From Spreadsheets to a Living Rental Registry.

Use Case: Foreclosures and Vacancies / Rent Licensing Products: BuildingBlocks and Slate City Size: ~75,000 residents, Northeastern Pennsylvania

The Challenge

Scranton, Pennsylvania has a housing stock with deep complexity: thousands of suspected rentals, an aging building inventory, an active foreclosure problem, and a code enforcement operation trying to keep pace with it all. When Tolemi first engaged with the city in 2022, staff were relying on OpenGov for their rental registry, assessor data that lagged months behind reality, and a collection of spreadsheets to track suspected rentals, unit discrepancies, and delinquent properties. Inspectors were scheduling manually. Enforcement correspondence had to be drafted by hand.

The city estimated somewhere between 13,000 and 16,000 rental units citywide, but that range itself illustrated the problem: nobody knew the actual number, and the data systems in place weren't built to answer it. Robin, the city's rental registry administrator, was personally flagging data issues, emailing the county when unit counts were wrong, and trying to maintain a no-fly list of chronic non-compliers alongside everything else.

The goal wasn't just better software. It was getting Scranton to a place where staff could see what they were actually dealing with and have real tools to act on it.

What Scranton Built

Tolemi's work in Scranton unfolded in two major phases, starting with BuildingBlocks as the analytical foundation and eventually expanding to a full Slate migration for both the rental registry and the foreclosure and vacancy registration program.

BuildingBlocks gave Scranton its first integrated view of property data across the city. Refuse data, assessor records, code enforcement violations, rental registration history from OpenGov, and Tolemi's suspected rental logic were all brought together in a single platform. For the first time, staff could filter suspected rentals by neighborhood, cross-reference garbage bill counts with unit counts to identify discrepancies, visualize which landlords controlled the most properties, and understand where naturally occurring affordable housing was concentrated in the city. The mayor used it regularly. Economic development staff used it for site selection and business development. Code enforcement used it to prioritize inspections.

The suspected rental logic itself became a significant piece of work. Tolemi built and refined a model pulling from multiple signals: active rental listings, Slate and OpenGov registrations, multifamily property indicators, refuse unit counts, and owner occupancy status, ultimately identifying approximately 9,835 suspected rental properties across the city. That model gave Robin and her team a defensible, data-driven universe to work from rather than a rough estimate.

When Scranton was ready to move its foreclosure and vacancy registration program off its legacy system in late 2024, Tolemi managed the full data migration into Slate. Over 3,500 historical registrations across 1,300 distinct parcels were brought in, preserving period data, payment status, and registration history. The rental registry workflow was built out with an inspection scheduling system, automated reinspection reminders at 30 days, letter generation tied directly to inspection checklist outcomes, and escalating citation tracking at $500, $750, and $1,000 per violation. Staff who had been generating enforcement letters manually now had them auto-populate from the inspection record.

The exoneration workflow was another notable addition. Scranton had an existing process allowing vacant property owners to claim a tax exoneration, but it required residents to physically bring in utility bills and fill out paper forms. Tolemi built a Slate workflow that moved this process online, with built-in task assignment, document uploads, and internal review steps, while automatically flagging any exoneration applicant who hadn't yet registered their property in the rental registry.

What It's Achieved

By early 2025, Robin reported that inspections were up significantly and the city was in a position it hadn't expected to reach this quickly. In a check-in that spring, she described it as "incredible, not where they thought they would be this time last year or even two years ago."

The practical picture behind that: over 3,500 foreclosure and vacancy registrations migrated and live in Slate, a suspected rental universe refined to nearly 10,000 properties, inspection workflows running across multiple inspectors with scheduling, reinspection tracking, and automated correspondence, and staff who could pull targeted compliance lists by neighborhood and send enforcement letters the same day rather than days later.

The city's next phase was drilling into downtown rental properties, many built as commercial but converted to residential use over the years, which the new data infrastructure made it possible to identify systematically for the first time.

Why Scranton's Approach Stands Out

Scranton is a city that came in with a lot of complexity and committed to building a coherent data operation from the ground up. The work here wasn't a clean single-program implementation. It was integrating legacy system history, layering in analytical tools, and connecting all of it to enforcement workflows that staff could actually use in the field.

When Robin says inspectors are doing more inspections and the city is ahead of where it expected to be, that's the story. Better tools didn't just make existing work easier. They made work possible that wasn't happening before.

[PLACEHOLDER: Client quote, reach out to Robin or Eileen for a testimonial]