The old real estate adage “location, location, location” remains as relevant today as when it was first cited in the Chicago Tribune in 1926. From markets to submarkets to clusters, how we define location shapes how we perceive real estate.

In this map, we apply the DBSCAN algorithm to the Philadelphia County submarket to define clusters of retail buildings based on minimum grouping size and maximum distance parameters1.  We then encased the clusters within Uber’s H3 hexagons to define the micro markets and facilitate statistical analysis.

Despite the challenges plaguing many urban cores nationwide, rent growth in Philadelphia’s Downtown/Avenue of the Arts North cluster (3.4% CAGR) has outperformed both the broader market (1.6% CAGR) and its submarket (1.8% CAGR) over the past five years.

High rents (defined by being in the top quartile of all retail clusters) are not an impediment to growth, as retailers clearly favor districts that offer the solid foot traffic that result from vibrant amenities and presence of prime office assets2.

This map illustrates continued strong demand for city living and proximity to amenities and provides a window into how post-pandemic consumer preferences are reshaping urban retail environments.

1 We did not guide the DBSCAN algorithm beyond setting the parameters. A trained researcher would likely define retail clusters using more parameters than distance and grouping size. For instance, the cluster we labeled as Downtown/Avenue of the Arts North would likely be broken into smaller clusters separated by the 676 and the northern section labeled Logan Square.
2 https://www.cbre.com/insights/reports/five-forces-shaping-the-future-of-retail