Accurately assessing property values is essential for ensuring that localities have the revenue to support public services like schools, roads, and law enforcement. However, traditional assessment methods face a litany of problems. Valuations can often be inconsistent and municipalities are typically understaffed and resource-constrained.

While there has been a lot of attention on generative AI systems like ChatGPT, predictive AI models trained on property characteristics, sales data, and market trends are increasingly being adopted to address the core challenges of traditional property tax assessment methods. 

As home sale data and tax assessments have become easily accessible, researchers have found systematic regressivity in property tax assessment in the past decade. Lower-value properties are typically over-assessed, while higher-value homes are under-assessed. A 2022 report from the Philadelphia Fed, for example, found that “owners of inexpensive houses pay almost 50% higher effective tax rates than owners of expensive houses.” Research on Atlanta’s property taxes, enabled by modeling from Center for Municipal Finance at the University of Chicago, found that 69 percent of the lowest-value properties in Atlanta are over-assessed, while 32 percent of the highest-value homes are under-assessed.

One cause of this regressivity comes from a lack of resources. Many small municipalities simply don’t have the budget to hire the right number of tax assessors. And although major urban areas face similar budget pressures, they tend to adjust both tax rates and property valuations annually, which creates uncertainty for homeowners trying to anticipate their tax obligations. Predictive AI systems could help alleviate those administrative burdens while also providing citizens tax equity.

In late 2022, the Cook County Assessor’s Office, which serves much of Chicago, released their Property Tax Simulator tool, PTAXSIM, a publicly available software package that approximates the effects of assessments, levies, tax increment financings, and other inputs on Cook County property tax bills. With this tool, “Researchers and policymakers can now use the data to analyze the whole system of appeals, exemptions, and tax levies – an unprecedented resource for academics, journalists, and policymakers.”

In 2023, California’s Riverside County signed a five-year contract with C3 AI after it found in a pilot project that the AI system sped up the appraisal process by 40 percent, reducing reliance from 30 separate appraisal models to just four unified AI models. Tasks that once took hours can now be done in minutes, easing backlogs and workload.

Earlier this year, New York City’s Finance Department launched a similar pilot program with C3 AI to revalue condo properties. Department of Finance spokeswoman Jackie Gold stressed that the agency would only be evaluating condos under the project but was hopeful that the approach would be “a more fair and transparent way to assess properties.”

Transparency is a key selling point for these systems. AI-generated appraisals are able to produce evidence packages and sales comparables to explain how the property value was generated. This data can then be used to autopopulate appeal forms with relevant comparable sales data, property characteristics, and market analysis tailored to each specific case. In other words, these systems have the potential to generate ready-to-file appeal forms, allowing property owners to successfully navigate the maze of paperwork, deadlines, and procedural requirements.

The adoption of AI in property tax assessment represents more than just a technological upgrade: It offers a path to reversing decades of regressive taxation that has unfairly burdened working-class homeowners. As these systems mature and expand beyond pilot programs, they hold the potential to create more equitable, efficient, and transparent property tax systems nationwide. As with any other change, however, successful implementation will require careful oversight to ensure these tools deliver on their promise of fairness while maintaining public trust.