Kansas Property Tax Estimator helps Kansans better understand agricultural property taxes
New online tool from K-State agricultural economics provides free, interactive resource for landowners, producers and other stakeholders
Manhattan, KANSAS, October 2, 2026 – A new online tool funded by the Information Network of Kansas, Inc. (INK) is helping Kansans better understand how agricultural property taxes are calculated and explore how changes in key factors may affect estimated taxes. Funding from INK made the development of the tool and its supporting educational resources possible and allows them to be available to the public at no cost.
The Kansas Property Tax Estimator Tool, developed through the Kansas State University Department of Agricultural Economics in collaboration with Cornell University, is a free, web-based resource that walks users through the agricultural land valuation process and allows them to compare “what-if” scenarios.
Kansas agricultural land is valued based on its use value rather than market value. The calculation incorporates numerous factors, including county, land use, land productivity, capitalization rate, assessment ratio, acreage and local mill levy. The Property Tax Estimator brings those components together in one application, allowing users to see each stage of the calculation.
“This tool is about making a complicated agricultural land valuation process easier for Kansans to understand,” said Leah Tsoodle, Ph.D., Kansas State University Department of Agricultural Economics. “We created this tool to increase the transparency of the property tax methodologies and to give users, from taxing districts to landowners, a planning tool.
Users can select a county, land use, land class and acreage, as well as adjust variables such as the capitalization rate and mill levy. The tool then provides estimated income per acre, appraised value per acre, assessed value per acre, tax per acre and total estimated tax. Results can also be downloaded to Excel for future reference.
The flexibility of the tool allows users to explore questions such as how a different land class or mill levy could affect estimated property taxes. It also provides a transparent way to see how individual components contribute to the final estimate.
The tool was designed to serve a broad range of users, including agricultural landowners and producers, taxing districts, legislators, lenders and advisers, and educators. Landowners, for example, can estimate taxes on land they own or are considering purchasing, while taxing districts can explore how changes in mill levies may affect revenue expectations.
INK support expands public access to Kansas information
The Information Network of Kansas, Inc. funded the development of the Kansas Property Tax Estimator as part of its work to expand public access to Kansas information and services. INK awarded $224,366 for the project, which has been completed and is now publicly available.
“We see this as an opportunity to help get a new tool in the hands of property owners, to help them in making important decisions regarding the use and management of their land,” said Murray McGee, Executive Director for the Information Network of Kansas.
The INK-funded project includes more than the estimator itself. A downloadable user guide, video tutorials and worked examples help users understand the valuation process and use the tool effectively.
The estimator is free, requires no account or software installation, and can be accessed through a web browser on a computer, tablet or phone.
The Kansas Property Tax Estimator was developed by Tsoodle and Dr. Allan Fabricio Pinto Padilla, Ph.D., Dyson School of Applied Economics and Management at Cornell University.
The tool is intended for planning and educational purposes. It does not calculate an official property tax amount or replace an appraisal notice, tax bill or property-specific information from county appraisal and tax records.
Access the Kansas Property Tax Estimator
Launch the Kansas Property Tax Estimator on AgManager.info.
Media contacts:
Leah Tsoodle, Ph.D., Department of Agricultural Economics, Kansas State University, ltsoodle@ksu.edu
Allan Fabricio Pinto Padilla, Ph.D., Dyson School of Applied Economics and Management, Cornell University, afp68@cornell.edu