Leaning against housing booms fueled by credit

This study aims to empirically identify the state of the US housing market and establish a countercyclical state-dependent macroprudential policy rule. I do so by estimating a Markov switching model of housing prices, in which mortgage debt affects house prices nonlinearly and drives state transition probabilities. Second, I propose a state-contingent policy rule fed with the probability of being in each state, which I apply to setting a housing countercyclical capital buffer, a mortgage interest deduction, and a dividend payout restriction. Finally, I show that such hypothetical tools contain early warning information in a forecasting exercise to predict the charge-off rates of real estate residential loans and a financial stress index. The significance of this study is that it informs policymakers about the state of the housing market mechanically, while also providing a general rule to implement a state-contingent and timely macroprudential policy. We propose a new method of dealing with the end point problem when filtering economic time series. The main idea is to replace filtered quarterly observations at the end of the sample with static forecasts from a MIDAS regression using higher frequency time series. This method is capable to improve stability of output gap estimates or other cyclical series, as we confirm by empirical analysis on selected CEE countries and the United States. We find that stability may still be violated due to structural breaks in business cycles, or by an excessive amount of short-term noise. While MIDAS regressions have the potential to improve output gap estimates compared to the HP filter approach, the country-specific circumstances play a considerable role and need to be considered.