Announcement, construction or delivery: When does value uplift occur for residential properties? Evidence from the Gold Coast Light Rail system in Australia

Abstract

This paper examines the timing of the increases in land value on residential housing following the delivery of the Light Rail Transit (LRT) system in the Gold Coast, Queensland, Australia.This paper thus addresses one of the most pertinent questions for policy and practice around the timing, shape and conditions for increases in land value or value uplift. Increasingly governments face funding constraints in the implementation of new infrastructure and so are keen to understand if capturing this land value uplift is a practical proposition to augment or provide funding for new transport infrastructure. This in turn depends on knowing how much uplift is generated, when it occurs, the size of the catchment effect, and the contours of the effects with increasing distance from the public transport facility. This paper uses a Difference-in-Differences model to show differences in impacts for properties, as measured by property prices, in catchment areas versus those in control areas across time. The results show property prices in the catchment areas start to increase after announcement with the highest increment of increase being found after solid financial commitment is made by government. Property prices then slow during construction and the operation period. These results provide an evidence base for operators, planners and government sectors in their planning for future LRT systems and for quantifying the potential funding that can be achieved through capturing the increases in land value.

Impact of rail transit station proximity to commercial property prices: utilizing big data in urban real estate

“…Most studies use geographical information system (GIS) and hedonic price modeling (HPM) by taking into account the contractual price of property market. For instance, Barbara et al [13] elaborated property sales data in Australia and New Zealand from a property business company. On the other hand, Mohammad et al [17] used repeated cross-sectional survey data in order to evaluate the value of commercial properties from metro existence.…”

“…Research topics related to the relationship between rail transit and property market have been extensively investigated in the past decades and have taken different types of rail transit into account, including mass rapid transit (MRT), light rail transit (LRT), and heavy rail. Debrezion, Pels, & Rietveld [8] and Mohammad, Graham, Melo, & Anderson [9] have contributed an extensive literature review that covers approximately 200 publications on the topic of rail transit and property values, while some recent studies have also discussed this topic in various cities worldwide such as Missouri, USA [10], Hamburg, Germany [11], Beijing, China [12], and Queensland, Australia [13]. However, most of them focused on the residential properties, and only a few studies acknowledged commercial or offices property values, such as those located in Arizona, USA [14], Australia [15], Seoul, Korea [16], Dubai, UAE [17].…”

An assessment of the impacts of large‐scale urban projects on land values: The case of Belo Horizonte, Brazil

“…It provided more than 200 thousand observations using pooled data from January 2009 to May 2017. This approach appears frequently in the literature (Maciel & Biderman, 2013; Rodríguez & Mojica, 2009; Yen, Mulley, Shearer, & Burke, 2018). There is a total of 246 neighbourhoods in the filtered datasets, 23 in the treatment group.…”

“…One of the implications of this type of dataset is that we do not have a panel data , but a cross‐sectional one, in which we used the year of transaction as an individual characteristic of the property and, as suggested by Dubé, Legros, Thériault, and Des Rosiers (2014), the coefficients on dummy variables work as a price index. Rodríguez and Mojica (2009) and Yen et al (2018) also inserted dummy variables to capture the yearly appreciation of land values, although they did not give much attention to it in their results. Yen et al (2018) studied only residential properties with data provided by a business company.…”

Analyzing Indicators Affecting Commercial Property Value in Metro Station Accessible Area Using Walking Time Consumption: Case of Xi’an, China

“…In the first HPM results, none of the indicators has a much larger "VIF," proving that all the initial indicators selected are independent from one another. Considering empirical evidences found in Wuhan [27], Queensland [38], Seoul [23], and Athens [39], multicollinearity in initial indicator set was reported. It is not observed in this research because indicators with similar Complexity effects on the rental price were intentionally merged when building the initial indicator set with the guidance of Abutaleb et al [14] and meaningful results from other related studies [12,13,21,23,[25][26][27].…”