Design a dynamic rental pricing system

Design an ML-driven system to help hosts set nightly rental prices. Describe the problem formulation (objective, constraints, business guardrails), data sources (historical bookings, search demand, competitor prices, calendars, events), and feature engineering (seasonality, lead time, availability, price elasticity, cancellations). Propose the modeling approach (e.g., time-series plus gradient boosting with elasticity estimation, or constrained reinforcement learning) and how you would incorporate uncertainty and guardrails. Outline the training pipeline, offline simulation/sandboxing, and online inference architecture; latency and scale requirements; exploration–exploitation strategy; handling cold-start listings and sparse regions; fairness, explainability, and abuse prevention; and a rollout plan with A/B testing and guardrail metrics.