Resources · PM Prep · Metrics Investigation
How do you measure success of the Hot Home feature in Redfin?
What this question is really testing ↓
The real test: The interviewer is testing whether you understand that measuring success of a prediction feature requires evaluating the prediction itself, not just user engagement with it.
Senior distinction: A mid-level PM proposes engagement metrics. A senior PM starts by asking what the prediction accuracy is and whether it's being tracked at all — then builds the measurement framework from accuracy upward.
Measuring Redfin's Hot Home: Start Here, Not There
Before you pick a metric, you need to understand what the feature actually claims — and why that changes everything.
The Three Metrics Redfin's Hot Home Actually Needs
Accuracy, utility, and trust — why you need all three and why most measurement frameworks only get one.
What to Build First When the Hot Home Badge Is Failing
The explicit call most candidates avoid — what to instrument first and what to cut.
Key Takeaways
For prediction features, measure accuracy of the prediction before engagement with it — engagement without accuracy is a vanity metric.
Separate stakeholder success metrics — buyer utility and seller satisfaction can conflict, and blending them hides the tension.
Measure trust decay through repeat behaviour — trust erodes slowly then breaks suddenly.