Prediction markets have been proposed for a variety of public policy purposes, but no one has considered their application in perhaps the most obvious policy area.: crime. This Article proposes and examines the use of prediction markets to forecast crime rates and the potential impact on crime policy, such as changes in resource allocation, policing strategies, sentencing, post-conviction treatment, and so on. First, we argue that prediction markets are especially useful in crime rate forecasting and criminal policy analysis because information relevant to decisionmakers is voluminous, dispersed, and difficult to process efficiently. After surveying the current forecasting practices and techniques, we examine the use of standard prediction markets-such as those being used to predict everything from the weather to political elections to flu outbreaks-as a method of forecasting crime rates of various kinds. Second, we introduce some theoretical improvements to existing prediction markets that are designed to address specific issues that arise in crime rate forecasting. Specifically, we develop the idea of prediction market event studies that could test the influence of real and hypothetical policy changes on crime rates. Given the high costs of changing policies, such as issuing a moratorium on the death penalty or lowering mandatory minimum sentences for certain crimes, these markets provide a useful tool for policymakers operating under uncertainty. But, the event studies and the other policy markets we propose face a big hurdle because predictions about the future imbed assumptions about the very policy choices they are designed to measure. We offer a method by which policymakers can interpret market forecasts in a way that isolates or unpacks underlying crime factors from expected policy responses, even when the responses are dependent on the crime factors. Finally, we discuss some practical issues about designing these markets, such as how to ensure liquidity, how to structure contracts, and the optimal ...