People spend approximately 90% of their lives in indoor spaces. This brings a growing need to ensure window views are designed to meet the health and comfort needs of occupants. To meet this requirement, design tools that measure many view features (e.g. nature, visual angles) and evaluate impacts of design decisions (e.g. building massing, window orientation) in the early design phase are needed. This research surveyed living room window view satisfaction on 590 view images with 181 participants to understand the effects of view features given by the tool. Moreover, this paper proposed a new view property computational tool, and aimed to verify its performance and suitability in the early design stage. The author suggests a new window view satisfaction evaluation framework that leverages a supervised machine-learning model, which synthesizes the survey data from the first step and the view parameters extracted from 3d model reconstructions of the 590 different window view samples. This new view satisfaction prediction performance was compared to an existing window view assessment framework. Results showed that prediction performance was generally high for most surveyed responses collected, verifying the reliability of the tool. Comparisons also showed that the tool outperformed the framework, and provided prediction accuracy. Through this research, the author proposed a new way to quantify and promote view quality and visual satisfaction using computational tools targeted toward the early architectural design process.