![Blighted buildings](/sites/g/files/omnuum10826/files/styles/hwp_21_9__1920x825/public/datasmart/files/benjamin-lehman-6-xlrdeeiva-unsplash.jpg?itok=4nbfHlmr) # Cities Are Not Overbuilt, But Underdemolished Data-Driven Strategies for Blight Removal April 28, 2023 [ Natalia Gulick de Torres ](#nataliagulickdetorres) The impact of blight on urban communities cannot be overstated. Abandoned properties, vacant lots, and other signs of neglect can lead to economic decline, increased crime, and [decreased quality of life for residents](https://www.sciencedirect.com/science/article/pii/S0264837721003847). However, cities across the United States are increasingly turning to data-driven strategies to identify and remove blight. By harnessing the power of data analytics, cities can more efficiently and effectively prioritize removal efforts, reduce blighted rates, and revitalize their communities. In this article, we will explore the current state of blight mitigation efforts in US cities, the role of data analytics in removal strategies, and the successful implementation in cities across the country. We will also discuss the potential benefits and challenges of using data metrics to address the importance of continued investment in these efforts. Blight has been a persistent issue in US cities for well over a century. Beginning in the late 19th and early 20th centuries, urbanization and industrialization led to rapid growth in cities across the country. However, as the economy shifted and populations changed, many of these cities began to experience decline. Factors such as white flight, suburbanization, and deindustrialization contributed to blight as businesses closed and residents moved away. [By the 1960s, blight had become a serious issue in many cities](https://www.pbs.org/johngardner/chapters/5b.html), and federal programs such as urban renewal and Model Cities were established to address it. However, these efforts often focused on demolition rather than revitalization, leading to the displacement of many low-income and minority residents. In recent years, cities have turned to more comprehensive and data-driven approaches to blight removal, recognizing the need to address the root causes and involve community members in the process. The current state of blight in US cities remains a significant challenge. According to aggregate data from the U.S. Census Bureau, Department of Housing and Urban Development, and Bureau of Economic Analysis, there are an estimated [16 million vacant and abandoned properties in the United States](https://anytimeestimate.com/research/most-vacant-cities-2022/). These properties can contribute to neighborhood blight by attracting crime and lowering property values, among [other negative impacts](https://web.archive.org/web/20250201084546/https://www.huduser.gov/portal/pdredge/pdr-edge-featd-article-012218.html). Additionally, blight can disproportionately affect low-income and minority communities, perpetuating economic and social inequality. However, there are also signs of progress. Cities such as Detroit, Baltimore, and New Orleans have implemented data-driven removal strategies that have reduced blight rates and revitalized neighborhoods. One of the key elements of data-driven blight removal strategies is the use of data analytics to identify and then prioritize critically blighted properties. For example, [Detroit's Motor City Mapping project](https://datadrivendetroit.org/files/DCPS/Motor%20City%20Mapping%20Mid-Level%20Overview%20Training%207.7.14.pdf) was a groundbreaking initiative that utilized data-driven strategies to identify and combat blight in the city. Launched in 2014, the project was a collaboration between the city of Detroit, the nonprofit Loveland Technologies, and the Skillman Foundation. The project sought to create a comprehensive database of blight throughout the city, using data from a variety of sources to create a detailed picture of the scope and severity of the problem. The Motor City Mapping project began by deploying a team of surveyors, who used mobile devices to collect data on blighted properties throughout the city. The surveyors were trained to identify signs of blight, such as boarded-up windows, overgrown vegetation, and missing doors. They also took photos of each property and geotagged the location, allowing the data to be analyzed and visualized on a map. Once the data had been collected, it was analyzed using a variety of data analytics techniques. The team used machine learning algorithms to classify the severity of blight for each property, taking into account factors such as the age of the building, the extent of the damage, and the level of disrepair. The resulting data was then visualized on an interactive map, allowing city officials, community members, and other stakeholders to explore and analyze the data in real-time. The Motor City Mapping project had a significant impact on Detroit's blight removal efforts. By using data analytics to identify the most severe cases of blight, the city was able to prioritize its resources more effectively and target its interventions more strategically. The project also helped to build trust and collaboration between the city government and community members, who were able to use the data to advocate for improvements in their neighborhoods. In addition to its impact on Detroit, the Motor City Mapping project has become a model for other cities seeking to address blight through data-driven strategies. The project has been replicated in other cities, including New Orleans and Philadelphia, and has inspired the development of similar initiatives in cities around the world. Another important aspect of data-driven blight removal strategies is the involvement of community members. In Baltimore, the [Vacants to Value](https://livebaltimore.com/resident-resources/financial-incentives/vacants-to-value-booster/) program (which has since evolved into the City’s [BuyIntoBmore](https://dhcd.baltimorecity.gov/nd/development-opportunities) program) is an initiative that has been successful in reducing blight rates and revitalizing neighborhoods. Launched in 2010, the program was designed to address the city's large number of vacant and abandoned properties, which were a major source of blight and crime. The Vacants to Value program uses data analytics to identify blighted properties and prioritize them for intervention. The program began by creating a comprehensive database of vacant properties throughout the city, using data from a variety of sources, including tax records, building permits, and 311 service requests. While vacant properties are not necessarily blighted properties, the city combined these into one category to more effectively carry out their goal of strategically intervening in these properties and revitalizing their surroundings. This data was then analyzed to identify properties that were likely to be blighted, based on factors such as the age of the building, the level of code violations, and the length of time the property had been vacant. Once the most severely blighted properties had been identified, the Vacants to Value program used a range of strategies to address them, including demolition, rehabilitation, and sale to responsible property owners. The program also provided incentives for property owners to rehabilitate their properties, such as tax credits and low-interest loans. One of the key strengths of the Vacants to Value program is its community engagement component. The program works closely with community organizations and residents to identify and address blighted properties. This collaborative approach not only helps to address blight, but also empowers community members to take ownership of the process and become more invested in their neighborhoods. The Vacants to Value program has been successful in reducing blight rates in Baltimore; since its launch in 2010, the program has demolished more than 4,000 blighted properties and rehabilitated more than 1,000 others. The program has also helped to increase property values and spur economic development in targeted neighborhoods. In addition to being more strategic than traditional blight removal strategies, data-driven approaches can also be more cost-effective. By using data analytics to identify properties with the most severe blight, cities can target their resources more efficiently and reduce the overall cost of blight removal efforts. This can be particularly important for cities with limited resources or competing priorities. [New Orleans' data-driven blight removal strategy](/news/article/new-orleans-brings-data-driven-tools-to-blight-remediation-915 "New Orleans Brings Data-Driven Tools to Blight Remediation") is a comprehensive approach to addressing blight and revitalizing neighborhoods. The strategy was developed in response to the city's high levels of blight, which were a major challenge in the aftermath of Hurricane Katrina. The strategy began with the creation of an extensive database of blighted properties throughout the city, based on data from multiple sources, including city records, code enforcement, and citizen complaints. This data was then analyzed using a range of data analytics tools to identify patterns and trends in blight, such as the most common types of blight and the neighborhoods that were most affected. Based on this analysis, the city developed a targeted intervention strategy, focusing its resources on the most severely blighted properties and the neighborhoods with the highest levels of blight. The strategy included a diversity of interventions, including demolishing blighted properties, rehabilitating homes, and selling to property owners who will either repair or renovate the housing depending on its condition. A highlight of New Orleans' strategy is the use of innovative technologies to streamline and improve the intervention process. For example, the city used a mobile app that allowed code enforcement officers to collect data on blighted properties in real-time, improving the accuracy and timeliness of the data. The city also used a digital platform that allowed residents to report blighted properties and track the city's progress in addressing them. Since the program's launch, the city has demolished more than 12,000 blighted properties and rehabilitated more than 2,500 others. As a result, property values have increased and there has been more economic development in those neighborhoods. Overall, data-driven blight removal strategies have shown significant promise in addressing one of the most pressing issues facing US cities. By using data analytics to identify and prioritize blighted properties, engaging community members in the process, and being more cost-effective than traditional approaches, cities such as Detroit, Baltimore, and New Orleans have been able to reduce blight rates, and therefore reduce the associated negative impacts, while revitalizing the local community. While there is still much work to be done, these successes demonstrate the potential of data-driven approaches to improve the quality of life for urban residents and create more vibrant and resilient communities. ## About the Author ![Headshot of Natalia Gulick de Torres ](/sites/g/files/omnuum10826/files/styles/hwp_1_1__100x100_scale/public/datasmart/files/natalia_gulick_de_torres_headshot.jpg?itok=slxJKAxj) ### Natalia Gulick de Torres Natalia Gulick de Torres is a graduate student at the Harvard Graduate School of Design and research assistant for Data-Smart City Solutions. Her academic research lies in the intertwined histories of urban and rural land development within Latin America and the Caribbean. Previously she was a research assistant for the Loeb Library at the Harvard Graduate School of Design and an engagement coordinator for the Institute for European Studies at Cornell University, where she obtained a Bachelor of Architecture. See also:- [ Housing ](/topics/housing) - [ Infrastructure ](/topics/infrastructure) Share on:- [ Facebook ](#) - [ Twitter ](#) - [ Linkedin ](#)