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<p>Research on house price prediction (HPP) often focuses on single urban areas, missing regional diversity. This study introduces a machine learning (ML) framework for region-specific HPP, analysing five Indian cities and a cumulative model. Six state-of-the-art ML approaches were compared at the same benchmark level in a multi-city context. The study also presents urban housing feature rankings and finds region-specific models superior to cumulative ones. These findings highlight the importance of regional attributes in price estimation, enhancing real estate investment planning and strategy, and demonstrating the successful application of ML models in a multi-city framework with regional diversity.</p>

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Can Flexible Machine Learning Examine the Housing Features in Urban Housing Market Systems? A Study from the Indian Consumer Perspective in a Multi-city Context

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Rohan Mukherjee,
  • Arnab Poddar,
  • Vinit Ghosh,
  • Tirthankar Nag

摘要

Research on house price prediction (HPP) often focuses on single urban areas, missing regional diversity. This study introduces a machine learning (ML) framework for region-specific HPP, analysing five Indian cities and a cumulative model. Six state-of-the-art ML approaches were compared at the same benchmark level in a multi-city context. The study also presents urban housing feature rankings and finds region-specific models superior to cumulative ones. These findings highlight the importance of regional attributes in price estimation, enhancing real estate investment planning and strategy, and demonstrating the successful application of ML models in a multi-city framework with regional diversity.