It has long been argued that the housing market is spatially compartmentalized within a metropolitan area. The argument has important implications in explaining how the housing market works – should the status quo be seen as an equilibrium state, or if no equilibrium is reached how do loosely interlaced submarkets function both independently and interdependently? The authors note that this body of literature has leaned toward testing the distinctiveness of housing submarkets given a priori housing submarkets. However, there seems to be lack of interest in developing methods for empirically deriving housing submarkets. Fuzzy clustering is well suited to this problem given that the boundary of housing submarkets is not often sharply delineated. The study applies a fuzzy c-means (FCM) algorithm to identify housing submarkets in the Buffalo-Niagara Falls region. The study is distinct from other FCM applications in three respects. First, we reflect on issues tied to choosing parameters of fuzzy clustering. Second, we introduce overlap measures to characterize the relationship between clusters produced. Third, we evaluate the performance of fuzzy clustering in terms of hedonic prediction accuracy. Results show that stratified hedonic models predict house price better than a market-wide hedonic model. Fuzzy clustering solutions also yield better prediction compared to hard clustering.