To get an idea of the problem we faced, consider three different situations.
Imagine you had lived in Brazil during the last football (soccer) World Cup. Your hometown will see a huge influx of travelers from all over the world, all united by the greatest football tournament on the planet. You have a spare room in your house, and you want to meet other football lovers and make some extra cash.
For our tool to help you figure out a price, there were a few factors to consider. First, this was a once-in-a-generation event in that country, so we at Airbnb have absolutely no historical data to look at. Second, every hotel was sold out, so clearly there was a massive imbalance between supply and demand. Third, the people coming to visit already had paid immense sums for their tickets and international travel, so they’d probably be prepared to pay a lot for a room. All of that had to be considered in addition to the obvious parameters of size, number of rooms, and location.
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The overall architecture of our tool was surprisingly simple to figure out: When a new host begins adding a space to our site, our system extracts what we call the key attributes of that listing, looks at other listings with the same or similar attributes in the area, finds those that are being successfully booked, factors in demand and seasonality, and bases a price tip from the median there.
The tricky part began when we tried to figure out what, exactly, the key attributes of a listing are. No two listings are the same in design or layout, there are listings in every corner of a city, and many aren’t just apartments or houses but castles and igloos. We decided that our tool would use three major types of data in setting prices: similarity, recency, and location.
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These tools are generating price tips for Airbnb properties globally today. But we think it can do a lot more than just better inform potential hosts as they choose prices for their online rentals. That’s why we’ve released the machine-learning platform on which it’s based, Aerosolve, as an open-source tool. It will give people in industries that have yet to embrace machine learning an easy entry point. By clarifying what the system is doing, it will remove the fear factor and increase the adoption of these kinds of tools. So far, we’ve used it to build a system that produces paintings in a pointillist style. We’re eager to see what happens with this tool as creative engineers outside of our industry start using it.
-More Here
Imagine you had lived in Brazil during the last football (soccer) World Cup. Your hometown will see a huge influx of travelers from all over the world, all united by the greatest football tournament on the planet. You have a spare room in your house, and you want to meet other football lovers and make some extra cash.
For our tool to help you figure out a price, there were a few factors to consider. First, this was a once-in-a-generation event in that country, so we at Airbnb have absolutely no historical data to look at. Second, every hotel was sold out, so clearly there was a massive imbalance between supply and demand. Third, the people coming to visit already had paid immense sums for their tickets and international travel, so they’d probably be prepared to pay a lot for a room. All of that had to be considered in addition to the obvious parameters of size, number of rooms, and location.
[---]
The overall architecture of our tool was surprisingly simple to figure out: When a new host begins adding a space to our site, our system extracts what we call the key attributes of that listing, looks at other listings with the same or similar attributes in the area, finds those that are being successfully booked, factors in demand and seasonality, and bases a price tip from the median there.
The tricky part began when we tried to figure out what, exactly, the key attributes of a listing are. No two listings are the same in design or layout, there are listings in every corner of a city, and many aren’t just apartments or houses but castles and igloos. We decided that our tool would use three major types of data in setting prices: similarity, recency, and location.
[---]
These tools are generating price tips for Airbnb properties globally today. But we think it can do a lot more than just better inform potential hosts as they choose prices for their online rentals. That’s why we’ve released the machine-learning platform on which it’s based, Aerosolve, as an open-source tool. It will give people in industries that have yet to embrace machine learning an easy entry point. By clarifying what the system is doing, it will remove the fear factor and increase the adoption of these kinds of tools. So far, we’ve used it to build a system that produces paintings in a pointillist style. We’re eager to see what happens with this tool as creative engineers outside of our industry start using it.
-More Here
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