How STR Data Gives Insights for Property-Level Optimizations

AirDNA provides us with credibility. Instead of pontificating about what we think the value of the property will be over the course of the year, we can show property owners the figures and explain exactly why we have come to that conclusion.

Hostmaker is one of the most respected property management companies in Europe, present in nine cities in six countries over the world. The company has quickly expanded from Airbnb to other channels including HomeAway, Booking.com, and Tripadvisor, currently managing over 2,000 properties. The team prides itself on achieving superior yields for hosts, which are driven by their proprietary pricing technology, thoughtful interior design, and global hospitality experience.

What issues did Hostmaker face pre-AirDNA? 

Before they started using AirDNA, Hostmaker did not have reliable access to market data. Their analysts were reduced to manually trawling through hundreds of homes listed on Airbnb to draw conclusions about a market. Before using AirDNA, in Hostmakers words, the methodology was a little more trial and error. Customers had to just trust the company’s judgment based on industry experience - but it couldn’t really back it up with data. AirDNA has been invaluable in plugging that information gap.

Predicting and Projecting Property Performance

Hostmaker now uses AirDNA data to predict and demonstrate property performance. AirDNA’s Forward Data Feed allows Hostmaker’s team of analysts to accurately judge the future performance of properties in a given period of time, and compare this to similar properties in the area. 

As a vacation rental management company, Hostmaker is able to prove to property owners that their techniques and services are some of the best available, allowing them to grow their customer base.

Another use of AirDNA data has been in improving guest satisfaction by ensuring that every property managed by Hostmaker is accurately represented on Airbnb and HomeAway. Hostmaker uses the Property Performance Data to identify potential data entry errors and verify that the listing characteristics, amenities, and descriptions accurately reflect the Hostmaker master inventory for the property. 

“We wanted to come up with our own assumptions analyzing the data. AirDNA - unlike other competitors – provides us with the raw data we need to utilize our own functions and methodology to interpret the data the way we want.”

As their data science team has grown, Hostmaker has appreciated being able to manipulate the raw data in the way that works best for their business. As a vacation rental management company, Hostmaker is able to prove to property owners that their techniques and services are some of the best available, allowing them to grow their customer base.

Take a concrete example: 2018’s World Mobile Congress in Barcelona.

When a compression event, the World Mobile Congress in Barcelona, occurred that year, Hostmaker finally had the data to back up their pricing strategy with property owners. 

“Our prices fluctuate for each Mobile World Congress according to demand that year. We saw a decline in bookings for 2018 in contrast to 2017, so our pricing was adjusted according to demand - using AirDNA data allowed us to explain the reason behind this to hosts in a tangible way. Pulling the information collected by AirDNA, we can reduce the human error in our own data, and do it in a way that's automated, rather than just one-by-one in a manual process. Before we started using AirDNA, our methodology was a little more trial and error. Customers had to just trust our judgment based on industry experience - but we couldn’t really back it up with data. AirDNA has been invaluable in plugging that information gap.

Pulling the information collected by AirDNA, we can reduce the human error in our own data, and do it in a way that's automated, rather than just one-by-one in a manual process”.

Discover how Hostmaker, a major property management company in Europe, used AirDNA data to predict and demonstrate property performance.

How STR Data Gives Insights for Property-Level Optimizations

How STR Data Gives Insights for Property-Level Optimizations

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