Final Report2

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



Hypotheses


•Method



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



Hypotheses


•Method



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



Hypotheses


•Method



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



Hypotheses


•Methodology



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



Hypotheses


•Method



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



Hypotheses


•Method



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.

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Final Report2