House Price Prediction using Machine Learning

House Price Prediction is crucial for informed decision-making in real estate markets. The accurate prediction of house prices holds significant importance in real estate markets, facilitating informed decision making for stakeholders. This study focuses on the application of machine learning (ML) techniques to predict house prices in Mumbai, India, a city renowned for its dynamic real estate landscape. By delving into the intricacies of Mumbai's real estate data, this research aims to identify the most influential factors driving property values and determine the optimal ML models for prediction. The study underscores the significance of locational and structural attributes in shaping house prices, highlighting the complex interplay between market dynamics and property characteristics. Furthermore, it investigates the impact of socio-economic factors, infrastructure development, and neighborhood amenities on housing values. Findings from this research contribute to the understanding of Mumbai's real estate market dynamics and provide actionable insights for stakeholders, including homebuyers, sellers, and real estate developers.

Institutions

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-09-23
DOI
https://doi.org/10.22214/ijraset.2025.74304
Primary Topic
Plant Taxonomy and Phylogenetics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

House Price Prediction using Machine Learning

International Journal for Research in Applied Science and Engineering Technology
Plant Taxonomy and Phylogenetics
article

House Price Prediction using Machine Learning

article en

Abstract

House Price Prediction is crucial for informed decision-making in real estate markets. The accurate prediction of house prices holds significant importance in real estate markets, facilitating informed decision making for stakeholders. This study focuses on the application of machine learning (ML) techniques to predict house prices in Mumbai, India, a city renowned for its dynamic real estate landscape. By delving into the intricacies of Mumbai's real estate data, this research aims to identify the most influential factors driving property values and determine the optimal ML models for prediction. The study underscores the significance of locational and structural attributes in shaping house prices, highlighting the complex interplay between market dynamics and property characteristics. Furthermore, it investigates the impact of socio-economic factors, infrastructure development, and neighborhood amenities on housing values. Findings from this research contribute to the understanding of Mumbai's real estate market dynamics and provide actionable insights for stakeholders, including homebuyers, sellers, and real estate developers.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(9)
Sardar Patel University (IN)
Openalex Percentile: Top 39%
Plant Taxonomy and Phylogenetics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.