The efficient functioning of urban land markets is crucial for sustainable urbanization, facilitating the agglomeration of labor and capital. China's rapid economic development and urbanization have created a dynamic land market with significant impacts on housing affordability, industrial investment, and regional development. The total land conveyance fees increased dramatically from 0.5 trillion yuan in 2005 to 8.8 trillion yuan in 2020, underscoring the importance of understanding the underlying mechanisms driving land price changes. Early empirical studies on land prices largely focused on Western countries with established market institutions. Recent research on China has primarily examined urban economics and governance, neglecting geographical factors such as natural restrictions and the hierarchical administrative system. This study uses a land transaction dataset to analyze the determinants of urban land prices in China, considering the impacts of natural restrictions, government interventions, and heterogeneity in land-use types across different time periods (2008-2015 and 2015-2020). The research aims to identify price determinants from both supply and demand perspectives, providing insights into the spatial-temporal dynamics of the Chinese land market and implications for land-use planners and developers.

Existing literature highlights the interplay between land demand, regional development, and urban land price inequality. The supply-demand relationship is a primary driver of land prices, with economic development influencing both. Price increases often originate in major metropolitan areas and spread outwards. In Western contexts, research emphasizes the role of amenities, such as public transportation access, in shaping housing prices. While some similar research exists in China, it is often limited to the intra-urban level. Socioeconomic factors, including population, income, and employment, are key determinants of land prices, with different land-use types (residential, commercial, and industrial) exhibiting varying sensitivities to these factors. However, critiques highlight the limitations of assuming flexible land supply due to variations in land quality, market participant objectives, and externalities. Studies increasingly consider both supply and demand sides, emphasizing geographical scale and spatial-temporal dynamics. The literature also explores the role of natural restrictions (water bodies, terrain) and regulatory constraints (development permits, zoning) in influencing land supply elasticity, with inconsistent findings suggesting the need for further investigation. In China, the incomplete land market and ambiguous property rights allow significant government intervention, affecting land supply strategies and pricing. Local governments pursue strategies to maximize revenue for infrastructure projects while incentivizing industrial investment through discriminatory pricing (lower prices for industrial land). This can lead to resource misallocation and regional inequality. The recent 'ecological redline' policy introduces another layer of complexity, potentially limiting land supply and influencing land prices. This study addresses these gaps by integrating natural restrictions, strategic land supply, and land-use type heterogeneity into its analysis of urban land price dynamics in China.

This study uses a city-level and parcel-level dataset obtained from the China Land and Resources Statistical Yearbook (2009-2018), China's land market website, and China City Statistical Yearbooks (2009-2021). The parcel-level data encompasses over 1.6 million land transaction records. The analysis focuses on residential, industrial, and commercial land transactions in 2008, 2015, and 2020, chosen to capture periods before, during, and after significant market shifts. Spatial inequality in land supply is examined using the Gini coefficient, Theil's T statistic, and the coefficient of variation. Hot-spot analysis (Getis-Ord Gi*) is employed to investigate spatial patterns in land prices. Ordinary least squares (OLS) regression and spatial regime models are used to identify price determinants, accounting for regional heterogeneity (eastern, central, and western China). A regression tree model is also used to determine the relative importance of variables. The OLS regression model is formulated as: ln *P_i* = *F*(S, E, G, L) = β₀ + Σβ_sS_i + Σβ_EE_i + Σβ_GG_i + Σβ_LL_i + ε_i, where *P_i* represents the average land price, S represents land supply, E represents economic and social development, G represents government intervention, and L represents land parcel characteristics. The spatial regime model allows coefficients to vary across regions. The dependent variable is the average land price for each land-use type. Independent variables include measures of natural restrictions (cultivated land per capita, percentage of water bodies, green space, elevation, slope), strategic land supply (per capita leased land for each land-use type, change rates of land supply), economic and social development (combined GDP, FDI, and fiscal revenue factor, population density, tertiary sector proportion), and parcel-level characteristics (size, source, grade, city rank, urban/rural location, transaction method).

The analysis reveals significant spatial inequality in urban land prices across China, varying by land-use type and time period. From 2008 to 2015, inequality in industrial and commercial land prices increased significantly, particularly in eastern China. This is linked to a 'race to the bottom' in offering land at low prices to attract foreign investment. After 2015, this inequality decreased as discriminatory pricing strategies in eastern China intensified. Residential land price inequality continued to rise throughout the study period, reflecting the government's reliance on land finance and its impact on housing prices. Spatial inequality in the western region remained relatively stable. The spatial regime model indicates that natural restrictions, such as the amount of cultivated land, significantly negatively affected land prices before 2015, particularly for industrial land. After 2015, this effect weakened, and socioeconomic factors became more important. The regression tree model corroborated these findings, showing the declining importance of cultivated land supply as a determinant of industrial land prices after 2015. This indicates a shift towards market-driven forces. For residential land, government interventions (transaction methods, parcel size) remained dominant, even after 2015, while the impact of natural factors decreased. The reliance on land finance continued to influence housing prices. Commercial land price is shown to be highly sensitive to location and natural environment. The gap between city and county land prices narrowed after 2015, which is likely due to the increased focus on developing rural and county regions.

The findings address the research question by demonstrating the heterogeneous mechanisms influencing urban land prices in China. The transition from government-dominated land markets (prior to 2015) to more market-oriented ones (after 2015) is evident. The decreasing influence of natural restrictions highlights the increasing role of market forces in shaping land prices. However, government intervention, particularly through land finance, continues to significantly influence residential land prices. The results underscore the complexity of the Chinese land market, highlighting the interaction between natural factors, government policies, and market dynamics. The study’s findings contribute to a better understanding of spatial inequality in the land market and the heterogeneous effects of policy interventions.

This study provides valuable insights into the spatial-temporal dynamics of urban land prices in China. The findings highlight the transition from government-dominated to more market-oriented land markets and the heterogeneous effects of natural restrictions and government interventions. The decreasing importance of natural restrictions and the increasing influence of market forces after 2015 are notable. Future research could further investigate the impacts of specific policies (e.g., 'ecological redline,' territorial space planning) on land supply and prices, and explore the dynamics of the city-county land price gap in greater detail.

The study relies on publicly available data, which may have limitations in terms of coverage and accuracy. The models used may not fully capture the complexities of the land market. The study focuses on China, so generalizations to other countries may not be appropriate.