This paper contributes to a growing body of scholarly literature on the regulation and impact of short-term rentals in the Global South more broadly, and Latin America more specifically, by examining the phenomenon's presence in Mexico City, one of Latin America's largest cities. Building on the author's prior research, it offers a case study on the rise of Airbnb in Mexico City, an issue which is growingly salient at the local level in the wake of rapid gentrification processes and a growing protest movement. Via a mixed-methods approach that combines tracing policy histories, quantitative and geospatial analysis, and semi-structured interviews, the article seeks to understand how the rise of short-term rentals in Mexico City is impacting residents' everyday lives and transforming the city's socio-spatial dynamics by examining three key issues: how the regulatory framework on the matter has evolved; what spatial factors correlate to its presence; and how its impact is felt by local residents. Findings reveal a highly unregulated environment where the platform is dominated by private actors and significantly tied to increased real estate development and new-build gentrification, and in which residents are increasingly concerned by the platform's encroachment on their neighbourhoods.
This study investigates whether local taxation decisions can be reliably predicted using machine learning (ML), focusing on the personal income tax sur charge set by Italian municipalities. We evaluate the predictive performance of ML models and identify key determinants of tax-setting behaviour. Results show that municipal tax choices follow systematic and predictable patterns driven by demographic, fiscal, socio-economic and institutional factors. A North-South comparison reveals stronger predictive accuracy in Southern regions, suggesting that tax increases are more closely linked to fiscal constraints than to discretionary political choices. Our findings highlight the usefulness of ML for understanding and supporting local fiscal planning.
