Product design question
If Facebook built a product to help users find rental housing, what would it look like?
- Meta
- Product design
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Product sense on a marketplace problem: identifying trust gaps in rental housing and how Meta's social graph could help.
How to approach it
- Confirm the user: renters searching for housing, or landlords listing units, and pick a primary focus.
- Identify the core pain point in rental search today: scams, stale listings, and no trusted reviews.
- Propose how Meta's asset helps, such as showing mutual friends who lived in a building or verified landlord identity.
- Sketch the core flow: search, filter, message the landlord, and see social trust signals.
- Define success as verified listings created or messages that lead to a viewing.
What a strong answer includes
- Leads with trust and safety, the biggest gap in rental search, rather than just another listings feed.
- Uses Facebook's real-identity graph to show mutual connections or reviews from past tenants.
- Names a concrete risk, like scam listings, and a mitigation, like identity verification for landlords.
- Proposes a measurable early goal, e.g. 20 percent of listings with a verified landlord badge.
Common mistakes
- Rebuilding a generic Zillow clone with no Facebook-specific advantage.
- Ignoring landlord-side incentives to list on a new platform.
- Not addressing scam and fraud risk, which is the category's biggest trust problem.
Likely follow-up questions
- How would you get landlords to list here instead of on Zillow?
- How would you handle fraudulent listings?
- What would the MVP look like in one city?
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More questions from Meta
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop