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Catering services play important role in the Spanish economy, accounting for 6.2% of GDP in 2021. To overcome the adverse economic impacts of COVID-19, catering services are considered one of the drivers to stimulate economic growth. Hence, the main aim of this paper is to analyse the sociodemographic profile of the family's main breadwinner who allocates most of his expenditure budget on different catering services before and during the pandemic caused by the COVID-19 in Spain.

The official Family Budget Survey in Spain was used. This offers information on expenditure by families in 2019 and 2020. CHAID multivariate analysis was employed. This has proved a valuable tool in predicting expenditure, as well as determining the cause–effect relationship of this expenditure.

Findings establish the main breadwinner's expenditure on catering services based on predictors such as “year” affected by the pandemic; “type of employment contract”; “gender”; and “age”. A gender “pub-gap” in consumption in bars and cafes has been revealed, and families with a male breadwinner, on a permanent contract, between the age of 40 and 60 spent the most on catering services.

This research presents a new interdisciplinary approach to family breadwinners as a company whose spend on catering is shaping the economic recovery and leading to new answers for hospitality management. Identified factors can lead to improved decision-making and contextualisation of economic models for food service providers in a post-pandemic future.

Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.