Daily Accessed Street Greenery and Housing Price: Measuring Economic Performance of Human-Scale Streetscapes via New Urban Data

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.

Measuring Impacts of Urban Environmental Elements on Housing Prices Based on Multisource Data—A Case Study of Shanghai, China

“…The reason why the HPR_DIS was chosen is that the distance from each neighborhood centroid to the Huangpu River notably affects residential housing prices. The housing prices decrease with the increase of the distance [30].…”

“…The rapid development of computer vision provides an efficient method for the information extraction of street view images. In this context, a great number of studies have been conducted to measure street-level green [27], estimate the spatiotemporal patterns of urban mobility [28], examine the relationship between street view and perceived safety [29] and assess the visual quality of urban environment [30] Therefore, in this study, street view data is used to evaluate the relationship between urban environmental elements and housing prices.…”

“…This method assumes that real estate is heterogeneous and three types of characteristics have significant impacts on housing price, namely structure, neighborhood and location characteristics [31,32]. In empirical research, HPM mainly has three forms, including linear models [24,30], semi-log models [1] and double-log models [33]. However, most studies combine linear regression with HPM to interpret the impact of different independent variables [34,35].…”

Impacts of Tourism Demand on Retail Property Prices in a Shopping Destination

“…It is widely documented that the price of a property is influenced by the attributes of the property, such as gross floor area, age, transportation accessibility, and landscape view. The (traditional) hedonic pricing model is an extensively-adopted reveal-preference method for quantifying the contributions of various characteristics, from which utility is derived, to the prices of heterogeneous goods (e.g., properties) sold in a particular market [39][40][41][42][43][44]. The hedonic pricing model assumes that implicit prices (or hedonic prices) for each characteristic of a good can be decomposed into prices of a set of observable attributes [45] and estimated by observing purchasers' willingness to pay.…”

From Functional Space to Experience Space:

“…Zhou et al (2018) employed space syntax to analyse the street network evolution in ancient Beijing. Ye et al (2019) identified street greenery in the daily lives of city residents and examined its relationship with housing prices. Sheng, Yang, and Liu (2014) assessed consumer demands of the Wangfujing Area in Beijing (China) with the employment of space syntax.…”