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We talk with Neil Bawa about using data science to buy when everyone else is afraid, from discovering depreciation benefits to building a repeatable way to rank markets and evaluate deals. We also break down why supply drives rents across both single-family and multifamily, how to pull useful CoStar insights for free, and how AI can speed up serious real estate research when you ask better questions.
• Neil’s origin story from tech to building campuses and learning depreciation
• using scraped housing and labor data to spot opportunity during the 2008 to 2011 downturn
• the Madera and Fresno strategy for turning empty new homes into leased rentals
• why supply is the most missed variable in market analysis
• the “one rental market” concept across Class C, Class B, Class A, and single-family tiers
• the CoStar report shortcut through brokers and what to read first
• five “Location Magic” metrics for comparing U.S. cities for investing
• current hot areas like Northwest Arkansas, Idaho, and Utah, plus why taxes and insurance matter
• defining a great multifamily property using comp set rent and vacancy performance
• raising capital with a contrarian mindset when prices are off peak
• using AI for deep research with weighted metrics and deal searches

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