By Rohan Mascarenhas.

The rate of new foreclosures increased fourfold during the Great Recession, and even now, they remain above the 2007 level. The burst of the housing bubble worsened real estate trends in many areas that were already grappling with decades-old shifts in population and the manufacturing sector. As a result, a set of difficult issues — vacancies, homelessness, and urban blight — has risen on the public agenda. Some policymakers have turned to new tools that use predictive analytics, crowdsourcing, and digital cartography to address these complex problems. In the process, they are in the early stages of redefining the provision of social services.

The problem of homelessness has become particularly acute in New York City. In 2010, an estimated 110,000 people slept in municipal shelters. Roughly a third of the people in these shelters have experienced a recent eviction, but it is difficult to predict which eviction notices will result in a homeless family. Predictive analytics may be able to help. In a joint project with the SumAll Foundation, the city Department of Homeless Services is analyzing data from all shelter check-ins, which include a family’s most recent address. Visualizations are then created to potentially identify eviction “hotspots,” where the city needs to focus its mitigation. “Programs like this will help us show we are using our resources in the absolute best way possible to help the people that need them,” Sara Zuiderveen, the assistant commissioner of prevention services at DHS, told website Fast Co-Exist.

Addressing vacant or abandoned properties requires a different approach. The problem is often divisive, as neighbors have differing views on whether a nearby property should be demolished or simply repaired. Histories of racial discrimination, which resulted in minority neighborhoods cut off from any investment, complicate the debate. To help gauge community opinion in South Bend, Indiana, a team of Code for America fellows developed CityVoice, a phone-based application that gathers feedback about vacant homes. The app focuses on roughly 250 homes that are “borderline” cases, where input is needed to decide the property’s fate. With CityVoice, residents can call a phone number and enter a property ID code. They then indicate if they want to see the property repaired or removed, and they have a minute to explain their decision. The city’s Code Enforcement inspector receives the input and considers it during discussion about a property.

In Detroit, the issue of blight, after decades of de-industrialization and population losses, has assumed a much larger size. The Blight Task Force estimates that roughly 100,000 homes may need to be remediated or demolished, but no one knows the true extent of the problem, or how much it would cost to tackle each blighted property. A new effort, led by Data Driven Detroit and local startup Loveland Technologies, aims to use an app to catalog all of the city’s 139 square miles. Survey teams, composed of three members, will drive around Detroit and, for each parcel, take a photograph of a building and enter notes about its condition. The information is digitally fed to a central office, which monitors the quality of each entry. The results should be available later this month, and it will drive the Task Force’s recommendations on addressing blight in Detroit. “Some of these really huge problems like blight are partially caused by problems with information,” Jerry Paffendorf, Loveland’s founder, told “You can’t fix things until you see fully what’s going on.”

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Originally posted at Data-Smart City Solutions.