This post originally appeared on the Urban Institute’s blog Metro Trends. It is the first post in a three-post series. Read the second and third posts.
As late as the early 1990s, people working in and for America’s low-income communities relied primarily on the Decennial Census for information. The Census was the only publicly available, reliable, and comprehensive source of data about the community—the people and the housing. If you wanted different or more recent data or information about places with non-Census boundaries, it was available (if at all) only in musty file cabinets and inaccessible computers in government offices, or through very expensive, time-consuming surveys.
Today, the landscape is a lot different, though some hurdles still remain. That’s why the Urban Institute and the Federal Reserve Bank of San Francisco released What Counts: Harnessing Data for America’s Communities, a book of short, accessible essays and a website. What Counts helps answer some of the major questions raised by the prior volume in the series, Investing in What Works for America’s Communities, namely “how do we tell what is needed, what could work, and what is actually working?”
What Counts not only explains how the data available to communities has expanded exponentially since the 1990s, it discusses both the challenges and opportunities that lie in turning those data into the information communities want and need. The book also highlights cultural, capacity, funding, privacy, infrastructure, and other challenges to the future uses of community-relevant data.
Last week’s launch event, hosted by Urban Institute’s Sarah Rosen Wartell, Federal Reserve Board of Governors’ Eric Belsky, and Robert Wood Johnson Foundation’s Donald Schwarz, brought together many of the book’s authors and editors for a discussion with an in-person and virtual audience. This is the first of three posts about that discussion.
Sharing data and measures to improve programs, raise capital, and influence policy
Timely, easily available data and outcome measures are critical to understanding how affordable housing and other traditional community development activities affect the lives of those who live in communities and the organizations that work there. Moreover, by using shared measures across multiple organizations working in the same community, or on the same issue in many communities, organizations can vastly improve their ability to understand and improve what works.
Asked how to convince organizations to move from measuring activities to measuring the outcomes of those activities for residents’ lives, Bill Kelly, strategic advisor to Stewards of Affordable Housing for the Future, said it wasn’t hard: the organizations care about the residents and know that measuring outcomes helps them improve their programs—and also raise capital and change policy.
But while the idea of shared measures is readily embraced, actually getting there is not easy. Annie Donovan of the Treasury Department (formerly CEO of CoMetrics) characterized the process as “wallowing in the data” to understand what is collected, the definitions of individual data points, the data’s accuracy, and its utility to answer important questions.
Donovan cited the experience of CoMetrics in building a shared data system for community land trusts (CLT) who wanted to know whether their housing model in fact stabilized both families and the housing stock. The HomeKeeper system provides answers to those questions, building on data the organizations collect to run their day-to-day business and establishing a strong feedback loop to make certain the data is properly interpreted. The system also enables each CLT to more effectively run its own organization.
Maggie Grieve of NeighborWorks America described how NeighborWorks’ Success Measures has helped organizations focus their programs, because they better understand what residents and clients need and what works to meet those needs. Building a library of tools that individual organizations can use to measure outcomes reduces the burden on organizations and enhances consistency.
Combining and comparing shared outcome measures with government data, Kelly said, could make them even more powerful. But notwithstanding an expanding “open data” movement, getting access to critical government data is still difficult. Kelly acknowledged privacy and proprietary business concerns, but asserted that to move the community development field forward and reduce poverty, the government should figure out how to address these issues without keeping data out of the hands of those who can use it to spark change.
Donovan, Kelly, and Grieve all said that the best way to build shared measures is to “just get started.” Building trust among those who are sharing data, as well as between the organizations developing the measures and those using them, is key. And the best way to build trust is to start small, make the measures useful to those who are providing the data, be persistent, and build from there.
