Helps you to budget and choose wisely.
Helps you on how to increase the value of any property.
Allows the realtors to help increase the value of a property for sale.
We consider various selling prices of a house on MLS, and check to see if any home renovations were made between two sales of the house.
Since different data points (permits, schools, hospitals, etc.) have different formats, we figured out a way to extract and process all relevant information. The data from different sources is then standardized and normalized.
We collect a range of geographical data – such information about schools, hospitals, malls, crime rate, neighborhood, and economy – that may influence the ROI.
Our algorithm pipeline involves data processing, feature engineering, and a learning function that predicts ROI.
House details: Basic information about house, year built, house configuration (#bedroom, #bathroom), size, etc.
Neighborhood details: A range of information related to schools, hospitals, malls, walk-score, crime rate, etc.
City level details: Economy, average family income, employment rates, etc.
Home Renovation details: Permit description, permit cost, permit date, etc.
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