In the management of road networks, it is often desired to know the condition of individual road sections, which is approximated using the values of condition indicators. The values of these indicators can be used, for example, to determine whether an intervention should be executed on the road sections in the upcoming year, or to predict the future condition of the road sections. Unfortunately, a common problem when working with these data is that there are numerous road sections where no information is available. This can happen either due to errors made during the inspection campaigns themselves or due to using multiple independent sets of geographical information system (GIS) indexed data, when the sets are recorded as noncoincidental GIS shapes. It is of interest to the road manager to estimate the values of the missing condition indicators as best as possible. In this paper, an investigation of the ability to estimate values of road section indicators based on their spatial correlation is presented. The investigation is done by estimating the values of condition indicators for surface defects, and longitudinal and transversal unevenness exploiting the spatial correlation between them, on the Swiss national highway network. It is shown that the values of road section indicators can be estimated based on their spatial correlation with reasonably high levels of accuracy. The variation of the predictive ability per condition indicator is shown.