Main goal of our research was to verify some of the widely-used media statements, that can be just impressions not based on the data. Examples of such statements are:
“Airbnb is still expanding at an astonishing pace!”
“Airbnb has a potential to destroy the hotel industry!”
Therefore, we set goals of our research as follows:
- Collect available data about Airbnb tourism in different European locations. Asses the data quality and usability.
- Collect available data about traditional hotel tourism in corresponding European locations. Assess the data quality and usability.
- Assess comparability of the collected data and transform them to a comparable form.
- Verify following hypotheses:
a) Airbnb establishment offer is expanding.
b) The expansion pace does not significantly change over the time.
c) Airbnb expansion does not significantly differ over European regions.
d) Airbnb expansion is reaching such significant market share that it negatively influences hotel industry.
Over the years, there were many start-up companies (example of such start-up companies is pronajmuse.cz or airdna.co.) established that built their business on helping people with renting their flats with Airbnb. One of the activities these companies do (except of advertising the place, cleaning, key exchange etc.) is estimating the optimal price daily. Therefore, they collect the data about all establishments, their prices and availability and suggest the most optimal price to their clients. Our data were collected from airdna.co.
We obtained the data about hotel tourism from Eurostat. Potential issue with Eurostat data quality is that they are based on surveys and they are not always complete (some years are missing for some regions), however we consider this to be a minor issue as it occurs only in small amount of regions.
From Eurostat, we gathered the data about tourists’ overnight stays. This side of Airbnb business is clearly not accessible – we can access only data about Airbnb offers, so Airbnb overnight stays can only be estimated. Our estimation is described in following chapters and is based on assumption, that structure of offered Airbnb establishments and their occupancy rate did not change significantly in time.
The gathered data from airdna.co and from Eurostat are of different regional granularity level. While Airbnb data are for cities (except of Crete and some other Mediterranean islands), Eurostat data are for NUTS2 regions. Therefore, from Eurostat we gathered data only for the cities from the NUTS2 region (they divide the data for cities/towns/rural areas) and from Airbnb we summed up statistics for the cities inside the region.
Regions of interest
We collected the data from 74 cities placed in 48 NUTS2 regions of 19 European countries. We divided the countries to four regions for comparison – West (Belgium, France, Netherlands, Austria, the United Kingdom, Germany), South (Spain, Portugal, Italy, Greece), East (Bulgaria, Hungary, Poland, Romania, Czech Republic, Slovakia) and North (Norway, Sweden, Finland). Figure 1 depicts the spread of the cities involved in the study and the regional division.
Figure 1: Map of the cities involved in the study. Green, blue, red and yellow colors indicate the regions.
Airbnb raw data:
- Number of establishments (2012, 2013, 2014, 2015, 2016).
- Actual establishment size structure (Studio, 1 bedroom, 2 bedrooms, 3 bedrooms, 4+ bedrooms).
- Actual occupancy rate (days rented in last year 30-90, 91-180, 181-270, 271-365).
Estimating Airbnb overnight stays
To be able to estimate Airbnb market share for comparison with hotel industry, we had to assume, that structure of offered Airbnb establishments and their occupancy rate did not change significantly in time. Without this assumption, we would not be able to estimate Airbnb overnight stays in past years for comparison with hotel industry. We derived two estimations from available data – low and high and chose the optimal estimation in ⅓ distance between these two for reasons, mentioned below.
Low estimation: Studio and 1 bedroom = 1 bed, 2 bedrooms = 2 beds, 3 bedrooms = 3 beds, 4 bedrooms = 4 beds, taking lowest day count from occupancy rate (30, 91, 181, 271), assuming occupancy rate in all types of flat are of uniform distribution.
High estimation: Studio and 1 bedroom = 1,5 bed, 2 bedrooms = 3 beds, 3 bedrooms = 4,5 beds, 4 bedrooms = 6 beds, taking highest day count from occupancy rate (90, 180, 270, 365), assuming occupancy rate in all types of flat are of uniform distribution.
Optimal estimation: In ⅓ distance between lower and higher, because bigger flat is usually not fully occupied (6-beds flat is usually not occupied by 6 people, but i.e. by smaller family). Second reason is that distribution of occupancy rate is decreasing, therefore more flats with occupancy rate 30-90 days in a year are occupied closer to 30 days than to 90 days.
Calculating Airbnb between-year growth
Calculating Airbnb between-year growth was based on establishment offer, as it reflects the growth (and overnight stays is only estimation assuming the structure did not change). Number of flats should correspond with number of tourists, as the supply-demand correspondence rate is very flexible in this case.
Calculating hotel between-year growth:
Calculating hotel between-year growth was based on overnight stays, because establishment offer does not reflect change on the demand side (number of hotels and hotel rooms offered does not correspond with number of tourists).
The results contain regions with the highest and lowest Airbnb growth rate, average Airbnb and hotel industry growth pace in the four regions and the estimation of Airbnb market share.
NUTS2 regions with Airbnb highest and lowest growth rate
Table 1 and Table 2 list the regions with Airbnb highest and lowest growth speed. The important trend we can see in the tables is that the lowest growth rate regions include a significant amount of the South European regions.
Our hypothesis is that the regions with a developed tourist market have a lower growth rate than regions with developing tourist market. These regions are growing faster because they are in a position farther from their maximum. In contrast, the developed tourist market is already operating, or closer, to the capacity frontier. It can be considered an analogy with economy – developing and developed countries.
Airbnb growth speed
Conclusions based on results depicted in Table 3 and Figure 2 are summarized in following list:
- The growth speed is gradually decreasing in average and in all the regions.
- The quickest growth was in the Northern Europe in 2012, however the growth is significantly decreasing in all regions except the Eastern Europe. The Eastern Europe was the only region above 200 % growth speed between 2015 and 2016.
- The decreasing growth rate, as the years pass, indicates this market is maturing but still hasn’t achieved its maximum.
- The numbers are huge in comparison with any business company in this market. It must be considered as a profitable opportunity.
|Mean||266 %||236 %||224 %||193 %|
|Standard error||17,28 %||1,06 %||0,70 %||0,51 %|
Table 3: Airbnb average growth speed 2012-2016
Figure 2: Airbnb regional average growth speed 2012-2016
Hotel tourism growth
The hotel growth rate is stable, doesn’t change significantly in the last years, as depicted in Figure 3. It indicates it is a mature market. The quickest growth among the regions is in the Eastern Europe, however the differences between regions are much smaller than in the case of Airbnb.
Figure 3: Hotel tourism growth rate 2012-2015
Airbnb estimated market share
Estimated Airbnb market share in comparison to hotel industry is gradually growing and in 2017 it could reach 10 % in the Western and the Eastern Europe, while in the Southern and Northern Europe will probably not cross this threshold next year.
Table 4: Airbnb estimated market share 2012-2015
Figure 4: Regional Airbnb market share 2012-2015
While collecting the data, we identified following main issues with their usability:
- Eurostat data have some years missing.
- Airbnb data reveal only part of the whole picture (offer side), demand side is airdna past extrapolation, corrected by sample data time-to-time issued by Airbnb itself.
- We had to create a mapping between NUTS2 region and cities to be able to compare the data.
- We had to do several assumptions and estimations about Airbnb data to be able to derive values, comparable to Eurostat data.
The hypothesis was verified. Airbnb establishment offer is expanding in all regions contained in the study.
The hypothesis was denied. Expansion pace is slowing down. We can see the quickest slow in the Northern and Western Europe.
Thy hypothesis was denied. Initially the slowest growth was in the Southern and Eastern Europe. However, in the Western and Northern Europe the growth slowed down significantly. Extrapolating this trend, we can still expect significant growth in the Eastern Europe, but in the rest the market will reach its maximum in the nearest years.
Airbnb expansion is reaching such significant market share that it negatively influences hotel industry
As we can expect estimated Airbnb market share to reach more than 10 % of market in 2017, it certainly has the potential to negatively influence hotel industry. However, hotel tourism is growing in stable pace since 2012, so we cannot affirm negative influence of Airbnb tourism.
Moreover, the quickest growth of hotel tourism is in the Eastern Europe and there is now also the quickest growth of Airbnb tourism. So far it seems the Airbnb growth does not influence the hotel industry, however it can change if Airbnb does not lose its growth pace.