Area (iii) predictive analytics · Area (i) revenue planning
Sales tax funds the great majority of VTA's budget, and Santa Clara County's tax base moves with the technology economy. This model explains 29 years of the county's taxable sales with local employment and income, then projects VTA's revenue under three forward scenarios.
Santa Clara County annual taxable sales, 1997–2025 (actual), with model projections to 2031 under three scenarios. Shaded band is the 90% prediction interval.
Source: CDTFA, Taxable Sales in California (Santa Clara County). Model by House Strategies Group.
Why the model works
County taxable sales move with how many people are employed in Santa Clara County and how much they earn. The information sector, the heart of Silicon Valley, drives both. That is why VTA's revenue is more exposed to the technology cycle than a typical transit agency's.
| Employment elasticity | |
| Income elasticity | |
| Correlation, tech employment & taxable sales | |
| Out-of-sample error (2020–2025) |
An employment elasticity near 0.7 means a 1% change in county employment is associated with about a 0.7% change in taxable sales. The model is trained through 2019 and tested on 2020–2025, including the pandemic shock, to show honest out-of-sample accuracy.
San Jose metro information-sector employment against county taxable sales, indexed.
What it means for VTA's budget
Applying VTA's effective sales-tax rate to the forecast translates each scenario into dollars VTA can plan against. The gap between the optimistic and pessimistic paths is the planning risk a stress-tested budget has to absorb.
Modeled VTA sales-tax revenue (all measures), applying the effective rate to forecast taxable sales.
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Replace a single point forecast with this three-scenario band in the long-range plan, so capital and operating commitments are tested against a downside, not just an expected case.
Run this forecast as an automated pipeline off CDTFA and BLS releases, refreshing VTA's revenue outlook every quarter without manual rework, the predictive-analytics capability the scope calls for.
Calibrate the stabilization reserve to the modeled downside gap rather than a fixed percentage.
See the 3D network map, the equity analysis, the SCIP funding engine, and the long-range operating model.
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