The Urban Redevelopment Authority (URA) is tasked with managing land recycling activities in the City of Pittsburgh, PA, including pricing real estate for acquisition and disposition
Staff were spending dozens of hours each quarter to derive a price per square foot that did not reflect diverse property attributes and market dynamics within neighborhoods
The URA partnered with the Western Pennsylvania Regional Data Center (WPRDC) and Tolemi to build a dynamic land valuation model based on dozens of open data sets
- Using a sales comparison approach, URA staff now have instant access to past sales, property
attributes, and comparables for every piece of vacant land in the City
The URA is the City of Pittsburgh’s economic development agency, focused on creating jobs, increasing the city’s tax base, and improving the vitality of businesses & neighborhoods. Since its inception in 1946, it has stewarded the Pittsburgh economy during the boom years of the post-WWII expansion, the decline of the steel industry in the mid-seventies, and the transition to a “21st century economy,” in the words of President Barack Obama. Driven by population loss and a proactive demolition program, Pittsburgh is home to over 20,000 vacant lots. The URA’s portfolio includes over 1,400 of these parcels, and the Real Estate Department oversees acquisition, maintenance, and conveyance of its property portfolio.
How do you accurately price over 20,000 parcels of vacant land in a dynamic real estate market?
Pricing vacant lots is difficult in any urban market due to the relatively small number of comparable sales of raw land. Additional factors—such as rapid revitalization of certain areas and the hilly topography that renders many parcels undevelopable—make the undertaking particularly challenging in Pittsburgh. To address the need for updated pricing and ensure the URA is responsibly stewarding vacant lots, Real Estate Department staff were spending hours each quarter analyzing bulk data on land sales and deriving a price per square foot for dozens of neighborhoods, then cross-referencing dozens of data sources on property inquiries to estimate a proxy price. But staff recognized their price per square foot calculations didn’t account for things like slope, zoning, type of transaction, and submarket dynamics within and across neighborhood boundaries. And this neighborhood-level price estimate still required them to cobble together dozens of data points to refine the pricing model to individual parcels.
“The pricing model in BuildingBlocks allows our team to instantly see a value derived from dozens of data sets, a task which would take hours to comb through without the help of the team at Tolemi. This value provides us with a jumping off point to determine a price at which to sell land that is both fair to the end user and the URA.”
Nathan Clark, Real Estate Director
To build a more dynamic and effective approach, the URA knew it had to expand the data points it considered in its sales comparison analysis. So it turned to the rich trove of open data maintained by the WPRDC, a community information intermediary managed by the University of Pittsburgh’s Center for Social and Urban Research in partnership with Allegheny County and the City of Pittsburgh. WPRDC regularly publishes a reliable inventory of over 300 data sets from city and county agencies throughout the region, including sales, public property, property attributes, Market Value Analysis, slope, zoning, and neighborhood boundaries. But incorporating these data points to its analysis would also add complexity—and time—to an already-intensive analysis.
Through its partnership with Tolemi, URA utilizes the BuildingBlocks application to aggregate and analyze this composite information on every piece of vacant land in the City. Upon receiving an inquiry on a property, staff can immediately look up every relevant attribute of that parcel of land, along with comparable land sales in the past few years, in a single profile that displays only the data most relevant to the Real Estate staff. Tolemi’s data science team worked closely with the URA team to build a land pricing model that utilized historical sales and over a dozen unique data points to associate a price per square foot with every piece of vacant land. The model included nine tiers of match types, based on how closely comparable parcels with recent sales matched the property being priced. Because the WPRDC maintains current data on its portal, BuildingBlocks connects directly via and API and automatically updates the pricing model regularly, giving every piece of vacant land a dynamic price estimate, a confidence score based on the match type, relative weighting in comparison to other parcel prices in the neighborhood, and links to information on comparable sales used. With all of this data organized at its fingertips, the URA team no longer has to toggle between data sets and systems to find the answers it needs.