Using AI to analyze your portfolio, and track important metrics

All of these are great post-acquisition workflows. The AI use case I've found most valuable is on the pre-acquisition side — specifically for tax deed auction underwriting, where the analysis problem is fundamentally different from portfolio management.

At a county tax deed auction, you might have 16 properties to evaluate simultaneously. You can't inspect any of them inside. You have the assessed value, the minimum bid, and public records — that's it. The margin for error is huge because you're buying sight-unseen with 100% cash.

Here's how AI changes the auction analysis:

1. Batch comparable screening: Feed the AI the full auction property list with assessed values and parcel details. Have it pull comparable sales within 0.5 miles for each property simultaneously. What would take a analyst days becomes a structured comparison table in minutes. For Broward County's upcoming Auction #113, I'm ranking all 16 properties by spread (assessed value vs. expected bid range) and flagging which ones have the thickest equity cushion.

2. Public data extraction: County appraiser records give you land value vs. building value, year built, square footage, zoning, and ownership history. AI can parse those records across multiple parcels and build a standardized underwriting sheet — something that would require manually pulling data from 16 different property appraiser pages.

3. Risk scoring: Aaron's point about T12 variance analysis applies even more at auction. Since you can't see inside the property, AI can flag risk factors from public data — properties with code enforcement liens, environmental overlays, flood zones, or title clouds that the clerk's database reveals. It's not a replacement for a title search, but it helps you decide which properties deserve deeper due diligence before bidding.

4. Rehab estimation from exterior data: This is where AI is still rough but improving. Street view images, property age, and neighborhood comparable conditions can give you a rehab range — not a precise estimate, but enough to know whether the spread justifies the risk.

The key difference from Justin's QBO workflow: you're not analyzing historical performance. You're predicting forward returns on properties where the data is incomplete and the acquisition is irreversible. Alison's point about treating AI like a fast junior analyst is exactly right — but at auction, that junior analyst is working with half the information and the stakes are higher because there's no inspection contingency.

For anyone doing tax deed auctions, the AI edge isn't in the reporting — it's in the speed of initial screening. You need to know within 48 hours of the auction which 4-5 properties deserve your full attention and which 11 you should pass on. That's where batch AI analysis pays for itself.