Investing In Real Estate With AI Science

**SPEAKER_1** (0:01)
Welcome to the Money Tree Investing Podcast. Stock market, wealth, personal finance, value stocks. Invest in your life.

**Kirk Chisholm** (0:10)
Hello, Smart Money Tree Podcast listeners. Welcome to this week's show. My name is Kirk Chisholm, and I'll be your host. So today, I'm joined with Neal Bawa. How you doing today, Neal?

**Neal Bawa** (0:19)
Fantastic, thanks for having me on the show, Kirk.

**Kirk Chisholm** (0:21)
For those of you listeners who don't know Neal, Neal, tell us a little bit about yourself.

**Neal Bawa** (0:24)
I am a technologist, a recovering technologist that accidentally fell into real estate for tax reasons. My degree is in computer science, data science is my area of interest, and worked as a technologist, running a company from 1999 to 2015
While I was doing it, making lots of money in California, but I like to call it tax-a-fornia, so I was not keeping a lot of money, and real estate proved to be the way for me to build my wealth and get out of a nine-to-five job. It took about 10 years, but it was a fun journey.

**Kirk Chisholm** (0:59)
As a data scientist, how does that play into real estate?

**Neal Bawa** (1:02)
Well, amateur data scientist, but it plays into real estate really well, because what I find is that while people in real estate are pretty tied to things like Excel spreadsheets for doing analysis, they don't go to the next step, and you really have to go to the next step, in my opinion, to set yourself up for better results.
And there's so much room, so much room for analysis, data analysis, for comparisons of various cities and how they're doing in terms of job growth, population growth, income growth, home price growth. Now, you don't always get things right. Sometimes that research ends up taking you into the wrong direction, and that's happened to me. But the bottom line is that we end up significantly setting ourselves up for better returns or results when we use data science. So lots and lots of room there, and I'll give you examples of that beyond what the typical Excel spreadsheet analysis stuff that most people do.

**Kirk Chisholm** (1:58)
I mean, I'd love to hear some examples because what you're talking about is great and everyone should do it. But I think, you know, looking at demographic trends in cities is important, but what are some examples of drilling down beyond that? Because I think there's probably a lot more you could do.

**Neal Bawa** (2:11)
First thing is, let's say that Kirk and I are doing some water cooler talk. We both work for the same company and we're talking in the water cooler, Kirk basically says, I think Grand Rapids, Michigan is the place to invest in. And Neal Bawa says, you know, I think Idaho Falls, Idaho is the place to invest in. Now, it may be possible that both of these markets are good markets to invest in, but which one's better and how much better? How do you quantify something like this? Let's say that I'm talking about Google stock and you're talking about Apple stock.
Isn't there a very easy way to quantify and look at last 10 years of performance for Google and last 10 years of performance for Apple and the dividends? So one could objectively say things like, well, Apple stock has appreciated three times more than Google or two times more than Google.
How about bringing that level of clarity, that level of objectivity to ranking cities for real estate, which cities are likely to be more profitable? You can never say it with any level of certainty, but you can certainly improve your chances.
So I became obsessed in 2008, 2009 with the idea of ranking cities for real estate investments, because as you can imagine, 2008, 2009, really bad time for real estate, and I'm interested in understanding what area is maybe the best. And so what we did is, as a group, I opened a meetup group in the San Francisco Bay Area. There's a lot of technologists here, lots of geeks here. So it was a very geeky group of people, and we'd get together and basically figure out how to mine data.
And the mind of data came from websites like the Bureau of Labor Statistics website and Redfin, Trulia, Zillow, and sites like that that existed back then. When we would mine that data, we'd basically put it into various software, like a statistical analysis software. And we try to correlate real estate profits with the data. Like, is there a strong correlation? Is there not a strong correlation? Obviously, you want a strong correlation between real estate profits and data.