This research investigates whether consumer sentiment indices and other leading economic indicators can improve sales tax revenue forecasting for Texas local governments. Using time-series analysis of statewide retail sales tax collections, we evaluate the predictive power of the University of Michigan Consumer Sentiment Index, the Conference Board Consumer Confidence Index, and the Dallas Fed Texas Leading Index (TLI). While consumer sentiment surveys showed correlations in simple regression models, their predictive power dissipated when incorporated into ARIMA models that account for autocorrelation. However, the Texas Leading Index at a three-quarter lag emerged as a statistically significant predictor of statewide retail sales tax performance. Cities can integrate TLI into their forecasting models as an exogenous variable, though local calibration is required to account for unique retail compositions and growth patterns.