Airbnb Leverages Machine Learning to Maximize Host and Company Revenue

Founded in 2008 by Brian Chesky, Joe Gebbia, and Nathan Blecharczyk, Airbnb has grown its lodging rental platform from a small Y Combinator startup to a $20 billion dollar unicorn. Airbnb’s website allows individuals to list their housing units – either an extra bedroom or the entire space – for others to rent. In exchange, the company captures a portion of the value (via a 3% host service fee and a 6-12% guest service fee) for setting up the transaction, managing the payment process, providing customer service and insurance, etc. Given the scale economies they capture via their asset-light model, the company’s goal is to maximize its revenue stream both by increasing the number of units available for rent on the platform and by ensuring that those units are priced to maximize the likelihood of being rented (as opposed to losing potential customers to hotels) – the latter of which is certainly aligned with users’ motivations.

While Airbnb has spent a lot of marketing money to increase the number of units available on its platform, the company has also begun hiring technical talent (e.g., data scientists) to analyze how the units already on the site could be better priced to satisfy all three stakeholders – the hosts, the occupants, and Airbnb. Historically, Airbnb hosts have priced their properties based on nearby comparables. However, this process is quite inefficient, as it not only forces users to spend time finding “comparable” properties, but also, the person(s) establishing to “baseline” that others use may have over- or underpriced the property, based on their financial goals and motivations. To help minimize these inefficiencies, Airbnb has recently launched Price Tips – a pricing guide that provides pricing suggestions for hosts, which helps hosts capture more revenue via increased likelihood of their unit being rented, while also providing Airbnb with more value capture by taking a portion of the rent. Airbnb’s Price Tips functionality is built using Aerosolve – the company’s open-source machine learning pricing tool.

Aerosolve and Price Tips evolved (and continue to evolve) using the research from Airbnb’s data scientists. These scientists have leveraged big data and statistical techniques (e.g., regression analysis) to help determine how to appropriate price a unit, based on supply, demand, and other factors. Further, via Aerosolve’s machine learning techniques, the increasing points of data (Airbnb has already used over 5 billion points for their algorithm) will continue to make the pricing suggestions smarter and more dynamic. Specifically, Airbnb uses some of the following categories (not exhaustive) to determine pricing:

• Day of the week
• Neighborhood of listing, with varied demand for different types of crowds (e.g., older populations may prefer quieter areas, but they also (often) have more disposable income than younger individuals)
• Demand (seasonality or special events (e.g., major concerts), the latter of which ties to neighborhood in some cases)
• Quality (e.g., property size, number of people the unit sleeps, access to wi-fi, air conditioning)
• Property reviews

Using critical attributes, Airbnb’s machine learning model examines which other listings with similar features are being booked, and bases their Price Tips suggestion from the median value (Exhibit 1).

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Exhibit 1. Price Tips offering suggested prices based on each day of the week, using demand forecasts.

A couple of interesting insights the Airbnb team has discovered, (for those of you interested in maximizing the likelihood of renting out your home) is that the number of reviews does increase the likelihood of a booking. No reviews makes guests feel uncertain, so even one review helps, with decreasing “returns” as the number of reviews increases. Also, although “stylish” photos of lit living rooms may be preferred by professional photographers, Airbnb guests seem to prefer homes featuring cozy bedrooms with warm colors.

While the Price Tips algorithm seems to certainly help, hosts still have the right to put their home up for above or below the suggested price. However, keep in mind that Airbnb’s model has shown, “When hosts price themselves within 5% of the suggested price, they are “nearly four times” as likely to get a booking…” (Quote from: E. Huet, Forbes).

It’s clear that Airbnb has been able to provide true value to its customers, as it has built (with the support of ~$120 MM in funding) a strong, convenient, and trustworthy brand, which continues to achieve rapid scale and maintain its competitive strength via strong network effects. With that said, the company still faces competition from incumbents in the lodging space, namely hotels. Hotel owners have not only begun lobbying against Airbnb, but they have also begun adjusting their own pricing algorithms using big data (e.g., Duetto). Faced with this competition, Airbnb will need to position itself to continue maximizing its revenue stream not only by optimizing pricing, but also by offering other tools (e.g., training users how to be great hosts, offering to have photographers take pictures) to support its growth.

Selected sources:

http://www.forbes.com/sites/ellenhuet/2015/06/05/how-airbnb-uses-big-data-and-machine-learning-to-guide-hosts-to-the-perfect-price/

https://www.youtube.com/watch?list=PLe_YVMnS1oXbQfnHoWyZwfc1_fhc1hhMT&v=Cwwu_AVh9n0

http://spectrum.ieee.org/computing/software/the-secret-of-airbnbs-pricing-algorithm