Price indicators for Airbnb accommodations

Abstract

New forms of hospitality grew increasingly more popular and successful during the last decades. Nowadays, they are chosen for different reasons, one of the most important certainly being price. Understanding the elements that can impact on price determination is crucial to increase profitability. We propose two price indicators for Airbnb accommodations, which are defined in three phases using proportional odds model as a reference model. The first phase focuses on the probability estimation of accommodations belonging to a specific class of price. The second phase aims to evaluate the ability of the model to make good predictions by computing three different indexes. Finally, the three indexes are combined to define the indicators q and r which evaluate, respectively, the impact that six different dimensions (transports, culture, crowd, property, management, and time) have with respect to price determination on Airbnb accommodations and their relative importance concerning neighborhoods. The analysis is focused on 61 neighborhoods of Rome. The findings show differences with respect to the impact of the dimensions on price for each neighborhood of Rome.