Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

REAL estate appraisal, which is the price estimation process for real estate properties, for both buyers and sellers it is essential as the basis for compromise and deal. Customarily, the repeat sales model has been widely adopted to estimate real estate price. Though, it depends on the design and calculation of a complex economic related index, which is challenging to estimate accurately the estate’s price. Nowadays, real estate brokers provide easy access to detailed online information on real estate properties to their clients. In a smart city, effective and accurate real estate assessments governed by a local government is crucial for determining the property taxes. Such assessments have never been trivial, and inappropriate assessments may result in disputes between property owners and the local government. Generally for price prediction Regression is used (Prediction of continuous valued-function). But here we are going to use Structured Deep Neural Network in order to improve efficiency and accuracy. We introduce a deep learning approach to smartly and effectively assessing 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 structured DNN model has been designed to learn from the most recently captured data points; therefore, it allows the model to adapt to the latest market trends.

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