Through a hedonic approach this study primarily focuses on how house prices vary systematically with respect to some general spatial structure characteristics in a Norwegian region. The introduction of a gravity based labor market accessibility measure contributes significantly to explain variation in housing prices, also in a model formulation where the distance from the city center is accounted for. Based on these results we suggest a distinction between an urban attraction effect and a labor market accessibility effect. Quantitatively, the two distinct effects are found to contribute about equally to intraregional variation in housing prices.
This study is devoted to empirical and modeling aspects on how characteristics of spatial structure influence commuting flows. Within a doubly-constrained framework, results from a competing-destinations formulation are evaluated and compared to results from the traditional gravity model. The evaluation depends critically upon the specification of within-zone journeys-to-work. Specific labor-market characteristics are found to be significant to explain how workers are absorbed in diagonal elements of the trip-distribution matrix. We also find that the parametric specification of the accessibility measure is important, and that the competing-destinations formulation is superior to the traditional gravity model.
In this paper we present a model for commuting in a network of towns. A basic assumption is that all individuals have a given residential location and that every node in the network has a fixed number of jobs. We then propose a general model for the commuting of labor between the nodes in the network.
Introduction Some substantial changes in the transportation network are planned in western Norway. An economic evaluation of the projects calls for predictions of generated traffic. Commuting represents one important component of travel demand. Based on data from the southern parts of western Norway, we focus on how characteristics of the transportation network and the spatial structure affect commuting flows.We estimate distance deterrence parameters which relate to both traveling by car and traveling by ferry boats on links without roads. Quantitative estimates of how discontinuities in the road network affect commuting flows represent an important input to an economic evaluation of investment projects where ferry connections are substituted by road links. In general, such estimates represent an important input to the ongoing political debate concerning the distribution of investment funds to various kind of transportation infrastructure projects.The estimates and predictions which are presented in this paper are obtained from a modified version of the so-called competing destinations model (see Fotheringham, 1983a). In Thorsen and Gitlesen (1998) the relevant model formulation is empirically tested and compared with alternative formulations. In addition to the empirical results, the motivation now is to contribute with an improved understanding of the theoretical and behavioral foundation for the relevant family of models. A rich literature exists that focuses on the theoretical foundation of this kind of spatial interaction model. Some of this literature will be reviewed in the discussion in the following sections. Much of the existing theory is, however, specific to kinds of spatial interaction other than commuting flows. In this paper, we argue that some modifications are required when job-search problems are considered. Hence, in addition to the empirical results, the main motivation for this paper is to offer a theoretical foundation for a competing destinations model for studying commuting flows.Pure spatial interaction models are, in general, constructed to predict a trip distribution matrix at a given point in time. In the long term, perspective fundamental
This paper primarily focuses on predicting housing price gradients in a Norwegian region with one dominating center. Spatial separation is represented by a function of the traveling distance from the city center in a traditional hedonic regression equation. Several functions are tested, and some alternatives provide a satisfying goodness-of-fit, consistent coefficient estimates, and intuitively reasonable predictions of housing price gradients. Still, not all commonly used functions are recommended. The findings also indicate that the strength of spatial autocorrelation is reduced when the hedonic function is properly specified. The main ambition of this study is to estimate a housing price gradient for a region in the southern part of Western Norway. This region has a dominating city (Stavanger), and the study also tests for the appropriateness of the monocentric city model in this kind of area. The basic idea underlying this model is represented by a steadily declining unit price for houses with an increasing distance from the central business district (CBD). For a presentation of the modeling framework, comparative static results, and interesting extensions, see Anas, Arnott, and Small (1998). Many empirical studies have aimed at finding rent gradients, land value gradients, and/or housing price gradients. In a few studies, the variable indicating the access to work came out with an insignificant sign, and occasionally a counter-intuitive sign was reported [see for instance Bartik and Smith (1987) for a review]. Such results can, for example, be explained by the fact that the area under study in some cases involves a restricted urban area rather than a housing market area. Another reason for such results is that modern metropolitan areas tend to be multicentric. Both Richardson (1988) and Heikkila, Gordon, Kim, Peiser, and Richardson (1989) state that the main reason for insignificant or counter-intuitive results stems from a misspecified hedonic price function. This is demonstrated in Waddell, Berry, and Hock (1993), who find that the impact of distance to the CBD is significant even when access to multiple employment centers and other nodes are accounted for.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.