In much of the developing world, the demand for high frequency quality household data for poverty monitoring and program design far outstrips the capacity of the statistics bureau to provide such data. In these environments, all available data sources must be leveraged. Most surveys, however, do not collect the detailed consumption data necessary to construct aggregates and poverty lines to measure poverty directly. This paper benefits from a shared listing exercise for two large-scale national household surveys conducted in Liberia in 2007 to explore alternative methodologies to estimate poverty indirectly. The first is an asset-based model that is commonly used in Demographic and Health Surveys. The second is a survey-to-survey imputation that makes use of small area estimation techniques. In addition to a standard base model, separate models are estimated for urban and rural areas and an expanded model that includes climatic variables. Special attention is paid to the inclusion of cell phones, with implications for other assets whose cost and availability may be changing rapidly. The results demonstrate substantial limitations with asset-based indexes, but also leave questions as to the accuracy and stability of imputation models.