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The price of a house is increased every year according to the location. It indicates the current economic situation so there is a need for a system to predict house sales in the future for both buyer and the seller. Here we use a dataset of Pune with more than 68,613 entries of train data and test data of housing sales in India. This analysis includes the effect of markdowns on sales and the extent of effects on the sales by size, price, area etc. has been analysed using different machine learning algorithms. Estimating home sales can help the developer determine the selling price of the home and the best time for the buyer to purchase the home. The output values of the algorithms are estimated based on the input characteristics from the data presented in the system and the analysis is a process. Physical conditions, concept and location are the three factors that determine the selling price of a property.