Machine Learning Engine for Real Estate Price Estimation

Accurate price estimation is crucial for informed decision-making in the real estate industry. This study explores machine learning (ML) methods for predicting housing prices in Ontario’s Halton Region from 2022 to 2023, using a dataset of over 7,000 detached home transactions. Data preprocessing involved feature engineering, including economic indicators like prime rates. Exploratory data analysis revealed transaction patterns and market shifts linked to interest rate changes.ML techniques, including linear regression, Random Forest, and XGBoost, were employed, with models achieving R-squared values between 0.93 and 0.997. Decision Tree and Random Forest models were the most effective in capturing price variability. Additionally, a Flask-Based price estimation tool was developed, and trained on several regions of the Greater Toronto Area (GTA) allowing users to predict home prices based on specific property features.The study demonstrates the value of ML in enhancing real estate market efficiency by providing reliable price predictions, benefiting stakeholders such as homebuyers, sellers, and investors.