This Room Is Too Dark and the Shape Is Too Long: Quantifying Architectural Design to Predict Successful Spaces



Historically, architects have relied primarily on rules-of-thumb to layout offices. In this paper we consider whether these assumptions can be improved by using machine learning to predict the success of an office layout. We trained a support vector classifier on data from 3276 private offices from 140 buildings. 56 features of the offices were used, including whether it had a window and the office’s squareness. The model was able to predict the lowest performing offices with a precision of 60–70% and a recall between 20 and 40%. This research suggests that many of the assumptions that drive architects will be able to be validated or refuted by applying machine learning to data gathered from people inhabiting the built environment.


Machine learning Predictive model Office design Building performance Support vector classifier 


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© Springer Nature Singapore Pte Ltd.  2018

Authors and Affiliations

  1. 1.New YorkUSA

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