Over the recent months, several initiatives have taken place to develop macro-prudential regulation in order to prevent systemic risk and the build-up of financial imbalances. These discussions took place in the aftermath of the recent financial crisis that is the consequence of the bust of an house price bubble that took place in the US and spread out to most developed countries. Crucial to the success of such policy is the ability of the macro-prudential authority to identify in due time such imbalances, generally featured by asset price boom-bust cycles. In this paper, we investigate the possibility of detecting house price booms according to alternative identification strategies and assess their robustness. In addition, we try to disentangle costless or low-cost from costly house price booms. Resorting both to a non-parametric approach and a discrete-choice (logit) model, we analyze the ability of a set of indicators to robustly explain costly house price booms. According to our results, real long term interest rates, total investment, real credit and real stock prices tend to increase the probability of a costly house price boom.