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.

中文翻译：

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使用大数据集分析房价的空间异质性

作为享乐主义模型可靠性的障碍，房屋子市场由于缺乏集成和标准化的分类框架和验证方法而受到了学术界的广泛关注。通过将多种空间统计和数据挖掘技术整合到混合空间数据挖掘方法中，本研究开发了一种创新的分类方法，该方法用空间连通性代替了空间连续性。以盐湖县为例，根据结构差异，城市空间的复杂性和邻里特征之间的差异，我们确定了43个住房子市场。随着将城市便利设施引入验证框架，基于子市场的模型与非子市场回归之间的比较表明，我们的分类不仅可以提高预测的准确性，而且可以更好地理解本地住房市场。除了有助于理解城市空间异质性，我们的研究还提供了一种可行的空间建模方法，该方法能够处理具有200,000多个观测值的大型数据集。