Anticipating Critical Transitions of Chinese Housing Markets

Abstract

We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which we apply to the housing markets of China's major cities. Building on the Log-Periodic Power Law Singular (LPPLS) model of self-reinforcing feedback loops, we use the quantile regression calibration approach recently introduced by two of us to build confidence intervals and explore possible distinct scenarios. We propose to consolidate the quantile regressions into the arithmetic average of the quantile-based DS LPPLS Confidence indicator, which accounts for the robustness of the calibration with respect to bootstrapped residuals. We make three main contributions to the literature of real estate bubbles. First, we verify the validity of the arithmetic average of the quantile-based DS LPPLS Confidence indicator by studying the critical times of historical housing price bubbles in the U.S., Hong Kong, U.K. and Canada. Second, the LPPLS detection methods are applied to provide early warning signals of the housing markets in China's major cities. Third, we determine the possible turning points of the markets in BeiJing, ShangHai, ShenZhen, GuangZhou, TianJin and ChengDu and forecast the future evolution of China's housing market via our multi-scales and multi-quantiles analyses.

Keywords: real estate bubbles, forecasting, Log-Periodic Power Law Singularity, multi-scale analysis, quantile regression, DS LPPLS Confidence indicator

JEL Classification: C22, C51, C53, E31, E37, G01, G17, R30