The recent proliferation of private tourist accommodations on what has been known as sharing economy has induced new models on urban tourism and on the use of traditional housing. Urban tourism pressure has caused many transformation processes with important impacts in neighborhoods with high tourist interest, which are shown in the evolution of certain urban sustainability indicators, such as those proposed by the UN-Habitat Agenda for Sustainable Cities in line with the principles of the Leipzig Charter on Sustainable European Cities. Due to the above, the objective of this work is to analyze the spatial distribution of Airbnb accommodations, and explore the factors associated with the situation of Airbnb rentals in relation to the indicators of urban sustainability of neighborhoods and variables related to the gentrification processes of neighborhoods in which there is crowding or overtourism, measured through the Global Tourist Stress Index. For this, a first-order spatial autoregressive panel (SAR) data model with fixed effects has been specified, the results of which provide us with information to understand how sustainability indicators in the neighborhoods of the city of Madrid in the time period 2015–2018, they would explain the location and number of Airbnb accommodations found there. Additionally, it allows us to observe the existence of a spillover effect from the central neighborhoods, with a high per capita income, to nearby neighborhoods with lower income due to the level of tourist crowding, which is a contribution to the scarce existing literature.

The aim of this article is to determine the socioeconomic level (SEL) with disaggregation of the Basic Statistical Area (BSA) in the Mexican Republic. The methodology used is the one established by the Mexican Association of Market Research Agencies (AMAI) along with the National Institute of Statistics and Geography (INEGI). The Clustering of the BSAs was carried out according to variables contained in the Population and Housing Census of 2010, through Gaussian mixture models, learning neural networks and finally, by defining the labels corresponding to each SEL. We found the existence of a representative SEL for each BSA. In addition, the definition of each socioeconomic level shows good results with an average of 90.86% of correctly labeled elements.

El objetivo que se persigue con el presente artículo es explorar los factores que están asociados a la disposición a pagar para mejorar la calidad del agua en la ciudad mexicana de Zapopan, Jalisco. Se lleva a cabo una encuesta a cuatrocientos hogares que arroja que los consultados toman agua embotellada (99%), consideran que el agua suministrada huele mal (53%) y que está contaminada (69%), temen por su salud o la de sus familiares por beber del grifo (74%) pero estarían dispuestos a tomar agua del mismo si la calidad mejorara (77%). Sin embargo, más de la mitad no estaría dispuesto a pagar más por mejorar la calidad del agua o pagaría no más de 40 pesos (31 y 22% respectivamente). A través de un modelo logit ordenado se encuentra que existe una relación significativa y positiva entre la disposición a pagar por mejorar la calidad del agua y estar abierto a tomar agua del grifo si esto sucede, a percibir que las autoridades encargadas del sector agua hacen lo correcto, a una creencia de escasez de agua en la colonia, a tener personas con problemas de salud en casa y al nivel de ingresos. Por otro lado, mayor disposición de pago se relaciona negativa y significativamente con la percepción de que el agua huela mal, el nivel de confianza en los otros y la edad.

In the context of the global growth of the collaborative economy, Airbnb has established itself as one of the most influential players in the transformation of the tourist accommodation market, especially in the reconfiguration of urban tourist accommodation. This article examines empirically and critically how this platform operates in Buenos Aires, the most visited city in Argentina and one of the main tourist hubs in South America. Based on a database of 17,249 active listings, the price formation of accommodations is analyzed using a comparative methodological approach between a general linear model (GLM) and a geographically weighted regression (GWR) model. While the GLM allows for capturing general patterns, the GWR reveals significant territorial differences, offering a detailed reading of the spatial behavior of prices in the city. The results show that variables such as the capacity of the accommodation, its type (full house), the host’s condition, the users’ ratings and the proximity to strategic points such as the subway or Plaza de Mayo have a significant influence on prices. In addition, it is shown that the influence of these variables varies by neighborhood, confirming that the pricing logic in Airbnb is deeply territorialized. This study not only provides novel empirical evidence for a Latin American city that has been little explored in the international literature, but also offers useful tools for hosts, urban planners and public decision makers. Its main contribution lies in showing that prices respond not only to accommodation attributes, but also to broader spatial inequalities, opening the debate on the effects of Airbnb on housing access and urban management in cities with strained real estate markets. By shedding light on these territorial asymmetries, the study offers valuable insights for public policy and urban governance and contributes directly to the achievement of Sustainable Cities and Communities (SDG 11), while also supporting Industry, Innovation and Infrastructure (SDG 9) and Reduced Inequalities (SDG 10), by providing practical knowledge that fosters more equitable and sustainable urban development.