The Secret of Airbnb’s Pricing Algorithm

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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WE-COMMERCE: The sharing economy's uncertain path to changing the world

Peer-to-peer collaboration is gaining ground and changing the economics of the future, but there are questions to answer and obstacles to overcome.

Here's a typical story you're hearing about the sharing economy: Millennial needs ride to music festival. She lives 15 minutes from downtown, which has hardly any parking, and her city doesn't have an efficient public transportation system.

No cabs are available. She remembers the ride sharing service Lyft recently came to her city and downloads the app. Three minutes later, a guy who drives a neon green Dodge pulls in her driveway.

She finds out the driver had just started his shift after a day of work at his full-time job at a health insurance company. He likes the extra money, but he also enjoys meeting people and helping them out. There's no pink moustache on the front of his car like most Lyft drivers — it attracts too much attention.

They arrive at the music festival, she tips him, bids him well. She probably won't see him ever again, but for a short period of time, they had a mutually beneficial relationship.

That anecdote is likely one of the many reasons the "sharing economy" is still considered a hippie movement by some, and a yuppie San Francisco trend by others. Why would a Lyft ride be important for the future of global business?

The answer: trust.

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