Spatial Heterogeneity of Sustainable Transportation Offer Values: A Comparative Analysis of Nantes Urban and Periurban/Rural Areas (France)

Abstract

Abstract:Innovative solutions have been implemented to promote sustainable mobility in urban areas. In the Nantes area (northwestern part of France), alternatives to single-occupant car use have increased in the past few years. In the urban area, there is an efficient public transport supply, including tramways and a "busway" (Bus Rapid Transit), as well as bike-sharing services. In periurban and rural areas, there are carpool areas, regional buses and the new "tram-train" lines. In this article, we focus on the impact on house prices of these "sustainable" transportation infrastructures and policies, in order to evaluate their values. The implicit price of these sustainable transport offers was estimated through hedonic price functions describing the Nantes urban and periurban/rural housing markets. Spatial regression models (SAR, SEM, SDM and GWR) were carried out to capture the effect of both spatial autocorrelation and spatial heterogeneity. The results show patterns of spatial heterogeneity of transportation offer implicit prices at two scales: (i) between urban and periurban/rural areas, as well as (ii) within each territory. In the urban area, the distance to such offers was significantly associated with house prices. These associations varied by type of transportation system (positive for tramway and railway stations and negative for bike-sharing stations). In periurban and rural areas, having a carpool area in a 1500-m buffer around the home was negatively associated with house prices, while having a regional bus station in a 500-m buffer was non-significant. Distance to the nearest railway station was negatively associated with house prices. These findings provide research avenues to help public policy-makers promote sustainable mobility and pave the way for more locally targeted interventions.

Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

“…The fluctuations in the perceived values of environmental variables across space are consistent with the literature. Bulteau et al 2018 found a similar result in their study of the impacts of sustainable transportation on housing markets in France. In some regions there was a surprising disutility associated with transportation, despite the general positive relationship, and the authors surmised that it might relate to noise pollution and other negative externalities of transportation systems [36].…”

“…In the hedonic modeling literature, GWR has been shown to improve global estimates by accounting for local spatial variation [33,34]. Hedonic models employing GWR have found spatially contingent relationships between housing values and proximity to transportation systems [35,36], and between air quality and housing prices in the USA [37]. A study in Austria used spatial regression to demonstrate that locally weighted models are the most appropriate for informing local planning and policy [38].…”

Valuing Environmental Amenities in Peri-Urban Areas: Evidence from Poland

“…Hedonic analyses have been conducted at different spatial scales, mainly representing administrative units, i.e., a county [17] or municipality [18] and its districts [19], sub-national [20], and country [21]. The peri-urban context [22] has not yet been well described in the hedonic pricing literature. Therefore, our research extends the existing body of valuation literature of environmental amenities with a peri-urban hedonic pricing case study and discusses its practical implications for authorities being responsible for the peri-urban environment.…”

Industrial legacy and hotel pricing: An application of spatial hedonic pricing analysis in Nord-Pas-de-Calais, France

“…There are several methods in spatial econometrics (Bulteau, Feuillet and Le Boennec 2018;Latinopoulos 2018) (Fotheringham, Brunsdon and Charlton 2002). We decided to use the first category of model for the following reasons: (i) The spatial Durbin model cannot be used here as one of the main purposes of the analysis is to capture the effects of public and network attributes.…”