# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 6 Conclusions  
In this study, a custom KAN-based deep neural network model is proposed for the house price estimation problem. The network architecture supports a deep neural network, consisting of multiple layers. To evaluate the performance of the KAN model, we used two datasets: the Greek Listings dataset, which consists of real-estate listings in Greece, and the California House dataset, which contains data related to houses in California. Both datasets were obtained from Kaggle and are available there.  
Based on the analysis conducted on the two datasets, the results show that the custom KAN-based model can outperform the best regression algorithms and traditional neural network models in terms of predictive accuracy (regarding MAE and MSE metrics that were tested). These findings demonstrate that KANs have significant potential for improving predictive performance in real-world applications. Furthermore, the outcomes prove the efficiency of spiking neural networks in modeling complicated, dynamic systems with incomplete and noisy data. Overall, the results provide strong evidence for the use of KANs as a powerful and adaptable tool for data analysis and decision-making.  
Regarding the future directions of this research, multiple plans have been outlined to further extend this study. One direction is to conduct more experiments with various types of KAN-based architectures to explore their full potential in greater detail. While the experiments conducted utilized public datasets, a future research direction includes the validation of the KAN model with real market data to assess its practical applicability. This validation strategy encompasses collaboration with real-estate firms to access actual transaction data and conducting targeted pilot studies in specific geographic regions. Our research roadmap includes the development of a mobile application to enhance accessibility and practical implementation.  
Beyond house price estimation, the KAN model demonstrates significant potential for broader applications in the real-estate industry. The model's versatility extends to investment analysis for property valuation, market trend forecasting through historical data analysis, risk assessment considering multiple economic indicators, and urban planning support through data-driven insights. These applications collectively position KAN as an innovative analytical tool that can significantly enhance decision-making processes in the real-estate sector.  
This comprehensive approach to practical implementation and diverse application scenarios underscores the potential of KAN as a transformative technology in real-estate analytics while maintaining rigorous standards for validation and continuous improvement. Finally, it is hoped that this research will inspire new research directions and collaborations in the field of neural networks, opening the way for further innovation and breakthroughs in the field of artificial intelligence.  
Conceptualization, I.V. and A.T.; Approach, I.V. and A.T.; Implementation, I.V.; Resources, I.V.; Writing--Original Draft Preparation, I.V.; Writing--Review and Editing, I.V. and A.T.; Supervision, A.T.; Project Administration, I.V. and A.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The following datasets can be downloaded at [[https://www.kaggle.com](https://www.kaggle.com)]([https://www.kaggle.com](https://www.kaggle.com)) accessed on 2 September 2024.  
The authors declare no conflicts of interest.