Approaches and comparison of valuation of Residential properties by using Deep Learning Technique.

This study presents an integrated approach to real estate price prediction and property valuation by combining advanced deep learning models with traditional regression techniques. The research leverages machine learning algorithms, including Artificial Neural Networks (ANNs) and Long Short-Term Memory (LSTM) networks, to improve the accuracy and efficiency of predicting residential property prices. A comparative analysis of valuation techniques such as the income approach, sales comparison method, and cost method is also conducted using a case study focused on residential properties in Pune City. The study explores spatial, temporal, and economic variables influencing market trends and integrates them into a hybrid model framework. By combining modern AI-driven predictive tools with traditional real estate valuation practices, this work aims to enhance decision-making for stakeholders such as investors, developers, and urban planners.