Affordable housing programs help millions of Americans keep a roof over their heads. Effective administration of these housing programs relies on the ability of relevant agencies to gather and analyze a wide range of data from multiple sources.
The administrators of public housing programs need to know things like the location and condition of housing units, as well as information about nearby schools, public transportation routes and more. They use these data points to analyze the efficacy of programs and policy changes, and to better inform administrators on how to provide quality housing in quality neighborhoods to households receiving assistance.
Simply getting the data is only half of the battle, however. Analyzing and interpreting it is what turns a mass of numbers and words into actionable information.
Within the federal government, the Department of Housing and Urban Development (and its agencies) is one of the largest users of this data, but it is also used by the broader housing ecosystem of state and local agencies as well as commercial businesses such as mortgage lenders, property developers and asset management companies.
The barriers to gathering data
Ironically, even in this era of sharing just about everything—from rides in personal cars and vacation rentals in private homes, to personal information on social media—data sharing in the housing assistance ecosystem remains a challenge. Almost all of the data lives in stove-piped systems that are held by many different owners. For example, a manager running a public housing program might need to access housing inspection reports, information on nearby employers and rankings of local public schools—but each data set is owned by a different entity, is stored in a different database structure, and uses different terminology.
Compiling all that data is often still a manual operation. Once it has been found and identified, however, data analytics and data visualization can dramatically increase its usefulness to housing authorities.
For example, digital maps based on selected data points can help officials understand the effects of policy changes. Users can overlay maps of housing units, median household incomes and public transportation to visualize trends in housing mobility, voucher concentration and proximity to amenities―and make better-informed decisions as a result.
Recently, my colleague Nydia Parries used digital mapping to help a client to analyze the outcomes of implementing a fair market rent program. The data visualization helped them determine that the program had not been as effective as desired in moving people into opportunity areas, and they are now considering additional steps to improve resident mobility. Without such user-friendly tools, housing authorities would need to have a data analyst or two onboard.
Housing data analysis is not a small challenge. The sheer diversity and quantity of data, siloed sources, and lack of standardization pose significant hurdles. However, as the population grows and the demand for housing increases, organizations that play a role in providing it must continue to modernize their methods and practices to achieve the best possible outcomes.
CGI has provided IT and business services to the affordable housing industry for 25 years. To learn more about our capabilities, download the brochure: Technology and services for the affordable housing industry.