Article
INVESTMENT MANAGEMENT IN REAL ESTATE
The real estate sector is one of the most dynamic and data-rich investment domains, yet it traditionally relies on manual assessments and intuition-based decision-making. With the advent of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), investment management in real estate is undergoing a transformative shift. This study explores how intelligent technologies can be leveraged to enhance the accuracy, efficiency, and profitability of real estate investment strategies.The research applies predictive modeling to historical property data, economic indicators, market trends, and customer behavior to forecast price appreciation, rental yields, and investment risks. ML algorithms such as Random Forest, Gradient Boosting, and Support Vector Machines are used to analyze factors influencing property values across residential, commercial, and industrial segments. Additionally, clustering techniques are applied to segment locations and property types based on investment potential. Sentiment analysis using Natural Language Processing (NLP) helps assess market sentiment by mining news articles, social media, and buyer reviews.Further, Deep Learning models such as LSTMs are employed to perform time-series analysis of real estate prices, enabling long-term trend forecasting and early detection of market shifts. Image-based DL models also support property condition assessment using aerial and on-site photographs. The findings suggest that integrating AI, ML, and DL into investment management not only improves forecasting precision but also automates routine analysis, reduces decision-making bias, and enhances portfolio performance.This study concludes that intelligent investment systems can empower real estate investors—whether individuals, developers, or institutions—to make datadriven, timely, and optimized decisions, ultimately maximizing returns while managing risks in a volatile market environment.
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