The limited availability of data on faecal sludge characteristics remains one of the major challenges faced by developing countries in proper management of faecal sludge. In view of the limited financial resources and expertise in these developing countries, there is a need to come up with less-resource-intensive approaches for faecal sludge characterisation. Despite being used substantially in wastewater, there is limited evidence on the use of predictive modelling as a tool for cost-effective characterisation of faecal sludge. In this study, first order multiple linear regression modelling is investigated as a less-resource-intensive approach for accurate prediction of organics (biochemical oxygen demand and chemical oxygen demand) in pit latrine sludge. The predictor variables explored in the modelling include pH, electrical conductivity, total solids, total volatile solids, fixed solids and moisture content. The modelling uses data collected from 80 latrines in unplanned settlements of four cities in Malawi. The study shows that it is possible to reliably predict chemical oxygen demand and biochemical oxygen demand in pit latrine sludge using electrical conductivity and total solids, which require low levels of resources and expertise to determine.