Forecasting Real Estate Prices and Rents Using LSTM Algorithms

Abstract

Predictive modelling constitutes a fundamental component in the utilization of data by organizations for decision-making purposes. When firms employ this methodology, they can anticipate future occurrences, discern emerging trends, mitigate risks, and optimize resource utilization. The real estate market significantly influences the economy, impacting consumer spending patterns, construction activity, and housing availability. Obtaining precise estimates of the costs associated with flats and rentals is crucial to financial planning and policy-making. The autonomous learning capabilities of machine learning have emerged as a pivotal element in the proliferation of automation. Likewise, it plays a vital role as a valuable tool in data analysis and a source of inspiration for individuals with artistic inclinations. In this recent study, we utilized a specific dataset to assess the effectiveness of a classifier in distinguishing between long-term and short-term memory. The conducted investigations revealed a significant correlation of 99% between the elements under investigation and a notable % accuracy rate of 72%. Line plots, heat maps, and bar graphs are illustrative visualizations that can effectively reveal concealed patterns and trends within a dataset.

DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.94-112