Academic journal article Fordham Urban Law Journal

Affordable Housing Law and Policy in an Era of Big Data

Academic journal article Fordham Urban Law Journal

Affordable Housing Law and Policy in an Era of Big Data

Article excerpt

Introduction                                                277 I. Why Might Big Data Matter to Affordable Housing Policy?  281  A. Big Data and Policy                                     281  B. From Outputs to Outcomes in Affordable Housing          284 II. Big Data in Affordable Housing Law and Policy           287  A. Some Examples of Big Data's Potential in Affordable     Housing                                                 288   1. Siting Decisions, Mobility, and Neighborhood Effects   288   2. Housing Portfolio Management                           290    a. Subsidy Targeting and Market Conditions               290    b. Enforcement and Housing Quality                       291    c. Unifying the Subsidized and Unsubsidized       Housing Portfolio                                     293   3. Resident Relations and Services                        293  B. Synthesis and Reflections on the Role of Law            295 III. Big Data's Dark Side: Caveats and (Some) Responses     297 Conclusion                                                  300 


Every year, federal, state, and local governments invest more than $50 billion to provide housing for people who cannot otherwise afford shelter. (1) In addition to this housing assistance, policymakers also make a variety of choices that impact the landscape of affordable housing, including in zoning, infrastructure, housing finance more broadly, and in a myriad of other policy domains. (2) These policies can make a profound difference for the millions of individuals and families helped, but are too often undertaken with only the vaguest, visceral sense of their consequences beyond the bare facts of putting roofs over people's heads.

However, affordable housing policy is beginning to experience a shift in perspective. To the extent that policymakers have collected data on impact, the focus traditionally has been primarily on outputs. These measures included the number of units built through a given investment, the number of people served under a given program, the number of construction or property management jobs created, and the like. But outputs are not always--indeed, not often--the same as outcomes, the actual short- and long-term consequences of policy interventions for those served by affordable housing programs and the communities at issue. (3)

In recent years, researchers and policymakers have begun to evaluate the results of policy interventions for people in subsidized housing on measures such as income, educational achievement, physical and mental health, and even subjective wellbeing. (4) Rather than merely track whether people have housing at a given level of affordability, this new focus understands that housing is a platform for a variety of life outcomes and that housing policy choices can meaningfully impact the arc of those outcomes.

This emphasis on outcome measures reflects a broader embrace of the use of data for decision making by managers and policymakers across the private and public sectors. (5) The ability to collect data in a more rigorous and systematic way and the development of tools to make that information actionable--particularly to make transparent patterns that would otherwise be opaque--is beginning to change a range of decisional processes. This shift can been seen in everything from how professional baseball teams select players to how Facebook, Google, and other companies target their advertising to how investors seek value. (6) And data increasingly means "big data," an admittedly fuzzy (and arguably hackneyed) concept that roughly refers to the use of relatively large data sets, often aggregated across previously disconnected areas, mined for the predictive value of underlying patterns and trends. (7)

Although much has been written about data-driven policymaking, the specific role of the legal system in improving program design and implementation deserves deeper exploration. …

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