Demand response (DR) programs motivate home users through dynamic pricing to shift electricity consumption from peak demand periods. In this paper, we introduce a day ahead electricity market where the operator sets the prices and multiple home users respond by scheduling their demands. The objective of the operator is to minimize electricity generation cost, whereas each user maximizes her utility function that captures the trade-off between timely execution of demands and financial savings. Since the operator is unaware of the users' utility functions, coordination of demands is a challenging task. Our DR model captures the diverse energy characteristics of different home appliances and shows that, in contrast to existing simplified models, in reality optimal demand scheduling is NP-hard. We propose a waterfilling-inspired price setting strategy, which requires only knowledge of the aggregate demand. Based on daily appliance demand traces, we show that our scheme reduces electricity generation cost significantly and derive useful insights regarding the electricity market operation. © 2012 IEEE.