Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Real estate properties assessment is the price estimation process for real estate properties. Nowadays, real estate brokers provide easy access to detailed online information on real estate properties to their clients. Regularly, the repeat sales model has been widely adopted to estimate real estate price. Generally for price prediction Regression is used i.e. prediction of continuous valued-function. But here we are going to use Deep Neural Network in order to improve productivity and accuracy. We introduce a deep learning approach to smartly and effectively evaluating real estate values. We propose a systematic method to derive a layered knowledge graph and design a structured Deep Neural Network (DNN) based on it. Neurons in a structured DNN are structurally connected, which makes the network time and space efficient; and thus, it requires fewer data points for training. The DNN model has been planned to learn from the most recently taken data points. We propose a systematic method to derive a layered knowledge graph and design a structured Deep Neural Network based on it. We introduce a deep learning approach to smartly and effectively assessing real estate values.

Keywords: Deep Neural Network (DNN), layered knowledge graph, structurally connected, deep learning approach.