Node classification in graphs, particularly those exhibiting heterophily, poses significant challenges for traditional methodologies. These include various graph neural network variants and approaches that simplify graph convolutions. This paper proposes a novel approach called nCASH, which combines an innovative label propagation method that utilizes features to compute soft labels and node homophily scores. It also incorporates multi-filter spectral convolutions and a redefined Laplacian matrix tailored for heterophilic graphs. nCASH allows for different types of feature transformations; each region of the graph is characterized by its homophily scores, which dictate the type of filter applied. Low-pass filters are used in homophilous regions, and high-pass filters in heterophilous regions to accentuate differences. nCASH, free from extensive training requirements, relies on sparse matrix multiplications. This enhances scalability and efficiency. Empirical results demonstrate the effectiveness of this approach, showing improvements in classification accuracy on several state-of-the-art heterophilic datasets.