Data and decontrol : a civic-tech approach for identification of predatory landlords in the New York City rent-regulated housing market

Abstract

With New York City in the throes of a severe affordable housing crisis, the City government and housing advocates have worked tirelessly towards the identification of landlords whose profit model is based on fraudulent deregulation of the rent-regulated housing stock. The problem is that these bad actors are not so easy to identify. With the refusal of the controlling agency, the New York State Department of Housing and Community Renewal (DHCR), to release data on units lost from the market, along the widespread use of limited liability companies (LLCs) to obscure ownership, it's difficult to both track changes in the market and to associate those changes with problematic actors.

The role of this thesis is to explore the creation of a methodology incorporating pre-existing work at the city and civilian level ("civic tech") to identify suspect patterns of behavior, recognizing that improved access to ownership data is key to identifying spatial and temporal patterns of change in the classification and pricing of rent-stabilized units. By leveraging tax data scraped by civic tech activists and cross-referencing it with property data, a relational database and associated SQL queries can make possible the identification of concentrated patterns of behavior occurring on properties by owners who have otherwise proven to be particularly adept at staying hidden. Look-up tables have been incorporated to create a method of analysis which is systematic and can be maintained and augmented as new information on ownership and management is accumulated over time.

This work is split into three parts: The first part of this work will begin with an initial exploration into the academic literature on rent-regulated housing, as well as the role of civic tech to supplement that literature. The second part of this work will outline the data integration methodology, using one census tract as a case study to test the feasibility of this approach. Finally, the work will explore ways in which this work could be implemented on a larger scale and the potential impacts of a successful execution of this methodology on legislation and prosecution targeting predatory landlords.