How do you measure the accuracy of an AI analyst in real estate?

Measure an AI analyst the way you would measure a new hire: give it deals you have already closed or passed on, compare its figures to the ones you know are right, and count. Track how many figures were correct, how many were flagged for review, how many were wrong, and whether it said when it could not check something. A simple test you can run 1. Pick three to five past deals with known outcomes: the final rent roll, the actual NOI, the price paid, the loan terms. 2. Send the AI only what you had at the start: the OM, the model, the rent roll, the T-12. 3. Score each key figure (price, units or square feet, in-place NOI, market rent, cap rate, DSCR, LTV) as right, close, wrong or not given. 4. Score each red flag it raised: was the issue real? 5. Score what it missed: problems you found later that it did not flag. What good looks like - Every figure traceable to a file or a record, with a date. Untraceable figures count as wrong, even when they happen to be close. - An explicit list of what it could not check. A tool that never says "I could not verify this" is guessing somewhere. - Consistency: the same deal sent twice should give the same core figures. - Speed that holds on a full package, not only on a single document. Why this matters more than demos A demo on a clean sample deal shows the tool at its best. Your own past deals, with their messy files, show how it will behave on Monday. Titleman opens every review with the count: how many files and items it read, how many it checked and how many it could not. You can test it on a deal of your own: https://titleman.ai/products/deal-review