Research linking characteristics of the neighborhood environment to health has relied on traditional regression methods where prespecified distances from participant’s locations or areas are used to operationalize neighborhood-level measures. Because the relevant spatial scale of neighborhood environment measures may differ across places or individuals, using prespecified distances could result in biased association estimates or efficiency losses. We use novel hierarchical distributed lag models and data from the Multi-Ethnic Study of Atherosclerosis (MESA) to (1) examine whether and how the association between the availability of favorable food stores and body mass index (BMI) depends on continuous distance from participant locations (instead of traditional buffers), thus allowing us to indirectly infer the spatial scale at which this association operates; (2) examine if the spatial scale and magnitude of the association differs across six MESA sites, and (3) across individuals. As expected, we found that the association between higher availability of favorable food stores within closer distances from participant’s residential location was stronger than at farther distances, and that the magnitude of the adjusted association declined quickly from zero to two miles. Furthermore, between-individual heterogeneity in the scale and magnitude of the association was present; the extent of this heterogeneity was different across the MESA sites. Individual heterogeneity was partially explained by sex. This study illustrated novel methods to examine how neighborhood environmental factors may be differentially associated with health at different scales, providing nuance to previous research that ignored the heterogeneity found across individuals and contexts.