COMPARING THE AMENITY VALUE OF
PUBLIC AND PRIVATE GREEN SPACE:
AN APPLICATION OF A TWO STAGE
HEDONIC MODEL
JAMES C. MINGIE, NEELAM C. POUDYAL,
AND DARLA H. MACDONALD
OUTLINE
• Introduction
• Overview of key literature
• Objectives
• Study site
• Methods
• Discussion
INTRODUCTION
• Green space provides numerous benefits to
communities
• Recreational opportunities, aesthetics,
ecosystem services
• However, green space is threatened by:
• Urbanization, population growth, development
INTRODUCTION
• Green space protection
• 935 policy measures from 1998 to 2004
• $25 billion directed to land conservation
• Diverse needs of the public need to be
balanced
• Understanding of amenity values is needed
HEDONIC METHOD
• Price of a good is related to its characteristics
• House: structural, neighborhood, and environmental
characteristics
• Using OLS, proportion of sell price attributable to
environmental characteristic of interest can be
obtained
• Metric: marginal implicit price
PREVIOUS LITERATURE
• Parks and open space
• Nearby public parks, greater acreage (Tyravainen
1997, Poudyal et al. 2009a) increases housing values
• Diversity (Poudyal et al. 2009b) increases housing
values
• Spatial variation impacts amenity values (Cho et al.
2008)
• Privately owned open space
• Private protected land increased housing values (Irwin
and Bockstael 2001)
• Proximity to public open space is valued over
proximity to private open space (Henderson and Song
2009)
PREVIOUS LITERATURE
• Implicit price does not adequately measure the
economic benefit of open space
• Two-stage hedonic framework needed to derive
demand and produce consumer surplus estimates
• Difficulty: data collection for multiple markets,
submarkets within a single city may not be adequately
varied
• Multiple market approach used
• Palmquist 1984, Boyle et al. 1999
• Single city submarket approach used
• Mahan et al. 2000, Poudyal et al. 2009
STUDY OBJECTIVES
• Use two-stage hedonic framework to derive
demand functions for:
• Public green space
• Privately owned green space
• Compare private and public green space
consumer surplus estimates
STUDY SITE
• Adelaide – capital of South Australia
• 1.2 million people
• Gridded layout, planned well, numerous public
environmental amenities
• Population growth
• 258,000 new dwellings expected by 2040
• Growth will come through infill (developing vacant
areas), consolidation, and urban expansion
• Environmental amenities
• Local reserves, nearby national parks, coastline
ADELAIDE,
SOUTH
AUSTRALIA
DATA OVERVIEW
• Compiled by researchers associated with
Mahmoudi et al. (2011)
• Two components
• Sales prices and housing attributes for private
residential dwellings (2005 to 2008)
• Spatial data involving residential and
environmental amenities (i.e. private green
space, area of nearest park, distance to nearest
park)
Source: Mahmoudi
et al. (2011)
METHODS OVERVIEW
• Two-stage hedonic approach
• First stage: Develop hedonic model
• Second stage: Use variation in implicit prices to
derive demand functions
• Single city submarket approach used to
obtain variation in implicit prices
METHODS: HEDONIC MODEL
• Hedonic price function
• Ph = f(Sh, Nh,Qh)
• Ordinary Least Squares regression
• Potential econometric concerns:
• Multicollinearity
• Non constant variance
• Spatial autocorrelation
METHODS: MARKET SEGMENTATION
• Two-Step Clustering analysis
• Optimal clusters generated from housing and
neighborhood attributes
• First step: Pre-clustered groups created using a
likelihood distance measure function
• Second step: Pre-clusters are regrouped and the
optimal number of clusters is determined by
information criteria (AIC or BIC)
• Characteristics considered for segmentation
• Structural, neighborhood, and location
METHODS: DEMAND FUNCTIONS
• Implicit prices for separate sub-markets are
obtained
• Two demand functions:
• Ln QPUB = πPPUB + λPSC + δZ + v
• Ln QPRIV = πPPRIV + λPSC + δZ + v
• Q: quantity of green space, P: implicit price of green space,
PSC : prices of substitutes and complements, Z: demand
shifters
• Potential econometric concerns
• Non constant variance, endogeneity
• Estimation: Two Stage Least Squares regression using
instrumental variables
SUMMARY STATISTICS
Structural Characteristics
Building size (m2)
Bathrooms
Age
Block Wall
Bluestone Slate Wall
Cement Wall
Basket Range Wall
Freestone Wall
Iron Wall
Rendered Wall
Condition
Very poor
Poor
Average
Fair
Good
Excellent
Pool
Garage
Mean
Min
Max
SD
147.51
1.38
39.93
0.02
0.02
0.04
0.01
0.08
0.01
0.14
35.00
1.00
2.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1085.00
6.00
170.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.01
0.04
0.25
0.11
0.56
0.03
0.09
0.37
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
55.39
0.55
27.00
SUMMARY STATISTICS
Neighborhood Characteristics
Distance to Interchange (km)
Distance to Bus stop (km)
Distance to Train (km)
Distance to Shopping Center (km)
Distance to Private School (km)
Distance to Public School (km)
Income (per week)
Crime (per 1000)
Population (per km)
Mean
3.50
1.05
3.41
3.04
1.11
0.72
986.07
168.98
2022.74
Min
0.04
0.00
0.01
0.00
0.01
0.04
298.89
59.00
20.00
Max
22.85
17.18
23.05
3.06
8.62
6.97
2639.00
1062.00
6640.00
SD
2.64
1.87
3.18
2.39
0.72
0.51
304.78
117.57
741.34
SUMMARY STATISTICS
Environmental Characteristics
Distance to Nearest Park (km)
Area of Nearest Park (ha)
Dist to Nearest Waterbody (km)
Dist to Nearest Coast (km)
Dist to Nearest Golf Course (km)
Private Green Space (sq m)
Mean
0.18
4.06
1.43
6.81
2.76
268.21
Min
Max
0.00
0.05
0.00
0.00
0.01
0.00
4.48
113.94
4.40
27.08
12.78
9292.00
SD
0.25
10.56
0.87
4.17
1.87
244.56
HEDONIC MODEL
• Dependent variable: ln Price
• 30 total independent variables
• Variance Inflation Factors (VIF): less than 3
• Breusch-Pagan = 2,375, p-value: <0.001
• Robust standard errors used
• R-Squared: 0.67
REGRESSION RESULTS
Structural Characteristics
ln Bld Size
Bathrooms
Age
Block Wall
Bluestone Slate Wall
Cement Wall
Basket Range Wall
Freestone Wall
Iron Wall
Rendered Wall
Condition
Very poor
Poor
Fair
Good
Excellent
Pool
Garage
*** - 0.1%, ** - 1%, * - 5%,
Coefficient
Std. Error
Significance
0.8045
0.0929
0.0049
-0.1256
-0.0823
-0.0011
0.0763
0.1579
-0.1480
0.1533
0.0063
0.0033
0.0001
0.0099
0.0099
0.0075
0.0126
0.0055
0.0221
0.0042
***
***
***
***
***
-0.0439
-0.0262
0.0093
0.0139
0.2356
0.0428
0.0280
0.0213
0.0086
0.0053
0.0037
0.0093
0.0048
0.0028
*
**
***
***
***
***
***
***
***
***
REGRESSION RESULTS
Neighborhood Characteristics
ln Distance to Interchange (km)
ln Distance to Bus stop (m)
ln Distance to Train (km)
ln Distance to Shopping Center (km)
ln Distance to Private School (m)
ln Distance to Public School (m)
ln Population Density (# per km)
*** - 0.1%, ** - 1%
Coefficients
-0.0021
-0.0038
0.0192
0.0107
-0.0656
0.0327
0.0033
Std. Error
0.0019
0.0013
0.0013
0.0017
0.0021
0.0023
0.0026
Significance
**
***
***
***
***
REGRESSION RESULTS
Environmental Characteristics (ln)
Dist to Nearest Waterbody (km)
Dist to Nearest Coast (km)
Dist to Nearest Golf Course (km)
*** - 0.1%
Coefficient Std. Error Significance
0.0114
-0.0538
-0.0187
0.0002
0.0001
0.0002
***
***
***
REGRESSION RESULTS
Green Space Characteristics (ln)
Distance to Nearest Park (km)
Area of Nearest Park (ha)
Private Green Space (sq m)
*** - 0.1%, * - 10%
Coefficient Std. Error Significance
0.0074
0.0001
***
0.0013
0.0001
*
0.0063
0.0001
***
Implicit prices
Size of Private Green Space: $8.20/meter sq.
Size of Nearest Public Green Space: $11.33/ ha
MARKET SEGMENTATION
• 4 distinct submarkets found
• 1: Smaller, lower priced, near center of city
• 2: larger, higher priced, good condition, far from
downtown
• 3: fairly small, moderately priced, older houses,
near downtown
• 4: fairly small, lower priced, good condition, far
from downtown
• ANOVA: characteristics varied significantly
among submarkets
DEMAND FUNCTIONS
• Two Stage Least Squares with robust standard errors
• Endogenous variables: green space prices
• Dependent variable: ln Public Green Space Area
Independent Variables
Intercept
ln Public Green Space Price
ln Private Green Space Price
ln Income
ln Population Density
ln Crime
*** - 1%, **- 5%, * - 10%
Coefficient Std. Error Significance
6.2544
2.8032
**
-0.3952
0.2329
*
0.5892
0.3427
*
0.2130
0.0446
***
-0.4343
0.0208
***
0.1228
0.0566
**
DEMAND FUNCTIONS
• Two Stage Least Squares with robust standard errors
• Endogenous variables: green space prices
• Dependent variable: ln Private Green Space Area
Independent Variables
Intercept
ln Private Green Space Price
ln Public Green Space Price
ln Income
ln Population Density
ln Crime
*** - 1%, **- 5%, * - 10%
Coefficient Std. Error Significance
13.7360
2.8032
***
-0.9786
0.3427
**
0.4378
0.2330
*
-0.2410
0.0446
***
-0.0220
0.0208
-0.1931
0.0566
***
DISCUSSION
• Price elasticities
• Public green space: inelastic
• Private green space: inelastic but nearly unitary
• Cross PEs: larger public green space demanded if
the price of private green space is high (vice
versa)
• Public green space and private green
space are substitutes
• Effects of exogenous demand shifters
FURTHER RESEARCH
• Consumer surplus estimates
• Welfare effect from a change in quantity
• Integrate demand function and evaluate at different levels
• Refined model needed
• Account for spatial autocorrelation
• Statistical versus real estate designated
submarkets
QUESTIONS???
James C. Mingie
Warnell School of Forestry and Natural Resources
jcmingie@uga.edu