GEOVALUE PREDICTOR HOUSE PRICE PREDICTION FOR INDIA

Abstract: Predicting the price of a house can be a complicated task. There are many factors involved in determining the price of property such as location, size, state of property, nearby facilities and type of property. However, there is a lot of variety even between different cities and within cities between different localities in India. This makes it hard to determine whether a property is a fair price based on the information provided.

This paper discusses GeoValue Predictor, a machine learning web application that can predict residential property prices for a selection of cities in India. This takes into account features such as city, locality type, property type, furnishing, number of bedrooms and bathrooms, built up area, floor, age of property, distance to metro stations and city centre, parking, lift and gated community facilities. Four different regression models are compared (Linear Regression, Ridge Regression, Random Forest and Gradient Boosting).

The prototype has been trained on a synthetic data set with 6000 rows. Gradient Boosting gave the highest R squared value (0.960) out of the four models. The mean absolute error was 10.80 lakh rupees and the root mean squared error was 16.10 lakh rupees. We also get an indication of how important each feature is, an indication of how prices vary between different cities, an indication of the predicted price range and an indication of how much it would cost to buy or rent.
This project does not aim to replace professionals who value properties but aims to provide a quick and easy way to give an indication of how much a property may be worth.