We present a demonstration of a Bayesian spatial probit model for a dichotomous choice contingent valuation method willingness-to-pay (WTP) questions. If voting behavior is spatially correlated, spatial interdependence exists within the data, and standard probit models will result in biased and inconsistent estimated nonbid coefficients. Adjusting sample WTP to population WTP requires unbiased estimates of the nonbid coefficients, and we find a $17 difference in population WTP per household in a standard vs. spatial model. We conclude that failure to correctly model spatial dependence can lead to differences in WTP estimates with potentially important policy ramifications.