Point cloud is a 3D image representation that has recently emerged as a viable approach for advanced content modality in modern communication systems. In view of its wide adoption, quality evaluation metrics are essential. In this paper, we propose and assess a family of statistical dispersion measurements for the prediction of perceptual degradations. The employed features characterize local distributions of point cloud attributes reflecting topology and color. After associating local regions between a reference and a distorted model, the corresponding feature values are compared. The visual quality of a distorted model is then predicted by error pooling across individual quality scores obtained per region. The extracted features aim at capturing local changes, similarly to the well-known Structural Similarity Index. Benchmarking results using available datasets reveal best-performing attributes and features, under different neighborhood sizes. Finally, point cloud voxelization is examined as part of the process, improving the prediction accuracy under certain conditions.