Real Estate Price Estimation Using Machine Learning

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Rupesh Manohar Sankale; K. N. Hande

Real Estate Price Estimation Using Machine Learning

Authors: Rupesh Manohar Sankale, K. N. Hande

Unique Paper ID: 178747
Volume: 11
Issue: 12
PageNo: 5911-5915

Keywords: No Keywords Found

Abstract:
The real estate market is highly dynamic and sensitive to price fluctuations, influenced by multiple economic stakeholders such as governments, agents, and buyers. Accurately predicting property sale prices remains a key challenge due to the market's complexity and volatility. This project proposes a property price prediction model using Decision Tree Regression, incorporating socioeconomic indicators like GDP, CPI, PPI, and HPI. By leveraging machine learning techniques alongside target encoding, the model aims to predict whether a property's final sale price will exceed or fall below its listed price. Geographic factors, historical pricing trends, and projected market shifts are used to enhance forecasting accuracy. Evaluation metrics such as Root Mean Square Error (RMSE) validate model performance. The ultimate goal is to assist individuals in estimating property values reliably, potentially reducing dependence on intermediaries.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 5911-5915

Real Estate Price Estimation Using Machine Learning

Available:https://ijirt.org/article?manuscript=178747

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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