Abstract: The real estate industry is an important part of China's economic, and monitoring housing price bubbles is a key measure for preventing real estate market risks and thus preventing and resolving financial risks. This paper adopts the GSADF test and the BSADF test to identify real estate price bubbles in 49 large and medium-sized cities in China, and analyzes the spatial autocorrelation and dependent structure of housing price bubbles among cities based on Moran's I index and the R-Vine Copula model. It is found that there are multiple periods of bubbles in China's real estate market, with high spatial autocorrelation and clustering characteristics at both the geographical and urban levels; the peak and duration of housing price bubbles are higher and longer in first-tier and new first-tier cities than in some third-tier cities; there is a significant dependency between cities' housing price bubbles, with a tendency for them to rise and fall together. The results of this study help local governments to timely monitor housing price bubbles and their spillover effects, which are of great significance for preventing risks in the real estate market.