Research Article
Optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models
@INPROCEEDINGS{10.1007/978-3-031-48888-7_12, author={Runkana Durga Prasad and Vemulamanda Jaswanth Varma and Uppalapati Padma Jyothi and Sarakanam Sai Shankar and Mamatha Deenakonda and Kandula Narasimharao}, title={Optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models}, proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I}, proceedings_a={IC4S}, year={2024}, month={1}, keywords={Real Estate United States Machine Learning Economy Regression Ensembling techniques}, doi={10.1007/978-3-031-48888-7_12} }- Runkana Durga Prasad
Vemulamanda Jaswanth Varma
Uppalapati Padma Jyothi
Sarakanam Sai Shankar
Mamatha Deenakonda
Kandula Narasimharao
Year: 2024
Optimizing Real Estate Prediction - A Comparative Analysis of Ensemble and Regression Models
IC4S
Springer
DOI: 10.1007/978-3-031-48888-7_12
Abstract
Valuation is a fundamental aspect of real estate for businesses. Land and property serve as factors of production, and their value is derived from the use to which they are put. This value is influenced by the demand and supply for the product or service produced on the property. Valuation involves determining the specific amount for which a property would transact on a given date. Accurate prediction of real estate prices is crucial for investors, house owners and industry professionals. In this article, analysis of USA real estate prediction using regression and ensemble models was presented, also evaluating the best model out of all the models that have been applied. The objective of this article is to provide accurate predictions for the real estate market, by making use of Multi-Variate Regression, Random Forest Regressor, Decision Tree Regressor, XGB Regressor and CatBoost Regressor. This analysis offers valuable insights for making wise and right choices in the real estate market.
