Integrating land use and transportation policy is widely understood as an efficient approach to meet sustainable transport objectives, yet impacts on residential location preference may limit policy effectiveness. Even though an integrated policy strategy is often proposed as more equitable, sustainable, and economically beneficial, choosing appropriate policy measures requires weighing a set of potentially conflicting goals, such as CO2 emissions, road-traffic safety, oil security, tax revenue, economic competitiveness, and consumer impact. Consequently, policy makers need to understand how combinations of land-use and transport policies effect land use and transportation consumer behavior and whether the policies complement or contradict each other. By gaining insight into the tradeoffs between policy mixes, planners and policy makers can more effectively align policy with preference to efficiently address the needs of the current land-use and transportation system. While many studies have focused on the effects of integrated land use and transportation policy on travel demand, the effects on residential location preference are largely unaddressed. In this paper, we argue incorporating the effects on residential location preference is especially important for aligning policy decisions with policy goals. This study uses the Atlanta Regional Commission (ARC) 2011 travel survey, matched to block group characteristics, to ask several questions. (1) How do households change their residential location preference in response to simultaneous changes in motor fuel tax and public transit provision? (2) How does this response influence changes in expected tax revenue, accessibility, and urban compactness? (3) What do the underlying tradeoffs mean for an optimal integrated policy mix? Using the travel survey data, matched to block group characteristics, this study uncovers an important constraint: an integrated consumer-driven policy mix can influence households to either a more compact and accessible city or a more sprawled, revenue-generating region. To understand the distribution of the policy mix impacts across the region, we estimate the effect of these policy decisions sub-regionally.