Much of the housing sub-market literature has focused on establishing methods that allow the partitioning of data into distinct market segments. This paper seeks to move the focus on to the question of how best to model sub-markets once they have been identified. It focuses on evaluating the effectiveness of multilevel models as a technique for modelling sub-markets. The paper uses data on housing transactions from Perth, Western Australia, to develop and compare three competing sub-market modelling strategies. Model 1 consists of a city-wide 'benchmark'; model 2 provides a series of sub-market-specific hedonic estimates (this is the 'industry standard') and models 3 and 4 provide two variants on the multilevel model (differentiated by variation in the degrees of spatial granularity embedded in the model structure). The results suggest that the more granular multilevel specification enhances empirical performance and reduces the incidence of non-random spatial errors.