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AbstractThe main aim of this paper is to apply a method based on fundamentals ─ which has already been applied in the stock market analysis ─ to detect boom/bust in the housing market, with a focus on the euro area. In this context, an underlying model is developed and tested. It turns out that the user cost rate, a demographic variable, the unemployment rate, disposable income (or disposable income per capita), the debt-to-income ratio and, finally, the housing stock are fundamental variables which significantly explain house price developments. Booms and busts are then selected as episodes when the house price index deviates excessively from the levels which would be implied by these economic fundamentals. In addition, a cross-check of the boom/bust episodes based on this method and other statistical and fundamental ones is carried out in order to substantiate the results obtained. Finally, money and credit aggregates are included in the specifications and are found to be useful in explaining boom/busts cycles in house prices.
Keywords: house prices, booms, busts, quantile regressions, monetary and credit aggregates
JEL-classification: E37, E44, E511
Non-technical summaryDuring the past decades, asset markets have played an increasingly important role in many economies, and large swings in asset prices have become a relevant issue for policy-makers, thus bringing new attention to the linkages between monetary policy and asset markets. Monetary policy has been cited as both a possible cause of asset price booms and a tool for defusing those booms before they can cause macroeconomic instability. Consequently, economists and policymakers have focused on how monetary policy might cause an asset price boom or turn a boom caused by real phenomena, such as an increase in aggregate productivity growth, into a "bubble", which may burst unexpectedly, thus rendering damage to the economy.The novelty of this study lies in the application of the methodology used in Machado and Sousa (2006) to house prices developments, by means of selecting underlying fundamental variables and applying the quantile regression approach to detect booms and busts. Our main results are as follows.First, house price developments are significantly explained by the user cost rate (with a negative coefficient), a demographic variable (working population or labour force) which affects the house prices positively, the unemployment rate (with a negative...