House Rent Management System using Machine Learning

Abstract

This research proposes an intelligent house rent management system leveraging machine learning to optimize rental pricing and tenant matching. The system addresses the inefficiencies of traditional methods by predicting optimal rental prices based on market trends, property features, and tenant profiles. It employs a recommendation engine to connect landlords with suitable tenants, minimizing vacancy periods and improving overall efficiency. The system's evaluation demonstrates a significant improvement in rental income and tenant satisfaction compared to existing methods, showcasing the potential of machine learning in property management.

Introduction

The house rental market is characterized by information asymmetry and inefficient processes. Landlords struggle to determine optimal rental prices, while tenants face challenges in finding suitable properties. Traditional methods rely on manual processes, leading to prolonged vacancy periods, price discrepancies, and dissatisfied parties. This research aims to address these challenges by developing an intelligent system that automates key tasks, enhancing efficiency and transparency within the rental market. The key gap lies in the lack of a comprehensive, data-driven approach that integrates property features, market dynamics, and tenant preferences to optimize both pricing and tenant matching.

Objectives

• To develop a machine learning model that accurately predicts optimal rental prices based on various factors.
• To design and implement a recommendation system that effectively matches tenants with suitable properties.
• To create a user-friendly interface for both landlords and tenants to interact with the system.