Airbnb Data: How to Earn More STR Revenue with AirDNA

Welcome to AirDNA! This is your beginner’s guide to unleashing the power of Airbnb data with the world’s most comprehensive tool for STR managers. AirDNA features over twenty-five metrics, such as the number of rentals, listing growth, and property sizes. Property-level and aggregate information is available by neighborhood, city, state, or country. The metrics are organized in a tabular fashion (tabs are navigable on the left side of the app), and are updated on a daily or monthly basis:

Forward-looking pricing data, on the Pricing Tab, are updated on a daily basis.

Key performance indicators, such as ADR, Occupancy, and Revenue, are displayed in the app for the last full calendar month and are updated on a monthly basis.

These AirDNA insights are available via paid subscription, however, there are many valuable charts with Airbnb data in the Overview Tab that are freely available to anyone who creates an account. Users are welcome to explore over 10,000,000 listings in over 80,000 markets worldwide within AirDNA.

Part I: Market Scores — Putting your Market in a Global Context

Every city and neighborhood is given a Market Score from 0 to 100 to indicate the current performance of that short-term rental market as compared to the biggest 2,500 markets around the world.


The grade is calculated by looking at four different metrics: investability, rental demand, revenue growth, seasonality, and regulation. They are the same metrics that go into calculating the best places to invest in a vacation rental in the United States.

Here’s a quick look at the logic for each metric:

Rental Demand:
How often have entire home rentals been booked over the course of the last year?
We combine the last twelve months of occupancy and with listing growth rates to make sure that while the market is growing, properties are still achieving high occupancy rates.

Revenue Growth
Are properties making more money this month than they did in the same month last year?
We isolate properties that have been listed on Airbnb and/or HomeAway for at least thirteen months and see if on average they made more this year than they did last year.

Seasonality
How much does travel demand fluctuate between peak season and low season?
Most hosts prefer to make money every month of the year instead of weathering the booms and busts of highly seasonal markets. We calculate the Revenue Per Available Rental (RevPAR) of the best and worst month of the previous year and calculate the difference.

Regulation
How strict is legislation and enforcement of that legislation?
Combining data on the number of listings per host, the change in the percentage of entire home properties, property churn and licensing requirements, we’ve created an algorithm that makes a pretty accurate guess as to whether local authorities are favorable or punitive towards Airbnb. A high score means there is likely to be a regulatory risk.

Investability (US Only)
Is this a promising market to buy a vacation rental investment property?
This metric pulls home value data to compare the average Airbnb and/or Vrbo income for full-time rental properties with the average cost of homes within the city. A market gets a high score in investability when house prices are low and vacation rental revenue is relatively high.

Plan your STR strategy based on real booking activity and trends—plus, see how things change year over year.

The map-centric view makes the Overview page the ideal place to navigate the area and look at comparable properties. Use the icons in the top right-hand corner of the map to filter properties by entire place, private room or shared room, and isolate your relevant comp set. Clicking on any property will make the pop-up sticky. Click the property title to view the listing directly on Airbnb or HomeAway. 

If you operate in a large market with thousands of rentals, make sure to use the neighborhood view when scouting properties using the map. We limit the number of results on the map to 10,000 to make the app load quicker. 

The icons in the map are dynamically sized based on the Average Daily Rate (ADR) for the property in the last year. When you use the map filters, the sizes of each dot will automatically adjust to the visible properties on the map. Additionally, helpful icons let you know whether each rental is listed on Airbnb, HomeAway, or both. This function is great for easily identifying the highest-performing rental properties of any size.

Part II: Implementing a Competitive Pricing Strategy Using Airbnb Data

Effective pricing is the single biggest challenge facing vacation rental owners and managers today. The sheer quantity of rental properties, the ebb and flow of travel demand, and the shortening booking window has left even the most seasoned pros scratching their heads.

In this section, you'll learn the in's and out's of how to use the MarketMinder to make smarter pricing decisions.

Tip: Watch this video for an overview of AirDNA's Pricing Tab.

Forward-looking trends in supply, demand, and rates

One of MarketMinder’s most influential aspects is the ability to see future supply, demand, and rates for a market. Because no two years or seasons look exactly the same, being able to see future booking activity can help hosts and property managers spot and intercept risks and opportunities with enough time to adjust pricing and beat out the competition.

Tip: Read this blog post to learn more about using forward-looking data to optimize pricing.

Here are two scenarios to watch out for, regarding forward-looking Airbnb data:

Booked rate is lower than the available rate
Sometimes, the rate at which people are actually booking skews lower than the rate all remaining available rentals are advertising. This is normal. However, if the gap begins widening as the date draws nearer, it’s a signal to lower your rate, or risk not being booked for the date.

High-demand events, like the Super Bowl, usually see an increase in supply from first-time hosts who are looking to cash-in on the event. A few days before the 2019 Super Bowl, the median available rate was $1,700 while the median booked rate was $899. Had the novice hosts known this, they could have lowered their rates and made about $900. Instead, many made $0.

Demand score is increasing ahead of a specific date
The calendars on the Pricing Tab are updated on a daily basis, which allows users to monitor changes in demand as future dates approach. If you notice specific days in the future turning to darker shades of green (indicating increasing demand), it’s a sign that you can begin increasing your rate without decreasing your chances of getting booked.

Historical trends in Average Daily Rate (ADR)

The short-term rental market has been growing at a rapid pace, as an increasing number of new and eager hosts are looking to make a side income.

The sudden addition of often hundreds of new rentals has left many managers scratching their heads, wondering if their past pricing strategies are still effective in this new, volatile environment. One of the first questions property managers often ask me is: “What effect has more short-term rentals had on my ability to price?”

We attempt to answer the basics of this supply and demand conundrum in the Average Daily Rate (ADR) chart. By overlaying the number of booked properties (supply) with ADR (demand) there are three conclusions that can be made. 

Demand is outpacing supply 
This is the best-case scenario. While there are more listings than a year ago, ADR is still on the rise. 

Supply and demand equilibrium 
In this case, ADR is remaining relatively flat year over year, and the delicate dance of travel demand and lodging supply is moving harmoniously over time. 

Supply is outpacing demand 
A surge in short-term rentals is having a material impact on the ability of property owners to charge the same rates they have historically. If this is the case, it is more important than ever to develop a proper pricing and marketing strategy. 

Tip: Read this blog post to learn more about using AirDNA's custom comps feature to drive greater revenue through competitor analysis. 

ADR is the average nightly rate that a property is being booked for (including cleaning fees). While the average is a good enough metric for the hotel industry, vacation rentals are extremely diverse in quality and pricing sophistication so a more comprehensive picture is needed to understand what is driving the changes in the average daily rate. 

The best visualization to dig deeper into the range of values that are driving the average daily rate is called a box and whisker chart. This chart displays an array of ADR quartiles that help you understand the distribution of trends over time. 

Tip: Watch this video to learn more about box and whisker charts.

Part III: Best Occupancy Rates and Booking Lead Times

Everyone fears a rental sitting vacant for extended periods of time. Getting as many bookings as possible is the name of the game and occupancy rate is the best gauge for projecting the percentage of days that a rental will be booked.

Occupancy Rate = Booked Nights / Available Nights

Although occupancy rates are important, in isolation it can be a very misleading metric. Many Airbnb hosts will say, "My rental is doing amazing! It’s almost 100% booked for the next 6 months." While that is great if you are hosting as a hobby, it's horrible news if you are running a business. Being more than 50% booked 3 months in advance almost always indicates your rates are too low.

In most cities, Airbnb data shows a large variance between the best and average properties. This can mostly be attributed to how aggressively the properties are being priced.

Properties in the 75th percentile that are achieving 90+% occupancy typically are willing to drop rates lower than their competitors in order to get booking during slow seasons. While the quality of the property, listing, great reviews, and host responsiveness will all have positive impacts, high occupancy is mostly dependent on an aggressive pricing strategy. 

Finding the right balance between price and occupancy

This is perhaps the biggest challenge and will require a certain amount of trial and error to perfect for each rental property. But, MarketMinder provides some great data points to give you a head start on leveraging Airbnb data.

A new metric that we've introduced is booking lead times. This is the number of days that properties are being booked in advance. On average this typically ranges between 30 and 60 days. Smaller, less expensive rentals usually are booked more last-minute while larger places are booked further in advance.


Booking Lead Time = Reservation Start Date - Booked Date


The shortening of booking lead times is one of the most disruptive trends in the industry today. Just a few years ago, vacation rental managers could anticipate that the majority of their booking to be made at least six months in advance. Reports from many large property managers suggest that booking lead times have been cut in half. The tighter booking window means there is a far less margin for error.

 AirDNA’s powerful yet easy-to-use tools make hosting and investing a breeze. This means having every insight you need, all in one place, always up-to-date.

What’s the optimal occupancy rate 14, 30, 60, 90 days into the future?

The booking lead time distribution pie chart helps answer this question. In the example below, you can see that 50% of bookings were booked less than fourteen days in advance. Only 11% of bookings were made more than three months in advance. Understanding the volume of bookings happening at different time periods will help you find the optimal booking curve.

Most vacation rental markets have a clearly defined peak, shoulder, and off-season. Do you know exactly what they are in your city?

For traditional vacation destinations, such as a mountain chalet or a villa by the sea, the seasons are easier to figure out. Metropolitan markets are a little trickier, however. Demand varies greatly each week, appearing sporadically around conferences, festivals, and other special events - all depending on the city or town you're looking at.
 
The Seasonality section of AirDNA uncovers these trends by using daily Revenue per Available Rental (RevPAR).



RevPAR = Revenue / Count of Available Rentals



Let's jump into our Seasonality calendar view. The inspiration for this visualization came from a presentation by Airbnb data scientists at the original Airbnb Open in 2014.

View this chart, and others, in AirDNA's Seasonality Tab

 The calendar above shows the RevPAR for the most recent 365 days of rental activity in Palm Springs, California. The dates filled with dark green are those with the highest travel demand, and the dark red dates show the slowest times of the year. 
 
Diving deeper into the yearly calendar, it looks like the peak season for Palm Springs starts February 1st and ends on April 30th, with strong weekend demand continuing through June. Weekdays start to get cold in May, and for the most part, stays that way until the holiday season starts ramping up in November and December. The shoulder season would be defined as December - January, and May. 

What now? How this information influences your booking strategy


Every property and location is unique, making specific recommendations difficult. But below are a few examples of data-driven strategies used by some of the best and brightest property managers in the world. 

Long Stay Discounts
No matter how bad a season may be, there are always a few people looking for a longer stay. For this market in July, August, and September, hosts should set a one-week minimum stay and try to fill as many Sunday to Thursday nights as possible by offering a large 40%+ weekly stay discount. Try setting a bargain daily rate for a 30-night minimum stay between six to twelve months in advance, and if nothing materializes, move to a week minimum stay. Continue to reduce your minimum stay requirement as the dates approach. 

Extending Stays
Another option for achieving higher occupancy rates in the slow season is enticing travelers to extend their stay by a day or two. This can be accomplished by reducing rates on Thursdays and Sundays by a significant amount. Many people make the mistake of changing their minimum night stay to try to achieve this. But this can backfire, as you'll be removed from the search results for all weekend searches. A better approach is to dramatically drop rates on dates around peak dates. For example, if your rate is $300 for Friday and Saturday, drop Sunday and Thursday to $99 and set your reservation settings to only allow 3 day stays when those dates are included (this option is only available on Airbnb).

The Hold Out 
On the opposite side of the spectrum is understanding how to maximize the booking value for high-demand dates. In March in Palm Springs, when RevPAR spikes by 400%, your approach should be completely different - as an Airbnb host, you become the suited not the suitor. You know the inquiries will be flooding in and the bargain hunters are going to be knocking down your door. This strategy requires patience and a tolerance for risk. (Remember, if you are reading this, you are more informed than 95% of vacation rental owners and operators.) Set your prices extremely high and let others start to take bookings. Use the Forward Supply and Demand chart on the ADR page to see how the going rate and demand are materializing. The goal here is to catch a booking in the last week or two from someone with lots of money and few options. 

Part V: Airbnb Revenue — Benchmarking and Prospecting

All the topics we've covered up to this point are building blocks of the single most important metric - revenue. Whether you are looking to increase your income on an existing property or acquire a new high performing rentals, the revenue section is the place to go to benchmark current rental performance and forecast the earning potential of new investment properties.

Tip: Check out AirDNA's Revenue Calculator to see how your rental's earning potential stacks up in the market.

Let's jump into exactly how AirDNA calculates property-level revenue.


Revenue = Booked Nightly Rate + Cleaning Fees


Our proprietary algorithm analyzes the calendar of every property, every day, to determine the value of each new reservation. We do this by monitoring every new 'set of greyed out days' in a property's calendar. We then run these days through our model to determine whether they are a real guest reservation or days that the Airbnb host has blocked from being booked.

How does AirDNA's data model determine what is a reservation vs. a blocked day?

This is the secret sauce. We've been able to reverse engineer the exact probability of each set of grey days being a reservation by using real data from over 30 million Airbnb reservations. Prior to October 2015, the source code in each Airbnb listing revealed whether each day was a reservation or a blocked day. We use Airbnb data 'signals' such as length of stay, booking lead time, the net change in monthly reviews, host response rate, and ten other indicators to assess the likelihood of each set of dates to be a reservation. We supplement and update our algorithm continuously with real reservation data on over 100,000 vacation rental properties around the world. While no model is perfect, we've tested ours to accurately identify 92% of reservations.

Does the revenue metric represent income generated by Airbnb bookings or revenue from all marketing channels?

The majority of professional hosts and property managers are using Airbnb and HomeAway as only one of many channels to market their property. Booking.com and other private websites will often make up the majority of total bookings. The good news is that no matter where a booking actually occurs, the core signals to our algorithm remain the same. In most circumstances, bookings from other platforms will also be picked up as a reservation by our model. That being said, if a property manager is not actively responding to Airbnb inquiries, doesn't have any reviews on the Airbnb platform, and is not actively using Airbnb as a substantial booking channel, our model will struggle to understand its true activity.

With the basics of understanding percentiles under our belts from Part II, the Rental Revenue chart should be pretty straightforward. But let's take a quick look at the city of Austin, Texas, and see what insights can be gained.

The chart is filtered for two-bedroom units and can see that there are 1,806 active rentals in the area. Two things strike us at first glance. The best performing properties (90th percentile) are earning over double what the average (50th percentile) properties are earning. Second, this revenue margin is amplified during the major event of SXSW in March and Austin City Limits in October. To my trained eye, this means that pricing is critical for these two events and that better located and operated rentals can charge a significantly higher premium.

Here you can view the total estimated revenue generated by all Airbnb and HomeAway listed properties. The Historic Market Revenue chart was created to show overall short-term rental growth. If you happen to be battling with any local regulators about fair short-term legislation, this data visualization is an eye-opener that can change the course of the conversation - it is perfect for estimating how much tax revenue could be collected with a transient occupancy tax. The data below was provided to the City of Los Angeles, which decided to implement a tax on Airbnb and HomeAway rentals. After collecting $28.5 million in Airbnb tax revenue in its first year, many cities of official have converted to be pro short-term rentals. 

Understand the investment potential of any address in the world. 

Part VI: Pacing and Forward-Looking Airbnb Data

In the context of the accommodations industry, pacing is the rate at which reservations are made for a particular date in the future. It's a metric used to know how far ahead (or behind) bookings are currently pacing compared to previous years. Pacing is a powerful forecasting tool that can be leveraged to gauge demand, revenue, and even expenses related to staffing and maintenance.

Our first feature shows how pacing can be used to optimize pricing.

The first new feature within the pacing tab is the Rate Analysis chart. This chart displays the difference between the median available rate versus the median booked rate for 6 months into the future.

In most markets, the price at which hosts advertise their properties doesn’t perfectly line up with the price guests actually book. Reasons for this run the gamut. Many hosts price themselves out of the market, while others continuously sell themselves short.
Knowing this discrepancy between booked and available rates is one of the easiest ways to stay ahead of the competition. In light of the current landscape, this chart sets out to provide answers to the following questions:

- How are hosts adjusting rates in response to the current landscape?
- If hosts are adjusting rates, when are they adjusting rates for? Short-term reductions with things normalizing after a few months?
- How do rates compare to last year when the Coronavirus had not yet taken hold?

The rates analysis chart within AirDNA's pacing section allows users to easily answer these questions and many more. 

Booking Trends: A Real-Time Tool Track Daily Reservations

With AirDNA's new Booking Trends chart, users can understand how future demand is evolving in real-time. Here’s how it looks:

Shown above is the number of bookings made in the last time period for any date in the future. The chart has four lines — total bookings, bookings in the last 60 days, bookings in the last 30 days, and those made in the last week — which you can toggle on or off. Hovering over any date in the future will show the number of bookings made for that day broken down by the four time frames. Airbnb data like this allows users to pinpoint exactly when bookings are happening so they can get out in front. 

This guide explains to you how to earn more STR with AirDNA. Unleash the power of Airbnb data using the most comprehensive vacation rental data tool.

Airbnb Data: How to Earn More STR Revenue with AirDNA

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