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Spatial heterogeneity and interaction effect of urban blue and green spaces on housing prices

    Huilin Chen Affiliation
    ; Lihui Hu Affiliation
    ; Ziyi Liu Affiliation
    ; Bo Chen Affiliation

Abstract

Rapid urbanization presents policymakers and planners with the challenge of balancing public open spaces design with the conservation and improvement of natural resources. A comprehensive understanding of the land economic value of urban blue-green spaces (UBGS) holds immense significance for urban sustainable development, urban spatial justice and the promotion of human well-being. In this study, the MGWR model is employed to discuss the heterogeneous effects of UBGS on housing prices in Hangzhou. Additionally, the interaction effect between blue space and green space was examined at the district level, and the specific locations and spatial patterns were identified. The results show that (1) different types, features and accessibility of UBGS have different degrees and spatial scale of effect on housing prices, and will be affected by other attributes of UBGS; (2) in 30.92% of the main urban area of Hangzhou, the effect of blue spaces and green spaces on housing prices exhibits an interactive effect. The spatial patterns are divided into blue-green positive synergistic, antagonistic and negative synergistic regions; (3) green space has positive and negative effects on housing prices, while blue space has positive effects on housing prices at the regional level. The existence of water bodies can promote the positive effect of green spaces on housing prices or alleviate the negative effect. The results indicate that planners must transcend the singular focus on blue or green space planning and instead consider both in an integrated manner. This outcome can provide valuable references for UBGS planning.

Keyword : urban blue-green spaces, MGWR, hedonic price model, housing prices, interaction effect, urban planning

How to Cite
Chen, H., Hu, L., Liu, Z., & Chen, B. (2024). Spatial heterogeneity and interaction effect of urban blue and green spaces on housing prices. International Journal of Strategic Property Management, 28(5), 302–319. https://doi.org/10.3846/ijspm.2024.22232
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References

Alriksson, S., & Öberg, T. (2008). Conjoint analysis for environmental evaluation. Environmental Science and Pollution Research, 15(3), 244–257. https://doi.org/10.1065/espr2008.02.479

Anderson, S. T., & West, S. E. (2006). Open space, residential property values, and spatial context. Regional Science and Urban Economics, 36(6), 773–789. https://doi.org/10.1016/j.regsciurbeco.2006.03.007

Anselin, L. (1990). Spatial econometrics: Methods and models. Journal of the American Statistical Association, 85, 905–906. https://doi.org/10.2307/2290042

Baker, S. (2007). Sustainable development as symbolic commitment: Declaratory politics and the seductive appeal of ecological modernisation in the European Union. Environmental Politics, 16(2), 297–317. https://doi.org/10.1080/09644010701211874

Basu, S., & Thibodeau, T. G. (1998). Analysis of spatial autocorrelation in house prices. The Journal of Real Estate Finance and Economics, 17(1), 61–85. https://doi.org/10.1023/A:1007703229507

Ben, S., Zhu, H., Lu, J., & Wang, R. (2023). Valuing the accessibility of green spaces in the housing market: A spatial hedonic analysis in Shanghai, China. Land, 12(9), Article 1660. https://doi.org/10.3390/land12091660

Bin, O., Landry, C. E., & Meyer, G. F. (2009). Riparian buffers and hedonic prices: A quasi-experimental analysis of residential property values in the Neuse River Basin. American Journal of Agricultural Economics, 91(4), 1067–1079. https://doi.org/10.1111/j.1467-8276.2009.01316.x

Bitter, C., Mulligan, G., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7–27. https://doi.org/10.1007/s10109-006-0028-7

Cao, K., Diao, M., & Wu, B. (2019). A big data-based geographically weighted regression model for public housing prices: A case study in Singapore. Annals of The American Association of Geographers, 109(1), 173–186. https://doi.org/10.1080/24694452.2018.1470925

Chen, S., Zhang, L., Huang, Y., Wilson, B., Mosey, G., & Deal, B. (2022). Spatial impacts of multimodal accessibility to green spaces on housing price in Cook County, Illinois. Urban Forestry & Urban Greening, 67, Article 127370. https://doi.org/10.1016/j.ufug.2021.127370

Chen, W. Y., Li, X., & Hua, J. (2019). Environmental amenities of urban rivers and residential property values: A global meta-analysis. Science of the Total Environment, 693, Article 133628. https://doi.org/10.1016/j.scitotenv.2019.133628

Cheung, L., & Fernandez, M. A. (2021). Close enough: Housing price effects of urban parks, reserves and volcanic parks in Auckland, New Zealand. International Journal of Housing Markets and Analysis, 14(5), 987–1003. https://doi.org/10.1108/IJHMA-05-2020-0064

Chin, T. L., & Chau, K. W. (2003). A critical review of literature on the hedonic price model. International Journal for Housing Science & Its Applications, 27(2), 145–165.

China State Council. (2019, January 2). Reply of the State Council on the Master Plan for Xiongan New Area of Hebei (2018-2035). https://www.gov.cn/zhengce/zhengceku/2019-01/02/content_5354222.htm

Crompton, J. L., & Nicholls, S. (2020). Impact on property values of distance to parks and open spaces: An update of U.S. studies in the new millennium. Journal of Leisure Research, 51(2), 127–146. https://doi.org/10.1080/00222216.2019.1637704

Dai, X., Felsenstein, D., & Grinberger, A. Y. (2023). Viewshed effects and house prices: Identifying the visibility value of the natural landscape. Landscape and Urban Planning, 238, Article 104818. https://doi.org/10.1016/j.landurbplan.2023.104818

Dell’Anna, F., Bravi, M., & Bottero, M. (2022). Urban Green infrastructures: How much did they affect property prices in Singapore? Urban Forestry & Urban Greening, 68, Article 127475. https://doi.org/10.1016/j.ufug.2022.127475

Fotheringham, A., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of The American Association of Geographers, 107(6), 1247–1265. https://doi.org/10.1080/24694452.2017.1352480

Fotheringham, A., Yu, H., Wolf, L., Oshan, T., & Li, Z. (2022). On the notion of ‘bandwidth’ in geographically weighted regression models of spatially varying processes. International Journal of Geographical Information Science, 36(8), 1485–1502. https://doi.org/10.1080/13658816.2022.2034829

Freeman III, A. M., Herriges, J. A., & Kling, C. L. (2014). The measurement of environmental and resource values: Theory and methods. Routledge. https://doi.org/10.4324/9781315780917

Ghofrani, Z., Sposito, V., & Faggian, R. (2017). A comprehensive review of blue-green infrastructure concepts. International Journal of Environment and Sustainability, 6(1), 15–36. https://doi.org/10.24102/ijes.v6i1.728

Gibbons, S., Mourato, S., & Resende, G. (2014). The amenity value of English nature: A hedonic price approach. Environmental & Resource Economics, 57(2), 175–196. https://doi.org/10.1007/s10640-013-9664-9

Gould, K., & Lewis, T. (2016). Green gentrification: Urban sustainability and the struggle for environmental justice. Routledge. https://doi.org/10.4324/9781315687322

Gu, J., Wang, X., & Liu, G. (2021). Rediscovering the amenity value of urban landscapes in the mountainous areas with high-rise buildings from the perspective of 3D vertical urban systems. Urban Forestry & Urban Greening, 60, Article 127018. https://doi.org/10.1016/j.ufug.2021.127018

Hangzhou Bureau of Statistics. (2021). Hangzhou statistical yearbook. Hangzhou Bureau of Statistics.

Hangzhou Municipal Government. (2024, January 15). Demographic overview of Hangzhou. The People’s Government of Zhejiang Province. https://www.hangzhou.gov.cn/col/col1229144823/index.html

Hou, H., & Estoque, R. C. (2020). Detecting cooling effect of landscape from composition and configuration: An urban heat island study on Hangzhou. Urban Forestry & Urban Greening, 53, Article 126719. https://doi.org/10.1016/j.ufug.2020.126719

Hu, L., & Li, Q. (2020). Greenspace, bluespace, and their interactive influence on urban thermal environments. Environmental Research Letters, 15(3), Article 034041. https://doi.org/10.1088/1748-9326/ab6c30

Huang, B., Feng, Z., Pan, Z., & Liu, Y. (2022). Amount of and proximity to blue spaces and general health among older Chinese adults in private and public housing: A national population study. Health & Place, 74, Article 102774. https://doi.org/10.1016/j.healthplace.2022.102774

Immergluck, D. (2009). Large redevelopment initiatives, housing values and gentrification: The case of the Atlanta Beltline. Urban Studies, 46(8), 1723–1745. https://doi.org/10.1177/0042098009105500

Irwin, E. G. (2002). The effects of open space on residential property values. Land Economics, 78(4), 465–480. https://doi.org/10.2307/3146847

Jennings, V., & Bamkole, O. (2019). The relationship between social cohesion and urban green space: An avenue for health promotion. International Journal of Environmental Research and Public Health, 16(3), Article 452. https://doi.org/10.3390/ijerph16030452

Jiao, L., & Liu, Y. (2010). Geographic field model based hedonic valuation of urban open spaces in Wuhan, China. Landscape and Urban Planning, 98(1), 47–55. https://doi.org/10.1016/j.landurbplan.2010.07.009

Jiao, X., Zhao, Z., Li, X., Wang, Z., & Zhang, Y. (2023). Advances in the blue-green space evaluation index system. Ecohydrology, 16(3), e2527. https://doi.org/10.1002/eco.2527

Kovacs, K. F. (2012). Integrating property value and local recreation models to value ecosystem services from regional parks. Landscape and Urban Planning, 108(2), 79–90. https://doi.org/10.1016/j.landurbplan.2012.08.002

Lamond, J., & Everett, G. (2019). Sustainable blue-green infrastructure: A social practice approach to understanding community preferences and stewardship. Landscape and Urban Planning, 191, Article 103639. https://doi.org/10.1016/j.landurbplan.2019.103639

Larson, E. K., & Perrings, C. (2013). The value of water-related amenities in an arid city: The case of the Phoenix metropolitan area. Special Issue: Urban Ecosystem Services, 109(1), 45–55. https://doi.org/10.1016/j.landurbplan.2012.10.008

Li, X., Chen, W. Y., Hu, F. Z. Y., & Cho, F. H. T. (2021). Homebuyers’ heterogeneous preferences for urban green–blue spaces: A spatial multilevel autoregressive analysis. Landscape and Urban Planning, 216, Article 104250. https://doi.org/10.1016/j.landurbplan.2021.104250

Li, Z., & Fotheringham, A. (2020). Computational improvements to multi-scale geographically weighted regression. International Journal of Geographical Information Science, 34(7), 1378–1397. https://doi.org/10.1080/13658816.2020.1720692

Lianjia. (n.d.). Hangzhou Lianjia. Retrieved December 6, 2022, from https://hz.lianjia.com/

Liang, J., Yang, R., Liu, M., Pu, Y., Bao, W., Zhao, Y., Hu, L., Zhang, Y., Huang, S., Jiang, N., Pu, X., Huang, S., Dong, G., & Chen, Y. (2024). Urban green, blue spaces and their joint effect are associated with lower risk of emotional and behavior problem in children and adolescents, a large population-based study in Guangzhou, China. Environmental Research, 240, Article 117475. https://doi.org/10.1016/j.envres.2023.117475

Lim, H., Kim, J., Potter, C., & Bae, W. (2013). Urban regeneration and gentrification: Land use impacts of the Cheonggye Stream Restoration Project on the Seoul’s central business district. Habitat International, 39, 192–200. https://doi.org/10.1016/j.habitatint.2012.12.004

Liu, G., Wang, X., Gu, J., Liu, Y., & Zhou, T. (2019). Temporal and spatial effects of a ‘Shan Shui’ landscape on housing price: A case study of Chongqing, China. Habitat International, 94, Article 102068. https://doi.org/10.1016/j.habitatint.2019.102068

Liu, N., & Strobl, J. (2023). Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model. Big Earth Data, 7(1), 156–179. https://doi.org/10.1080/20964471.2022.2031543

Liu, T., Hu, W., Song, Y., & Zhang, A. (2020). Exploring spillover effects of ecological lands: A spatial multilevel hedonic price model of the housing market in Wuhan, China. Ecological Economics, 170, Article 106568. https://doi.org/10.1016/j.ecolecon.2019.106568

Liu, Z., Ma, X., Hu, L., Liu, Y., Lu, S., Chen, H., & Tan, Z. (2022). Nonlinear cooling effect of street green space morphology: Evidence from a gradient boosting decision tree and explainable machine learning approach. Land, 11(12), Article 2220. https://doi.org/10.3390/land11122220

Lu, B., Charlton, M., Harris, P., & Fotheringham, A. S. (2014). Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. International Journal of Geographical Information Science, 28(4), 660–681. https://doi.org/10.1080/13658816.2013.865739

Lu, B., Ge, Y., Shi, Y., Zheng, J., & Harris, P. (2023). Uncovering drivers of community-level house price dynamics through multiscale geographically weighted regression: A case study of Wuhan, China. Spatial Statistics, 53, Article 100723. https://doi.org/10.1016/j.spasta.2022.100723

Lundy, L., & Wade, R. (2011). Integrating sciences to sustain urban ecosystem services. Progress in Physical Geography-Earth and Environment, 35(5), 653–669. https://doi.org/10.1177/0309133311422464

Lutzenhiser, M., & Netusil, N. R. (2001). The effect of open spaces on a home’s sale price. Contemporary Economic Policy, 19(3), 291–298. https://doi.org/10.1093/cep/19.3.291

McMillen, D. (2004). Geographically weighted regression: The analysis of spatially varying relationships. American Journal of Agricultural Economics, 86, 554–556. https://doi.org/10.1111/j.0002-9092.2004.600_2.x

Ministry of Natural Resources. (2019, June 2). Notice of the Ministry of Natural Resources on comprehensively carrying out the work of territorial spatial planning. https://www.gov.cn/xinwen/2019-06/02/content_5396857.htm

Ministry of Natural Resources of the People’s Republic of China. (2021, July 1). Spatial planning guidance to community life unit (TD/T 1062-2021). http://www.nrsis.org.cn/portal/stdDetail/240432

Moore, M., Doubek, J., Xu, H., & Cardinale, B. (2020). Hedonic price estimates of lake water quality: Valued attribute, instrumental variables, and ecological-economic benefits. Ecological Economics, 176, Article 106692. https://doi.org/10.1016/j.ecolecon.2020.106692

Morancho, A. B. (2003). A hedonic valuation of urban green areas. Landscape and Urban Planning, 66(1), 35–41. https://doi.org/10.1016/S0169-2046(03)00093-8

Musterd, S., van Gent, W. P., Das, M., & Latten, J. (2016). Adaptive behaviour in urban space: Residential mobility in response to social distance. Urban Studies, 53(2), 227–246. https://doi.org/10.1177/0042098014562344

Neumann, B. C., Boyle, K. J., & Bell, K. P. (2009). Property price effects of a national wildlife refuge: Great Meadows National Wildlife Refuge in Massachusetts. Land Use Policy, 26(4), 1011–1019. https://doi.org/10.1016/j.landusepol.2008.12.008

Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). MGWR: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), Article 269. https://doi.org/10.3390/ijgi8060269

Owens, A. (2019). Building inequality: Housing segregation and income segregation. Sociological Science, 6(19), 497–525. https://doi.org/10.15195/v6.a19

Panduro, T., & Veie, K. (2013). Classification and valuation of urban green spaces-A hedonic house price valuation. Landscape and Urban Planning, 120, 119–128. https://doi.org/10.1016/j.landurbplan.2013.08.009

Peng, C., Xiang, Y., Chen, L., Zhang, Y., & Zhou, Z. (2023). The impact of the type and abundance of urban blue space on house prices: A case study of eight megacities in China. Land, 12(4), Article 865. https://doi.org/10.3390/land12040865

Potter, J. D., Brooks, C., Donovan, G., Cunningham, C., & Douwes, J. (2023). A perspective on green, blue, and grey spaces, biodiversity, microbiota, and human health. Science of the Total Environment, 892, Article 164772. https://doi.org/10.1016/j.scitotenv.2023.164772

Reynaud, A., & Lanzanova, D. (2017). A global meta-analysis of the value of ecosystem services provided by lakes. Ecological Economics, 137, 184–194. https://doi.org/10.1016/j.ecolecon.2017.03.001

Sander, H. A., & Zhao, C. (2015). Urban green and blue: Who values what and where? Land Use Policy, 42, 194–209. https://doi.org/10.1016/j.landusepol.2014.07.021

Shi, D., Song, J., Huang, J., Zhuang, C., Guo, R., & Gao, Y. (2020). Synergistic cooling effects (SCEs) of urban green-blue spaces on local thermal environment: A case study in Chongqing, China. Sustainable Cities and Society, 55, Article 102065. https://doi.org/10.1016/j.scs.2020.102065

Steingröver, E., Geertsema, W., & van Wingerden, W. (2010). Designing agricultural landscapes for natural pest control: A transdisciplinary approach in the Hoeksche Waard (The Netherlands). Landscape Ecology, 25(6), 825–838. https://doi.org/10.1007/s10980-010-9489-7

Tajima, K. (2003). New estimates of the demand for urban green space: Implications for valuing the environmental benefits of Boston’s big dig project. Journal of Urban Affairs, 25(5), 641–655.

Tapsuwan, S., Ingram, G., Burton, M., & Brennan, D. (2009). Capitalized amenity value of urban wetlands: A hedonic property price approach to urban wetlands in Perth, Western Australia. Australian Journal of Agricultural and Resource Economics, 53(4), 527–545. https://doi.org/10.1111/j.1467-8489.2009.00464.x

Troy, A., & Grove, J. (2008). Property values, parks, and crime: A hedonic analysis in Baltimore, MD. Landscape and Urban Planning, 87(3), 233–245. https://doi.org/10.1016/j.landurbplan.2008.06.005

Tuofu, H., Qingyun, H., Dongxiao, Y., & Xiao, O. (2021). Evaluating the impact of urban blue space accessibility on housing price: A spatial quantile regression approach applied in Changsha, China. Frontiers in Environmental Science, 9, Article 696626. https://doi.org/10.3389/fenvs.2021.696626

Tyrväinen, L., & Miettinen, A. (2000). Property prices and urban forest amenities. Journal of Environmental Economics and Management, 39(2), 205–223. https://doi.org/10.1006/jeem.1999.1097

Voskamp, I., & Van de Ven, F. (2015). Planning support system for climate adaptation: Composing effective sets of blue-green measures to reduce urban vulnerability to extreme weather events. Building and Environment, 83, 159–167. https://doi.org/10.1016/j.buildenv.2014.07.018

Walsh, P., Milon, J., & Scrogin, D. (2011). The spatial extent of water quality benefits in urban housing markets. Land Economics, 87(4), 628–644. https://doi.org/10.3368/le.87.4.628

Wang, R., Yang, B., Yao, Y., Bloom, M. S., Feng, Z., Yuan, Y., Zhang, J., Liu, P., Wu, W., Lu, Y., Baranyi, G., Wu, R., Liu, Y., & Dong, G. (2020). Residential greenness, air pollution and psychological well-being among urban residents in Guangzhou, China. Science of the Total Environment, 711, Article 134843. https://doi.org/10.1016/j.scitotenv.2019.134843

Wen, H., Bu, X., & Qin, Z. (2014). Spatial effect of lake landscape on housing price: A case study of the West Lake in Hangzhou, China. Habitat International, 44, 31–40. https://doi.org/10.1016/j.habitatint.2014.05.001

Wen, H., & Jia, S. H. (2004). Housing characteristics and hedonic price: Based on hedonic price model. Journal of Zhejiang University (Engineering Science), 38, 38–42.

Wen, H., Jin, Y., & Zhang, L. (2017). Spatial heterogeneity in implicit housing prices: Evidence from Hangzhou, China. International Journal of Strategic Property Management, 21(1), 15–28. https://doi.org/10.3846/1648715X.2016.1247021

Wen, H., Zhang, Y., & Zhang, L. (2015). Assessing amenity effects of urban landscapes on housing price in Hangzhou, China. Urban Forestry & Urban Greening, 14(4), 1017–1026. https://doi.org/10.1016/j.ufug.2015.09.013

Wolch, J. R., Byrne, J., & Newell, J. (2014). Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’. Landscape and Urban Planning, 125, 234–244. https://doi.org/10.1016/j.landurbplan.2014.01.017

Wolf, L. J., Oshan, T. M., & Fotheringham, A. S. (2018). Single and multiscale models of process spatial heterogeneity. Geographical Analysis, 50, 223–246. https://doi.org/10.1111/gean.12147

Wu, C., Ye, X., Du, Q., & Luo, P. (2017). Spatial effects of accessibility to parks on housing prices in Shenzhen, China. Habitat International, 63, 45–54. https://doi.org/10.1016/j.habitatint.2017.03.010

Wu, C., Ye, X., Ren, F., Wan, Y., Ning, P., & Du, Q. (2016). Spatial and social media data analytics of housing prices in Shenzhen, China. PLoS ONE, 11(10), Article e0164553. https://doi.org/10.1371/journal.pone.0164553

Wu, L., & Rowe, P. G. (2022). Green space progress or paradox: Identifying green space associated gentrification in Beijing. Landscape and Urban Planning, 219, Article 104321. https://doi.org/10.1016/j.landurbplan.2021.104321

Xiao, Y., Hui, E., & Wen, H. (2019). Effects of floor level and landscape proximity on housing price: A hedonic analysis in Hangzhou, China. Habitat International, 87, 11–26. https://doi.org/10.1016/j.habitatint.2019.03.008

Xiao, Y., Lu, Y., Guo, Y., & Yuan, Y. (2017). Estimating the willingness to pay for green space services in Shanghai: Implications for social equity in urban China. Urban Forestry & Urban Greening, 26, 95–103. https://doi.org/10.1016/j.ufug.2017.06.007

Yu, H., Fotheringham, A., Li, Z., Oshan, T., Kang, W., & Wolf, L. (2020). Inference in multiscale geographically weighted regression. Geographical Analysis, 52(1), 87–106. https://doi.org/10.1111/gean.12189

Yuan, Y., Zhang, J., Tang, S., & Guo, W. (2023). Progress and review of research on urban blue-green space based on bibliometric analysis. Landscape Architecture Academic Journal, 40(4), 59–67. https://doi.org/10.12193/j.laing.2023.04.0059.008

Zhang, Y., Zhang, T., Zeng, Y., Yu, C., & Zheng, S. (2021). The rising and heterogeneous demand for urban green space by Chinese urban residents: Evidence from Beijing. Journal of Cleaner Production, 313, Article 127781. https://doi.org/10.1016/j.jclepro.2021.127781