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Bringing Machine Learning to the Vacation Rental Industry

by on May 4, 2017

This blog post is the second of our series of guest posts authored by the speakers to present at 2ML: Madrid Machine Learning in Madrid, Spain, on May 11. The first blog post covered how Stats4Trade applies Machine Learning to help active investment fund managers select stocks and make buy/sell decisions using a new software-driven approach.

This post introduces a different application area of Machine Learning, namely Marketing and Sales. Nick Mote, the Director of Innovation at Vacasa, will be presenting how his company determines the value of accommodations and the process to automate such tasks by using Machine Learning. Below there is a high level overview of his upcoming talk. For more on the presenters and topics, please reach out to the 2ML event site.

Much like a hotel operator, Vacasa provides housekeeping and marketing services to create a hotel-like experience across a diverse portfolio of more than 5,000 vacation rentals. However, unlike the hotel industry, Vacasa faces many challenges resulting from managing single properties that are not operating under the same roof.

For instance, pricing vacation rentals in the same market is much harder than pricing a hotel room, since Vacasa’s homes are geographically spread throughout the market. The old adage “location, location, location” is absolutely true when determining the value of accommodations. Neighborhood and proximity to attractions have a huge impact on pricing within a given market, and qualities such as direct beach access add much greater value compared to a similar home three blocks from the same beach.

A hotel also has the advantage of pricing mostly identical rooms, whereas Vacasa faces the challenging task of determining the value of many completely unique properties with non-obvious comparable properties. Two houses in the same neighborhood can have entirely different amenities and quality compared to each other, preventing you from easily grouping and learning from even similar units. For example, how does a six bedroom house on the outskirts of town compare to a one bedroom cottage on the beach? Finding a way to automate the consideration of these factors when setting pricing is no easy task, but a critical one for vacation rental managers to be able to tackle at a large scale.

At Vacasa, the analytics team knew that they could use Machine Learning to find high value correlations that can automatically be adapted to specific markets to predict the appropriate price for any given day of the year. In other words, the Machine Learning algorithms pick the relevant features that are most important in driving the optimal prices. Armed with this understanding, Vacasa launched the second version of their yield management algorithm, code named Alan (named after computer scientist Alan Turing) to automate their pricing of the entire portfolio of properties based on past and current market conditions.

However, Vacasa’s use of Machine Learning technology doesn’t stop at pricing. Machine Learning techniques also predict the time it takes to drive to and clean different sized homes, as well as to predict fraudulent transactions before they occur.

Want to know more about ML in Marketing and Sales?

Don’t miss the opportunity to learn more about how Vacasa is applying Machine Learning to the vacation rental industry at the upcoming 2ML event on May 11 in Madrid, Spain. If you are interested in these business sectors, you may also attend Faraday.io‘s presentation. Stay tuned for future blog posts.

If you haven’t yet done so, there’s still time to register for #2ML17. We hope to see you there!

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