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The equity and efficiency of the property tax depends on the ability of taxing jurisdictions to accurately predict the market value of properties. The application of predictive modeling in other domains, such as pretrial detention, lending, and healthcare, has given rise to a broad literature on tradeoffs between equity and efficiency. Using data from CoreLogic on home sales in the US between 2018-2023, we demonstrate that in the property tax setting, there is no equity-efficiency tradeoff. That is, as models become more accurate, they also become less regressive, on average. We ground these findings in underlying algorithmic fairness theory and propose that this principle applies to wealth taxation in general.