HomeScope is a machine learning tool designed to estimate home prices in the Austin, TX area. By analyzing historical data from over 13,000 property sales, HomeScope generates predictions based on property features like size, number of bedrooms, and lot area. While it isn’t a replacement for professional appraisals, HomeScope offers a data-driven way to explore housing prices in the Austin market.
Regression is a type of machine learning used to find relationships between variables. For HomeScope, regression models analyze how property features (like square footage) influence sale prices. These models then use those patterns to predict the price of a home based on its features.
Think of it like connecting the dots: regression creates a mathematical "line" that helps estimate where a new property might fit based on past trends.
HomeScope’s predictions are powered by a machine learning model called LightGBM. This model was trained on historical data from the Austin area, where it learned patterns like how property size or the number of bedrooms affect sale prices. When you enter property details, the model processes them to generate an estimated price.
It’s important to note that predictions are based on the data the model has seen. Factors like neighborhood reputation or recent market trends - things not included in the dataset - can impact real-world prices and may not be reflected in the predictions.
HomeScope’s best-performing model achieved an R² score of 0.603, meaning it explains about 60% of the variance in home prices within the dataset. The average error (RMSE) is $306,553, so predictions should be taken as a broad estimate rather than an exact value.
While not perfect, HomeScope demonstrates how machine learning can provide insights into complex problems, even with noisy or incomplete data.