# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 1 Introduction
### Contribution  
The contribution of this study can be summarized as follows:  
1. Scientific contribution: This study expands the methodological landscape in regression by presenting Kolmogorov-Arnold networks (KANs) as a robust alternative to traditional models like gradient boosting, ensemble methods, and neural networks such as MLPs, GRUs, and LSTMs. Gradient boosting algorithms like XGBoost and LightGBM,while effective on structured datasets, struggle with capturing intricate, nonlinear relationships in high-dimensional data and face scalability and interpretability challenges. Similarly, MLPs require extensive tuning to handle complex, high-dimensional data effectively, while GRUs and LSTMs, optimized for sequential data, are less suitable for non-sequential regression tasks and introduce significant computational overhead. KANs address these limitations by leveraging the Kolmogorov-Arnold representation theorem, which decomposes multivariate functions into sums and compositions of univariate functions. This approach allows KANs to model complex nonlinear relationships efficiently while maintaining interpretability, scalability, and adaptability to high-dimensional data. The spline-based architecture further enhances their ability to generalize well across diverse regression tasks, offering a significant advantage over both traditional and modern machine learning models. By outperforming these methods in terms of accuracy and computational efficiency, KANs demonstrate their potential as a transformative tool for tackling complex regression problems across various domains.
2. Economic and social impact: By improving prediction accuracy in housing price estimation, this research contributes to a more transparent and informed real-estate market. Enhanced predictive capabilities help stakeholders make better decisions, optimize investments, and mitigate risks. Furthermore, the insights gained from the study can lead to the development of smarter urban planning and housing policies, ultimately benefiting society by fostering equitable and sustainable growth in the housing sector.
3. Broader applicability across industries: While the focus is on house price estimation, the versatile nature of KANs offers potential applications beyond real estate. KANs can be applied in areas such as financial modeling, healthcare predictions, environmental analysis, and supply chain optimization. The study underscores the adaptability of this approach, setting a precedent for its application in diverse regression and prediction tasks.  
This work not only establishes the utility of KANs in regression problems but also sets the stage for a future exploration of their potential across a wide range of applications. Section 2 provides an overview of related works in the field of KANs and house price prediction, while Section 3 explains the research methodology and the proposed KAN model in detail. In Sections 4 and 5, the experiments are described, and the results are discussed. Finally, Section 6 concludes the paper and provides insights for future research in this area.