Although consumption expenditure data is crucial for assessing the level of people’s welfare and calculating important welfare measures such as the poverty headcount rate, collecting such data requires significant time and effort. In this study, we experiment with three approaches to predict consumption expenditure and poverty at household and aggregate level as simpler alternatives to using consumption expenditure. The idea is not to use these alternatives as a substitute for consumption expenditure data, rather to use it for the purposes of rapid monitoring and appraisal of welfare. The three approaches are i) consumption correlates model, ii) poverty probability model, and iii) the wealth index Principal Components Analysis (PCA). We test each approach’s performance and found that the consumption correlates model is the best approach to predict poverty quickly and relatively accurately. We found that education level, asset ownership, and consumption pattern are the best predictors of expenditure and poverty.