Housing Price Prediction by Using Generative Adversarial Networks

Abstract

Housing price forecasting is a highly complex and vitally important field of research. Recent advancements in deep neural network technology allow researchers to develop highly accurate models to predict financial trends. Deep learning has recently achieved great success in many areas due to its powerful capabilities in the data processing. For instance, it has been widely used in financial areas such as stock market prediction, portfolio optimization, financial information processing, and trade execution strategies, etc. In this paper, we proposed a novel architecture of Generative Adversarial Network (GAN) with the Long Short-Term Memory (LSTM) for forecasting the price of houses. To train and evaluate our methodology. The dataset was House Sales in King County, USA, which includes several pings, floor or room age, etc. In the analysis of this input information, the network model will output the predicted house price.