Abstract
The protective effects of street greenery on ecological, psychological, and behavioral phenomena have been well recognized. Nevertheless, the potential economic effect of daily accessed street greenery, i.e., a human-scale and perceptual-oriented quality focusing on exposure to street greenery in people’s daily lives, has not been fully studied because a quantitative measuring of this human-scale indicator is hard to achieve. This study was an attempt in this direction with the help of new urban data and new analytical tools. Shanghai, which has a mature real estate market, was selected for study, and the housing prices of 1395 private neighborhoods in its city center were collected. We selected more than forty variables that were classified under five categories—location features, distances to the closest facilities, density of facilities within a certain radius, housing and neighborhood features, and daily accessed street greenery—in a hedonic pricing model. The distance and density of facilities were computed through a massive number of points-of-interest and a geographical information system. The visible street greenery was collected from Baidu street view images and then measured via a machine-learning algorithm, while accessibility was measured through space syntax. In addition to the well-recognized effects previously discovered, the results show that visible street greenery and street accessibility at global scale hold significant positive coefficients for housing prices. Visible street greenery even obtains the second-highest regression coefficient in the model. Moreover, the combined assessment, the co-presence of local-scale accessibility and eye-level greenery, is significant for housing price as well. This study provides a scientific and quantitative support for the significance of human-scale street greenery, making it an important issue in urban greening policy for urban planners and decision makers.
Highlights
The recent emergence of new urban data, e.g., large amounts of geo-referenced data provided by street view images and open street maps, and of new analytical tools, e.g., machine-learning algorithms and space syntax tools, bring new research possibilities [33,34]
Based on the regression analyses above, we identified the positive impact of indicators from daily accessed street greenery on housing price
By combining new urban data, including many street view images, PoIs, and detailed street networks from OSM, with new tools, including machine-learning algorithms and space syntax, the level of accessed visible street greenery in residents’ daily lives was identified and its impact analyzed in relation to housing prices
Summary
The definition of daily accessed street greenery contains both visual and accessible dimensions [9], which can be regarded as a series of indicators including purely visible greenery and street accessibility and combined categories based on the high or low values of both greenery and accessibility. It is a human-scale, perceptual-oriented quality focusing on exposure to street greenery in people’s daily lives. Less research has focused on the accessibility of roadside vegetation, which is a highly visible form of urban vegetation that many residents may pass through and experience daily [15]
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