This paper develops a new solution method for a broad class of discrete-time dynamic portfolio choice problems. The method efficiently approximates conditional expectations of the value function by using (i) a decomposition of the state variables into a component observable by the investor and a stochastic deviation; and (ii) a Taylor expansion of the value function. The outcome of this State Variable Decomposition (SVD) is an approximate problem in which conditional expectations can be computed efficiently without sacrificing precision. We illustrate the accuracy of the SVD method in handling several realistic features of portfolio choice problems such as intermediate consumption, multiple risky assets, multiple state variables, portfolio constraints, non-time-separable preferences, and nonredun- dant endogenous state variables. We finally use the SVD method to solve a realistic large-scale life-cycle portfolio choice and consumption problem with predictable expected returns and recursive preferences.