'How We Used Machine Learning to Predict Real Estate Prices' machinelearning realestate
All these stats and trends play a part when you estimate and predict the price of a house. Now, we are going to move on to the machine learning case we had recently where all the stats came in handy.It's hard to estimate a price of a particular house and predict its behavior in the future. Even if this prediction covers only a short period of time. Too many factors can influence the final numbers.
Real estate professionals look for comparable homes in the area and define the value of a property based on how those houses behaved on the market. Comparable homes are chosen by size, number of rooms, style, and recent sales price.A Broker Price Opinion is another option for a person to get a professional opinion on the house. It is usually made by a professional broker who is aware of the local market.
Such an amount of data that a broker should bear in mind while predicting the house price can get extremely voluminous. This task can be tedious even for the most experienced specialists. Besides, there’s still a possibility of a simple human error. That was the exact reason why we started working on this project a client has come to us with: To automate house price prediction and minimize the influence of possible human error. Our main goal was to make a highly accurate machine learning model that will predict the house price in a month with an accuracy of around 85 to 90 percent.for price prediction, what tech stack we used, and what main challenge we faced during the process. Let’s start with the overall strategy.
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