In recent years, artificial intelligence (AI) and machine learning (ML) have begun to revolutionize the real estate sector in areas such as price forecasting, demand analysis, and portfolio management. However, these technologies not only interpret current data but also hold promise in predicting critical scenarios for the future. One of these is real estate bubbles: situations of financial crisis where prices artificially rise by deviating from the supply-demand balance and then burst suddenly. So, can artificial intelligence really predict these bubbles? This is the question we are pursuing.

Can Predictive Models Forecast Real Estate Bubbles?

Overview of Real Estate Bubbles

A real estate bubble is formed when housing prices rise artificially, disconnected from economic fundamentals. This increase is generally fueled by factors such as consumer behavior, speculative investment, cheap credit, and insufficient regulation. At some point, prices become unsustainable, leading to sudden drops ("bursts"). The 2008 crisis is a striking example of this. The ability to detect such scenarios in advance could make a significant difference for investors and policymakers.

Why Have Traditional Methods Proved Insufficient?

Many economic crises could not be predicted using traditional economic models. These models typically rely on linear assumptions, work with static data, and ignore the complex relationships among all variables. However, housing bubbles arise from the interplay of social psychology, behavioral economics, and financial architecture. Therefore, more complex, multidimensional, and data-intensive analyses were needed. It is at this point that artificial intelligence comes into play.

Is Bubble Prediction Possible with Artificial Intelligence?

The most significant difference of AI is its ability to recognize relationships, correlations, and patterns among a multitude of complex variables. This skill can be utilized to identify "anomalies" in the real estate market.

Example: Detecting Anomalies with Time Series Analysis

Machine learning algorithms can analyze changes in housing prices over time, identifying deviations from the average (outlier situations). These deviations can be examined to determine whether they signal calm increases or potential bursts.

Supervised Learning: Learning from Past Crises

Models can be trained using data from crises like 2008 through supervised learning. In other words, the model is fed with examples such as "these data indicate the presence of a bubble." Similar signals can be sought in current markets.

Unsupervised Learning: Detecting Unknown Anomalies

Considering that bubbles do not always appear the same, unsupervised learning may be more flexible. For instance, abnormal deviations in the ratios between housing prices and rental yields could signal potential bubbles.

Case Study: What Would Have Happened if AI Had Been Applied During the 2008 Crisis?

(The graph shows mortgage default rates between 2004 and 2010.

The areas marked with red circles are points that could be identified as "abnormal increases" by AI.

Such early signals could indicate the approaching collapse of a market.)

The 2008 crisis represents a burst crisis resulting from highly complicated mortgage-backed securities, the unchecked distribution of mortgage loans, and a rapid speculative increase on the market surface. When analyzing the data of this period, numerous "anomalies" were evident, such as the disproportionate increase of housing prices relative to income, sharp rises in mortgage borrowing levels, and the decrease in rental yields compared to prices.

Had price predictions been made with deep learning derivatives such as LSTM during this period, deviations from trends could have served as early warning signals. Similarly, behavior trends such as "housing prices are skyrocketing," "everyone is buying houses," could have been highlighted through social media analysis or news sentiment analysis.

Additionally, the acceleration of interest in the subprime mortgage segment in the U.S. could have indicated the deterioration of credit portfolios through supervised learning models. By combining these signals, alarm levels could have been reached in specific markets before the 2008 crisis.

At this point, one might ask: If these models had truly existed in their current form at that time, would the crisis have been completely avoided? Perhaps not, but at least its impact could have been minimized, and damages could have been limited with tighter regulations and early policy actions.

What Data Can Be Used?

The following types of data are critically important for bubble forecasting:

• Housing price indexes (Turkish Statistical Institute, REIDIN, etc.)
• Rent-price ratios
• Credit usage rates and interest levels
• Consumer confidence indices
• Listing and sales data regarding the supply-demand balance
• Macroeconomic data (GDP, unemployment, inflation)
• Social media sentiment analysis (trends like "These properties have become very expensive")

Tools and Technologies Used

Time Series Forecasting: ARIMA, Prophet, LSTM

Anomaly Detection: Isolation Forest, One-Class SVM

Classification Models: XGBoost, Random Forest, Neural Networks

NLP and Social Data Analysis: BERT, sentiment analysis models

Potential Applications

- Early warning systems for policymakers: Systems that notify central banks or municipalities of the market's overheating areas.

- Investor insights: Identifying areas with potential bubble signals for portfolio managers.

- Credit risk assessment: More careful evaluation of mortgages in risky areas for banks.

Challenges and Limitations

• Limitations of data quality and access
• Interpretability of model complexity
• Variability of market dynamics (what works today may not work tomorrow)
• Ethical questions: Risks of price fluctuations in areas flagged for bubble warnings

LeadOcean: AI-Supported Lead Generation in the Real Estate Sector

The real estate sector is rapidly evolving under the influence of digital transformation, and competition is increasing daily. In this dynamic environment, reaching the right customers and optimizing sales processes is critically important for success. PlusClouds' AI-supported lead generation tool, LeadOcean, provides significant advantages for real estate professionals in this area.

What is LeadOcean?

LeadOcean is an AI-based tool that analyzes your website to identify potential customers who are most aligned with the services you offer. It matches customer needs with the services you provide, identifying the most suitable customer candidates for you.

Advantages of LeadOcean in the Real Estate Sector

Smart Data Analysis: LeadOcean analyzes data available on the internet to identify potential customers that fit your industry. This way, you can focus on buyers who are genuinely interested.

Automated Customer Tracking: It helps you identify customers who are close to purchasing real estate by analyzing the behaviors of potential buyers.

Targeted Marketing: By reaching individuals who meet your specified criteria, it increases the efficiency of your advertising campaigns and allows you to reach the right people at the right time.

LeadOcean is a powerful tool that accelerates customer acquisition processes for professionals in the real estate sector, increases sales, and optimizes marketing strategies. With its AI-supported solutions, it helps you stand out in this competitive industry.

Conclusion: AI Doesn't Burst Bubbles, But It Speaks Before They Do

Artificial intelligence has immense potential in predicting anomalies and possible price bubbles in real estate markets. However, the goal of these systems should not be to create panic, but to provide timely and analytical information to decision-makers. In the future, such early warning systems may play a key role in maintaining financial stability.

Tracking the signs of crises can sometimes be the best way to prevent new ones. Artificial intelligence can illuminate this path for us.