Real Estate Price Range Prediction Using Artificial Neural Network and Grey Wolf Optimizer
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Real estate market price prediction is less popular than the stock market prediction and other branches of sales markets, however, there's evidence that the real-estate market is growing substantially out of proportions, and this lack of knowledge is a setback to understanding the elements that effect the real-estate market behaviors, and the relationship between these elements and the market prices. In this paper we propose a Deep learning scheme for predicting the housing market prices by using an artificial neural network and a grey wolf optimizer, we use data collected from 1.2 million leased property listing recorded over the course of two months, and we train our hybrid ANN-GWO algorithm to analyze the data through a generalized linear regression and predict the market prices, then we evaluate our method by using the MSE and Accuracy indexes and find that our method have a 98.7951% accuracy rate with very low MSE, we also find that the accuracy of our model can be increased by increasing the number of neurons. © 2020 IEEE.