Neighborhoods by Numbers

Author(s): Alan Mallach (Senior Fellow)

When we talk about data, we are talking about numbers, or quantitative information, that can be gathered, analyzed, and interpreted to help us understand wha tis going on in some part of the world, from international trade or migration statistics to the code violations on a single building. The Youngstown Neighborhood Development Corporation in Youngstown, Ohio, uses data to design neighborhood plans, identifying the most appropriate strategy for each neighborhood. South Bend, Indiana, and Baltimore, Maryland, use data to tackle vacant properties, designing different strategies for different neighborhoods based on their market conditions. Minneapolis and other cities in Minnesota are using data to track problem rental properties, and design strategies to improve landlord stewardship.

Every community has many potential data users. They include many people in local government, including city planners, as well as those working on community and economic development, housing inspections, public safety, neighborhood services, and more. They include people in the local nonprofit sector, including community development corporations (CDCs) as well as social service providers. They may include realtors, developers, lenders, and people in other parts of the local business community, as well as residents of the community’s neighborhoods, who may be involved in civic associations, block clubs, and crime watch organizations. All of these people may be able to use data to help benefit their block, neighborhood, or community.

Often, however, people don’t know which datasets can give them what information, how reliable they are, and where to find them. Many people are intimidated by what they see as the difficulties of obtaining and understanding data. Once they have the data, they may not know how to present it in the most effective way to get their point across. The purpose of this guide is to make data more accessible and encourage people to use it to make better decisions about the future of their communities. It provides basic information about finding and using three types of information a local data user is likely to need: information about individual properties, information about neighborhood housing market conditions; and information about other neighborhood condition relevant to neighborhood conditions. This guide is designed to help answer the following questions: where to find the data; how to extract the data; what each dataset tells you, and what its limitations are; how to use these datasets to better understand neighborhood conditions; and how this data can be presented.