Background: The purpose of this study was to examine the association between measures of housing quality, stability, and access on breast cancer stage at diagnosis among older women living in the United States. Methods: This study included 67,588 women aged 66-90 with data from the SEER-Medicare linked database. The primary outcome was breast cancer stage at diagnosis. Multinomial regression models were performed using a three-category outcome (stage 0, early-stage (I-II), late-stage (III-IV)). The key independent variables were median housing value, percentage living in the same house as the previous year, percentage owner occupied homes, and an index of contemporary mortgage lending bias (redlining). Results: In adjusted models, higher contemporary mortgage lending bias was significantly associated with later-stage diagnosis (RR = 1.10 1.02-1.20; RR = 1.31, 95% CI 1.16-1.49; RR = 1.41, 95% CI 1.24-1.60 for Least to High, respectively). Median housing value was inversely associated with later-stage diagnosis, but to a lesser degree than mortgage lending bias (RR = 0.88, 95%CI 0.80-0.96; RR = 0.77, 95% CI 0.68-0.88 for second and third tertiles, respectively). Owner occupancy and tenure were not significantly associated with late-stage diagnosis in adjusted models. Conclusions: Contemporary mortgage lending bias demonstrated a significant dose-response relationship with later stage at diagnosis of breast cancer in this cohort of elderly women. Policy interventions aimed at reducing the effects of redlining with the goal of decreasing late-stage breast cancer diagnosis to improve prognosis should be considered. Table. Relative risk of late stage breast cancer diagnosis based on measures of housing quality and stability, as well as redlining. Risk is relative to the base outcome, stage 0. Values for the first tertile of housing quality and stability as well as the “least” category for redlining are not shown in this table as they are the base outcome which the other values are compared to. Standard error was adjusted for MSA clustering effects in all models.