At the small area scale simple methods for forecasting total populations are often employed because of a lack of data for cohort-component models, concerns about the reliability of these models for forecasting small population totals, and resource constraints. To date, a select number of authors have assessed the forecast accuracy of several individual, averaged, and composite models. This paper extends this stream of work by evaluating a large number of models on new datasets. The aims of the paper are to examine the performance of (a) 10 individual forecasting models (some of which are well known; others less so); (b) averages of every combination of 2, 3, 4, and 5 of the individual models (627 in total); and (c) composite models based on population size and growth rates (200,000 in total). Do averaged and composite models outperform individual models? Using new small area population datasets, forecasts from 2001 to 2031 were produced for three case study countries, Australia, New Zealand, and England & Wales. Both forecast accuracy and credibility (avoidance of negatives; degree of constraining to state populations) were assessed in 2011; for 2031, just credibility was evaluated. Of the individual models, constant share of growth (positive shares only) and constant share of population performed the best. A small proportion of averaged and composite models outperformed the best individual models in forecast accuracy. Several recommendations for the practice of small area population forecasting are made.