Block rate pricing is often applied to income taxation, telecommunication services, and brand marketing in addition to its best-known application in public utility services. Under block rate pricing, consumers face piecewise-linear budget constraints. A discrete/ continuous choice approach is usually used to account for piecewise-linear budget constraints for demand and price endogeneity. A recent study proposed a methodology to incorporate a separability condition that previous studies ignore, by implementing a Markov chain Monte Carlo simulation based on a hierarchical Bayesian approach. To extend this approach to panel data, our study proposes a Bayesian hierarchical model incorporating the random and fixed individual effects. In both models, the price and income elasticities are estimated to be negative and positive, respectively. Further, the number of members and the number of rooms per household have positive relationship to the residential water demand when we apply the model with random individual effects, while they do not in the model with fixed individual effects.