Estimating and mapping Tokyo Window View Desirability using 3D model imagery and the hedonic price approach
Bibliographic Data
| ID | 22413367 |
|---|---|
| Authors | Stephen Law (0000-0003-3184-572X, University College London, corresponding author), Masato Horinouchi (Osaka Metropolitan University), Esra Suel (0000-0001-9246-3966, University of Zurich), Steven Stalder (0009-0000-4568-8652, ETH Zurich), Michele Volpi (0000-0003-2771-0750, ETH Zurich), Yao Shen (0000-0002-7261-4234, Tongji University), Moran Li (0000-0002-6406-1983), Maosu Li (0000-0002-9970-4053), Atsushi Takizawa (0000-0001-6474-6985, Osaka Metropolitan University) |
| Year | 2026 |
| Volume | 194 |
| Pages | 104110 |
| Publication date | 2026-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Applied Geography (JOURNAL) |
| Journal identifiers | ISSN: 0143-6228 • E-ISSN: 1873-7730 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.apgeog.2026.104110 |
| OpenAlex | W7168158474 |
| Language | EN |
| References cited | 39 |
We long for a connection with nature and a sense of refuge in the views from our living spaces. Views from homes shape both our well-being and housing values, yet there are limited research that quantify the holistic visual desirability of a residential window scene, as reflected in housing rents. This study introduces a pipeline that combines photorealistic synthetic window-view images from Google 3D Tiles with a hedonic price model to estimate Tokyo’s Housing Window View Desirability ( HWVD ) from rents. Leveraging three-bedroom rental property records in Tokyo, we infer HWVD as the rent component explained by window-view imagery after controlling for structural and locational attributes. Incorporating HWVD improves out-of-sample rent prediction (R 2 increases from 0.62 to 0.71) with a building-level spatial resampling (train-test split). These findings were further validated through a human user survey with experts. To interpret the metric, we relate HWVD to the semantic content of the views, finding significant positive correlations with visible greenness and negative correlations with building views. Spatial analysis reveals significant spatial clustering, with higher HWVD concentrated in southwestern Tokyo, particularly in wards such as Shinagawa-ku and Minato-ku and lower HWVD in eastern wards like Katsushika-ku. We found more desireable views are concentrated in wealthier wards, reflecting a clear socio-economic divide in Tokyo’s visual landscape. In summary, this research provides a scalable approach to revealing’unseen’ visual amenities from residential spaces, with significant implications for applied geography, urban planning, real estate, and architectural design
3D city models · Pipeline (software) · Renting · Resampling · Scalability · Spatial analysis · Visualization · Window (computing) · Housing Market and Economics · Urban Design and Spatial Analysis · Urban Green Space and Health
Green gentrification in European and North American cities
High-Resolution Image Synthesis with Latent Diffusion Models
Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China
A configurational approach to analytical urban design
Natural Versus Urban Scenes
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States
View Through a Window May Influence Recovery from Surgery
Difference-in-Differences with multiple time periods
Implementing a deep-learning model using Google street view to combine social and physical indicators of gentrification
Self-supervised learning unveils urban change from street-level images
A room with a view
Visual Capital
Efficient and accurate assessment of window view distance using City Information Models and 3D Computer Vision
Street view imagery in urban analytics and GIS
Privileged Socionatures and Naturalization of Privilege
The value of views and open space
Environmental drivers of housing prices and thermal inequality using satellite imagery
The Workplace Window View
Measuring the value of window views using real estate big data and computer vision
Value of scenic views
The restorative benefits of nature
What's in a View
| Highly cited | No |
|---|