Volatility on the housing and financial markets in China is more similar to the dynamics we find in Japan. Large volatility, as expected to be present, is visible on the housing market in China. Such price dynamics on the housing market is expected as a consequence of the changes in the Hukou system (policy regulated migration from the countryside to the cities) having a direct influence on the demand for housing in China. Housing demand dynamics is reflected in the credit demand (mortgage loans) that is observed in figure 4 (graph on the right). Cyclical fluctuations both for housing prices and credit to the private non-financial sector is statistically significant for the cycle period between 8 to 15 years. The cyclical fluctuation on the credit market (% share in the GDP) after testing for deterministic cycles also exhibits an 8 to 15 years pattern with smaller volatility in comparison to the volatility on the housing market and credit to a private non-financial sector (volume). 
Deterministic cycle test results validate the results of the previous studies on financial cycles finding a cyclical pattern in the financial time series ranging from 8 to 15 years on average for the UK, USA, Japan and China. After the financial cycles identification by the means of spectral analysis, we proceed by testing if Big data can help to improve the financial cycles forecasting.

Using Financial Big Data for Improving Financial Cycles Forecasting Accuracy

To test the hypothesis that financial big data improve financial cycles forecasting we proceed in several steps. First by the means of the singular spectrum analysis (SSA) of the form \citep{Vautard1992}
\(c_{i j}=\frac{1}{N-|i-j|} \sum_{t=1}^{N-|i-j|} X(t) X(t+|i-j|)\)(5)
where
cij = lag covariance matrix
N = number of data points in time series
i,j = time indices
t = continuous time t ∈ R
x(t) = observed time series.
using data on housing prices and credits we isolate financial cycles for the UK, USA, Japan and China. Following on the isolate (SSA) components, we apply a linear recurrent formula for the purpose of (SSA) forecasting. As a final outcome, we get an R-forecasting (SSA forecasting method) for residential property prices, credit to the private non-financial sector, credit share in the GDP over the eight quarters (2017Q2 2019Q1).