Predictive Analytics for Housing Market Trends and Valuation

Introduction: The demand for housing in major cities is exceptionally high due to the concentration of offices and economic hubs in these areas. The combination of limited available land and increased demand drives house prices upward. Objective: To accommodate this, developers are increasingly constructing residential areas on the outskirts of cities, offering easier access to transportation such as trains and highways. These developers compete by offering competitive pricing, diverse housing options, simplified mortgage processes, and attractive promotions like zero down payments. Buying a house is a significant long-term investment, as property values typically appreciate over time. Therefore, a thorough analysis is crucial when purchasing a home. Several key factors, such as location, land size, building area, and property type, play a role in determining house prices. Method: This study adopts a quantitative approach, which involves systematically investigating phenomena by collecting measurable data and analyzing it through statistical, mathematical, or computational methods. Result: This paper discusses the most effective techniques for data collection, pre-processing, feature extraction, model training, and evaluation. The purpose of this research method is to develop theoretical frameworks related to real-world phenomena. Conclusions: Measurement plays a pivotal role in this quantitative study, as it is central to understanding the data and drawing meaningful conclusions. Finally, we evaluate the current state of research, identifying trends and gaps in the field