VRMA Administrator
The value of having good data in evaluating the potential of a short-term rental investment and/or in operating a profitable short-term rental business goes without saying. However, there is an increasing trend toward providing more and more data, and the belief that the “data will tell us the right pricing and revenue strategy.” Unfortunately, there are many factors and reasons why such reliance on data-driven strategies can lead to suboptimum results. There are a few key reasons why.
First, the overwhelming majority of properties are unique one-off properties. Unlike multi-inventory properties (such as hotels) where one listing represents many similar (or same) units, every short-term rental property is unique. For example, even neighboring houses in a development each have different characteristics (one may be closer to a busier street, one may have less obstructed views, etc.). Furthermore, the listing pictures and the listing description may be different, and the reviews on one property may be better than on the other. There are so many different potential variables, it is unrealistic to pinpoint what the right price should be based solely on a property’s physical characteristics. Is a property with a hot tub, with a 4.5-star review rating, and iPhone pictures worth more or less than a similar property without a hot tub, a 5-star rating, and professional photography? It is not possible to know what the right price of any given property should be. Instead, the guests who are booking the property are the ultimate determinants of the right price for a given property at any given point in time.
Second, unlike the hotel or airline industries where revenue managers are taught the “proper way” of managing revenue, in the short-term rental industry, it is pretty much every person for themselves. In these other industries, there are generally agreed-upon rules of engagement by which the overall industry operates. But in the short-term rental industry, because there truly are no industry agreed-upon rules of engagement, the data is an aggregation and average of a lot of operators all playing by their own set of rules. Oftentimes, mom-and-pop operators (which account for the vast majority of the short-term rental industry) whisper quietly that their secret to pricing is to see what their competitors are priced at, and setting their rates 5 percent below their competitors. With this type of a strategy, operators relying on a data-driven revenue strategy will continue to bias the data set toward a race to the bottom.
Third, the investment strategy and/or risk-tolerance of the owner materially impacts the revenue management strategy of a short-term rental. Some operators willingly forgo generating the maximum revenue in order to have the assurance of seeing three to four months of solid bookings on their calendar. Others are more risk tolerant, and are willing to wait until the very last minute to try to get the highest rate even at the risk of missing out. The data gives minimal consideration to these important factors that typically are a part of any other investment vehicle.
Here’s a concrete example to show that just “trusting the data” can lead to incorrect decisions. The summer of 2021 exemplified this for my short-term rental business. A few of the properties that my company provides revenue management for achieved revenue of almost twice that of the 90 percent of comparable properties (credit to Tim Speicher and Buoy for the data and visualization). Of course, due to the pent-up demand for travel, the summer of 2021 was a record summer all across the country, and these properties were similarly benefiting. However, I know for a fact that had I been blindly following the data, the data would have limited my ability to push the revenue because the data would not support doubling the revenue of the top of the market. My properties likely would have outperformed the market by just 30-50 percent rather than the nearly 100 percent increase achieved. Following the data blindly would have led me to leaving a lot of money on the table.
Data is incredibly important to understanding the overall market and benchmarking performance against the market. Without data, revenue managers would be operating blindfolded and every decision would be reactive. However, too much data can lead to confusion and indecision, and can also ultimately lead to the incorrect decisions.
With the few points I made earlier in this article, I believe that the importance of data in the highly fragmented short-term rental industry does not lie in the precise and exact numbers of any specific data point. Instead, using the data to track relative trends and patterns in the market provide the signals that can be used to establish proactive forward-looking revenue strategies.
From my perspective, the goal of revenue management in short-term rentals is to find what the market will bear over an extended and ongoing period of time. A data-informed approach to revenue management helps to identify what the market will bear without being unduly influenced by the specifics of the vast data available. Below is an outline of a process to use data to inform revenue strategy. This is a high-level and simplistic approach, and of course there is much more nuance and details to execute this, but this shows a structured approach.
- Understand pacing in your market versus pacing of your portfolio over an extended period of time (three to six months) – Pacing is the rate at which reservations are made for any particular date range.
- Establish target occupancy – Based on general understanding of pacing historically, establish a target occupancy profile for the upcoming three to six months. This target occupancy is constantly shifting as time passes.
- Adjust the average nightly rate – In order to maintain the target occupancy and stay on pace with the market. The nightly rate is the lever adjust to keep the occupancy on pace.
Established dynamic pricing software platforms in the short-term rental industry are moving away from purely a data-driven “black box” approach; instead, they are moving toward one that provides the operators with market data in order to establish a data-informed revenue management strategy. Pricelabs is one dynamic pricing software company that has always embraced the notion that the data helps to inform the dynamic pricing strategy but always incorporated many additional controls to customize and tweak almost every aspect of the dynamic pricing software. Recently, Wheelhouse introduced what they called Hybrid Pricing, which enables operators to determine how much the revenue strategy is purely data-driven and how much is driven by their own custom rules.
Dynamic pricing tools are moving away from black boxes where we are expected to simply trust the data, and toward enabling increased customizations. At the same time, there is increasing availability of granular and vast data sets that can be sliced and diced in any way desired, and some data providers helping to simplify the data into concise and digestible form. The convergence of these trends sets the stage for a move toward a broader adoption of data-informed—not data-driven—revenue management strategy in the short-term rental industry. With increasingly more granular data available, blindly following a purely data-driven path can create a false sense of assurance operators are managing their revenue with surgical precision. However, at this early stage in the short-term rental with the vast fragmentation of the overall market, using the data to understand broad trends, and then adjusting to find what the market will bear could be a winning revenue management approach.
John An is a short-term rental industry expert, with particular focus in optimizing and customizing the technology stack, driving up revenue, and continually streamlining operations. Leveraging his experience in building, growing, and operating a successful and profitable short-term rental brand from the ground up, An constantly utilizes his own short-term rental business as a fertile testing ground to roll up his sleeves and dig in to experiment with new systems, technologies, and approaches.
