The Impact of the Light Rail System on Single Family Property Values in Mecklenburg County, NC, from 1997 to 2008

1. PROJECT
DEFENSE
THE IMPACT OF A LIGHT RAIL
SYSTEM (EXISTING BLUE LINE) ON
SINGLE FAMILY PROPERTY VALUES
IN MECKLENBURG COUNTY, NC, FROM
1997 TO 2008
SisiYan 2009
Master of Arts in Geography
University of North Carolina at Charlotte
Department of Geography and Earth Science
Committee Members: Dr. Eric Delmelle, Dr. Mike Duncan and Dr. Harrison Campbell

2. Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
Introduction
•Study Area & Data
•Method
•Results & Discussion
•Conclusion & Future
Study

4.  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

5. 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?

6. Content Table
•Introduction
•Literature Review
•Research Design & Location Theory and
Hypotheses Transit Capitalization
•Study Area & Data
Hedonic Price Studies
•Method
•Results & Discussion Empirical Studies on
•Conclusion & Future Transit Impact
Study

7. Literature Review
Location 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 “

8. 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)

9. 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

10. Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses Research Design
•Study Area & Data
•Method
Research Hypotheses
•Results & Discussion
•Conclusion & Future
Study

11. Study Time Frame
T1 T2 T3- T4
• Pre- • Planning • Rail • Rail
Planning period; Construction Operation
period; From 1999 period; period;
From 1997 to 2004 • From 2005 to After
to 1998 2007 Nov.1st
2007 till
July 2008

12. Research Hypothesis
T4
T3
T2
T1

13. Content Table
•Introduction
•Literature Review
•Research Design & Charlotte
Hypotheses
•Study Area & Data Light Rail Station Area
•Method
•Results & Discussion
Data Sources
•Conclusion & Future
Study

14. 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

15. LYNX Rail

16. 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).

17. 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

19. Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
Methodology
•Study Area & Data
•Methodology
•Results & Discussion
•Conclusion & Future
Study

20. 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;

21. Spatial Dependence
Neighborhood
Boundary
Moran’ s I
0.85
Block group
Boundary 0.8
0.75
Moran's I t1
0.7
t2
t3
0.65
t4
0.6
0.55
300 500 600 650 700 800 900 1100 2000
Threshold Distance (feet)

22. Variables Discussion
 Sales value vs. assessed value
 Network distance vs. Straight-line Distance;
 Variable List

23. Data Transformations
 Ln_ad_price (HPI)
 Ln_net_dis
 Ln_heatedarea
 Age2

24. Content Table
•Introduction
•Literature Review
•Research Design &
Hypotheses
Models’ Results
•Study Area & Data
•Method Light Rail Impact
•Results & Discussion
•Conclusion & Future
Study

25. Model1. HPR with neighborhood dummy variables:
Model 1 regression coefficients for
four time periods T1 T2 T3 T4
Notes: * insignificant at p < 0.05
Variable Coefficient Coefficient Coefficient Coefficient
(since most of the variables are
(constant) 7.028 6.502 7.248 7.796
significant in this table, for a Property characteristics
better distinguish, I chose using * to age -0.003* 0.002* -0.002* -0.005
represent insignificant variables) 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

26. Model2. HPR with block group dummy
variables:
T1 T2 T3 T4
Model 2 regressions coefficients for
Variable Coefficient Coefficient Coefficient Coefficient
four time periods (constant) 8.196 7.406 8.123 8.249
Notes: * insignificant at p < 0.05 Property characteristics
(since most of the variables are age -0.004 -0.001* -0.003* -0.006
significant in this table, for a agesqr 4.37E-05 2.59E-05* 4.16E-05 8.21E-05
better distinguish, I chose using * to height 0.125 0.062 0.076 0.053*
represent insignificant variables) 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

27. Models T1 T2 T3 T4
Models Comparisons R2
M1 0.746 0.750 0.781 0.829
M2 0.779 0.790 0.811 0.837
M1 0.742 0.748 0.777 0.824
Adjusted R2
M2 0.773 0.783 0.805 0.83
M1 0.167 0.185 0.238 0.064
Moran’s I
M2 0.097 0.110 0.167 0.021

28. Light Rail Impact
Notes: *
insignificant at
p < 0.05
Models T1 T2 T3 T4
M1 0.129 0.147 0.153 0.054* Z Test
Ln_Net_Dist
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

29. Content Table
•Introduction
•Literature Review
•Research Design &
Conclusions
Hypotheses
•Study Area & Data Future Studies
•Method Suggestions
•Results & Discussion
•Conclusion & Future
Study

30. 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

32. 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

33. 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

34. Questions and Comments?

35. 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

36. Variables Description Data Sources Justification
PROPERTY VALUE (dependent variable)
Amount($) for which the single family
the sales price generally reveals the
property was sold during the study time the Property Ownership Land Records
value of the property. (Bowes and
Ln_ad_Price period. Dollar values are adjusted to the third Information System (POLARIS)
Ihlanfeldt, 2001; Voith,1993;Al-
quarter of 2005 based on HPI(Housing Price Federal Housing Finance Agency
Mosaind et al,1993)
Index).
RAIL PROXIMITY
semi-log of network distance(in feet) to the real access distance.(Duncan, 2007;
Ln_Netdis Calculated using GIS
nearest rail station Landis et al.1995)
PROPERTY CHARACTERISTICS
age of the structure(in year) 2008 age may affect the price of the
Age POLAIRS
substract building year building.
squared age may capture the
Age2 squared age POLAIRS nonlinear relationship between
age and price (Coulson, 2008)
semi-log of heated area(in square feet) of
ln_HeatedArea POLAIRS same as above
the property
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, Primary type of air conditioning system
POLAIRS same as above
Ac03, Ac04,) used (4 categories of AC)
the quality of the structure(below average
Qality_bui POLAIRS same as above
to excellent, 1-5)
UNITS Number of living units in the structure POLAIRS same as above
HEATEDFUEL (Fuel01, Primary type of fuel used for heating (5
POLAIRS same as above
02, 03, 04, 05, ) categories of Fueltypes)
HEIGH story height POLAIRS same as above
NUM_FIRE number of fireplace POLARIS same as above

37. LOCATIONAL & NEIGHBORHOOD CHARACTERISTICS (based on two scales)
Consider the
whether or not the property is neighborhood boundary
City of Charlotte
F(i) within a neighborhood as dummy variables to
Quality of life study and GIS
i(0,1,6,900,etc) control for loccation and
neighborhood characters
Consider the block
whether or not the property is group boundary as
Dum(i) within a block group i(0- US Census and GIS dummy variables to
34,etc) control for location and
neighborhood characters

38. Table 4 Price Statistics for four time periods
Note: ad_price is the adjusted price that is calculated by House Price Index.
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