Rail Accessibility and Residential Unit Prices: Evidence from Melbourne’s Cranbourne Line

Guanyu Pan

doi:10.54254/2754-1169/2025.CAU28492

Advances in Economics, Management and Political SciencesOpen access

Rail Accessibility and Residential Unit Prices: Evidence from Melbourne’s Cranbourne Line

Research Article

Open Access

Rail Accessibility and Residential Unit Prices: Evidence from Melbourne’s Cranbourne Line

Guanyu Pan ^1*

¹ Shanghai Zhongqiao Vocational and Technical University

^*Corresponding author: a1711135746@gmail.com

Published on 22 October 2025

AEMPS Vol.231

ISSN (Print): 2754-1177

ISSN (Online): 2754-1169

ISBN (Print): 978-1-80590-463-2

ISBN (Online): 978-1-80590-464-9

Download Cover

Abstract

The paper assesses the capitalization of residential unit values along Melbourne's Cranbourne Line route. Beyond station proximity, this paper jointly assesses transportation accessibility, everyday service availability, dwelling attributes, and neighborhood socio-demographics as determinants of price. To enhance cross-sectional comparability in a market characterized by varied lot sizes and layouts, outcomes are assessed based on per-square-meter prices instead of total selling values—an approach that remains underutilized in Australian studies but is immediately beneficial for planners and investors. Utilizing a hedonic pricing framework, this paper estimates a global ordinary least squares (OLS) model and a geographically weighted regression (GWR) to differentiate corridor-wide connections from regionally variable effects. Findings demonstrate that an increase in the number of bedrooms and the availability of on-site parking correlates with elevated unit costs, whereas increased distance from both the nearest rail station and the central business district relates to diminished unit prices. The GWR exhibits significant geographic variation, with the distance-to-station gradient differing markedly among station catchments. The evidence emphasizes the necessity for households to balance housing costs with effective access to the rail network; for policymakers, it illustrates that public transport initiatives influence property values and the distribution of urban opportunities, thereby informing station-area design, last-mile services, and transit-oriented development up-zoning. This method provides a replicable framework for corridor analysis and establishes a foundation for subsequent causal evaluations of specific investment occurrences.

Keywords:

Transport Infrastructure, Spatial Modelling, Geographically Weighted Regression, Land Value Capture

View PDF

References

[1]. Benjamin, J., & Sirmans, G. S. (1996). Mass transportation, apartment rent and property values. Journal of Real Estate Research, 12(1), 1–8.

[2]. Cervero, R., & Duncan, M. (2002). Land value impacts of rail transit services in Los Angeles County. Report prepared for National Association of Realtors and Urban Land Institute.

[3]. Debrezion, G., Pels, E., & Rietveld, P. (2011). The impact of rail transport on real estate prices: an empirical analysis of the Dutch residential property market. Urban Studies, 48(5), 997–1015.

[4]. Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298.

[5]. Bitter, C., Mulligan, G. F., & Dall’erba, S. (2007). Incorporating spatial variation in residential property attribute prices: A comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9, 7–27.

[6]. Wen, H., Jin, Y., & Zhang, L. (2017). Spatial heterogeneity in implicit residential property prices: Evidence from Hangzhou, China. International Journal of Strategic Property Management, 21(1), 15–28.

[7]. Lu, B., Charlton, M., & Fotheringhama, A. S. (2011). Geographically weighted regression using a non-Euclidean distance metric with a study on London residential property price data. Procedia Environmental Sciences, 7, 92–97.

[8]. Ren, P., Li, Z., Cai, W., Ran, L., & Gan, L. (2021). Heterogeneity analysis of urban rail transit on residential property with different price levels: A case study of Chengdu, China. Land, 10(12), 1330.

[9]. Mathur, S. (2020). Impact of transit stations on residential property prices across entire price spectrum: A quantile regression approach. Land Use Policy, 99, 104828.

[10]. Suhaimi, N. A., Maimun, N. H. A., & Sa'at, N. F. (2021). Does rail transport impact residential property prices and rents? Planning Malaysia, 19, .

[11]. Chorus, P., & Bertolini, L. (2011). An application of the node place model to explore the spatial development dynamics of station areas in Tokyo. Journal of Transport and Land Use, 4(1), 45–58.

[12]. Knaap, G. J., Ding, C., & Hopkins, L. D. (2001). Do plans matter? The effects of light rail plans on land values in station areas. Journal of Planning Education and Research, 21(1), 32–39.

[13]. Haque, M. M., Chin, H. C., & Debnath, A. K. (2013). Sustainable, safe, smart—three key elements of Singapore’s evolving transport policies. Transport Policy, 27, 20–31.

[14]. Sirmans, S., Macpherson, D., & Zietz, E. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1–44.

[15]. Hill, R. J. (2013). Hedonic price indexes for residential property: A survey, evaluation and taxonomy. Journal of Economic Surveys, 27(5), 879–914.

[16]. Kang, C. D. (2019). Spatial access to metro transit villages and residential property prices in Seoul, Korea. Journal of Urban Planning and Development, 145(3), 05019010.

[17]. Duan, J., Tian, G., Yang, L., & Zhou, T. (2021). Addressing the macroeconomic and hedonic determinants of residential property prices in Beijing Metropolitan Area, China. Habitat International, 113, 102374.

[18]. Hall, P. (2014). Cities of tomorrow: An intellectual history of urban planning and design since 1880. John Wiley & Sons.

[19]. Clapp, J. M., & Giaccotto, C. (1998). Residential hedonic models: A rational expectations approach to age effects. Journal of Urban Economics, 44(3), 415–437.

[20]. Eshet, T., Ayalon, O., & Shechter, M. (2005). A critical review of economic valuation studies of externalities from incineration and landfilling. Waste Management & Research, 23(6), 487–504.

[21]. Goodman, A. C., & Thibodeau, T. G. (2003). Residential property market segmentation and hedonic prediction accuracy. Journal of Residential Property Economics, 12(3), 181–201.

[22]. Harrison Jr, D., & Rubinfeld, D. L. (1978). Hedonic residential property prices and the demand for clean air. Journal of Environmental Economics and Management, 5(1), 81–102.

[23]. Rosenzweig, C., Solecki, W. D., Blake, R., Bowman, M., Faris, C., Gornitz, V., & Zimmerman, R. (2011). Developing coastal adaptation to climate change in the New York City infrastructure-shed: Process, approach, tools, and strategies. Climatic Change, 106, 93–127.

[24]. Wang, Y., & Baddeley, M. (2016). The problem of land value betterment: A simplified agent-based test. The Annals of Regional Science, 57, 413–436.

[25]. McMillen, D. P., & McDonald, J. (2004). Reaction of residential property prices to a new rapid transit line: Chicago's Midway Line, 1983–1999. Real Estate Economics, 32(3), 463–486.

[26]. Munshi, T. (2020). Accessibility, infrastructure provision and residential land value: Modelling the relation using geographic weighted regression in the city of Rajkot, India. Sustainability, 12(20), 8615.

[27]. Nam, K. M., Ou, Y., Kim, E., & Zheng, S. (2022). Air pollution and residential property values in Korea: A hedonic analysis with long-range transboundary pollution as an instrument. Environmental and Resource Economics, 82(2), 383–407.

[28]. Neuwirth, R. (1990). Economic impacts of transit on cities. Transportation Research Record, 1274, 142–149.

[29]. Nguyen-Hoang, P., & Yinger, J. (2011). The capitalization of school quality into residential property values: A review. Journal of Residential Property Economics, 20(1), 30–48.

[30]. Ooi, J. T., Le, T. T., & Lee, N. J. (2014). The impact of construction quality on residential property prices. Journal of Residential Property Economics, 26, 126–138.

[31]. Ruo-Qi, L., & Jun-Hong, H. (2020, September). Prediction of residential property price along the urban rail transit line based on GA-BP model and accessibility. In 2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE) (pp. 487–492). IEEE.

[32]. Wang, Y., Feng, S., Deng, Z., & Cheng, S. (2016). Transit premium and rent segmentation: A spatial quantile hedonic analysis of Shanghai Metro. Transport Policy, 51, 61–69.