Tax Increment Finance Illumination Project

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CivicLab Tax Increment Finance (TIF) Illumination Project

To investigate, illuminate and educate around the total impacts of TIFs in Chicago on a ward-by-ward basis. There are 163 TIF districts in Chicago, covering 30% of the city's footprint. In 2011 they extracted $454 million in property taxes from property owners in those districts and placed those funds in essentially what is an "off-the-books" second city budget - one that has very few checks and balances. The TIF Program is scandal ridden and highly controversial. See stories written in our local weekly, The Chicago Reader at [1].

Presentation parked here: [2]

We have begun investigation of one ward - the 27th, on the city's Near North side, which has 12 TIFs in it.


GitHub: [4]


Data were compiled from multiple sources:

City of Chicago:

   * TIF Projection Reports: [5]
   * Boundaries - Wards: [6]
   * Boundaries - Tax Increment Finance Districts: [7]
   * Boundaries - Building Footprints: [8]
   * District Annual Reports: [9]

Cook County:

   * Property Info Portal: [10]

Additional data were acquired through various Freedom of Information Act (FOIA) requests, notably the taxcode data that enumerates the percentage a taxing body takes from each property based on the taxing bodies' overalpping jurisdictions.

The geospatial data (shapefiles) were loaded into a PostgreSQL ([11]) database with the geospatial PostGIS ( [12] ) extension (see OpenGeo's excellent tutorial here: [13] ). From there, buildings were matched to addresses so that any new data with an address could be matched to a building (which could then be matched to a TIF, which could then be matched to a ward, etc...).

Financial data from the District Annual Reports had to be manually entered, as they are all in PDF form and the necessary fields could not be easily extracted programmatically.

Through a FOIA request, we obtained every Property Index Number (PIN) in Chicago, with the total property tax amount billed to each of those PINs. Using h[ttp://] , PINs were matched to addresses, which were then geocoded by matching them to the buildings in the buildings footprint database (98.7% of non-vacant PINs were successfully geocoded). A spatial query could then be written to sum the property tax bills for every PIN for each TIF in the 27th ward.