1# RECORD dbfc419fae85b2cf607f68db238ec77b3593a5e02# TITLE: Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park3# YEAR: 2025.0 VENUE: International Journal of Strategic Property Management DOI: 10.3846/ijspm.2025.248134# AUTHORS: Haizhen Wen; Fangziyi Huang; Shu-Yuan Li; E. Hui; Ling Zhang5# PAGES: 13 CANDIDATE TABLE PAGES (1-based): [4, 7, 8, 9]6# 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.789===== PAGE 1 (score 13, numeric-paren 0, table-words 0, var-words 13) =====1011This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), 12which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.13Copyright © 2025 The Author(s). Published by Vilnius Gediminas Technical University14ISSN: 1648-715X / eISSN: 1648-917915INTERNATIONAL JOURNAL 16OF STRATEGIC PROPERTY 17MANAGEMENT18DYNAMIC EFFECT OF SPORTS PARKS ON HOUSING PRICES: EVIDENCE FROM 19HANGZHOU ASIAN GAMES PARK20Haizhen WEN1, Fangziyi HUANG2, Shuyuan LI 1,3*, Eddie C. M. HUI 4, Ling ZHANG 2,5211 School of Spatial Planning and Design, Hangzhou City University, Hangzhou, China222 Department of Civil Engineering, Zhejiang University, Hangzhou, China233 School of Public Affairs, Zhejiang University, Hangzhou, China244 Department of Public and International Affairs, City University of Hong Kong, Hong Kong, China255 Center for Real Estate Studying, Zhejiang University, Hangzhou, China26Article History: 27Abstract. The construction of sports parks often involves significant investment and extended development 28timelines, making it crucial to evaluate their economic impacts for informed policymaking and strategic ur-29ban planning. While research on the externalities of urban parks has grown, the specific economic effects of 30sports parks remain underexplored. This study examines the impact of the Hangzhou Asian Games Park on 31housing prices using 31,329 transaction records from 2018 to 2023. Employing a hedonic pricing model and 32a difference-in-differences approach, we find that housing prices increase by 1.6% for every 1 km reduction 33in distance to the park. Spatial heterogeneity is observable, as the effect diminishes with increasing distance. 34Temporally, the park’s planning announcement triggered a 0.7% increase in price-distance elasticity, com-35pared to a 0.2% increase after its opening. These findings suggest that the anticipatory market response was 36stronger than the realized benefits after the park’s completion. This study quantifies the spatial and temporal 37dynamics of the economic value of sports parks, providing valuable insights for urban planning, public policy, 38and investment strategies.39 ■received 18 March 202540 ■accepted 28 July 202541Keywords: sports parks, housing prices, hedonic pricing, spatial heterogeneity, temporal dynamics, Hangzhou Asian Games Park.42 * Corresponding author. E-mail: shuyuanli@zju.edu.cn431. Introduction44Urban parks play a critical role in enhancing the livability 45and sustainability of cities. Extensive research has dem-46onstrated the environmental, social, and economic con-47tributions of urban parks. For instance, urban parks help 48mitigate air pollution (Kim & Coseo, 2018), reduce urban 49heat island effects (Yan et al., 2018), and provide spaces for 50recreation, social interaction, and physical activity (Brown 51et al., 2014; Peters et al., 2010; Chiesura, 2004). These 52benefits, along with their influence on real estate values, 53have sparked interest in assessing their impact on housing 54markets (Jim & Chen, 2010; Mueller et al., 2022). How-55ever, most existing studies focus on general green spaces 56or conventional urban parks, leaving a significant gap in 57understanding the economic value of sports parks as a 58distinct category of public infrastructure. 59Unlike traditional urban parks, sports parks blend fit-60ness and recreational facilities with natural landscapes, of-61fering sports venues, exercise amenities, and leisure spac-62es. As multifunctional public goods, they contribute to ur-63ban vitality and public health while also serving as venues 64for organized sports events. Previous research on sports 65parks has predominantly examined their planning and de-66sign (Luo & Wang, 2022), social benefits (Sun et al., 2022), 67and ecological functions (Li, 2020). For instance, Sun et al. 68(2022) demonstrated how sports parks promote commu-69nity interaction, while Wu and Li (2022) emphasized their 70role in meeting public exercise needs. Access to sports 71parks has also been linked to improved health outcomes 72and subjective well-being (Dadvand et al., 2016; Kou et al., 732021; Zhang et al., 2021b). However, their economic exter-74nalities, particularly their impact on housing prices, remain 75underexplored. This gap in the literature limits our under-76standing of their broader value and constrains evidence-77based urban planning and investment decisions.78The Hangzhou Asian Games Park provides a compel-79ling case study to address this gap. Developed to support 80the 19th Asian Games, this large-scale sports park inte-81grates green space with sports facilities and commercial 82areas, representing an innovative approach to urban park 83development. Its phased development process, including 84the announcement of the project in 2019 and its official 85opening in 2021, offers a unique opportunity to examine 86202587Volume 2988Issue 589Pages 316–32890https://doi.org/10.3846/ijspm.2025.24813919293===== PAGE 4 (score 22, numeric-paren 0, table-words 2, var-words 15) =====9495International Journal of Strategic Property Management, 2025, 29(5), 316–3289631997The architectural characteristics of housing represent the 98physical attributes of the buildings. In this study, build-99ing age and size were selected to capture these features. 100Location characteristics reflect the fixed and relative posi-101tions of housing, typically quantified through accessibil-102ity measures such as straight-line distance, travel time, or 103path distance. To better represent the spatial structure of 104Hangzhou, this study includes the distances to West Lake, 105the traditional cultural and commercial center, and Qianji-106ang CBD, the emerging central business district, as the pri-107mary location characteristic variables. West Lake serves as 108a key natural and cultural landmark, with its historical and 109aesthetic value significantly influencing housing prices. In 110contrast, Qianjiang CBD represents the city’s modern eco-111nomic development and the shift in its commercial core, 112playing a crucial role in shaping housing price patterns. In 113addition, neighborhood characteristics focus on the natu-114ral environment, social surroundings, and supporting facili-115ties around the housing. This study selected the distances 116to major hospitals, key schools, bus stops, metro stations, 117and green spaces as neighborhood characteristic variables. 118These factors reflect the accessibility to public services, 119quality of life, and proximity to essential resources, further 120enriching the explanatory power of the model and ensur-121ing the robustness of the conclusions. Definitions and de-122scriptive statistics for each variable are provided in Table 1.1233.2. Data and variables124The secondhand housing transaction data and architec-125tural characteristics, including area and age, used in this 126study were obtained from real estate intermediary com-127panies and the CRIC database. The housing price is the 128total transaction price of a single housing unit, which 129spanned from January 2018 to March 2023 in 1,671 resi-130dential communities in Hangzhou. After excluding imper-131fect and anomalous samples, we obtained valid data for 13231,329 housing transaction samples.133The housing price was selected as the dependent vari-134able in this study. The independent variables were catego-135rized into three aspects according to hedonic price theory: 136architectural characteristics, neighborhood characteristics, 137and location characteristics. Data related to neighborhood 138and location characteristics were collected at the residen-139tial community level, and relevant latitude and longitude 140coordinates were obtained through the electronic map. 141This approach allowed for the direct calculation of dis-142tances or the use of map ranging tools, thus quantifying 143the relevant variables. Drawing on previous empirical stud-144ies on the factors influencing housing prices in Hangzhou 145(Du & Huang, 2018; Wen et al., 2015, 2018, 2022; Xiao 146et al., 2019), two architectural characteristic variables, two 147locational characteristic variables, and eight neighborhood 148characteristic variables were adopted as control variables. 149Table 1. Variable definitions and descriptive statistics150Type151Variable Definition152Mean153S.D.154Min155Max156Dependent 157variable158HP159Total transaction price of a housing ($)1603,157,000 1,756,000 400,000 17,000,000161Architectural 162features163Age164Building age (years)16517.69016610.110167116846169Area170Floor area of a housing (m2)17182.14017235.83017324174372175Locational 176features177DWL178Distance from residential community to West Lake (km)1796.3081803.6961810.20518217.950183DQJ184Distance from residential community to Qianjiang CBD (km) 9.7051855.0671860.29918723.250188Neighborhood 189features190Green191Availability of green spaces within a 1 km radius of the 192community (1 for availability, 0 otherwise)1930.8411940.36619501961197Bus198Number of bus routes within a 1 km radius of the 199community20062.93020136.4502020203205204Metro205Availability of metro stations within a 1 km radius of the 206community (1 for availability, 0 otherwise)2070.729 2080.444 20902101211PriSch212Availability of key primary schools within a 1 km radius of 213the community (1 for availability, 0 otherwise)2140.412 2150.492 21602171218MidSch219Availability of key middle schools within a 1 km radius of 220the community (1 for availability, 0 otherwise)2210.3642220.48122302241225HighSch Availability of high schools within a 1 km radius of the 226community (1 for availability, 0 otherwise)2270.3132280.46422902301231Univ232Availability of universities within a 1 km radius of the 233community (1 for availability, 0 otherwise)2340.249 2350.432 23602371238DHosp239Distance from community to nearest tertiary hospital (km)2402.6172412.6142420.03324313.440244DAGP245Distance from community to Asian Games Park (km)2466.0832473.1002480.34824912.000250Dj251Eight distance buffers between community and the Asian 252Games Park: j = 1 is within 1 km, j = 2 is 1–2 km, j = 3 is 2532–3 km, j = 4 is 3–4 km, j = 5 is 4–5 km, j = 6 is 5–6 km, 254j = 7 is 6–9 km, j = 8 is 9–12 km255256257===== PAGE 5 (score 6, numeric-paren 0, table-words 0, var-words 6) =====258259320260H. Wen et al. Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park261Accessibility to the Asian Games Park is the primary 262variable examined in this study. Prior research has consist-263ently demonstrated that accessibility can be effectively an-264alyzed using linear distances (Ardeshiri et al., 2016; Jim & 265Chen, 2006; Vom Hofe et al., 2018). Studies on the effects 266of sports stadiums and urban parks frequently highlight 267significant impacts on housing prices within a 3 km ra-268dius, with diminishing effects observed beyond this range 269(Ahlfeldt & Kavetsos, 2014; Mueller et al., 2022). For large-270scale cultural and sports facilities constructed for public 271welfare, previous research suggests that the service radius 272is typically larger, often extending beyond 5 km (Zhang 273et al., 2019).274To account for these varying impacts and explore 275their attenuation across distance, this study categorizes 276residential communities into eight groups based on their 277straight-line distance from the Hangzhou Asian Games 278Park: 0–1 km, 1–2 km, 2–3 km, 3–4 km, 4–5 km, 5–6 km, 2796–9 km, and 9–12 km. Communities within the 9–12 km 280range are used as the control group, as this distance is 281well beyond the typical impact radius of similar facilities. 282This categorization allows for a nuanced analysis of how 283the park’s influence on housing prices varies with distance.284It is worth noting that some communities near the 285boundaries of these distance categories may be subject 286to overlapping influences, which could lead to a certain 287degree of spatial correlation or spillover effects. However, 288existing literature indicates that the effects of policies or 289facilities tend to attenuate with increasing distance, signifi-290cantly reducing the likelihood of spillover effects impact-291ing the control group. Additionally, the use of multiple 292finely defined distance categories, rather than a simple bi-293nary classification of treatment and control groups, helps 294capture subtle variations in impact across distances and 295mitigates potential concerns about spatial autocorrelation.296Figure 1 illustrates the spatial distribution of the Hang-297zhou Asian Games Park and the selected residential com-298munities, providing a visual representation of the study 299area and distance classification.300The date of the public announcement of the plan-301ning of the Hangzhou Asian Games Park coincides with 302the date when the organizing committee first clarified the 303date and events of the Asian Games. Since then, residents 304have become aware that professional sports facilities and 305stadiums would be built on the site for the Asian Games 306events. We consider this time point, 5 April 2019, as the 307date of the announcement. The second key time point is 308the official opening of the sports park to the public on 3091 December 2021.310The total time period is represented as T, and all the 311data were divided into four time periods:312T1: 1 January 2018 to 4 April 2019 (preannouncement 313period),314T2: 5 April 2019 to 31 July 2020 (postannouncement 315period),316T3: 1 August 2020 to 30 November 2021 (preopening 317period),318T4: 1 December 2021 to 31 March 2023 (late opening 319period).320In addition, we define the following broader time pe-321riods for analysis:322Figure 1. Locations of the Hangzhou Asian Games Park and housing samples323324325===== PAGE 7 (score 90, numeric-paren 58, table-words 8, var-words 8) =====326327322328H. Wen et al. Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park329In contrast, during the post-treatment periods (post_1, 330post_2, and post_3), the interaction term coefficients be-331come significant, with confidence intervals that do not 332cross zero. This demonstrates the emergence of a clear 333treatment effect following the project announcement. 334These findings confirm that the parallel trends assumption 335holds in the pre-treatment period, and the observed dif-336ferences in the post-treatment period can be attributed to 337the impact of the Asian Games Park project.3384. Results and analysis3394.1. Baseline impact of sports park on 340housing prices341The basic model measures the overall impact of the Asian 342Games Park on housing prices, which is estimated using 343OLS regression. The results are shown in column (1) of 344Table 2. The adjusted R2 is 0.770, thus indicating that the 345model has a good fit. The coefficient of DAGP is −0.016, 346thereby suggesting that the park has a positive external 347effect on housing prices. For every 1 km increase in dis-348tance from the park, housing prices decrease by 1.6%. 349In terms of architectural characteristics, floor area has 350a positive impact on housing prices, while newer build-351ings have higher prices. Variables related to transportation 352facilities show different results. Bus routes have a slightly 353negative impact on housing prices possibly because of 354noise and exhaust emissions. Proximity to metro stations 355has a positive impact on housing prices. Proximity to key 356primary schools, middle schools, high schools, and green 357spaces all have positive impacts, which is consistent with 358other empirical studies on Hangzhou (Du & Huang, 2018; 359Wen et al., 2015, 2018). For every 1% decrease in distance 360to a tertiary hospital, housing prices increase by 0.009%. 361Regarding location variables, for every 1% decrease in dis-362tance to West Lake and Qianjiang New City CBD, housing 363prices increase by 0.164% and 0.013%, respectively.364Four models were estimated for the time periods T1, 365T2, T3, and T4, with results shown in columns (2) to (5) of 366Table 2. The results show that the coefficient of DAGP is not 367significant in T1 before the planning announcement. Nev-368ertheless, the coefficients in the subsequent three periods 369are −0.019, −0.017, and −0.020, respectively. These values 370indicate that the impact of distance to the sports park on 371housing prices becomes significant after the planning an-372nouncement. Overall, these coefficients suggest that for 373every 1 km decrease in distance to the sports park, hous-374ing prices increase by 1.9%, 1.7%, and 2.0% in the T2, T3, 375and T4 periods, respectively.3764.2. Spatial heterogeneity of external effects377To examine the spatial heterogeneity of the impact of the 378sports park on housing prices, dummy variables for dif-379ferent distance ranges to the Asian Games Park were in-380troduced. These ranges include 0–1 km, 1–2 km, 2–3 km, 3813–4 km, 4–5 km, 5–6 km, 6–9 km, and 9–12 km, which cor-382respond to D1, D2, D3, D4, D5, D6, D7, and D8 respectively, 383with D8 serving as the reference variable.384Column (1) of Table 3 shows the overall regression re-385sults for the distance dummy variables. Most variables are 386statistically significant, thus indicating good explanatory 387power. The results show that the Asian Games Park has 388a premium effect on housing prices within a 5 km range. 389The coefficients of D1, D2, D3, D4, and D5 are 0.349, 0.115, 390Table 2. Baseline regression results391(1)392T393(2)394T1395(3)396T2397(4)398T3399(5)400T4401DAGP402−0.016***403−0.003404−0.019***405−0.017***406−0.020***407lnArea4081.084***4091.082***4101.053***4111.054***4121.177***413lnAge414−0.030***4150.030***416−0.030***417−0.037***418−0.044***419Green4200.046***4210.068***4220.042***4230.034***4240.060***425Bus426−0.001***427−0.001***428−0.001***429−0.000*4300.000 431Metro4320.054***4330.067***4340.055***4350.042***4360.054***437PriSch4380.009***4390.020**4400.013**4410.0034420.006443MidSch4440.009**4450.0014460.0074470.012**4480.015*449HighSch4500.045***4510.070***4520.063***4530.000 4540.061***455Univ4560.002457−0.027***458−0.015**4590.021***4600.010 461lnDHosp462−0.009***463−0.003464−0.009**465−0.013***466−0.006467lnDWL468−0.164***469−0.132***470−0.168***471−0.178***472−0.147***473lnDQJ474−0.013***475−0.029***476−0.065***4770.013**4780.000 479Constant48010.562***48110.300***48210.869***48310.716***48410.087***485Month486YES487YES488YES489YES490YES491Region492YES493YES494YES495YES496YES497Obs.49831,3294993,7685009,57250111,0545026,935503R25040.7705050.7855060.7915070.7695080.759509Notes: * p < 0.1, ** p < 0.05, *** p < 0.01.510511512===== PAGE 8 (score 51, numeric-paren 28, table-words 7, var-words 3) =====513514International Journal of Strategic Property Management, 2025, 29(5), 316–3285153235160.054, 0.047, and 0.028, respectively. These results sug-517gest that housing prices within 1 km of the sports park 518are approximately 41.7% ( 0.3495191520e521−, Halvorsen & Palmquist, 5221980) higher than those in the outermost distance ring. 523This effect decreases with increasing distance, with hous-524ing premiums within 4–5 km being 2.8%. 525Similar to Table 2, columns (2) to (5) of Table 3 corre-526spond to the sample time ranges T1, T2, T3, and T4, respec-527tively. In the period before the announcement (T1), only 528D1 is significant at the 1% level, whereas other distance 529variables for the Asian Games Park are not significant. In 530the period after the announcement (T2), the coefficients 531of D1 to D5 are significant, with values of 0.345, 0.154, 5320.084, 0.058, and 0.030, respectively. However, D6 and D7 533are not statistically significant. In the period before the 534opening (T3), the coefficients of D1 to D4 indicate that the 535corresponding housing price premiums are approximately 53644.5%, 12.7%, 7.1%, and 5.8%, respectively. The external ef-537fect of the sports park becomes insignificant beyond 4 km. 538In the post-opening period (T4), the premium effect results 539shown in column (5) indicate that the premium rates for 540D1 and D5 have further increased compared with other 541periods, with coefficients of 0.452 and 0.086, respectively. 542These results preliminarily indicate that, before the 543planning announcement, the Asian Games Park had an ex-544ternal effect only on housing within a 0–1 km range. After 545the announcement, premium effects appeared within dif-546ferent distance ranges from 1–5 km, which have decreased 547with increasing distance, thus suggesting an anticipatory 548effect among homebuyers. Comparing the periods before 549and after the park’s opening, the premium effects of close 550distance within 1 km and longer distance within 4–5 km 551have increased significantly. Overall, the results indicate 552that residents have an additional willingness to pay for 553housing within a 5 km range of the Asian Games Park, 554with the premium being higher with closer distance. This 555outcome demonstrates significant spatial heterogeneity in 556the external effect on housing prices.5574.3. Dynamic effects around key time periods558The estimation results of Equation (3), shown in Table 4, 559provide a detailed analysis of how the planning and open-560ing of the Hangzhou Asian Games Park affected nearby 561housing prices. The model captures the dynamic relation-562ship between housing prices and distance to the park (DAGP) 563across key time periods, with a focus on the effects of the 564park’s planning announcement and eventual opening.565Column (1) examines the entire period (T), including the 566pre-announcement (T1), post-announcement (Tannounced, 567covering T2 and T3), and post-opening (Topen, covering T4) 568phases. The coefficient of DAGP is −0.009, which is statis-569tically significant at the 1% level. This indicates that, on 570average, housing prices decreased by 0.9% for every ad-571ditional kilometer of distance from the park during the 572overall study period. This result highlights the significant 573positive externality of the park, as properties closer to it 574tend to have higher prices.575The interaction term 576*577AGP578announced579D580T581, with a coeffi-582cient of −0.007, also reaches statistical significance at the 5831% level. This indicates that, after the planning announce-584ment, the effect of distance on housing prices became 585stronger. Specifically, for every 1 km closer to the park, 586housing prices increased by 1.6% (0.9% + 0.7%) during 587the post-announcement period. This reflects the market’s 588anticipation of the park’s future benefits, as expectations 589of improved amenities and public space began to be in-590corporated into housing prices. After the park opened, the 591interaction term 592*593AGP594open595D596T597 has a coefficient of −0.009, 598which is also significant at the 1% level. This suggests that 599the opening of the park further strengthened the effect, 600with housing prices increasing by 1.8% (0.9% + 0.9%) for 601every 1 km closer to the park. These results demonstrate 602that the external effects of the park were significantly 603enhanced after the planning announcement. However, 604the increase in external effects after the park opened was 605relatively marginal (only 0.2%).606Table 3. Regression results considering spatial heterogeneity607(1)608T609(2)610T1611(3)612T2613(4)614T3615(5)616T4617D16180.349***6190.180***6200.345***6210.368***6220.452***623D26240.115***625−0.0036260.154***6270.120***6280.160***629D36300.054***631−0.0256320.084***6330.069***6340.066***635D46360.047***6370.026380.058***6390.056***6400.062***641D56420.028***643−0.0186440.030***6450.0176460.086***647D6648−0.010649−0.0306500.012651−0.023**6520.022653D7654−0.018***6550.000656−0.017657−0.008658−0.024*659Controls660YES661YES662YES663YES664YES665Constant66610.486***66710.308***66810.765***66910.641***6709.966***671Month672YES673YES674YES675YES676YES677Region678YES679YES680YES681YES682YES683Obs.68431,3296853,7686869,57268711,0546886,935689R26900.7776910.7896920.7986930.7786940.767695Notes: * p < 0.1, ** p < 0.05, *** p < 0.01.696697698===== PAGE 9 (score 74, numeric-paren 39, table-words 12, var-words 4) =====699700324701H. Wen et al. Dynamic effect of sports parks on housing prices: Evidence from Hangzhou Asian Games Park702Column (2) narrows the analysis to the pre-announce-703ment and post-announcement phases (T12). The coefficient 704of DAGP is −0.010, implying that during these two periods, 705housing prices decreased by 1.0% for every additional ki-706lometer of distance from the park. The interaction term 707*708AGP709announced710D711T712 has a coefficient of −0.005, which is sig-713nificant at the 1% level. This indicates that immediately 714after the planning announcement, the negative effect of 715distance on housing prices increased by 0.5% per kilo-716meter relative to the pre-announcement period. Although 717this is slightly smaller than the overall effect reported in 718column (1), it still demonstrates that the announcement of 719the park created immediate market anticipation. 720Column (3) focuses on the pre-opening and post-721opening phases (T34), offering a comparison of housing 722price dynamics immediately before and after the park 723became operational. The coefficient of DAGP is −0.018, 724significant at the 1% level, indicating that during these 725two periods, housing prices decreased by 1.8% for every 726additional kilometer of distance from the park. This sug-727gests that as the park neared completion and its impact 728became more tangible, the spatial price premium for prop-729erties closer to the park intensified. However, the interac-730tion term 731*732AGP733open734D735T736 has a coefficient of −0.001 and is 737not statistically significant. This indicates that the open-738ing of the park did not result in a significant additional 739change in the relationship between distance and hous-740ing prices compared to the pre-opening phase. The lack 741of significance suggests that much of the park’s positive 742effect on housing prices had already been realized in an-743ticipation of its completion, and the actual opening simply 744confirmed these expectations without further enhancing 745the premium.746Table 4. Results of DID model based on baseline regression747(1)748T749(2)750T12751(3)752T34753DAGP754−0.009***755−0.010***756−0.018***757DAGP * Tannounced758−0.007***759−0.005***760DAGP * Topen761−0.009***762−0.001763Controls764YES765YES766YES767Constant76810.559***76910.707***77010.473***771Month772YES773YES774YES775Region776YES777YES778YES779Obs.78031,32978113,34078217,989783R27840.770 7850.7897860.762787Notes: *** p < 0.01.788Table 5 shows how the external effects of the Asian 789Games Park on housing prices vary spatially and over time. 790By incorporating interaction terms between ring distance 791variables and time variables, the table illustrates the price 792impacts at different distances before and after the park’s 793planning announcement and opening.794The baseline coefficients (D1 to D7) indicate a clear 795spatial gradient in housing prices, with higher premiums 796observed closer to the park. In column (2) of Table 5, 797the coefficients of the interaction terms 7981799D *800announced801T802, 803D2 * announced804T805, and D3 * announced806T807 are 0.113, 0.107, and 8080.057, respectively. This outcome indicates that, after the 809planning announcement, housing prices within 3 km of 810the Asian Games Park increased by 12.0%, 11.3%, and 8115.9%, respectively. The coefficients of the interaction terms 812D4 * announced813T814 and D5 * announced815T816 are not significant, there-817by suggesting no significant changes in the park’s external 818effects within the 3–5 km ring distance before and after 819the planning announcement. Column (3) presents the re-820sults for the period T34, which covers the time before and 821after the park’s opening. After the opening, the external 822effects are only strengthened within 1 km of the park and 823in the outer 4–5 km ring distance, with premiums of ap-824proximately 6.7% and 2.9%, respectively, which are signifi-825cant at the 5% level.826Column (1) of Table 5 uses sample data from the en-827tire period to examine the impacts of the planning an-828nouncement and park opening on the external effects 829simultaneously. Using the period before the planning 830Table 5. Results of DID model considering spatial 831heterogeneity832(1)833T834(2)835T12836(3)837T34838D18390.195***8400.214***8410.375***842D28430.0218440.030 8450.124***846D38470.0058480.0088490.073***850D48510.033***8520.045***8530.061***854D58550.0098560.0058570.033***858D6859−0.038**860−0.030**861−0.006862D7863−0.006864−0.0038650.0118661867D *868announced869T8700.153***8710.113***8722873D *874announced875T8760.104***8770.107***8783879D *880announced881T8820.060***8830.057***8844885D *886announced887T8880.015889−0.0018905891D *892announced893T8940.0148950.0138966897D *898announced899T9000.033**9010.036**9027903D *904announced905T906−0.002907−0.0199081909D *910open911T9120.252***9130.065**9142915D *916open917T9180.128***9190.0279203921D *922open923T9240.047***925−0.0189264927D *928open929T9300.020 931−0.0079325933D *934open935T9360.047***9370.029**9386939D *940open941T9420.027943−0.0059447945D *946open947T948−0.037**949−0.059***950Controls951YES952YES953YES954Constant95510.484***95610.638***95710.377***958Month959YES960YES961YES962Region963YES964YES965YES966Obs.96731,32996813,34096917,989970R29710.7789720.7959730.771974Notes: ** p < 0.05, *** p < 0.01.975976977===== PAGE 10 (score 9, numeric-paren 0, table-words 0, var-words 9) =====978979International Journal of Strategic Property Management, 2025, 29(5), 316–328980325981Figure 3. Kernel density distribution of placebo test results982announcement (T1) as a comparison baseline, the park’s 983external effects within 0–3 km significantly increased after 984the announcement and the opening. The coefficients of 985the interaction terms D1 * announced986T987, D2 * announced988T989, and D3 990*991 992announced993T994are 0.153, 0.104, and 0.060, respectively, while 995the coefficients of D1 * open996T997, D2 * open998T999, and D3 * open1000T1001 are 10020.252, 0.128, and 0.047, respectively. All these values are 1003statistically significant at the 1% level. In addition, the an-1004nouncement and opening up have also generated signifi-1005cant premiums for 5–6 km and 4–5 km, respectively. 1006These results are consistent with the previous model, 1007thus further confirming that the planning announcement 1008and park opening have had significant impacts on the ex-1009ternal effects of the Asian Games Park. In terms of magni-1010tude and impact range, the anticipatory effect of the plan-1011ning announcement is evidently greater than the changes 1012in the external effects caused by the park’s opening.1013To verify the robustness of the results, we conducted 1014additional analyses. First, we performed a robustness check 1015using log-transformed distance as an alternative specifica-1016tion to examine potential non-linear effects of distance on 1017housing prices. The results of this specification are consist-1018ent with those from the main analysis, providing further 1019support for the validity of the findings. Second, a placebo 1020test was conducted to validate the robustness and effec-1021tiveness of the DID model in estimating the intervention 1022effects. This test aims to confirm that the observed results 1023are not driven by random fluctuations or omitted variables 1024but are instead caused by the actual policy intervention. 1025Following established methods, we randomly assigned 1026treatment groups using computational simulation and re-1027peated the regression analysis 1,000 times.1028Figure 3 presents the results of the placebo test. The 1029kernel density of estimated coefficients is centered around 1030zero, and most p-values exceed 0.1, indicating that the 1031observed effects are unlikely to result from random noise 1032or omitted variables. Only a small proportion of p-values 1033fall below 0.1, as highlighted by the vertical line, confirm-1034ing that outliers are minimal. These results reinforce the 1035robustness of the main findings and the validity of the 1036causal effects identified in the study.10375. Discussion1038As cities continue to invest in mega sporting events and 1039their associated infrastructure, understanding the econom-1040ic impact of such projects becomes increasingly important. 1041The Hangzhou Asian Games Park serves as a compelling 1042case study, demonstrating that well-planned sports parks 1043can yield substantial gains for nearby housing markets. 1044However, these gains vary across locations and over time, 1045highlighting the need for strategic urban planning and in-1046vestment decisions.1047Previous research has documented the positive impact 1048of urban parks on housing prices (Czembrowski & Kro-1049nenberg, 2016; Panduro et al., 2018; Piaggio, 2021). Our 1050findings extend this understanding by showing that large 1051urban sports parks, such as the Hangzhou Asian Games 1052Park, also enhance housing prices. Specifically, we found 1053that for every 1 km decrease in distance from the park, 1054housing prices increase by 1.6%. This is consistent with 1055studies by Feng and Humphreys (2012, 2018) and Yang 1056et al. (2018), which reported significant positive externali-1057ties of sports spaces on nearby housing prices.1058Spatial heterogeneity is another critical aspect of this 1059study. While previous research has noted varying degrees 1060of impact based on proximity to parks and stadiums (e.g., 1061Tu, 2005; Iqbal & Wilhelmsson, 2018; Dell’Anna et al., 10622022), our study provides a more granular analysis by 1063introducing dummy variables for different ring distance 1064ranges. This variation in the park’s effects further high-1065lights the importance of proximity in shaping the eco-1066nomic impact. Housing prices within 1 km of the park 1067experienced the most significant increases, while the ef-1068fects diminished with greater distances and became neg-1069ligible beyond 5 km. This pattern suggests that the per-1070ceived value of the park’s benefits, such as access to green 1071spaces, recreational amenities, and improved air quality, 1072is highly localized. Residents living closer to the park can 1073enjoy these benefits more conveniently, which enhances 1074the desirability of properties in the immediate vicinity. This 1075spatial gradient also reflects the challenges of ensuring 1076equitable access to urban public goods, as those living 1077further away may not experience comparable benefits.1078The DID analysis reveals that the Hangzhou Asian 1079Games Park had a substantial and dynamic impact on 1080housing prices over time. The planning announcement 1081generated strong market anticipation, as evidenced by the 1082increased impact of proximity on housing prices during 1083the post-announcement phase. The park’s opening further 1084reinforced these effects, though the short-term dynamics 1085around the opening phase suggest that much of the value 1086had already been capitalized into prices prior to its com-1087pletion. This phenomenon can be attributed to several fac-1088tors. First, the announcement of a large-scale urban sports 1089park often generates significant market expectations, as it 1090signals the government’s commitment to improving lo-1091cal infrastructure, enhancing environmental quality, and 1092providing recreational amenities. These expectations are 1093rapidly reflected in housing prices as buyers and investors 1094