How Data-Driven Decision Making Can Inform Affordable Housing

By Betsy Gardner • June 11, 2019

A 2017 study commissioned by the National Multifamily Housing Council (NMHC) and National Apartment Association (NAA) found that the U.S will need 4.6 million new apartments by 2030 to accommodate an increase in renters caused by demographic and lifestyle changes. Rents — and home prices — are skyrocketing, and there is a severe lack of affordable housing in the U.S. Unfortunately, building affordable housing is a divisive and highly nuanced issue, with a significant amount of opposition based on fear of crime, lower home prices, and changing “neighborhood character.” Choosing locations for necessary, affordable housing can be politically fraught, but in Charlotte, North Carolina city officials are using data to help guide their decision making and compare proposed sites in a transparent and standardized way.

Until recently, gentrification wasn’t a huge concern in Charlotte, but over the past decade city demographics have been rapidly changing and there is growing attention to tenant displacement. The Charlotte Housing Trust Fund is an eighteen-year-old fund for financing affordable housing, managed by the Housing & Neighborhood Services' (HNS) Housing Services division. Traditionally the city council reviewed site requests from developers, giving a binary yes or no to potential affordable housing sites. As gentrification became a more pressing concern, and as the city council filled with new data-engaged members, Housing Services staff saw an opportunity to introduce data-driven decision making into the site selection process.

Housing Services, working with the Charlotte City Data Analytics, anticipated an interest from the council and began to brainstorm how to create a new process for building; they landed on a data map that would provide scoring for proposed areas, to help inform the council when they reviewed site proposals. Warren Wooten, the Housing Services Operations Manager at HNS, explained that the mapping tool works by giving a score, which is really a combination of four different scores for four different measures: proximity to amenities, car and public transit access to jobs, neighborhood change score, and neighborhood diversity.

Tool mock-up with fictional proposed project, including score and project information.


Wooten and his team didn’t come up with those measures themselves but adapted them from the metrics that the city council discussed when doing traditional site selection. On the map, the graphic shows all the scores in a 1-10 range, with increments of a tenth of a point. Hovering over the box shows the underlying data that informed the score.

Tool mock-ups showing scores for a fictional proposal, including underlying score breakdown information.


The HNS team had the staff capacity to build this, with a small amount of custom GIS from mapping company Esri. All the data for this tool comes from Charlotte’s publicly-available open data. The funding came from HNS’ established budget and the tool was a relatively low-cost build. The city council members, as the main users, were pleased to have a tool that addressed the metrics they wanted to consider, in a way that was transparent and rooted in data. With this scoring, proposed sites can now be compared based on technical standards, which factor into the council’s decisions.

Along with HNS as the builders and the city council as the main users, the developers and the community are the other stakeholders in the affordable housing conversation. The developers know that their proposals will be graded by this tool, and in some cases, it has increased transparency and helped ensure that developers are following the correct standards in their proposals. Wooten also knew that the community would be interested in the tool, since it could influence if affordable housing was built in their neighborhood. HNS did six or seven community meetings for community outreach to roll out the tool and introduce it to residents of Charlotte.

According to Wooten, the tool was well received; most people were just curious about how it works. He wasn’t surprised that the majority of pushback and questions came from people who didn’t like the results generated by the tool, but since it uses open data and comparing sites with standard metrics, HNS and the council are able to be transparent about the information. They also reassured residents that it’s not just policy and data that’s making the decision; proposals still go through the council. As such, the scoring tool is not the only criteria used to make local Housing Trust Fund decisions. While it is a robust tool that assists in guiding the location of housing trust fund investments, the city council also considers the financial strength of each development, both the developers track record and experience, and the overall long-term affordability of the development.

As Wooten described, council decisions can be used to “backstop gentrification” in places like the West Side and increase access to the amenities and transit in wealthier areas. Overtime it is both the factors and the scoring tool that will help create more mixed-income neighborhoods and make Charlotte a more equitable city. Other cities struggling with gentrification and staggering rental prices can learn from Charlotte’s innovation. City governments will need to explore creative and data-driven solutions to guarantee the amount of affordable housing necessary in the near future.

About the Author

Betsy Gardner

Betsy Gardner is the editor of Data-Smart City Solutions and the producer of the Data-Smart City Pod. Prior to joining the Ash Center, Betsy worked in a variety of roles in higher education, focusing on deconstructing racial and gender inequality through research, writing, and facilitation. She also researched government spending and transparency at the Lincoln Institute of Land Policy. Betsy holds a master’s degree in Urban and Regional Policy from Northeastern University, a bachelor’s degree in Art History from Boston University, and a graduate certificate in Digital Storytelling from the Harvard Extension School.