It can be hard to let go. Especially when it comes to something with such a direct impact on your livelihood and lifestyle.
That’s right, I’m talking about pricing your vacation rental.
Although the temptation to control your own destiny and manage your own pricing is strong, the number of factors influencing short-term rental pricing is staggering and growing by the minute. It is virtually impossible for a human to manually assimilate all of the necessary data to come to an actionable conclusion. And then repeat that process daily, always looking 6 months out.
AirDNA’s Path to Solving the Pricing Problem
Earlier this year, AirDNA launched its forward-looking Market Rates calendar, which allows users to filter daily prices of available rentals on the market based on size and type of rental.
Taking that pricing algorithm one step further, AirDNA released its new Recommended Rates calendar earlier this month, which further relies on AI by combining a rental’s actual historical performance with local market dynamics to produce personalized price recommendations.
By analyzing several years worth of global vacation rental pricing data and developing an automated dynamic pricing algorithm, AirDNA is helping vacation rental managers save time and maximize their revenue by removing the guesswork.
The Not-So-Secret Sauce
Over the past four years, AirDNA has grown to be the most-trusted source for vacation rental data. With over 10,000 active subscribers in over 80 countries, AirDNA has accelerated the success of hosts and property managers worldwide.
AirDNA uses a mix of scraped data and source data to analyze global vacation rental performance. Scraped data involves collecting calendar data at the listing level and modeling whether unavailable days were actually reserved or blocked by the listing owner. Our source data comes directly from more than 300,000 active short-term rental listings via property management systems, channel managers and short-term rental hosts.
AirDNA’s integrated data set has been vetted by third parties and is more accurate than source data alone, based on a more diverse and comprehensive sample size. As of August 2019, AirDNA was collecting daily data on over 10,000,000 listings.
So it’s no secret that AirDNA would turn to its massive cache of performance data to help solve the vacation rental pricing problem.
The hard part was building a model that understands the pricing behavior of different rental types in markets with different booking dynamics. This enabled AirDNA to determine optimal rates for virtually any rental at any point within the next six months.
How AirDNA’s Recommended Rates Model Works
Vacation rental pricing starts with accurately understanding booking demand. With the help of a million points of ground truth derived directly from Airbnb and another proprietary model that deciphers between reserved, blocked, and available calendar days.
The next fundamental piece is creating a base rate. AirDNA has discovered that pricing models work best when fluctuating a price around a base anchor rate instead of independently predicting a new rate each day.
AirDNA looks at all booked days for properties over the past twelve months to get their average daily rate (ADR) and compares it to seasonal trends in the market to understand when the property received those bookings. We then use that data to generate a seasonally agnostic base rate that works any time of year, and scales up and down based on overall market seasonality.
Base rates are recalculated once a month and the seasonal adjustment works much like our revenue potential model.
For example: if your rental was only booked during the Coachella music festival last year, the pricing model would recognize that your property was booked during a high demand event in Palm Springs’ high season, and scale down your base rate based on what other rentals in your market earned on every day other than during Coachella.
The Nitty Gritty
The Recommended Rates model is based on the daily dynamic calculation of over two dozen property and market characteristics.
Some of the most important features include:
- Demand Score (Future Expected Occupancy in Market)
- Historical Daily RevPAR
- Booking Lead Time (Days until reservation start date)
- Comps’ Booked Rates
- Maximum Guests
- Amenities (Pool, Pets..)
- ADR Over the Past Year
Individual rentals can be influenced by hyper-localized events, meaning that rentals on one block may see soaring rates, while rentals in the neighborhood next door remain fairly unaffected.
Because of these varying micro economies, AirDNA considers some metrics—like ADR and Occupancy —based on the following:
- City-wide: calculated across the entire city
- Nearest-neighbor: calculated across the nearest 100 rentals
- Personalized comp set: calculated across a personalized comp set of about 20 nearby rentals that are similar in size and rate to the subject property
By calculating a metric like ADR on all three levels, AirDNA can better understand what’s going on around individual rentals, and serve up the best possible rate for each day.
Validating the Results
Recommended Rates has been launched in beta status. Although more than a year of development and testing has already gone into the algorithm, the team at AirDNA realizes in order to truly optimize future rate recommendations, it needs to “live” test over a longer period of time, with rentals across thousands of global markets.
Additionally, pricing is never done. Global and market forces are always shifting, and that needs to be accounted for—in real time—by continually iterating our vacation rental pricing algorithm.
With that being said, here are some of the earlier results from tests done by AirDNA’s data science team using a professionally managed, 1-bedroom short-term rental just off Bourbon Street in New Orleans:
In the chart above, the yellow line represents AirDNA’s recommended rate for the rental each day. The green dots represent the actual booked rate for the property. Happily, you can see that our pricing algorithm nailed the demand spikes in the market, and the booked rate for many days fell along the yellow line, in alignment with our recommended rates.
For other days, the booked rate was less than the recommended rate, indicating that the property manager potentially left money on the table for those days. Similarly, many of the days still available (in blue) are priced below AirDNA’s recommended rates. Even with a professional revenue manager actively managing this listing, you can see that he has some risk aversion and has lowered rates since a rental is still available only two weeks out.
But the numbers don’t lie; there is additional margin to be made on the property. Our recommended rates dynamically adjust pricing recommendations to capitalize on the demand peaks in the markets, and have the potential to outperform even the best revenue managers in the space.
A Small Price to Pay for Better Pricing
So, you might ask, what is the cost to access Recommended Rates for your rental?
Already a MarketMinder subscriber? This great new feature is available for free for properties within your subscribed markets!
Not yet a MarketMinder subscriber? Pricing depends on the size of your market. Most MarketMinder subscriptions range in cost from $19.95 – $99.95 per month, depending on the number of vacation rentals in your area.
MarketMinder, unlike many commission-based pricing tools, charges a flat monthly fee regardless of how many rentals you have, or how much revenue you earn each month. This makes MarketMinder an especially economical option for pricing insights for those with two or more vacation rentals.
Beta Means Your Feedback is Needed
Because the new Recommended Rates was released in beta, AirDNA needs your feedback! You can reach out with questions, comments, concerns to AirDNA directly by emailing [email protected] or messaging AirDNA directly from within MarketMinder using the “contact” widget for live chat.
Here are the kinds of feedback that are particularly helpful:
- Usability: is the tool easy to understand and easy to use?
- Sentiment: how do you feel about Recommended Rates?
- Impact: have you noticed any measurable impact on your overall short-term rental revenue?
- Bugs/issues: are you getting any error messages?
- Competitive: have you used a different pricing tool? How does Recommended Rates compare?