Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (7): 434-442.doi: 10.23940/ijpe.23.07.p2.434442

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Suitability Index Prediction for Residential Apartments Through Machine Learning

Kshitij Kumar Sinhaa,b,*, Manoj Mathura, and Arun Sharmab   

  1. aSchool of Planning and Architecture, New Delhi, India;
    bIndira Gandhi Delhi Technical University for Women (IGDTUW), Delhi, India
  • Contact: * E-mail address:
  • About author:Kshitij Kumar Sinha is a PhD Scholar at School of Planning & Architecture and an Assistant Professor at Department of Architecture & Planning at IGDTUW, Delhi. His research interests include design management and application of emerging technologies in the design processes.

Abstract: The value a good designed apartment adds to the life of the occupants has largely remained a lived out experience. Additionally, the architectural decision that ensures ‘value’ in designed homes has been restricted to the predetermined housing typologies influenced by both internal and external variables. The lack of exploration to reflect the ‘lived out experience’ as a key internal factor has instigated the continuous and rampant build-rebuild chain, which seems to only end with irreparable environmental degradation. Partnering with Artificial Intelligence to promote mindfulness amongst various stakeholders to address these pressing and urgent concerns has opened numerous possibilities. This paper demonstrates that by mapping the user group (represented through nine family structure scenarios) with their day to day requirements (derived through their indulgence in conducting an activity in terms of time spent and criticality) to help us to assign weightage to the spaces of a residential apartment based on the user group preference (by applying the TOPSIS method). Acting as an extension to the previous works on predicting the usability of a 3BHK apartment, the research presents a strong case of Machine Learning applications to predict the Suitability Index of a residential apartment.

Key words: usability, user group, suitability, value, TOPSIS, machine learning