THE IMPACT OF A LIGHT RAIL SYSTEM (EXISTING BLUE LINE) ON SINGLE FAMILY PROPERTY VALUES IN MECKLENBURG COUNTY, NC, FROM
1997 TO 2008
SisiYan 2009Master of Arts in Geography
University of North Carolina at CharlotteDepartment of Geography and Earth Science
Committee Members: Dr. Eric Delmelle, Dr. Mike Duncan and Dr. Harrison Campbell
PROJECTDEFENSE
Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
•Study Area & Data
•Method
•Results & Discussion
•Conclusion & Future
Study
Introduction
1984---the Charlotte-Mecklenburg Planning commission made its first recommendation;
1998, Dec---tax voted, the planning for the South Corridor to Pineville commenced;
2005, Feb26---groundbreaking;
2007, November 24---Opened
Research Questions:
how much is the property value change as it proximity to rail transit in Charlotte area?
Was there such impact during plan time? How have this relationship changed over time?
Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
•Study Area & Data
•Method
•Results & Discussion
•Conclusion & Future
Study
Location Theory and Transit Capitalization
Hedonic Price Studies
Empirical Studies on Transit Impact
Literature ReviewLocation Theory
Von Thünen 1826 (land use theory)
Different land use will be adopted accordingly in order to maximize the overall profits. land that is closer to the market place will bear less transportation costs and therefore has higher value.
Alonso 1964, Muth 1969:(bid for rent)
higher land value appears in a shorter distance to center and this rent gradient will decline nonlinearly as distance to center increases.
“ good accessibility results in higher property values “
Hedonic Price Model
Knaap (1998) summarized:
Property character: the size, age and quality of any structure, etc;
the location character: distance to CBD, transit and other amenities.
the neighborhood character: median household income and crime rate, etc.
Sale _price =f (Pr, H, L, N)
Empirical Studies
Light rail in Portland, Oregon (Lewis-Workman and Brod, 1997)
on average, property values increase by $75 for every 100 feet closer to the station
Metrorail in Miami, FL (Gatzlaff and Smith, 1993)
weak evidence that there was any major effect on residential values because of the rail
Rapid Transit in Chicago (McMillen and McDonald, 2004)
the housing market anticipated the opening of the line and house prices have been affected by proximity to the stations six years before its construction
Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
•Study Area & Data
•Method
•Results & Discussion
•Conclusion & Future
Study
Research Design
Research Hypotheses
Study Time Frame
T1
• Pre-Planning period; From 1997 to 1998
T2
• Planning period; From 1999 to 2004
T3-
• Rail Construction period;
• From 2005 to 2007
T4
• Rail Operation period; After Nov.1st 2007 till July 2008
Research Hypothesis
T4
T2
T3
T1
Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
•Study Area & Data
•Method
•Results & Discussion
•Conclusion & Future
Study
Charlotte
Light Rail Station Area
Data Sources
Charlotte
Since the 1980s, Charlotte has been one of the nation’s fastest growing urban areas. Between 1980 and 2005, Charlotte grew from the 47th to the 20th most populated city in the United States (Charlotte Chamber).
Due to the development of the banking industry, Charlotte became a financial city attracting many new businesses
LYNX Rail
Data Sources
Mecklenburg County & UNC Charlotte Urban
Institute
Charlotte Area Transit (CATS)
Federal Housing Finance Agency
US census
other secondary data generated by Geographical
information technology (GIS).
Table 3 Descriptive Statistics
Minimum Maximum Mean Std. Deviation
sales_pric 10000.00 992000.00 197997.82 146058.84
age 1.00 108.00 46.49 21.31
heatedarea 480.00 7003.00 1722.25 743.16
height 1.00 3.00 1.32 0.48
NUM_fire 0.00 4.00 0.67 0.49
qality_building 0.00 4.00 1.47 0.89
fullbaths 1.00 6.00 1.69 0.71
bedrooms 1.00 9.00 3.12 0.64
units 1.00 2.00 1.00 0.05
lnheatarea 6.17 8.85 7.38 0.38
lnnetdis 6.27 9.93 8.42 0.53
t1lnnetdis 0.00 9.93 2.11 3.67
t2lnnetdis 0.00 9.92 3.39 4.14
t3lnnetdis 0.00 9.93 1.72 3.40
t4lnnetdis 0.00 9.93 1.20 2.95
Valid N 6381
Note: t(i) Lnnetdisrepresents the ln_net_dis (in feet) at t(i) (i=1,2,3,4) time period
Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
•Study Area & Data
•Methodology
•Results & Discussion
•Conclusion & Future
Study
Methodology
Methods:
hedonic regression model for four time periods:Model 1:
Sale _price =f (Pr, H, N(i))
Model 2:Sale _price =f (Pr, H, BG(i))
Specify model:
Ln(ad_sale_ price) =β0+βi * hi +βj * ln_net_distance+βk * Dumk + εi
Where, dependent variable is the natural logarithm of the adjusted sales price; hiis a vector of asset-specific characteristics of the properties; ln_net_distance is the logarithm of proximity variable; Dumkis spatial dummy variables; βistands for the coefficients of each independent variable;
Spatial Dependence
0.55
0.6
0.65
0.7
0.75
0.8
0.85
300 500 600 650 700 800 900 1100 2000
Mo
ran
's I
Threshold Distance (feet)
t1
t2
t3
t4
Moran’ s I
Neighborhood Boundary
Block group Boundary
Variables Discussion
Sales value vs. assessed value
Network distance vs. Straight-line Distance;
Variable List
Data Transformations
Ln_ad_price (HPI)
Ln_net_dis
Ln_heatedarea
Age2
Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
•Study Area & Data
•Method
•Results & Discussion
•Conclusion & Future
Study
Models’ Results
Light Rail Impact
Model1. HPR with neighborhood dummy variables:
T1 T2 T3 T4
Variable Coefficient Coefficient Coefficient Coefficient
(constant) 7.028 6.502 7.248 7.796
Property characteristics
age -0.003* 0.002* -0.002* -0.005
agesqr 4.24E-05 1.06E-05* 5.99E-05 7.93E-05
height 0.152 0.083 0.076 0.091
Fule_None -0.762 0.327* 0.063* -0.718
AC-Central 0.061 0.097 0.100 0.117
Building_Grade 0.040 0.034 0.059 0.041
Num_Fire 0.114 0.092 0.074 0.016*
ln_Heatedarea 0.490 0.538 0.443 0.502
Rail Impact
Ln_Net_Dis 0.129 0.147 0.153 0.054*
Neighborhood Dummy Variables
York Road -0.729 -0.712 -0.960 -1.038
Wilmore -0.920 -0.611 -0.185 0.036*
Dilworth 0.233 0.351 0.467 0.483
Starmount Forest -0.491 -0.609 -0.716 -0.912
Sterling -0.159 -0.202 -0.323 -0.317
Montclaire South -0.238 -0.355 -0.394 -0.594
Yorkmount -0.588 -0.601 -0.793 -0.779
See other neighborhoods in appendix table
R2 0.746 0.750 0.781 0.829
Model 1 regression coefficients for
four time periods
Notes: * insignificant at p < 0.05
(since most of the variables are
significant in this table, for a
better distinguish, I chose using * to
represent insignificant variables)
T1 T2 T3 T4
Variable Coefficient Coefficient Coefficient Coefficient
(constant) 8.196 7.406 8.123 8.249
Property characteristics
age -0.004 -0.001* -0.003* -0.006
agesqr 4.37E-05 2.59E-05* 4.16E-05 8.21E-05
height 0.125 0.062 0.076 0.053*
Fule_None -0.796 0.302* 0.032* -0.794
AC-Central 0.045 0.080 0.090 0.101
Building_Grade 0.034 0.027 0.032 0.059
Num_Fire 0.089 0.064 0.057 0.004*
ln_Heatedarea 0.337 0.392 0.338 0.455
Rail Impact
Ln_Net_Dis 0.123 0.169 0.148 0.052*
Sample of Block Group Dummy Variables
First Ward blkg3 -0.110* 0.603 0.708 0.441
YorkRoad blkg26 -0.637 -0.622 -0.937 -1.078
Dilworth blkg19 0.558 0.720 0.881 0.578
Dilworth blkg20 0.325 0.492 0.597 0.434
Dilworth blkg23 0.772 0.923 0.849 0.638
Sterling blkg32 -0.506 -0.488 -0.669 -0.961
Yorkmount
blkg28 -0.528 -0.552 -0.750 -0.781
See other block dummy variables in appendix table
R2 0.779 0.786 0.811 0.837
Model 2 regressions coefficients for
four time periods
Notes: * insignificant at p < 0.05
(since most of the variables are
significant in this table, for a
better distinguish, I chose using * to
represent insignificant variables)
Model2. HPR with block group dummy variables:
Models T1 T2 T3 T4
R2M1 0.746 0.750 0.781 0.829
M2 0.779 0.790 0.811 0.837
Adjusted R2M1 0.742 0.748 0.777 0.824
M2 0.773 0.783 0.805 0.83
Moran’s IM1 0.167 0.185 0.238 0.064
M2 0.097 0.110 0.167 0.021
Models Comparisons
Light Rail Impact
Notes: *
insignificant at
p < 0.05Models T1 T2 T3 T4
Ln_Net_Dist
M1 0.129 0.147 0.153 0.054*
M2 0.123 0.169 0.148 0.052*
T1-2 T2-3 T1-3 T3-4 T1-4 T2-4
Z/neighbor -0.56 -0.19 -0.68 2.59 1.97 2.64
Z/blkgrp -1.29 0.60 -0.64 2.25 1.64 2.95
Z Test
Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
•Study Area & Data
•Method
•Results & Discussion
•Conclusion & Future
Study
Conclusions
Future Studies Suggestions
Conclusions
Contradictory to many studies, single family housing
value in Charlotte area tend to increase value as distance
to rail increases
Comparing across four time periods, pre-
planning, planning, construction and operation, rail
operation diminish the proximity disadvantage that
appears at the station area
Future Studies
Apply model to other available property types such as multiple family and commercial
Analyze the impact of rail when the line is completed
Integrate spatially-explicit regression models such as geographical weighted regression Local patterns in residuals
Divide study time period according to station plan time
Acknowledgements
Thanks for Eric’s advice from Idaho to Charlotte
Thanks for Mike’s great help and guidance through this study
Thanks for Harry’s support
Thanks for Tom Ludden’s data support
Thanks for Paul McDaniel's great tolerance during editing my ‘professional’ Chine-glishwriting
Thanks for Amos’s Coding support
Thanks you all for coming today
Questions and Comments?
References Selected: Al-Mosaind, M.A., Dueker, K.J., Strathman, J.G.
(1993), "Light rail transit stations and property values: a hedonic price approach", Transportation Research Record, No.1400, pp.90-4.
Alonso, W. (1964). Location and land use: Toward a general theory of land rent. Cambridge, MA: Harvard University Press.
Bajic V (1983). The effects of a new subway line on housing prices in metropolitan Toronto. Urban Studies 20: 147–158.
Duncan, Michael (2007) The Conditional Nature of Rail Transit Capitalization in San Diego, California. Dissertation No. D07-003
Variables Description Data Sources Justification
PROPERTY VALUE (dependent variable)
Ln_ad_Price
Amount($) for which the single family
property was sold during the study time
period. Dollar values are adjusted to the third
quarter of 2005 based on HPI(Housing Price
Index).
the Property Ownership Land Records
Information System (POLARIS)
Federal Housing Finance Agency
the sales price generally reveals the
value of the property. (Bowes and
Ihlanfeldt, 2001; Voith,1993;Al-
Mosaind et al,1993)
RAIL PROXIMITY
Ln_Netdissemi-log of network distance(in feet) to the
nearest rail stationCalculated using GIS
real access distance.(Duncan, 2007;
Landis et al.1995)
PROPERTY CHARACTERISTICS
Ageage of the structure(in year) 2008
substract building yearPOLAIRS
age may affect the price of the
building.
Age2 squared age POLAIRS
squared age may capture the
nonlinear relationship between
age and price (Coulson, 2008)
ln_HeatedAreasemi-log of heated area(in square feet) of
the propertyPOLAIRS same as above
Fullbaths number of bathroom in the unit POLAIRS same as above
Bedroom number of bedroom in the unit POLAIRS same as above
Actype (Ac01, Ac02,
Ac03, Ac04,)
Primary type of air conditioning system
used (4 categories of AC)POLAIRS same as above
Qality_buithe quality of the structure(below average
to excellent, 1-5)POLAIRS same as above
UNITS Number of living units in the structure POLAIRS same as above
HEATEDFUEL (Fuel01,
02, 03, 04, 05, )
Primary type of fuel used for heating (5
categories of Fueltypes)POLAIRS same as above
HEIGH story height POLAIRS same as above
NUM_FIRE number of fireplace POLARIS same as above
LOCATIONAL & NEIGHBORHOOD CHARACTERISTICS (based on two scales)
F(i)
whether or not the property is
within a neighborhood
i(0,1,6,900,etc)
City of Charlotte
Quality of life study and GIS
Consider the
neighborhood boundary
as dummy variables to
control for loccation and
neighborhood characters
Dum(i)
whether or not the property is
within a block group i(0-
34,etc)
US Census and GIS
Consider the block
group boundary as
dummy variables to
control for location and
neighborhood characters
Time_Preiod avg_ad_price min_ad_price max_ad_price N
t1 197,950 13,422 1,133,820 1,592
t2 206,720 10,527 1,007,040 2,568
t3 213,300 15,000 990,000 1,308
t4 227,840 13,849 845,585 913
Table 4 Price Statistics for four time periods
Note: ad_price is the adjusted price that is calculated by House Price Index.