The Role of Subjective Perceptions and Objective Measurements of the Urban Environment in Explaining House Prices in Greater London: A Multi-Scale Urban Morphology Analysis

Abstract

House prices have long been closely related to the built environment of cities, yet whether the subjective perception (SP) of these environments has a differing effect on prices at multiple urban scales is unclear. This study sheds light on the impact of people’s SP of the urban environment on house prices in a multi-scale urban morphology analysis. We trained a machine learning (ML) model to predict people’s SP of the urban environment around properties across Greater London with survey response data from an online survey evaluating people’s SP of street view image (SVI) and linked this to house price data. This information was used to construct a hedonic price model (HPM) and to evaluate the association between SP and house price data in a series of linear regression models controlling location information and urban morphological characteristics such as street network centralities at multiple urban scales, quantified using space syntax (SS) methods. The findings show that SP influences house prices, but this influence differs depending on the urban scale of analysis. Particularly, a sense of ‘enclosure’ and ‘comfort’ are important factors influencing house price variation. This study contributes by introducing SP of the urban environment as a new dimension into the traditional HPM and by exploring the economic impact of SP on the house price market at multiple urban scales.

Measuring the Spatial-Temporal Heterogeneity of Helplessness Sentiment and Its Built Environment Determinants during the COVID-19 Quarantines: A Case Study in Shanghai

“…The human-eye level perceptions of the residential street qualities include subjectively measured enclosure and complexity, and other objective measures (e.g., view index of the tree, person, etc.) [19][20][21][22] have profound impacts on human behaviors [15,[23][24][25][26] like walkability and running [20,23], biking [21,22], housing prices [27], and crime rates [24]. Therefore, we extracted streetscape perception data (subjective perceptions) from the research of Qiu et al [25], and also added other residential environment data (including plot ratio, greening rate, construction year, etc.)…”

Day-to-Night Street View Image Generation for 24-Hour Urban Scene Auditing Using Generative AI

“…The shapefile of the road network was obtained from OpenStreetMap [84]. 42,306 points were sampled, and an 800-point subset was randomly selected to request SVIs with Google Street View API [85].…”

Assessing Subjective and Objective Road Environment Perception in the Bangkok Metropolitan Region, Thailand: A Deep Learning Approach Utilizing Street Images

“…Here, the subjective measure was gauged through the perceived neighborhood environment, and the objective measure involved the application of GIS to assess an accessibility index. Furthermore, Yang et al [20] explored the relationship between house prices and the urban environment, considering the role of both subjective perceptions and objective measurements.…”

“…[5,13,22] Vitality The surrounding road environment creates a sense of vitality in the traveler. [6,14,20] Beauty The road environment induces a sense of beauty and pleasantness throughout the journey. [6,16] Negative perception…”