The spatial varying relationship between services of the train network and residential property values in Melbourne, Australia

Abstract

This study investigates the spatial effects of the service frequency and transport interchange facilities of rail stations on residential property values for the entire metropolitan train network in Melbourne. Residential properties are classified as either detached or attached dwellings. Given that a traditional hedonic price model cannot handle the spatial dependence and spatial non-stationarity of the housing market, several geographically weighted regression (GWR) models are used and multicollinearity is considered; the model with the Euclidean distance metric outperforms others. Results indicate that the service frequency and facilities of the stations influence the residential property values in a spatially variable way. For every 1 km closer to the train stations, an increase in the frequency of the train services per unit results in a change in the residential property values ranging from −4.01% to 2.71%; an improvement in the transport interchange facilities per unit results in a change in the residential property values ranging from −29.93% to 47.04%. Crime and retail activities that indirectly affect the relationship between rail stations and residential property values are also identified. For every 1 km closer to the train stations, the crime density increases significantly from 5.64% to 42.88% and this occurs in one-fifth of the areas in Melbourne. In contrast, the relationship between retail activities and train stations remains spatially stable. This study complements the relatively scarce literature on the link between railway service levels and residential property values while extending the case study to the local level.

Multiscale Effects of Multimodal Public Facilities Accessibility on Housing Prices Based on MGWR: A Case Study of Wuhan, China

“…Similarly, although the distance to bus station is generally negatively correlated to housing price, which may be supported by most studies [6], it also changes over space. Such a phenomenon is consistent with a recent case study in Melbourne [47], which verifies the spatial varying relationship between housing price and transportation facilities. However, the result here further indicates that it has a local spatial effect and is insignificant in most regions.…”

Urban Rail Transit in Bangkok: Chronological Development Review and Impact on Residential Property Value

“…Urban rail transit was found to have a great impact on real estate. In many cases, it was found that rail transit station proximity had a positive impact on residential property value, such as in Shanghai [20], Kuala Lumpur [21], Bangkok [6,[22][23][24], Portland [25], Buffalo [26], London [27], Hamburg [28], Lisbon [29], Melbourne [30], etc. However, in some cities the impact was not spatially even across the city, i.e., being positive and negative in different areas, such as reported in San Diego [31], Tyne and Wear region [32], Ottawa [33], etc.…”

“…The dependent variable, which is the property value, could be the listing price of the property [29,32], the assessed property value [38], or the actual transaction price [21,[39][40][41][42][43]. The price in different years would be adjusted for inflation and other external factors [30]. The value of the property price may be considered as the total price [24,25,27,28,33,36,37,[43][44][45][46] or the price per unit area, i.e., price per square foot or per square meter of the area [6,20,22,23,26,31,34,35,39,[47][48][49][50][51][52].…”

“…Structural characteristics may include size, building age, number of rooms, other functions, etc. Transport accessibility includes proximity to transport facilities such as (mostly) rail transit station, bus stop [45,48], freeway, major highway, bike-sharing station [48], bus frequency [53], rail service [30], etc., while some studies consider activity opportunities [32,52]. The proximity of a rail transit station could be represented as straight line distance [23,31,44,47], network distance [54], or another impedance measure such as travel time [39].…”

Identifikasi Faktor-Faktor Yang Mempengaruhi Nilai Properti Residensial Berdasarkan Persepsi Penilai Pemerintah

“…P a d a praktiknya terdapat dua kelompok besar terkait model penelitian. Kelompok pertama adalah kelompok yang menggunakan data sekunder atau data pasar aktual seperti harga transaksi atau harga sewa dari pihak ketiga (Hamidi, Bonakdar, Keshavarzi, & Ewing, 2020;Kauko, 2003), faktor lokasi yang didapat dari pihak ke tiga (Rahadi et al, 2020), informasi iklim (Hino & Burke, 2020) atau data jaringan transportasi dari otoritas terkait (Li, Wang, Callanan, Lu, & Guo, 2020). Kelompok kedua adalah kelompok yang menggunakan data primer yang diperoleh dari kuesioner atau wawancara (Abidoye & Chan, 2016;Babawale & Omirin, 2012;Kauko, 2003;Strand & Vågnes, 2001).…”