The authors employ a structural time series modelling approach to investigate the presence and significance of unobserved components within the Australian housing finance data. More precisely, the objectives of the paper are to: (i) establish whether or not the cyclical and seasonal variations exist; and (ii) whether seasonality, if present, is stochastic or deterministic. In doing so, the authors deviate from conventional regression analysis by employing a state space smoothing algorithm to extract these unobserved components allowing them to test for the presence of stochastic versus deterministic seasonal and cyclical components. The results of the empirical analysis confirm presence of the end of financial year seasonal effect. The value of transactions and hence demand for housing finance is greatest during the end of financial year season. The paper also points to some suggestions for future research.