Abstract

Approximately 70 percent of New York City residents rent the place they call home, making landlords of residential properties some of the most influential people in the lives of New Yorkers. A variety of high profile instances of illegal and harmful landlord practices, particularly around illegal and coercive actions taken to push tenants of rent regulated units out in order to create a larger return on these units. These cases and the public outcry around them motivate further exploration into the question of how to better identify and prosecute landlords engaging in illegal behavior. Based on an original model of predatory landlord behaviors, our team presents a set of methods to identify this behavior across a variety of datasets. The scale of this analytic work has never been tackled before, and can substantially improve the way illegal landlord activity is discovered, prosecuted and regulated.