Suwanee, GA · Member since 2017 · 5 posts · 0 votes
Hey everyone. I'm looking for data on the Charlotte area for AirBnb and I stumbled onto AirDNA. I only have one property there and I'm trying to determine whether to go with long-term rentals or short-term, so it doesn't make sense for me to subscribe. If someone has a subscription, would you mind sharing just a few data points?
1) What is the average annual occupancy in the greater Charlotte area, and more specifically, around the Mallard Creek area?
2) What is the median nightly rate in the same areas?
I really appreciate any input. Even if the data isn't from AirDNA, I just need some numbers. Thank you!
Rental Property Investor · San Diego, CA · Member since 2016 · 306 posts · 205 votes
9y
Hi @Vash P. you can find all of this information on Airdna by simply creating a free account. No need to buy reports to gather this info you're looking for.
I'd caution to estimate lower than the amounts shown to you. Airdna gets its data by scraping Airbnb, but occupancy rates can be completely inaccurate because it has no way to indicate an actual reservation versus a blocked date on the calendar.
Suwanee, GA · Member since 2017 · 5 posts · 0 votes
9y
@Ariel Smith do you have any experience with the data on the website? Is it significantly off to the point I may as well not use it, or is it within a good margin of error for some estimates?
@Mike DeLong the owners you've talked to--are they all in one market or several areas? Thanks, just trying to gauge whether or not it's accurate just for one area or if you've seen it across the board.
Rental Property Investor · San Diego, CA · Member since 2016 · 306 posts · 205 votes
9y
@Vash P. I do. I'd say it is within a good margin of error but I would estimate low on the rental rates and occupancy. Airbnb has a forum for hosts so I'd post the question there and see what other hosts say. You mentioned having to create a listing first, but you can create your listing and leave it deactivated in order to access the forums I believe.
Specialist · Denver · Member since 2016 · 9 posts · 5 votes
8y
@Ariel Smith Great to see you've been putting our data to the test. You are correct that the majority of our data is pulled Airbnb listing calendars - we supplement this with real data from 100,000 rentals around the world. Real reservation data allows us to build out models that can determine if a property is actually being booked with a high level of accuracy.. about 94% at last count. This means our occupancy calculation is based on reserved days only.
@Vash P. We offer data on every single Airbnb listing, as Ariel mentioned we do offer some of this data for free. Please get in touch with us and a member of the team would be happy to run through our data options with you.
Rental Property Investor · San Diego, CA · Member since 2016 · 306 posts · 205 votes
8y
@Scott Shatford How is it possible that you are able to gather real data? When I negotiate pricing with someone for the stay, how do you capture that? How do you capture whether I block my Airbnb calendar or whether it is an actual booking? Interested to know.
Specialist · Denver · Member since 2016 · 9 posts · 5 votes
8y
Hey @Ariel Smith The majority of properties are booked at listing price, for those anomalies which are not, we would consider this unusual behaviour and would not pick this price change up. In terms of how we determine the difference between a reserved day and a blocked day, this is an integral part of our data.
We started picking up data on Airbnb in 2014, and in Q4 2015, Airbnb stopped showing the real reservation information. So until then, every day we'd see an A (available for rent and not reserved), R (reserved) and B (blocked), so we knew the exact information of Airbnb reservations.
At the end of 2015, they closed that door, and henceforth in the calendar, we can now only see A (available for rent) or U (unavailable), just as the human eye sees. We then built out an algorithm based on the year and a half of historical data to determine which of the Us (unavailable) are Rs (reservation) and which are B (blocked).
Our model takes into account 16 different criteria picked up by looking at the historical data set (the length of the booking, the booking lead time, the historical performance of the property) to make that determination. That algorithm is historically correct within a margin of error of 5%. In aggregate, this makes our information extremely accurate in the market, although sometimes on individual properties - if there is some quite unusual behaviour, it can get the odd reservation wrong.
We have subsequently augmented this information with data partnerships and channel managers who see several hundred thousand reservations. This is the machine learning component of our algorithm that continues to make it more accurate, to adjust for changes in booking behaviour over time, for example.
Feel free to get in contact if you want to understand the algorithm in more detail.