The sharing economy needs machine intelligence to set prices
How much should you charge someone to live in your house? Or how much would you pay to live in someone else’s house? Would you pay more or less for a planned vacation or for a spur-of-the-moment getaway?
Answering these questions isn’t easy. And the struggle to do so, my colleagues and I discovered, was preventing potential rentals from getting listed on our site -- Airbnb, the company that matches available rooms, apartments, and houses with people who want to book them.
In focus groups, we watched people go through the process of listing their properties on our site—and get stumped when they came to the price field. Many would take a look at what their neighbors were charging and pick a comparable price; this involved opening a lot of tabs in their browsers and figuring out which listings were similar to theirs. Some people had a goal in mind before they signed up, maybe to make a little extra money to help pay the mortgage or defray the costs of a vacation. So they set a price that would help them meet that goal without considering the real market value of their listing. And some people, unfortunately, just gave up.
Clearly, Airbnb needed to offer people a better way—an automated source of pricing information to help hosts come to a decision. That’s why we started building pricing tools in 2012 and have been working to make them better ever since. This June, we released our latest improvements. We started doing dynamic pricing—that is, offering new price tips daily based on changing market conditions. We tweaked our general pricing algorithms to consider some unusual, even surprising characteristics of listings. And we’ve added what we think is a unique approach to machine learning that lets our system not only learn from its own experience but also take advantage of a little human intuition when necessary.
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