Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation

Abstract

This paper offers an analysis of the supply of Airbnb accommodation in Rome, one of the main tourist destinations in the world, the third-largest city in Europe, by the number of Airbnb listings. The aim is to focus on the recent spatial trend of Airbnb listings, including the period of the COVID-19 pandemic, and highlight the main housing and socioeconomic characteristics of the neighbourhoods associated with a strong presence of Airbnb listings. The study is developed with quantitative methods and spatial regression (spatial lag and spatial error using OLS as a benchmark), based on data collected from the Inside Airbnb and Tomslee websites. In the period 2014–2019, the listing trend in Rome has been increasing in absolute numbers. After the start of the pandemic, the trend became negative, and the decline of Airbnb offerings is more substantial for shared accommodation. Airbnb supply is related to the distance from the city centre, the average income of the area, empty apartments, singles and the share of foreign residents coming from high-income countries. A signal of spatial diffusion of Airbnb listings emerges in the coastal area, even if they are increasingly concentrated in the historic centre, where there is a monoculture of short-term renting.

Pandemic disorientations and reorientations as legacies: Scoping review of COVID‐19 impacts on European cities

“…Regarding the urban economy, the pandemic’s disruption in cities that depend heavily on tourism notably reinvigorated controversies about short‐term rentals, which were affecting housing affordability and housing markets before the pandemic (Batalha et al, 2022; Crisci et al, 2022; Tomal & Helbich, 2022). At the same time, the shift from overtourism—that is, “an excessive negative impact of tourism on the host communities and/or natural environment” (Koens et al, 2018 in Amrhein et al, 2022, p. 2)—to “no‐tourism” uncovered the fragility of the “growth‐driven economic monoculture of tourism” (Amrhein et al, 2022, p. 9).…”

Investigating metropolitan change through mathematical morphology and a dynamic factor analysis of structural and functional land-use indicators

“…Our study introduces a novel approach based on landscape indicators derived from mathematical morphology, whose outcomes were analysed through a multi-way factor analysis 101 summarising the latent relationship between form (morphological classes) and functions (land-use). More specifically, the study estimates the net impact of different socioeconomic and territorial configurations on landscape morphology as reflected in three developmental www.nature.com/scientificreports/ stages: urbanisation, suburbanisation, and counter-urbanisation 63,102,103 . The empirical results of our analysis correctly distinguished these three stages characteristic of Rome's post-war development trajectory, in line with the outcome of earlier studies 17,73,104 .…”

Touristification and Conflicts of Interest in Cruise Destinations: The Case of Main Cultural Tourism Cities on the Spanish Mediterranean Coast

“…Moreover, recreational amenities and tourist services-such as restaurants [31], terraces and souvenir shops [32], monuments [33] or even cinemas [34]-also determine the Airbnb offer. When the spatial contextual factors of short-term rentals are identified, Airbnb studies use the distance to the city centre as an explanatory descriptor (e.g., [35]). Furthermore, sociodemographic variables-such as inhabitants [36,37] and population change (e.g., [38]); together with housing [39,40] and long-term rental [41,42]-are taken into account in the spatial distribution of Airbnb listings.…”

“…Indicator systems have been extensively analysed using density maps and, to a lesser extent, cluster maps [9,22,28,30,34,36,37]. In addition, the considered variables in each study have been likewise analysed using correlations [23,25,38,39,42,43,47] or regression models [8,9,28,30,34,35,40,43,48], and even both statistics used for the same study [12,36,37]. It should be noted that univariate global spatial autocorrelations (Global Moran's Index)-rather than the bivariate one [9,28,30,49]-have been extensively performed in the studies [16,[34][35][36][37]41].…”

“…In addition, the considered variables in each study have been likewise analysed using correlations [23,25,38,39,42,43,47] or regression models [8,9,28,30,34,35,40,43,48], and even both statistics used for the same study [12,36,37]. It should be noted that univariate global spatial autocorrelations (Global Moran's Index)-rather than the bivariate one [9,28,30,49]-have been extensively performed in the studies [16,[34][35][36][37]41]. Thus, the spatial dimension of Airbnb has been extensively analysed in quantitative indicators based on tourist facilities and traditional accommodation, together with socio-demographic and economic indexes.…”