Earthquake-triggered landslides are a significant hazard in seismically active regions, but our ability to assess the hazard they pose in near real-time is limited. In this study, we present a new globally applicable model for seismically induced landslides based on the most comprehensive global dataset available; we use 23 landslide inventories that span a range of earthquake magnitudes and climatic and tectonic settings. We use logistic regression to relate the presence and distribution of earthquake-triggered landslides with spatially distributed estimates of ground shaking, topographic slope, lithology, land-cover type, and a topographic index designed to estimate variability in soil wetness to provide an empirical model of landslide distribution. We tested over 100 combinations of independent predictor variables to find the best-fitting model, using a diverse set of statistical tests. Blind validation tests show the model accurately estimates the distribution of available landslide inventories. The results indicate that the model is reliable and stable, with high “balanced accuracy” (correctly vs. incorrectly classified pixels) for the majority of test events. A cross validation analysis shows high balanced accuracy for a majority of events as well. By combining near-real time estimates of ground shaking with globally available landslide susceptibility data, this model provides a tool to estimate the distribution of co-seismic landslide hazard within minutes of the occurrence of any earthquake worldwide for which a USGS ShakeMap is available.