REAL ESTATE PRICE PREDICTION USING ADVERTISEMENT DATA
DOI:
https://doi.org/10.24867/01BE48VidovicKeywords:
data analysis, machine learning, regression analysis, neural networksAbstract
This paper presents a real estate price prediction model using advertisement data. Real estate technical specifications, images and, if available, geographical coordinates are extracted from advertisements, acquired from a real estate advertising website. The coordinates are used to form location ratings. The images are used to train a neural network for the detection of a real estate's equipment on images.Three separate datasets for training the price prediction models were formed. The first dataset contains only technical specifications of the real estates, the second dataset also contains location ratings, while the third dataset contains location ratings and objects detected on images. These datasets were used to train different regression models for predicting real estate prices. The performances of the models, represented by their achieved R2 scores, were compared in order to establish which model had the best performances.. The best performing model was the GBT (Gradient Boosted Trees) model on the dataset with location ratings and detected objects, with an achieved R2 score of 0.856.
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