Optimization Techniques for Deep Learning Based House Price Prediction

With the persisting increment in the rates of houses, the cost of homes is escalating every year. So it is necessary to design a system that can predict future housing prices. This intelligent system undertakes the task to play a crucial role between learning and optimization. But because these systems are dynamic, complex, and multidisciplinary, optimizing them is particularly challenging. The House Price Index (HPI) is one of the most useful indicators for calculating frequent changes in home prices. The data processing of the current home pricing involves several approaches and takes into account several variables, including housing design, layouts, climate control conditions, etc. To incorporate the benefits of external factors and utilize the increased home price, this approach makes use of a multiple regression model and optimization approaches. Using enhanced housing price composition and multiple regression prediction models, such as particle swarm optimization, genetic, firefly, and mayfly algorithms, etc., it is possible to include the benefits of outside influences. The purpose of this work is to provide an overview of the latest and most popular deep learning optimization techniques which predicts the more precise and accurate prices of houses.