Transportation infrastructure often crosses large, slow-moving landslides. Full stabilization of these landslides can be prohibitively expensive, but measures to reduce landslide movement rates combined with regular monitoring and maintenance activities can often maintain an acceptable level of infrastructure performance. A general relationship between infrastructure condition state and landslide movement rate can be established from an operator’s experience with known active landslides but predicting the future probabilities of different landslide movement rates occurring over the infrastructure design life can be particularly challenging. This paper describes an approach to estimate annual landslide displacements and associated impacts on infrastructure performance and cost using Markov Chain models and Monte Carlo Simulation. Order-of-magnitude landslide displacement rate categories are treated as landslide condition states. Markov models provide future landslide velocity class probability distributions when combined with knowledge of the current state of the landslide. Monte Carlo simulation samples from the Markov model outputs and compares against infrastructure condition state criteria to generate annual estimates of infrastructure condition state probability. These are combined with estimates of associated owner and user costs to generate estimates of risk and lifecycle cost.