# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 3 The Proposed Scheme
### Hyperparameter Tuning of LightGBM and Feature Importance Analysis  
To further refine the performance of the chosen LightGBM model, hyperparameter tuning is essential. For this task, we utilized the OPTUNA library, an open-source Python tool designed specifically for hyperparameter optimization. While it shares similarities with GridSearch, RandomSearch, and BayesianOptimization, OPTUNA offers a more streamlined and efficient approach to optimize both machine learning and deep learning models. It significantly reduces the time and effort typically required for model selection and hyperparameter adjustment. Considering that a lower RMSLE signifies better accuracy, our optimization objective was set to'minimize'. In order to balance optimization quality with computational efficiency, we limited the number of iterations to \(n\_trial=100\). Post-optimization, the model's accuracy saw a marked improvement, with the RMSLE value decreasing from the initial 0.24230 to 0.21999. The reduction of the RMSLE value by 0.02 after logarithmic transformation of the actual values through hyperparameter tuning signifies that if the pre-adjustment prediction was made with an error rate of 24% at KRW 100 million, the post-adjustment prediction would be at an error rate of 22%, resulting in a value of KRW 104.5 million. This equates to a difference of approximately KRW 4.5 million per SMUP, potentially leading to substantial financial losses for homeowners due to this error. The accuracy of our predictions improved with hyperparameter tuning. In the LightGBM-based analysis, our primary objective was to pinpoint the salient features influencing housing prices. To gauge the specific impact of subway stations on the 'SMUP' prediction, we conducted a separate performance assessment across the eight models, excluding the two subway-related features, 'WTD_SUBW_RANK' and 'NEAR_SUBW_DIST'. Figure 7 depicts a discernible decrease in predictive accuracy for all models, indicating the significant role these subway attributes play. It can be inferred that the proximity to subway stations, coupled with their high usage, exerts a positive influence on housing price predictions. In our prediction analysis, we scrutinized the influence of these two features and subsequently assessed their importance. We divided the feature importance analysis into two scenarios, one including these two features and the other excluding them; the results are presented graphically in Figure 8. In the importance analysis graph which includes the two features, the feature with the most significant influence is WTD_SUBW_RANK, representing the subway stations frequently used by many people, followed by INC_BY_REG,  
Figure 6: Cross-validation results for the target value SMUP: average RMSLE and RRMSE values for the eight regression models, complemented with a bar chart highlighting the differences in average RMSLE values among the models. (**a**) Average RMSLE and RRMSE values for the eight regression models; (**b**) Bar chart comparison of the average RMSLE values across eight models. indicating purchasing power, and then AGE_BLDG, representing the age of the building. The other graph showcases the influence of features when the same two specific features are excluded. Interestingly, while the housing area (XUAR) has a lesser influence than AGE_BLDG in the top graph, it ranks second in the bottom graph. This can be inferred as those with greater purchasing power tending to prefer houses with a larger area. In both graphs, the influence of the construction company's brand was the least impactful. Thus, it can be discerned that features related to proximity to subway stations and socio-infrastructure facilities exert the most substantial influence on housing price predictions. These findings suggest that location-related external factors, especially transit accessibility, have a more pronounced impact on housing prices than the inherent attributes of the properties. Additionally, the age and size of a property play crucial roles in its valuation.