The monocenter model of urban metropolitan areas has come under increasing criticism in recent years. Many urban areas are in fact multicentered. This paper describes a methodology for identifying and ranking the multiple "centers" of a metropolitan area by inferring them from real estate price data. The method generalizes from previous methodologies developed to identify the urban center from density distributions and gravity models. It involves applying maximum likelihood methods to infer the location of each center, testing the relative importance of each, and testing for the significance of the addition of each incremental center. It allows the joint identification of the locations of metropolitan centers as well as their (local) price gradients (or elasticity of price with distance from the centers). It is readily applicable to GIS tools and data. In addition, there are a number of interesting issues that may be addressed by assuming that the nuclei of the city are "unknown" and must be inferred from the data. It is possible, theoretically, for the pinnacles of commercial and residential land price functions to be located in different places. It is possible to describe land pricing functions through identifying a series of "centers" in metropolitan areas that have irregular topography, or where prices follow the contours of transportation arteries. It is possible to identify "inverse centers" that act as troughs - rather than pinnacles - of land gradient functions. It is possible for the center or centers to be located in areas outside the traditional central business district, to move over time, and even for pinnacles to lie outside the urban area itself. The methodology is then applied to real estate transaction prices for Haifa Israel.
ASJC Scopus subject areas
- Economics and Econometrics