Abstract
As an obstacle to the hedonic model's reliability, housing submarkets have drawn plenty of scholarly attention because they lack an integrated and standardized classification framework and validation methods. By incorporating multiple spatial statistics and data mining techniques into a hybrid spatial data mining method, this study develops an innovative classification methodology that replaces spatial continuity with spatial connectivity. Employing Salt Lake County as the case, we identify 43 housing submarkets based on differentiation among structural differences, the complexity of urban space, and neighborhood characteristics. With the introduction of urban amenities into the validation framework, the comparison between the submarket-based model and non-submarket regression shows our classification not only enhances prediction accuracy but also achieves better theoretical comprehension of local housing markets. Besides contributing to an understanding of urban spatial heterogeneity, our study also provides a feasible spatial modeling method which is capable of processing a large dataset with more than 200,000 observations.
