We yearn for a connection with nature and a sense of refuge from where we live and the views from our homes. Despite these inclinations, existing window view indices are often time-consuming to collect and over-simplified in their construction. The limited research can be attributed to the lack of data and computational methods for analyzing vistas from properties. To address this gap, this study will leverage the newly available photo-realistic 3D modelled imagery from Google Maps, along with machine learning techniques to improve housing price/rent prediction and to establish a novel window view index for automatically assessing the appeal of views.