Determinants of Agricultural Land Prices in the Mendoza River Basin: A Spatial Hedonic Analysis with Emphasis on Irrigation Water

This research examines the determinants of agricultural land prices in the Mendoza River Basin, with emphasis on the marginal contribution of irrigation water as a strategic resource in an arid context. An original georeferenced database of 297 cultivated rural properties (2003-2024) was compiled, using advertised asking prices expressed in 2024 constant US dollars. A mixed spatial hedonic model with a log-log functional form was applied, incorporating hierarchical random effects (district nested within department), exponential spatial correlation, and heterogeneous variance across districts. The results indicate that properties with definitive surface irrigation rights command prices approximately 21% higher than comparable properties that rely exclusively on groundwater, confirming surface irrigation water as a pivotal economic asset. Soil quality and productive improvements increase land values by approximately 20% and 21%, respectively. In contrast, land size and distance to urban centres have significant negative effects (elasticities of -0.39 and -0.47). These findings highlight the central role of surface irrigation water as an economic asset and indicate a territorially segmented land market. The proposed methodology provides empirical evidence to support the design and implementation of water management and land-use planning policies in irrigated agricultural regions. 


Highlights:  


• A spatial mixed log–log hedonic model is estimated for 297 agricultural properties in an arid irrigated basin.

• Secure surface or mixed irrigation water increases farmland prices by approximately 21% relative to groundwater-only properties.

• Soil quality and productive improvements generate land value premiums of approximately 20% and 21%, respectively.

• Larger farms and greater distance to urban centres significantly reduce land values.

• Accounting for spatial dependence and territorial hierarchy improves inference and policy relevance.