Property taxes are one of the highest and least understood costs of homeownership. Each year, millions of property values are calculated through automated systems that rely on statistical models, comparable sales data, and large-scale valuation frameworks. The process is efficient. It is also largely invisible to the people it affects.
In Taxed by Algorithm, Michael Reeves examines how modern assessment systems operate, how automated valuation models influence property tax bills, and why transparency and accountability matter in a system that affects household finances nationwide.
Through the lens of a composite homeowner navigating annual assessment notices, Reeves explains how assessed value differs from market value, how local tax rates interact with valuation models, and why small compounding increases can materially affect affordability over time. The book explores how administrative systems scale decisions, how appeals function as a feedback mechanism, and why participation in those systems remains low even when assessments are inaccurate.
This is not a critique of taxation itself. Property taxes fund schools, infrastructure, and essential public services. Rather, this book asks a structural question: when valuation decisions are made at scale through automated processes, what safeguards ensure accuracy, transparency, and meaningful recourse?
Written in clear, accessible language, Taxed by Algorithm translates complex institutional processes into plain English. It offers readers a grounded understanding of how property tax systems work, where errors can occur, and what rights homeowners retain within the process.
At a time when housing costs continue to rise, and administrative systems increasingly rely on automation, this book provides a thoughtful examination of how numbers acquire authority-and why accountability must keep pace with technology.
