Accessibility is a key factor in the utility from living in different areas. In urban models, accessibility is theoretically expected to be internalized by the residential market, creating an 'accessibility premium' in areas with better accessibility. Previous case-study literature found significant and largely unexplained variation in the transit accessibility premium in different urban contexts. This paper proposes a new approach to uncovering the determinants of this variation in a unified framework, utilizing a theoretically grounded measure of accessibility, and both causal machine learning and standard econometric methods applied to highly granular nationwide data on rents and the transportation network.

I find that high residential density, mixed-use zoning, and a demographic composition better reflecting typical transit users imply a larger transit accessibility premium. This premium is also higher in areas with a low level of services compared to a reasonable reference point, and positive only up to a threshold level of services. There is some evidence that proximity to rail systems implies a premium over and above the expected premium implied by a reduction in travel times alone. The estimated effect is usually modest.

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