Identification and Critical Time Forecasting of Real Estate Bubbles in the U.S.A and Switzerland

Abstract

We present a hybrid model for diagnosis and critical time forecasting of real estate bubbles. The model combines two elements: 1) the Log Periodic Power Law (LPPL) model to describe endogenous price dynamics originated from positive feedback loops between economic agents; and 2) a diffusion index method that creates a parsimonious representation of multiple macroeconomic variables. We examine the behavior of our model on the housing price indices of 380 US metropolitan areas, using 15, 35, and 90 national-level macroeconomic time series and a dynamic forecasting methodology. Empirical results suggests that the model is able to forecast the end of the bubbles and to identify variables highly relevant during the bubble regime. In addition, the same methodology is applied to the national housing price index of Switzerland, diagnosing a bubble in which global imbalances and Switzerland's status as a safe haven seem to be playing a dominant role.

Keywords: real-estate bubbles, USA and Switzerland, diffusion index, forecasting, log-periodic power law, criticality, positive feedback, sparse partial least squares

JEL Classification: C12, C22, C52, G01, G17