Implications of Market Rating-based Segmentation on Intra-platform Competition

An application to Airbnb’s market in Barcelona. Competition and Market Regulation master project by Paul Arenas and Saúl Paredes ’21

Aerial view of Barcelona's Eixample neighborhood
Photo by Erwan Hesry on Unsplash

Editor’s note: This post is part of a series showcasing BSE master projects. The project is a required component of all Master’s programs at the Barcelona School of Economics.

Abstract

In recent years, large platforms have raised concerns that they may engage in anti-competitive practices that affect market competition. Therefore, analyzing the competition structure inside platforms is a relevant issue that has not been treated in much empirical research.

This study analyzes how a platform’s owner could affect the degree of competition among members of one group in the platform through biasing search results using rating classifications. In this paper, we perform an application to Airbnb‘s market in Barcelona given the particularity of rating is an unavailable searching filter to guests.

We found evidence that listing’s rating classification represents an important market segmentation in the Airbnb’s market in Barcelona that could imply a possible practice of biasing search results. Moreover, we found that the intensity of competition is differentiated by the rating-related segments, which means that these segments are concentrating competition.

Conclusions

We found an inelastic demand for Airbnb’s listings in Barcelona in a market that is divided by rating classification. In particular, our empirical results show the following two points:

First, the majority of hosts face an inelastic demand. These results are consistent under the two main models we used. From the nested logit model under rating segmentation, we found that when there is a 10% increase in price of available nights, there is an expected decrease in booked nights of 4.5%. These results imply that there is room to increase the price without reducing the revenues of the hosts.

Second, even though the rating is not available as a filter in the Airbnb web page, it creates an important market segmentation. This means that the competition between two listings that belong to the same segment is different from the competition faced by two listings that belong to different rating classifications. Moreover, we found differences in intensity of competition faced by listings that belong to different segments.

Finally, these results show that the existence of segmentation suggests that Airbnb is performing a rating-based market division. Yet the rating segmentation does not show a clear pattern of competition intensity in each group.

Connect with the authors

About the BSE Master’s Program in Competition and Market Regulation

Data visualization: London property prices

Barcelona GSE Data Science student Stefano Costantini ’15 shares a data viz exercise that explores London property prices from 1995-2013.

StefanoData Science student Stefano Costantini ’15 has posted this data viz project exploring London property prices on his website. Have a look and follow Stefano on Twitter @stefanoc.


 

London property prices: Visualising the evolution of the residential market (1995 to 2013)

The London residential property market has always been strong. However, it is only in the last twenty years or so that property prices have increased to such levels that previously “cheap” areas have now turned into prime locations. The gentrification process, together to an increase in population, have pushed up the prices even in peripheral areas. The purpose of this exercise is to visualise these changes, covering the period 1995-2013.

graphic
Evolution of local average prices by quarter for the whole period 1995-2013

See more graphics, read about the methodology and tools used in the project, and download the code and the data from Stefano’s website.