Association for Public Policy Analysis & Management

Within the large body of literature evaluating the role of various demographic, geographic, and economic factors in housing-related outcomes, law is often neglected as an influential variable. The growing field of legal epidemiology is popularizing the use of law as data in quantitative analysis. As with any other dataset, it is imperative that legal data is accurate and meets quality control standards. To that end, a method known as policy surveillance was developed to ensure the reliability and reproducibility of legal data that can be used to evaluate the impact of law. Policy surveillance is a type of legal mapping that produces robust, scientific data for empirical research by tracking laws and policies across jurisdictions and over time.

This article introduces readers to policy surveillance as a method to create empirical legal datasets, using two examples. The first is a cross-sectional state-level dataset covering fair housing protections in all 50 states and the District of Columbia, as of August 1, 2017. The second is a cross-sectional city-level dataset covering nuisance property ordinances in the 40 most populous cities in the U.S., as of August 1, 2017. These types of legal datasets identify gaps and trends in policy, and facilitate evaluation studies exploring the impact of law on housing outcomes.