Design a dynamic rental pricing system

Q: Design a dynamic rental pricing system

This is a ML System Design interview question from Airbnb for Software Engineer roles. View the full question and solution on PracHub.

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Question

System Design: ML-Driven Nightly Pricing for Short-Term Rentals

Context

Design a production ML system that recommends (and optionally auto-sets) nightly prices for hosts on a two-sided rentals marketplace. The system should maximize long-term marketplace health while protecting hosts and guests with business guardrails.

Requirements

Problem formulation
- Objective(s)
- Constraints and business guardrails
Data sources
- Historical bookings, search demand, competitor prices, calendars, local events
Feature engineering
- Seasonality, lead time, availability/inventory, price elasticity, cancellations
Modeling approach
- E.g., time-series + gradient boosting with elasticity estimation, or constrained reinforcement learning
- How to incorporate uncertainty and guardrails
Training pipeline and evaluation
- Offline training, offline simulation/sandboxing
Online inference and architecture
- Service design, latency and scale targets
Exploration–exploitation strategy
Handling cold-start listings and sparse regions
Fairness, explainability, and abuse prevention
Rollout plan with A/B testing and guardrail metrics

Design a dynamic rental pricing system

System Design: ML-Driven Nightly Pricing for Short-Term Rentals

Context

Requirements

Solution (Locked)

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