Predictive Data Analytics: How REALTORS® Identify Sellers Before They List

Learn how predictive data helps REALTORS® identify likely sellers, prioritize leads, and win more listings with less guesswork. Learn how predictive data reveals who is most likely to sell—and how REALTORS® use it to win more listings with less guesswork. Most REALTORS® are still chasing listings after sellers raise their hand. Top producers do something different. In Predictive Data Analytics, you’ll learn how consumer behavior, data aggregation, and real-world triggers can identify homeowners who are most likely to sell—before they list their property publicly. This session breaks down a complex topic into simple, actionable strategies REALTORS® can apply immediately to increase listing opportunities and improve quality of life. What You’ll Learn • What predictive data analytics actually is (without tech jargon) • How online behavior signals when someone is thinking about selling • The most powerful seller “triggers” that predict future listings • How absentee owners, landlords, and vacant properties fit into predictive models • Why handwritten notes and direct outreach still outperform automation • How REALTORS® use predictive data to prioritize sellers instead of guessing • How this system consistently leads to more listing-side transactions Why This Matters Predictive data is already used by the world’s largest companies—Amazon, Apple, Zillow, and major insurers. This class shows how REALTORS® can apply the same thinking ethically and practically at the local level. You’ll walk away understanding how to: • Spend less time prospecting the wrong people • Focus on sellers with higher probability of listing • Shift from buyer-heavy production to listing-driven income • Build repeatable systems that scale over time Who Should Attend • REALTORS® who want more listings • Agents tired of cold prospecting with low conversion • REALTORS® interested in systems, leverage, and smarter marketing • Professionals who want data-supported decision making—not guesswork