April 29, 2009 By Andy Opsahl
More than 300 local governments are utilizing a $3.9 billion Community Development Block Grant (CDBG) that the U.S. Department of Housing and Urban Development (HUD) is offering in response to the national foreclosure crisis. Municipalities plan to use the money to buy foreclosed homes and turn them into low-income housing. To get those funds, governments must identify the areas within their boundaries likely to be hardest hit by foreclosures. Not surprisingly, cities and counties used GIS to perform that analysis.
Moreno Valley, Calif., received $11.4 million from HUD after submitting GIS data that mapped out all foreclosed and likely-to-be-foreclosed properties. The city plans to rehabilitate and sell them to low-income families or manage the houses as low-income rental properties. When appropriate, the city will turn some homes into multitenant structures.
Local foreclosure information for the project came from real-estate information vendor DataQuick and HUD. The city also gathered information from government staff who enforce codes that prevent neighborhood blight. Houses that weren't yet in foreclosure but appeared abandoned or rundown were entered into a GIS database. Technicians watched those properties in case they fell into foreclosure.
In another effort, Johnson County, Kan., secured $2.1 million in HUD funds and uses a similar GIS tool for tracking foreclosures. (Only counties and what HUD considers to be large cities may access HUD money directly. Smaller cities must get their counties to apply for the grant.)
"Our system can tell us what percentage of homes in a certain community have been foreclosed on or are within eight to 10 days of being foreclosed on," said John Harrenstein, a management analyst for Johnson County.
In another instance, GIS staff in San Bernardino County, Calif., tracked potential foreclosures in all of the county's 24 cities, saving each city from doing the job itself. The project generated $22.5 million from HUD.
To decide what areas to target, San Bernardino County used HUD criteria when it developed a point system for ranking homes most in danger of foreclosure. On a zero-to-nine scale, county staff calculated each home's "Total Severity Score" in Microsoft Excel. Any home scoring three points or above was considered a targeted area for foreclosure watch.
"We figured that anyone with any point is already hard-hit, but we could not target 82 percent of the region. So we decided to target the areas with three or more points, which covered about 47 percent of the county," the project's targeting narrative document explained.
For example, if HUD considered a household's revenue to be "low income," the house got 1 point. If it was located in an area where more than 40 percent of homes have subprime mortgages, the house received another point. Two points were added for houses in areas where 50 to 60 percent of mortgages are subprime. Homes in areas where 60 percent or more of mortgages are subprime received three points. Several other criteria also influenced the scores. The county then fed the Excel spreadsheet into its GIS tool to map the target properties.
George Huang, economic analyst of San Bernardino County, was surprised to learn that all 24 cities in the county had buildings scoring three points or higher -- even affluent cities. He said measuring the entire county gave all 24 mayors within it a political gift so they could take advantage of that extra money and boast of it to voters.
"Even the best of the cities, [economically speaking], have tiny bits of areas that are high-risk," Huang said.
This Digital Communities white paper highlights discussions with IT officials in four counties that have adopted shared services models. Our aim was to learn about the obstacles these governments have faced when it comes to shared services and what it takes to overcome those roadblocks. We also spoke with several members of the IT industry who have thought long and hard about these issues. The paper offers some best practices for shared government-to-government services, but also points out challenges that government and industry still must overcome before this model gains widespread adoption.