# RECORD 2f1c2029c5f2034d797bc4a5ae390cff3782ab60
# TITLE: Area-Based Hedonic Pricing of Urban Green Amenities in Beijing: A Spatial Piecewise Approach
# YEAR: 2024.0  VENUE: Environmental and Resource Economics  DOI: 10.1007/s10640-024-00861-2
# AUTHORS: Zhaoyang Liu; Heqing Huang; J. Siikamäki; Jintao Xu
# PAGES: 26  CANDIDATE TABLE PAGES (1-based): [17, 18, 19]
# ABSTRACT: This study explores a spatial piecewise approach for the hedonic valuation of the area of urban green space at different distances from a property, using a rich census dataset collected from Beijing. We explore three novel empirical strategies that improve the identification of the spatial boundary or threshold distance within which green space is capitalised into housing prices. We first delineated a series of concentric circles surrounding each property and measured the area of green space within each doughnut-shaped ring. We next estimated the hedonic price using three methods. The first is a regression spline model combined with a machine learning type of model selection procedure which objectively selects the exact location of the threshold distance that optimises the model’s predictive performance. The second is a novel matching algorithm that minimises covariate imbalance for a continuous treatment variable (i.e., the area of green space) to provide stronger causal evidence on the hedonic prices of green space at different distances. The third is a spatial difference-in-differences approach that further accounts for endogeneity bias associated with unobserved factors. For our dataset, we found that housing prices are more likely to be affected by green space within a 1 km radius.


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Vol.:(0123456789)
Environmental and Resource Economics (2024) 87:1223–1248
https://doi.org/10.1007/s10640-024-00861-2
1 3
Area‑Based Hedonic Pricing of Urban Green Amenities 
in Beijing: A Spatial Piecewise Approach
Zhaoyang Liu1   · Heqing Huang2 · Juha Siikamäki3 · Jintao Xu2
Accepted: 1 March 2024 / Published online: 25 March 2024 
© The Author(s) 2024
Abstract
This study explores a spatial piecewise approach for the hedonic valuation of the area of 
urban green space at different distances from a property, using a rich census dataset col-
lected from Beijing. We explore three novel empirical strategies that improve the identi-
fication of the spatial boundary or threshold distance within which green space is capital-
ised into housing prices. We first delineated a series of concentric circles surrounding each 
property and measured the area of green space within each doughnut-shaped ring. We next 
estimated the hedonic price using three methods. The first is a regression spline model 
combined with a machine learning type of model selection procedure which objectively 
selects the exact location of the threshold distance that optimises the model’s predictive 
performance. The second is a novel matching algorithm that minimises covariate imbal-
ance for a continuous treatment variable (i.e., the area of green space) to provide stronger 
causal evidence on the hedonic prices of green space at different distances. The third is a 
spatial difference-in-differences approach that further accounts for endogeneity bias associ-
ated with unobserved factors. For our dataset, we found that housing prices are more likely 
to be affected by green space within a 1 km radius.
Keywords  Urban green space · Hedonic pricing · Regression splines · Model selection · 
Covariate balancing matching for continuous treatment · Spatial difference-in-differences
JEL Classification  Q51 · Q57 · R30
This paper is an output of the Ecosystem Services Accounting for Development project jointly initiated 
by the Environment for Development Initiative and the Swedish Environmental Protection Agency 
(SEPA Reference Number: NV-05230-14), and funded by the Swedish International Development 
Cooperation Agency (SIDA Reference Number: 13/000230). The authors declare that they have no 
relevant or material financial interests that relate to the research described in this paper. The authors 
sincerely appreciate the very helpful comments provided by Dr. Jessica Alvsilver, Dr. Christopher 
Campbell-Duruflé, Dr. Timothy Hamilton, Prof. Nick Hanley, Prof. Thies Lindenthal, Dr. Richard 
Mulwa, Dr. Matías Piaggio, Dr. Carolin Hoeltken, Dr. Per Stromberg, Dr. Byela Tibesigwa, Dr. Jane 
Turpie and Dr. Dawit Woubishet. In addition, the authors are greatly indebted to Mrs Cyndi Berck for 
editing the paper. Last but not least, the authors gratefully acknowledge two anonymous referees and 
the editor for their very helpful comments on an earlier version of the paper.
Extended author information available on the last page of the article


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We estimated four DID regressions (Models 4–7, Table 4), which have the same speci-
fication as in Eq. (5) but differ in whether Radiussi is defined as within 1, 2, 3 or 4 km. In 
Model 4 where the radius is 1 km, the positive and statistically significant coefficient on the 
interaction term ‘ After × Radius ’ suggests that a new green space tends to increase housing 
prices within a 1 km radius. In our DID dataset, the average size of green spaces is 4.14ha. 
This suggests that, approximately speaking, a new hectare of green space increases housing 
prices in a 1 km radius by CNY 147.82 or USD 22.26 per ­m2 of housing. This is notably 
higher than the counterpart estimates from the regression spline and matching analyses, 
although the DID estimate refers to only a subset of the green spaces in the regression 
spline and matching analyses. In Models 5–7 (where the radius is 2, 3 or 4 km), the coef-
ficient on the interaction term ‘ After × Radius ’ is statistically indistinguishable from zero 
(the lowest p value = 0.49), and the absolute value of the estimated effect is much smaller 
than that for a 1 km radius. These results provide further evidence that a new green space is 
more likely to be capitalised into housing prices within a 1 km radius.
Our DID approach has controlled for all location- and time-specific factors, in addition 
to the control variables listed in Table A1. If there are no other confounders, the mean dif-
ference in the (residualised) treated and control housing prices would not vary over time 
before the creation of the green spaces, known as the parallel trends assumption (Wool-
dridge 2020). Otherwise, the main causal strength of this DID analysis would be confined 
to accounting for location-specific time-invariants (both observed and unobserved) at a 
more detailed level (compared to the regression spline and matching analyses), at the cost 
of focusing on only a subset of the green spaces in our dataset. Figure A5 in the appen-
dix provides some visual evidence for the applicability of the parallel trends assumption 
in our analysis. A Wald test further corroborates that the mean differences between the 
treated and control housing prices are statistically equal across the two pre-treatment years 
(p value = 0.64). These assessments find no evidence that the parallel trends assumption is 
violated.
Table 4   Difference-in-differences estimates
Asterisks indicate statistical significance: * p value < 0.10, ** p value < 0.05, *** p value < 0.01. Standard 
errors are in parentheses
DV: log (housing price)
Model 4
Model 5
Model 6
Model 7
Radius = 1 km
Radius = 2 km
Radius = 3 km
Radius = 4 km
EV
After
−1.04 × ­10−2*
−1.01 × ­10−2*
−9.09 × ­10−3
−8.83× ­10−3
(5.35 × ­10−3)
(5.47 × ­10−3)
(5.73 × ­10−3)
(6.52 × ­10−3)
Radius
−4.13 × ­10−3
−1.01 × ­10−3
1.11 × ­10−2***
2.22 × ­10−2***
(8.04 × ­10−3)
(4.45 × ­10−3)
(3.56 × ­10−3)
(3.64 × ­10−3)
After × Radius
2.31 × ­10−2*
4.62 × ­10−3
−6.58 × ­10−4
−1.11 × ­10−3
(1.23 × ­10−2)
(6.73 × ­10−3)
(5.38 × ­10−3)
(5.57 × ­10−3)
Green space location fixed effects
Yes
Yes
Yes
Yes
Time fixed effects
Yes
Yes
Yes
Yes
Housing controls (Table A1)
Yes
Yes
Yes
Yes
Model significance (p value)
< 0.01
< 0.01
< 0.01
< 0.01
R2 (within)
0.40
0.40
0.40
0.40
Obs
124,137
124,137
124,137
124,137


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3.4  Further Robustness Tests
The DID analysis, despite focusing on a subset of the green spaces in our dataset, lends 
further support to our finding that housing prices are less likely to be affected by green 
spaces outside a 2 km radius. On the other hand, the spatial distribution of green space 
in neighbouring locations in Beijing tends to be positively correlated (Wang et al. 2023; 
Zhang et al. 2021). A location is likely to have more green space if its neighbouring loca-
tions have more green space. In fact, for the residential blocks in our dataset, the area of 
green space within 1 km has a correlation coefficient above 0.60 with the area of green 
space at 1–2 km, 2–3 km, and 3–4 km. In other words, green space outside a 2 km radius of 
a property is less likely to directly influence housing price, but tends to be highly correlated 
with green space within a 1 km radius. This suggests the possibility of instrumenting green 
space within a 1 km radius using green space outside a 2 km radius, as a means to further 
explore the implications of potential endogeneity issues. Bayer et al. (2009) adopted a sim-
ilar ‘spatial lag’ type of instrument for air quality.
To facilitate the instrumental variable (IV) estimation, we first estimated an OLS 
hedonic pricing regression (Model 8, Table 5). The right-hand side has only one green 
Table 5   Instrumental variable and placebo estimates
Standard errors are in parentheses
Asterisks indicate statistical significance: * p value < 0.10, ** p value < 0.05, *** p value < 0.01
DV: log (housing price)
Model 8
Model 9
Model 10
Model 11
OLS
IV-2SLS
OLS
OLS
Hedonic price
 Green area 0–1 km
5.90 × ­10−4**
1.40 × ­10−3**
1.05 × ­10−3***
(2.57 × ­10−4)
(6.42 × ­10−4)
(3.73 × ­10−4)
Future green area 0–1 km
−3.92 × ­10−4
(1.01 × ­10−3)
Housing controls (Table A1)
Yes
Yes
Yes
Yes
Excluded instruments
Green area 2–3 km
No
Yes
No
No
Green area 3–4 km
No
Yes
No
No
Weak IV test
Cragg–Donald Wald F stat
110.83
(H0: Weak IV)
R2 ­(1st stage)
0.50
Over-identification test
Sargan statistic: p value ­(H0: Valid IV)
0.19
IV redundancy test
LM test: p value ­(H0: Redundant IV)
< 0.01
Endogeneity test
Diff-in-Sargan–Hansen Statistic: p value
0.17
(H0: Exogenous ‘Green area 0–1 km’)
Hedonic price model sig.: p value
< 0.01
< 0.01
< 0.01
< 0.01
Hedonic price model R2
0.68
0.68
0.65
0.66
Obs
1270
1270
889
889


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space variable, which represents the total area of all green space within a 1 km radius, 
controlling for other housing and location-specific variables listed in Table A1. The OLS 
estimate of the hedonic price (the estimate for ‘Green area 0–1 km’ in Model 8 in Table 5) 
is smaller than that derived from the regression spline approach (Model 1 in Table 2), but 
remains statistically significant at the 5% level. In Model 9, ‘Green area 0–1 km’ is instru-
mented using the area of green space at 2–3 km and 3–4 km. The F statistic from the weak 
IV test (110.83) is markedly greater than the conventional rule of thumb (10) and exceeds 
a more recently recommended threshold (104.7) (Lee et al. 2022), which provides evidence 
against the null hypothesis of weak identification. The p value from the over-identification 
test is well above the critical value (0.10), which further justifies the validity of the instru-
ments. Although the p value from the endogeneity test does not reject the null hypothesis 
of no endogeneity bias at the conventional critical level (0.10), the IV estimate for ‘Green 
area 0–1 km’ is notably higher than the OLS and regression spline estimates. This suggests 
that the OLS and regression spline estimates are likely to be conservative estimates of the 
true hedonic price of green space.
Lastly, we undertook placebo tests to further assess whether the foregoing analyses have 
picked up some distance-dependent patterns other than the effects of green space on hous-
ing prices. For each residential block, we measured the total area of green space at differ-
ent distances that was created after the residential block was sold. In theory, such ‘future’ 
green space is less likely to affect housing prices, compared to preexisting green space. We 
next re-estimated Model 8 after replacing the variable ‘Green area 0–1 km’ (green space 
inside 1  km that existed before housing transactions) with ‘Future green area 0–1  km’ 
(green space inside 1 km that was created after housing transactions), to test whether the 
model would falsely attribute any unexpected effect to future green space. As in Model 
10 in Table 5, the estimate on ‘Future green area 0–1 km’ is statistically insignificant (p 
value = 0.70), which is in line with intuition. Note that this placebo analysis is confined to 
housing transactions that happened before 2014, because our green space data were col-
lected in 2014, and thus do not include any future green space for housing transactions 
from 2014 onward. We thus re-estimated Model 8 using housing transactions before 2014 
to test whether the estimates could be affected by dropping subsequent housing transac-
tions. This gave rise to Model 11, where the estimate on ‘Green area 0–1 km’ remains 
positive and statistically significant, although larger in size compared to the estimate for the 
full sample. Moreover, we performed another placebo test by adding to Model 10 the area 
of future green space at 1–2 km, 2–3 km, 3–4 km and 4–5 km. As can be seen in Figure A6 
in the appendix, all these placebo estimates are statistically insignificant; the lowest p value 
is above 0.35. In comparison, the non-placebo estimates in Figure A6 were derived from 
an expanded version of Model 11, which contains preexisting green space at different dis-
tances; here, the estimate on ‘Green area 0–1 km’ is almost identical to that in Model 11.
4  Aggregate Hedonic Prices of Green Space in Central Beijing
This section derives the aggregate hedonic prices of green space using the unit hedonic 
price estimates from Sect. III. To reduce computational workloads, we confined the analy-
sis to green spaces that are larger than 0.5ha and located in Beijing’s six central districts. 
For each of these green spaces, we first searched in our dataset for all residential blocks 
located in the green space’s 1 km radius. The aggregate hedonic price was then calculated 
by multiplying the unit hedonic price (the coefficient on the variable ‘Green area 0–1 km’ 


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after being converted from a semi-elasticity estimate to a monetary marginal effect esti-
mate) by the total area of the green space and the total floor area of the residential blocks 
within the green space’s 1 km radius. As an example, Fig. 6 maps the aggregate hedonic 
prices of these green spaces individually (based on the regression spline estimate in Model 
(1). Admittedly, owing to the nature of the hedonic approach, the spatial distribution of the 
Fig. 6   Hedonic prices (billion CNY in 2016 prices) of green spaces in Central Beijing
Fig. 7   Aggregate hedonic prices 
(in 2016 prices) of green spaces 
in Central Beijing. Note: M1: 
Model 1, regression spline; M2: 
Model 2, parametric covariate 
balancing generalised propen-
sity score (CBGPS) matching; 
M3: Model 3, non-parametric 
CBGPS matching; M4: Model 
4, difference-in-differences (sub-
sample); M8: Model 8, ordinary 
least squares (OLS); M9: Model 
9, instrumental variable; M11: 
Model 11, OLS (sub-sample)