Household consumption expenditure data is crucial for calculating important welfare measures such as poverty headcount rate. However, collecting such data is difficult and cumbersome. As an alternative, we experiment with three methods – consumption correlates model, poverty probability model, and wealth index principal components analysis (PCA) – to predict consumption expenditure and poverty using non-consumption indicators. The purpose is to use these alternatives for rapid monitoring and appraisal of social welfare as an early warning system. We test each method’s performance and find that the consumption correlates model is the best method to predict poverty quickly and relatively accurately. We find that education level, asset ownership, and consumption pattern are the best predictors of expenditure and poverty.