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Giada Natale
Martina Pia Giuliani
Caterina Boschi
The spatial and quality dimensions of Airbnb markets
Augusto Voltes-Dorta University of Edinburgh Business School, UK
Federico Inchausti-Sintes Universidad de Las Palmas de Gran Canaria, Spain
The research question of the paper refers to the correlation between quality and the spatial
dimension of Airbnb markets. The authors task is to give appropriate and in-depth insights of
Airbnb markets prices and to understand until what extent they are affected by location and quality.
They referred to a case study of Bristol (UK), by collecting data and analysing 818 entire houses
and apartment listings from the city.
In recent years, the hotel business has been subjected to drastic changes and within such a changing
environment it was also exposed to several challenges. One of them is the development of peer-
to-peer (P2P), in other words sharing economies, such as Airbnb, a platform that made space for
new suppliers: the private owners.
The opinions of the experts about the impact of these “new entries” in the hotel sector are in
conflict. Some assert that the effect is deleterious for hotels and some highlight that the success of
Airbnb and P2P accommodation in respect to hotel is limited to cities and small towns, while in
summer destinations the hotels are still winning the market. Despite the negative consequences for
hotels and residents, the introduction of new P2P platforms led to a big growth in the supply of
private accommodations in many cities in addition to a greater availability of information. In such
a way, the competition is more intense, and the product differentiation becomes the basis for
attracting guests. Differentiation is mainly determined by location, since in most cities there is a
greater concentration of hotels and Airbnb in the areas near the touristic attractions. Another
important factor to analyze is that location has a big influence on prices, in fact, in central touristic
areas prices are expected to be higher. However, the quality differentiation can reduce the impact
of location.
As mentioned above the main objective of the article is to analyze the role of location and
competitor prices (representing quality) as factors affecting the price of Airbnb listing in Bristol,
UK. For this purpose, the authors used the OLS methodology, to reach an optimal market definition
concerning the distance and the price variations between listings. Furthermore, in the literature
review the author analyses all the factors influencing price with the exemption of the location. The
determinants that resulted statistically significant are: room characteristics as number of beds,
bedrooms, or bathrooms; reputation of the host and the experience; rental rules; reviews and ratings
of other customers.
Nonetheless, it is to take into consideration that most of the study on this topic also acknowledge
location as fundamental driver in travelers’ decision making. As a matter of fact, the location
differentiation is given by the vicinity to touristic attractions and city-center, as well as touristic
facilities, like: convention centers; bus/train stations; coastline; sightseeing, nightlife, etc
Giada Natale
Martina Pia Giuliani
Caterina Boschi
The first Airbnb listing in Bristol was in 2011 and since then, the level of competition in the
traditional tourism accommodation raised. While hotel companies are concerned about the unfair
competition brought by Airbnb, they are confident about their positive impact on visitor arrivals
to Bristol as a result of the increased accommodation supply, as well as the fact that the usage of
P2P platforms allows locals (as hosts) to catch a greater portion of the benefits associated with
tourism activity, thus benefiting locals rather than multinational hotel chains.
The authors obtained a data set containing all Airbnb listings in Bristol. These data were scraped
from the internet and made publicly accessible at http://www.insideairbnb.com. There are 2056
listings in the database, with a broad range of property and room styles, focusing on the most
popular property category. This results in a subsample of 818 listings, accounting for 39.8% of
Bristol’s Airbnb supply. In specific, 534 apartments are distributed in the city center, while the
remaining listing are spread in different clusters around Bristol. From the raw data set, they
selected a variety of variables related to room costs, host characteristics, room features, and
customer reviews. House values function as a control variable, collecting unique factors of the
areas where the listings are located that can influence short-term rental rates. For the objectives of
this article, the most relevant location factors will have a variable radius around the listing which
are: the number of tourist attractions, the average hotel price for person and, the amount of
competitive Airbnb listings with the same property category.
They used OLS methods to estimate a set of price equations to investigate the spatial and quality
parameters of local Airbnb markets. The equations are meant to determine the price of single
listings in comparison to property and host features, local home values, and distance to tourist
attractions, hotels, and other listings. The proximity variables, which are calculated with increasing
distance radius and quality margin, will vary in each model (measured as a price difference).
The Ordinary Least Squares regression is statistical method for analysis. It calculates the
interaction between one or more independent variables and a dependent variable by minimizing
the sum of squares in the difference between the observed and expected values of the dependent
variable in a straight-line configuration. The authors chose the natural logarithm of the listing price
of the property as dependent variable, while other dependent variables such as the price per capita
were also investigated but dropped due to poor results. The quantity of bedrooms and bathrooms,
as well as the average consumer rating of the property or the host’s “superhost”status, are all well-
known price factors. The house value variable represents the average price paid for all sold homes.
The distances to the city center (distcenter) and the nearest bus/train station (diststation), both in
kilometers, were also included. The number of points of interest (tourist attractions) within a given
radius of the listing is referred to as POIS radius. The average hotel price (for a double room)
within a given radius is referred to as price hotel radius. Price_hotel_radius relates to the average
hotel price within a given radius (for a double room). The amount of competing Airbnb listings of
the given property category is referred to as N_comp_ radius_diff.
From the data analyzed the authors concluded that the optimal market is defined by a smaller radius
when the listings are not differentiated, for each level of price difference. In other words, when the
Giada Natale
Martina Pia Giuliani
Caterina Boschi
price difference increases the optimal radius for market definition proportionally decreases. For
instance, when the difference in prices within listings is not more than 10% it is easier to define
prices of the competitors within 1700m radius. These are the “optimal” spatial and price/quality
dimension of Airbnb listings in Bristol. However, when the price difference is higher than60%
and with a distance higher than 2000m radius, it increases the adjusted R² (the highest= optimal
“local” model). This suggests that also the non-location price factors (like number of bedrooms
and bathrooms) are important to determine the price. Another price determinant factor to consider
is accessibility, from the result the distance to the city center has a slightly negative coefficient. In
fact, it is 5%, which is not statistically significant; This may be because of the Point of interest
(POIS) variable that already includes the influence of the distance from the city center on Airbnb
prices. The number of competitors is negative and significant, which is consistent to the strong
competition notion where higher supply led the prices down.
Outcomes indicates that the price of listings are negatively influenced by the number of other
listings within a maximum difference price of 40%. The fierce competition makes the prices go
down within the local markets. In this case, the homogeneity and similarity within the listings have
an adverse effect on the prices, which cause the market to have greater boundaries. Apart from
these quality differentiation aspects, there is the city-wide effect to consider. The authors obtained
that when the price differences are higher than 70%, the presence of competitors influences
positively the prices, since hosts can exploit the big average differences in prices to also higher
their own prices. This effect is reduced together with distance for the agglomeration effect, since
a high distribution of listing in the same radius decreases the differentiation of tourism
accommodation and increases the competition.
Despite the deep analysis and calculations there are some weaknesses that does not allow the paper
to be totally reliable and generalizable. The main reason is that the analysis is restricted to the
sample of the tourism destination of Bristol, which is a city in United Kingdom that cannot
represent every city in the world. Furthermore, there is a lack of information since the only data
about prices are at the time of the collection and the hosts may decide to apply pricing tactics.
Seasonal fluctuations in the geographical or pricing aspects of Airbnb local marketplaces have also
been neglected in the analysis. These difficulties can be addressed with further studying or by
extending this study to additional locations. This could help to confirm these findings outside of
the geographical and chronological constraints imposed by the data.
Bibliography:
(Ordinary Least Squares Regression | Encyclopedia.com, 2021)
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