Description: In 2018, the Mobile Source Air Pollution Reduction Review Committee (MSRC) commissioned the UCLA Luskin Center for Innovation (LCI) to identify locations within Southern California where investments in charging infrastructure could unlock latent demand for PEV ownership. LCI focused on two types of locations have higher than usual hurdles for installing charging equipment locations: workplaces and MUDs, such as apartments and condos. This spatial layer focuses on MUD properties and provides a score for ranking MUD parcels in the South Coast Air Basin according to the relative demand of building residents for PEV ownership, assuming barriers to chargers are removed. The score accounts for (a) the historical adoption rate of PEVs in each census tract, (b) the likelihood that PEVs are likely to belong to households of different income groups, and (c) the likelihood that those income groups are likely to live in a home of a certain value. The score is based on the average value of the unit within the MUD. Final scores are not weighted by the size of the MUD (i.e., the total number of units). MUDs will less than 4 units are excluded from this dataset altogether because residents at these properties are often able to charge using available 110/220 volt outlets with low-cost, portable Level 1 electric vehicle service equipment (EVSE). Data development/processing methodology:For a full documentation of methods, visit: https://innovation.luskin.ucla.edu/transportation/electric-vehicle-planning/Note: The propensity to purchase score metric is based in part on administrative data from the tax assessors offices of each of the four counties that form the SCAQMD. These data contain errors across several dimensions that can impact the final scores delivered by the propensity to purchase algorithm. While the authors have endeavored to identify and isolate errors, the large size of the parcel datasets makes exhaustive review impossible. While the authors believe that errors have been minimized, users are advised that some level of error is inevitable.