Cities today are home to more than half of the world’s population, where growing wealth in Western economies is pushing demand for better quality of housing. Many residents prioritise living in tranquil, green environments that offer peace and natural beauty, yet they also place significant importance on maintaining convenient and easy access to their cars for everyday mobility and flexibility. The competition for land to accommodate these landscaping amenities intensifies pressure on limited urban space, often conflicting with consumers’ desires for green, accessible environments and government goals for compact, mixed-use development. As a result, urban planning increasingly grapples with balancing car parking accessibility and exposure to green spaces, an issue that is especially evident in new car-free districts and cities worldwide adopting car-restricted zones for sustainability. Against this backdrop, this study investigates the trade-off between car parking accessibility and exposure to green environments in residential decision-making. Using street-level images generated by artificial intelligence to represent variations in residential environment, the study applies a state-of-the-art computer vision-enriched discrete choice model (CV-DCM) to analyse how both visual and numerical information influence residential choices. Results reveal a preference for a greener environment among all age groups, but a higher sensitivity for accessible parking. These insights highlight the complex trade-offs residents make and equip policymakers with valuable tools for more nuanced residential planning.