Community colleges serve many critical purposes for residents within their local service areas by providing relatively low-cost (Ma & Pender, 2023), open-access postsecondary education and workforce-focused training (Jacobs & Worth, 2019). A recent national survey of previously enrolled community college students finds that "gaining skills to be successful in work" was the most frequently cited (74%) motivation for enrollment, yet only about half of career-motivated students reported meeting their work-related goals (e.g., financially support self and family, earn more money) after leaving their community college (Strada Education Foundation, 2023). In response to the public's questioning of the value of higher education, the Postsecondary Value Commission (2021) and its resulting Postsecondary Value Framework (PVF) have encouraged higher education researchers and data practitioners to quantify the value postsecondary institutions provide to students more explicitly. For example, the Institute for Higher Education Policy (IHEP) has used publicly available data to measure students' earnings against the PVF, using the resulting data to construct the Equitable Value Explorer (EVE), an interactive data tool. Given the hyperlocal enrollment of community college students and their primarily economic reasons for choosing to enroll in college, place-based measures of economic value are increasingly important to understand. AIR used publicly available federal data sources, including the Integrated Postsecondary Education Data System (IPEDS) and the American Community Survey (ACS), along with data from IHEP's EVE data tool to create a novel analytic data set that included institution-level and CBSA-level variables. Following the methodology advanced by the Postsecondary Value Commission, we calculated measures of economic value (dependent variable) for each community college in our analytic sample based on two value thresholds: the minimum economic return threshold, or T0, and the earnings premium threshold, or T1. We examined economic value using descriptive statistics (e.g., percent distribution) and through correlation and regression analyses.