Year: 2025
Type: article
Abstract: : With the rapid expansion of the real estate sector, determining property values accurately has become increasingly important. Conventional methods for estimating house prices are slow and rely largely on expert judgment, which can result in inconsistencies. This project presents an AI-driven approach using machine learning to forecast house prices based on parameters like location, square footage, number of rooms, and additional key attributes. The system applies regression-based models to analyze historical property datasets and uncover the correlation between different features and price values. This predictive model assists homeowners, buyers, and real estate professionals in making better investment decisions while reducing manual effort. The study highlights the effectiveness of artificial intelligence and data science in enhancing accuracy and efficiency in real estate price prediction.
Language: English
Topic: Housing Market and Economics
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4416208573