I started with supply side (number of listings, occupancy rates, average nightly price) and worked toward a GMV number before applying a take rate.
Start by clarifying the scope (e.g., global annual revenue, including all fees) and then use a top-down approach: estimate the number of bookings (nights) and average daily rate (ADR), then apply Airbnb's take rate. Alternatively, segment by host type (individual vs. professional) or by region to improve accuracy. State assumptions clearly and sanity-check the final number against known benchmarks.
Pro tip: Show product thinking by discussing how Airbnb's revenue model has evolved (e.g., from just booking fees to Experiences and long-term stays) and how that impacts the estimate. Also, mention that you'd validate your estimate with public data (e.g., earnings reports if public) or industry reports.
Define what 'annual revenue' means (e.g., net revenue after host payouts, or gross booking value) and set the time frame (e.g., pre-COVID or current). State any assumptions about global reach, average stay duration, etc.
Calculate the number of nights booked annually by estimating the number of active listings, average occupancy rate, and average nights per booking. Alternatively, estimate from the demand side: number of travelers using Airbnb per year times average nights per trip.
Estimate the average price per night across all listings, considering mix of budget and luxury, and regional differences. Use a range (e.g., $80-$150) and justify with reasoning.
Airbnb typically charges guests a service fee (around 14%) and hosts a fee (around 3%), but for simplicity, assume a blended take rate of ~10-15% of gross booking value. Multiply total nights by ADR to get gross booking value, then apply take rate to get revenue.
Compare your estimate to known figures (e.g., Airbnb's 2019 revenue was ~$4.8B) and adjust assumptions if needed. Discuss potential sources of error and how you'd improve the estimate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.