Modelling transport and real-estate values interactions in urban systems

Abstract

a b s t r a c tThis article presents hedonic Multiple Linear Regression models (MLR), spatial autoregressive hedonic models (SAR), Spatial autoregressive hedonic in the Error term Models (SEMs) and spatial Durbin hedonic models (SDMs) to estimate house price variations in metropolitan areas as a result of changing environmental and accessibility conditions. The goodness of fit of the different models has been compared along with a series of hypotheses about the performance of the specifications considering spatial relationships between observations. The case study for such analysis was the urban area of Santander (Spain). The models which considered spatial dependence between observations offered a greater degree of fit in a scenario showing strong spatial correlation in MLR residuals. The SEM model combined with a QueenContiguity matrix provided a good fit to the data and at the same time presented significant parameters with theoretically coherent signs. This model estimated increases of 1.8% for each additional transit line present in the areas of housing, as well as a reduction of 1.1% in their prices for each additional minute in travelling time to the Central Business District. Closeness to the train stations, however, implied reductions in house prices.

The Effects of Environmental Quality on Residential Choice Location

“…The TRAIN variable representing accessibility using suburban railway transport, presented a different sign parameter in agreement with previous research (Ibeas at al., 2012). This can be justified considering that railway infrastructures imply diseconomies related to noise and a lower landscaping and environmental quality in the city of Taranto, in this case had negative sign.…”

“…A systematic review of the various research work carried out can be found in Fujita (1989). The hedonic studies relating real estate prices with transport conditions have complemented the theories on urban economy and tested their hypotheses through multiple case studies (Ibeas and al., 2012).…”

Impact of high speed rail on housing values: an observation from the Beijing–Shanghai line

“…Some studies adopt spatial econometric estimation to address the spatial dependence in hedonic modeling, but the spatial impact of transportation infrastructure does not seem to be quantified appropriately. For example, Ibeas et al (2012) adopted three spatial econometric models including a spatial autoregressive, a spatial error and a spatial Durbin to evaluate the impact of transport on real-estate value in the case of a Spanish metropolitan area. The impact of transport was measured by two dummy variables that indicate whether there is a bus stop or a suburban train station near a housing property.…”

Investigating the impact of recession on transportation cost capitalization: a spatial analysis

“…In Madrid, Dorantes et al (2011) concluded that a new metro line had positive impacts on house prices in south Madrid. In another case study, Ibéas et al (2012) found that, in Santander (Spain), proximity to http://dx.doi.org/10.1016/j.jtrangeo.2014.10.009 0966-6923/Ó 2014 Elsevier Ltd. All rights reserved. train stations drives house prices lower.…”