Owning a home is one of the most important ways of building wealth in America. As rises in home prices outpace rises in income, fewer and fewer Americans can afford to buy a home, causing the generational wealth gap to widen. Housing assistance programs are imperative to expand access to homeownership – particularly for Black Americans, who have historically been excluded from stable homeowning through policies and practices including redlining and exclusionary zoning. Designing effective housing assistance requires understanding where gaps in affordability currently exist for potential homeowners. Read more.

OpenTSS aims to develop a crowdsourcing tool and/or campaign to audit tenant screening services and reveal the patterns of their inner algorithms, data structures, and representations, by collecting tenant screening reports, as well as denied renters’ experiences. Read more.

The racial wealth gap in the US remains a persistent issue; white individuals possess six times more wealth than Black individuals. Leading scholars and public figures have pointed to slavery and post-slavery discrimination as root cause factors, and called for reparations. Yet the institutionalization of race-neutral ideologies in policies and practices hinders a reparative approach to closing the racial wealth gap. Read more.

iBuyers use automated valuation algorithms and streamlined home-buying processes, including exemption of repairs before selling and cash offers, to purchase homes. Previous literature has examined the roles and limitations of iBuyers in the housing market, but there is a lack of empirical research on the racial implications of these algorithmic home-buying processes. Using spatial lag model, this study identifies spatial clustering of iBuyer profit margins and a statistically significant and positive correlation between profit margins and the rise in the proportion of Black or Latinx residents within a given neighborhood tract. This work will be submitted to Journal of Urban Affairs. Read more.

Using data from 2000+ police departments, this project measures the effects of policing technologies, including face recognition, drones, body-worn cameras, predictive policing, and home security partnerships, on racial biases in law enforcement.

A large proportion of police-citizen interactions are initiated by 911, and thus pairing 911 call data with police stop data provides a step toward a more systematic causal framework for estimating racial bias.

Predictive policing systems use narrowly scoped data and narrowly defined objectives that lead to 'hotspot' policing — disproportionate policing of small areas. What impact does this have on communities beyond how it effects crime? We examine how algorithms can lead to changes in police practices and policies.

Applying a systematic framework to causally understand the effect of race on policing, the policing team looks at linked data quantitatively to estimate how different races benefit or suffer differently from the same policy interventions.

Linking law enforcement data, which is collected through heterogeneous administrative processes, into a stitched data set will provide a more systematic characterization and insightful understanding of law enforcement.

Researchers are building datasets from various law enforcement-related sources, like body camera images, cell phone mobility data, and social media posts. With interfaces that support users from different programming backgrounds, this initiative will benefit law enforcement researchers across the US.