Building Hierarchies of Retail Centers Using Bayesian Multilevel Models

Sam Comber, Daniel Arribas-Bel, Alex Singleton, Guanpeng Dong and Les Dolega

Annals of the American Association of Geographers, 2020, vol. 110, issue 4, 1150-1173

Abstract: The perceived quality of urban environments is intrinsically tied to the availability of desirable leisure and retail opportunities. In this article, we explore methodological approaches for deriving indicators that estimate the willingness to pay for retail and leisure services offered by retail centers. Most often, because the quality of urban environments cannot be qualified by a natural unit, the willingness to pay for an urban environment is explored through the lens of the residential housing market. Traditional approaches control for individual characteristics of houses, meaning that the remaining variation in the price can be unpacked and related to the availability of local amenities or, equivalently, the willingness to pay. In this article, we use similar motivations but exchange housing prices for residential properties with property taxes paid by nondomestic properties to glean hierarchies of retail centers. We outline the applied methodological steps that include very recent, nontrivial contributions from the literature to estimate these hierarchies and provide clear instructions for reproducing the methodology. Using the case study of England and Wales, we undertake a series of econometric experiments to rigorously assess retail center willingness to pay (RWTP) as a test of the methods reviewed. We build intuition toward our preferred specification, a Bayesian multilevel model, that accounts for the possibility of a spatial autoregressive process. Overall, the applied methodology describes a blueprint for building hierarchies of retail spaces and addresses the limited availability of spatial data that measure the economic and social value of retail centers. Key Words: econometrics, retail geography, spatial statistics.

Date: 2020
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DOI: 10.1080/24694452.2019.1667219

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