Leveraging Deep Learning Intelligence for Real Estate Analytics

Ms. Pinninti Pujita*,  Mrs. S. Ratna Kumari#

*MCA  Student, SITAM, Vizianagaram.

#Associate Professor, Dept. of CSE, SITAM, Vizianagaram.

E-mails: [pujitapinninti@gmail.com, ratnakumari@gmail.com]

Abstract—Predicting the price of a house is a hard problem. Many things affect the final number at the same time, such as location, the size and condition of the building, nearby amenities, the state of the local economy, and how buyers happen to feel about the market that month. Older valuation methods, which are mostly built on straight-line regression, tend to miss the bends and jumps that show up in real data. In the last few years, deep learning has given researchers new tools to deal with this kind of messy, high-dimensional information. In this paper, we develop a price prediction model around a Convolutional Neural Network (CNN). CNNs were first made famous by image tasks, but they also work well on well-organised tabular data once the columns are arranged with some thought. Our system pulls together property attributes, basic location details, and market signals, and learns the hidden patterns that push prices up or down. Structured real estate records are reshaped into small feature maps, and the CNN then extracts layered representations that a simple regressor cannot easily pick up. The goal of this work is to help buyers, sellers, investors, and local authorities by giving them more trustworthy price estimates and lowering the guesswork that usually comes with a house purchase. In our experiments the CNN-based approach beats standard machine learning baselines on both accuracy and stability, which makes it a practical option for smarter real estate analytics.Index Terms—Deep Learning (DL), Convolutional Neural Network (CNN), 1D-CNN, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Structured Deep Convolutional Neural Network (SDCNN), real estate analytics, price prediction.