The Challenge

At the height of economic uncertainty, one fast-growing city faced a familiar dilemma: how do you plan responsibly when one of your largest revenue sources feels impossible to predict?

Growth was rapid. New retail centers were opening, incentive agreements were coming online, and development timelines were shifting as quickly as the market itself. Yet traditional forecasting models, with their tidy “high, medium, low” projections, couldn’t capture the complexity or the risk. City leaders needed more than just a range of numbers, they needed a clear understanding of likelihood.

The Approach

Our team approached the problem by leaning into uncertainty rather than fighting it. We built a probability-based model designed to simulate thousands of possible revenue outcomes, each accounting for different combinations of factors: development timelines, consumer spending trends, audit adjustments, and anomalies in the city’s historical sales tax data.

We started by normalizing the data: filtering out one-time payments, missed filings, and other noise that can distort long-term trends. From there, we worked with city staff to identify which developments were likely to open during the forecast window and the revenue ranges each might produce. Those assumptions became the building blocks of a Monte Carlo-style simulation, producing a probability curve instead of a single-point estimate.

The result: a model that didn’t pretend to know the future, but quantified just how uncertain it was.

The Findings

What emerged from the analysis was both intuitive and illuminating. The city’s growth had made revenues more volatile but also more resilient. While certain industries, like construction, showed early signs of softening, others tied to e-commerce, food, and daily essentials continued to expand.

More importantly, the probability model revealed that severe downturn scenarios (the kind that dominate headlines and drive panic budgeting) were statistically unlikely. The city’s underlying revenue base was broad enough to absorb short-term shocks, giving decision-makers permission to move from fear-based budgeting to probability-informed confidence.

The forecast became more than a spreadsheet; it became a policy tool. Staff could now explain not just what might happen, but how likely each scenario was to occur.

The Impact

Armed with this analysis, the city adjusted its financial plan with clarity and composure. Departments could prepare contingency plans tied to specific probability thresholds, knowing what tradeoffs would be required if revenues tracked toward the lower end of the curve. Leadership could communicate with their council and community in plain terms: “Here’s what we expect, and here’s how confident we are.”

This framework didn’t just improve one year’s forecast; it permanently raised the bar for how the city evaluates risk and opportunity.

The Takeaway

Forecasting, at its core, isn’t about prediction, it’s about preparation. By transforming uncertainty into a measurable, communicable insight, this project helped one city replace anxiety with agency.

In the end, the data showed what good management already knows instinctively: that resilience isn’t luck, it’s planning informed by probabilities and a willingness to look uncertainty squarely in the eye.