Determinants of industrial property rents in the Chicago metropolitan area

Abstract

Urban economists have long understood the theoretical importance of transportation infrastructure and accessibility on the location choice of households and firms. We utilize a readily available data set of transaction rents in the Chicago metropolitan area to investigate the determinants of industrial property rents. Among the factors considered are proximity to transportation infrastructure, characteristics of the property, the term structure of lease agreements, and local attributes of the neighborhood.Empirical results suggest property, lease, and local demographics play important roles in determining rents. Despite the fact that industrial property tends to locate very close to rail lines and interstate highways, transportation infrastructure has much less influence. There is evidence that there is an upward sloping lease term structure premium and that the premium varies over time. The model is also used to develop a constant quality rent index for the Chicago commercial property market. Compared to average rents and asking rents, the estimated constant quality index shows a smaller run up in rents from 2003 through 2008 and a larger drop off in rents through the end of 2011.

Modeling the Spatial Dimensions of Warehouse Rent Determinants: A Case Study of Seoul Metropolitan Area, South Korea

“…Rent is used as part of the many important criteria for deciding whether a firm should own a property or rent and at the same time, where to locate it [14]. The role of rent and its determinants have been the research subjects in urban and transportation studies for many decades.…”

“…Therefore, a consumer implicitly chooses a set of different characteristics by selecting a specific property [28]. A hedonic price function explains the relationship between the real estate price and the property attributes, including physical, locational, transactional, and neighborhood-related characteristics [14,27]. Usually, the hedonic price function is estimated by multiple regression analyses using a semi-log or log-log specification.…”

“…To explicitly account for the effect of the spatial autocorrelation, spatial autoregressive models proposed by Anselin [23] can be adopted to hedonic price analysis. For instance, Clark and Pennington-Cross [14] analyzed the relationship between industrial property rents and its determinants in the Chicago metropolitan area using spatial error model (SEM) where spatial autocorrelation is reflected in the error term. For significant rent determinants, they identified the physical characteristics of the property, lease term structure, and local demographic attributes of the neighborhood.…”

“…Thus, a warehouse user implicitly chooses a set of different warehouse attributes and services by renting a storage unit from the warehouse. Previous studies on industrial property rent used characteristics related to physical property, lease structure, market condition, and accessibility to transportation infrastructures [14,19]. Under this framework of hedonic price modeling, the warehouse rent ® can be expressed as the following function of transactional (T), physical (P), locational (L), and service (S) related characteristics of the warehouse, as well as the error term ε :…”

“…Warehouse rent represents the value of the rental storage unit in terms of its physical, locational, and lease characteristics [13,14]. The warehouse rental price is determined at an equilibrium in which the storage space provided matches the customer's need and ability to rent the storage space.…”