Secondary data and survey information are used to develop a large data set for analyzing water demand in 221 communities. The resulting monthly data are employed to examine seasonal variability in consumer price sensitivity. Several functional forms are contrasted for their abilities to identify monthly price elasticities. Results demonstrate the statistical contribution of a new climate variable for fitting monthly data, generally indicate that summer price elasticities exceed winter price elasticities by 30%, and appear to reject the use of the translog functional form as well traditional linear and Cobb-Douglas forms for statistical analyses of pooled monthly data. The generalized Cobb-Douglas and augmented Fourier forms are more viable alternatives for pooled monthly data.