Source: JLL, Oxford Economics, Euromonitor, MSCI RCA, and other market sources (data collected as of Feb 2023)

Here, we visualise the results of the analysis using two variable groupings – one focused on variables which indicate economic development levels and the other focused primarily on macroeconomic and real estate market characteristics. If we categorise the cities into four clusters, we see that major retail hubs, such as Hong Kong and Singapore, fall in the same cluster as they have higher economic development levels along with a stronger international tourism market. Meanwhile, cities from the same country or subregion also tend to cluster together as they share similar characteristics. For example, mainland Chinese markets fit neatly within the same cluster, and yet also separately from Indian cities, given that Chinese markets generally have higher economic development levels, lower population growth and higher retail investment market liquidity than their Indian counterparts.

This analysis gives investors a better idea of differentiating characteristics of different retail markets across the region and how similar locations cluster together, before they proceed with their investment decisions. While this analysis looks at cities only on a macro market level, it provides another possible way to examine real estate markets and could serve as a helpful first step before enabling a deeper analysis at the asset level.

^{[1] These techniques include principal component analysis (PCA) and k-means clustering.
[2] PCA is used to group similar variables together and reduce the size of a dataset.
[3] The k-means clustering technique is used to identify and group observations that roughly share similar characteristics to one another.}