EstimaRent: Data Driven Rental Housing Optimisation and Market Analysis for Enhanced Decision-Making

Authors

  • Shwetambari Chiwhane Department of Computer Science, Symbiosis Institute of Technology, (SIT) affiliated to Symbiosis International (Deemed University), Pune, India
  • Pooja Bagane Department of Computer Science, Symbiosis Institute of Technology, (SIT) affiliated to Symbiosis International (Deemed University), Pune, India
  • Akshansh Sourabh Department of Computer Science, Symbiosis Institute of Technology, (SIT) affiliated to Symbiosis International (Deemed University), Pune, India
  • Srushti Nagrale Department of Computer Science, Symbiosis Institute of Technology, (SIT) affiliated to Symbiosis International (Deemed University), Pune, India
  • Saakshi Jha Department of Computer Science, Symbiosis Institute of Technology, (SIT) affiliated to Symbiosis International (Deemed University), Pune, India
  • Saumya Pandey Department of Computer Science, Symbiosis Institute of Technology, (SIT) affiliated to Symbiosis International (Deemed University), Pune, India

Keywords:

Housing Market, Machine Learning, Real-Time Data, Rental Price Prediction, User-Friendly Platform

Abstract

Estimarent is a cutting-edge research programme that aims to change the housing industry by leveraging the power of machine learning. The goal of this project is to deliver very accurate rental pricing projections for specific cities via a user-friendly web platform. Estimarent provides a useful resource for those looking for rental houses by combining data from social networks like Facebook and regional WhatsApp groups. Key goals include developing expert machine learning models, specifically using LSTM for sequence data, to estimate rental charges with high precision. Property owners can optimize rental listing pricing, renters can find cheap accommodation, and real estate specialists can obtain insight into rental market dynamics. Beyond mathematics, Estimarent simplifies one of life's most important decisions: whether to rent or buy a home. As it grows, the initiative aims to revolutionise the rental housing market by providing a dynamic solution that responds to the ever-changing real estate landscape.

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References

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Published

25.12.2023

How to Cite

Chiwhane, S. ., Bagane, P. ., Sourabh, A. ., Nagrale, S. ., Jha, S. ., & Pandey, S. . (2023). EstimaRent: Data Driven Rental Housing Optimisation and Market Analysis for Enhanced Decision-Making. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 20–28. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4216

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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