This article presents an assessment of the quality of prediction of a Markov-based statistical sewer deterioration model using the extensive CCTV dataset of a German city, Braunschweig. Additionally, a sensitivity analysis has been performed in order to assess the influence of input data availability on model performance. Results indicate that models are able to simulate quite accurately the condition distribution of the network with deviations smaller than 1%. Results also indicate that the performance of deterioration models is quite independent of the amount of CCTV data available to calibrate the model. Even when using very few data (˜3%, i.e. 1000 inspections) to calibrate the model, very good model performance can be obtained.This article presents an assessment of the quality of prediction of a Markov-based statistical sewer deterioration model using the extensive CCTV dataset of a German city, Braunschweig. Additionally, a sensitivity analysis has been performed in order to assess the influence of input data availability on model performance. Results indicate that models are able to simulate quite accurately the condition distribution of the network with deviations smaller than 1%. Results also indicate that the performance of deterioration models is quite independent of the amount of CCTV data available to calibrate the model. Even when using very few data (˜3%, i.e. 1000 inspections) to calibrate the model, very good model performance can be obtained.