House prices need careful guessing because choices here carry big money risks. Old ways of judging value usually depend on people looking closely, using their experience - this can bring bias or mixed results. As AI and ML grew stronger, number-based techniques started offering sharper estimates, changing how homes are priced. Looking at how different machine learning methods predict home values, this work uses organized real estate information. Features like size of the land, count of rooms, age of construction, space for vehicles, general condition ratings, and neighborhood details make up the data set. Cleaning steps - fixing gaps in records, turning categories into numbers, adjusting scale differences, eliminating odd entries - helped sharpen predictions. Instead of just one approach, four were tested: straight-line fitting, tree-style splitting, forest-based averaging, then boosting-driven refinement. Each was judged by average mistake size, error spread, plus explained variance - not magic, just math tracking accuracy. When tested, ensemble techniques did better than standard regression. Random Forest stood out by predicting most accurately. These outcomes show artificial intelligence models boost how well property values are estimated. Efficiency gets a clear lift from using such systems.
House Price Prediction, Artificial Intelligence, Machine Learning, Regression Models, Random Forest, Real Estate Analytics.
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