Matteo Beghelli (p)

Nicola Camatti

The aim of this paper is to assess the relationship between sharing-economy touristic accommodations and those professionally managed (typically hotels) in terms of direct/indirect competition. Moreover, the analysis of turnover associated with different kinds of structures bookable on Airbnb drive to the assessment of the proportion of “core sharing” in Airbnb business and to an esteem of new touristic flows activated by the sharing portal. The analysis has been carried out for Bologna metropolitan area in Italy.
The analysis shows that only 20% of the turnover recorded by Airbnb in Bologna metro area can be associated to a “pure” sharing economy, meanwhile the remaining 80% has various levels of professional management that in many cases undermine the peer-to-peer nature of the relationship established between the parties involved, typical of sharing economy. Of this 80% of turnover, while 44% is associated with structures that can be considered in direct competition with hotels, 36% is made by touristic structures that, to a various degrees, play indirect competition with hotels, thus activating new touristic flows to Bologna metro area.
The paper also tried to illustrate the different consequences of the spread of tourist sharing in the urban and extra-urban frames, highlighting the risk of displacement of habitual dwellers from touristic parts of towns and the permanent destination of the latter to the service of mass-over-tourism with obvious angry reactions.
Net of this, the phenomenon of sharing-economy in tourism has a remarkable scope that has only begun to show its potential and its effects on the world of tourism. It is reasonable to expect that there will be no trend reversals in the foreseeable future, that is, the sharing economy - in tourism as in other sectors - has come to stay and must, therefore, be managed.
A unique solution to avoid distortions has not yet been found but this is not a good reason not to insist. Radical technological innovations that take the form of radical economic and social innovations require adequate measures to protect them from the externalities they produce, even to the detriment of themselves.
It is not easy. It is necessary.

Luca Salmasi (p), Nicola Camatti, Jan Van Der Borg, Isabella Procidano

Matteo Beghelli

The use of online reviews has become very popular to measure a destination reputation. For instance, TripAdvisor’s data are often used to obtain indicators measuring how a city compares with respect to other destinations, or to provide rankings of different areas within the same destination. These measures are generally obtained by aggregating ratings assigned by reviewers to a variety of attractions belonging to the area of interest. However, it may be that reviewers use different scales to rate attractions, or that the rating is assigned with a certain degree of uncertainty.
We propose to isolate reviewers’ rating uncertainty from real preferences by using CUB models, recently proposed by Iannarino and Piccolo (2011). We assume that the distribution of ratings of a certain attraction can be modelled as a mixture of distributions, one capturing informed evaluations and another associated to ratings assigned with uncertainty. Applying CUB models we are able to isolate the two effects and obtain a measure of reputation that does not depend on uncertainty.
In this work we use data from TripAdvisor for the city of Venice. We collected information about ratings assigned to museums, churches, historical buildings and places of interest for each review assigned since the first available review. Moreover, we also collected information about reviewers (e.g., age, gender, nationality and interests). We use this hitherto unexploited data source to obtain a dynamic measure of reputation for various areas of Venice. Then, we apply the CUB model to isolate true ratings, netting out the effect of reviewers’ uncertainty, and compare the two indicators.
We find that uncertainty plays an important role in reviewer’s judgements and with differing effect through across time periods. According to our results uncertainty in reviewer’s ratings affects significantly the distribution of ratings and should be accounted for in order to obtain more precise measures of reputation for touristic destinations.

Nicola Camatti (p), Dario Bertocchi (p), Jan Van der Borg, Luca Salmasi

Matteo Beghelli

In the last few years sharing economy and collaborative economy represents a new business opportunity for different economic sectors. Tourism is one of the most effected business sectors, especially regarding accommodation and mobility. Airbnb platform offers a great opportunity to visit a destination promoting a different style of experience it, developing the concept of “live like a local”, a now a day very popular way of travelling. If the impacts on urban level have been studied from many researchers, the effects on this phenomenon on a vast and mixed (urban and rural area). territory still need to be investigated.
The analysis of spatial autocorrelation is a fundamental tool for the understanding of all the physical as well as anthropological processes which naturally take place within the geographical space, and which cannot be studied independently from it. In this paper the spatial autocorrelation has been applied to a Airbnb lodging dataset regarding the tourism destination of Dolomiti, in Veneto region. The results represent clusters of lodgings that represent the characteristics of the accommodation system represented by the sharing economy. Furthermore, giving a management and governance perspective to those clusters is possible to identify niche of tourism offer, features of the destinations and measures to monitoring the performance of this large territory.

David Boto, Matias Mayor (p), Pablo de la vega Suarez

Matteo Beghelli

The appearance of the sharing economy has disrupted the traditional business practices. In the hospitality industry, peer-to-peer accommodations are becoming increasingly demanded by travellers, representing an expanding share of the market. Originally, P2P accommodation services started as a way by which regular people rented spare bedrooms or properties to exploit the underuse capital. However, nowadays a non-negligible share of hosts behaves closer to business intermediaries and tends to fix prices efficiently. Thus, price fixing is a very important tool to understand the market. Previous literature has widely studied the price determinants for the P2P accommodations, focusing on properties’ characteristics. Our aim is to extend the literature by estimating a hedonic price model including not only the listings’ characteristics but also the effect of the accessibility to various sightseeing spots. Also, we use a spatial econometric model to control for the spatial heterogeneity that may affect price fixing by a spatial competition on prices among the close properties. To do so, we use a public-access database of AirBnB’s in the central district of Madrid for the second trimester of 2017. To the best of our knowledge this is the first paper that jointly analyse spatial heterogeneity, accessibility and properties’ characteristics on price for the AirBnB market.