Grid Density Algorithm-Based Second-Hand Housing Transaction Activity and Spatio-Temporal Characterization: The Case of Shenyang City, China

Abstract

Second-hand housing transactions constitute a significant segment of the real estate market and are vital for its robust development. The dynamics of these transactions mirror the housing preferences of buyers, and their spatial and temporal analysis elucidates evolving market patterns and buyer behavior. This study introduces an innovative grid density clustering algorithm, dubbed the RScan algorithm, which integrates Bayesian optimization with grid density techniques. This composite methodology is employed to assess clustering outcomes, optimize hyperparameters, and facilitate detailed visualization and analysis of transaction activity across various regions. Focusing on Shenyang, a major urban center in Northeast China, the research spans from 2018 to 2023, exploring the second-hand housing transaction activity and its spatio-temporal attributes. The results reveal temporal fluctuations in transaction intensity across different Shenyang regions, although core areas of high activity remain constant. These regions display a heterogeneous pattern of irregularly stepped and clustered distributions, with a notable absence of uniformly high-activity zones. This study pioneers a novel methodological framework for investigating second-hand housing transactions, offering crucial insights for market development and policy formulation in Shenyang.