Simple Scores are Messy Signals: How Users Interpret Scores on Real Estate Platforms

DOI

Real estate platforms serve as an online version of traditional real estate listings, assembling comprehensive information for potential buyers and renters. However, along with traditional details like photos, a description of the property, and a price, these platform listings include supplementary information in the form of scores or ratings of schools, walkability, and crime. This information is presented with little explanatory context, as a simple numerical score or shaded map implying a hierarchy from better to worse. While these scores and maps are presented as self-evident, my research shows that people interpreted the scores in considerably varied ways. Different users, for example, could draw opposing conclusions from the same data. I present findings from 32 interviews with real estate platform users. In the context of housing decisions, I found people readily inferred meanings about protected groups, particularly about the racial makeup of neighborhoods. The use of maps and scores on online real estate platforms raises questions and concerns about their broader societal consequences. While real estate platforms likely consider their role to be a provider of information principally to an audience of people buying, selling, and renting real estate, I argue that they have become a form of consequential civic data with the potential to reshape urban life. Some real estate platforms are beginning to recognize this, removing sensitive information about crime, and acknowledging that they cannot present it effectively or responsibly.