I've been investing in Columbus for a few years and lately I've been going deeper into county data — building permits, code violations, tax delinquency — to find motivated sellers before they hit MLS.
The combination that's been working best for me: properties with 90+ days on market + a price drop + tax delinquency flags. Those sellers have usually accepted reality on price.
Curious what others are using. Anyone having luck with code violation lists? Skip tracing? I've been experimenting with pulling county ArcGIS data directly but it's a lot of manual work per county.
Investor · Austin, TX · Member since 2014 · 144 posts · 86 votes
2w
I don’t think there’s one signal I’d trust by itself. The value comes from stacking signals that strengthen each other.
Tax delinquency, code violations, absentee ownership, high equity, vacancy, or a long ownership period may each mean very little alone. When several overlap (and the underlying events are recent) you have a much stronger reason to prioritize that owner.
Skip tracing is separate. It doesn’t tell you whether someone is motivated; it only determines whether you can reach them.
Ultimately, I’d track each signal and combination through actual conversations, offers, and contracts. That’s the only reliable way to learn which data is creating opportunities versus just creating a larger list.
Great points on stacking signals - that matches my experience exactly. Any single flag is noise, but recency plus overlap is where the real signal lives.
I've been scoring my lists with a composite approach: yield-weighted scoring that blends tax delinquency, time on market, price cuts, and pre-foreclosure status. The recent-event filter you mentioned made a big difference - a 2-year-old code violation means much less than one filed last month.
On skip tracing being separate from motivation - agreed, and I'd add that it's the biggest hidden cost. Building a strong list you can't reach is just an expensive spreadsheet.
What's your process for tracking signal performance through to contracts? I've been meaning to set up a simple feedback loop - conversation rate by signal combination - but haven't gotten further than noting it manually.
Great points on stacking signals - that matches my experience exactly. Any single flag is noise, but recency plus overlap is where the real signal lives.
On tracking signal performance through to contracts - here's what's been working for me. I log every outreach attempt against the signal combination that produced the lead: which flags were present, how recent, and the source (tax delinquency list vs code violation vs DOM + price cut). Then I track three conversion points: contact rate (did they answer), conversation rate (did they engage), and contract rate.
The pattern so far: tax delinquency alone converts worst - those owners are often in genuine hardship and hard to reach. The best performing combination is 90+ DOM with a price cut plus absentee ownership - those sellers are already motivated by the market's verdict, they just need an offer that respects their timeline. Code violations filed within the last 60 days add real signal on top of either.
The feedback loop is simple but you have to be disciplined about logging it. Even a shared spreadsheet with signal combo, date, outcome, and next step tells you within 20-30 outreach attempts which combinations deserve more of your list budget.
What I've found most valuable is refusing to trust any list I can't tie back to conversations. The data is only worth what the conversations prove.