Construct comprehensie indicators through a signal extraction approach for predicting housing price crises

Published: 10 September 2021| Version 1 | DOI: 10.17632/gb6ksc4k8g.1
yan xu


Because of the availability of data, we select Beijing, Shanghai, Tianjin, and Chongqing as the research objects. The time span is from 2005Q3 to 2018Q4. Data are obtained from the China Economic Network Statistics Database, China Economic Network Industry Database, Wind Information, and the National Bureau of Statistics. On the basis of existing studies on the real estate market in China, we select 13 economic variables as individual indicators. These include the M2 growth rate, the exchange rate, the SSE Real Estate, the inflation rate, the medium-term and long-term loan interest rates, the ratio of the completed residential investment in real estate enterprises to the completed residential investment in fixed assets, the ratio of residential property sales to the GDP, the ratio of residential area for sales to the completed residential area, the ratio of residential area under construction to the completed residential area, the residential CPI, the funds in place for real estate enterprises, the land transaction price for real estate enterprises, and the GDP growth rate. We integrate the early warning information from those individual indicators into four comprehensive indicators. The reliability of the early warning system for crises in the urban housing market is verified through the in-sample early warning results. In addition, current housing price movements in the four urban housing markets are analyzed through the out-of-sample results and the crisis prediction probability curves. Because some of the selected individual indicators have both monthly and quarterly data, some individual indicators only have monthly data, and others only have quarterly data, we use quarterly data only in order to ensure the accuracy and reliability of the data. For individual indicators with only monthly data, we adopt the price index conversion method with a fixed base. We first convert monthly chain data to monthly fixed data (with the base period being December 2005), then convert the monthly fixed data to quarterly fixed data (with the base period being 2005Q4), and finally we calculate quarterly year-on-year data. Because the absolute value of the indicator variable is relatively large, in order to facilitate comparison, we convert all indicator variables into relative values of year-on-year or comparison with other variables. We take the first three policy cycle time periods of China’s real estate market (from 2005Q3 to 2014Q2) as the in-sample time for building an early warning system for urban housing price crises in China. We use the fourth policy cycle time period (from 2014Q3 to 2018Q4) to evaluate the out-of-sample performance of the early warning system for urban housing price crises.