Measuring the value of window views using real estate big data and computer vision: A case study in Wuhan, China
Abstract
Window views significantly influence residential quality and real estate value, particularly in high-rise residential buildings. Previous studies have predominantly focused on water and green views, resulting in a lack of clarity regarding the influence of other types of views on house prices. In this study, we quantified and analyzed the impacts of 9 window view elements, including sky, high-rise buildings, low-rise buildings, trees, grass, water, hard ground, roads, and barren land, on housing prices using online real estate images and computer vision techniques. Focusing on high-rise buildings constructed in the past five years, our findings, based on spatial hedonic pricing models, reveal that an increased proportion of water views through windows has a significant positive effect on property prices. Conversely, the presence of grass and hard ground is associated with significant negative impacts. This study examines the influence of various window view elements on apartment prices, offering valuable insights for urban planning, architectural design, and property development.
Peng Chucai
Visiting Scholar
