• Volume 19, Number 3
  • Managing Editor: Mark D. Shroder
  • Associate Editor: Michelle P. Matuga
 

Residential Demographic Multipliers: Using Public Use Microdata Sample Records To Estimate Housing Development Impacts

Sidney Wong
Community Data Analytics

Daniel Miles
Gabrielle Connor
Brooke Queenan
Alison Shott
Econsult Solutions, Inc.


Data Shop

Data Shop, a department of Cityscape, presents short articles or notes on the uses of data in housing and urban research. Through this department, the Office of Policy Devel- opment and Research introduces readers to new and overlooked data sources and to improved techniques in using well-known data. The emphasis is on sources and methods that analysts can use in their own work. Researchers often run into knotty data problems involving data interpretation or manipulation that must be solved before a project can proceed, but they seldom get to focus in detail on the solutions to such problems. If you have an idea for an applied, data-centric note of no more than 3,000 words, please send a one-paragraph abstract to david.a.vandenbroucke@hud.gov for consideration.


Impact analysis plays a critical role in evaluating development proposals, devising housing policies, and developing comprehensive plans. To assess how enrollment and population increase and how net fiscal impacts affect municipalities and school districts, analysts require the latest evidence-based demographic multipliers that are specific to housing types. However, because outdated statewide multipliers are still widely used, an urgent need exists to devise up-to-date demographic multipliers to reduce imprecise impact assessment. In this article, we discuss a new generation of demographic multipliers based on the latest annually released American Community Survey (ACS) Public Use Microdata Sample (PUMS). We introduce a larger and more stable sample of households that have recently moved into both new and old housing units. We believe that these new multipliers are more accurate and relevant to impact assessment. With improved methodology, we can make these multipliers more geographically specific. Finally, we discuss the challenges and the new direction in using ACS PUMS data to generate statistically valid multipliers in small geographical units.


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