# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 4 Discussion
### Housing Characteristics  
Several nuanced insights valuable for real estate market analysis and predictive modeling can be derived by analyzing the SHAP plots in Figure 4 concerning the housing characteristics--specifically, the Bedroom and Property Condition (PropCond) features. The beeswarm plot reveals that the number of bedrooms (Bedroom feature) generally positively impacts the predicted property prices. The distribution of SHAP values for this feature is spread out, suggesting that additional bedrooms tend to increase the dwelling's price, but the degree of this increase varies significantly from property to property. This variability may be due to interactions with other influential features, such as the size of the property (PGA) or its location. It also hints at a possible diminishing return on value as the bedroom count increases, which would be an essential consideration for market segmentation and pricing strategies. The analysis of SHAP values highlighting the Bedroom feature significance aligns well with [PERSON] et al. 's (2018) [96] research, which established a statistically significant positive relationship between the number of bedrooms and property value. This correlation underlines the market's valuation of space and utility, particularly in how bedrooms cater to the needs of families or individuals seeking more living space. Furthermore, [PERSON] et al. (2023) [37]'s study reinforces this perspective by ranking bedrooms as an upper mid-range feature in their SHAP analysis. This insight suggests that bedrooms reflect a property's practical utility and significantly influence its market appeal. The consistency of these findings across different studies validates the conclusion that bedrooms are a critical, flexible indicator of a property's utility and attractiveness to buyers in the real estate market. For stakeholders in the real estate market, these insights could be instrumental in guiding development priorities, investment choices, and targeted marketing campaigns. In contrast, the Property Condition (PropCond) feature exhibits a more clustered distribution of positive SHAP values, especially for new properties. This clustering indicates a more consistent market expectation that new dwellings command a premium, likely due to lower anticipated maintenance costs and contemporary amenities. Whether a dwelling is new or used also affects its price, with new properties generally commanding higher prices, reflected by the average SHAP value for this feature. However, the Global Summary plot indicates that while PropCond is significant, its average impact on the model's output is less than that of the number of bedrooms, suggesting that while buyers value newness, the size and utility of the property play a more crucial role in determining price. The consistent value attributed to the PropCond feature, particularly for newer dwellings, aligns with [PERSON] et al. (2021) [97] findings, establishing a direct functional correlation between property condition and selling price. This insight reinforces the observed market trend where buyers are willing to pay a premium for new construction. This effect could be due to the modern design and amenities, energy efficiency, or reduced need for immediate repairs and renovations. [PERSON] et al. 's (2022) [98] study further supports this by identifying OverallQual, which rates a house's overall material and finish as a significant determinant of housing prices, indicating that buyers value not just the age of a property but its overall quality and condition, which are often superior in newer constructions. Additionally, [PERSON] et al. 's (2021) [95] research further corroborates this viewpoint, highlighting that the accumulation of individual absolute Shapley values indicates that the age of a property, closely related to its condition, is a critical factor in market valuation.