Matching buyers with sellers depends a lot on price. Price too high and buyers are turned off. Price too low and the seller loses out.
To increase the odds of matching buyers and sellers, Airbnb’s team of data scientists and engineers have worked hard to develop pricing technology, said Bar Ifrach, Airbnb data scientist, during a recent OpenAir 2015 talk.
“We’re trying to empower our hosts with tools to price their listings and get bookings seamlessly and effectively, so we have more hosts and stays on Airbnb and more matches on the platform,” Ifrach said.
Airbnb displays a host’s calendar. Days shown in white are available to book. Gray indicates days already booked, in the past, or that are days the host doesn’t want to book.
At the bottom of days in white, the host sees a color bar, which indicates how likely the host is to get a booking for that day at a given price. Green indicates a high likelihood. Yellow means there’s a medium chance of booking at the current price; red suggests a low probability of booking.
The technology behind this is divided into two parts, Ifrach said: Modeling and Aerosolve.
With modeling, Airbnb is “trying to predict for every day of year, for every listing, what will be the likelihood of getting a booking for any possible price,” Ifrach explained. “Then we can find a price that works best.” Airbnb modeling is doing this “on a huge, global scale” by looking at millions of derived features and over 5 billion training points, he said.
Three main considerations go into the pricing model:
- Demand, or the impact of seasonality or special events (such as Austin’s SXSW conference) on an area. Airbnb’s model translates demand features into pricing predictions.
- A listing’s location, such as the market, neighborhood, or street block. Example: San Francisco has many distinct neighborhoods that appeal to different crowds, and “we need to account for that” in pricing, said Ifrach. This is accomplished through grids and k-d trees.
- A listing’s type and quality. Airbnb’s pricing model is “unique,” Ifrach said, because it incorporates such factors as a property’s size and specific qualities.
As an example, he compared a houseboat situated across from the Eiffel Tower in Paris and a private room with a view of the tower. To determine pricing for each, Airbnb’s model takes into account the differences in the two listing types (houseboat vs. a room), amenities (such as air conditioning), and other qualities. To quantify a listing’s quality, Airbnb’s modeling uses Dirac and Cubic splines to capture the effect of guest reviews.
The second part of Airbnb’s dynamic pricing technology is Aerosolve, “machine learning for humans,” said Airbnb engineer Hector Yee, who took the stage after Ifrach.
Airbnb introduced Aerosolve at OpenAir 2015 as an open-source machine learning library available on GitHub. Aerosolve is designed to interpret complex data sets so that humans can easily understand them. While Airbnb’s modeling suggests pricing to hosts, Aerosolve’s goal is to add context to the pricing recommendation so the host understands it. “You have to understand why the model makes it decisions. Maybe the host’s pricing is too high because they have no reviews or because it’s low season,” Yee explained.
The blog post “Aerosolve: Machine learning for humans” offers details about how Aerosolve works and how developers can use it.