Metrics question
How do you measure the success of the Hot Home feature in Redfin?
- Redfin
- Metrics
- Medium
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What this question tests
Ability to build a metric tree for a real estate discovery feature and separate top-of-funnel signals from bottom-of-funnel outcomes.
How to approach it
- Clarify what Hot Home flags, likely listings predicted to sell fast or draw many offers, and who sees it.
- State the goal: help buyers act faster on competitive listings, driving more tours and offers through Redfin.
- Build a metric tree: exposure (views of flagged homes), engagement (tour requests and saves versus unflagged listings), and outcome (offer rate, time to offer).
- Add a guardrail: prediction accuracy, the share of flagged homes that actually sold within the predicted window.
- Compare conversion rate on flagged versus unflagged listings to isolate the feature's lift.
- Segment by market, since "hot" varies a lot between a market like San Francisco and a slower metro.
What a strong answer includes
- Names a north star like offer submission rate on flagged listings within 7 days, not just page views.
- Distinguishes correlation from causation by comparing against a control group of similar unflagged homes.
- Adds prediction precision as a guardrail so the label stays trustworthy.
- Notes false hot labels can push rushed, worse decisions, a real risk worth tracking.
Common mistakes
- Using raw click count as the success metric.
- Ignoring the guardrail on prediction accuracy.
- Not segmenting by market conditions.
Likely follow-up questions
- How would you validate the underlying hot prediction model?
- What would you do if flagged homes converted no better than unflagged ones?
- How do you avoid the label unfairly increasing bidding wars?
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More questions from Redfin
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop