What technique is used to group similar subdivisions into neighborhoods?

The technique that is most appropriate for grouping similar subdivisions into neighborhoods is cluster analysis. This method is designed to identify and accommodate the natural groupings within a dataset based on selected attributes or characteristics. In the context of real estate or urban planning, cluster analysis can analyze various factors such as demographic data, property types, socioeconomic status, and other relevant characteristics that define neighborhoods.

By applying cluster analysis, practitioners can segment areas into distinct neighborhoods that exhibit homogeneity within the groups and heterogeneity between different groups. This is particularly useful for assessment and planning purposes, as it helps in understanding patterns and relationships in urban development.

Factor analysis, while also a statistical technique, focuses on identifying underlying relationships between variables rather than grouping distinct entities. Regression analysis is primarily used for determining relationships between dependent and independent variables, which doesn't directly relate to grouping subdivisions. Geospatial mapping involves visual representation of data geographically but doesn't inherently provide a mechanism for clustering similar subdivisions. Therefore, cluster analysis is the correct and most effective technique for this purpose.