Residential Property Price Forecasting with a Machine Learning Approach

The location, economic trends, infrastructure, and regulatory changes are just a few of the many variables that impact the dynamic and complicated housing market. For investors, buyers, sellers, and legislators to make wise choices, accurate home price forecasting is essential. Conventional assessment techniques mostly rely on manual value, which is frequently biased and inconsistent. By revealing hidden patterns in vast and intricate datasets, machine learning has become a potent tool for modeling and predicting real estate prices in recent years. Using a variety of property-related characteristics and historical sales data, this study suggests a strong machine learning framework for predicting home values. Numerous factors are included in the model, including location, square footage, number of bedrooms and baths, property age, ease of access to amenities, and neighborhood data. To find the best model, a number of techniques are investigated and contrasted, such as Linear Regression, Decision Trees, Random Forests, and Gradient Boosting. Performance optimization involves several crucial phases, including feature selection, data preprocessing, and hyperparameter adjustment. Model correctness is evaluated using the evaluation metrics Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. The findings show that ensemble-based models perform better in terms of prediction, especially Gradient Boosting. This study offers a flexible and scalable method for real-time price estimation that can be incorporated into real estate platforms, improving the efficiency and transparency of real estate transactions.