What is the right Price for your Airbnb listing?
Updated on March 12, 2020
Airbnb’s suggested pricing is a great place to start, but is that best possible rent? Probably not, because each listing is unique in its own way (maybe your house is bigger, more beds, a penthouse or other such factors). Below are some considerations when pricing your listing:
- How are other listings in your neighborhood priced? How does your listing compare?
- How are the hotel prices in your area?
- Does your city/ location – experience tourism seasonality due to the weather? Tourist numbers are often dependent on the temperature. Nice weather and you have more tourists, bad weather and you have more people traveling away from your city (more Airbnb houses?)
- Are there any major events attracting tourists to your area? Any big music festivals, conferences, university commencement?
- Are you close to the booking date and don’t have a booking yet?
All these are some starting points to think about your listing price. These factors and many more affect the price for your listing. And what’s more, these factors change daily. So how do you set the optimum price for your listing? PriceLabs’ predictive algorithm predicts supply and demand of tourists coming into a city on any given day, and then calculates the optimum rent for a listing. Our algorithm updates the pricing for your listing every time we sense a change in the market.
Often times we find that our clients either price their property to low where they leave money on the table, or in case of events too high and remain unbooked. While pricing low and leaving money on the table is still ok. It is pricing high for events and not receiving a booking that particularly concerning.
One of our client, Mike (name changed) who typically charges $140 for their 1 bedroom received a few requests for the Chicago marathon weekend. Intuitively, he raised the price to $175 for Sept 9 - Sept 13. However, after doing so, he received no booking requests at all. This is when Mike started using PriceLabs. Our algorithm predicted maximum probability of booking with the pricing structure as shown below in the image.
Result? Mike received a booking. Made 10% more than his usual pricing structure, and avoided the possibility of not receiving a booking.
At PriceLabs, our first goal is to drive occupancy and then we look at what is the maximum rent you should charge for filling that night.