Airbnb rental data: A new method to forecast demand at your hotel?

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The hotel industry has been overlooking the impact of short-term rentals on their business for some time, perceiving it as a niche form of lodging that would not affect traditional hotel stays.

However, the rapid growth of short-term rentals, led by platforms like Airbnb and VRBO, has made it impossible to ignore.

As a result, the hotel and short-term rental industries are increasingly overlapping, with customers comparing both options on prices, amenities, and value when booking rooms.

Fortunately, a study conducted by our data science team has established a correlation between short-term rental pick-up data and hotel demand, providing revenue managers with a powerful new mechanism to predict demand at their property.

Our data science team has uncovered findings that demonstrate short-term rental bookings actually start to pick up before hotel bookings in most major markets

So, if you’re armed with this data, you can predict elevated demand levels in your market and adjust your pricing and marketing strategies accordingly, giving you a crucial advantage over the competition.

Understanding the relationship between Airbnb rental data and hotels

To understand the relationship between short-term rental accommodations and hotels, we analyzed 29 major travel destinations comparing Airbnb rentals to representative hotels using occupancy levels from August 2022 to February 2023.

While both had similar final occupancy, the pickup time differed. Airbnb rentals had an earlier pickup while hotels picked up closer to the stay date.

We calculated the "hotel pick-up lag" by measuring the difference in lead time at which short-term rentals and hotels reached 5% occupancy.

By comparing the two curves, we gained insights into the relationship between short-term rental accommodations and hotels.

Short-term rentals tend to book earlier than hotels, indicating rising market demand for hotels in the same area. Hotels, on the other hand, tend to fill up later than short-term rentals, but attract a higher number of bookings in the last three weeks leading up to the stay date. 

This relationship could be a useful tool for revenue managers to sense emerging demand and adjust rates accordingly.

The 21-day marker out from the point of stay appears to be a key data point for many destinations, as it's when hotels catch up to the same level of occupancy as the Airbnb market. 


Visualizing hotel pick-up lag

We gained deeper insights into the correlation between short-term rental and hotel occupancy by analyzing the average share of accommodations booked over a specific period and plotting lead time in days.

This methodology provides valuable information for revenue managers to optimize their business strategies.

We see similarities in how short-term rentals and hotels track each other as the booking to stay lead time shortens. Below you can see the pattern of the top two most correlated destinations, Paris and Amsterdam.

Paris average pick-up curves

Hotel pick up lag paris

Paris has a hotel pick-up lag of 79 days, with the correlation of final occupancies in the city reaching 94%. Amsterdam has a hotel pick-up lag of 69 days, with the correlation of final occupancies in the city reaching 88%.

Amsterdam average pick-up curves

Hotel pick up lag Amsterdam

In all cases, the final share of rooms sold across the market converges or hotels slightly exceed the OTB reservation levels of short-term rental properties. Below are two example destinations from the USA, in New York City and San Francisco. New York has a hotel pick-up lag of 18 days, with the correlation of final occupancies in the city reaching 85%. Amsterdam has a hotel pick-up lag of 44 days, with the correlation of final occupancies in the city reaching 83%.

New York average pick-up curves

Hotel pick up lag New york

San Francisco average pick-up curves

Hotel pick up lag San Francisco

Why does hotel pick-up lag vary in certain markets?

This initial analysis reveals that there is a clear relationship between short-term rentals and traditional hotels, but it can vary across different markets.

Some markets have a highly correlated positive relationship like Amsterdam and Berlin, while others have the complete inverse. Such as the Maldives, where there is still a strong positive correlation but the lag is in fact negative, meaning that hotels are booked before short term rentals.

In the Maldives, hotels tended to start seeing demand an average of 208 days before their Airbnb counterparts.

Maldives sea lodges

Therefore, some caution is advised, and further research is required. However, the potential for linking the two is clear, as the majority of destinations show a strong relationship, which represents an exciting finding for the industry and provides valuable insights for revenue managers to optimize their business strategies.

We don't know for sure what causes this pattern and why it varies by location, but there are some hints.

Large groups and high-end travelers may book desirable Airbnb properties early, creating an earlier-developing curve, and then fall back on hotels if necessary. When hotels are booked earlier, it may be due to customer profile, regulation, and available properties.

Bangkok and Singapore have weak correlations because short-term rentals are illegal for most private properties. The Maldives has low availability of truly alternative accommodation, which affects the market structure. These outliers show the importance of market structure in this metric.


With the rapid growth, professionalization, and increasing market share of short-term rentals, they have become a formidable competitor to hotels.

Over 46% of those who paid for lodging in 2021 stayed in a short-term rental at least once, and potential guests are now considering short-term rentals as a viable alternative to hotels.

The problem is that hoteliers have not had a solution to truly understand this new competitor, until now. 

Lighthouse's Rate Insight+ provides a complete view of your competitive landscape by combining hotel and short-term rental data into a single platform.

This solution not only enables better pricing decisions based on demand, occupancy, and rates across your true compset, but based on the evidence laid out in our new whitepaper it also gives you a competitive edge by providing a new method for demand forecasting.

If you want to dive into the data for your market and find out how demand and pricing is shaping up, take a free trial here.

Do you want to predict demand before your competition?