Estimation model of vacant houses in population decline areas using machine learning

Abstract

This study addresses the increasing issue of vacant houses in urban areas, particularly in South Korea, by introducing a predictive framework that integrates machine learning and spatial autocorrelation. Focusing on detached housing in Jinju-si, a mid-sized city experiencing population decline, we employ generalized additive models (GAM), random forest (RF), and support vector machines (SVM) to identify high-risk areas for vacancy. Among the models tested, GAM achieved the highest predictive accuracy (R2 = 0.62), outperforming OLS (R2 = 0.51), RF (R2 = 0.48), and SVM (R2 = 0.39). The analysis highlights key influencing factors such as building age, land price, and proximity to pollutants, and shows how incorporating spatially lagged variables improves prediction performance. Findings reveal that vacant houses tend to cluster in older neighborhoods and spread spatially, underscoring the need for early intervention. This study provides data-driven insights for urban regeneration policies targeting housing stability and vacancy mitigation.