In addition to atomic charges, the relationship of computed atomic properties such as NMR chemical shifts to Hammett parameter is appealing due to their ability to capture local electronic effects and having a corresponding experimental observable. Perhaps due to more complex nuclear coupling relationships captured in this experimental measurement, correlations to partial atomic charge values may not be as high. Still, successful studies have utilized NMR shifts in their relationship to Hammett parameters and partial atomic charges. A variety of methods also exist to compute NMR shifts, including Gauge-Independent Atomic Orbital (GIAO), Individual Gauges for Atoms in Molecules (IGAIM), and the Continuous Set of Gauge Transformations (CSGT) 36 methods. Machine learning (ML) models have been trained to compute partial atomic charges and NMR shifts. When utilized within their domain of applicability, these models give a highly accurate surrogate prediction of QM or experimental properties much faster than traditional QM calculations. These models typically use simple molecular representations, such as a SMILES string, as inputs. The qmdesc Python library utilizes a model trained to predict Hirshfeld charges and GIAO NMR shifts, among other QM properties. The CASCADE Python library also can be utilized to predict experimental 1 H and 13 C NMR shifts. In this work, we perform a quantitative comparison of a wide range of computed parameters obtained from different protocols against experimental Hammett parameters. We examine the performance of various computational methods, including density functional theory (DFT), semi-empirical QM, and ML models (Figure ). Due to their broad generality and widespread availability of empirical values, our study focuses on Hammett's original definition of σm and σp values.