Investing In Real Estate With AI Science
Money Tree Investing
Money Tree Investing Podcast
4.6 • 732 Ratings
🗓️ 17 April 2026
⏱️ 53 minutes
🧾️ Download transcript
Summary
Neal Bawa is here today to discuss the investing intersection of real estate with ai science. Neil explains how he transitioned from a tech career into real estate by applying data science to identify high-performing markets, emphasizing that factors like job growth, population growth, income growth, home price trends, and crime reduction can significantly improve investment outcomes. He outlines how his team uses advanced analytics and AI tools to rank cities, analyze deals, and uncover insights that humans often miss, while also integrating AI deeply into company operations through structured systems like EOS. He highlights selective opportunities in distressed multifamily assets and emerging areas like senior housing, while cautioning that single-family and industrial assets remain expensive.
We discuss...
- Neil Bawa transitioned from tech to real estate, using it as a tax-efficient path to build long-term wealth.
- Key drivers of real estate performance include job growth, population growth, income growth, home price trends, and crime reduction.
- He developed a data-driven system to rank U.S. cities and identify high-performing markets like Madera, California.
- AI is deeply integrated into his company, with employees required to use it daily and contribute to building internal tools.
- AI improves efficiency and insight generation, even if it occasionally makes calculation errors.
- He expects modest interest rate declines in 2026, with mortgage rates around 6–6.3%.
- Home prices are likely to remain flat or grow slightly (1–2%) due to improving supply and demand dynamics.
- The "lock-in effect" from ultra-low pandemic-era mortgages has constrained housing supply and prevented price declines.
- As rates ease, more sellers and buyers are expected to re-enter the market, balancing prices.
- Multifamily real estate saw price declines with rising rates, unlike the single-family market.
- Distressed multifamily deals present niche opportunities, especially in overleveraged markets.
- The office sector is likely near a bottom, with gradual recovery driven by return-to-office trends and limited new supply.
- Private credit is growing but carries elevated risk, requiring careful selection of managers.
- Real estate overall is in a transitional phase after several challenging years, particularly for commercial sectors.
Today's Panelists:
- Kirk Chisholm | Innovative Wealth
- Barbara Friedberg | Barbara Friedberg Personal Finance
- Marc Walton | Forex Mentor Pro
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For more information, visit the full show notes at https://moneytreepodcast.com/real-estate-with-ai-science-neal-bawa-808
Transcript
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| 0:00.0 | Welcome to the Money Tree Investing Podcast. |
| 0:04.0 | Stock market, wealth, personal finance, value stocks, invest in your life. |
| 0:10.0 | Hello, Smart Money Tree Podcasts listeners. |
| 0:12.0 | Welcome to this week's show. |
| 0:13.0 | My name is Kirk Chisholm and I'll be your host. |
| 0:15.0 | So today I'm joined with Neil Bawa. |
| 0:18.0 | How you know what I did, Neil? |
| 0:19.0 | Fantastic. Thanks for having me on the show, Kirk. |
| 0:21.1 | For those of you listeners who don't know, Neil, |
| 0:23.2 | Neil, tell us a bit about yourself. |
| 0:24.9 | I am a technologist, a recovering technologist, |
| 0:28.1 | that accidentally fell into real estate for tax reasons. |
| 0:31.8 | My degrees in computer science, data science is my area of interest |
| 0:35.2 | and worked as a technologist running a company from 1999 to |
| 0:41.0 | 2015 while I was doing it, making lots of money in California. But I'd like to call it taxifonia. |
| 0:48.3 | So I was not keeping a lot of money. And real estate proved to be the way for me to build my wealth |
| 0:53.6 | and get out of a nine to five job. |
| 0:56.2 | So it took about 10 years, but it was a fun journey. |
| 0:59.2 | As a data scientist, how does that play into real estate? |
| 1:02.7 | Well, amateur data scientists, 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. |
| 1:16.3 | And you really have to go to the next step, in my opinion, to set yourself up for better results. |
| 1:22.0 | And there's so much room, so much room for analysis, data analysis, for comparisons of various cities and how they're |
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