Quantifying Retail Agglomeration using Diverse Spatial Data

TL;DR

This paper introduces a new model based on the Cross-Nested Logit framework to quantify how retail agglomeration and floor space influence retail location choices using detailed spatial data, tested on London.

Contribution

It develops a theoretical model at the retail unit level incorporating agglomeration effects, filling a gap in existing aggregate-level models.

Findings

Retailer attractiveness scales super-linearly with floor space.

Agglomeration effects are significant within a 325m radius.

Model results align with observed retail spatial patterns.

Abstract

Newly available data on the spatial distribution of retail activities in cities makes it possible to build models formalized at the level of the single retailer. Current models tackle consumer location choices at an aggregate level and the opportunity new data offers for modeling at the retail unit level lacks a theoretical framework. The model we present here helps to address these issues. It is a particular case of the Cross-Nested Logit model, based on random utility theory built with the idea of quantifying the role of floor space and agglomeration in retail location choice. We test this model on the city of London: the results are consistent with a super linear scaling of a retailer's attractiveness with its floor space, and with an agglomeration effect approximated as the total retail floorspace within a $325m$ radius from each shop.