Exploring the Determinants of Rural Housing Vacancy in Mountainous Regions: Evidence from Jinshan Town, Fujian Province, China

The rational management of vacant rural housing is critical for optimization of Territorial Spatial Patterns. Although the issue of rural housing vacancy (RHV) has attracted widespread attention, systematic investigations in mountainous regions remain limited. This study is based on census data covering 3039 rural houses across six villages in Jinshan Town, Nanjing County, Zhangzhou City, Fujian Province, China. Using binary logistic regression and the XGBoost machine learning model, it systematically identifies the dominant determinants of rural housing vacancy in mountainous areas and evaluates their relative importance. The results show that the relative importance of the influencing factors is ranked as follows: locational conditions, physical housing characteristics, and topographic features. Specifically, among locational factors, the distances to the national road, county government, township government, and village committee centers are the most critical determinants of housing vacancy. In terms of physical attributes, the number of stories, the structural type, the floor area per story, and the orientation of the house are key variables. Regarding topographic factors, slope and aspect have limited overall influence. The two models yielded consistent directions and magnitudes of the key predictors, confirming the robustness and reliability of the results. The findings of this study help address the existing gaps in research regions, influencing factors, and methodological approaches, thereby contributing to the promotion of sustainable rural development.