Leveraging Hybrid Machine Learning Technique For Effective House Rental Price Forecasting

These days, figuring out the house's sale price is crucial because both the land and the house's prices rise annually. Therefore, the next generation needs a simple approach for forecasting future rents for houses. Accurate and data-driven strategies are crucial for estimating house rental prices in the rapidly shifting real estate market. This study investigates the application of machine learning to accurate rental price forecasting. Conventional methods of rental estimation tend to rely on intuition and knowledge of the marketplace in the area, but they are incapable of taking into consideration the complex network of variables which influence rental rates. This study explores how rental prices are influenced by factors including the age of the property, whether it is furnished, the flat area, the size of the house etc. It also shows how machine learning models may be used to find hidden connections and get precise rental price predictions. The dearth of highly accurate models currently in use, the need to reduce overpricing, adjust to changing conditions in the market, maximize the return on investments, support data-driven decision-making, advance market transparency, and create a simple to use interface are among the reasons behind this research. The main goal is to create a reliable and accurate model for predicting the price of a rental home so that tenants and property owners can benefit from data-driven insights.