When COVID-19 hit in early 2020, city halls across the country braced for impact. With lockdowns spreading faster than the virus itself, local leaders faced an agonizing question: how deep would the fiscal damage go?

The prevailing wisdom at the time was grim. Cities feared catastrophic drops in sales tax revenue, potential layoffs, and budget cuts that would ripple through essential services. But where many saw uncertainty, we saw a solvable problem—one grounded not in panic, but in data.

The challenge

At the beginning of the pandemic, economic forecasting models were ill-equipped for what lay ahead. The traditional “three-scenario” approach - optimistic, realistic, and pessimistic - couldn’t capture the unprecedented nature of a global shutdown. Cities needed more than scenarios. They needed probabilities.

Our goal was simple: help local governments understand how bad things might get and, just as importantly, how likely those worst-case fears really were.

The approach

We started by taking a microscope to each city’s sales tax base. Not just aggregate numbers, but the composition of their local economies down to the business category level. We asked hard questions: Which industries would take the biggest hit from lockdowns? Which might actually benefit as consumer habits shifted?

For example, while restaurants and entertainment venues were clearly at risk, sectors like grocery stores, home improvement, and online retail were likely to grow as people adjusted their spending. We also examined the emerging landscape of remote sales (an entirely new revenue source after the Supreme Court’s Wayfair decision) offering cities an unexpected buffer against losses.

Each business classification was assigned a probability distribution based on these behavioral shifts. We ran thousands of simulations to model potential revenue outcomes under varying lockdown durations and consumer substitution effects. The result was a set of probabilities tied to real-world likelihoods.

The Findings

The analysis told a story that surprised nearly everyone. While cities were right to be cautious, the doomsday scenarios (those 30–50% revenue collapses making headlines) were statistically improbable. Our models showed that, in most cases, losses were likely to be moderate and short-lived, cushioned by both essential-sector resilience and the rise of e-commerce.

These findings didn’t just offer comfort, they provided confidence. With a data-backed sense of what was truly at stake, local leaders could avoid the reactionary budget cuts that often create long-term harm. Instead, they could focus on targeted contingency planning and smart use of reserves.

The impact

Across dozens of client communities, our analytics helped reframe the conversation from fear to focus. Cities that initially considered furloughs and service reductions were able to maintain staff levels and continue essential programs. Just as important, they entered the next fiscal year with a better understanding of their economic base and a renewed trust in their own resilience.

The takeaway

In a crisis, clarity is the most valuable commodity. By combining advanced analytics with deep municipal experience, we helped city leaders replace uncertainty with insight. Even in unprecedented times, data can light the path toward steadier ground.