A Comprehensive Review of Machine Learning Techniques for House Price Prediction

Abstract

Methods for calculating the sale price of houses in cities remain a difficult and time-consuming task. The purpose of this article is to forecast the coherence of non-house prices. Using Machine Learning, which can intelligently optimize the optimum pipeline fit for a task or dataset, is a key technique to simplify the difficult design. Predicting the resale price of a house on a long-term temporary basis is vital, particularly or those who will be staying for a long time but not permanently. Forecasting house prices is an important aspect of real estate. The literature tries to extract relevant information from historical property market data. The price of real estate causes land price bubbles to expand, causing macroeconomic instability. The reasons that drive up real estate prices are important investigating so that the government may use them as a guide to help stabilize location, and various economic elements influencing at the time are all factors that influence the house selling price.