Making Housing Tradeoffs Clear in Pittsburgh

More homes can mean demolished apartments and higher nearby rents, even as extra supply eases housing pressure. Treating displacement as a weighted score lets other benefits outweigh harm to existing residents. Colorado’s water-rights system offers a different rule: users diverting water out of priority must replace what existing rights-holders would lose. Apply that rule to Pittsburgh housing scenarios. First, estimate three effects separately: homes demolished, displacement from nearby rent increases, and any relief from added housing supply. Then price a protection package of affordable homes, purchases to preserve existing affordable housing, and tenant assistance. Count each remedy according to how likely it is to arrive, arrive on time and keep residents housed. Planners and communities choose the confidence threshold: how far into the estimated displacement range the package must cover. Keep that policy choice visibly separate from the estimates. Only scenarios with enough expected protection enter the ranking; show shortfalls separately as partial-protection cost comparisons. Readers can see how much stronger protection costs, rather than burying displacement in a score.

A duplex can fill a housing gap in one neighborhood and add little in another. A fixed score for duplexes misses both local needs and the shrinking benefit of adding more of the same. Grid planners solve a similar problem: they credit a solar farm or battery for its contribution to reliable electricity, given the resources already on the grid. This proposal applies that method to housing. For each census tract, it estimates the chance of missing targets across seven dimensions: demand, zoning feasibility, affordability, displacement risk, infrastructure capacity, access to jobs and services, and carbon emissions. Each housing type earns an “adequacy credit” for how much its next addition reduces those chances. As a tract gains more of that type, its credit diminishes. Displacement estimates account for three competing effects: extra supply can ease rents, neighborhood improvements can attract wealthier newcomers, and demolition can remove existing homes. Repeated simulations show how often housing options rank above one another under different conditions. Planners can adjust targets and priorities, while every displayed value stays labeled as observed data, an uncertain estimate, or a policy choice. The result is a locally specific comparison that shows when a different housing mix offers more benefit.

A planning commission can give affordability its highest priority, then repeatedly choose housing options that score poorly on it. A ranking alone leaves that pattern hard to see. The proposed tool borrows the recurring accountability record used in EU fisheries, where watchdogs compare ministers’ catch limits with scientific advice. For each neighborhood, it keeps three inputs separate: observed data, analysts’ assumptions about how housing options perform, and the commission’s declared priorities. When the commission chooses an option, the tool saves those inputs and compares the choice with the highest-scoring eligible option under those same priorities. It records the gap on each measure, such as affordability, displacement risk and carbon emissions, alongside the commission’s explanation. Later changes to priorities cannot quietly rewrite earlier records. Across many decisions, residents and planners can see which stated priorities repeatedly lose out and examine the reasons.