Real Estate Price Prediction Using Machine Learning Algorithms
Year: 2025
Type: preprint
Abstract: This report delves into the realm of real estate price prediction utilizing machine learning algorithms. Given the paramount importance of precise property valuation in real estate transactions, this study is dedicated to crafting and assessing predictive models aimed at forecasting property prices with accuracy. The methodology encompasses a meticulous process of data collection from diverse sources, followed by rigorous feature engineering, and comprehensive evaluation of various machine learning algorithms, including regression, ensemble methods, and neural networks, to ascertain their efficacy in predicting real estate prices. The outcomes of this investigation furnish invaluable insights into the intricate dynamics of real estate markets, furnishing stakeholders with practical tools to make wellinformed decisions concerning property investment and valuation. This research also holds practical significance for industry professionals, policymakers, and investors alike by offering robust predictive models and elucidating the underlying factors shaping property prices, this study equips stakeholders with actionable intelligence to navigate the complexities of real estate markets more effectively. The findings presented herein serve as a cornerstone for informed decision-making, facilitating prudent investment strategies
Author: Uddin, Md Rasel
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7101835460
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