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The constant growth and development in the Mumbai Metropolitan Region have resulted in the change of housing prices a lot of times, making it very important for buyers and sellers to predict the right price for a house. Predicting the price for a house in a city like Mumbai is a challenging task as it is based on various factors like carpet area, geographic coordinates (latitude and longitude), number of bedrooms and bathrooms in a house, number of parking slots, and many more. In this paper, we use machine learning algorithms to predict the house price in Mumbai and compare various machine learning algorithms to determine the best performing algorithm. Linear regression, CatBoost Regressor, Ridge regression, Support Vector Regression, Random Forest Regressor, Decision Tree Regression, and XGBoost Regressor are the algorithms that have been compared.

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Leveraging Machine Learning for Accurate Real Estate Price Forecasting in Mumbai Metropolitan Region

  • 会议论文

  • 发表日期：

    2025年

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  • Aditya Apandkar,
  • Huzefa Dohadwala,
  • Kumkum Saxena

摘要

The constant growth and development in the Mumbai Metropolitan Region have resulted in the change of housing prices a lot of times, making it very important for buyers and sellers to predict the right price for a house. Predicting the price for a house in a city like Mumbai is a challenging task as it is based on various factors like carpet area, geographic coordinates (latitude and longitude), number of bedrooms and bathrooms in a house, number of parking slots, and many more. In this paper, we use machine learning algorithms to predict the house price in Mumbai and compare various machine learning algorithms to determine the best performing algorithm. Linear regression, CatBoost Regressor, Ridge regression, Support Vector Regression, Random Forest Regressor, Decision Tree Regression, and XGBoost Regressor are the algorithms that have been compared.