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| Real Estate Price Bubbles and the Spatial Contagion Effect:Evidence from 100 Cities in China |
| LI Lunyi, ZHANG Xiang
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| School of Finance/Institute of Big Data, Southwestern University of Finance and Economics |
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Abstract This study uses the log period power law (LPPL) model to measure price bubbles in real estate markets in China, and the spatial econometric model to study the price bubble contagion effect. This study asks the following questions. Compared with other financial assets, how can price bubbles be measured in the real estate market？ Are price bubbles in the real estate market spatially contagious？ What is the mechanism behind the contagion？ Do macro-control policies effectively prevent real estate price bubbles from expanding？ This study not only quantitatively analyzes the real estate price bubbles in various Chinese cities, but also discovers the relationship between real estate bubbles in different regions. The findings will help local governments to regulate real estate based on local conditions and will help them to develop appropriate policies. According to the LPPL model used in this study, the financial market generated by price bubbles and their eventual collapse is in many ways similar to the seismic system; that is, the price of financial assets changes in a cyclical pattern, and the price continues to rise until a critical state leads to a reversal.
The LPPL model is mainly used for seismic research. Johansen, Ledoit, and Sornette (2000) and Zhou and Sornette (2005) are the first to apply LPPL to the analysis of asset price bubble behavior in financial markets. Many studies use the LPPL to predict historical financial crises and bubble and anti-bubble phenomena in Western financial markets. They all find that the LPPL model has the best simulation and prediction results for studies of these phenomena.
The LPPL model used in this study is a commonly used and mature model for studying the bubble theory. It uses observed price time series data to detect the formation of price bubbles and their expected collapse point, that is, the end point of the bubble. It focuses on the simulation of the price formation process itself and the prediction of the price reversal point. Unlike the stock market, real estate price bubbles are characterized by medium-to-long-term continuous rises in price formation, and they occur slowly. The LPPL model can better simulate the process of real estate price growth and reversal. Unlike previous studies, this study considers the characteristics of real estate price bubbles in both the upward and downward stages of the bubble. The biggest difference between the two stages lies in the price dynamics before and after the price collapse point. In a positive bubble, the price appears to grow faster than exponentially with accompanying oscillations, and a price collapse point appears at a future point. The reverse bubble stage begins at the price collapse point, after which prices trend downwards.
The second innovation of this study is to use the combined cross-sectional physical and economic distances between cities to explore whether real estate price bubbles measured by LPPL have a spatial contagion effect.
Finally, this study uses the recent real estate control policies issued by some cities to conduct an event study of the impact of these policies on real estate price bubbles. Therefore, this study examines the behaviors of real estate price bubbles before and after the implementation of real estate control policies in some first-tier cities, and whether the contagiousness of the bubble space is weakened or strengthened by these control policies.
This study uses microstructure real estate market data from 100 cities from the June 2010 to November 2017 period. The LPPL model identifies real estate price bubbles in 100 cities in China, and the bubbles have two main states: a positive bubble (housing prices continue to rise) and reverse bubble (housing prices decline overall, but there is a reversal point). In each city (area), real estate prices have a strong spatial contagion. The spatial infectivity of areas with a positive bubble is more obvious than that of areas with a reverse bubble. When economic measures are used to define spaces instead of physical measurements, the spatial infectivity between cities is stronger. Unlike previous studies, this study finds that increases in the new house price index in reverse bubble areas, especially the second-hand house price index, has a strong positive impact on the real estate price index in surrounding cities. Finally, this study finds that the real estate regulation policies in cities to some extent restrain the traditional impact of housing prices (such as credit and new and second-hand housing prices). Purchase restrictions, price restrictions, loan restrictions, and sales restriction all affect the real estate market through different channels. Loan restrictions create an inflection point in the market, whereas sales restrictions can effectively inhibit market investment. However, the link between real estate prices in various cities should be considered by regulators seeking to control real estate price bubbles.
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Received: 03 December 2018
Published: 13 January 2020
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