Two different models, two different bets. Here's what each one really delivers, with numbers we can back up.
How does PropScored compare to Offrs? Offrs uses AI to predict which homeowners might sell in the next 12 months and runs ads to capture their info; PropScored skips prediction entirely and sources leads from public-record filings — liens, pre-foreclosure, bankruptcy — that confirm a homeowner is already in a distressed or motivated situation, delivered exclusively to one agent per zip.
Both companies are chasing the same insight: in real estate, being first in the door wins listings. Where they differ is in how they decide who to knock on the door for, and how fresh that information is by the time you get it.
Credit where it's due. Offrs has built a real business around predictive modeling — they claim to leverage over 250 data points per homeowner and say their algorithm predicts more than 70% of listings in a market. They back their pitch with a documented NAR statistic that 3 out of 4 homeowners list with the first agent they talk to, which is a legitimate and well-cited industry finding.1 Their Lead Guaranty — a promised lead volume per tier, with automatic territory expansion if they fall short — is a straightforward, agent-friendly commitment. And their three-stage funnel (predictive sellers → seller signal leads → in-market leads via email nurture) is a coherent system, not just a data dump. If you want broad-market prediction with built-in ad infrastructure and nurture campaigns, Offrs has clearly invested in that.
The honest answer is: not everywhere, and not for everyone. PropScored doesn't predict who might sell — it sources leads from homeowners who have already filed something in the public record that signals real motivation: a lien, a pre-foreclosure notice, a bankruptcy. Every source is scraped daily, and across 1,142 timestamped filings, the median gap between a filing hitting the public record and it becoming a sourced lead is 2 days (bankruptcy in 1 day, pre-foreclosure in 3, liens in 7 — county lien lists arrive as periodic bulk loads, the one exception to daily refresh). For comparison, most batch-based platforms in this space, like PropStream, refresh on roughly 30-90 day cycles — read more on why freshness compounds over a filing's lifecycle.
Across 5,265 sourced prospects, 84% carry a verified phone or email (69% phone, 64% email), and it varies meaningfully by signal: FSBO sits at 96%, liens at 97%, pre-foreclosure at 95%, expired listings at 82%, and tax delinquent at 81%. Offrs doesn't publish a comparable contactability figure on their site, so we can't compare that number directly — only note that PropScored's is independently measured and disclosed.
PropScored sells one agent per zip code, full stop — no splitting a territory with competitors chasing the same lead. Coverage currently spans 11 distress signal types across 15+ counties in Georgia and Florida (DeKalb, Gwinnett, Hillsborough FL, Cobb, Fulton, Pinellas FL, Cherokee, Forsyth, Hall, Henry, Clayton), with distressed-seller leads as a core category. Offrs operates nationally with a broader territory model. If your farm is outside those GA/FL counties, that alone may settle the decision.
| Dimension | Offrs | PropScored |
|---|---|---|
| Data source | AI prediction + ad capture | Public-record filings |
| Refresh cycle | Not disclosed | Daily (median 2 days) |
| Exclusivity | Territory-based | 1 agent per zip |
| Contactability | Not disclosed | 84% verified phone/email |
| Motivation signal | Predicted (12-mo model) | Confirmed (county record) |
| Best for | Broad-market farming, national reach | GA/FL distress niches, exclusivity |
Predictive models guess at motivation; PropScored confirms it. Every lead is sourced from a real public-record filing—bankruptcy, pre-foreclosure, liens—not a propensity score. Harvard Business Review shows the winning move is reacting quickly to customer-driven signs of interest. A court filing or tax delinquency is exactly that—a recorded event, not a probability. That's why freshness and verified distress beat algorithmic guessing.
If you want a national predictive-lead engine with built-in ad campaigns and email nurture, and you're comfortable with a broader definition of