Marketing

Engineering

Ecology

Biology

Identification (biology)

Tourism

Archaeology

Variables

Variable (mathematics)

Economics

Data mining

Civil engineering

Computer science

Business

Mathematics

Geography

Amenity

Econometrics

Mathematical analysis

Artificial intelligence

Decision tree

Industrial organization

Finance

Listing (finance)

Perspective (graphical)

Host (biology)

Renting

Machine learning

International Journal of Hospitality Management

This research primarily contributes to the identification of the important variables that significantly influence room pricing on the Airbnb rental platform. The study adopted a comparative approach by using three different methods—OLS, random forest, and decision tree—and applied it to a vast amount of data from the Airbnb listing dataset of 11 US cities. Each individual amenity mentioned in the listing in the textual format was used as an independent variable. We also added six other common listing variables to obtain interesting insights into the influence of these variables from the perspective of the host, guest, traveler, and tourist. Apart from identifying city-specific variable importance using different models, we estimated a composite score of variable importance that may be helpful to generalize the influence of amenities and other explanatory variables in the presence of any city-specific regional heterogeneity on the shared rental platform.

Do airbnb host listing attributes influence room pricing homogenously?