## 3 HELIOS Concept in the Real Estate Market--Methodology and Implementation
### Leveraging LLMs for Resolving Homogeneity Market Analysis Challenges  
The attempt to apply natural language processing (NLP), specifically large language models (LLMs), represents a distinct approach in the NLP field. LLMs, being a specialized type of model, concentrate on comprehending and generating human-like text by leveraging deep learning techniques. Figure 1 shows the prospective usefulness of LLM application in the most troublesome aspects of homogenous property market analyses, elaborated based on the authors' experience and literature review. The challenges identified in the analysis of homogenous property markets, as depicted in Figure 1, primarily arise from the following factors. 1. UPDATING PRICES: The paramount challenge in real estate price updates is the quality and timeliness of data. The model's effectiveness hinges on the accuracy and recency of available data. Only accurate and complete information can result in good forecasts. Additionally, the intricacies of real estate markets, influenced by unpredictable factors like political, economic, social, and environmental changes, need help integrating them into predictive models. In the realm of property analysis and valuation, the large language model emerges as a valuable tool for updatingtends in real estate price changes as of the valuation date. The application of the LLM model enriches the analytical process by providing nuanced insights into evolving market dynamics. Data Collection: LLMs can sift through and analyze vast amounts of data from various sources, such as real estate listings, articles in the real estate market, industry reports, and market data, to identify current price trends. Natural Language Processing: Leveraging natural language processing capabilities, LLMs can interpret the language used in property descriptions, market analyses, and forecasts, providing insights into factors influencing property prices. Historical Analysis: Through the analysis of historical price data, LLMs can assist in understanding how property prices have evolved in the past, which can be utilized for forecasting future trends. Price Modeling: LLMs can support the creation of predictive models for property prices by analyzing large datasets and identifying patterns that may not be apparent through traditional analytical methods. Adaptation to Local Factors: Models can be adjusted to account for local factors influencing prices, such as changes in infrastructure, regional policies, or investor interest. 2. UNIFORM INFORMATION SOURCES SCARCITY: The scarcity of uniform information sources is the primary challenge within the real estate domain. The absence of consistent and comprehensive data hampers accurate analyses and impedes the development of reliable predictive models. The varying formats, incomplete datasets,  
Figure 1: The biggest challenges in homogenous property market analyses. Source: own study. and disparities in information representation across different sources contribute to the complexity of aggregating and processing real estate data. Addressing this issue, the application of LLMs emerges as a potential solution. LLMs offer assistance in various aspects of data processing and aggregation related to real estate. Automatic Information Aggregation: LLMs can search diverse data sources, including listings, cadastral databases, real estate sales or rental portals, and information from social media and online forums. They analyze and aggregate this information, providing a more cohesive and integrated view of available properties. Data Extraction and Classification: LLMs can extract essential information from unstructured text data, such as property descriptions. They identify relevant attributes, such as location, price, property size, number of rooms, etc., and classify listings based on these parameters. Sentiment Analysis: Using LLMs allows sentiment analysis of opinions about properties posted online.