Applying the LightGBM model and SHAP interpretation to understand the urban land price index and its influencing factors in China

Investigating the key factors affecting land prices is conducive to establishing targeted market-oriented reforms of the land market and promoting its healthy development. Traditional linear analysis cannot comprehensively compare the importance of feature variables and is unable to analyse the nonlinear influence process of feature variables. Therefore, based on the land transaction dataset from 2011 to 2020 in China, we used machine learning and the Shapley Additive exPlanations method to analyse the importance ranking of factors affecting land prices and analyse the nonlinear influence processes. The government fiscal, industrial development, and financial system indices are determined as the most important factors affecting residential, commercial, and industrial land price indices, respectively, and each exhibits different characteristics. The financial system index exerts the strongest impact on land price indices in large cities, whereas transportation and communication indices are the most dominant in small cities. Moreover, industrial development index is the most important influencing factor in industrial cities, whereas regional economic index exerts the greatest impact in small cities. By employing machine learning methods, we determined nonlinear effects that traditional linear models fail to obtain. Based on the results, we propose policy recommendations for market-oriented reforms on the land market, real-estate policies, and a stable monetary system.