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Cityscape: Volume 18 Number 3 | Chicago Multifamily Market Characterization: Developing a Comprehensive Picture of the Multifamily Housing Landscape

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Gentrification

Volume 18, Number 3

Editors
Mark D. Shroder
Michelle P. Matuga

Chicago Multifamily Market Characterization: Developing a Comprehensive Picture of the Multifamily Housing Landscape

Rachel Scheu
Margaret Garascia
Elevate Energy



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 Development 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.


A data-driven description of a community’s housing stock can help identify community needs and inform decisionmaking regarding energy efficiency and other types of programs. This article presents the data and methods used in an analysis characterizing the multifamily building stock in Chicago, which segmented Chicago’s multifamily buildings by age, size, construction type, and energy use. Conducting this analysis presented several thorny data challenges: building-level data are not collected in any central location; in Chicago and many other cities, the local property assessor has the most complete data of this kind, but the data are compiled for the purpose of tax assessment and not for the purpose of population-level building segmentation; and many disparate data sets must be combined with assessor data into a cohesive whole, presenting difficulty in matching, cleaning, and determining the appropriate level of granularity. This article describes a multifamily market characterization study in Chicago for which different data sources were merged for the analysis; presents a general methodology that could be used by other cities or program implementers; and discusses insights about the Chicago multifamily market. Identifying and locating geographic concentrations of certain building types enable more precise targeting for energy, housing, and other building programs.


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