What is churn prediction in property platform users?

Churn prediction in property platform users is the application of machine learning models to identify which users of a real estate platform whether buyers, sellers, renters, or agents are likely to become inactive or move to a competitor.

What Constitutes 'Churn' on a Property Platform

• A registered buyer who stops logging in and searching for properties.
• A property owner who lists elsewhere or delists without transacting.
• An agent who reduces activity on the platform.
• A subscriber who cancels their premium membership.
• A tenant whose rental agreement expires and does not renew.

Signals Used in Churn Prediction Models

• Declining session frequency and duration.
• Reduction in listing views or property saves.
• No enquiries submitted in a defined period.
• Low email open rates or unsubscribes.
• Complaints logged but unresolved.
• Search activity narrowing or stopping.
• Competitor platform activity detected (if data is available).

Churn prediction is essential for PropTech platforms that depend on active, engaged user bases for marketplace liquidity. By catching at-risk users early and delivering relevant value, platforms can protect their supply-demand balance and drive long-term sustainable growth.