An investigation of the open-system Bus Rapid Transit (BRT) network and property values: The case of Brisbane, Australia

Transportation Research Part A 134 (2020) 16–34

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Transportation Research Part A journal homepage: www.elsevier.com/locate/tra

An investigation of the open-system Bus Rapid Transit (BRT) network and property values: The case of Brisbane, Australia

T

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Min Zhanga, Barbara T.H. Yenb, , Corinne Mulleyc, Neil Sipea a b c

School of Earth and Environmental Sciences, The University of Queensland, Australia Department of Transportation and Logistics Management, National Chiao Tung University, Taiwan, ROC Institute of Transport and Logistics Studies, Business School, The University of Sydney, Australia

A R T IC LE I N F O

ABS TRA CT

Keywords: Value uplift Value capture Bus rapid transit (BRT) Property values BRT network Open system BRT

This paper presents an investigation of the open-system Bus Rapid Transit (BRT) network on property values. An open-system BRT is one where bus feeder lines can enter and leave the BRT system, depending on their origin or destination so the BRT system infrastructure is shared by multiple routes. Brisbane, Australia is the empirical setting where smartcard (GoCard) data shows that 43% of bus passengers accessed the BRT system from feeder line stops. The paper investigates whether feeder line stops are important for increasing network accessibility in Brisbane’s open-system BRT. The hypothesis underpinning the study is that the improved accessibility resulting from an open-system BRT network results in higher property values within feeder line corridors in addition to simply around the BRT system. A Geographically Weighted Generalized Linear Model (GWGLM) is used to investigate property value premiums and their spatial distribution. GWGLM makes an improvement over Geographically Weighted Regression Model (GWR) by providing flexible local and global variable settings that decrease the risk of multi-collinearity in local models. The results identify property value uplift of up to 1.64% for every 100 m closer to feeder bus stops with frequent services in western and eastern Brisbane suburbs. Future studies should pay attention to the type of BRT operation (whether open- or closed-) in investigating the value of accessibility from BRT implementation. The results are policy relevant for the debate between whether BRT systems should be open or closed.

1. Introduction Recently, there has been increased interest in land value change that is induced by enhanced transport accessibility. These accessibility premiums are seen as a potential source of alternative funding to contribute to transport infrastructure through land value capture (LVC) which is important in an environment where new infrastructure is expensive and long-lived and difficult to fund. The willingness to pay for transit accessibility is a critical prerequisite in LVC implementation (McIntosh, 2015). The literature has documented these capitalisation effects of transit for some time. The earliest LVU studies typically focused on highways (e.g., Allen, 1981; Buffington et al., 1985; Burkhardt, 1984; Palmquist, 1980; Langley and John, 1981; Lewis et al, 1997; Spawn and Hartgen, 1997; Ryan, 1999) but with the increasing popularity of investment in rail-based transport infrastructure, researchers added a considerable body of empirical research to the literature on rail-based transit, including heavy rail, light rail and metro (e.g., Ryan, 1999; Debrezion et al., 2007; Mohammad et al., 2013; Yen et al., 2018a). More recently, the emergence of bus rapid transit (BRT), a

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Corresponding author. Tel.: +886 3 5712121 ext. 57213. E-mail address: [email protected] (B.T.H. Yen).

https://doi.org/10.1016/j.tra.2020.01.021 Received 19 October 2017; Received in revised form 27 November 2019; Accepted 28 January 2020 0965-8564/ © 2020 Elsevier Ltd. All rights reserved.

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Fig. 1. Brisbane bus journeys. Source: Data are from GoCard (the public transport smartcard in Brisbane) for one day, Tuesday 12 March 2013.

bus-based transport mode, has received more attention with a growing number of papers exploring its ability to capitalise accessibility into land values (e.g., Mulley, 2014; Mulley and Tsai, 2016; Munoz-Raskin, 2010; Pang and Jiao, 2015; Rodríguez and Mojica, 2009; Rodríguez and Targa, 2004). More recently, Higgins and Kanaroglou (2016) reviewed more than 130 public transport analyses, completed over the last 40 years that looked the relationship between accessibility and price premiums for all modes. They observed that premiums vary according to station zoning, built environment and system characteristics and that empirical work would be improved if these elements were included in the analysis. BRT became the newly established public transport system in Brisbane, Australia primarily because of the lower construction costs relative to other rapid transit modes (Hook and Wright, 2007; United States General Accounting Office, 2001; Vuchic et al., 2013) and the ability to provide a larger network of rapid services. BRT systems can be either open (where bus feeder lines can enter and leave the BRT system, depending on their origin or destination so the BRT system infrastructure is shared by multiple routes). Or closed (BRT services only run on the BRT system infrastructure and passengers interchange at the BRT infrastructure from local services). In the case of open BRT systems, ignoring the accessibility potential offered by the feeder line corridors for the BRT system may underestimate the accessibility benefits of the BRT system as a whole. The hypothesis underpinning the study is that the improved accessibility resulting from an open-system BRT network results in higher property values within feeder line corridors in addition to simply around the BRT system. Drawing on the open Brisbane BRT system as a case study, this paper aims to investigate connections between the open-system BRT network and property values by considering feeder bus routes as part of the BRT system as a whole. The Brisbane BRT system is a typical open BRT system. The core bus routes in the system travel on and off the BRT corridor to serve surrounding areas using the BRT corridor where appropriate as part of their journey. Fig. 1 provides the number of passenger journeys from BRT and non-BRT stops while accessing BRT. Services on Tuesday 12 March 2013. The passenger journey data are collected from GoCard (public transport smartcard) data in Brisbane. BRT plays a significant role in Brisbane as it contributes more than half (52%) of the total bus journeys in the network. Of these, 43% of bus journeys accessed the BRT from non-BRT stations (i.e., regular bus stops used by feeder bus lines, referred to hence forth as feeder line stops and 92% of these journeys accessed the BRT system directly without any transfers as feeder line journeys. This indicates that the feeder line stops and feeder line journeys provide significant system accessibility advantages, which should be considered when examining connections between the presence of the BRT and property values. Connections between property values and the Brisbane BRT have been the focus of three studies (i.e., Mulley et al., 2016, 2017; Zhang and Liu, 2015). In general, relatively lower value property value premiums have been reported around Brisbane BRT system stations compared to BRT systems around the world. In the case of Brisbane’s South Eastern Busway (SEB) and Northern Busway (NB), some negative premiums have been identified for surrounding residential housing properties (Zhang and Liu, 2015). However, none of these studies considered the open operation of the BRT system. This study attempts to overcome this bias by including feeder bus routes as part of the BRT network in the investigation of the connections between property values and an open-system BRT. The paper is motivated by providing an evidence base to planners or government agencies when a BRT system is being planned and in particular to provide a commentary on the critical decision as to whether the system should be open or closed. A comprehensive understanding of the connections between BRT and land values across different systems (open vs. closed) can help transport planners and decision-makers improve their BRT system planning if a concern is to understand how the accessibility benefits have been capitalised. This is particularly relevant for cities implementing newly designed BRT systems as well as for those cities with open systems, such as Brisbane, which are considering turning the BRT system into a closed system. Moreover, if a land value capture scheme is being considered as a way of helping to fund new transport infrastructure, this paper demonstrates that the net must be cast wider than simply the area around the new system infrastructure in an open BRT system. This paper is structured as follows: the next section provides the literature context for the impact of transport infrastructure in the transport field. It highlights issues that should be considered when exploring the implications of an open BRT system. The study area and data used in this research are summarised in Section 3, while Section 4 describes the study methodology. The results are provided in Section 5, followed by the discussion of key findings and areas for future research. This paper ends with a conclusion, a discussion of the study’s significance and limitations of the results.

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2. Literature review 2.1. BRT presence and property values BRT, which combines the capacity and speed of rail-based transit with lower costs and higher flexibility, is an alternative transport mode to improve urban mobility (Weinstock et al., 2011). In the urban context, public transport investment is recognised as not only a solution for traffic congestion, but also as an economic development strategy. Land rent theories developed by Alonso (1964) and Muth (1969) identify that land value increases reflect improvements in accessibility. In essence, rents are higher for land with higher accessibility since this offers land holders greater opportunities in terms of destinations. This theory underpins the notion that positive land value ‘uplift’ could be generated by investment in high-capacity public transport systems. TransMilenio, a BRT system implemented in Bogotá, Colombia in 2000, gained considerable attention from scholars for its performance and efficient operation (Hidalgo et al., 2013). Rodríguez and Targa (2004) found that the rental price of a property increased by between 6.8% and 9.3% for every 5 min of walking time closer to a TransMilenio BRT station using a spatial hedonic price model. Rodríguez and Mojica (2009) further examined the capitalisation effects of TransMilenio network expansion before and after the BRT implementation. The results showed that BRT network investment could increase property values in a 500 m catchment area by 13% to 14% when compared to those properties outside this catchment. Another case study of TransMilenio conducted a citywide econometric hedonic analysis, across different walking distances, subsystems (trunk, feeder), socio-economic strata and time (Munoz-Raskin, 2010). It concluded that the housing market places value premiums on properties in the immediate walking proximity of feeder lines. Middle-income properties were valued more if they were closer to the system with small average annual increases in property values that were correlated with the implementation of the system. There have been other empirical studies of property value uplifts from BRT investment in densely populated cities in Asia. In Seoul, Korea, (Cervero and Kang, 2011) reported that a dedicated median-lane BRT system improvement prompted property owners to convert single-family residences to higher density apartments and condominiums. They found that land prices for the residential areas within 300 m of BRT stops were higher. In Guangzhou, China, the BRT system has resulted in an increase of property values by 30% during the first two years of its operation (Suzuki et al., 2013). In contrast, Zhang and Wang (2013) measured the proximity premium of the Southern axis BRT line in Beijing, finding no measurable association with housing prices. They suggested that it was because the BRT system does not have a permanently fixed guide way, this gives rise to a higher level of uncertainty and investment risk than rail system and a lower capitalisation of accessibility. Mixed results are reported by the US Federal Transit Administration (FTA) (2009) for the Los Angeles Metro BRT Lines. The FTA (2009) indicated that residential properties within one-half mile from BRT system stops sold for less, while commercial properties sold for more, relative to other properties in the city. These lower prices were attributed to air pollution and noise around the BRT lines which are operating in mixed-traffic conditions along freeways. A more recent study by Mulley and Tsai (2016) examined the timing of the impact of a BRT system on residential housing prices in Sydney, Australia. They reported that sales price of residential properties within 400 m of BRT stops were marginally higher than those outside of the BRT service area immediately after the opening of the Liverpool-Parramatta Transitway in 2003/4. A review study conducted by Stokenberga (2014) concluded that property value associations with BRT systems have been mixed. Part of the mixed outcome is the result of different methods and study design but there is consensus amongst the results of different studies that different designs of BRT system infrastructure - for example, BRT operating in mixed traffic or on separate rights-of-way will have different links with property values. 2.2. BRT systems: Open vs. Closed Few studies have considered the way in which the flexibility of BRT operations can influence the capitalisation effect of the BRT system. As noted above, there are two types of BRT systems: closed and open. Fig. 2 shows examples of these two types of BRT. Table 1 below summarises the characteristics of the different BRT system infrastructures. In a closed system, BRT buses do not operate outside the BRT system infrastructure (e.g., TransMilenio, Bogotá). Feeder buses operating between the trunk corridors and the city’s peripheral areas are used to provide service for passengers outside the BRT corridor. But this requires passengers to transfer between buses operating on the BRT system infrastructure and the local bus services. However, this closed operation mechanism ensures that buses running on BRT corridor provide frequent and punctual rail-like public

Fig. 2. Examples of closed and open-system BRT. 18

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Table 1 Closed and Open-system BRT Characteristics. Indicator

Closed-system

Open-system

BRT Station Bus BRT corridor service (Trunk line)

Transfer stations are needed. High capacity; distinctive from local buses. Frequent, efficient and punctual service on the BRT corridor.

Feeder line service

Local bus services that start and/or end at BRT system infrastructure stations. All passengers access services of the BRT system infrastructure from BRT stations; BRT stations have system accessibility advantages over feeder line bus stops. Public transport demand is high around the corridor area and passenger volumes decrease sharply beyond the immediate corridor.

No specific transfer stations required. Usually not distinctive from local buses. Less efficient and punctual service on the BRT corridor because interactions between feeder buses can cause delay. Direct bus services that feeds into the BRT corridor.

Accessibility

City characteristics

Passengers can access service from both BRT system infrastructure stations and feeder line bus stops. Public transport demand does not differ greatly from area to area in the city.

transport service that removes the delays found in local bus services operating in mixed traffic. Closed systems are more effective if the public transport demand around the corridor is high but with passenger volumes decreasing sharply beyond the corridor. Buses in open BRT systems have more flexible service routes because they can move in and out of the dedicated BRT system infrastructure according to their route requirements (e.g., Seoul; Guangzhou; Brisbane). The open system enables a larger number of passengers to access the system by feeder line services without transferring giving a ‘single seat’ experience. Empirical studies show that requiring transfers along public transport routes can reduce public transport ridership and public transport mode share since transfer normally brings inconvenience to passengers, such as extra walk and wait time referred to as “transfer penalties” (Han, 1987; Liu et al., 1997; Yen et al., 2018b). This suggests that feeder line services in an open BRT system might be more valuable – in terms of their accessibility contribution to the network -than the local bus services servicing a closed BRT system. However, this greater accessibility contribution is by no means guaranteed as the mixed use of the BRT system corridor might lead to inefficient system performance by diluting the accessibility advantage of BRT stations over other bus stops. Closed- and open- BRT systems have distinguishable operational characteristics suggesting different system performance and thus potentially different capitalisation effects. To date, there is a lack of systematic examination of the capitalisation effects within open BRT systems. Most of the existing literature has focused on the BRT corridor catchment areas and the areas around BRT system infrastructure stations. However, the potential of a BRT system to extend its catchment area in terms of implications for property prices through the presence of an open system network is the focus of this paper.

3. Study area 3.1. The Brisbane BRT system Brisbane is the capital of Queensland and Australia’s third largest city with a population of 2.2 million in 2015. It is one of the major business hubs and fastest growing regions in Australia, averaging 4.7% economic growth between 2011 and 2012 (Queensland Treasury and Trade, 2012). Brisbane has a well-developed multi-modal transportation network of heavy rail, bus and ferries. The bus system is anchored by the most extensive BRT infrastructure system in Australia. In the mid-1990s, inspired by the BRT in Ottawa, Canada, the introduction of BRT was put on Brisbane’s transport policy agenda. Brisbane quickly designed and implemented its first BRT corridor, the South Eastern Busway (SEB), which opened in September 2000. Along with the patronage success of SEB, the BRT system expanded rapidly. To date, the Brisbane BRT Busway system has a 30 km network. The network is radial with most lines originating in the Brisbane's Central Business District (CBD). It now comprises SEB, Northern Busway (NB) and Eastern Busway (EB) with several more extensions under consideration. The Busway is a BRT system infrastructure consisting of high capacity buses with stations at wider spacing that operate similar to light rail transit (Hoffman, 2008; Tanko and Burke, 2013). It has been praised as the most advanced example of the Quickway model1 together with TransMilenio in Bogotá (Hoffman, 2008). A key difference between Brisbane BRT Busway and the TransMilenio is how they are operated. TransMilenio is a closed system, while Brisbane’s Busway is an open system. Fig. 3 shows the bus routes on SEB network. As a radial network, most routes in the network travel into or out from the CBD to serve suburban areas using the BRT system infrastructure, called the Busway, for part of their journey. Brisbane uses a zonal fare system, which means that no extra fares are charged for journeys that use the Busway. There are around 70 routes (each with two directions, inbound and outbound to Brisbane CBD) that operate on the Busway for part of their routes. In 2003, Brisbane City Council introduced the “BUZ” concept of high frequency routes giving direct access to the Busway with 10minute headways during peak hours and 15-minutes outside the peak (Yen et al., 2018). BUZ is therefore a branding strategy to 1 The focus of the Quickway model is that of creating a primarily grade-separated infrastructure, which then permits the cost-effective operation of a range of public transport services, many of which may not be identified during the infrastructure planning stage (Hoffman, 2008).

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Fig. 3. South Eastern Busway Network map. Source: https://translink.com.au/.

promote those high frequent routes that use the Busway as part of their journey. In peak hours, more than 300 buses in total operate on the Busway. Fig. 4 presents the bus mode share for the journey to work at Statistical Area 1 (SA12) level based on 2011 census data. Australia is a highly car-dependent country, and the mode share of the private car is around 86% (Cosgrove, 2011). Brisbane’s mode share is little different, but Fig. 4 shows the bus mode share around the Busway is much higher than in other parts of the city with some areas adjacent to high frequency routes having levels of bus use ranging from 25.1% to 38.9%. Fig. 4 also illustrates the importance of feeder lines by showing the number of BRT users by boarding stop (based on smart card data, Tuesday 12 March 2013). This shows the highest boardings are along the high frequency feeder branded ‘BUZ’ routes, shown as green lines, suggesting that these high frequency feeder line services might be a key factor in attracting passengers to public transport in Brisbane. 2 Statistical Area1 (SA1) are typically the smallest geographical area for the release of census data in Australia. The population of SA1 areas vary between 200 and 800 persons with an average of 400 persons.

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Fig. 4. The bus mode share for journey to work at SA1 level and the number of BRT passengers by boarding stop.

3.2. Case study area Fig. 4 also shows the many bus stops where bus services operate in the wider Brisbane urban area. The Brisbane network consists of Busway services on separate BRT system infrastructure which fan out into corridors as part of an open BRT system. The study area is defined as the 400 m (straight line distance) buffer of feeder line bus stops and 800 m (straight line distance) buffer of Busway stations. To investigate the contribution of feeder lines to the network as a whole, a method is needed to identify which bus stops along these corridors could be considered as being a stop providing feeder services to the BRT. A feeder line of the open-system BRT in Brisbane is defined here as a service from a bus stop providing direct Busway access without requiring a transfer. One method could be to have a distance cut off on each of the corridors to separate the feeder lines from local buses: this however would be treating all 21

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Fig. 5. Method identifying feeder line stops.

corridors as homogeneous whilst, in reality, feeder lines present variations in service frequency, which have a significant implication for their attraction to passengers. This study instead uses a method which takes account of the different levels of usage on a corridor and its likelihood of being used as a feeder line rather than a local route. The method uses smart card data on boardings, ordered by distance from the nearest stop on the Busway. Investigating the boardings in this way identified a discontinuity at 47 boardings per day. This is shown in Fig. 5. Bus stops nearer to the nearest stop on the Busway than this discontinuity were identified as a feeder line stops (irrespective of whether there were more or less than 47 boardings): these are shown as bus stops in blue3 in Fig. 5. Bus stops further from the nearest stop on the Busway than this discontinuity, coloured red in Fig. 5, are considered local routes. This method, as it happens, captures all the branded BUZ routes which are designed as high frequency services and is taken as being supportive of the method being able to differentiate high frequency feeder lines from local routes. Since these feeder line bus stops are regular bus stops, the accessibility benefits of these stops are not limited to BRT access but also accessibility to other local destinations. However, this study assumes the influence of feeder line stops on property values arise only from the open-system BRT network service. This is an oversimplification and could lead to an overestimate of the benefits of the BRT system. However, it is likely that this overestimate is small as Fig. 1 shows 92% of BRT journeys access the Busway from feeder line stops without transfer. Thus, there is a strong association between feeder line bus accessibility and BRT system accessibility justifying the inclusion of the feeder line bus stops as part of the open-system BRT network in this investigation. In total the analysis uses the 84 Busway stations of the BRT system infrastructure together with 1137 feeder line bus stops on 72 feeder routes.

4. Methods and data 4.1. Geographically Weighted Generalized Linear model (GWGLM) In previous studies, the hedonic price model has been widely used, based on the assumption that property values are independent of one another within the study area. However, this is not usually the case in real estate markets and their analysis where substantial geographic data is involved. One characteristic of geographic data, spatial dependency, is described as “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970). For instance, the value of a property is more likely to be high if properties nearby are expensive. Besides, spatial heterogeneity (also named non-stationarity), which refers to spatial structure, is also likely to occur (Anselin, 1988). Consequently, housing markets are generally local and diverse and ignoring the presence of these spatial effects may lead to biased results. The first decision is the choice of model. One choice would be to choose between the many spatial error models which have become more accessible as computing power has grown in recent times. These models correct for spatial issues and provide single values for the coefficients explaining the variation in house prices across a study area. An alternative choice is to use a local model approach which is probably more data hungry but provides an output which is more finely articulated, and which can be presented 3

For interpretation of color in Figs. 5 and 7, the reader is referred to the web version of this article. 22

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on maps. In line with the motivation to provide evidence as to how price premiums may vary over space, and in particular how this may vary between different areas of the Brisbane context, this paper has chosen to implement a local model approach. (Olaru et al., 2011). Geographically Weighted Regression (GWR), developed by Fotheringham et al. (2003), has been increasingly used to control for spatial autocorrelation and spatial heterogeneity in housing markets. The global model is a hedonic price model, which is fitted into the entire study region, with local models developed to explore how the relationship between the dependent variable and independent variables might vary geographically. The basic GWR model is shown in Eq. (1).

Pi (μi,νi ) = β0 (μi,νi ) +

∑ βj (μi , νi) Xij + εi

(1)

j

where (μi,νi) are the location coordinates of property i; Pi is the sale prices of property i which is predicted by a vector of observable property attributes Xij. Specifically, β0(μi,νi) and βj(μi,νi) would contain coefficients to be estimated for location (μi,νi) by a weighted least squares method used to calibrate the global model. The best bandwidth needs to be selected to generate the geographical weight for each point in the study area. Usually this process is calculated by the GWR software with reference to a specific criterion, such as minimising AIC (Akaike Information Criterion). As a result, a set of location-specific parameter estimates is generated, which can be mapped and analysed to provide information on spatial non-stationarity in relationships. The GWR model in Eq. (1) calibrates all the estimated parameters in the global model without specifically justifying the calibration for individual variables. Using this method, for instance, if nominal/categorical data is included in a GWR model, there is a strong risk of encountering local collinearity issues since categories cluster spatially. In order to ameliorate this, this study uses the improved Geographically Weighted Generalized Linear Model (GWGLM) which permits semi-parametric variants to investigate the spatial variation in the association between the BRT system and property values. Thus, one particular advantage of GWGLM is that the opportunity to include non-spatially varying variables allows the inclusion of nominal/categorical variables which usually need to be excluded in the traditional GWR. GWGLM is a more generalized GWR model and can be written as:

Pi (μi,νi ) = β0 (μi,νi ) +

∑ βLj (μi,νi) XLij + ∑ βGj XGij + εi j

(2)

j

where (μi,νi) are the location coordinates of property i; Pi is the sale price of property i, which is predicted by two vectors of observable property attributes XLij and XGij. For XLij, the coefficient βLj(μi,νi) is location-specific and estimated using the observations within a bandwidth by solving a maximisation problem of geographically weighted likelihood as in GWR model, while for XGij, vector the coefficients βGj remain constant over space and are without spatial calibration and are global (for the study area) in nature. 4.2. Data description Following Eq. (2), property prices are expected to be determined by accessibility to the Busway and other amenities, the internal characteristics of the property, and neighbourhood attributes. Property prices are based on 2012 property transaction data extracted from the Australian Urban Research Infrastructure Network (AURIN) database provided by the commercial company RP data. Fig. 6 shows the property sale observations within the study area which form the sample for this analysis. The dataset has 5391 property sales and the natural log of the property sales price is defined as the dependent variable in GWGLM. The transformed dependent variable has the advantage of mitigating heteroscedasticity as a result of reducing scale of the values (Rodríguez and Mojica, 2009; Mulley and Tsai, 2016). 4.2.1. Accessibility attributes In order better to understand how BRT accessibility influences property values, this study measured accessibility in two ways. The first is the network distance from the property to the nearest BRT stop (“D_BRTsystem”, including Busway stops and feeder line stops). The second is a set of dummy variables using an all-or-nothing approach to represent whether the property is located within the BRT system catchment area. For Busway stops, the catchment area is defined as a 800 m network distance from a Busway stop (“Catchment_Trunk”). This is based on many previous studies that assume that 800 m is a walkable catchment area for public transport (Guerra et al., 2013). For feeder line bus stops that are closer to each other, a narrower catchment area of 400 m network distance (“Catchment_Feeder)” is used. Properties that are located in both the catchment areas of the Busway and the feeder line are defined as in the catchment area of the BRT network system, and are included in the variable “Catchment_BRTsystem”. We also created a dummy variable (“Noise_50m”) to show properties that are within a 50 m buffer of all stations on the Busway to capture the any negative externalities (e.g., noise and air pollution) of being too close to BRT corridor. Finally, the network distances from a property to the nearest shopping centre and train station (i.e., heavy rail) are calculated to control for accessibility variations across the study area. 4.2.2. Property attributes The dataset comprises market clearing prices for properties in the study area. Market clearing prices of properties relate to improved land prices and are composed of two elements: the quality and attributes of the property and its location amenity. In order to control for the quality and attributes of the property, this study creates a variable by considering the land value, and the construction value of the property. The land value is the value of unimproved land used for taxation by the Queensland government. The construction value variable “ConstructionValue” is calculated as the difference between the market clearing property price (the 23

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Fig. 6. The property sale observations and station buffers within the study area.

improved land value) and the land value (the unimproved land value). The variable “Construction Value” is therefore used as a proxy of property attributes and property quality as it captures internal area size, layout, built year, building materials, etc. The location amenity of the property is captured by BRT access with the variable D_BRTsystem. The property type variable (i.e., “PropertyType”, representing house or townhouse) is provided alongside the market clearing property sale price by the data provider and is included in the model to provide an additional control. Further, because Brisbane is hilly (Gregory, 2007), an elevation variable (“Dem”) is included in the model as properties in elevated positions might have better views and lower flood risks, both of which could influence property prices. 4.2.3. Neighborhood attributes Neighbourhood attributes are used to control for the social-economic characteristics that could influence property prices. These 24

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Table 2 Variable description and summary statistics. Variables

Description

Mean

Std. Deviation

Dependent Variable LnPrice

Natural log of the property sale price

13.231

0.376

0.880

0.320

ConstructionValue Elevation (Dem)

A categorical variable equal to 1 if property is a house; equal to 0 if property is a townhouse. Reference value is townhouse. Property sale price-underlying land price (unit: $AU10,000) Elevation of the property in metres

2.762 28.320

2.290 17.948

Neighborhood Attributes Unemployment (%) CrimeCases Age65 (%) SchoolQuality

The unemployment rate at SA1 level Number of crimes at meshblock level The percent of persons older than 65 years old at SA1 level ICSEA of the nearest school for the property

0.0511 2.590 0.1108 1072.450

0.028 4.635 0.0574 77.535

The network distance from the property to the nearest heavy rail station in kilometres The network distance from the property to the nearest shopping centre in kilometres A categorical variable equal to 1 if property is within 50 m buffer of Busway stop, otherwise equal to 0 The network distance from the property to the nearest bus station that can access BRT without transferring in kilometres A categorical variable equal to 1 if property is within BRT feeder line stop catchment area (400 m network distance) or Busway station catchment area (800 m network distance), otherwise equal to 0 A categorical variable equal to 1 if property is only within a Busway station catchment area (800 m network distance), otherwise equal to 0 A categorical variable equal to 1 if property is only within BRT feeder line stop catchment area (400 m network distance) and not within Busway station catchment area (800 m network distance), otherwise equal to 0

2.590 1.604 0.180 0.283

1.830 0.9192 0.388 160.544

0.780

0.415

0.060

0.239

0.720

0.450

Property Attributes PropertyType

Accessibility Attributes D_Train D_Shopping Noise_50m D_BRTsystem Catchment_BRTsystem Catchment_Trunk Catchment_Feeder

variables are taken from the Australian Bureau of Statistics (ABS) 2011 Census at the SA1 level. Although a number of variables could be considered as important, e.g., the percentage of older people over 65 years old, the percentage of unemployed population, the percentage of high-income population, the percentage of married population, and the percentage of population with college and higher qualifications, these exhibited high collinearity and so only two variables - the unemployment rate (“Unemployment”) and the percent of older people (“Age65”) - are included. In the results, it is important to remember that these two variables will be capturing the influence of the variables with which they are highly colinear. Some control variables that might influence property values, for example, the distance to CBD, are not included due to collinearity problems with the elevation and unemployment rate variables. Other studies have suggested that school quality should be considered as one of the neighbourhood attributes and this is captured by the variable of the quality of the nearest primary/high school using the Index of Community Socio-Educational Advantage (ICSEA) for 2011. ICSEA is a scale created by the Australian Curriculum, Assessment and Reporting Authority (ACARA) to provide a fair and reasonable comparison among schools. Finally, crime can be viewed as a neighbourhood disamenity leading to a decrease in property value. A variable of the number of crime cases at the meshblock level was created (“CrimeCases”) from the Queensland Government Public for 2011. The meshblock is the smallest geography in the Australian Statistical Geography Standard (ASGS) with approximately 30–60 dwellings. Table 2 describes all variables used in this study and Table 3 provides the correlation matrix which gives no concern for potential multicollinearity. 5. Results Geographically Weighted Regression (GWR) proceeds in two stages. First, a global model is estimated using Ordinary Least Squares (OLS): this is the hedonic price model. The estimated parameters of the global model give the average effect of the independent variables on the dependent variable for the study area as a whole. This is followed by a local estimation whereby a regression is performed at each observation in the dataset using neighbouring observations for calibration. The results below first consider the global models and then the local models, each with more details on the specific method employed. 5.1. Global estimation Three different models were developed in order to explore the association between the BRT system and property values, including the feeder bus lines of the open BRT system network. Table 4 presents the results from three specifications each using different measures for BRT accessibility. In each case, all the coefficients estimated are statistically significant at the 5% level. 5.1.1. BRT network proximity and price premium The use of a semi log functional form allows the estimated parameters to be interpreted as a percentage change on the dependent 25

LnPrice PropertyType Construction Value Elevation (Dem) CrimeCases SchoolQuality Age65 Unemployment D_Train D_Shopping Noise_50m D_BRTsystem

1.000 0.273 0.761 −0.100 −0.110 0.393 −0.138 −0.255 −0.147 −0.080 −0.093 0.004

LnPrice

1.000 −0.072 0.087 −0.024 −0.072 0.106 −0.025 0.041 0.058 −0.026 0.021

Property Type

Table 3 Simple correlation matrix of model variables.

1.000 −0.099 −0.040 0.241 −0.128 −0.141 −0.119 −0.068 −0.038 0.015

Construction Value

1.000 −0.101 −0.115 0.187 0.146 0.244 −0.086 −0.014 0.114

Elevation (Dem)

1.000 −0.122 −0.003 0.103 −0.117 0.071 0.024 −0.019

Crime Cases

1.000 −0.165 −0.227 −0.200 −0.085 −0.014 −0.008

School Quality

1.000 0.080 0.217 0.002 0.021 0.025

Age65

1.000 0.016 −0.079 0.000 0.054

Unemployment

1.000 0.154 0.025 −0.002

D_Train

1.000 0.006 0.023

D_Shopping

1.000 −0.504

Noise_50m

1.000

D_BRTsystem

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Table 4 Global models estimation results. Model 1 BRT network service area (network distance)

Model 2 BRT network service area (all- or-nothing approach)

Model 3 BRT trunk line service area vs. feeder line service area (all- or-nothing approach)

Variables

Coef.

p-value

Coef.

p-value

Coef.

p-value

(Constant) PropertyType (base = townhouse) Elevation (‘Dem’) CrimeCases SchoolQuality ConstructionValue Age65 Unemployment D_Train D_Shopping Noise_50m D_BRTsystem Catchment_BRTsystem Catchment_Trunk Catchment_Feeder

11.662 0.402 0.000 −0.003 0.001 0.012 −0.218 −1.233 −0.004 −0.015 −0.070 −0.095 – – –

0.000 0.000 0.042 0.000 0.000 0.000 0.000 0.000 0.005 0.000 0.000 0.000 – – –

11.616 0.402 0.000 −0.003 0.001 0.012 −0.220 −1.252 −0.004 −0.015 −0.056 – 0.023 – –

0.000 0.000 0.023 0.000 0.000 0.000 0.000 0.000 0.006 0.000 0.000 – 0.000 – –

11.605 0.402 0.000 -0.003 0.001 0.012 -0.214 −1.265 -0.004 -0.014 -0.056 – – 0.052 0.021.

0.000 0.000 0.046 0.000 0.000 0.000 0.000 0.000 0.015 0.000 0.000 – – 0.000 001

AIC Adj R2

−2,943.142 0.76

−2,945.201 0.76

−2,944.380 0.76

variable, here the property sale price. Table 4 shows that in Model 1 where Busway network proximity is measured as the distance to the nearest bus stop with access to the BRT without the need to transfer, the estimated coefficient is −0.095. This indicates a significant price premium from the Busway proximity with, on average, each 100 m closer to the bus stop increasing the property price by 0.95%. Model 2, in contrast, compares the price premium between properties within the Busway network service area defined by “Catchment_BRTsystem” and those that are not. This approach gives an estimated coefficient of Busway accessibility of 0.023, suggesting that direct BRT access (i.e., within BRT network catchment area that includes a feeder bus lines that allow travel on the Busway without transfer) has a price premium of 2.3% on property value, all else being equal. Model 3 further separates the influence of property values to the Busway catchment and to the feeder line catchment. The estimated coefficients for “Catchment Trunk” and “Catchment Feeder” indicate that properties in the Busway trunk line catchment area and feeder line catchment area appreciated by an average of 5.2% and 2.1%, respectively. As discussed above, the nuisance/noise effect from bus operations was estimated using the variable “Noise_50m” and the estimated parameter suggests properties located within 50 m of a bus line would price 5.6% lower (Models 2 and 3) or 7% lower (Model 1). 5.1.2. Control variables Table 4 shows the models are very similar for all the independent variables used as control variables. Property type is an important factor in determining property sale prices: a house has a sale price 40.2% higher than a townhouse, ceteris paribus, in all models. The estimated coefficient for property construction value is 0.012 in all models, indicating that for every AU$ 10,000 increase in property construction value, there is an increase in the sale price of 1.2%. Two location specific characteristics of property, distance to the nearest train station and distance to the nearest shopping centre show positive associations, adding price increases of 0.4% (Model 1), 0.15% (Model 2) and 0.14% (Model 3) for every 100 m closer to train station and shopping centre, respectively. As for the neighbourhood attributes, both the unemployment rate and percentage of older people are negatively related to property value with every 1% increase yielding a 0.02% (all models) and 1.2% (Model 1) and 1.3% (Models 2 and 3) lower property values, respectively. An increase of 10 crimes per year within the mesh block is associated with a 3% lower property price (all models). Finally, as expected, proximity to quality schools had a positive influence on property value. The good fit of the global model and the significance of the explanatory variables validates the appropriateness of the model formulation and suggests that property prices are indeed influenced by the accessibility attributes, property attributes and neighbourhood attributes. Although global models provide some insights into the association between the open-system BRT and property values at the case study or city scale, they cannot show whether there are local variations which could give more informative policy implications. This is the role of the local models where spatial variability of the price premium can be explored. In addition, a test to see if the residuals of the global model are spatially independent by using the spatial autocorrelation coefficient, Moran’s I. The hypothesis that there is no spatial autocorrelation among property sales samples in the case study area is rejected by the Moran’s I test (z-score = 9.365, p-value = 0.000). This is an additional reason to adopt a local model to address this issue. 5.2. Local estimation Overall, all three global models fit well with no significant difference between the models in terms of their fit diagnostics (i.e., AIC, Adj R2). This paper uses an improved version of GWR, GWGLM, for local estimation. As discussed above, a major advantage of GWGLM is that independent variables which are dummy variables can be included in the estimation but restricted to being non27

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Table 5 GWGLM local and global variable settings. Variable type

Variables

Dependent variable Independent variable with fixed (Global) coefficient Independent variable with varying (Local) coefficient

LnPrice PropertyType, ConstructionValue, Noise_50m Intercept, Elevation (Dem), CrimeCases, Unemployment, SchoolQuality, Age65, D_BRTsystem, D_Train, D_Shopping

spatially varying. In this local model, this attribute of GWGLM is used to include two non-spatially varying dummy variables: “PropertyType” and “Noise_50m”. For other variables, the local model is based on the global Model 1 (shown in Table 4) since the accessibility variable “D_BRTsystem” is continuous being the network distance from the property to the nearest bus station that can access BRT without a transfer. Models 2 and 3 (shown in Table 4) include dummy variables attempting to capture the spatial influence of feeder bus lines and the BRT system (“Catchment_BRTsystem”; “Catchment_Trunk”; and “Catchment_Feeder”) and are therefore not suitable to be included as spatially varying variables in the local models since they would provide limited additional spatial explanation within a local model. Using the selected explanatory variables from Model 1, the local model is estimated using GWGLM in the GWR 4 package with adaptive kernels and a Gaussian function, as expressed in Eq. (2). The use of adaptive kernels, as opposed to the fixed kernels, ensures each of the data points is estimated by the same number of neighbouring data points and this approach is generally recommended for data points that are not evenly spatially distributed across a study area, as is the case here where residential properties tend to cluster in areas and where there are areas such as parklands that have no development. The bandwidth of the kernels is determined using the “golden selection search method” embedded in the GWR 4 software which determines the optimal bandwidth based on small sample bias corrected AICc minimisation. Table 5 summarises the variable settings for the finally selected GWGLM local model. The results from GWGLM are presented in Table 6. The GWGLM local model provides an improvement on the Global model given the smaller AIC (−4770.67 as compared to −2943.14 in the Global model) and with a rather higher model fit (pseudo R2 = 0.83). For the global variables, the estimated coefficients are similar to those estimated by global model (Table 4). The coefficients for local variables vary across space as shown in Table 6. The concern of this paper is in the spatial variability of the independent variable, BRT network accessibility (“D_BRTsystem”). Table 6 shows that this varies with a range of −0.164 to 0.276 (or −2.76% to 1.64% for each 100 m closer to a Busway stop without requiring transfer), showing a considerable spatial variation in housing prices. One of the advantages of the GWR method is the ability to visualise estimated local coefficients on a map: this is done in Fig. 7 that shows only significant parameters (at the 90% level) in graduated colour points while the insignificant properties are marked in light grey. Of the significant parameter estimates shown on Fig. 7, 66% are positive and 34% are negative. Fig. 7 shows that the local estimates of the relationship between BRT system accessibility and property values vary spatially. However, over half (58%) of the properties included in the analysis have insignificant coefficients for the Busway network accessibility, which suggests that BRT network accessibility does not have a significant influence on housing prices for much of the study area which may well be the result of the highly car-dependent nature of Brisbane. Fig. 7 does however show areas of distinct significant clustering around some – but Table 6 GWGLM estimation results. Fixed (Global) coefficients

Property Type ConstructionValue Noise_50m

Coefficient

Standard Error

t (Estimate/SE)

0.412 0.012 −0.056

0.007 0.001 0.007

59.468*** 101.882*** −8.475***

Summary statistics for local coefficients

Intercept Elevation (Dem) Crime Cases School Quality Age65 D_BRTsystem Unemployment D_Train D_Shopping AICc: Adj R2:

Mean

STD

Min

Max

Range

12.120 0.002 −0.001 0.001 0.002 −0. 090 −0.566 −0.002 −0.001 −4,770.676 0.832

0.597 0.002 0.003 0.000 0.424 0. 820 0.615 0.026 0.025

10.331 −0.002 −0.010 0.000 −0.867 −0.164 −2.499 −0.049 −0.066

14.859 0.007 0.013 0.001 1.385 0.276 1.183 0.078 0.065

4.527 0.009 0.022 0.001 2.252 4.400 3.682 0.127 0.131

***

p-value < 0.01, **p-value < 0.05, *p-value < 0.10. 28

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Fig. 7. Significant local parameter estimates associated with BRT network accessibility (variable “D_BRTsystem”).

not all – of the high frequency routes which, by Brisbane standards, have high levels of bus use mode share between 25.1% and 38.9%. But these levels of bus use are much lower than other cities where significant property value premiums have been found for BRT infrastructure (e.g., the public transport mode share is 59% for Bogotá’s TransMilenio BRT system, where strong property premiums are reported). Surprisingly, Fig. 7 shows no significant property premiums directly around the SEB corridor. One possible reason is that the majority of houses located close to the SEB can conveniently access the frequent BRT service, so Busway accessibility is not a distinguishing feature of only some properties. However, Fig. 7 shows significant price premiums in Brisbane’s eastern and western and suburbs (i.e., green points) with many properties at some distance from the BRT corridor enjoying price premiums of up to 1.64% for every 100 m closer to feeder bus stops - the highest value price premium reported for any Australian BRT system. Most of the areas benefiting from the Busway access are located around feeder lines with high frequency service routes (i.e., shown as green lines in 29

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Fig. 7) showing how the property premiums can extend along BRT feeder lines. This also implies that high frequency feeder line services are important in getting suburban residents to use the BRT system and a key variable to generate property premiums (Landis et al., 1994). Moreover, this shows that restricting an examination of an open-system BRT to its main corridor might underestimate the benefits from BRT investments. The highest price premiums are around feeder lines away from the SEB corridor. One possible reason is the strong proximityrelated negative externalities of bus transit such as noise, crime rate, etc. These are perceived as key disadvantages by some decisionmakers and public transport officials (Hecker, 2003). The situation tends to be worse because the SEB was constructed adjacent to the six-lane Pacific Motorway which reduces the desirability of wanting to living nearby, lowers the walking catchment, and decreases the quality of the station environments (Currie, 2006). It is also the case that the Busway feeder lines increase accessibility in peripheral areas providing an efficient way to get to the CBD and other desired destinations. Similar results have been reported by Bowes and Ihlanfeldt (2001) and Mulley (2014) where stronger price premiums are observed at stations further away from the CBD compared to those that are closer. For the southeast suburbs, the largest price premium area is located in the suburban area linked to the EB by high frequency services. Several factors could be responsible for these price premiums. First, a major bus interchange centre, the Carindale bus station, is located there. This interchange serves as a regional transport hub with services connecting to major destinations including Brisbane City, Redland City, Brisbane Airport, and the two of the large universities (i.e., The University of Queensland and Griffith University). It therefore has a larger catchment area than a typical feeder line bus stop. Second, the area around the feeder bus stop is walkable and adjacent to a large shopping centre forming a compact and mixed-use public transport station development. The catchment area of Carindale bus station was identified as having the highest level of the residential Transit Oriented Development (TOD) (top 15%)4 in Brisbane (Kamruzzaman et al., 2014). Finally, several bus stations in this area have been planned as Busway stations to be built as part of the EB. Though no specific date has been announced for construction, many routes servicing this area now travel via the completed sections of the EB. Therefore, property price premiums here could be due to the improved accessibility from direct BRT access via feeder line stops together with the expectation of future Busway extensions. In Fig. 6, the most significant price premiums are found in southwest neighbourhoods (i.e., Forest Lake, Inala, and Sherwood) that are far away from BRT corridor but featuring the quality of an existing activity centre TOD suggesting that the nature of the TOD, with its greater focus on public transport as opposed to the private car, could explain greater price premia. (Kamruzzaman et al., 2014). In the northern suburbs, the estimated coefficient of BRT network accessibility has a positive sign (Fig. 6), which indicates that the relationship between BRT accessibility and housing price is negative. This study is not alone in finding the pattern of negative property premiums around the NB (e.g., Mulley et al., 2016). A number of different local issues, not controlled in the modelling, might be at play to explain the negative property premiums of the NB which correspond almost directly to the positive property premiums of the same area for train accessibility (Fig. 8). First, in this northern part of Brisbane there are a number of train lines which together provide a more frequent service and larger reach or coverage – such factors are known to be key determinants of capitalisation effects (Landis et al., 1994). This contrasts with the situation in the south and west of the Brisbane where the rail routes provide significant (uncovered) coal train operations to the Port of Brisbane, providing night-time nuisance and pollution externalities, including on some inner-city sections which may be limiting the benefits of living proximate to rail stations in the south of Brisbane. Second, the rail lines in Brisbane are much more mature than the BRT system and the premiums – particularly in the central area - may have long since been capitalised into house prices. A final explanation, and perhaps the most important, is that the data of this paper, covering the whole of 2012, is in time very close to the opening of the NB which opened in mid-2012. All transport systems need a ramping up period and so an explanation of the negative premia around the NB may be that the benefits of the NB have not been realised by the time of this study’s data collection. Fig. 8 also shows there are house premiums around feeder line bus stops close to train stations where the feeder lines serve both the rail and the Busway networks. It is possible that rail station accessibility from these bus stops has also contributed to the property price premiums. However, as a limitation of this study, we are not able to isolate price premiums arising from feeder-line network accessibility to rail stations. Figs. 7 and 8 show negative house premiums in the river bend, close to the Brisbane CBD. This is a rich suburb of Brisbane which, although benefiting from feeder line access to the CBD, does not benefit from core BRT services nor train services as these are not located closeby. This area has, however, been identified as an area where there are positive premiums associated with access to the linear ferry service of Brisbane (Mulley, 2014; Tsai et al., 2017). Access to ferry services was not included in the models of this paper as such access only affects a very small part of the study area. 6. Conclusions This study investigates the associations between an open-system BRT network and property values by considering feeder bus lines as part of the Busway system in Brisbane, Australia. Using smart card data from the BRT system, 43% of journeys were identified as accessing the Busway from non-BRT infrastructure stops daily, highlighting the importance of feeder line services. In this study, the global models show the improved accessibility due to the open-system BRT network results in property price premiums both within 4 Kamruzzaman et al (2014) identified four different neighbourhoods including neighbourhoods featuring the quality of an existing residential TOD (15%); neighbourhoods featuring the quality of an existing activity centre TOD (10%); neighbourhoods featuring the quality of potential TODs (46%); and neighbourhoods requiring both land use and transport investment to qualify as a TOD (29%).

30

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Fig. 8. Local parameter estimates for rail station accessibility (variable “D_Train”).

the Busway main corridor but also in the feeder line catchment areas. Global models were applied for preliminary analysis. The results show that the relationship between BRT network accessibility and housing prices is positive. On average, for every 100 m closer to a bus stop, the property price premium is 0.95%. In general, property sales prices within the BRT and feeder line catchment areas are 5.2% and 2.1% higher than those outside the catchment, respectively. However, the diagnosis of the global (i.e., OLS) model using spatial autocorrelation coefficient, Moran’s I, shows spatial autocorrelation among property sales samples. Thus, the global model results may well be biased. A GWGLM was then used to explore the non-stationary nature of the relationship between Busway system accessibility and property values across Brisbane controlling for spatial autocorrelation and spatial heterogeneity. By visualising the local parameter estimates for BRT system accessibility, significant spatial variability can be seen for housing prices. Properties at some distance from the BRT corridor enjoy price premiums up to 1.64% for every 100 m closer to feeder bus 31

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stops. This suggests that the price premiums can exist around the BRT feeder lines as well as the main corridor. The main findings from the local model are:

• The association between the BRT catchment areas and housing prices is insignificant for more than half (58%) of the study area. • • • •

This may be because BRT proximity is not seen to be so valuable in the local real estate market because Brisbane is a highly cardependent city. In the southeast and southwest areas, the highest price premiums were around feeder lines further away from the SEB corridor itself. One possible explanation is that the stronger proximity-related negative externalities of BRT along the corridor itself reduces the desirability to reside in areas adjacent to the Busway and therefore affects the local price premium. High frequency feeder line services are a key factor in driving suburban residents to BRT and a key determinant of price premiums. The area with the greatest price premium was around the feeder line stop areas that could be identified as an existing residential TOD, and as an existing activity centre TOD, as defined by Kamruzzaman et al. (2014). In northern suburbs, the relationship between BRT accessibility and housing price is negative, suggesting that the less mature northern BRT has not yet resulted in property price premiums. An additional explanation is that in this part of Brisbane there are a number of train lines which together provide a more frequent service and a greater reach of destinations which are known to be important determinants of capitalisation.

While the results presented here are only valid for Brisbane, there are implications for future research. How a BRT is operated can make big differences for its service area, passenger ridership and the land appreciation. For the Brisbane Busway, the open-system BRT accessibility appears to significantly associated with property premiums in the wider area covered by the feeder lines. This suggests that future studies looking at the connections between BRT and property prices should pay attention to the nature of the BRT operation to better capture the property premiums associated with a BRT network, including both the main BRT corridor and feeder line services where appropriate. A comprehensive understanding of the distribution and size of property premiums resulting from public transport accessibility is critical in the preparation of LVC policies. One of the debates about LVC schemes is whether the capture tax should be applied only within the transit station catchment area (i.e., BRT corridor in this case) or the whole city area. The results of this study provide another LVC option for consideration: The evidence in Brisbane suggests the price premiums of BRT accessibility spreads to the feeder lines of the open system, and this can be distant from the BRT system infrastructure (e.g., EB). Based on the results from this study, districts further away from BRT corridors should also be included in an equitable and effective LVC policy. However, implementation a LVC, including feeder line routes, could be politically challenging as the argument would be that these routes are potentially more flexible and that if they were removed the premium would also go. However, some feeder line stops can be permanent, such as the large-scale stops seen in the eastern bus interchange centre in Brisbane where the benefits can be entrenched by enhancing infrastructure, optimising BRT routes and the most importantly, facilitating the development of TOD in these areas. A second issue for LVC is whether a uniform rate of tax should be applied or whether it should be scaled in some way. The analysis here shows that price premiums do vary over space which means that BRT accessibility is being seen to have a positive effect on land value in some places, a negative or insignificant effect in others. In these circumstances applying a LVC policy where each property pays the same contribution thus engendering horizontal equity is likely to create winners (where the price premium exceeds the tax) and losers (the reverse) or vertical inequity. Finally, house premiums were found around feeder line bus stops close to train stations. It is possible that rail station accessibility from these bus stops has also contributed to the premium. This study is not able to isolate price premiums arising from feeder-line services to rail services. An avenue for future research would be to explore multi-modal public transport networks associations with land value at the city scale. In addition, exploration of other factors which are currently excluded from the modelling, such as the walkability of neighbourhoods, could help explain the observed spatial variability in the local model. There are some issues that have limited our ability to carry out this research. First, due to the limited data resources, this study uses a single day of smart card data to investigate how feeder bus services work in an open BRT system and therefore design the study area. Future research should seek to provide a more comprehensive understanding of feeder lines and their implications for housing prices using travel data over a longer period. Another area for future study is extending this cross-sectional research to a longitudinal analysis of BRT feeder-line impacts on housing value uplift. This would generate results on the causal relationship between BRT network accessibility and property value uplifts, eliminating the bias if properties within feeder line catchment area are more expensive than those outside before the BRT implementation. Other methods (e.g., multi-level regression models or difference in difference models) could be implemented to see if they provide more robust results. Further, there are different services within the multi-modal public transport system, e.g., BUZ services in Brisbane. The implications of different service types should be considered for future analysis. Finally, this study has used fixed sized catchment areas (800 m and 400 m as catchment areas for Busway stations and feeder line bus stops respectively). Enhanced sensitivity analysis using different ranges or maybe continuous variable measuring distance from the station or stop would be useful and help to confirm the robustness of the results. CRediT authorship contribution statement Min Zhang: Concept and method development, analysis, writing initial draft Barbara T.H. Yen: Supervision of concept development and method, analysis, writing- review and editing, funding acquisition. Corinne Mulley: Writing- review and editing, 32

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analysis advice, funding acquisition. Neil Sipe: Supervision, writing - review and editing, funding acquisition Acknowledgements Funding: This research was supported partially by the Australian Government through the Australian Research Council's Linkage Projects funding scheme (project LP150100078) and by the Queensland Department of Transport and Main Roads, Transport for NSW, Gold Coast City Council and Queensland Airports Limited. For Professor Mulley, the paper also contributes to the research program of the Volvo Research and Educational 3 Foundations Bus Rapid Transit (BRT+) Centre of Excellence at the Institute of Transport and Logistics Studies, University of Sydney. The authors wish to thank anonymous reviewers for their time in making constructive comments which have significantly improved the paper. The views expressed are solely those of the authors, who are responsible for all errors and omissions. 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