Market Dashboards: Methodology & Terminology

Market Dashboards: Methodology & Terminology

Glossary of Terms

Active Listing: A listing that has an active page on Airbnb or had one at some point in the last 15 days.

Bedroom Category: By default listings in the Market Dashboard are grouped by the number of bedrooms they advertise on Airbnb. In addition, there is a “Room” category that groups all listings with 0 or 1 bedroom that is advertising as a shared, private, or hotel room.

Booked Dates: This refers to a time period where we consider the bookings that were received, whether or not the stay dates for those bookings have occurred yet. For example, “Past 30 Booked Dates” can be read as “for bookings received in the last 30 days”.

Booked Nightly/Weekly/Monthly Price: The price per night for bookings that have a certain length of stay. Booked Nightly Price would consider all bookings, Booked Weekly Price would consider bookings with length of stay greater than or equal to 7, Booked Monthly Price would consider bookings with length of stay greater than 30. Length of Stay discounts are included.

Booking Window: The number of days between when the booking was made and the first stay date for that booking. A same day booking, where the first stay date is the same as the booked date, would have a Booking Window of 0. If someone books today to start their stay tomorrow that would be a Booking Window of 1. Etc.

Length of Stay (LOS): The number of nights a booking is for. For a booking where guests check in Friday and checkout Sunday the Length of Stay would be 2 (Friday night and Saturday night)

Percentile: The percentage of listings that fall at or below the given value. For example, if the 25th percentile price is $144, that means 25% of listings have a price equal to or lower than $144.

Scraped Data: Data that comes from publicly viewable websites and pages. For listings some examples of data that we scrape are future prices, future available dates, listing info such as number of bedrooms and amenities included. See Data Source and Processing section for more details on the data we gather and how we process it.

Stay Dates: This refers to a time period where we consider bookings where guests have stayed at the listing during the period. “Past 30 Stay Dates” can be read as “In the past 30 days for bookings where guests have stayed”.

Methodology: Data Sources and Processing

Data Gathering 
Currently Market Dashboards is using only scraped data from Airbnb. For a listing all data we gather could be found by going to that listing’s public Airbnb page and looking through their calendar and the listing info they provide. We then keep records of how their calendar has changed, what dates have become unavailable (or re-available), how their prices have changed, etc. and we build up a history for that listing. We currently do this for all listings that appear on Airbnb. Using scraped data enables us to provide Market Dashboards for any location around the world (regardless of whether we have customers there or not). 

Data Processing 
Much of the booking data we show on Market Dashboards is not directly available from a listing’s calendar. Data like Booking Window, Length of Stay, Booked Date, and Booked Price are instead inferred from the changes we see to a listing’s calendar over time. If consecutive dates become unavailable from one scraping to the next, we will initially mark it as a single booking. Due to the frequency at which we scrape the booked date is well determined and the chances that it is two separate consecutive bookings is low. We can then calculate the Booking Window and Length of stay for this booking. The booked price is then assign based on the last listed prices for those dates we saw prior to the dates becoming unavailable.

Block Removal 
One of the main challenges for scraped data is that there is no guaranteed way of determining if specific dates are not available because they were booked or if the owner has decided to block those dates. Everyone using scraped data faces this issue and generally has some way in which to try and remove these blocked dates from the data, and no method is perfect. PriceLabs has its own block removal logic that looks at patterns in the whole market as while as individual listing data to help us determine whether a booking is real or a block. Some of the factors of a booking we look at to determine if it is a block or not are: Length of Stay, Booking Window, Market Occupancy, extreme Price variations, and more. We also automatically remove any stay greater than 60 days as we feel they do not fall under the Short-Term Rental category and can have a large impact on the data. Once a block has been found the corresponding dates for that listing are changed to available and do still count as the listing being empty when calculating market level Occupancy for those dates. All other booking info is also removed.

Bookings made on other OTAs or Direct Website 
As long as a property is listed on Airbnb in addition to other OTAs, we still are able to deduce bookings on other OTAs as they block the calendar on Airbnb. As described above, once a date is unavailable (either due to a booking on Airbnb, or from any other OTA) we use our block detection logic to identify whether it's a booking (made through any channel) or a blocked date. 

Dynamic Pricing Analysis for listings in your market/comp-set
When creating listing comp-sets, you will see a column named "Dynamic Pricing." Here's what it means:

We track the prices of each listing and score them on a 0 to 1 scale on both day to day to day price variation as well as how much their prices have changed for the same day since the last time we checked. A 1.0 would indicating their prices vary strongly from one day to the next and are constantly changing, while a zero indicates the price is always constant. I can't divulge exactly how this score is calculated but to give you a rough reference an average PriceLabs user generally falls into the 0.6 - 0.8 range depending on their customizations.

For the table we then bin these scores:
None: score 0.0 - 0.1, these listings almost never change their prices
Low: score 0.1 - 0.25, these listings may change their prices for holidays and events, but their overall price week to week is pretty constant
Moderate: score 0.25 - 0.5, these listings prices vary week to week but the variance is overall small and prices don't update too often (someone manually updating their prices every week may fall into this category)
High: score >0.5, these listings prices account for DOW and holiday/event demand changes and update every few days (listing is most likely using some dynamic pricing software to update their prices on a daily basis).

Frequently Asked Questions

  1. Why do we sometimes use Median instead of Mean? 
    There tends to be a few outliers in every market that set extremely high prices or only take extremely long bookings, the listings are not indicative of the market but would have a large effect when calculating the mean for these values. Median on the other hand is more stable and resistant to outlier behavior.
  2. Why is the area covered in the listing map smaller than the inputted radius? 
    We currently cap the number of active listings in each Dashboard to 1,000. If the area you selected has more than 1,000 listings in it we will only show you data for the closest 1,000.
  3. In the listing table (under Location Map) how do you classify professionally managed listings? 
    We use 4 categories: Individual, Small, Moderate, Large. The individual category is for property managers that only have a single listing tied to their account, Small means they have between 2 and 10 listings, Moderate between 11 and 50 listings, and Large is 51 or more listings. Unknown may appear if we haven’t yet determined how many listings the property manager has.
  4. Is there a way to search for Airbnb Luxe listings using the dashboard? 
    Not currently
  5. Why do some listings have extremely high occupancy/revenue in the past 30 days even though the market as a whole isn’t doing well? 
    It is likely that at least some of the bookings we have assigned to that listing are actually blocks that we missed. Our block removal isn’t perfect but do send us cases you find suspicious so that we can try to improve!


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