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Unprecedented urbanization in China has directly resulted in residential vacancies, which has seriously stunted sustainable development, a part of China's new-type urbanization plan. Understanding the various types and mixes of residential vacancies is critical for the advancement of our knowledge of speculative urbanism and for devising vacancy-mitigation policies, but this issue remains insufficiently studied. Using municipal water consumption data, this study proposes a feasible and general-purpose framework for providing innovative insights into the variability in residential vacancies at the household level and the mixture of residential vacancies at the building level. This framework was applied to the city of Changshu, China, and four categories of vacant residences at the household level were identified: seasonally vacant residences, long-term vacant residences, newly built residences and occasionally vacant residences. The first category is closely related to tourism and seasonal industries, while the last three exhibit a Matthew effect. In addition to revealing significant and intensifying spatial clustering and three patterns of changes in vacancy mixtures (i.e., emergence, disappearance, and increases or decreases), the results identify particular types of vacant residences at the building level (e.g., extremely low-entropy long-term multihousehold buildings). The insights from this study can contribute to devising customized policies for alleviating residential vacancies. • The proposed framework reveals variability in and mixtures among residential vacancies using municipal water consumption. • The document representations of vacant residences are extracted based on the vacant residence corpus and Doc2Vec method. • Four categories of vacant residences at the household level are identified using the Doc2Vec and K-means++ methods. • Seasonally vacant residences are closely related to tourism and seasonal industries. • Long-term, newly built and occasionally vacant residences exhibit a Matthew effect. [ABSTRACT FROM AUTHOR]