Ever wonder what's fuelling your revenue management system? It's all about the data. But here's the twist - not all data is created equal.
A revenue management system that uses data which hasn’t been reviewed and screened for accuracy is a dangerous practice. It can result in an inaccurate view of market demand.
We recently explored the different types of short-term rental data available to highlight the importance of high-quality, accurate data, and we’re continuing this series to call attention to another important aspect of data quality: cleanliness.
Focusing on Short-term Term Rentals
Many different types of software in the short-term rental industry use scrape data, or data that is literally scraped from web pages around the internet and online travel agency (OTA) websites like Airbnb, Booking.com, and Vrbo. This type of data can provide insight into posted prices and calendar availability, but it can also include long-term rentals that are listed on these websites.
Short-term rental occupancy data can be skewed when blended with long-term rental occupancy data, so it’s important to use software that can differentiate between the two based on minimum stay requirements. Here’s an example of how short and long-term occupancy can impact a market:
In this chart, we can see how listings with long-term minimum stay requirements and mixed short and long-term stay requirements can impact the view of total listings in a market.
Here’s a look at future occupancy for that same market. The impact of low occupancy for long-term listings is affecting the total view of occupancy in the market when blended with short-term listings.
At Beyond, our data science team works to ensure that long-term rental occupancy doesn’t misrepresent occupancy in the market, since long-term rentals are often not available to guests looking for stays shorter than 28 days. Properties are also not exclusively rented for short or long periods of time, and minimum stay requirements can vary widely by day. This is also why it is important for a dynamic pricing tool to identify the minimum stay requirements for each listing in a market by day.
💡 Have you asked your revenue management system partner if they exclude data for long-term rentals when pricing short-term rentals?
Data Freshness
Revenue management systems and market data providers are tasked with collecting and analysing billions of data points on a regular basis from many different sources. The process of refreshing this data can often take a long time, sometimes days on end. Short-term rental markets often react to things like event announcements and demand shifts fairly quickly, so it’s important to understand how often your revenue management system collects, analyses, and digests these different data sources.
At Beyond, our “data pipeline” as we call it refreshes every 24 hours for all markets around the world! This helps ensures your listings are always using the latest market data for accurate pricing.
💡 Do you know how often the data refreshes for your system?
The Impact of Calendar Blocks
When using scrape data, calendar blocks on calendars can create confusion within the data. Scrape data looks at publicly available calendars on OTA websites, but an unavailable date may be booked or blocked (typically due to an owner stay or maintenance reason).
Basic dynamic pricing tools read this scrape data as “booked” nights for the property, which may overinflate occupancy data for that property and therefore the rest of the market.
At Beyond, our data science team has worked to detect and exclude blocked calendar dates from OTA scrape data sources. This ensures that we always have an accurate picture of market data without overinflating
Interested in learning more about the data that powers your revenue management system? Schedule a free consultation today!
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