The importance of measuring performance over different time aggregations – a Berlin hostel Study
When reporting, writing and presenting on data, STR frequently switches between four different time aggregations: monthly, running 3-month, running 12-month and year to date. Aggregations can produce different results, but when used correctly, can also create a holistic picture of performance. Below we look at the Berlin hostel market to understand the use and interpretation of each time configuration.
Monthly data is the first and the simplest of STR’s time aggregations. As its name suggests, monthly data looks at one month’s performance. This data is inherently seasonal due to weather, holidays, events, and even school break schedules. Year-over-year monthly data is best viewed one month at a time in comparison to a hostel’s market or competitive set.
Looking at absolute values allows operators to isolate high-performing months. Berlin hostel total revenue per available bed (TRevPAB) peaks near €30 in September, likely as a result of Oktoberfest beginning late in the month. Using monthly performance to determine which times are historically high or low performing can help operators in their future strategies.
Running 12-Month (R12) or 12-Month Moving Average (12MMA) data is the average of data over the past 12 months. R12 data condenses one year into one number and is most useful for viewing performance trends over time, as it removes seasonality from data.
In Berlin, R12 data highlights the slow deceleration of hostel occupancy growth by smoothing out the peaks and troughs present in the monthly data. Monthly occupancy comparisons fluctuated between +20% and -5% over the past 12 months; comparatively, R12 occupancy growth steadily fell from 8.4% to 1.4% over the same time period.
Fortunately, slowed occupancy growth does not spell bad news for Berlin hostels. Using running 3-month (R3) data, the average of performance over the past three months, we can view quarterly hostel performance in Berlin. What we see aligns with the R12 data and adds additional context.
As occupancy growth decelerated in 2019, growth in average daily rate (ADR) accelerated, leading to solid gains in revenue per available bed (RevPAB) during the first two quarters of the year. While ADR increased 2.1% in Q3, occupancy losses effectively ate most of the gain, and RevPAB rose a modest 0.9%.
R3 data is most useful at a quarter or season’s end, as a way of measuring performance over a small subset of time. This aggregation is best viewed as a bar chart rather than a time trend, as it lags but does not completely erase seasonality in the data.
Year to date
Year-to-date (YTD) data provides the most accurate picture of current-year performance. With the exception of December, 12MMA aggregations include prior-year data, while monthly and R3 snapshots won’t capture the full year. YTD data is best used at least four months into the year; prior to that, R3 or monthly data would serve nearly the same purpose.
Despite a total turnaround in the drivers of Berlin hostel growth, RevPAB and TrevPAB increases have remained strong over the past two years. Even near-zero occupancy growth in 2019 isn’t a cause for concern. Year to date, Berlin hostels filled three of every four beds while increasing rate 4.4% year over year.
Berlin hostels have enjoyed two years of strong performance, but by viewing performance through just one time aggregation, you might not necessarily draw that conclusion. Using all four time aggregations to analyze a market, class or competitive set provides context and a broader perspective of performance.