Employing a small number of predictive variables, the authors of this report created statistical models to forecast violent and property crime rates in New York City. The models estimated yearly changes in New York City’s crime rates from the early 1960s through 2021, estimates that corresponded very closely to the actual rates. The authors then used these models to forecast annual changes in crime rates through 2026. The forecast for violent crime is a slight decrease each year through 2026, while the forecast for property crime shows slight yearly increases. Finally, the projected impact on New York City’s violent crime rate of reducing the state imprisonment rate by 25% would be minimal. No association was found between imprisonment rates and property crime.

Read the full report [PDF] here.

In this report, the authors devised statistical models to “predict” past yearly changes in Los Angeles’s rates of violent and property crime from the early 1960s through 2021, employing a very small set of predictive variables known to be associated with levels of crime. The yearly changes projected for those years corresponded quite closely to the actual changes. The authors then used the models to forecast crime trends through 2026. Violent crime is forecast to decline through 2026, while property crime is expected to rise modestly in the same period. The analysis also finds that if California imprisonment rates were reduced by 20%, the effect on crime in Los Angeles would be minimal.

Read the full report [PDF] here.

The American public, like citizens elsewhere, care about current and future levels of crime and the factors that drive them. But policymakers, who can greatly influence such factors, often lack knowledge from careful studies on the causes and control of crime to guide their decisions. HFG commissioned three leading criminologists to address this deficit by developing a predictive model of national violent and property crime rates.

The researchers identified a small set of demographic, economic, and criminal-justice variables whose changes over nearly four decades closely track trends in crime. As in previous research, they found that higher incarceration is associated with lower crime—but only to a limited degree. And, more important, they found that other factors, such as manufacturing employment or the population’s proportion of young people, combine to have a greater influence on crime than does incarceration.

In addition to accurately tracking past crime rates, this new model can also be used for “what-if” analyses in which predictive variables are changed to obtain estimates of future crime rates. Such exercises show that, using reasonable assumptions about future levels of those variables, the United States can substantially lower its imprisonment rate without incurring an increase in crime.

Explaining the Past and Projecting Future Crime Rates provides a readable and comprehensive account of the findings and implications of this HFG study, an important resource for practitioners, policymakers, and scholars alike.