Borrowed sizes: A hedonic price approach to the value of netowrk structure in public transport systems

Abstract

Property prices are known to be higher in places with high accessibility, such as in proximity to train stations and especially to commuter rail, than in places without this access. This study provides a better understanding of how regional accessibility, through the structure of railway networks, can influence local agglomeration economies by providing accessibility to large labor markets. Previous literature has shown a positive impact of proximity to railway stations on housing prices, and our study adds to the literature by analyzing the impact of network structure. We argue that public transport systems can support the benefits of city networks in line with Alonso’s concept of borrowed sizes (1973). Using network theory to measure accessibility provided by the network, we show that stations that provide accessibility to large labor markets across the region are perceived as more attractive by households. Cities in proximity to other cities are strengthened through their public transport links, which allow agglomeration benefits to be exploited by residents.

A Research Agenda for Small and Medium-Sized Towns

“…While borrowing and shadowing are made possible through accessibility (Bohman and Nilsson, 2021) and (limited) distance, we previously concluded that it is mostly a product of true interaction (Meijers and Burger, 2017). Further integration definitely is a driver of borrowing and shadowing patterns.…”

The Impact of Urban Street Network on Land Value: Correlate Syntactical Premises to the Land Price

“…Therefore, urban planning and design are essential for economic expansion, especially in the arrangement of urban land. The land can be used as an exchange economy as the infrastructure can contribute to economies of scale by providing faster markets [13].…”

“…The value of land increases with the provision of access which affects land prices. Accessibility is measured directly through the size of the local labor market and indirectly through accessibility provided by the network [13]. Eventually, it can be stated that the urban structure and street network, including accessibility, ultimately affect land value and price.…”

Influence of Urban Railway Network Centrality on Residential Property Values in Bangkok

“…For instance, in Hong Kong, the influence of closeness centrality was explored [32], and in Shanghai, the focus was on degree centrality [33]. In the Scania region, the most southern region of Sweden, a study found that the centrality of the regional train network, specifically degree and closeness centralities, influenced single-family house prices, though betweenness centrality did not show a statistically significant influence [34]. On a larger scale, the centrality of China's high-speed rail network was also found to affect land values and housing prices [35,36].…”

“…This lagged variable represents the average value of the dependent variable in neighboring locations, effectively acknowledging that the value of the dependent variable in one location may be influenced by the values in nearby locations. Conversely, the spatial error model considers that there is spatial autocorrelation in the error terms of the regression model, so the spatial autocorrelation is explicitly incorporated into the error term through spatially weighted values derived from nearby observations [20,34,67]. This model acknowledges that observations in close proximity may share unobserved characteristics that impact the dependent variable.…”