House Price Prediction Using Texture and Visual Features

Abstract

Real estate sector has been growing at a rate never seen before. For this sector, a key role is played by the pricing of the property. Gone are the days when the price of the property was based on whims and facies of the real estate dealers. Machine learning has numerous applications in the domain of real estate, and one of the most popular ones is predicting house prices. The application of machine learning in house price prediction involves training a model on a dataset that includes a variety of visual and texture features related to the property. The model is then used to predict the price of a new property based on its features. This paper successfully explores machine learning based house price prediction. The methodology followed was to first use data sets to train the model. Later, using correlation-based hybrid GA-reinforcement strategy, a suitable set of features has been selected. In the end, these features are applied to a XG boost regressor to get results. The accuracies are compared with the cases of without feature selection of different regressors. This algorithm, if successfully deployed will be beneficial to both sellers and buyers, because it sets a data-based benchmarking for pricing the property.