The property performance data offers up some of our most granular data insights. The report shows historical individual property performance across various metrics for as long as the listing has been active as a short-term rental.

This level of information is designed to give an entire picture of properties in a chosen market and can be applied to many use cases. Whether you are researching STR's impact on an area or you are a vacation rental manager looking at property performance, the PPD gives you valuable STR understanding.

AirDNA’s PPD gives information on specific listings across two data files, the Property, and the Monthly files.

This file will give you performance figures for a listing for the last twelve months from when the file was created. For example, if the file was created in January 2023, the figures will show January 2022 to December 2022. Each listing in the file is listed and identifiable by its Airbnb or VRBO ID number, which can be found in the listing URL. The property file shows data on all listings in the chosen market, even those no longer active or available to book.

Property ID – AirDNA's unique identifier for the property

Host ID – The listing host's unique identifier

Property Type – The listing property type, entire home, private room, or shared room

Real Estate Type – The kind of property, house, villa, apartment, condo, etc

Property Manager – If a professional property management service manages the listing

Host Type – The type of host based on the number of listings they manage

Listing Creation Date – The date the listing was first published on an STR booking platform

Property Latitude & Longitude – Coordinate location of the property

Average Daily Rate (ADR) – Average daily rate of booked nights in the currency specified in the Currency field. ADR = Total Revenue / Booked Nights. Includes cleaning fees

Price Tier – Average Daily Rate Price Tiers in MarketMinder segments listings within a market into different price points; Budget, Economy, Midscale, Upscale, and Luxury

Occupancy Rate LTM – Occupancy Rate = Count of Reservation Days / (Count of Reservation Days + Count of Available Days). The calculation excludes blocked days and properties with no bookings.

Revenue LTM – Last twelve months (LTM) listing revenue in the currency specified on the Currency field. Includes cleaning fees but not other additional fees

Revenue Potential – Amount that could have been generated if available full-time, includes cleaning fee, but no other fees or taxes

Number of Bookings (Last twelve months) – Number of Reservations in the Last twelve months (LTM). If the property has listings in both Airbnb and VRBO, it will be the addition of reservations on each platform

Count Available days – Total number of listing calendar days that were classified as available for reservation but not booked during the last twelve months

Cleaning Fee – The cleaning fee charged per reservation in the currency specified on the Currency field

The monthly file gives monthly performance for a property as far back as 2014, providing there is data to show. As with the property file, listings are identifiable by their ID number. The metrics are much the same as the property file but are displayed monthly rather than annually.

Reporting Month – The monthly reporting period for the listing. The earliest dated month will represent the first month of performance for the listing, and the most recent month represents the latest property performance figures

Revenue Potential – Amount that could have been generated if available full-time in the given month, inclusive of cleaning fee

Active Listing (True/ False) – Active vacation rentals are those that had at least one calendar day classified as reserved or available during the month and that are currently active on the listing site

Scraped During Month – True if the listing was scraped during the month. If the listing is not Active on the site, Scraped during the month will show as false

The two files are designed to be used in tandem, using the property file to identify a listing or group of listings of interest and then using the monthly file to drill down into their historical performance in more detail.

Properties can be identified and cross-referenced from the property file to the monthly file using their property ID.

AirDNA’s Property Performance Data shows a projection of the relative frequency of a property booking and the rate a guest pays for that reservation. These estimates are based on a machine learning model trained off of actual booking data, but can not perfectly distinguish between booked and blocked nights or capture nights booked while a property is not listed on either Airbnb or Vrbo. Our revenue estimates leverage the last available rate for a given night based on the date we scrape the property and the night the guest booked. By tracking changes in the calendar, we can not identify the booking channel through which a guest books a property and thus do not account for differences in the rates offered through other booking platforms. We also do not apply or account for weekly, monthly, or annual discounts that a guest may receive for longer term stays.