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Filippo Celata and Antonello Romano
1
Overtourism and online short-term rental platforms
in Italian cities
Abstract
Although Italian cities have undergone several waves of touristification, concerns about overtourism
have only recently become widespread. In the article, we suggest that the diffusion of short-term rental
platforms is not merely a concomitant factor, but is crucial to understanding the how and where of
contemporary overtourism. To this end we apply a fractal methodology to identify, map and compare
those parts of the city that are most affected, and measure the pressure short-term rentals have on city
centres as places of residence. By allowing the conversion of residential apartments into tourist
accommodation, we argue, short term rentals contribute to the displacement of residents more directly
than a generic process of gentrification or touristification. Second, platforms such as Airbnb not only
contribute to increasing the accommodation capacity of urban areas, but radically change the
morphology of the tourist city. The growing concerns about overtourism are not due to the rising
number of tourists per se, but to their increasing penetration into the residential city. We suggest,
therefore, that to conceive of overtourism merely as overcrowding is not only inadequate but
counterproductive. Even though the depopulation of city centres is difficult to reverse, the coronavirus
emergency is an opportunity to plan a different city where tourism coexists with other urban uses and
functions.
Keywords: Overtourism; Airbnb; Short-term rentals; Platform economy; City centres; Neighbourhood
effects.
Introduction
In recent years overtourism has been on the agenda of various cities worldwide. The term has been used
in Google searches since 2006; it became a hashtag on Twitter in 2012 and was first discussed in an
article on the travel industry site Skift.com in 2016. Since then the term has gained increasing
popularity: a simple search on Google Scholar for the keyword “overtourism” returns approximately
400 papers in 2019 and 150 in 2018, while the same search in 2017 returned only 12 results (Goodwin,
2017). The term “tourismphobia” is also recent; it first appeared in 2008 and since then has been widely
used to label, or rather stigmatize anti-tourism protests. These protests have been observed in many
European cities (Barcelona, Venice, Palma de Mallorca, Paris, Dubrovnik, Berlin, Bologna, Reykjavik, and
others), and elsewhere (Koens et al., 2018). Anti-tourism movements have also flourished in recent
years (Hughes, 2018; Colomb and Novy, 2016). Some may argue that these concerns belong to the past,
given that the coronavirus emergency has practically halted tourism flows worldwide. However, the
epidemic may change mass tourism more or less permanently, but will not stop it indefinitely. However,
many of the effects overtourism produced are difficult to reverse, as we will discuss further in the paper.
Despite the relevance of the issue and its effects, there is still lack of conceptual clarity about what
overtourism is, how contemporary concerns about it differ from earlier worries, what are its causes and
1
This is the postprint version of the article published in the Journal of Sustianable Tourism:
https://doi.org/10.1080/09669582.2020.1788568.
consequences and, consequently, how it should be investigated and managed. In this paper, we first
provide a review of current conceptualizations in order to highlight the specificities of contemporary
concerns about overtourism, and how previous research has attempted to define, measure and monitor
the pressure tourism is exerting on cities. In particular, we discuss the crucial role played by the spread
of digital accommodation platforms, which sparked a huge and uncontrolled expansion in cities’
accommodation capacity with the potential to impact housing availability and affordability, displace
permanent residents, and transform the social ecology of the most affected urban neighbourhoods. The
article focuses upon Airbnb.com, given that it is the most widely-used short-term rental platform in Italy,
and based on the idea that such diffusion is an important part of the problem. The hypothesis is that
short-term rentals do not merely contribute to increasing the accommodation capacity of urban areas,
but radically change the morphology of the tourist city and, consequently, the relationships between
residents and visitors.
On this basis, we develop a methodology aimed at identifying and mapping sub-municipal areas that are
most affected by overtourism, and apply this methodology to the most touristified metropolitan cities
in Italy – Venice, Florence, Rome, Naples, Palermo, and Bologna. The aim is to provide comparable
evidence about the incidence and impact of short-term rentals upon the liveability of city centres, and
their contribution to the depopulation of the urban core.
The case study cities have been identified based on the number of short-term rentals listed on the
accommodation platform Airbnb.com (Picascia et al., 2017). All of these cities have seen a proliferation
of initiatives and social movements denouncing the effects of overtourism and short-term rentals, in
particular in terms of housing availability for residents or students
2
. Hotel associations have criticized
short-term rentals as a form of unfair competition, given their unregulated status
3
. These views are often
countered by those who argue instead that short-term rentals represent a precious source of (extra)
income and urban regeneration. Concerns from local public authorities have initially been limited to
attempts to avoid excessive tourism congestion, to ‘educate’ or ‘discipline’ tourists, or to limit their
access to certain parts of the city. Mayors in some of those cities (Florence, Venice, Rome) have, for
example, issued ordinances that ban tourists from consuming meals in public spaces or sitting on
monuments. The Mayor of Florence announced in 2017 that he would have church steps watered to
prevent tourists from sitting there. In Venice, entry gates were set up to regulate access to the city centre,
so that they can be closed when the number of accesses exceeds a certain threshold (the gates were
removed shortly afterwards as they were never used). The same has been attempted around specific
attractions, like Fontana di Trevi in Rome, which tourists are invited to visit quickly. Visitors entering
Venice have recently been asked to pay an entry ticket that ranges from 3 to 10 euros depending on the
degree of congestion in each period, with the exception of tourists staying in local accommodation
facilities and other categories of city users. Several local associations and (anti-tourism) social
movements have protested vehemently against these measures, which they judge counter-productive.
What those associations criticize is the transformation of cities into some sort of theme park: access
gates and entry tickets cannot but promote and accelerate such process. It is clear, however, that current
2
See, for example, the manifesto of the SET network, "Sud Europa di fronte alla Turistizzazione", which many
Italian cities signed up to: https://setfirenze.noblogs.org/post/2019/02/13/founding-manifesto-of-set-
network/.
3
See for example the report “Tourism and the shadow economy” published by Federalberghi, the main Italian
association of hotels:
http://www.federalberghi.it/UploadFile/2018/09/turismo%20e%20shadow%20economy%20-
%20edizione%20settembre%202018.pdf.
approaches are far from constituting an appropriate and definitive management of overtourism, which
is not simply an issue of overcrowding, as we will discuss in the next sections.
With regard to the widely debated issue of short-term rentals and digital accommodation platforms,
none of those cities have taken any formal steps, but some of them (Bologna and Firenze) have declared
very recently their intention to introduce specific regulations and even to ‘stop’ the conversion of
residential dwellings into lodgings for tourists. Proposals have been made, moreover, to change existing
regional and national laws in order to provide cities with some tools for monitoring and regulating
short-term rentals, which are currently very weak, for example by introducing an ad-hoc licence.
Moreover, national authorities have attempted to limit tax evasion and tax avoidance, with limited
success. The paper aims both to contribute to existing research and to put forward a more appropriate
system for management of overtourism and of its effects.
The how and where of platform-mediated overtourism
Although the term has gained popularity only very recently, concerns about overtourism are by no
means new. In tourism research, the topic has been discussed at least since the early seventies (Wall,
2020; Capocchi et al. 2019). For example, an index for measuring residents' ‘irritation’ towards tourists
was proposed by Doxey in 1975. Within Butler’s well-known theory of the Tourism Area Life Cycle, the
“consolidation” stage is described as the moment when the number of visitors exceeds that of
permanent residents (1980). According to Butler, this situation can easily lead to stagnation and decline,
as well as causing “opposition and discontent among permanent residents, particularly those not
involved in the tourist industry in any way, and result in some deprivation and restrictions upon their
activities” (Butler, 1980, p. 8). More recent definitions of overtourism are basically similar, except that
the emphasis is more on residents’ discontent and perceptions, rather than overcrowding per se (Butler,
2019)
4
.
The first difference with respect to previous concerns about overtourism is indeed this ‘discontent’.
Concerns and protests about the negative effects of excessive tourism are today particularly widespread
(Milano et al., 2019) whereas previously they were more limited (Dodds and Butler, 2019). The second
difference is that concerns about overtourism arise today mainly in big cities. The question we must ask
therefore is: why? The easiest answer is that tourism is simply growing too much and that this growth
is particularly concentrated in cities. This view has been advanced by a recent UNWTO report on the
topic (2018), and is common in the burgeoning literature about overtourism (Sequera and Nofre, 2018;
Capocchi et al., 2019; Oklevik et al., 2019; Dodds and Butler, 2019). However, this is just part of the
answer since the how of this growth is, in our view, at least equally important. In this regard , we believe
that the role of digital accommodation platforms is crucial for understanding contemporary
overtourism. The diffusion of “network hospitality” or platform-mediated short-term rentals is in fact
often mentioned as a concomitant factor in the literature about overtourism (Goodwin, 2017; Bouchon
and Rauscher, 2019; Dodds and Butler, 2019), but it is rarely the main focus of the analysis.
4
The UNWTO defines overtourism as “the impact of tourism on a destination, or parts thereof, that excessively
influences perceived quality of life of citizens and/or quality of visitors experiences in a negative way” (2018, p.
4). The Responsible Tourism Partnership (Goodwin, 2017) defines overtourism as “destinations where hosts or
guests, locals or visitors, feel that there are too many visitors and that the quality of life in the area or the quality
of the experience has deteriorated unacceptably” (p. 1). According to a report commissioned by the European
Parliament, “overtourism describes the situation in which the impact of tourism, at certain times and in certain
locations, exceeds physical, ecological, social, economic, psychological, and/or political capacity thresholds”
(Peeters et al. 2018, p. 22).
One hypothesis that we wish to explore further in this paper is that platforms such as Airbnb have not
only hugely increased the accommodation capacity of many destinations, they have also changed
substantially the morphology of the tourist city, which “plays an important role in the sentiment of
contested spaces between residents and visitors” (Bouchon and Rauscher, 2019, p. 14). Inhabitants, it
has been argued, feel increasingly alienated from their own city which they feel has been appropriated
by tourists (Diaz-Parra and Jover, 2020).
Evidence about the spatial effects of accommodation platforms is indeed ambivalent (for a review, see
Guttentag, 2019). Short-term rentals, it has been shown, are causing both the over-touristification of
already highly touristified city centres (Arias Sans and Quaglieri Domínguez, 2016; Picascia et al., 2017;
Benítez-Aurioles, 2018; Alizadeh et al., 2018) and the invasion and gentrification of non-touristic
neighbourhoods (Cocola-Gant, 2016; Wachsmuth and Weisler, 2018; Ioannides et al., 2019). This
apparent ambivalence can easily be solved by assuming that short-term rentals are much more diffused
and widespread all over the cities’ central and near-central areas than hotels and traditional
accommodation facilities (Gutiérrez et al., 2017; Celata, 2017; Gyòdi, 2017). The rising concerns about
overtourism may therefore be due not to the growing number of tourists per se, but to their growing
penetration into the residential city, closer to where the inhabitants live.
Moreover, the diffusion of short-term rentals may have a much more direct effect on the socio-spatial
ecology of city centres than a ‘standard’ gentrification process (Sequera and Nofre, 2018; Jover and Diaz-
Parra, 2019) and even than touristification in general, whose effects are mainly indirect. By allowing the
conversion of thousands of residential apartments into tourist lodgings, short-term rentals immediately
cause a substantial decrease in the housing stock available for long-term residents and contribute
directly to the depopulation of city centres, as we will show.
Another difference with respect to previous debates about overtourism, as already mentioned, is in the
typology of destination that is today more exposed (Bouchon and Raucher, 2019; Phi, 2019; Butler,
2019; Wall 2020). Traditionally, concerns about the number of tourists exceeding an acceptable
threshold have been raised with regard to, for example, natural parks and areas of ecological
importance, small islands, specific tourist sites, or “resort cities” where “a major part of the area’s
economy will be tied to tourism” (Butler, 1980, p. 8). Since today overtourism predominantly affects
big cities, the conceptual and empirical lens through which we observe and eventually react to over-
touristification must change.
For example, based on previous experiments in destinations affected by overcrowding, the application
of "carrying capacity" or “the limits of acceptable change” methods is frequently suggested
(Papathanassis, 2017; Bouchon and Rauscher, 2019; Phi, 2019; Capocchi et al., 2019; Milano et al., 2019;
Goodwin, 2017; Koens et al. 2018; UNWTO, 2018; Peeters et al., 2018; Dodds and Butler, 2019). A wealth
of “urban carrying capacity” assessment methods exists (Wei et al., 2015), and these have been applied
to determine the maximum amount of tourism allowable in, for example, Venice (Bertocchi et al., 2020).
The option attracted several criticisms (Saarinen, 2006; Koens et al., 2018; Wall, 2019, 2020). The
measurement of the maximum acceptable number of tourists may be based on the physical capacity of,
e.g., accommodation facilities, public transport or the waste treatment system (Bertocchi et al., 2020).
However, touristification can cause irreversible and detrimental effects, as well as raising concerns and
protests from the local population, much before such an extreme threshold and the city’s complete
saturation is reached. Contemporary overtourism is not, moreover, simply due to congestion or
overcrowding; the concern is about how touristification affects and interacts with the social fabric of the
city, and what the consequences are for residents. At the same time, to measure carrying capacity based
on residents’ perceptions or sociocultural variables is problematic, equivocal, and potentially flawed, as
long as what is an “acceptable” pressure is based on a complicated and debatable aggregation of
individual preferences (Seidl and Tisdell, 1999). Additionally, the relationship between the density and
degree of touristification and the “acceptable change” it induces is not linear (Wall, 2019). And what
should we do once we know that the number of tourists is excessive? Such a view implicitly calls for an
approach based on limiting tourist numbers, which is not only problematic, but also far from being a
proper management of the causes and consequences of overtourism in an urban context (Phi, 2019), as
mentioned in the introduction. Moreover, cities have a much more diversified social and economic base
with respect to those over-specialized destinations that have been traditionally affected by tourism
congestion. The issue is therefore not merely overcrowding, but how touristification relates to – and
potentially conflicts with – other urban functions, and how it contributes together with a wealth of other
factors and processes to urban change. Finally, as long as the destinations that are the most affected by
contemporary overtourism are big metropolitan areas, the issue is not “how much” but “where”
overtourism is in the urban area (UNWTO, 2018).
In the following sections, we will provide evidence about some of the issues mentioned above, issues
that, in our view, are crucial for understanding the how and where of contemporary overtourism.
Fig. 1- Airbnbscapes in Italian cities. The bars’ height is proportional to the portion of the housing stock that is
available for rent on Airbnb.com per census tract. The bars’ colour is the proportion of “entire homes” out of total
Airbnb listings. Data sources: Insideairbnb, ISTAT.
Data and methodology
The analysis presented below provides first, the identification of those areas in the city that are affected
by overtourism and, secondly, some evidence about the socio-spatial impact of platform-mediated
touristification. The methodology and measurements are aimed at providing comparable evidence
across some of the main Italian tourism cities: Bologna, Florence, Naples, Palermo, Rome, and Venice.
The study is based on data scraped from Airbnb.com in 2019 by Insideairbnb.com, microdata from
official statistics (ISTAT), Municipal statistics and Openstreetmap geodata.
We first identify within the six cities the area that may be defined as the ‘tourist city’, based on a common
method, and using the location of Airbnb listings. In particular, we apply a fractal methodology (Jiang &
Miao, 2015) in order to make the different cities comparable or, more precisely, to avoid the so-called
modifiable area unit problem (MAUP). The analysis presented in the paper is in fact applied to various
urban areas that range from medium-sized cities such as Venice (260,000 inhabitants) to big
metropolitan areas such as Rome (2.9 million inhabitants). In order to properly compare those cities,
their different sizes as well as their different internal structures should be taken carefully into account.
Figure 1 enables us to appreciate such variation: the city’s ‘skyline’ is composed of bars whose height is
proportional to the ratio of the city’s housing stock that is for rent on Airbnb.com per each census tract.
Bars are coloured based on the percentage of entire homes over total listings. Tall blue bars, in short,
indicate areas in the city where not only are there more Airbnb listings, but where the impact on the
availability of housing for permanent residents is higher, an issue that we will discuss in greater detail
below. At this stage, the figure is useful to provide some sort of 3D visualization of the pervasive but
non-homogeneous distribution of short-term rentals over the urban space, and to outline a preliminary
taxonomy of the tourist city’s morphology. In Florence and Bologna the spatial pattern is concentrated
in and more or the less equally distributed all over the city centre. Naples shows a multi-polar pattern.
Venice is heavily polarized, while Palermo and Rome are both multi-polar and hierarchical.
The fractal methodology permits us to account for such variability by taking into account those areas
where Airbnb listings are most concentrated, but also the overall structure of the (tourist) city, without
adopting any predefined spatial partition. Previous analyses of the distribution and impact of short-term
rentals are often affected by the MAUP. Such impact is in fact analysed sometimes at the city scale, e.g.
based on municipal boundaries (Wegmann and Jiao, 2017; Alizadeh et al., 2018), sometimes on a sub-
municipal scale using predefined divisions such as neighbourhoods or census tracts (Wachsmuth and
Weisler, 2018; Gutierrez et al., 2016; Cocola-Gant, 2016), and other times focusing on specific
neighbourhoods (Smith et al., 2018; Cocola-Gant and Gago, 2019; Ioannides et al. 2019). Estimates are
therefore affected by the scale and shape of the geographical divisions adopted, which is particularly
problematic if we wish to compare cities or neighbourhoods. Municipal and sub-municipal boundaries
are in fact not only very different in size and shape but also arbitrary, being imposed from the top down
by public authorities. The actual extent of cities in more geographical/spatial terms is defined and
delineated based on their physical morphology, for example in terms of the average distance between
buildings. The same applies to the ‘tourist city’: our methodological option is to identify these tourist
cities based on the distance between Airbnb listings or, more precisely, based on the head/tail breaks
rule. In detail, the approach “involves dividing things around an average into large and small, which
respectively constitute the head and the tail of the rank-size plot” (Jiang, 2015, p.6). The process has
four steps (Figure 2): we first calculated the Triangular irregular networks of Airbnb listings (A); we
then measured the length of the interpolation edges (B), and (C) selected those whose length is below
the median value, and those below the 75th centile. Finally, we created the fractal areas (D) by
aggregating those high proximity features (point C) into single-part polygons. Figures 3-8 report the
results: in orange the fractal area obtained by aggregating edges whose length is below the median, and
in blue those below the 75th centile. In the analysis that follows, the “tourist city” corresponds to the
fractal area with proximity of listings below the median value. The methodology allows us to obtain
homogeneous and comparable spatial units, as well as to highlight the spatial structure of the tourist
city, e.g. the extent to which it is more or less compact or, on the contrary, fragmented.
A B
C D
Figure 2. Fractal methodology to identify the tourist city: A) Airbnb listings (1 dot = 1 listing), B) interpolation
edges between listings, C) selection of edges shorter than the median length, D) identification of the fractal area
(in orange). Naples. Data source: Insideairbnb.com, 2019.
Using those spatial units, we calculated several indicators such as the extent of the tourist city, the
concentration of Airbnb listings in this area, their growth rate, the ratio of short-term rentals on the
residential housing stock, and the relation between their accommodation capacity and the resident
population (Table 1).
Finally, we present and discuss the trends of population variation within and outside of the tourist city,
and we then focus on some of those cities in order to provide further evidence about the association
between the city centre’s depopulation and the diffusion of short-term rentals, and about how the
distribution of Airbnb listings in the city differs from that of hotels and registered accommodation
facilities.
The spatiality and impact of platform-mediated overtourism
The fractal approach described in the previous section permits us, first, to obtain a comparable
delimitation of the ‘tourist city’ within the metropolitan areas that are the object of our analysis. The
results are presented in figures 3-8. These maps adopt the same geographical scale, and show that the
overall extension of the tourist city is more or less similar despite these cities having very different sizes
and populations, with the exception of Rome, where the tourist city is bigger, and Bologna, where it is
smaller and more fragmented.
Figure 3. The tourist city in Venice, identified based on the distribution of Airbnb listings. Data source:
insideairbnb.com, May 2019.
Figure 4. The tourist city in Bologna, identified based on the distribution of Airbnb listings. Data source:
insideairbnb.com, May 2019.
Figure 5. The tourist city in Florence, identified based on the distribution of Airbnb listings. Data source:
insideairbnb.com, May 2019.
Figure 6. The tourist city in Rome, identified based on the distribution of Airbnb listings.
Data source: insideairbnb.com, May 2019.
Figure 7. The tourist city in Naples, identified based on the distribution of Airbnb listings.
Data source: insideairbnb.com, May 2019.
Figure 8. The tourist city in Palermo, identified based on the distribution of Airbnb listings. Data source:
insideairbnb.com, May 2019.
As reported in Table 1, the ‘tourist city’ is relatively small in terms of extent with respect to the entire
municipality (2.2% of the municipal area in Florence, 1.5% in Venice, approximately 0.5% in the other
cities), but also quite significant as it covers most of the city centre, and includes between one third and
three quarters of the entire supply of Airbnb listings. The demand for those listings is even more heavily
concentrated in this central area: the percentage of reviews obtained by central listings (which can be
considered proportional to the number of guests) is always above the percentage of listings located in
this area, with the single exception of Florence, due to the attractiveness of villas in less central areas
for rent on Airbnb.
In terms of impact, as already mentioned, the most direct and worrying effect is the subtraction of
housing units available for permanent residents, and their conversion into short-term rentals. In order
to assess this, we calculate the ratio between the entire residential housing stock in the census tracts
that have their centroid in the ‘tourist city’, and the number of entire apartments for rent on Airbnb.com
in the same area. The ratio ranges from 11% (Naples) to 30% (Florence and Bologna). Census data is
only available for 2011; however, in the ‘tourist city’ the housing stock is relatively stable, given that the
area includes heavily regulated historic neighbourhoods
5
.
Table 1. Tourist city’s extent and incidence of Airbnb listings in Bologna, Florence, Naples, Palermo, Rome,
Venice. Data source: Insideairbnb.com, Istat.
City
Fractal area / Tourist city (km2)
Percentage of Airbnb listings in the
fractal area
Percentage of Airbnb reviews in the
fractal area
Density of Airbnb listings in the
fractal area (per Km2)
Yearly growth rate (%) of listings
within the fractal area (2018-2019)
Ratio between entire homes on
Airbnb and the total residential
housing stock in the fractal area
Ratio between entire homes on
Airbnb and the number of families
residing in rented apartments in
the fractal area
Ratio between the accommodation
capacity of Airbnb listings and the
resident population in the fractal
area
Bologna
0.25
34%
41%
5632
+288%
32.4%
136.8%
99.7%
Florence
2.3
77%
70%
3599
+39%
29.1%
149.5%
118.5%
Naples
1.76
64%
71%
2823
+84%
10.9%
30.3%
34.8%
Palermo
0.93
54%
71%
3266
+91%
25.0%
85.7%
95.2%
Rome
5.78
62%
74%
3300
+57%
17.0%
118.4%
75.9%
Venice
2.01
73%
75%
2986
+46%
21.8%
124.3%
86.0%
5
Census data shows that from 2001 to 2011 the number of apartments in residential buildings has indeed
decreased in Venice (-9%), Bologna (-3%), Florence (-16%) and Rome (-8%), probably due to their conversion
into office space, and increased only slightly in Naples (+2%) and Palermo (+6%) (dati.istat.it). More recent data
shows moreover that the average surface of residential apartments in the historic centres of Rome and Naples
didn’t change from 2012 to 2016 (https://www.agenziaentrate.gov.it/portale/web/guest/agenzia/agenzia-
comunica/prodotti-editoriali/pubblicazioni-cartografia_catasto_mercato_immobiliare/immobili-in-italia); we
can therefore exclude that the number of these apartments increased due to their subdivision into smaller units.
It should be noted that in most Italian cities the availability of rentals is very limited, as the great
majority of families live in homes they own. The conversion of residential apartments into short-term
rentals impacts therefore, in particular, upon the already small proportion of the housing stock which is
available for long-term rentals. To measure such pressure, we compare the number of entire apartments
listed on Airbnb with the number of families renting in the year 2011, i.e. before the Airbnb ‘invasion’
began. In four of the six cities, the number of apartments listed on Airbnb in 2019 exceeds those rented
to residents in 2011. The indicator is not meant as a ratio but simply, as already mentioned, as a proxy
of pressure. In fact, not only rented apartments but also those occupied by their owners may have been
converted into short-term rentals. The available data do not allow us to measure actual conversion rates.
However, while the percentage of families living in owned apartments increased consistently over the
past decades, the percentage of residential apartments for rent (to either tourists or residents)
increased in Rome and Naples from 2012 to 2016, and more in the city centres (+5.5%) than in the
whole city (+3%). This may be due to various factors. What the above-mentioned data show is that the
growth of short-term rentals is probably one of those factors.
The ratio of tourists to the permanent population is also a potential indicator of (over)touristification
and of the pressure short-term rentals exert on city centres as places of residence. We therefore
compared the entire accommodation capacity of Airbnb listings with the number of residents in 2011:
with the single exception of Naples, such ratio is always close to or even above (in the case of Florence)
100%. Obviously, the resident population may have changed since 2011, as we discuss below. It is also
unlikely that the total accommodation capacity of Airbnb listings is permanently and completely
occupied by tourists. On the other hand, we only considered Airbnb listings. When tourists staying in
hotels or in accommodation facilities advertised through other digital platforms are added, these
numbers increase substantially.
Short-term rentals and the depopulation of city centres
How are such numbers and trends actually impacting the liveability of cities? The primary and most
visible impact is upon the resident population of city centres. Figures 9, 10 and 11 show population
trends within and outside the “tourist city”, i.e. those neighbourhoods that correspond more closely to
the fractal areas identified in Figures 3-8.
In Rome, the central and most touristified part of the city is indeed depopulating fast (Figure 9), in
particular since 2010, and especially after 2014: in four years, the two most touristified neighbourhoods
– the zone labelled “historical centre” and Trastevere – have lost approximately one third of their
inhabitants. In Venice (Figure 10), the municipal population is also more or less stable overall, while
the number of residents in the historic city is decreasing. Unlike in the case of Rome, in Venice this trend
seems to predate the Airbnb ‘invasion’ (which explains also why the ratio between the accommodation
capacity of Airbnb listings and the resident population reported in Table 1, is lower in Venice than
Florence or even Palermo). In Bologna, the municipal population is growing, but this growth does not
affect the most touristified parts of the city, which are in fact slightly depopulating (Figure 11).
Figure 9. Variation of the resident population in Rome, 2006-2018 (Base: 2006 = 100). The tourist city’s
neighbourhoods are those that fall almost completely within the fractal area (Figure 5): Centro Storico,
Trastevere, Esquilino, XX Settembre, Prati and Eroi. Data source: Municipality of Rome
(https://www.comune.roma.it/web/it/roma-statistica-popolazione.page).
Figure 10. Variation of the resident population in Venice, 2006-2018 (Base: 2006 = 100). The tourist city
corresponds to the zone “centro storico”, i.e. the main central islands (neighbourhoods: S.Marco-Castello-S.Elena-
Cannaregio and Dorsoduro-S.Polo-S.Croce-Giudecca). Data source: Municipality of Venice
(https://www.comune.venezia.it/it/content/serie-storiche).
Figure 11. Variation of the resident population in Bologna, 2006-2018 (Base: 2006 = 100). The ‘tourist city’
corresponds to the zone “Irnerio”. Data source: Municipality of Bologna
(http://dati.comune.bologna.it/node/1033).
For the other cities, a complete historical series is not available. Based on the limited data available, we
can see that in Florence the population of the “historical centre” zone (an area similar to that of Figure
5) decreased its weight with respect to the total municipal population, from 18.2% in 2012 to 17.3% in
2018
6
. In Naples, the sub-municipal areas are too big to match with the ‘tourist city’ identified in Figure
7, and data is only available until 2016; however, from 2010 to 2016, the resident population of the
central area of the city shows a small decrease in absolute numbers, but not with respect to the rest of
the city, as the whole urban population is decreasing
7
. In Palermo, the zone “circoscrizione 1” – which
includes the ‘tourist city’ identified in figure 8, although it is bigger – the resident population decreased
by 4.9% between 2012 and 2018, while the total urban population decreased by 4.25%
8
.
The depopulation of city centres is certainly not a new phenomenon. However, the population in the
‘tourist city’ from 2001 to 2011 – i.e. before the “Airbnb invasion” – remained stable in Bologna (+0.2%),
increased in Naples (+3.4%) and Palermo (+9.3%), and decreased in Florence (-4.5%). The population
trend for the historic centre of Rome is reported in figure 12, and it had been more or less stable since
1991. Venice (Figure 13) experienced the highest decrease between 2001 and 2011 (-10%), which is in
any case lower with respect to both the previous five decades, and the most recent one (-15% from 2009
to 2019).
Figure 12. Resident population in the historic centre of Rome, 1901-2011
Source: Sonnino et al., 2011 (1901-1991 data) and Istat (2001-2011 data)
Figure 13. Resident population in the historic centre of Venice, 1901-2011
Source: Municipality of Venice
6
Data accessed at http://dati.toscana.it, February 7th 2020.
7
Data accessed at http://www.comune.napoli.it/flex/cm/pages/ServeBLOB.php/L/IT/IDPagina/34362,
February 7th 2020.
8
Data accessed at https://opendata.comune.palermo.it, February 7th 2020.
In order to assess to what extent depopulation is associated with the spread of short-term rentals, we
calculated the Pearson correlation between the variation of the resident population in each of the 155
neighbourhoods of Rome (“zone urbanistiche”) and in the 12 neighbourhoods of Venice (“quartieri”),
with several measures of the concentration of Airbnb listings in those neighbourhoods. As shown in
Table 2, the correlations are always significant, above a 99% confidence level, and also quite high. The
highest correlation is, not surprisingly, with the number of entire apartments for rent on Airbnb. The
same correlation for the 18 neighbourhoods of Bologna is significant (the correlation is -0.585,
significant at the 0.05 level) only if the variation of the population is calculated from 2012, and if two
low-income but central zones (Bolognina and Marconi) where both the number of residents and of
Airbnb listings have grown in the last years are eliminated.
Table 2. Pearson correlation between the resident population variation in the neighbourhoods of Rome (2014-
2018) and Venice (2014-2019), and the concentration and variation of Airbnb listings
Number of
Airbnb listings,
2019
Number of entire
apartments for rent
on Airbnb, 2019
Cumulate number
of Airbnb listings
reviews, 2019
Absolute difference in
the number of Airbnb
listings, 2016-2019
Rome
-,616**
-,699**
-,629**
-,648**
Venice
-,862**
-,897**
-,857**
-,834**
** Correlation is significant at the 0.01 level (2-tailed). Data source: Insideairbnb, Municipality of Rome,
Municipality of Venice.
Finally, in order to assess how the morphology of the tourist city is changing due to the expansion of
short-term rentals, we calculated the average distance between the resident population and the closest
accommodation facility, which measures how ‘close’ tourists are to where residents live. Table 3 shows
that this distance is substantially lower for Airbnb listings with respect not only to hotels and similar,
which are obviously fewer in number, bigger and consequently more concentrated, but also with respect
to registered accommodation facilities such as bed & breakfasts or ‘formal’ rooms and apartments for
rent to tourists. Such ‘closeness’ is relatively even higher in the central area we defined as the ‘tourist
city’ than in the entire municipality
9
.
Table 3. Average distance between the resident population and the closest accommodation facility in Rome.
Entire Municipality
‘Tourist city’
Hotels and similar
649.5 mt
279.2 mt
Airbnb listings
136.7 mt
10.5 mt
Non-hotel registered
accommodation facilities
351.1 mt
51.1 mt
Data source: Insideairbnb, Municipality of Rome, ISTAT
9
It is worth noting that in Rome the average distance to the three ‘top’ attractions (the Colosseum, the Pantheon
and Fontana di Trevi), which measures how ‘conveniently’ located tourists are in the city, is higher for tourists
staying in hotels (4.6 km) than for those staying in Airbnb listings (3.6 km), when calculated for the entire
municipality. This result is in line with the evidence provided by Gutierrez et al. (2016). However, when the same
indicator is calculated only for the ‘tourist city’, i.e. for a more central area, the opposite is true: Airbnb guests are
relatively more distant from the three top attractions (2 km) than tourists staying in hotels (1.5 km).
Discussion and conclusions
Although Italian cities have undergone several waves of touristification, concerns about overtourism
are very recent. The hypothesis explored in this article is that the growth of digital short-term rental
platforms is not merely a concomitant factor contributing to an excessive growth in the number of
tourists but crucial for understanding how such growth is distributed in the city and, consequently, how
it impacts upon city centres as living spaces.
Accommodation platforms such as Airbnb produce two primary effects. First, platform-mediated
touristification radically changes the most affected neighbourhoods, producing more direct and
immediate effects compared to a generic process of gentrification (Sequera and Norfe, 2018; Jover and
Diaz-Parra, 2019) or of touristification in general. As mentioned in Section 2, gentrification causes
resident displacement mainly indirectly, by driving up rents and prices. Indeed, several studies
demonstrate how the spread of short-term rentals influences the cost of rents and real estate values (for
a review, see Guttentag, 2019). The conversion of residential units into short-term rentals, however,
reduces the housing stock that is available for permanent residents directly and immediately, without
even having to assume or to demonstrate any impact on the cost of housing
10
. The impact is dramatic in
those parts of the city where the concentration of short-term rentals exceeds a certain threshold.
In the article, we applied a methodological approach to identify those parts of the city that are more
greatly affected. Such an ad-hoc delimitation was also aimed at obtaining comparable evidence for cities
with very different sizes and structures. In those ‘tourist cities’, short-term rentals listed on Airbnb.com
occupy a substantial portion of the total residential housing stock; their number in the majority of cases
exceeds the number of long-term rentals; and their capacity is close to or above that of apartments
occupied by residents.
We showed, moreover, that the resident population of those city centres is decreasing. Such
depopulation may indeed have many causes, not limited to touristification. Population may decrease
because residential dwellings are converted into short-term rentals, or because of the indirect effects
touristification has on, for example, the commercial fabric, congestion, noise, etc., but also due to
unrelated factors such as ageing, decreasing occupancy rates, the conversion of residential units into
office space, or other factors. The depopulation of city centres is also a much older process, but it had
slowed considerably before the last decade, even if it had not stopped completely. It goes beyond the
scope of the paper to demonstrate any direct causality between the spread of digital accommodation
platforms and population de-growth. Intuitively, however, in Rome in particular, there is a clear
temporal coincidence between the depopulation of the city centre and not touristification in general,
but platform-mediated touristification, which started in around 2013. As a confirmation of this, the
correlation between population de-growth and the growth in Airbnb listings, in Rome and Venice, is
high and significant: those areas in the city where the resident population decreases the most are also
the areas with the highest concentration and the fastest growth of Airbnb listings. The available data
does not permit us to test the same correlation for the other cities.
The analysis has also some limitations. The evidence presented in the paper is in fact mainly indirect,
although consistent with our hypothesis. The numbers are in any case impressive. The conversion of
10
In most Italian cities, real estate values have decreased in the last years because of the economic recession.
Between 2012 and 2016 the average value per square metre of a residential apartment decreased by -27% in
Bologna, -20% in Naples, -15% in Rome, -11% in Florence and -1.2% in Venice
(https://www.agenziaentrate.gov.it/portale/web/guest/agenzia/agenzia-comunica/prodotti-
editoriali/pubblicazioni-cartografia_catasto_mercato_immobiliare/immobili-in-italia).
thousands of residential apartments into short-term rentals cannot but contribute to the depopulation
of city centres where the housing stock is stable, if not decreasing. However, future research should
confirm the validity of our hypothesis and findings, based both on direct evidence and longitudinal
micro-data to be obtained through, for example, an ad-hoc survey of residential apartments and their
actual usage through the years, or more recent secondary data and more robust analytical techniques
that permit testing for casual relationships, or through a comparison with less touristy cities.
A second hypothesis we explored in the paper is that short-term rentals penetrate the residential city
much more deeply than hotels or other more traditional accommodation types. In terms of spatial
pattern, the fractal methodology whose results are reported in Figures 3-8 outlines a very compact and
dense ‘tourist city’ that covers more or less homogenously a substantial proportion of the city centre.
The only exception is Bologna, where the spread of Airbnb listings is more recent and less widespread,
i.e. more clustered in specific locations. The assessment of the average distance between places of
residence and tourism accommodation in Rome confirms the extent to which Airbnb brings tourists
‘closer’ to where people live (see also Gutiérrez et al., 2017; Gyòdi, 2017). The distribution of short-term
rentals, in other words, is pervasive and invades central or near-central zones that were more marginal
during previous waves of touristification.
By allowing its guests to "live like a local", short-term rental platforms cause visitors and inhabitants to
make use more often of the same spaces, infrastructure and services, causing discontent in the resident
population (Bouchon and Rauscher, 2019). The perceived impact of these transformations goes well
beyond the areas of the city that are more heavily affected. These changes affect in fact predominantly
a central and relatively small part of the urban area, but one which is crucial for both the material life of
the city and for its inhabitants’ sense of belonging to the city. Permanent residents, consequently, are
both physically displaced from the urban centre and feeling increasingly alienated from their own city
(Diaz-Parra and Jover, 2020).
It is not surprising, then, that most of the discontent about overtourism is today addressed to Airbnb
and short-term rental platforms, rather than against tourism per se. Slogans such as “go to hotels” are
indeed common in protests and campaigns against overtourism; those slogans are implicitly calling for
a more segregated tourist city in which inhabitans and visitors are more functionally and physically
separated.
The problem of overtourism is, therefore, not simply the growth or overcrowding of tourists (Butler,
2019), but their increasing penetration into the residential city. The case of Italian cities confirms
moreover that the relationship between the degree of tourism congestion and the effects it causes in
terms of residents’ perceptions and reactions is not linear (Wall, 2019). For example, our analysis shows
that in Bologna the incidence of short-term rentals is much lower than in the other cities, and no
significant correlation with the variation of the resident population has been found. Bologna has,
however, seen some of the strongest protests against short-term rentals and the city was the first to
declare its will to stop any further increase in Airbnb listings, especially because they are severely
limiting the availability of apartments for rent to students
11
. In Palermo, on the contrary, impact
indicators are much higher, similar to Venice or Florence, but overtourism has only recently induced
some reactions from residents and local social movements
12
.
11
https://bologna.repubblica.it/cronaca/2019/11/13/news/case_bologna_il_sindaco_in_arrivo_un_freno_ad_airbn
b-240978631/
12
https://www.facebook.com/turistificazionepalermo/.
In this framework, approaches to the management of overtourism based on limiting tourists’ access to
and use of the city are useless, as these do not address the root causes nor the more worrying effects of
touristification. Those approaches even risk being counterproductive, as they contribute to the
‘museumification’ of city centres and increase the alienation of inhabitants from such an important part
of the city. Instead, based on our hypothesis and findings, appropriate regulation of short-term rentals
could make a difference. The problem is that the same elements that cause platform-mediated
touristification to be so pervasive and impactful prevent adequate governance of the short-term rentals
market. Since lodgings advertised through platforms such as Airbnb are predominantly residential
apartments, they are not subject to ad-hoc planning regulations, and the instruments available to
monitor and regulate the phenomenon are very weak if not non-existent (Gurran and Phibbs, 2017;
Ferreri and Sanyal, 2018). Even more pressing and more challenging is the need to guarantee that the
urban centres of big tourist cities remain lively and liveable for both visitors and inhabitants, through
for example (social) housing policies, rental support or urban planning more generally.
The coronavirus emergency has thrown us, at least temporarily, into a different world. At the time of
writing, lockdown measures have been implemented in many countries worldwide that have radically
reduced movement and activities. The impact of those measures upon tourist destinations and
especially upon the centres of historic cities has been particularly dramatic
13
. Local authorities are
therefore now desperately looking for alternatives, while some short-term rentals are being converted
into longer-term ones (Celata, 2020). The emptiness that the lockdown created in tourist cities’ centres
may eventually be filled again by tourists, or by a return of residents, or both. It is also a matter of what
kind of policies will be adopted after the emergency. To outline those policy options in detail goes
beyond the scope of the paper. The crisis is having a terrible impact, but it is also making the problem
evident and providing an opportunity to prepare for a different future. Our cities, we believe, are
perfectly capable of again hosting masses of tourists, but only if we take this opportunity to understand
how these numbers can be made compatible with other urban uses and functions.
Acknowledgments: We thank Massimiliano Crisci (IRPPS-CNR), Patrizia Veclani (Osservatorio civico
indipendente sulla casa e sulla residenzialità, Venezia) and Grazia Galli (Progetto Firenze) for providing
detailed population data, and the three anonymous referees for their constructive comments.
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