# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 4 Results
### Dataset A--Greece Listings  
The Greece House Listings dataset provides information on numerous properties for sale in Greece. The dataset comprises 20,000 rows and 25 columns, where each row represents a distinct property for sale. The columns that are included are the following:  
* location_name (the municipal of the house; categorical feature; 73 unique values; most common: Athens, 21%).
* location_region (the region of the house; categorical feature; possible values: Attiki/ Thessaloniki; most common: Attiki, 94%).
* res_type (the type of the property; possible values: building, apartment, etc.; five unique values).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Optimal Values: Dataset A** & **Optimal Values: Dataset B** \\ \hline Model architecture & 256-128-64-32-1 & 50-40-30-20-1 \\ Optimizer & Adam & Adam \\ Activation function & ReLU & ReLU \\ Number of knots & 7 & 7 \\ Spline layer & 9 & 9 \\ Learning rate & 0.001 & 0.01 \\ Batch size & 8 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal parameters for KAN.  
* res_address (secondary location attribute; this stands for the exact neighborhood of the property; categorical feature; 987 unique values).
* res_price (advertised price for the property (in euros). This is the feature that the model has to predict. The mean value is 367,000.
* res_sqr (square meters of the property. The mean value is 169).
* construction_year (the construction year of the property).
* levels (levels for the property, e.g., 1 st floor, 2 nd floor, etc.).
* bedrooms (the number of bedrooms. The mean is 2.58).
* bathrooms (the number of bathrooms. The mean is 1.48).
* status (the current status for the property; categorical feature; possible values such as 'good','renovated', etc.; eight unique values).
* energyclass (categorical feature; the energy class is from the lowest level (H) to the highest (A+) in the following order: H, Z, E, \(\Delta\), \(\Gamma\), B, B+, A, and A+. There are also three possible values for the energy class: Non-effective, Excluded, and Pending).
* auto_heating (autonomous heating: 1 for Yes, 0 for No).
* solar (solar water heater: 1 for Yes, 0 for No).
* cooling (cooling: 1 for Yes, 0 for No).
* safe_door (safety door: 1 for Yes, 0 for No).
* gas (1 for Yes, 0 for No).
* fireplace (1 for Yes, 0 for No).
* furniture (1 for Yes, 0 for No).
* student (Is it appropriate for students? 1 for Yes, 0 for No).  
Beginning with the data cleaning process, dependent columns, such as price per square meter, and columns that showed no statistically significant correlation (e.g., parking) were removed. Also, rows that contained empty fields were removed. Additionally, 5% of the outliers with respect to the price were removed, including the minimum and maximum values. Categorical variables were encoded using one-hot encoding and ordinal encoding techniques. Numerical variables were standardized using a standard scaler. Table 2 and Figure 3 below demonstrate the results sorted by performance (error), showing KAN outperforming significantly all of the existing state-of-the-art approaches. Figure 4 presents a comparison plot between the values predicted by KAN and the actual values.  
Figure 3: Models’ performance in Dataset A.