Short-term and vacation rental data are encountering a post-Coronavirus renaissance. The data shows the steady worldwide popularity of alternative accommodations, from flats to farms and igloo stays. The data depicts that more companies in the business bind their bright future with – data.
According to the Worldwide Vacation Rental Data Report 2022, 40% of property supervisors rely on market business insight (BI). It is a major leap compared to just 13% before the COVID-19 outbreak. Furthermore, 58% are planning to utilize more data or spend more on data items. By doing so, they ready themselves with competitive insights into pricing, accessibility, and different aspects of the rental business. It helps them make smarter choices on the best way to maximize revenue.
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In this article, we’ll focus on short-term and vacation rental data analysis. We’ll also share our experience gained from the collaboration with Japan’s largest booking platform Rakuten Travel, vacation rental data supplier Key Data, and other tech partners.
Why Short-term and Vacation Rental Data is Important
Prior to going any further, let’s discuss what vacation rental data are. This term refers to hotel accommodation options that can be reserved for a few days or weeks (thus, the other name — short-term rentals.) Such facilities embrace a large number of properties, from extravagant apartments and villas to rooms or even tents.
Vacation rentals typically offer visitors more appealing prices than hotels alongside a more confidential setting. Another selling point is the accessibility of a kitchen that allows customers to get a good deal on food. While these advantages are self-evident, companies managing accommodations should have more tricks up their sleeves to flourish in such a competitive market.
How does data prove to be useful? Having applicable observations and strong analytical tools within reach, property supervisors can
- Compare their rates and performance with those of contenders,
- Analyze demand
- Monitor supply,
- change costs to rise occupancy, and more.
Vacation Rental Data Sources: PMS, Vrbo, Airbnb, and More
The biggest aggregators of data on alternative accommodations are global platforms like Airbnb or Vrbo. However, the most data conceivable aggregates directly in your own vacation rental software. You need to utilise internal and external sources to get trusted insights. And look at a one-year history of appointments at least (a few years of data will bring far superior outcomes.)
Vacation Rental Data Attributes: What To Look For
Depending upon your objectives, you might have to examine various data attributes. Generally, we can group them as follows.
Accommodation depiction includes different attributes of the property, from its area and landmarks to everything connected with its type, accessibility, size, and household conveniences.
Booking conditions state when, for which period, and based on what conditions, the reservation was made. Key points here are days to booking, dates, season, and cancellation policy.
Key performance metrics correspond with those used all through the hotel business, including length of stay and average daily rate (ADR). However, for example, the occupancy rate for vacation rentals is assessed in a different way than for hotels. You want to partition the number of days when the property is reserved by the number of days when the property is free.
Customer-side data embraces information on your property produced by your visitors. Surveys depicting various parts of the client experience, preferences, evaluations, and different sorts of input allow you to track down opportunities to get better.
However, it’s worth noting that vacation rental data, in many cases, are often organized in sequential order gathered over a specific timeframe, called time series. Such a structure allows for tracing patterns, cycles, and occasional instances. And changes in the data, as well as foreseeing their repetition in the future.
Let’s now see how exactly your vacation rental business can benefit from data.
Vacation Rental Data Analytics: What Questions Data Can Answer
Data can tell you nearly everything — if you have the right inquiries, have strong business knowledge tools to determine replies about the current situation, and apply machine learning to tell what’s in future. The amount of accessible data and information quality also influence the dependability of the insights.
Which Vacation Rental Data strategy is right for your business
The size of those investments will rely upon your business size. For instance, the custom development of an ML-based indicator that fits huge tech enterprises is excessively costly and looks bad for a host dealing with a few properties by means of Airbnb. Only for reference:
- 70% of vacation rental businesses are small, with no more than 19 properties;
- 20% are medium-sized, implying that they operate 20 to 99 properties; and
- 10% are large, managing over 100 properties.
Each of these groups can get benefit from data but in its own specific manner.
Small Vacation Rental Companies: 1 to 10 Properties
If you sell your properties through Airbnb or other vacation rental platforms, you can use their local data analytics tools to follow your performance metrics. Besides this, all data providers mentioned in this article deliver ready-to-use rental dashboards and market insights. You can get started with data analysis free of cost. Utilization of tools like Airbnb data analysis by Rabbu or a plan by Transparent that tracks over 35 million listings from Airbnb, Vrbo, Booking.com, as well as TripAdvisor.
Medium-Sized Vacation Rental Companies: 20 To 100 Properties
Medium-sized businesses need more elaborated and customized functionality for further growth. This includes features such as data integration, dynamic pricing tools, performance metric filters, occupancy rate forecasts, review analysis, etc. Still, in most cases, solutions from data providers will fulfil your requirements.
Large Vacation Rental Companies: More Than 100 Properties
Large businesses can utilize their PMS as the main source of information and directly integrate it with data analytics tools through APIs. They can benefit from the same solutions as mid-sized companies; often, enterprise-scale companies have unique challenges that make them set their sights on bespoke software and machine learning model development. A custom approach allows big platforms to get the highest forecast accuracy and make most of the available data.
