
Efficiency in Land Investing: Using AI to Maximize Deal Flow
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Dec 4, 2025 Join Fred, co-founder of Reworked/WeWorked AI, as he shares his innovative journey from land investing to developing 'Betty'—an AI that optimizes mailing efficiency. He discusses how machine learning eliminates human bias, improving seller targeting and cutting costs. Discover surprising demographic indicators predicting sales readiness and why traditional scrubbing methods may hide valuable leads. Fred emphasizes the importance of data over assumptions, and how smarter mailing strategies reduce waste and enhance deal flow.
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Credit Score Changes Signal Motivation
- Credit-score movement is the strongest indicator of a seller becoming motivated.
- Recent drops or rises often reflect life events that make owners open to offers.
Validate And Feed The Feedback Loop
- Test Betty on lists you mailed before to validate performance and build trust.
- Run periodic bottom-half mailings as required to keep Betty's feedback loop healthy and avoid bias.
Quit Manual List Scrubbing
- Stop spending days manually scrubbing lists; use ML to score and prioritize contacts.
- Reinvest saved time and postage into more frequent, targeted list runs.

