## 4 Discussion  
The proposed HELIOS system aims to streamline the real estate market analysis procedure, focusing on a crucial stage: the selection of homogeneous objects. The concept encompasses considerations that hinder the effective and satisfactory attainment of results in this area. This innovative concept is currently undergoing in-depth testing, and its primary foundation is to showcase its capabilities in addressing challenges. To address the aforementioned challenges in real estate market analyses, the authors have developed the HELIOS system, utilizing artificial intelligence (AI) through the application of natural language processing (NLP), specifically large language models (LLMs), as its foundational framework. A significant added value of the elaborated solution is the ability to obtain a unique \"fingerprint\" for real estate through an innovative synergistic combination of standardized tokens, capturing the unique characteristics of transactions within these homogeneous groups. This unique approach allows us to group properties based on semantic analysis of transaction descriptions, revealing complex patterns and relationships often hidden using traditional methods. The resulting semantically homogeneous groups provide new insights into buyer preferences and market dynamics, contributing to a deeper understanding of the factors influencing property values. This breakthrough demonstrates the exceptional potential of this methodology in advancing real estate research and enhancing data interpretation. However, one of the main limitations of our study is the dependence on the quality and detail of transaction descriptions. Converting data into textual descriptions and then into S-BERT embeddings requires careful preprocessing and may introduce subjectivity in data interpretation. Additionally, language models such as S-BERT, despite their advanced natural language processing capabilities, may not be fully effective in capturing all nuances and specifics of the real estate market, which can affect clustering accuracy. Despite these limitations, the findings of our study have significant implications for practice in the real estate market. By offering market analysts, agents, and other practitioners new \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No Group** & **Quantity** & **Mean Distance** & **No Group** & **Quantity** & **Mean Distance** \\ \hline  & & 312 & 16 & 2472 \\ \hline  & & 313 & 47 & 4490 \\ \hline  & & 314 & 243 & 13,019 \\ \hline  & & 315 & 4 & 3115 \\ \hline  & & 316 & 296 & 142,724 \\ \hline  & & 317 & 198 & 204,622 \\ \hline  & & 318 & 2 & 2851 \\ \hline  & & 319 & 7 & 2943 \\ \hline Average Mean Distance & & & & 33,108 \\ \hline  & & & 4372 & \\ \hline \hline \end{tabular} Source: own elaboration.  
\end{table} 
Table 4: _Cont._tools for deeper analysis and better understanding of the market, clustering properties based on semantic analysis can help identify trends and customer preferences, as well as forecast future market changes. It can also facilitate matching offers to buyer needs, thereby increasing sales efficiency. Moreover, while the property market is characterized by complexity, the adoption of mass appraisal techniques offers a promising solution for accurately valuing properties for taxation purposes. The added value lies in the strong incorporation between humans and machines, enabled in the \"Early Validation\" component. A machine can never fully replace a human in aspects that involve nuanced judgment and contextual understanding. The developed system considers this and introduces a specific component to address it. This component focuses on collaboration between human expertise and machine efficiency. Given that it is a critical element of the HELIOS Nexus system, every output or input within the system can be subjected to validation or is recommended for human validation from a substantive perspective.