From Data to Insights: Segmenting Airbnb’s Supply

Introduction

At Airbnb, our supply comes from hosts who decide to list their spaces on our platform. Unlike traditional hotels, these spaces are not all interchangeable units in a building that are available to book year-round. Our hosts are people, with different earnings objectives and schedule constraints — leading to different levels of availability to host. Understanding these differences is a key input into how we develop our products, campaigns, and operations.

Over the years, we’ve created various ways to measure host availability, developing “features” that capture different aspects of how and when listings are available. However, these features provide an incomplete picture when viewed in isolation. For example, a ~30% availability rate could indicate two very different scenarios: a host who only accepts bookings on weekends, or a host whose listing is only available during a specific season, such as summer.

This is where segmentation comes in.

By combining multiple features, segmentation allows us to create discrete categories that represent the different availability patterns of hosts.

But traditional segmentation methodologies, such as “RFM” (Recency, Frequency, Monetary), are focused on customer value rather than calendar dynamics, and are often limited to one-off analyses on small datasets. In contrast, we need an approach that can handle calendar data and daily inference for millions of listings.

To address the above challenges, this blog post explores how Airbnb used segmentation to better understand host behavior at scale. By enriching availability data with novel features and applying machine learning techniques, we developed a practical and scalable approach to segment availability for millions of listings daily.

From Data to Insights: Segmenting Airbnb’s Supply