, learning this research through these Facebook posts and talking to different agents, I went with a group that has done 18 apartment complexes within the Playa del Carmen to loom area, and that's how I decided to go with them.

17:10 - Vikas Gupta (Co-host)
And were you able to like, did you ask like what percent of your presale commitment? And have you hit? Do that kind of diligence?

17:18 - Ariel Herrera (Guest)
I did Thank you, just to make sure that I wasn't, I guess, number one on the list.

17:27 - Vikas Gupta (Co-host)
I'm sure they. I wonder if they give discounts to number one probably.

17:31 - Ariel Herrera (Guest)
They do yeah.

17:33 - Vikas Gupta (Co-host)
Yeah, Cool. So you have properties in three states plus Mexico. Mexico one's not up and running yet, but you still have properties across the United States. How, how are you managing this? Do you have property managers? Are you doing it yourself? Tell us a little bit more about the operations of your business.

17:52 - Ariel Herrera (Guest)
I have a property manager out in Detroit and then in New Jersey I still have my parents that live there, so my mom is actually my property manager. I am looking to get a little bit more cohesive in terms of like the software I'm using, which is why in 2024 I plan to reset everything up in Zibo, which I'm excited about, and then here in Tampa I have one property manager, since I just converted a house into a short-term rental and then I do it myself for the other property.

18:25 - Vikas Gupta (Co-host)
Well, I love to hear that 2024 coming over to Zibo. So you do have some self-management experience. Tell us about. You know why you have a mix of self-management and property managers and the pros and cons of each.

18:38 - Ariel Herrera (Guest)
I ideally like to have control, so that's why I like to do some property management myself. But ultimately I realized that having my hands in too many different things doesn't allow me to be good at one particular thing. So if I am committed to being a data scientist, to researching about the latest and greatest in tech, being distracted by phone calls or things that come with managing a property is going to hinder me from doing that. So it's definitely been a tough decision to kind of let go of a little bit control, but it's been really worthwhile and again, also like screening these folks that I've given control to deeply has also helped.

19:18 - Vikas Gupta (Co-host)
Well it's also expensive, right.

19:20 - Ariel Herrera (Guest)

19:22 - Vikas Gupta (Co-host)
That's what we hear the most. I mean, the two things we hear the most about why folks want to self-manage is are exactly it's expensive and control.

19:31 - Ariel Herrera (Guest)
Definitely. But then when I look back on it and I'm like, well, if I were to have the time to build out a certain tool and I can make $10,000 on it, it's probably worth it versus being that person still behind the phone of answering texts and stuff.

19:47 - Vikas Gupta (Co-host)
So how are you thinking about your real estate investments going forward? Are you actively investing? Are you taking a break in the market, like what's your read and what's going on?

19:58 - Ariel Herrera (Guest)
I'm taking a slight break, looking to pick back up in spring and I'm looking for more creative financing deals. So one of the cons of going full time into real estate is you do lose your W2 and financing is a little bit tougher to get. So I'm looking to have more creative strategies and maybe seller financing to acquire some deals as well. I would like to in the near term, maybe in the next three years or so, to take some of my investments and scale up using a 1031 exchange. So, for example, that duplex that I have in Detroit being able to leverage, that maybe go into a four to eight unit.

20:38 - Vikas Gupta (Co-host)
How do you think about that versus hold forever? Frequently, if Brandon were here, he would tell you that the folks in his tax practice customer base who are some of the wealthiest in the market some of the wealthiest and the most successful have pretty much never sold a property. Buy and hold forever.

20:57 - Ariel Herrera (Guest)
Yeah, I think it's definitely a great approach. Find hold forever. You can leverage a lot more in terms of borrowing against the property too. In my head right now I'm looking to have as little variation or risk with management, and if I could have things within one single building versus split between different units across different states, I think that would give me as well lower risk, but also peace of mind.

21:28 - Vikas Gupta (Co-host)
That's an interesting consideration that actually hasn't come up on the podcast yet as a potential decision factor or risk factor. Tell us a little bit more about the operational risk that you're thinking through.

21:42 - Ariel Herrera (Guest)
Yeah. So I think mostly in terms of like capex expenses. So being able to replace one roof versus three, and then being having three different teams of contractors, for example like I never thought I would be as diversified as I am today is kind of just how life has taken me, especially with the house hacking part. But if I can get things down to maybe just a couple of buildings and within one max two teams, I think that would really help. Yeah, again with my peace of mind, but also when it comes to repairing things, having just sold one to two people that are the contractors that are helping me out with that.

Yeah, one other thing with contractors I've never had like a horror story, but I definitely have had like this person's working really great, able to communicate with them, and then like just out of nowhere, I reach back out to them a month later hey, can you help me with this? Like sink that's leaking, and then it's just no response, just a ghost. So having to continue that conversation over and over again is a little bit tiresome. So the hope would be with a larger unit, larger building, more units could just have one person who finds it maybe worthwhile to stick around.

22:59 - Vikas Gupta (Co-host)
So, going back to your expertise, your data science, your Python, so if I don't know Python and I'm not as data oriented as you are, how do I compete?

23:12 - Ariel Herrera (Guest)
Luckily, there are a lot of resources today that weren't available, probably even like last year, ever since GPT. So, taking a step back, what are the benefits of being able to use a programming language like Python? So imagine if you are collecting leads maybe you have foreclosure leads, tax lien leads, a whole assortment. To have eight different spreadsheets up and trying to do VLOOK-ups or analyzing data across them is pretty cumbersome, and especially if you are doing that across like 30 cities at once. Programming languages like Python allow you to work with large data sets in a cleaner fashion and to be a little more accurate as well as faster.

Beforehand, you kind of needed to know Python to do this, but now, with some readily available tools, you could use things like Tableau as well for data visualization to understand trends within a data set just by uploading an Excel file that's free to use as well. Using GPT, you could train your own bot basically to understand the data that you have, ask it questions and deliver insights. So, for example, if I were to download data from Redfin that has days on market for cities, median sale, price, off market listings I could then train a model on that all without code with chat GPT and then ask it questions like which cities have had the most growth over the last six months, it will understand a data set and then be able to bring up those insights.

24:53 - Vikas Gupta (Co-host)
Well, that's really, that's a really cool application of GPT. As an aside, I have to say that I love that. 30 different Excel sheets in a VLOOKUP was the JV version of what you do, because that's beyond. That's already advanced for most people, including me. So so chat GPT so I can get the data from those sources that you mentioned. I can visualize them in Tableau for free. I can upload them to chat GPT. Ask it questions, it'll do my data analysis for me. I can use Zapier to automate the notifications to myself. So that's all pretty cool, relatively low cost to free, and I can build, cobble together my own sort of analytics offer engine for real estate 100%.

25:51 - Ariel Herrera (Guest)
And one thing that people tend to ask me too is well, in that process of being able to have the data visualize, it use tools like Zapier. Well, how do you even get the data? To begin with, for example, for closure data, you usually have to go to the county, maybe manually copy some information down, but there's also tools like Rouse AI, which are no code solutions to do web scraping, to pull information from the web. For all you do is spend 15, 20 minutes of walking through the process of how you would just click through things on a website, what information you would copy. It then basically watches your actions and is able to automate that, so you can just have that go off, say, daily, to pull foreclosures from a website automatically.

26:39 - Vikas Gupta (Co-host)
Oh wow, Is there a YouTube video on your channel that I can watch? That's going to walk me through this end to end.

26:46 - Ariel Herrera (Guest)
Yes, there is. I could send that to you to put into the show notes.

26:52 - Vikas Gupta (Co-host)
Yeah, definitely Help to point the audience to that and I'm going to go check that out. So tell me more about coffee closers. What are you up to there?

27:01 - Ariel Herrera (Guest)
Yeah, coffee Closers is an app that I've co-developed with two other folks to help real estate investors find deals in up-and-coming areas, and the reason why I created this app was in the last four years, as I've been able to have the YouTube channel, I've synced with some really amazing people agents, lenders, investors, wholesalers and the common theme was how do we automatically find cash flowing deals in up-and-coming areas? So I built some tutorials of how to do this in Python, how to do this through a course, some tools for no coding, but ultimately the ask was can you just put this some sort of app that I could just utilize and quickly find deals? Save me time? So that's why we created Coffee Closers, and some things we look into is one, being able to do automatic deal analysis.

Two, look for areas that are appreciating in up-and-coming so maybe there's a new whole fruit coming out. We take that into account to show this may be an area that's going to be appreciating and you may want to get in now. And then the third thing value add opportunities. So, like that property I mentioned in Detroit, the duplex, it was actually listed as a single family home, but the agent listed it incorrectly. But once you read a description you could see. Oh, it's actually a duplex, not a single family home, and I was able to get the property for about 15k less than other comparables just because of that mislisting. So we also take into account mislistings, value add opportunities like out of bedroom and some other to help you with this kind of trifecta to find the best deal in the city.

28:41 - Vikas Gupta (Co-host)
So you're looking at listings, you're doing some sort of AI image analysis. You're doing description textual analysis. You're pulling in census data. You're pulling it. Where are you getting the whole foods data? Where is that retail data coming from?

28:59 - Ariel Herrera (Guest)
Google Places mostly, and then some of it is also like using GPT to read news articles to find out what's coming.

29:09 - Vikas Gupta (Co-host)
And is it live? And I go check it out now and start playing around.

29:13 - Ariel Herrera (Guest)
Yeah, you could check it out today 7-day free trial, and it's available across 55 cities. For the cities that we don't support right now, we do allow one-off analysis, so if you had a property in a smaller town, you could just plug in the address and then we return information that's relevant to it.

29:36 - Vikas Gupta (Co-host)
And what's the gating factor behind enabling more cities? Is it just onboarding some of the local data sources, or something else?

29:44 - Ariel Herrera (Guest)
Looking to get everything right before going nationwide. So one of the common things we've seen with other softwares is that they have nationwide data, but when you actually try to analyze things, a data is inaccurate, rent rates are inaccurate, and that really hurts being able to utilize the product. So we'd rather get these 55 cities right and iterate constantly on them before going nationwide.

30:11 - Vikas Gupta (Co-host)
All right, so you've done these 55 cities, you're iterating, You've told us some interesting things, but like what were the few things if there were that the most sort of interesting or the most insightful things that you jumped out in terms of learnings about? Whether it's common things in images or in descriptions that maybe lead to mispricings, or it's data elements that you didn't think would be significant factors in your model but turned out to be? Like what are those things? The things that I wouldn't think of?

30:45 - Ariel Herrera (Guest)
Great question. Some things that we found initially very challenging was being able to accurately assess multifamily rental properties, because there may be like two bed, one bath for one unit and then three bed, one bath for the other. We can't just assess this just by getting the beds and baths. By reading the descriptions we actually realized that a lot of agents put that information there and it was really hard to tease out initially just by doing keyword searches, because every agent has their own style and sometimes there's misspellings as well. Being able to leverage LLMs and GPT in that sense to parse the description and read it out, be able to tease out what those breakouts are for each unit, really helped us. We didn't realize how messy this text data from the MLS can be, but it does show that there's opportunity to find some of those gems just by parsing out the right information.

31:48 - Vikas Gupta (Co-host)
Yeah, Well, certainly if you were able to get something for 15K less. I can't imagine that. I mean, it just blows my mind that an agent would list the duplex as a single family home.

32:01 - Ariel Herrera (Guest)
It's pretty crazy, but we still are. In a time where agents are doing so many things at once. There's fat fingers of putting things in correctly, and then also there's some agents that maybe just less experience and got the listing from their uncle who's selling their property and maybe they don't have second pair of eyes to help them with reviewing that data.

32:27 - Vikas Gupta (Co-host)
Cool. What is one data set that you haven't been able to get your hands on that you wish you could get your hands on for your model?

32:37 - Ariel Herrera (Guest)
AirDNA. Airdna is short-term rental data. They are the number one for that space. It's not a matter of not being able to obtain it, it's just pretty high cost, which everyone who tries to acquire short-term rental data is aware of in the industry. We are going to focus on buy and hold primarily for right now. When there's enough of a need to focus on short-term rental strategies, we'll make that leap.

33:09 - Vikas Gupta (Co-host)
What data does AirDNA have?

33:12 - Ariel Herrera (Guest)
They take information from Airbnb VRBO. There's two other sources that are popular for people to do short-term vacation rentals. Their data goes back several years. They're able to see occupancy rates, say, if I'm looking to get a rental property in Tampa Florida that I'm looking to put as a short-term rental, I could use their data to accurately assess what my cash flow should look like each season, based on occupancy and based on comps, whereas right now, if you weren't using an AirDNA, you would have to sift through Airbnb manually or use some web scraping services. They're the leaders in that space right now.

33:55 - Vikas Gupta (Co-host)
Oh, wow. Yeah, I can see that that'd be a transformative data set for short-term rental analysis. Do they have deals with each of these companies or do you know how they're getting the data?

34:07 - Ariel Herrera (Guest)
I assume they have deals yeah. All right Well they're getting it via like an API, which is a structured way of obtaining the data.

34:18 - Vikas Gupta (Co-host)
Well, if anyone from AirDNA is listening to this, then maybe you can cut her a deal for her for burgeoning startup. I would appreciate that, Cool. Well, I think we've covered a lot of ground and I want to get to building my own bot, my own real estate bot. So let's go into the closing questions and wrap this thing up. Closing question number one what is your favorite book? And it doesn't have to be real estate related.

34:45 - Ariel Herrera (Guest)
My favorite book is Extreme Ownership by Jaco Willink. So it's about a pair of Navy SEALs during the Iraq war and their essence is that as an individual, as a leader, you should always take full responsibility of whatever occurs, no matter the circumstance. An example that I have here of how I applied it was after March 2020, I had tenants that were unable to pay the rent, so I had to cover that extra amount, since they had lost their jobs, and in that process I was so angry. I was like, oh, why is this happening to me? But then I thought back like how can I make this better?

So there was a couple of things that I was able to improve upon by taking Extreme Ownership. So I learned how to screen better, how to more appropriately look at credit scores and types of jobs relative to the area, better times to list the property which could really matter the day of the week as well as the season, and then also having more appealing photos. So even though I was able to rent it out in the past with an iPhone, take some pics I realized that if I could have more appropriately staged photos that are better quality, I can attract better tenants as well. So through that book I was able to get those foundational principles that helped me down the line.

36:08 - Vikas Gupta (Co-host)
Very nice. I think now there's some AI tools that will clean up your iPhone pics for you.

36:16 - Ariel Herrera (Guest)
There are and make it like super bright. It's like looks like it's ready for Airbnb rental.

36:23 - Vikas Gupta (Co-host)
Yeah, I mean, I've seen one that's that like I don't know, this is probably pushing it, but they'll like take a deadline and make it look like it's live which seems a little bit disingenuous to me, but I mean, I guess you know that's what AI can do.

36:38 - Ariel Herrera (Guest)
Yeah, that's interesting.

36:42 - Vikas Gupta (Co-host)
Well, great, great recommendation. Question number two what is more important to you in real estate investing cashflow or appreciation?

36:50 - Ariel Herrera (Guest)
Hands down appreciation, reason being helps you scale faster. So as an example, when I tried to make my purchase for that property out in Mexico, I took a HELOC against one of my properties to leverage war money $90,000. Whereas if I had a cash flowing property of $500 a month, it would take me 15 years to get that $90,000. And I think that really shows the power of being able to leverage appreciation and scale in real estate.

37:22 - Vikas Gupta (Co-host)
Well, you need cashflow to cover that HELOC right.

37:25 - Ariel Herrera (Guest)
Yes, so you need both. But yeah, in terms of scale, I think, appreciation.

37:32 - Vikas Gupta (Co-host)
No, I'm just playing with you. I appreciate the picking one instead of hedging. Final question is there any last bit of advice or insight you'd like to leave? With our audience that we didn't get a chance to cover already.

37:49 - Ariel Herrera (Guest)
I think it goes down to making informed decisions using data. Maybe five, 10 years ago, kind of picking a property on a map, could it work, but now it's high competition and all the available resources that are out there, it's really important to use data-driven decisions. So, whether you're using coffee closers, another sort of application, I think it's worthwhile especially if you're putting thousands of dollars into an investment to use some sort of tool, some sort of analysis, to help you in that process and alongside that, with making informed decisions using systems and software like Zeebo in place.

38:32 - Vikas Gupta (Co-host)
Well, thank you, this has been great. Really appreciate you going into detail and depth on how to do some of these things, especially if one is not so technically inclined. Before we let you go, where can our audience find you?

38:47 - Ariel Herrera (Guest)
You can find me on LinkedIn, ariel Herrera, as well as YouTube channel, which is tech and real estate, and if you wanna reach out to me directly, my email is arielharrera at analytics arielcom.

38:59 - Vikas Gupta (Co-host)
Thank you.

39:00 - Ariel Herrera (Guest)
Thanks so much, Vikas.

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