# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 4 Discussion
### Geolocation  
The insights gathered from the SHAP analysis reveal the significant impact of geospatial features, namely Latitude, Longitude, and ZIP7 (7-digit postal code), on real estate valuation. Longitude emerges as a particularly influential factor, with a mean absolute SHAP value of 16,871.72. The beeswarm plot indicates its pronounced effect on property prices, suggesting that properties within certain longitudinal zones, potentially due to their location within sought-after neighborhoods, proximity to commercial hubs, or other desirable amenities, tend to command higher prices. The SHAP beeswarm plot illustrates this trend: red dots skewed to the right indicate areas with higher property values, while blue dots to the left denote lower-valued areas. Figure 6b also displays a similar pattern, where Longitude exhibits pronounced peaks and troughs, indicating that certain longitudinal zones significantly impact property prices positively or negatively. The broader range of SHAP values and clear clusters suggest a more localized and complex impact on property prices. This variation implies that east-west positioning within the city has diverse implications on property values, thus reflecting geographical desirability. Figure 6: SHAP dependence plots for geographic predictors of real estate prices: This series of plots illustrates the relationship between the SHAP values of real estate prices and three geographic features: (**a**) Latitude, (**b**) Longitude, and (**c**) ZIP code (ZIP7). Each plot visualizes the individual contribution of these features to the predictive model’s output. Plot (**a**) shows the SHAP values for Latitude, displaying a trend where certain latitudes correspond to higher or lower impacts on price predictions, suggesting a geographical preference or aversion in the real estate market. Plot (**b**) for Longitude reveals a complex, non-linear relationship with the price predictions, indicating that some longitudinal regions might be more favorable or unfavorable for property values. Lastly, plot (**c**) demonstrates the variability of the ZIP code’s impact on price predictions, with some ZIP codes having a distinctly positive or negative influence, reflecting the localized real estate market trends. Collectively, these plots enable a nuanced understanding of how specific geographic locations contribute to the valuation of real estate properties as per the model’s predictions. Latitude, although less influential than Longitude with a mean absolute SHAP value of 11,298.82, still plays a significant role in the model. It indicates the north-south desirability of properties within the city. The SHAP Partial Dependence plot in Figure 6a for the Latitude shows a broadly descending trend, where the SHAP value decreases as the Latitude increases. This trend suggests a consistent north-south axis relevance in property valuation, and the impact on property prices tends to decrease as we move north or south from a certain point. The spread of the points is relatively uniform, with no apparent clusters, indicating that the impact of Latitude on property prices is more evenly distributed and possibly less localized than Longitude or ZIP7. This plot also seems to exhibit a less dramatic range of SHAP values than Longitude and ZIP7, indicating a more stable but widespread influence on property prices. ZIP7, representing the 7-digit postal code, also shows a notable influence on property prices, with a mean SHAP value of 11,034.59. This feature reflects the desirability of different areas, capturing how local dynamics, such as school districts, local market conditions, or community features, impact property values. The SHAP values for ZIP7 underscore the intricacies of regional price variations, indicating that specific postal codes correspond to areas with differing levels of desirability. In Figure 6c, the dependence plot for ZIP7 shows a series of vertical lines, indicating that discrete categories within the data correspond to individual postal codes.