Spatial Characteristics of CA: A Narrative into San Francisco

This study delves into the spatial characteristics of housing prices within California, with a specific focus on San Francisco. We explore various models to predict housing prices based on different feature sets, including coordinate and noncoordinate attributes. Through extensive analysis, we find that incorporating geographic coordinates significantly enhances the predictive accuracy of housing prices. Utilizing a neural network model, we achieve nearly 100% accuracy in predicting the, the quartile of median housing prices, underscoring the complexity and influence of location-specific features. Finally, we utilized iSLFN algorithm for developing a precise price prediction model and describing communities characteristics.