Jerry Chu Real Estate Background: #SkillsetSunday
- CEO & Founder of Lofty AI
- From San Francisco, California
- 2 years of real estate experience.
- Lofty has helped over 200 real estate investors find undervalued neighborhoods to invest in since 2019.
- Say hi to him at: www.lofty.ai
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Best Ever Tweet:
“We leverage artificial intelligence to make the most accurate real estate predictions in the world” – Jerry Chu
Joe Fairless: Best Ever listeners, how are you doing? Welcome to the best real estate investing advice ever show. I’m Joe Fairless. This is the world’s longest-running daily real estate investing podcast where we only talk about the best advice ever; we don’t get into any of that fluffy stuff. With us today, Jerry Chu. How are you doing, Jerry?
Jerry Chu: I’m doing well, Joe. How about yourself?
Joe Fairless: Well, I’m glad to hear that. I’m doing well as well and looking forward to our conversation. And first off Best Ever listeners, hope you’re having a best ever weekend. Because today is Sunday, we’ve got a special segment called Skillset Sunday. That’s why we got Jerry back on the show, if you recognize Jerry’s name.
First off, Jerry is the CEO and founder of Lofty AI, based in San Francisco, and Lofti AI has helped over 200 real estate investors find undervalued neighborhoods and properties to invest in since 2019. If you want to learn more about the launch of the business, then you can listen to Episode 1601 and it’s titled, How To Identify The Next Hot Neighborhood. So we’re not going to repeat what our previous discussion was. What we thought would be good for discussion today would be to talk about some new findings that Jerry and his team have uncovered as a result of being in business over a year from the last time we spoke. So first off, Jerry, do you want to give the Best Ever listeners just a quick overview or refresher of what Lofty AI does for investors? And then you can go right into some interesting findings that you think we should talk about?
Jerry Chu: Yeah, definitely. So essentially, at Lofty AI, we help real estate investors make better decisions using artificial intelligence and unique data, and what we mean by unique data are things like the average wait times for ride sharing services, to weather patterns, to – believe it or not – the number of French Bulldogs in the neighborhood if you could track that, people’s music preferences, even sewage data, air quality, the growth of Airbnb listings in an area. So all these data points that traditionally might not have been used by real estate investors, but it turns out not only do they have a strong correlation to price appreciation in single-family market, a lot of times, they tend to be leading indicators as well, which means that if you were paying attention to these data points, you would know ahead of the curve which areas you should be looking at to invest in.
Joe Fairless: So will you dig into that a little bit more, as far as using some of those indicators to then determine, “Okay, here’s some areas that are likely headed on the upswing”, and just talk about your process that you use to do that?
Jerry Chu: Yeah, definitely. The prototyping stage that we do – this is before any of the data is fed into the AI – we have to do a lot of trial and error, and a lot of that just has to do with thinking about data in a different way than the industry has traditionally thought about it, and just being creative with it. So to give you an example, what we’re working on internally right now that hasn’t been deployed yet, was we read from another report and actually another company, also a technology startup – they were talking about how in the future, you could track pandemics and diseases early on based on sewage data; and the rationale for that, they claim, is apparently before people – so take Coronavirus as an example – before people develop symptoms, a lot of times, you can detect traces of the virus or disease within picometers in the sewage data. So that’s something they’re working on. So when we heard about that, we were like, “Hey, that’s pretty interesting. Could we use that for real estate in some way? Would it make sense to tell investors, if we could do this accurately, not to invest right now, because there might be a pandemic breaking out in the city or in this neighborhood? Or maybe if someone worked about to invest, we say, ‘Hey, maybe you should wait three, four months and maybe prices will be cheaper by then because there’s going to be this panic?” So how can we use these weird data and how can we tie that back to important information we can pass on to our customers and real estate investors. So that’s how you have to think about data to do what we do.
But once we get past that stage, it’s a lot of prototyping in getting the data. If it’s online in a public source, can it be scraped? If not, can it be purchased? Can we develop partnerships with whichever vendor generates that type of data? And then from there, do a lot of nerdy data science stuff to test out and see over a historical time period whether that has any correlation to real estate prices or not. Because at the end of the day, no matter how cool something sounds, if it doesn’t really contribute or affect price growth or decline, it’s not really that useful for us or our customers. So that’s, in essence, a step. And if we test this data source for a certain period of time and the result shows that it has a consistent correlation or a consistent ability to help predict which neighborhoods or properties will do well in the future, then that’s when we officially add it as an ingestion data source for our AI and that’s when the users on our platform can actually benefit from that data source.
Joe Fairless: That is incredibly interesting. When you’re talking about the track diseases based on sewage, I didn’t know what direction you were going to go with that… But yeah, I think there’s a less likelihood of don’t buy there because that area might be quite quarantined for decades, but it is interesting if you’re a fix and flipper, to get ahold of that information, because that could kill you on a fix and flip over a three month period of time where there’s, all of a sudden, a bunch of new stories or on the flip– pun intended, I guess… On the flip side, if you’re about to buy something, then yeah, maybe you wait until something happens. That’s fascinating stuff. What are some other data sources and correlations that you have looked at, that you have seen increases the value?
Jerry Chu: This one probably won’t come necessarily as a shock, but the air quality. So the government actually publishes what’s called the AQ ISO, the air quality index, and if you have an iPhone or presumably an Android as well, if you go to your weather app, somewhere on there, it’ll actually show you that value… And it is pretty granular, at least down to neighborhood or zip code levels, and we have seen consistently that areas with more expensive real estate properties tend to be in neighborhoods where over a long period of time the average AQI is very low. So the index works in a way – the higher it is, the more polluted the air is. So essentially, it’s not very shocking, but it’s probably something people don’t talk to their broker and agent and say, “Before I buy this, tell me what the average AQI is for the last five years.” It’s not something they overtly think about, but it turns out has a pretty powerful, I guess, subconscious motivator. You’d think that when people are buying or investing in neighborhoods, when people want to move to an area, if they’re walking around and the area is not very good, there might be something just in the back of their mind like, “I don’t really like this area”; it turns out that’s been true historically for a pretty long time period.
Joe Fairless: See, I would have guessed the opposite, because I think of New York City, I think of Los Angeles, I think of Chicago, and my guess is that the air quality is worse in those areas compared to Fort Worth, or Waco, or Abilene, or Lubbock, Texas. In those areas, the latter, the values are lower. So help me reconcile that.
Jerry Chu: You’re definitely right about that. So if you’re only looking at this indicator, you’re not going to be able to have a pretty good batting average, as you can picture. It’s what we call a powerful additional indicator to use. So you have all your other factors – you’re looking at demographics, job growth in a region. So all else equal, especially when it comes down to job growth and economics of an area, then looking at this additional indicator can help you rank the neighborhoods you’re looking at. If you’re looking at all these metropolitan cities and neighborhoods within these cities, the one with the lower AQI will tend to do better, historically speaking. However, if you just look at that, then obviously places that are underdeveloped like national parks or things like that would have the best AQI, but in reality, you’re not gonna really be able to make a lot of money doing real estate investment there.
Joe Fairless: So we spoke a little over a year ago… What are some things that you’ve come across over the last 12 or so months that you think would be interesting to share?
Jerry Chu: I don’t think I mentioned this the last time, but the wait times for ride sharing services has consistently, over the last year, have been one of the highest quality predictor of neighborhood appreciation, and it has a lot to do with the proliferation of the service. Almost everyone, at some point or another, uses Lyft and Uber, and what’s unique about that is if you actually pay attention to the different ride categories – so you’ll have UberX, and then on the higher end, you’ll have, I don’t know what they call it anymore, UberLux, I think… But essentially, the drivers of the luxury category, they often have those big SUVs that aren’t exactly gas efficient. So for them, the unit economics doesn’t make sense for them to just drive around the city and wait for rides to be routed to them. So a lot of them, they even have these hangout groups on the weekend. They trade information, and they’ll know this area has a lot of high-end restaurants. Celebrities visit that on the weekends very often, so they’ll be parked near or around those neighborhoods, and they’ll just park on the side of the street until they get a call, and that’s when they actually go pick up their customers. So with that said, the shorter the amount of time it takes for you to wait for a luxury ride, the more expensive neighborhood is.
So imagine Beverly Hills, if you wanted to call the most expensive Uber ride, it doesn’t take that long for one to get to you. But on the other end of the spectrum, if a neighborhood isn’t typically the most high-end neighborhood, if you request one of these luxury rides, you could be waiting up to 20 minutes. So we’ve noticed that consistently over a time period, if the average wait times for this neighborhood starts decreasing when it comes to ride sharing services, the luxury category specifically, that’s a really powerful indicator. It’s saying that something is going on in this area where for one reason or another, there’s either a lot of wealthier people moving there or visiting there, and regardless of what the exact reason is, we’ve noticed a very powerful correlation between price growth and neighborhoods that have a rapid decrease in the wait times of these luxury ride services.
Joe Fairless: Bravo! This is fun. I like this stuff.
Jerry Chu: Thank you.
Joe Fairless: Yeah. What’s something that you learned that maybe surprised you over last year with the data that you’ve gotten? That might have been a surprise; it sounds like that’s been something that you’ve been aware of for a little while. So what’s something over the last 12 months that you’re like, “Oh, you know what? For better or worse, that was surprising.”
Jerry Chu: That’s part of the fun aspect of the job that we’re doing, is discovering all these unique interesting insights. So one thing that shocked me personally was prior to starting the company and working all of this, I had assumed that anything you see in a more developed, wealthier neighborhood, if you start seeing the exact same things happen in another neighborhood, it typically would mean a good thing. So something like a wine bar, for example. I would imagine, oh well, if this area didn’t have any wine bars before and one popped up, and it’s very expensive, bougie, then the neighborhood must be doing pretty well. It turns out, it’s actually the opposite. So we discovered something really interesting – the neighborhoods with more beer gardens or beer-centric, like breweries and things like that, those neighborhoods grow faster in terms of median home values, whereas neighborhoods with more wine bars and things like that tend to not grow as quickly. So that was something that was very shocking to me.
Joe Fairless: Yeah, because I picture beer to be not as sophisticated as wine, and beer drinkers belching and wine drinkers sipping and with their pinky stuck out.
Jerry Chu: Yeah.
Joe Fairless: I drink wine for the record, by the way, and beer.
Jerry Chu: Yeah, I do both as well.
Joe Fairless: I don’t want to offend anyone when I just– [unintelligible [00:15:10].21]
Jerry Chu: Exactly. That would be the stereotype, and I think that’s why I was shocked. But I personally dug into it a bit more, and I’m going to preface and say this isn’t scientifically sound per se, but in my opinion, it seems like once you look into it more, there’s a huge resurgence in craft breweries, and it’s now the cool thing to do, and so people with a lot of money are now taking tours of these microbreweries and the brewers are trying out different techniques, and it’s like this whole underground subculture that’s gotten and actually started to become more mainstream, and I think that probably plays into why the data is the way it is.
Joe Fairless: I’ve enjoyed this thoroughly. I just find this stuff incredibly interesting. So Jerry, how can the Best Ever listeners learn more about what you’re doing at your company at Lofty AI?
Jerry Chu: They can just come visit our website; it’s www.lofty.ai. We have a lot of information on our landing page, and in the future, what we actually plan on doing is we want to build a record [unintelligible [00:16:16].17] leader as the most transparent data company as well, and so what we’re actually doing is we’re going to publish our internal results just completely for free and some other unique insights we’ve discovered. So the last time we spoke, we only had a couple. To keep things proprietary, we couldn’t really share and detail a lot of what we had, but now given some time, we’re up to 50 unique leading indicators of neighborhood growth, so we’re happy to share a portion of that with the public. So fairly soon, we’re gonna have a results page to our website where you can see the exact addresses that our AI has picked in the past year and how well they’re doing, and you can track that and they’ll just be up there; it’ll automatically update every single month.
On top of that, you can see some of our unique data sources, what we’re tracking and how those data points have been moving for certain neighborhoods and zip codes over a period of time, and we think that can generate a lot of interesting data points for your listeners, and just people out there who are interested in adding additional insights to their real estate investment strategy.
Joe Fairless: Jerry, thank you so much for being on the show. Again, I enjoyed learning the thought process for how you think about potentially correlating things, like tracking diseases based on sewage data and hey, maybe we might be able to have an application there for maybe probably more short term buyers, if they should wait or if they should jump in… To wait times with ride sharing services, and specifically if the average wait time of a luxury category ride sharing decreases, then it’s an indicator of price growth and neighborhood, and then also beer gardens being better for the neighborhood in terms of property values then wine bars. So great stuff, I enjoyed our conversation. I hope you have a best ever day and talk to you again soon.
Jerry Chu: Thank you, Joe. It’s always a pleasure.
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