These were some of the questions that led Janita Chalam, an independent researcher with a background in software engineering and machine learning, to begin their research journey into discovering how open data, statistical modeling, and AI can help us tackle the housing affordability crisis.
This presentation will walk through what Janita has learned about the variables at play in NYC’s housing landscape and present a statistical analysis of the Bloomberg-era upzonings as a case study in examining the frictions to building more housing in NYC.
Finally, Janita will propose some ideas for what kind of data and methodologies we might need in order to make bolder claims about what it takes to get us out of the housing crisis. By the end of this talk, we will hopefully have a better understanding of the role that data and empiricism can and should play in our conversations about housing policy.
This talk is for anyone interested in housing affordability and will not require any expertise in the technologies mentioned.
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