(Data-Smart City Solutions) -- Whether the result of a destructive natural disaster like Hurricane Katrina or the consequence of the foreclosure and home loan crisis of the mid-2000s, cities across America have been burdened with blight, the presence of vacant and abandoned properties. The effects are seen in cities large and small, from New York City to Cleveland, New Orleans, and Youngstown, Ohio.
The cost of these empty lots and abandoned homes is not simply a cosmetic black eye for cities; blight causes real, tangible problems, prompting action by cities across the country. A study published this June by The Center on Urban Poverty and Community Development looked at the relationship between vacant properties and community health and crime in Cleveland, Ohio. The findings were definitive: there is “significant correlation” between vacant properties and violent crime, homicides, and elevated lead levels in blood tests — 83 percent of homicides, 65 percent of violent crime hotspots, and 62 percent of elevated lead levels overlapped with vacancy hotspots in Cleveland.
In response to what has proved to be an urgent urban crisis, cities are deploying a wide range of digital and data-driven strategies to address vacant and abandoned properties. From using data to drive efficiency in code enforcement to crowdsourcing the mapping of properties, cities across the country are making significant strides in the battle against blight.
In the wake of Hurricane Katrina, the city of New Orleans was faced with a an abundance of vacant and abandoned properties across the city. Mayor Mitch Landrieu, when elected to office in 2010, made it a major priority of his administration to address the issue of abandoned and vacated properties across the Big Easy.
The city crafted a blight reduction strategy centered around results and improved performance management through public monthly BlightStat meetings. To further advance the work, the city began sending “nudging” letters to property owners when 311 complaints were filed, which resulted in more homeowners coming forward to bring properties into compliance before the city needed to take further action.
Taking the efforts a step further, the city also created a decision support scorecard that organizes the steps for handling a reported property and makes the code enforcement process significantly more efficient. Previously, there was a backlog of as many as 1,500 properties awaiting inspections and hearings — the digital scorecard streamlined the process. They implemented a machine learning model that makes recommendations for next steps based on a score for the property input by a mid-level supervisor, prioritizing the workflow for the Code Enforcement Department.
In South Bend, Indiana, Mayor Peter Buttigieg heard from residents on the campaign trail that they wanted to see the next administration tackle the issue of vacant and abandoned properties. In short order, Buttigieg assembled a team to address the issue and by February of 2013, the city released a Vacant and Abandoned Properties Task Force Report which laid out the publicly stated goal of addressing 1,000 vacant or abandoned properties in 1,000 days.
Residents were given a window into the code enforcement process and were able to track the city’s progress on the city’s website. The public eye proved valuable when local media picked up on a bug in the city’s progress tracking system that incorrectly showed 100 properties pending review by code enforcement officials as already addressed. Santiago Garces, the city’s Chief Innovation Officer, said in an interview with Data-Smart that the revelation of this problem led to significant improvements in the city’s code enforcement process.
In the end, the city reached its stated goal of addressing 1,000 problem properties in September 2013, about two months ahead of schedule. By the 1,000th day, the city had taken action on 1,122 abandoned properties, repairing almost 40 percent of them, according to the city’s website. By taking a data-driven approach to tackling vacant and abandoned properties and using a digital platform to increase transparency to the public, Buttigieg’s administration sets a strong example for cities looking to solve public concerns over blighted properties.
Detroit's Blight Removal Task Force, in partnership with Michigan Nonprofit Association, Data Driven Detroit, and Loveland Technologies launched in 2013 a physical survey to gather property condition data for all 380,000 parcels of land in the city. The city collaborative set out to create a thorough database of conditions of every property in Detroit to give the city a sense of where problem properties needed to be addressed — and an all-in-one place to keep track of properties over time.
Through a mobile application aptly called “Blexting,” a team of about 150 residents and volunteers surveyed the entire city to compile the database and citywide property map. Surveyors used the mobile app to photograph the front of every property — residential and non-residential — and answered a standardized series of questions about the property. Their responses assessed individual properties based on estimated occupancy, vacancy, fire damage, the existence of any “dumping” and the use of property (commercial, public, etc.)
To maintain consistency and reliability in the crowdsourced data, the collaborative maintained a “mission control” center where staff performed a quality check of the data submitted from the field in real time. The task force website reports that of the total 84,641 structures and vacant lots in the city, about 40,000 were deemed to fit the definition of “blight” and prioritized for removal or intervention. The detailed and data-driven survey questionnaire approach also allowed for the estimation of properties with indicators of future blight, of which the task force reported about 38,000 fit the bill. The task force recommended further inspection of these properties and a variety of interventions including rehabilitation, removal, and securing.
In 2015, the University of Chicago team of data scientists at the Center for Data Science and Public Policy (DSaPP) worked with the Cincinnati Department of Buildings and Inspections to develop a predictive model that allows for early intervention by building inspectors at homes and properties most at risk of vacancy or violations.
The predictive models the team at DSaPP developed combined data about home values, fire, crime, tax, census, and water shutoff information with historical inspection data to develop a list of properties prioritized by their need for inspection. The logic is that the earlier an inspector can visit a property likely to be in violation of city code, the earlier problems can be addressed, and the more likely it will be that the property is fixed as opposed to abandoned.
DSaPP’s blog post detailing the project says that the traditional method of using citizen complaints to inform property inspections leads to a violation found in 53 percent of cases. The initial results from 2015 show that using the predictive model increases the likelihood of finding a building code violation in a specific property to 78 percent.
These examples of data-driven strategies set a great example for cities across the nation burdened with blight. Deploying data in the fight against blight can significantly improve the code enforcement process to prevent vacancies before they happen, give residents a window into a city’s progress in the process of addressing blight like in South Bend, and bring them directly into the process of mapping properties to increase citywide knowledge with apps like “Blexting.” Cities in need of blight reduction strategies would be wise to learn from Detroit, South Bend, New Orleans, and Cincinnati to tackle blight while building trust with residents and increasing transparency along the way.