ROC Real Estate, led by Ramon Casaus, operates in Phoenix, AZ, and Albuquerque, NM, relying on cold calls and outbound marketing to fill its pipeline—an expensive and often hit-or-miss process. Seeking better efficiency, they partnered with HouseCanary to deploy a propensity-to-sell machine-learning model that scores homeowners on their likelihood to list in the next 12 months.
Data Calibration: ROC received 5,000 property records across five ZIP codes. Initially targeting only 99%-score homeowners, they found many already in market—proof the model was accurate.
Refined Targeting: They shifted focus to homeowners scoring between 87–95%, unlocking a larger, actionable pool of off-market prospects.
CRM & Dialer Integration: Using Brivity CRM for tracking and dialers for efficient calling, agents completed 5–6 follow-ups per contact, scrubbing bad numbers and verifying details.
Funnel & Nurture: Verified leads moved into long-term marketing flows—home valuation reports and market updates—ensuring sustained engagement.
4 confirmed listings, including two luxury homes valued at $1.2M and $1.5M.
175 high-quality leads added to ROC’s nurture pipeline.
10% projected conversion on nurtured leads over the next year—a dramatic improvement over random cold-call conversion rates.
Ramon Casaus sums it up:
“I haven’t found a better way to have high-quality conversations with sellers and build a robust database for ongoing marketing”.
By harnessing data-driven insights, ROC Real Estate transformed its outreach from shotgun to sniper precision—focusing effort where it counts and unlocking premium listings that traditional methods missed entirely.
