WEB SERVICE MANAGEMENT SYSTEM FOR PREDICTING REAL ESTATE PRICES USING MACHINE LEARNING TECHNIQUES
DOI:
https://doi.org/10.31891/csit-2024-4-9Keywords:
real estate, price forecasting web service, apartment characteristics, price prediction, machine learningAbstract
Today there are many different web services for renting real estate, but none of them provides price forecasting capabilities. There is a need to create a platform that allows users to receive accurate real estate price forecasts with minimal time expenditures. The aim of this paper is to develop the architecture of a web service for real estate price forecasting, considering various apartment characteristics. We have prepared a review and analysis of existing analogues of real estate rental web services, functional and non-functional requirements for a web service for apartment price forecasting. The high-level architecture and technical tasks for the participants of our web service were also developed and described in our research.
The paper proposes the development of a web service that predicts real estate prices based on various property characteristics. The key objectives are: analyze existing real estate rental web services and identify functional gaps, particularly the lack of price prediction capabilities; establish technical requirements for a comprehensive web service that unifies tenants, landlords, and administrators to facilitate informed decision-making; utilize machine learning techniques, such as linear regression, random forest, and decision trees, to develop a price forecasting module within the web service; evaluate the performance of different machine learning models using RMSE metric.
The paper presents the high-level architecture of the web service, including modules for user registration, data validation, apartment search and interaction, and price forecasting. The experimental results demonstrate that the random forest model outperforms linear regression and decision trees in predicting apartment rental prices in Kyiv. Overall, the study highlights the potential of integrating machine learning into real estate web services to enhance transparency and informed decision-making for both tenants and landlords.
