This research examines trends in U.S. homicide rates at the city level during the so-called homicide epidemic in the latter decades of the 20th century. Using spline regression techniques to locate structural breaks in city-level time series, we model the true trends of homicide rates to identify those cities that exhibited a meaningful boom and bust cycle. We then use Tobit regressions for all cities at risk of experiencing a cycle to estimate unbiased effects of theoretically important predictors on the timing of the phase changes. Our findings reveal that larger cities were more likely to experience an epidemic-like pattern, and that densely populated cities characterized by high levels of deprivation tended to exhibit the rise and fall in homicide rates earlier than other cities.