PROPERTY ADVISOR

Abstract

People looking to invest in a property tend to be more conservative with their budgets and market strategies. The present method doesn’t predict future prices of the houses mentioned by the customer. Due to this, the risk in investment in an apartment or an area increases considerably. To minimize this error, customers tend to hire an agent which again increases the cost of the process. This leads to the modification and development of the existing system and customer approaches a real estate agent to manage his/her investments and suggest suitable estates for his investments. But this method is risky as the agent might predict wrong estates and thus leading to loss of the customer’s investments. The existing system involves calculation of house prices without the necessary prediction about future market trends and price increase. So to make it easier we are coming up with an idea of property advisor. The goal of the project is to predict the efficient house pricing for real estate customers with respect to their budgets and priorities. By analyzing previous market trends and price ranges, and also upcoming developments future prices will be predicted. The functioning of this paper involves a website which accepts customer’s specifications and then combines the application of multiple linear regression algorithm of data mining. This application will help customers to invest in an estate without approaching an agent. It also decreases the risk involved in the transaction.