## 1 Introduction
While this approach enhances model performance, it demands high-quality data and robust model generalization capabilities, potentially underperforming in regions with limited or heterogeneous data [8]. [PERSON] et al. employed the least squares method alongside real estate transaction data and the stand-alone board rate to dynamically adjust the batch appraisal benchmark price for taxation purposes, using real estate agency appraisals as a benchmark. Although this method offers innovative approaches for tax assessment, the speed and precision of its adjustments require further validation in the context of the highly volatile real estate market [9]. [PERSON] and [PERSON] used a twin support vector regression method, based on data mining and big data analytics, to predict second-hand house prices, providing insights for government macro-control and market demand forecasting. However, effectively applying these forecasts to actual regulatory measures remains a challenge [10]. In examining the transaction behavior of second-hand houses and their influencing factors, [PERSON] and [PERSON] explored the bargaining dynamics between buyers and sellers in the second-hand housing market, developing both indefinite and finite bargaining models and deriving equilibrium solutions through game theory analysis. However, actual market bargaining often includes irrational behaviors, rendering the model's application scenario somewhat idealized [11]. [PERSON] and [PERSON] investigated bargaining issues using the final offer arbitration model to determine a Nash equilibrium solution, but the generalizability and practical applicability of this model require further validation [12]. [PERSON] et al. discovered that the entry of Chain Home Realty, a prominent Chinese platform, leads to reduced prices and enhanced sales of second-hand houses. Despite attempts to control for endogeneity bias using various methods, the broad applicability and depth of the findings are somewhat constrained by the limitations of relying on secondary data and the absence of detailed operational insights from the platform [13]. [PERSON] and [PERSON] initially explored the Venturi effect of the Special Stamp Duty (SSD) on Hong Kong's real estate market using spatial scan statistics and multivariate logistic regression analyses. Although this approach introduced a novel spatial perspective, the generalizability of the results and the long-term impacts require further examination [14]. Regarding the investigation into the determinants of second home prices, [PERSON] and [PERSON] utilized interpretative structural modeling to elucidate the key factors influencing these prices. However, the explanatory power of the model and the practical relevance of their findings might be constrained by the limited breadth and representativeness of the data [15]. [PERSON] and [PERSON] employed a multi-scale geographically weighted regression (MGWR) model to examine the spatial distribution characteristics and influential factors of second-hand house transaction prices in Binhu New Area. They identified significant spatial positive correlations and heterogeneity, with the proximity to schools being a pivotal factor. Nonetheless, the applicability of their conclusions to broader urban contexts may be limited due to data and study scope constraints [16]. [PERSON] et al. analyzed second-hand housing data using TF-IDF and K-means methods, pinpointing housing type as a primary determinant of price. However, they did not extensively explore other potential factors such as socio-economic characteristics and policy influences, and the necessity for manual setting of cluster numbers in K-means clustering introduces subjective bias, thus limiting the thoroughness of the analysis [17]. [PERSON] et al. explored the influence of residential characteristics and location on the asking prices of second homes in Alicante using a characteristic price model.