# RECORD dbfc419fae85b2cf607f68db238ec77b3593a5e0
# TITLE: Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park
# YEAR: 2025.0  VENUE: International Journal of Strategic Property Management  DOI: 10.3846/ijspm.2025.24813
# AUTHORS: Haizhen Wen; Fangziyi Huang; Shu-Yuan Li; E. Hui; Ling Zhang
# PAGES: 13  CANDIDATE TABLE PAGES (1-based): [4, 7, 8, 9]
# ABSTRACT: The construction of sports parks often involves significant investment and extended development timelines, making it crucial to evaluate their economic impacts for informed policymaking and strategic urban planning. While research on the externalities of urban parks has grown, the specific economic effects of sports parks remain underexplored. This study examines the impact of the Hangzhou Asian Games Park on housing prices using 31,329 transaction records from 2018 to 2023. Employing a hedonic pricing model and a difference-in-differences approach, we find that housing prices increase by 1.6% for every 1 km reduction in distance to the park. Spatial heterogeneity is observable, as the effect diminishes with increasing distance. Temporally, the park’s planning announcement triggered a 0.7% increase in price-distance elasticity, compared to a 0.2% increase after its opening. These findings suggest that the anticipatory market response was stronger than the realized benefits after the park’s completion. This study quantifies the spatial and temporal dynamics of the economic value of sports parks, providing valuable insights for urban planning, public policy, and investment strategies.


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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), 
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright © 2025 The Author(s). Published by Vilnius Gediminas Technical University
ISSN: 1648-715X / eISSN: 1648-9179
INTERNATIONAL JOURNAL 
OF STRATEGIC PROPERTY 
MANAGEMENT
DYNAMIC EFFECT OF SPORTS PARKS ON HOUSING PRICES: EVIDENCE FROM 
HANGZHOU ASIAN GAMES PARK
Haizhen WEN1, Fangziyi HUANG2, Shuyuan LI   1,3*, Eddie C. M. HUI   4, Ling ZHANG   2,5
1 School of Spatial Planning and Design, Hangzhou City University, Hangzhou, China
2 Department of Civil Engineering, Zhejiang University, Hangzhou, China
3 School of Public Affairs, Zhejiang University, Hangzhou, China
4 Department of Public and International Affairs, City University of Hong Kong, Hong Kong, China
5 Center for Real Estate Studying, Zhejiang University, Hangzhou, China
Article History:  
Abstract. The construction of sports parks often involves significant investment and extended development 
timelines, making it crucial to evaluate their economic impacts for informed policymaking and strategic ur-
ban planning. While research on the externalities of urban parks has grown, the specific economic effects of 
sports parks remain underexplored. This study examines the impact of the Hangzhou Asian Games Park on 
housing prices using 31,329 transaction records from 2018 to 2023. Employing a hedonic pricing model and 
a difference-in-differences approach, we find that housing prices increase by 1.6% for every 1 km reduction 
in distance to the park. Spatial heterogeneity is observable, as the effect diminishes with increasing distance. 
Temporally, the park’s planning announcement triggered a 0.7% increase in price-distance elasticity, com-
pared to a 0.2% increase after its opening. These findings suggest that the anticipatory market response was 
stronger than the realized benefits after the park’s completion. This study quantifies the spatial and temporal 
dynamics of the economic value of sports parks, providing valuable insights for urban planning, public policy, 
and investment strategies.
	■received 18 March 2025
	■accepted 28 July 2025
Keywords: sports parks, housing prices, hedonic pricing, spatial heterogeneity, temporal dynamics, Hangzhou Asian Games Park.
 * Corresponding author. E-mail: shuyuanli@zju.edu.cn
1.	Introduction
Urban parks play a critical role in enhancing the livability 
and sustainability of cities. Extensive research has dem-
onstrated the environmental, social, and economic con-
tributions of urban parks. For instance, urban parks help 
mitigate air pollution (Kim & Coseo, 2018), reduce urban 
heat island effects (Yan et al., 2018), and provide spaces for 
recreation, social interaction, and physical activity (Brown 
et  al., 2014; Peters et  al., 2010; Chiesura, 2004). These 
benefits, along with their influence on real estate values, 
have sparked interest in assessing their impact on housing 
markets (Jim & Chen, 2010; Mueller et  al., 2022). How-
ever, most existing studies focus on general green spaces 
or conventional urban parks, leaving a significant gap in 
understanding the economic value of sports parks as a 
distinct category of public infrastructure. 
Unlike traditional urban parks, sports parks blend fit-
ness and recreational facilities with natural landscapes, of-
fering sports venues, exercise amenities, and leisure spac-
es. As multifunctional public goods, they contribute to ur-
ban vitality and public health while also serving as venues 
for organized sports events. Previous research on sports 
parks has predominantly examined their planning and de-
sign (Luo & Wang, 2022), social benefits (Sun et al., 2022), 
and ecological functions (Li, 2020). For instance, Sun et al. 
(2022) demonstrated how sports parks promote commu-
nity interaction, while Wu and Li (2022) emphasized their 
role in meeting public exercise needs. Access to sports 
parks has also been linked to improved health outcomes 
and subjective well-being (Dadvand et al., 2016; Kou et al., 
2021; Zhang et al., 2021b). However, their economic exter-
nalities, particularly their impact on housing prices, remain 
underexplored. This gap in the literature limits our under-
standing of their broader value and constrains evidence-
based urban planning and investment decisions.
The Hangzhou Asian Games Park provides a compel-
ling case study to address this gap. Developed to support 
the 19th Asian Games, this large-scale sports park inte-
grates green space with sports facilities and commercial 
areas, representing an innovative approach to urban park 
development. Its phased development process, including 
the announcement of the project in 2019 and its official 
opening in 2021, offers a unique opportunity to examine 
2025
Volume 29
Issue 5
Pages 316–328
https://doi.org/10.3846/ijspm.2025.24813


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International Journal of Strategic Property Management, 2025, 29(5), 316–328
319
The architectural characteristics of housing represent the 
physical attributes of the buildings. In this study, build-
ing age and size were selected to capture these features. 
Location characteristics reflect the fixed and relative posi-
tions of housing, typically quantified through accessibil-
ity measures such as straight-line distance, travel time, or 
path distance. To better represent the spatial structure of 
Hangzhou, this study includes the distances to West Lake, 
the traditional cultural and commercial center, and Qianji-
ang CBD, the emerging central business district, as the pri-
mary location characteristic variables. West Lake serves as 
a key natural and cultural landmark, with its historical and 
aesthetic value significantly influencing housing prices. In 
contrast, Qianjiang CBD represents the city’s modern eco-
nomic development and the shift in its commercial core, 
playing a crucial role in shaping housing price patterns. In 
addition, neighborhood characteristics focus on the natu-
ral environment, social surroundings, and supporting facili-
ties around the housing. This study selected the distances 
to major hospitals, key schools, bus stops, metro stations, 
and green spaces as neighborhood characteristic variables. 
These factors reflect the accessibility to public services, 
quality of life, and proximity to essential resources, further 
enriching the explanatory power of the model and ensur-
ing the robustness of the conclusions. Definitions and de-
scriptive statistics for each variable are provided in Table 1.
3.2. Data and variables
The secondhand housing transaction data and architec-
tural characteristics, including area and age, used in this 
study were obtained from real estate intermediary com-
panies and the CRIC database. The housing price is the 
total transaction price of a single housing unit, which 
spanned from January 2018 to March 2023 in 1,671 resi-
dential communities in Hangzhou. After excluding imper-
fect and anomalous samples, we obtained valid data for 
31,329 housing transaction samples.
The housing price was selected as the dependent vari-
able in this study. The independent variables were catego-
rized into three aspects according to hedonic price theory: 
architectural characteristics, neighborhood characteristics, 
and location characteristics. Data related to neighborhood 
and location characteristics were collected at the residen-
tial community level, and relevant latitude and longitude 
coordinates were obtained through the electronic map. 
This approach allowed for the direct calculation of dis-
tances or the use of map ranging tools, thus quantifying 
the relevant variables. Drawing on previous empirical stud-
ies on the factors influencing housing prices in Hangzhou 
(Du & Huang, 2018; Wen et  al., 2015, 2018, 2022; Xiao 
et al., 2019), two architectural characteristic variables, two 
locational characteristic variables, and eight neighborhood 
characteristic variables were adopted as control variables. 
Table 1. Variable definitions and descriptive statistics
Type
Variable Definition
Mean
S.D.
Min
Max
Dependent 
variable
HP
Total transaction price of a housing ($)
3,157,000 1,756,000 400,000 17,000,000
Architectural 
features
Age
Building age (years)
17.690
10.110
1
46
Area
Floor area of a housing (m2)
82.140
35.830
24
372
Locational  
features
DWL
Distance from residential community to West Lake (km)
6.308
3.696
0.205
17.950
DQJ
Distance from residential community to Qianjiang CBD (km) 9.705
5.067
0.299
23.250
Neighborhood 
features
Green
Availability of green spaces within a 1 km radius of the 
community (1 for availability, 0 otherwise)
0.841
0.366
0
1
Bus
Number of bus routes within a 1 km radius of the 
community
62.930
36.450
0
205
Metro
Availability of metro stations within a 1 km radius of the 
community (1 for availability, 0 otherwise)
0.729 
0.444 
0
1
PriSch
Availability of key primary schools within a 1 km radius of 
the community (1 for availability, 0 otherwise)
0.412 
0.492 
0
1
MidSch
Availability of key middle schools within a 1 km radius of 
the community (1 for availability, 0 otherwise)
0.364
0.481
0
1
HighSch Availability of high schools within a 1 km radius of the 
community (1 for availability, 0 otherwise)
0.313
0.464
0
1
Univ
Availability of universities within a 1 km radius of the 
community (1 for availability, 0 otherwise)
0.249 
0.432 
0
1
DHosp
Distance from community to nearest tertiary hospital (km)
2.617
2.614
0.033
13.440
DAGP
Distance from community to Asian Games Park (km)
6.083
3.100
0.348
12.000
Dj
Eight distance buffers between community and the Asian 
Games Park: j = 1 is within 1 km, j = 2 is 1–2 km, j = 3 is 
2–3 km, j = 4 is 3–4 km, j = 5 is 4–5 km, j = 6 is 5–6 km, 
j = 7 is 6–9 km, j = 8 is 9–12 km


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320
H. Wen et al. Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park
Accessibility to the Asian Games Park is the primary 
variable examined in this study. Prior research has consist-
ently demonstrated that accessibility can be effectively an-
alyzed using linear distances (Ardeshiri et al., 2016; Jim & 
Chen, 2006; Vom Hofe et al., 2018). Studies on the effects 
of sports stadiums and urban parks frequently highlight 
significant impacts on housing prices within a 3  km ra-
dius, with diminishing effects observed beyond this range 
(Ahlfeldt & Kavetsos, 2014; Mueller et al., 2022). For large-
scale cultural and sports facilities constructed for public 
welfare, previous research suggests that the service radius 
is typically larger, often extending beyond 5 km (Zhang 
et al., 2019).
To account for these varying impacts and explore 
their attenuation across distance, this study categorizes 
residential communities into eight groups based on their 
straight-line distance from the Hangzhou Asian Games 
Park: 0–1 km, 1–2 km, 2–3 km, 3–4 km, 4–5 km, 5–6 km, 
6–9 km, and 9–12 km. Communities within the 9–12 km 
range are used as the control group, as this distance is 
well beyond the typical impact radius of similar facilities. 
This categorization allows for a nuanced analysis of how 
the park’s influence on housing prices varies with distance.
It is worth noting that some communities near the 
boundaries of these distance categories may be subject 
to overlapping influences, which could lead to a certain 
degree of spatial correlation or spillover effects. However, 
existing literature indicates that the effects of policies or 
facilities tend to attenuate with increasing distance, signifi-
cantly reducing the likelihood of spillover effects impact-
ing the control group. Additionally, the use of multiple 
finely defined distance categories, rather than a simple bi-
nary classification of treatment and control groups, helps 
capture subtle variations in impact across distances and 
mitigates potential concerns about spatial autocorrelation.
Figure 1 illustrates the spatial distribution of the Hang-
zhou Asian Games Park and the selected residential com-
munities, providing a visual representation of the study 
area and distance classification.
The date of the public announcement of the plan-
ning of the Hangzhou Asian Games Park coincides with 
the date when the organizing committee first clarified the 
date and events of the Asian Games. Since then, residents 
have become aware that professional sports facilities and 
stadiums would be built on the site for the Asian Games 
events. We consider this time point, 5 April 2019, as the 
date of the announcement. The second key time point is 
the official opening of the sports park to the public on 
1 December 2021.
The total time period is represented as T, and all the 
data were divided into four time periods:
T1: 1 January 2018 to 4 April 2019 (preannouncement 
period),
T2: 5 April 2019 to 31 July 2020 (postannouncement 
period),
T3: 1 August 2020 to 30 November 2021 (preopening 
period),
T4: 1 December 2021 to 31 March 2023 (late opening 
period).
In addition, we define the following broader time pe-
riods for analysis:
Figure 1. Locations of the Hangzhou Asian Games Park and housing samples


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322
H. Wen et al. Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park
In contrast, during the post-treatment periods (post_1, 
post_2, and post_3), the interaction term coefficients be-
come significant, with confidence intervals that do not 
cross zero. This demonstrates the emergence of a clear 
treatment effect following the project announcement. 
These findings confirm that the parallel trends assumption 
holds in the pre-treatment period, and the observed dif-
ferences in the post-treatment period can be attributed to 
the impact of the Asian Games Park project.
4.	Results and analysis
4.1. Baseline impact of sports park on 
housing prices
The basic model measures the overall impact of the Asian 
Games Park on housing prices, which is estimated using 
OLS regression. The results are shown in column  (1) of 
Table 2. The adjusted R2 is 0.770, thus indicating that the 
model has a good fit. The coefficient of DAGP is −0.016, 
thereby suggesting that the park has a positive external 
effect on housing prices. For every 1 km increase in dis-
tance from the park, housing prices decrease by 1.6%. 
In terms of architectural characteristics, floor area has 
a positive impact on housing prices, while newer build-
ings have higher prices. Variables related to transportation 
facilities show different results. Bus routes have a slightly 
negative impact on housing prices possibly because of 
noise and exhaust emissions. Proximity to metro stations 
has a positive impact on housing prices. Proximity to key 
primary schools, middle schools, high schools, and green 
spaces all have positive impacts, which is consistent with 
other empirical studies on Hangzhou (Du & Huang, 2018; 
Wen et al., 2015, 2018). For every 1% decrease in distance 
to a tertiary hospital, housing prices increase by 0.009%. 
Regarding location variables, for every 1% decrease in dis-
tance to West Lake and Qianjiang New City CBD, housing 
prices increase by 0.164% and 0.013%, respectively.
Four models were estimated for the time periods T1, 
T2, T3, and T4, with results shown in columns (2) to (5) of 
Table 2. The results show that the coefficient of DAGP is not 
significant in T1 before the planning announcement. Nev-
ertheless, the coefficients in the subsequent three periods 
are −0.019, −0.017, and −0.020, respectively. These values 
indicate that the impact of distance to the sports park on 
housing prices becomes significant after the planning an-
nouncement. Overall, these coefficients suggest that for 
every 1 km decrease in distance to the sports park, hous-
ing prices increase by 1.9%, 1.7%, and 2.0% in the T2, T3, 
and T4 periods, respectively.
4.2. Spatial heterogeneity of external effects
To examine the spatial heterogeneity of the impact of the 
sports park on housing prices, dummy variables for dif-
ferent distance ranges to the Asian Games Park were in-
troduced. These ranges include 0–1 km, 1–2 km, 2–3 km, 
3–4 km, 4–5 km, 5–6 km, 6–9 km, and 9–12 km, which cor-
respond to D1, D2, D3, D4, D5, D6, D7, and D8 respectively, 
with D8 serving as the reference variable.
Column (1) of Table 3 shows the overall regression re-
sults for the distance dummy variables. Most variables are 
statistically significant, thus indicating good explanatory 
power. The results show that the Asian Games Park has 
a premium effect on housing prices within a 5 km range. 
The coefficients of D1, D2, D3, D4, and D5 are 0.349, 0.115, 
Table 2. Baseline regression results
(1)
T
(2)
T1
(3)
T2
(4)
T3
(5)
T4
DAGP
−0.016***
−0.003
−0.019***
−0.017***
−0.020***
lnArea
1.084***
1.082***
1.053***
1.054***
1.177***
lnAge
−0.030***
0.030***
−0.030***
−0.037***
−0.044***
Green
0.046***
0.068***
0.042***
0.034***
0.060***
Bus
−0.001***
−0.001***
−0.001***
−0.000*
0.000 
Metro
0.054***
0.067***
0.055***
0.042***
0.054***
PriSch
0.009***
0.020**
0.013**
0.003
0.006
MidSch
0.009**
0.001
0.007
0.012**
0.015*
HighSch
0.045***
0.070***
0.063***
0.000 
0.061***
Univ
0.002
−0.027***
−0.015**
0.021***
0.010 
lnDHosp
−0.009***
−0.003
−0.009**
−0.013***
−0.006
lnDWL
−0.164***
−0.132***
−0.168***
−0.178***
−0.147***
lnDQJ
−0.013***
−0.029***
−0.065***
0.013**
0.000 
Constant
10.562***
10.300***
10.869***
10.716***
10.087***
Month
YES
YES
YES
YES
YES
Region
YES
YES
YES
YES
YES
Obs.
31,329
3,768
9,572
11,054
6,935
R2
0.770
0.785
0.791
0.769
0.759
Notes: * p < 0.1, ** p < 0.05, *** p < 0.01.


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International Journal of Strategic Property Management, 2025, 29(5), 316–328
323
0.054, 0.047, and 0.028, respectively. These results sug-
gest that housing prices within 1 km of the sports park 
are approximately 41.7% ( 0.349
1
e
−, Halvorsen & Palmquist, 
1980) higher than those in the outermost distance ring. 
This effect decreases with increasing distance, with hous-
ing premiums within 4–5 km being 2.8%. 
Similar to Table 2, columns (2) to (5) of Table 3 corre-
spond to the sample time ranges T1, T2, T3, and T4, respec-
tively. In the period before the announcement (T1), only 
D1 is significant at the 1% level, whereas other distance 
variables for the Asian Games Park are not significant. In 
the period after the announcement (T2), the coefficients 
of D1 to D5 are significant, with values of 0.345, 0.154, 
0.084, 0.058, and 0.030, respectively. However, D6 and D7 
are not statistically significant. In the period before the 
opening (T3), the coefficients of D1 to D4 indicate that the 
corresponding housing price premiums are approximately 
44.5%, 12.7%, 7.1%, and 5.8%, respectively. The external ef-
fect of the sports park becomes insignificant beyond 4 km. 
In the post-opening period (T4), the premium effect results 
shown in column (5) indicate that the premium rates for 
D1 and D5 have further increased compared with other 
periods, with coefficients of 0.452 and 0.086, respectively. 
These results preliminarily indicate that, before the 
planning announcement, the Asian Games Park had an ex-
ternal effect only on housing within a 0–1 km range. After 
the announcement, premium effects appeared within dif-
ferent distance ranges from 1–5 km, which have decreased 
with increasing distance, thus suggesting an anticipatory 
effect among homebuyers. Comparing the periods before 
and after the park’s opening, the premium effects of close 
distance within 1 km and longer distance within 4–5 km 
have increased significantly. Overall, the results indicate 
that residents have an additional willingness to pay for 
housing within a 5  km range of the Asian Games Park, 
with the premium being higher with closer distance. This 
outcome demonstrates significant spatial heterogeneity in 
the external effect on housing prices.
4.3. Dynamic effects around key time periods
The estimation results of Equation (3), shown in Table 4, 
provide a detailed analysis of how the planning and open-
ing of the Hangzhou Asian Games Park affected nearby 
housing prices. The model captures the dynamic relation-
ship between housing prices and distance to the park (DAGP) 
across key time periods, with a focus on the effects of the 
park’s planning announcement and eventual opening.
Column (1) examines the entire period (T), including the 
pre-announcement (T1), post-announcement (Tannounced, 
covering T2 and T3), and post-opening (Topen, covering T4) 
phases. The coefficient of DAGP is −0.009, which is statis-
tically significant at the 1% level. This indicates that, on 
average, housing prices decreased by 0.9% for every ad-
ditional kilometer of distance from the park during the 
overall study period. This result highlights the significant 
positive externality of the park, as properties closer to it 
tend to have higher prices.
The interaction term 
*
AGP
announced
D
T
, with a coeffi-
cient of −0.007, also reaches statistical significance at the 
1% level. This indicates that, after the planning announce-
ment, the effect of distance on housing prices became 
stronger. Specifically, for every 1 km closer to the park, 
housing prices increased by 1.6% (0.9% + 0.7%) during 
the post-announcement period. This reflects the market’s 
anticipation of the park’s future benefits, as expectations 
of improved amenities and public space began to be in-
corporated into housing prices. After the park opened, the 
interaction term 
*
AGP
open
D
T
 has a coefficient of −0.009, 
which is also significant at the 1% level. This suggests that 
the opening of the park further strengthened the effect, 
with housing prices increasing by 1.8% (0.9% + 0.9%) for 
every 1 km closer to the park. These results demonstrate 
that the external effects of the park were significantly 
enhanced after the planning announcement. However, 
the increase in external effects after the park opened was 
relatively marginal (only 0.2%).
Table 3. Regression results considering spatial heterogeneity
(1)
T
(2)
T1
(3)
T2
(4)
T3
(5)
T4
D1
0.349***
0.180***
0.345***
0.368***
0.452***
D2
0.115***
−0.003
0.154***
0.120***
0.160***
D3
0.054***
−0.025
0.084***
0.069***
0.066***
D4
0.047***
0.02
0.058***
0.056***
0.062***
D5
0.028***
−0.018
0.030***
0.017
0.086***
D6
−0.010
−0.030
0.012
−0.023**
0.022
D7
−0.018***
0.000
−0.017
−0.008
−0.024*
Controls
YES
YES
YES
YES
YES
Constant
10.486***
10.308***
10.765***
10.641***
9.966***
Month
YES
YES
YES
YES
YES
Region
YES
YES
YES
YES
YES
Obs.
31,329
3,768
9,572
11,054
6,935
R2
0.777
0.789
0.798
0.778
0.767
Notes: * p < 0.1, ** p < 0.05, *** p < 0.01.


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324
H. Wen et al. Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park
Column (2) narrows the analysis to the pre-announce-
ment and post-announcement phases (T12). The coefficient 
of DAGP is −0.010, implying that during these two periods, 
housing prices decreased by 1.0% for every additional ki-
lometer of distance from the park. The interaction term 
*
AGP
announced
D
T
 has a coefficient of −0.005, which is sig-
nificant at the 1% level. This indicates that immediately 
after the planning announcement, the negative effect of 
distance on housing prices increased by 0.5% per kilo-
meter relative to the pre-announcement period. Although 
this is slightly smaller than the overall effect reported in 
column (1), it still demonstrates that the announcement of 
the park created immediate market anticipation. 
Column (3) focuses on the pre-opening and post-
opening phases (T34), offering a comparison of housing 
price dynamics immediately before and after the park 
became operational. The coefficient of DAGP is −0.018, 
significant at the 1% level, indicating that during these 
two periods, housing prices decreased by 1.8% for every 
additional kilometer of distance from the park. This sug-
gests that as the park neared completion and its impact 
became more tangible, the spatial price premium for prop-
erties closer to the park intensified. However, the interac-
tion term 
*
AGP
open
D
T
 has a coefficient of −0.001 and is 
not statistically significant. This indicates that the open-
ing of the park did not result in a significant additional 
change in the relationship between distance and hous-
ing prices compared to the pre-opening phase. The lack 
of significance suggests that much of the park’s positive 
effect on housing prices had already been realized in an-
ticipation of its completion, and the actual opening simply 
confirmed these expectations without further enhancing 
the premium.
Table 4. Results of DID model based on baseline regression
(1)
T
(2)
T12
(3)
T34
DAGP
−0.009***
−0.010***
−0.018***
DAGP * Tannounced
−0.007***
−0.005***
DAGP * Topen
−0.009***
−0.001
Controls
YES
YES
YES
Constant
10.559***
10.707***
10.473***
Month
YES
YES
YES
Region
YES
YES
YES
Obs.
31,329
13,340
17,989
R2
0.770 
0.789
0.762
Notes:  *** p < 0.01.
Table 5 shows how the external effects of the Asian 
Games Park on housing prices vary spatially and over time. 
By incorporating interaction terms between ring distance 
variables and time variables, the table illustrates the price 
impacts at different distances before and after the park’s 
planning announcement and opening.
The baseline coefficients (D1 to D7) indicate a clear 
spatial gradient in housing prices, with higher premiums 
observed closer to the park. In column (2) of Table  5, 
the coefficients of the interaction terms 
1
D *
announced
T
, 
D2 * announced
T
, and D3 * announced
T
 are 0.113, 0.107, and 
0.057, respectively. This outcome indicates that, after the 
planning announcement, housing prices within 3 km of 
the Asian Games Park increased by 12.0%, 11.3%, and 
5.9%, respectively. The coefficients of the interaction terms 
D4 * announced
T
 and D5 * announced
T
 are not significant, there-
by suggesting no significant changes in the park’s external 
effects within the 3–5 km ring distance before and after 
the planning announcement. Column (3) presents the re-
sults for the period T34, which covers the time before and 
after the park’s opening. After the opening, the external 
effects are only strengthened within 1 km of the park and 
in the outer 4–5 km ring distance, with premiums of ap-
proximately 6.7% and 2.9%, respectively, which are signifi-
cant at the 5% level.
Column (1) of Table 5 uses sample data from the en-
tire period to examine the impacts of the planning an-
nouncement and park opening on the external effects 
simultaneously. Using the period before the planning 
Table 5. Results of DID model considering spatial 
heterogeneity
(1)
T
(2)
T12
(3)
T34
D1
0.195***
0.214***
0.375***
D2
0.021
0.030 
0.124***
D3
0.005
0.008
0.073***
D4
0.033***
0.045***
0.061***
D5
0.009
0.005
0.033***
D6
−0.038**
−0.030**
−0.006
D7
−0.006
−0.003
0.011
1
D *
announced
T
0.153***
0.113***
2
D *
announced
T
0.104***
0.107***
3
D *
announced
T
0.060***
0.057***
4
D *
announced
T
0.015
−0.001
5
D *
announced
T
0.014
0.013
6
D *
announced
T
0.033**
0.036**
7
D *
announced
T
−0.002
−0.019
1
D *
open
T
0.252***
0.065**
2
D *
open
T
0.128***
0.027
3
D *
open
T
0.047***
−0.018
4
D *
open
T
0.020 
−0.007
5
D *
open
T
0.047***
0.029**
6
D *
open
T
0.027
−0.005
7
D *
open
T
−0.037**
−0.059***
Controls
YES
YES
YES
Constant
10.484***
10.638***
10.377***
Month
YES
YES
YES
Region
YES
YES
YES
Obs.
31,329
13,340
17,989
R2
0.778
0.795
0.771
Notes:  ** p < 0.05, *** p < 0.01.


===== PAGE 10 (score 9, numeric-paren 0, table-words 0, var-words 9) =====

International Journal of Strategic Property Management, 2025, 29(5), 316–328
325
Figure 3. Kernel density distribution of placebo test results
announcement (T1) as a comparison baseline, the park’s 
external effects within 0–3 km significantly increased after 
the announcement and the opening. The coefficients of 
the interaction terms D1 * announced
T
, D2 * announced
T
, and D3 
*
 
announced
T
are 0.153, 0.104, and 0.060, respectively, while 
the coefficients of D1 * open
T
, D2 * open
T
, and D3 * open
T
 are 
0.252, 0.128, and 0.047, respectively. All these values are 
statistically significant at the 1% level. In addition, the an-
nouncement and opening up have also generated signifi-
cant premiums for 5–6 km and 4–5 km, respectively. 
These results are consistent with the previous model, 
thus further confirming that the planning announcement 
and park opening have had significant impacts on the ex-
ternal effects of the Asian Games Park. In terms of magni-
tude and impact range, the anticipatory effect of the plan-
ning announcement is evidently greater than the changes 
in the external effects caused by the park’s opening.
To verify the robustness of the results, we conducted 
additional analyses. First, we performed a robustness check 
using log-transformed distance as an alternative specifica-
tion to examine potential non-linear effects of distance on 
housing prices. The results of this specification are consist-
ent with those from the main analysis, providing further 
support for the validity of the findings. Second, a placebo 
test was conducted to validate the robustness and effec-
tiveness of the DID model in estimating the intervention 
effects. This test aims to confirm that the observed results 
are not driven by random fluctuations or omitted variables 
but are instead caused by the actual policy intervention. 
Following established methods, we randomly assigned 
treatment groups using computational simulation and re-
peated the regression analysis 1,000 times.
Figure 3 presents the results of the placebo test. The 
kernel density of estimated coefficients is centered around 
zero, and most p-values exceed 0.1, indicating that the 
observed effects are unlikely to result from random noise 
or omitted variables. Only a small proportion of p-values 
fall below 0.1, as highlighted by the vertical line, confirm-
ing that outliers are minimal. These results reinforce the 
robustness of the main findings and the validity of the 
causal effects identified in the study.
5.	Discussion
As cities continue to invest in mega sporting events and 
their associated infrastructure, understanding the econom-
ic impact of such projects becomes increasingly important. 
The Hangzhou Asian Games Park serves as a compelling 
case study, demonstrating that well-planned sports parks 
can yield substantial gains for nearby housing markets. 
However, these gains vary across locations and over time, 
highlighting the need for strategic urban planning and in-
vestment decisions.
Previous research has documented the positive impact 
of urban parks on housing prices (Czembrowski & Kro-
nenberg, 2016; Panduro et al., 2018; Piaggio, 2021). Our 
findings extend this understanding by showing that large 
urban sports parks, such as the Hangzhou Asian Games 
Park, also enhance housing prices. Specifically, we found 
that for every 1 km decrease in distance from the park, 
housing prices increase by 1.6%. This is consistent with 
studies by Feng and Humphreys (2012, 2018) and Yang 
et al. (2018), which reported significant positive externali-
ties of sports spaces on nearby housing prices.
Spatial heterogeneity is another critical aspect of this 
study. While previous research has noted varying degrees 
of impact based on proximity to parks and stadiums (e.g., 
Tu, 2005; Iqbal & Wilhelmsson, 2018; Dell’Anna et  al., 
2022), our study provides a more granular analysis by 
introducing dummy variables for different ring distance 
ranges. This variation in the park’s effects further high-
lights the importance of proximity in shaping the eco-
nomic impact. Housing prices within 1  km of the park 
experienced the most significant increases, while the ef-
fects diminished with greater distances and became neg-
ligible beyond 5 km. This pattern suggests that the per-
ceived value of the park’s benefits, such as access to green 
spaces, recreational amenities, and improved air quality, 
is highly localized. Residents living closer to the park can 
enjoy these benefits more conveniently, which enhances 
the desirability of properties in the immediate vicinity. This 
spatial gradient also reflects the challenges of ensuring 
equitable access to urban public goods, as those living 
further away may not experience comparable benefits.
The DID analysis reveals that the Hangzhou Asian 
Games Park had a substantial and dynamic impact on 
housing prices over time. The planning announcement 
generated strong market anticipation, as evidenced by the 
increased impact of proximity on housing prices during 
the post-announcement phase. The park’s opening further 
reinforced these effects, though the short-term dynamics 
around the opening phase suggest that much of the value 
had already been capitalized into prices prior to its com-
pletion. This phenomenon can be attributed to several fac-
tors. First, the announcement of a large-scale urban sports 
park often generates significant market expectations, as it 
signals the government’s commitment to improving lo-
cal infrastructure, enhancing environmental quality, and 
providing recreational amenities. These expectations are 
rapidly reflected in housing prices as buyers and investors