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Abstract: The purpose of the paper is to predict the market value of the property being sold. This program helps to find the starting price of a location based on location variables. Similarly, consider a situation in which a person needs to sell a house. By using a real estate pricing system, the seller will be able to determine what features he can add to the house so that the house can be sold at a higher price. Therefore, in both cases, we can be sure that the home price is good for both the buyer and the seller. Housing prices go up every year, so there is a need for a real estate forecasting system. Estimating the price of a house can help a developer determine the selling price of a house and can help clients set a reasonable time to buy a home. Buying a house is one of the biggest financial goal of everyone. Owning a house is not only a basic need but it also represents prestige. However, buying a house is one of the most crucial decision of a person’s life as there are so many factors to be consider before buying a property. House prices keeps changing based on location, area, population, house condition and structure, availability of parking, backyard, size of house etc. From past few years a lot of data has been generated regarding Real Estate. Machine learning prediction techniques can be very useful to predict an accurate pricing of the houses. The study focuses on developing an accurate prediction model for house price prediction. Machine learning is sub-branch of artificial intelligence that deals with statistical methods, algorithms. Using machine learning we can build a model which can make prediction based on past data. In this paper we will review different machine learning algorithms which can be used for house pricing prediction.