The amenity value of natural views

Abstract

We estimate nonmarket values for natural views in an urban setting. These views contain the aesthetics of natural areas commonly found in public parks and open space, and offer an aspect of property valuation that previous research is unable to disentangle from proximity to parks and open space. We incorporate machine learning techniques on Google Street View images to identify natural views in an urban setting. We find positive capitalization rates associated with household views of park‐like properties. Estimates are robust to a variety of specifications, including models that are identified off of new developments on neighboring properties and falsification tests that help to rule out the effect of a broader neighborhood environment. From a policy perspective, our results inform as to the optimal size, location, and shape of open space. Furthermore, machine learning methods used in the construction of our view variable provide a potentially powerful tool for other nonmarket valuation studies.