Abstract
The aim of the current study is to determine which models are most suited for forecasting a property's rental price given a variety of provided characteristics and to develop a predictive model using machine learning techniques to estimate the rental prices of apartments in Cluj-Napoca, Romania, in relation to market dynamics. Given the absence of a comprehensive dataset tailored for this specific purpose, a primary focus was placed on data acquisition, cleaning, and transformation processes. By leveraging this dataset, the model aims to provide accurate predictions of fair rental prices within the Cluj-Napoca real estate market. Additionally, the research explores the factors influencing rental prices and evaluates the model's performance against real-world data to assess its practical utility and effectiveness in aiding rental market stakeholders.
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