| Abstract: |
Several authors have stressed rising housing prices associated with
intensifying tourism activity as a significant negative externality of tourism
development (e.g., Mikuli, Vizek, Stoji, Payne, eh asni, & Barbi, 2021). The
relationship between levels of tourism activity, typically measured in terms
of arrivals and/or overnights, and housing prices has also been confirmed
empirically in a number of studies (e.g., Biagi, Lambiri & Faggian, 2012;
Biagi, Brandano & Caudill, 2016; Balli, Balli, Flint-Harle & Yang, 2019;
Paramati & Roca, 2019, Churchill, Inekewe, & Ivanovski, 2021). This negative
side-effect of tourism development gained new momentum with the proliferation
of Airbnb and other peer-to-peer platforms that initiated a structural change
in accommodation capacities at many destinations, essentially by turning flats
into short-term rentals (STR) aimed at tourists (Dolicar, 2019). The negative
consequences of these developments are well-documented in studies set within
the context of “bucket-list” destinations struck by over-tourism, like, for
example, Barcelona, Dubrovnik, New York, or Venice. Even the COVID-19
pandemic, which has hit the tourism sector very hard and led to a short period
of under-tourism, did not stop this trend, with some European towns like,
e.g., Split and Venice again being overrun by tourists during the peak of the
2021 summer season.Generally, besides rising housing prices, negative
consequences associated with STR-induced overtourism are gentrified city
centers (e.g., Wachsmut & Weisler, 2018; Ardura Urquiaga, Lorente-Riverola, &
Ruiz Sanchez, 2020), crowdedness (e.g., Park & Agrusa, 2020), and increasing
retail prices (e.g., Stynes, 1997, Gholipour, Tajaddini, & Andargoli, 2021),
to name only some of the most significant ones. Together, these effects
significantly contribute to a lowering of quality-of-life of residents in
tourist destinations (Biagi, Ladu, Meleddu, & Royuela, 2020), especially for
residents who do not own a property and/or have no significant benefits from
the local tourism industry, either direct (e.g., via employment) or indirect
ones (e.g., boosting economy). However, what if these effects were not
constrained to neighborhoods in the area or destination under investigation
(e.g., Zou, 2020) but rather extended to other regions via spatial spillovers?
On the one hand, this would imply that the pressure of tourism activity on
housing prices is an externality that also affects those who do not have any
direct or indirect benefits from tourism activity, as described above. On the
other hand, this would also imply that actions taken at the local level, like
housing policies, aimed at providing relief to residents who have difficulties
affording housing at popular tourist destinations, should, in fact, be
developed more holistically by taking into account tourism impacts on real
estate markets at a regional or supra-regional, rather than focusing only on
the local level.As described above, potential regional spillovers have not
been addressed in the tourism, housing, nor regional economics literature so
far, which is a research gap this study intends to fill. The empirical
analysis is set within Croatia, an increasingly popular Mediterranean
destination, ranked 27th according to the World Economic Forum’s Travel &
Tourism Competitiveness Report, putting it at a similar level like, e.g.,
Greece (#25) (WEF, 2019). Tourism activity in this country is densely
concentrated in proximity to the sea and along the whole coast, whereas there
are only a few tourist spots in the continental parts of this country. This is
also reflected in a share of 97.3% of all accommodation establishments located
in one of two of Croatia’s NUTS-2 regions, i.e., Adriatic Croatia (as opposed
to Continental Croatia), which is also ranked first among all European NUTS-2
regions according to accommodation capacity (Eurostat, 2021a). At the same
time, Croatia also has the largest share of private accommodation in overall
tourist capacities compared to its Mediterranean peers. For example, the
percentage of holiday and other STRs in total bedplaces in pre-pandemic 2019
was 61.3% in Croatia, whereas the same share was 34.5% in Italy, 32, 2% in
Greece, or 24.6% in Spain (Eurostat, 2021b). Accordingly, although
geographically small, Croatia is ideal for examining regional spillovers as
described above because its tourism sector heavily relies on apartment houses
and is highly concentrated in its coastal area.The research question we are
addressing in this paper should also be placed in the context of the
ever-increasing global tourism demand. International tourism receipts
increased 4.9 percent in real terms to reach US$ 1.34 trillion in 2017,
whereas tourist arrivals amounted to 1.336 billion in the same year (UNWTO,
2018). In addition, international tourist arrivals worldwide are expected to
increase by 3.3% annually to reach 1.8 billion by 2030 (UNWTO, 2017), which
suggests popular destinations already overwhelmed with tourists will start to
experience more negative externalities of tourism activity, including the
degradation of sociocultural and environmental conditions and the rise of
house prices and rents coupled with declining housing affordability. In
geographically smaller or island countries which depend more on tourism
receipts, the emergence of over-tourism could potentially be more damaging to
the local housing markets due to limited availability of building plots,
higher population density, and often rigid urban zoning rules. In such
countries, house price hikes taking place in a very popular tourist
destination (local government units - LGUs) could easily spillover to
neighbouring LGUs, then regions and eventually the entire country. For this
reason we study local/regional house price spillovers which are due to tourism
activity in Croatian LGUs.In order to understand the spillover process better,
we will use spatial panel data estimators to model house prices and its
determinants, which in turn will enable us to detect the main spillover
characteristics and modalities. To the best of our knowledge, this would be
the first study of its kind in the literature. The only other study addressing
this issue is Kavarnou and Nanda (2018) who use dummies for neighboring
regions as controls for spatial spillover effects, which is a static and
rather limited way of addressing this issue.The empirical part of our analysis
relies on techniques from the family of spatial panel estimators. We use the
Durbin spatial panel autoregression technique (DSM) (LeSage and Pace, 2009;
Elhorst, 2013). This technique enables us to establish a direct relationship
between the dependent variable and its effects in other spatial units (cities
or regions), spatial effects of independent variables such as measures of
tourism activity and unobserved spatially correlated heterogeneity. Moreover,
we employ different types of spatially weighted matrices, symmetric matrices
that define relationships between units in space. To this end, we explore
whether spatial effects are limited to neighboring municipalities and cities
or do they exert wider scale. Another advantage of spatial econometric
analysis is the ability to estimate direct and indirect effects of the
observed process. The change in individual city or region generates two types
of impacts. A direct impact on itself and an indirect impact that goes first
to other spatial units and partially returns through feedback loops (LeSage
and Pace, 2009). These feedback loops arise because each spatial unit is
considered a neighbor to its neighbors so the impact passing through
neighboring units will create a feedback impact on the initial unit itself.
There is reason to expect such feedback loops will arise with changes in
tourism activity and dynamics of the housing market. To the best of our
knowledge, such analysis has not been performed in context of tourism sector
or its relationship with the housing market in general. Our dataset combines
variables constructed from several reliable data sources. Most of the
variables, including the tourism related measures, come from the National
Statistical Office of Croatia, the official focal point for statistical data
collection. We use five different proxies for tourism activity and intensity:
number of nights spend per inhabitant, number of arrivals per inhabitant, the
share of private accommodation in total tourism accommodation, the share of
rental housing in total housing stock and the length of stay of tourists.This
database is supplemented with datasets obtained from the Ministry of Finance,
i.e. its Tax Office from which Property income and Average wage data come
from. Finally, the Institute of Economics Zagreb (EIZ) provides the data on
housing transactions and housing and construction land prices from their
annual reports on real estate trends prepared for the Croatian Ministry of
Construction. The analysis covers the 2012-2019 period and contains 556
Croatian cities and municipalities in which real estate transactions took
place over the years analyzed. |