Does transportation network centrality determine housing price?. (July 2022)

• Record Type: Journal Article
• Authors: Chakrabarti, Sandip; Kushari, Triparnee; Mazumder, Taraknath
• Is Part Of: Journal of transport geography. Volume 103(2022)
• Publication Date: 2022-07
• Subjects: Network centrality -- Transportation planning -- Housing price -- Land use -- Real-estate; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2022.103397
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract: In this paper, we investigate whether transportation network centrality determines housing price in cities. We find that it does. Using housing price data from >400 neighborhoods across the city of Kolkata, India, our research shows that a neighborhood's centrality within the intra-urban road transportation network is positively associated with the average price per sq. ft. of ownership units in multistory apartment buildings in the neighborhood. We test the relationships of three alternative centrality indices – closeness, betweenness, and eigenvector – capturing different dimensions of network-wide connectedness with housing price, independently and in combination. We employ two alternative network weights to derive centrality considering peak-period and off-peak travel conditions and road transportation network performance. We address the spatial autocorrelation issue to derive robust evidence on the centrality-price relationship. Our results suggest that centrality is an intrinsic location advantage that positively influences urban housing price. Different centrality indices have different effects on price but they collectively reinforce each other. The estimated magnitudes of association between centrality indices and housing price are significant for policy and practice. In addition to contributing to scholarship in the domain of transportation planning, our research offers specific takeaways for metropolitan planning agencies and real-estate developers, especially in resource constrained geographic contexts. Highlights: The transportation network centrality – housing price relationship is analyzed. The effects of closeness, betweenness, and eigenvector centrality indices are tested. Both peak-period and off-peak road network performance are considered. Spatial Error models are used to address spatial autocorrelation. Centrality is found to be positively associated with ownership apartment price.