value uplift of brt in brisbane

The University of Sydney Source: http://cincinnatitransforum.org/wp-content/uploads/2010/08/brt_bogota_poster.jpg

THE EFFECTS OF BUS RAPID

TRANSIT ON RESIDENTIAL

PROPERTY VALUES IN

BRISBANE, AUSTRALIA

Corinne Mulley

Liang Ma

Geoffrey Clifton

University of Sydney

Barbara Yen

Matthew Burke

Griffith University

The University of Sydney

Introduction

– Public transport investment typically targeted at increasing accessibility which land rent theory identifies will increase land values.

– There is clear interest in how much land values increase to establish whether uplift sufficient to capture to help pay or contribute to investment plans.

– Identifying uplift for residential land has been well studied in the context of new light rail systems and bus rapid transit (BRT) systems in developing countries but there is little evidence for BRT in developed countries.


Objectives

– First, to examine long term impact of BRT in a developed world context in Brisbane, Australia.

– Second, to consider the spatial distribution of uplift which is an essential pre-requisite to understanding the distributional impact if uplift is used to contribute to infrastructure funding.


The Brisbane BRT

– Brisbane’s 32km busway network services the inner and middle suburbs of Brisbane

– Fully segregated and physically protected rights-of-way

– Single-seat journeys

– The system in Brisbane is relatively mature, with the first sections opened in the year 2000

– Over 300 buses per hour travel on key links of the South East Busway, carrying over 20,000 passengers per hour in the peak


BRT Vs. Train in Brisbane

Historical trends in public transport patronage (millions of

passengers), Brisbane, 1900 to 2013.

Train Busway

Peak headway <= 15 min <= 5 min

Off peak headway 15 to 30 min <= 5 min

Span of hours

04.00 to 00.30 (Friday and

Saturday)

04.00 to 24.00

(other days) 05.00 to 24.00

Number of routes 10 4

Length of network 220 km 32 km

Number of stations 146 24

Average stop spacing 1.5 km 1.2 km

Comparison of Brisbane Train and Busway LOS

– In 2013, the mode shares for bus, train, and ferry were 54%, 44% and 2%, respectively

– Brisbane’s train services have traditionally operated at lower frequencies than the corresponding bus routes

– The Brisbane busway network offers passengers faster, more frequent and reliable bus services


Methodology

– Hedonic Price Model

�� = � + ∑ �� +�� ∑ � � +�

� ∑ �� + ∑ �� + ��

� (1)

– Spatial Lag Model

�� = � + �� + ∑ �� +�� ∑ � � +�

� ∑ �� + ∑ �� + ��

� (2)

– Spatial Error Model

�� = � + ∑ �� +�� ∑ � � +�

� ∑ �� + ∑ �� +�

� �� + � (3)

– Geographically Weighted Regression Model (GWR)

�� = � �� + ∑ � �� +��

∑ � �� +�� ∑ �� + ∑ ��

�� + ��

�� (4)


Data

– transaction price, property type (house or unit), area size of the plot, number of bedrooms, bathrooms and parking places, and latitude/longitude of the property (supplied by RP data)

– All properties sold in 2011 that are in a 5-kilometer buffer of the BRT were selected for this cross sectional analysis

– Other data added from 2011 Census


Variables - Property

VariableDescription Mean

Std.

Dev.

SalePrice Sale price of the property576,45

0

328,81

2

PropertyType Type of property (1=house, 0=unit) 72%

Bedrooms Number of bedrooms 3 1

Baths Number of baths 2 1

Parking Number of parking places 2 1


Variables - Accessibility

Variable Description Mean Std. Dev.

AmenitiesNumber of common destinations within walking

distance (400 meters) of the property2 5

DBRTStreet network distance to the nearest BRT stations

(100's meters)38.5 19.1

DTrainStreet network distance to the nearest Train stations

(100's meters)24.7 19.1

Hwy100mProperty located within 100 meters of a highway

(1=yes)2%

DCBDEuclidean distance from the property to the CBD

(1,000's meters)4.8 4.9

DRiverEuclidean distance from the property to the river

(1,000's meters)2.6 2.5

Rail50mProperty located within 50 meters of a train line

(1=Yes)0.9%

BRT50mProperty located within 50 meters of a BRT line

(1=Yes)0.2%


Variables - Neighbourhood

VariableDescription Mean

Std.

Dev.

PopDen Population density (1,000's persons) 3.7 3.6

OlderPercentage of elder people of the statistical

area where the property located10% 5%

HHincomeMedian household income of the statistical

area where the property located1,661 493

CrimeTotal crimes per acre within walking distance

(400 meters) of the property29.5 95.9


Model Results

OLS Spatial Lag Spatial ErrorCoef. t Coef. t Coef. t

DBRT -0.0013 -8.28 *** -0.0013 -9.09 *** -0.0011 -4.23 ***

DTrain 0.0015 7.87 *** 0.0010 5.74 *** 0.0017 5.37 ***

PropertyType 0.1743 22.25 *** 0.1564 20.84 *** 0.2116 26.99 ***

Bedrooms 0.1434 34.19 *** 0.1336 33.60 *** 0.1396 35.92 ***

Baths 0.1457 30.73 *** 0.1378 30.83 *** 0.1291 29.11 ***

Parking 0.0627 16.22 *** 0.0612 16.80 *** 0.0610 17.30 ***

Amenities 0.0040 4.31 *** 0.0035 4.04 *** 0.0042 3.01 ***

PopDen 0.0035 4.26 *** 0.0042 5.40 *** 0.0018 1.74 *

Older 0.7164 13.29 *** 0.5028 9.82 *** 0.5975 9.15 ***

HHincome 0.0002 36.20 *** 0.0001 18.84 *** 0.0002 22.64 ***

Hwy100m -0.0514 -2.42 ** -0.0357 -1.78 * -0.0271 -1.18

DCBD -0.0171 -24.72 *** -0.0113 -16.36 *** -0.0188 -15.37 ***

DRiver -0.0463 -29.38 *** -0.0391 -25.88 *** -0.0490 -17.97 ***

Rail50m -0.0773 -2.72 *** -0.0708 -2.65 *** -0.1011 -3.65 ***

BRT50m -0.0057 -0.09 -0.0045 -0.07 0.0038 0.06

Crime -0.00012 -2.58 ** -0.00004 -1.03 -0.00014 -2.17 **

Constant 11.9951 771.40 *** 7.8689 54.85 *** 12.0991 546.15 ***

Lambda/Rho Coefficient 0.3296 28.91 *** 0.4973 33.55 ***

Number of obs. 7693 7693 7693

R-squared 0.70 0.73 0.75

Akaike info

criterion -455.87 -1266.76 -1435.30


Model Results

OLS Spatial Lag Spatial Error

Coef. t Coef. t Coef. t

DBRT -0.0013 -8.28 *** -0.0013 -9.09 *** -0.0011 -4.23 ***

DTrain 0.0015 7.87 *** 0.0010 5.74 *** 0.0017 5.37 ***PropertyType 0.1743 22.25 *** 0.1564 20.84 *** 0.2116 26.99 ***

Bedrooms 0.1434 34.19 *** 0.1336 33.60 *** 0.1396 35.92 ***

Baths 0.1457 30.73 *** 0.1378 30.83 *** 0.1291 29.11 ***

Parking 0.0627 16.22 *** 0.0612 16.80 *** 0.0610 17.30 ***

Amenities 0.0040 4.31 *** 0.0035 4.04 *** 0.0042 3.01 ***

PopDen 0.0035 4.26 *** 0.0042 5.40 *** 0.0018 1.74 *

Older 0.7164 13.29 *** 0.5028 9.82 *** 0.5975 9.15 ***

HHincome 0.0002 36.20 *** 0.0001 18.84 *** 0.0002 22.64 ***

Hwy100m -0.0514 -2.42 ** -0.0357 -1.78 * -0.0271 -1.18

DCBD -0.0171 -24.72 *** -0.0113 -16.36 *** -0.0188 -15.37 ***

DRiver -0.0463 -29.38 *** -0.0391 -25.88 *** -0.0490 -17.97 ***

Rail50m -0.0773 -2.72 *** -0.0708 -2.65 *** -0.1011 -3.65 ***

BRT50m -0.0057 -0.09 -0.0045 -0.07 0.0038 0.06Crime -0.00012 -2.58 ** -0.00004 -1.03 -0.00014 -2.17 **

Constant 11.9951 771.40 *** 7.8689 54.85 *** 12.0991 546.15 ***

Lambda/Rho

Coefficient 0.3296 28.91 *** 0.4973 33.55 ***

Number of obs. 7693 7693 7693

R-squared 0.70 0.73 0.75

Akaike info criterion -455.87 -1266.76 -1435.30


Crime Model Estimation

Coef. t

DBRT -0.6955 -12.38 ***

DTrain -0.2675 -3.89 ***

PopDen 6.8022 23.33 ***

Older

-

151.18

07 -7.64 ***

HHincome -0.0154 -6.93 ***

Hwy100m

85.022

1 11.08 ***

DCBD -0.7967 -3.14 ***

DRiver -2.3363 -4.15 ***

Constant

86.765

0 16.47 ***

Number of obs 7693

R-squared 0.18

– OLS model with crime density as the dependent variable was estimated

– results indicate that the crime rates are higher at areas nearby BRT and train stations


Local model with GWR

– Better fit with lowest AIC of all models

– But improvement in model fit set against poor performance in controlling for spatial autocorrelation

– GWR allows the spatial variation to be mapped


GWR Model Results

– In general, most parts of the study show the expected negative relationship between access to the BRT station and housing price

– This effect is relatively stronger at stations further away from the CBD with non-significant effects for some BRT stations closer to the CBD

– In the northern suburbs, the association between access to the BRT station and housing price is also negative, suggesting that either nuisance there more than offsets the benefits or that the less mature Northern Busway has not fully developed uplift

Local estimates of distance to the BRT station


GWR Model Results

– By contrast, this shows the unexpected negative association between access to the train station and housing price throughout most of the study area

– The strongest impact was at the train stations located close to the CBD -possibly because the benefits of access are small in terms of accessibility/time savings while the negative effects (e.g. noise, crime, pollution) might be higher

– In the north of the study area there are several passenger train routes which combined will provide both more frequent services and larger coverage giving positive capitalization effects

Local estimates of distance to the train station


Conclusion and Policy Implications

– Results from global models, OLS and spatial models, consistently suggest that, ceteris paribus, being close to BRT adds a premium to the housing price

– Results from the GWR local model further indicate that proximity effects vary over space. In general, the proximity effects are relatively stronger at stations further away from the CBD, indicating people living in suggesting suburban dwellers more likely to pay extra for being close to a BRT station

– Areas with high premia on house prices are served by the BUZ (Bus Upgrade Zone) routes which provide extensive feeder buses and very high frequency– Operators argue that open system (single seat) expands service area

and contributes to the significant and widespread capitalization effects– Open and closed systems may deliver different uplift (typical

developing country systems are closed) and needs to be considered in the design of a BRT system

– Uplift exists to be captured – by tax increment financing (TIF) as used widely in the USA? Or other mechanisms?


Thank [email protected]

value uplift of brt in brisbane

Engineering