## 3 HELIOS Concept in the Real Estate Market--Methodology and Implementation
### Unveiling the Helios System Concept  
Considering the considerations, the authors advocate for the creation of a versatile and adaptable cognitive information system tailored to the homogeneity of object classification in the real estate market. The proposed solution with the acronym HELIOS (Homogeneity Estate Linguistic Intelligence Omniscient Support) is depicted in Figure 2.  
The concept of the HELIOS system aims to enhance the efficiency and quality of analyses, leading to the identification or classification of homogenous objects in the real estate market. The term \"object\" can refer to either a part of the property (e.g., a building), the entire property, or even a real estate submarket. This research area focuses on an attempt to create a universal algorithm capable of solving or at least minimizing challenges and issues that limit the effectiveness of analyses in a crucial stage of most real estate market analyses, namely the selection of similar or homogenous objects. The most significant advantage of this concept lies in the synergy between human expertise, needs, and requirements and the efficiency of AI in pattern recognition, large-scale data processing, and anomaly detection. The proposed procedure consists of the following components:  
* The \"Real Estate Data Landscape Reality\" component reflects the generic and unstructured data from diverse sources. * The \"ETL (Extract, Transform, Load) process\" is a data processing step that allows data to be obtained in the required format for further processing steps. * The \"Artificial Homogeneous Property Database with Flaws\" is a database developed as a verifier for results with artificially introduced flaws tailored to detect potential weaknesses in the LLM. * The \"General LLM\" component delivers a comprehensive set of rules that serve as the cornerstone for the system's adept understanding and interpretation of linguistic patterns and structures. * \"Early validation\" provides a mechanism for incorporating expert opinions and feedback from entities within the real estate market, contributing to refining and improving the system's accuracy and effectiveness. This component is particularly significant because it emphasizes collaboration between human expertise and machine efficiency, ensuring that human insight complements and enhances the system's analytical capabilities, addresses discrepancies, and improves overall performance. * The \"HELOS Nexus\" serves as a system component designed to enhance cognitive processes by utilizing a custom LLM asset. * The \"Released LLM\" comprises a tailored language model customized to align with the real estate market's specific linguistic nuances and requirements. * The \"Real Estate Fingerprint\" utilizing LLM technology, represents a comprehensive approach involving the synergistic combination of standardized tokens to capture and analyze unique characteristics and features within the real estate domain. This method aims to create a distinct and refined profile of properties by leveraging linguistic rule-based processes. * \"Fingerprints Matching\" employs a round-robin approach to assess the magnitude of differences between each and every pair of objects through iterative analysis. * The \"Non-Homogeneity Feedback\" is utilized for fine-tuning, involving the adjustment of model parameters in the event of not achieving a satisfactory result at the level set by the operator. * The \"Helios Homogeneity Output\" provides the final approved classification of homogenous objects. Figure 2: The concept of HELIOS—system for homogeneity real estate analyses. Source: own study. This concept ultimately leads to the classification of objects into homogeneous groups based on the assumed level of similarity (indiscernibility). However, the most significant advantage of the proposed solution, compared to commonly used methods, lies in addressing the significant challenges inherent in analyses within the property market.