Predicting Real Estate Prices Using Deep Learning and Regression Techniques

Predicting Real Estate Prices Using Deep Learning and Regression Techniques

Mr. Arbaaz I. Mujawar

Dept. Computer Engineering

JSPM’s JSCOE Pune, India

arbaazmujawar01@gmail.com
Mr. Uzair S. Mulla

Dept. Computer Engineering

JSPM’s JSCOE Pune, India

uzairamulla10@gmail.com

Mr. Rohan V. Thakre

Dept. Computer Engineering

JSPM’s JSCOE Pune, India

rohanthakre1342003@gmail.com
Mr. Pranav B. Mahadik

Dept. Computer Engineering

JSPM’s JSCOE Pune, India

pranavmahadik1973@gmail.com

Prof. N.R. Zinzurke

Prof. Dept. Computer Engineering

JSPM’s JSCOE Pune, India

ni3zinzurke2021@gmail.com

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Abstract - The real estate market is highly dynamic, influenced by a variety of factors such as location, property features, economic conditions, and market trends. Predicting real estate prices accurately is a challenging yet crucial task for buyers, sellers, and investors. In this study, we explore the use of deep learning and regression techniques to predict real estate prices. Deep learning models, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), are leveraged to capture complex patterns and relationships within large datasets. In addition, traditional regression techniques, including linear regression and decision trees, are used for comparison to understand their effectiveness in predicting property values. The study demonstrates that combining deep learning methods with regression approaches can enhance prediction accuracy, offering valuable insights for stakeholders in the real estate market.

Key Words: Real Estate Prices, Deep Learning, Regression Techniques, Artificial Neural Networks, Convolutional Neural Networks, Property Value Prediction, Machine Learning, Predictive Modelling, Real Estate Market Analysis