Abstract
This study investigated the extent to which subjectively and objectively measured street-level perceptions complement or conflict with each other in explaining property value. Street-scene perceptions can be subjectively assessed from self-reported survey questions, or objectively quantified from land use data or pixel ratios of physical features extracted from street-view imagery. Prior studies mainly relied on objective indicators to describe perceptions and found that a better street environment is associated with a price premium. While very few studies have addressed the impact of subjectively-assessed perceptions. We hypothesized that human perceptions have a subtle relationship to physical features that cannot be comprehensively captured with objective indicators. Subjective measures could be more effective to describe human perceptions, thus might explain more housing price variations. To test the hypothesis, we both subjectively and objectively measured six pairwise eye-level perceptions (i.e., Greenness, Walkability, Safety, Imageability, Enclosure, and Complexity). We then investigated their coherence and divergence for each perception respectively. Moreover, we revealed their similar or opposite effects in explaining house prices in Shanghai using the hedonic price model (HPM). Our intention was not to make causal statements. Instead, we set to address the coherent and conflicting effects of the two measures in explaining people’s behaviors and preferences. Our method is high-throughput by extending classical urban design measurement protocols with current artificial intelligence (AI) frameworks for urban-scene understanding. First, we found the percentage increases in housing prices attributable to street-view perceptions were significant for both subjective and objective measures. While subjective scores explained more variance over objective scores. Second, the two measures exhibited opposite signs in explaining house prices for Greenness and Imageability perceptions. Our results indicated that objective measures which simply extract or recombine individual streetscape pixels cannot fully capture human perceptions. For perceptual qualities that were not familiar to the average person (e.g., Imageability), a subjective framework exhibits better performance. Conversely, for perceptions whose connotation are self-evident (e.g., Greenness), objective measures could outperform the subjective counterparts. This study demonstrates a more holistic understanding for street-scene perceptions and their relations to property values. It also sheds light on future studies where the coherence and divergence of the two measures could be further stressed.
Highlights
With a comprehensive investigation into the relationship between both subjective and objective streetscape perceptions and property values, this study provides a scientific basis for policy makers, planners, and real-estate developers to adequately address the economic value of street environments
We calculated the view indices of more than thirty physical features from the 300 training images through a PSPNet pre-trained semantic segmentation algorithm according to the general formula (1)
While the implicit return from investing in streetscapes on improving property values is noneligible [32,36,96], our finding suggests that the urban design process for deciding the streetscape could be more participatory, allowing different stakeholders to contribute to a better street environment [30]
Summary
Physical disorder visible in the street (e.g., broken windows, abandoned housings, graffiti, and decayed street lighting) correlates to crime, decreases residents’ sense of safety, and lowers residents’ willingness to live there [3,4]. A welldesigned and maintained street environment increases residents’ physical activities, lessens their stress [5], and improves their health [6,7] in part due to the outdoor thermal microclimate [8,9,10] or perceived safety [1]. Streetscapes affect pedestrians’ route choices [11], perceived thermal comfort and walking comfort [12] influenced by heat exposure [13]. Streetscapes affect driving safety as a result of sun glare effects in urban roads [14]
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