AirDNA’s Short-Term Rental Data Methodology

Leslie James | October 26, 2018

How do we develop our short-term vacation rental metrics?

We only report active vacation rental properties.

AirDNA collects short-term vacation rental data from hundreds of sources, including Airbnb and HomeAway, to build a comprehensive view of the short-term rental market.

We only report “active” properties that are actually located in the geographic boundaries of each city. A large percentage of listings on Airbnb and HomeAway are no longer being actively rented, haven’t updated their calendar in many months, or haven’t accepted a reservation for an extended period of time.

We remove these listings from our analysis to provide a more accurate picture of the current competing properties in each area. In addition, Airbnb and HomeAway display many short-term rental listings that are located outside of the queried location. AirDNA’s reports only displays properties actually located within the boundaries of each city, postal code, or neighborhood.

Don’t see your city in MarketMinder? Contact us to get started.

 

We geolocate short-term vacation rental listings.

We create a much more exact property location than is shown on the Airbnb or HomeAway websites by taking the center point of the average displayed latitude and longitude coordinates. AirDNA has built out one of the most robust GIS databases in the world to report on short-term vacation rentals by zip code, neighborhood, city, MSA, state and country.

 

We calculate revenue based on daily rates + cleaning fees.

We are constantly reviewing the calendar information of short-term rental properties to determine when a place was booked and for how long. When a new reservation is recorded, we calculate the advertised daily rate of each of those days directly before the booking occurred and then add in the cleaning fee for each unique reservation.

At the end of the month we sum up exactly how many days have been booked and at what rate, and add in cleaning fees to calculate monthly revenue. Fees for additional guests or last-minute discounts are not visible.

 

 

What will you make on your rental property?

Rentalizer is the first automated valuation model (AVM) to accurately predict what a property can earn as a vacation rental.

Assess revenue potential, ADR, and occupancy of a property based on local Airbnb and HomeAway comps over the last twelve months.

Make the best real estate investment with Rentalizer.

Better predictive algorithms and AI.

We know booked vs. blocked days on your calendar.

AirDNA has developed advanced artificial intelligence and machine learning technology that allows for accurate identification of blocks of unavailable dates observed on short-term rental platforms as either booked by a customer or blocked by the host. This ability to discern between booked and blocked days is core to any analysis of Airbnb data.

Airbnb originally did not obscure booked vs blocked information and only started that practice in Q4 2015. AirDNA’s ability to develop such a model is possible because of the extensive historical data set which captured actual booked and blocked data for 18 months prior to Airbnb’s implementation of the practice of obscuring data types, as well as institutional knowledge on Airbnb host behavior and smart application of modern artificial intelligence technology.

AirDNA utilizes statistical pattern-recognition techniques, which define a mathematical relationship between what is known about a property and actual classification of genuine reservations or dates blocked by the host. These are similar in concept to the algorithms that enable Amazon to recommend new products that you might be interested in, Netflix to recommend new movies and OkCupid to recommend potential partners.

Our predictive model is always learning.

The accuracy of AirDNA’s prediction model is tested by setting aside a portion of AirDNA’s known booked/blocked historical data, hiding it from the model training data and then asking the system to classify the blocks of unavailable dates once it’s been trained. This output is then compared against the actual known booked/blocked status of each grouping of days to assess the degree of predictive accuracy.

True to its machine learning classification, AirDNA’s artificial intelligence model continues to learn and improve as time goes on. This is important as short-term rental booking trends are likely to continuously evolve over time as evidenced by past behavior.

AirDNA’s AI continues to observe behavior, extract patterns from new information and historical knowledge and, as a result, predict short-term vacation rental booking information on Airbnb and HomeAway with accuracy.

Ready to optimize your vacation rental?