Solving the grand challenges of a dynamic and uncertain world requires analytical tools that help us understand and model its complexity. This is the particular expertise of data scientists like Christopher Glynn, assistant professor of decision sciences, who develops computational methods and statistical models to take on these challenges.
One societal issue, homelessness, has confounded attempts to address it, says Glynn, “because the data that we have on the scope of homelessness is incomplete, and these gaps have made it nearly impossible to identify characteristics of a community related to homelessness – such as housing costs, poverty and economic opportunity.”
Backed by funding from the online real estate and rental marketplace Zillow, Glynn and his colleagues studied 25 major metropolitan areas and found a connection between escalating rents and growing numbers of people experiencing homelessness. The relationship was strongest in New York, Washington, D.C., Los Angeles and Seattle.
Glynn forecasted that nearly 3,000 more people in New York City alone would fall into homelessness with just a five percent average rent increase in the coming year. Says Glynn, “Making an explicit connection between affordable housing and homelessness is the first, critical step to helping cities budget to provide affordable housing to more people.”
Considered a landmark, Glynn’s study received attention in The New York Times, Los Angeles Times, U.S. News and Fast Company. He is a Zillow Research Fellow and received $60,000 from Zillow to support his work.