Spatial crime analysis relies not only on accurate geocoding but also the achievement of a high level of geocoding success. Geocoding is the task of converting locations, such as the addresses of burglary victims, into grid coordinates and is a task performed regularly by many crime analysts. Data sources include police offence and incident databases where the quality of geographical references can vary. The reality of dealing with this real world data means that achieving a completely successful geocoding process is rare and few crime analysts can get a hit rate (the percentage measure of success) of 100%. This paper seeks the answer to a seemingly simple question: what is an 'acceptable' minimum geocoding hit rate for crime data? This paper uses a number of different crime patterns and Monte Carlo simulation to replicate a declining geocoding hit rate to answer this question. Reduced crime rates of mapped points, aggregated to census boundaries, are compared for a statistically significant difference. The result indicates 85% as a first estimate of a minimum reliable geocoding rate, and this result is applicable to many address-based, point pattern datasets beyond the crime arena.