Design a price prediction model like Airbnb
PremiumML systemEasy
- Price prediction
- Regression
- MAPE
Quick answer
This ML system interview question asks you to design a price prediction model like Airbnb. Aim for a design that turns data into useful predictions in production, then explain the data, training, serving and monitoring. Interviewers want to see how you frame the problem, measure success and keep the model healthy after launch.
The problem
Design a price prediction model like Airbnb's smart pricing: suggest a nightly price for every listing and every future date so hosts get booked at a fair price. The hard parts are seasonality and local events, brand-new listings with no history, and labels that only show prices hosts chose, not what guests would have paid.
Hosts see a suggested price and a range on their calendar and can accept it or type their own. The suggestion must be good enough that hosts trust it, and it must be explainable in simple terms ("similar homes nearby are booking at 120 dollars").
What must a price prediction model like Airbnb do?
- Daily suggestions: Suggest a nightly price and a low-high range for every active listing for each of the next 365 nights.
- New listings: Give a sensible price to a listing on its first day, before it has any bookings.
How well must it do it?
- Accuracy: Median absolute percentage error on booked nightly prices under 15%.
- Calendar latency: The host calendar reads 365 suggestions in under 200 ms p99.
What scale should you design for?
Use these numbers in your estimates.
The scale numbers unlock with this questionUnlock
Key concepts
- Price prediction
- Regression
- MAPE
- Seasonality
- Cold start
- Gradient boosted trees
How you’ll practise
- Problem framing
- Data and labels
- Features and model
- Training and evaluation
- Serving and monitoring
- Wrap-up
Lala, personalized to you, gives a hint when you ask and reviews your design when you are ready.
What do interviewers look for?
In any ML system design round.
- Frames the product goal as a clear ML problem
- Defines offline and online success metrics
- Explains where the data and labels come from
- Separates training from serving and keeps features consistent
- Plans for drift, retraining and rollbacks
- Weighs model quality against latency and cost
Questions people ask about this interview question
- Which design type is this question?
- It is an ML system question. You design a price prediction model like Airbnb and explain your choices the way you would in an interview.
- Is this question asked at entry level?
- It is marked Easy, so it suits engineers who are new to ML system interviews. It is also a good warm-up before harder questions.
- What should I cover first?
- Start with the product goal, how success is measured and what data is available. Agree on scope with the interviewer before you design anything.
- Can I practise it for free?
- It is a premium question. Sign up and unlock it once for 10 credits; it then stays unlocked. When you finish, Lala, your personalized AI mentor, can review your design for 30 credits.
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Updated · Written by the SevyDevy team