Comparing Equity and Effectiveness of Different Algorithms in an Application for the Room Rental Market

Machine Learning (ML) techniques have been increasingly adopted by the real estate market in the last few years. Applications include, among many others, predicting the market value of a property or an area, advanced systems for managing marketing and ads campaigns, and recommendation systems based on user preferences. While these techniques can provide important benefits to the business owners and the users of the platforms, algorithmic biases can result in inequalities and loss of opportunities for groups of people who are already disadvantaged in their access to housing. In this work, we present a comprehensive and independent algorithmic evaluation of a recommender system for the real estate market, designed specifically for finding shared apartments in metropolitan areas. We were granted full access to the internals of the platform, including details on algorithms and usage data during a period of 2 years. We analyze the performance of the various algorithms which are deployed for the recommender system and asses their effect across different population groups. Our analysis reveals that introducing a recommender system algorithm facilitates finding an appropriate tenant or a desirable room to rent, but at the same time, it strengthen performance inequalities between groups, further reducing opportunities of finding a rental for certain minorities.