Developed a Python-based system for generating automated rural land valuation reports. The process starts by composing land use information through internal data and public land use datasets from Brazil. It then classifies the productive capacity and applies appreciation or depreciation factors based on ESG compliance, proximity to infrastructure, irrigation use, and crop type.

To estimate the land's market value, the system compares the classified land use with a custom land price database, built by use type and municipality across the country. Additionally, it performs web scraping of rural property listings in the same municipality, extracting price and area data. Descriptions are analyzed to infer the primary land use of each listing, enabling a direct comparison of per-hectare prices with the model's valuation.

This fully automated approach delivers fast, consistent, and scalable results for use in compliance, monitoring, and financial reporting contexts.