## 1 Introduction
Their findings indicate substantial variations in residential asking prices based on these factors, yet other socio-economic influences on prices were not considered [18]. [PERSON] et al. demonstrated the positive influence of metro station proximity on housing prices by examining land use changes around metro stations and the prices of second-hand houses in Wuhan City. However, the absence of authoritative sample data on house prices constrains a comprehensive analysis of the relationship between subway stations and house prices [19]. [PERSON] et al. investigated the spatial heterogeneity of shopping center accessibility on house prices using a geographically weighted regression model based on second-hand housing transaction data in Hangzhou. Nevertheless, due to data and temporal limitations, the broad applicability of their findings warrants further investigation [20]. [PERSON] analyzed the failures and policy impacts on China's real estate market through a dynamic equilibrium model, concluding that the 2010 policy enhanced market efficiency and mitigated the rapid increase in house prices. However, the analysis did not adequately consider the long-term economic changes and regional disparities affecting policy efficacy [21]. [PERSON] et al. assessed the appreciation, dynamic, and heterogeneity effects of elevator installations on house prices using transaction data from second-hand houses with elevators in old residential areas of Beijing, employing a multi-period double-difference model. Nevertheless, the homogeneity of the data region and the stringent assumptions of the model restrict the generalizability of the results [22]. In exploring the trends in the spatial and temporal evolution of second-hand house prices, [PERSON] et al. used spatial autocorrelation and spatial Markov methods to empirically analyze second-hand houses in Hefei City. They found that house prices show divergent trends in the north-south direction with notable spatial autocorrelation. However, the small sample size and brief time span of the study are inadequate to fully capture the complexity and dynamics of the overall market [23]. The existing body of literature provides a robust theoretical and empirical framework for investigating government policies, assessment methods, transaction behaviors, and influencing factors within the real estate sector. Despite these advances, further enhancements are needed in the innovation of research methodologies, the expansiveness of model applications, and the practicality of results, particularly in the context of second-hand housing prices. Furthermore, a more detailed analysis of the second-hand housing market is warranted, taking into account specific regional characteristics as well as spatial and temporal dynamics. These studies typically consider variations in economic development, market supply and demand, geographical location, distribution of educational resources, and living conveniences [16; 17; 18]. Consequently, the frequency of second-hand housing transactions exhibits significant regional disparities. To date, the concept of \"activity\" in this context, which could serve as a metric to quantify the frequency of transaction-related events and thus reflect the intensity of market activity, has not been thoroughly investigated. This oversight represents a notable gap in the research. In this study, we define transaction activity of second-hand houses as the rate of change in the number of transactions per region, where transactions are recorded when houses are registered with the housing authority for a title change and successfully transferred within a specified timeframe. Research on activity has primarily focused on exploring influencing factors and developing models in various domains, employing big data analytics and machine learning to uncover complex patterns and challenges in the real world. The relevant literature includes studies on urban hotspot area mining [24; 25; 26; 27; 28], public space utilization [29], and location recommendation frameworks [30].