Capitalization effects of rivers in urban housing submarkets – A case study of the Yangtze River
Abstract
The study aims to investigate the heterogeneity of the Yangtze River’s impact on housing prices, using the data of 12,325 residential transactions within 8 kilometers of the Yangtze River in Wuhan, based on submarkets divided according to geographical location and buyer groups. The kernel density plots reveal that properties near the Yangtze River have the highest price and the lowest density, while properties further away from the river exhibit the opposite trend. Then the Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model show the following results, respectively: (1) The Yangtze River has an influence range of roughly 5 kilometers on adjacent dwellings, with an average impact of 0.035%. However, within the chosen geographical interval, the impact rises from 1.582% to 2.072%. (2) The Yangtze River has the greatest impact on middle-priced houses, followed by high-priced houses, and the least impact on low-priced houses. (3) The Spatial Generalized Additive Model and the Spatial Quantile Generalized Additive Model have been proven to be effective at capturing spatial and temporal impacts on data. In conclusion, this article advises that the government should pay more attention to non-central locations with limited natural resources.
Keyword : ecological landscape, hedonic price method, housing submarket, the spatial generalized additive model, the spatial quantile generalized additive model
How to Cite
Ke, X., Yang, C., Zheng, M., Amal, M., & Zeng, Y. (2024). Capitalization effects of rivers in urban housing submarkets – A case study of the Yangtze River. International Journal of Strategic Property Management, 28(2), 76–92. https://doi.org/10.3846/ijspm.2024.21184
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Alas, B. (2020). A multilevel analysis of housing submarkets defined by the municipal boundaries and by the street connections in the metropolitan area: Istanbul. Journal of Housing and the Built Environment, 35(4), 1201–1217. https://doi.org/10.1007/s10901-020-09735-7
Bangura, M., & Lee, C. L. (2023). The determinants of homeownership affordability in Greater Sydney: Evidence from a submarket analysis. Housing Studies, 38(2), 206–232. https://doi.org/10.1080/02673037.2021.1879995
Bohman, H. (2021). Same, same but different? Neighbourhood effects of accessibility on housing prices. Transport Policy, 107, 52–60. https://doi.org/10.1016/j.tranpol.2021.04.016
Bonetti, F., Corsi, S., Orsi, L., & De Noni, I. (2016). Canals vs. streams: To what extent do water quality and proximity affect real estate values? A hedonic approach analysis. Water, 8(12), Article 12. https://doi.org/10.3390/w8120577
Chen, G., Zhu, D., Su, Y., & Zhang, L. (2015). The effects of large-scale urban park green spaces on residential prices exemplified by Olympic Forest Park in Beijing. Resources Science, 37(11), 2202–2210.
Chen, K. D., Lin, H. M., You, S. Y., & Han, Y. (2022). Review of the impact of urban parks and green spaces on residence prices in the environmental health context. Frontiers in Public Health, 10, Article 993801. https://doi.org/10.3389/fpubh.2022.993801
Cho, S.-H., Roberts, R. K., & Kim, S. G. (2011). Negative externalities on property values resulting from water impairment: The case of the Pigeon River Watershed. Ecological Economics, 70(12), 2390–2399. https://doi.org/10.1016/j.ecolecon.2011.07.021
Chwiałkowski, C., & Zydroń, A. (2022). The impact of urban public transport on residential transaction prices: A case study of Poznań, Poland. ISPRS International Journal of Geo-Information, 11(2), Article 74. https://doi.org/10.3390/ijgi11020074
Coulson, N. E., & McMillen, D. P. (2007). The dynamics of intraurban quantile house price indexes. Urban Studies, 44(8), 1517–1537. https://doi.org/10.1080/00420980701373446
Elhorst, J. P. (Ed.). (2014). Linear spatial dependence models for cross-section data. In Spatial econometrics: From cross-sectional data to spatial panels (pp. 5–36). Springer. https://doi.org/10.1007/978-3-642-40340-8_2
Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., & Goude, Y. (2021). Fast calibrated additive quantile regression. Journal of the American Statistical Association, 116(535), 1402–1412. https://doi.org/10.1080/01621459.2020.1725521
Fernandez, M. A., & Bucaram, S. (2019). The changing face of environmental amenities: Heterogeneity across housing submarkets and time. Land Use Policy, 83, 449–460. https://doi.org/10.1016/j.landusepol.2019.02.024
Geng, X., Yu, Z., Zhang, D., Li, C., Yuan, Y., & Wang, X. (2022). The influence of local background climate on the dominant factors and threshold-size of the cooling effect of urban parks. Science of the Total Environment, 823, Article 153806. https://doi.org/10.1016/j.scitotenv.2022.153806
Grainger, C. A. (2012). The distributional effects of pollution regulations: Do renters fully pay for cleaner air? Journal of Public Economics, 96(9), 840–852. https://doi.org/10.1016/j.jpubeco.2012.06.006
Gu, Z. N., Luo, X. L., Tang, M., & Liu, X. M. (2023). Does the edge effect impact the healthcare equity? An examination of the equity in hospitals accessibility in the edge city in multi-scale. Journal of Transport Geography, 106, Article 103513. https://doi.org/10.1016/j.jtrangeo.2022.103513
Huang, T., He, Q., Yang, D., & Ouyang, X. (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://www.frontiersin.org/articles/10.3389/fenvs.2021.696626
Jia, J., & Zhang, X. (2021). A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan. Journal of Cleaner Production, 316, Article 128321. https://doi.org/10.1016/j.jclepro.2021.128321
Lamond, J., Proverbs, D., & Hammond, F. (2010). The impact of flooding on the price of residential property: A transactional analysis of the UK market. Housing Studies, 25(3), 335–356. https://doi.org/10.1080/02673031003711543
Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74(2), 132–157. https://doi.org/10.1086/259131
Łaszkiewicz, E., Heyman, A., Chen, X., Cimburova, Z., Nowell, M., & Barton, D. (2022). Valuing access to urban greenspace using non-linear distance decay in hedonic property pricing. Ecosystem Services, 53, Article 101394. https://doi.org/10.1016/j.ecoser.2021.101394
Le Boennec, R., Bulteau, J., & Feuillet, T. (2022). The role of commuter rail accessibility in the formation of residential land values: Exploring spatial heterogeneity in peri-urban and remote areas. The Annals of Regional Science, 69(1), 163–186. https://doi.org/10.1007/s00168-022-01113-1
Lee, H., Lee, B., & Lee, S. (2020). The unequal impact of natural landscape views on housing prices: Applying visual perception model and quantile regression to apartments in Seoul. Sustainability, 12(19), Article 8275. https://doi.org/10.3390/su12198275
Liebelt, V., Bartke, S., & Schwarz, N. (2019). Urban green spaces and housing prices: An alternative perspective. Sustainability, 11(13), Article 3707. https://doi.org/10.3390/su11133707
Mittal, J., & Byahut, S. (2019). Scenic landscapes, visual accessibility and premium values in a single family housing market: A spatial hedonic approach. Environment and Planning B: Urban Analytics and City Science, 46(1), 66–83. https://doi.org/10.1177/2399808317702147
Nutsford, D., Pearson, A. L., Kingham, S., & Reitsma, F. (2016). Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city. Health & Place, 39, 70–78. https://doi.org/10.1016/j.healthplace.2016.03.002
Olszewski, K., Waszczuk, J., & Widłak, M. (2017). Spatial and hedonic analysis of house price dynamics in Warsaw, Poland. Journal of Urban Planning and Development, 143(3), Article 04017009. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000394
Panduro, T. E., & Veie, K. L. (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
Piaggio, M. (2021). The value of public urban green spaces: Measuring the effects of proximity to and size of urban green spaces on housing market values in San José, Costa Rica. Land Use Policy, 109, Article 105656. https://doi.org/10.1016/j.landusepol.2021.105656
Potrawa, T., & Tetereva, A. (2022). How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market. Journal of Business Research, 144, 50–65. https://doi.org/10.1016/j.jbusres.2022.01.027
Rajapaksa, D., Wilson, C., Hoang, V.-N., Lee, B., & Managi, S. (2017). Who responds more to environmental amenities and dis-amenities? Land Use Policy, 62, 151–158. https://doi.org/10.1016/j.landusepol.2016.12.029
Ridker, R. G., & Henning, J. A. (1967). The determinants of residential property values with special reference to air pollution. The Review of Economics and Statistics, 49(2), 246–257. https://doi.org/10.2307/1928231
Rivas Casado, M., Serafini, J., Glen, J., & Angus, A. (2017). Monetising the impacts of waste incinerators sited on brownfield land using the hedonic pricing method. Waste Management, 61, 608–616. https://doi.org/10.1016/j.wasman.2016.10.036
Shao, J., Zhou, Y., Luo, H., Wang, J., & Zhang, Q. (2023). Comparative analysis of visual amenity services valuation: A nationwide assessment through propensity scoring matching and hedonic regression. Journal of Environmental Management, 325, Article 116564. https://doi.org/10.1016/j.jenvman.2022.116564
Shehata, W., Abu Arqoub, M., Langston, C., Elkheshien, R., & Sarvimaki, M. (2021). From hard bed to luxury home: Impacts of reusing HM Prison Pentridge on property values. Journal of Housing and the Built Environment, 36(2), 627–643. https://doi.org/10.1007/s10901-020-09766-0
Shi, L., Chen, B., Chen, X., & Chen, Z. (2022). Assessing the impact of wildfires on property values in wildland-urban intermix and interface in Colorado: A hedonic approach. Journal of Environmental Management, 319, Article 115672. https://doi.org/10.1016/j.jenvman.2022.115672
Tawfeeq Najah, F., Fakhri Khalaf Abdullah, S., & Ameen Abdulkareem, T. (2023). Urban land use changes: Effect of green urban spaces transformation on urban heat islands in Baghdad. Alexandria Engineering Journal, 66, 555–571. https://doi.org/10.1016/j.aej.2022.11.005
Tiebout, C. M. (1956). A pure theory of local expenditures. Journal of Political Economy, 64(5), 416–424. https://doi.org/10.1086/257839
Wang, J., & Lee, C. L. (2022). The value of air quality in housing markets: A comparative study of housing sale and rental markets in China. Energy Policy, 160, Article 112601. https://doi.org/10.1016/j.enpol.2021.112601
Wen, H. Z., Xiao, Y., & Zhang, L. (2017). Spatial effect of river landscape on housing price: An empirical study on the Grand Canal in Hangzhou, China. Habitat International, 63, 34–44. https://doi.org/10.1016/j.habitatint.2017.03.007
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., Li, S., Hui, E. C. M., Jia, S., & Cui, W. (2021). Purchase motivation, landscape preference, and housing prices: Quantile hedonic analysis in Guangzhou, China. Journal of Urban Planning and Development, 147(3), Article 04021033. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000734
Wen, H., Xiao, Y., & Hui, E. C. M. (2019). Quantile effect of educational facilities on housing price: Do homebuyers of higher-priced housing pay more for educational resources? Cities, 90, 100–112. https://doi.org/10.1016/j.cities.2019.01.019
Worku, G. B. (2017). House price drivers in Dubai: Nonlinearity and heterogeneity. International Journal of Housing Markets and Analysis, 10(3), 384–409. https://doi.org/10.1108/IJHMA-06-2016-0048
Wu, J., Wang, M., Li, W., Peng, J., & Huang, L. (2015). Impact of urban green space on residential housing prices: Case study in Shenzhen. Journal of Urban Planning and Development, 141(4), Article 05014023. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000241
Xiao, Y., Hui, E. C. M., & 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
Xu, T., & Zhang, M. (2016). Influences of regional development conditions on the railway transit access premium: Evidence from Wuhan City. Urban Problems, 9, 48–57.
Zemo, K. H., Panduro, T. E., & Termansen, M. (2019). Impact of biogas plants on rural residential property values and implications for local acceptance. Energy Policy, 129, 1121–1131. https://doi.org/10.1016/j.enpol.2019.03.008
Bangura, M., & Lee, C. L. (2023). The determinants of homeownership affordability in Greater Sydney: Evidence from a submarket analysis. Housing Studies, 38(2), 206–232. https://doi.org/10.1080/02673037.2021.1879995
Bohman, H. (2021). Same, same but different? Neighbourhood effects of accessibility on housing prices. Transport Policy, 107, 52–60. https://doi.org/10.1016/j.tranpol.2021.04.016
Bonetti, F., Corsi, S., Orsi, L., & De Noni, I. (2016). Canals vs. streams: To what extent do water quality and proximity affect real estate values? A hedonic approach analysis. Water, 8(12), Article 12. https://doi.org/10.3390/w8120577
Chen, G., Zhu, D., Su, Y., & Zhang, L. (2015). The effects of large-scale urban park green spaces on residential prices exemplified by Olympic Forest Park in Beijing. Resources Science, 37(11), 2202–2210.
Chen, K. D., Lin, H. M., You, S. Y., & Han, Y. (2022). Review of the impact of urban parks and green spaces on residence prices in the environmental health context. Frontiers in Public Health, 10, Article 993801. https://doi.org/10.3389/fpubh.2022.993801
Cho, S.-H., Roberts, R. K., & Kim, S. G. (2011). Negative externalities on property values resulting from water impairment: The case of the Pigeon River Watershed. Ecological Economics, 70(12), 2390–2399. https://doi.org/10.1016/j.ecolecon.2011.07.021
Chwiałkowski, C., & Zydroń, A. (2022). The impact of urban public transport on residential transaction prices: A case study of Poznań, Poland. ISPRS International Journal of Geo-Information, 11(2), Article 74. https://doi.org/10.3390/ijgi11020074
Coulson, N. E., & McMillen, D. P. (2007). The dynamics of intraurban quantile house price indexes. Urban Studies, 44(8), 1517–1537. https://doi.org/10.1080/00420980701373446
Elhorst, J. P. (Ed.). (2014). Linear spatial dependence models for cross-section data. In Spatial econometrics: From cross-sectional data to spatial panels (pp. 5–36). Springer. https://doi.org/10.1007/978-3-642-40340-8_2
Fasiolo, M., Wood, S. N., Zaffran, M., Nedellec, R., & Goude, Y. (2021). Fast calibrated additive quantile regression. Journal of the American Statistical Association, 116(535), 1402–1412. https://doi.org/10.1080/01621459.2020.1725521
Fernandez, M. A., & Bucaram, S. (2019). The changing face of environmental amenities: Heterogeneity across housing submarkets and time. Land Use Policy, 83, 449–460. https://doi.org/10.1016/j.landusepol.2019.02.024
Geng, X., Yu, Z., Zhang, D., Li, C., Yuan, Y., & Wang, X. (2022). The influence of local background climate on the dominant factors and threshold-size of the cooling effect of urban parks. Science of the Total Environment, 823, Article 153806. https://doi.org/10.1016/j.scitotenv.2022.153806
Grainger, C. A. (2012). The distributional effects of pollution regulations: Do renters fully pay for cleaner air? Journal of Public Economics, 96(9), 840–852. https://doi.org/10.1016/j.jpubeco.2012.06.006
Gu, Z. N., Luo, X. L., Tang, M., & Liu, X. M. (2023). Does the edge effect impact the healthcare equity? An examination of the equity in hospitals accessibility in the edge city in multi-scale. Journal of Transport Geography, 106, Article 103513. https://doi.org/10.1016/j.jtrangeo.2022.103513
Huang, T., He, Q., Yang, D., & Ouyang, X. (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://www.frontiersin.org/articles/10.3389/fenvs.2021.696626
Jia, J., & Zhang, X. (2021). A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan. Journal of Cleaner Production, 316, Article 128321. https://doi.org/10.1016/j.jclepro.2021.128321
Lamond, J., Proverbs, D., & Hammond, F. (2010). The impact of flooding on the price of residential property: A transactional analysis of the UK market. Housing Studies, 25(3), 335–356. https://doi.org/10.1080/02673031003711543
Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political Economy, 74(2), 132–157. https://doi.org/10.1086/259131
Łaszkiewicz, E., Heyman, A., Chen, X., Cimburova, Z., Nowell, M., & Barton, D. (2022). Valuing access to urban greenspace using non-linear distance decay in hedonic property pricing. Ecosystem Services, 53, Article 101394. https://doi.org/10.1016/j.ecoser.2021.101394
Le Boennec, R., Bulteau, J., & Feuillet, T. (2022). The role of commuter rail accessibility in the formation of residential land values: Exploring spatial heterogeneity in peri-urban and remote areas. The Annals of Regional Science, 69(1), 163–186. https://doi.org/10.1007/s00168-022-01113-1
Lee, H., Lee, B., & Lee, S. (2020). The unequal impact of natural landscape views on housing prices: Applying visual perception model and quantile regression to apartments in Seoul. Sustainability, 12(19), Article 8275. https://doi.org/10.3390/su12198275
Liebelt, V., Bartke, S., & Schwarz, N. (2019). Urban green spaces and housing prices: An alternative perspective. Sustainability, 11(13), Article 3707. https://doi.org/10.3390/su11133707
Mittal, J., & Byahut, S. (2019). Scenic landscapes, visual accessibility and premium values in a single family housing market: A spatial hedonic approach. Environment and Planning B: Urban Analytics and City Science, 46(1), 66–83. https://doi.org/10.1177/2399808317702147
Nutsford, D., Pearson, A. L., Kingham, S., & Reitsma, F. (2016). Residential exposure to visible blue space (but not green space) associated with lower psychological distress in a capital city. Health & Place, 39, 70–78. https://doi.org/10.1016/j.healthplace.2016.03.002
Olszewski, K., Waszczuk, J., & Widłak, M. (2017). Spatial and hedonic analysis of house price dynamics in Warsaw, Poland. Journal of Urban Planning and Development, 143(3), Article 04017009. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000394
Panduro, T. E., & Veie, K. L. (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
Piaggio, M. (2021). The value of public urban green spaces: Measuring the effects of proximity to and size of urban green spaces on housing market values in San José, Costa Rica. Land Use Policy, 109, Article 105656. https://doi.org/10.1016/j.landusepol.2021.105656
Potrawa, T., & Tetereva, A. (2022). How much is the view from the window worth? Machine learning-driven hedonic pricing model of the real estate market. Journal of Business Research, 144, 50–65. https://doi.org/10.1016/j.jbusres.2022.01.027
Rajapaksa, D., Wilson, C., Hoang, V.-N., Lee, B., & Managi, S. (2017). Who responds more to environmental amenities and dis-amenities? Land Use Policy, 62, 151–158. https://doi.org/10.1016/j.landusepol.2016.12.029
Ridker, R. G., & Henning, J. A. (1967). The determinants of residential property values with special reference to air pollution. The Review of Economics and Statistics, 49(2), 246–257. https://doi.org/10.2307/1928231
Rivas Casado, M., Serafini, J., Glen, J., & Angus, A. (2017). Monetising the impacts of waste incinerators sited on brownfield land using the hedonic pricing method. Waste Management, 61, 608–616. https://doi.org/10.1016/j.wasman.2016.10.036
Shao, J., Zhou, Y., Luo, H., Wang, J., & Zhang, Q. (2023). Comparative analysis of visual amenity services valuation: A nationwide assessment through propensity scoring matching and hedonic regression. Journal of Environmental Management, 325, Article 116564. https://doi.org/10.1016/j.jenvman.2022.116564
Shehata, W., Abu Arqoub, M., Langston, C., Elkheshien, R., & Sarvimaki, M. (2021). From hard bed to luxury home: Impacts of reusing HM Prison Pentridge on property values. Journal of Housing and the Built Environment, 36(2), 627–643. https://doi.org/10.1007/s10901-020-09766-0
Shi, L., Chen, B., Chen, X., & Chen, Z. (2022). Assessing the impact of wildfires on property values in wildland-urban intermix and interface in Colorado: A hedonic approach. Journal of Environmental Management, 319, Article 115672. https://doi.org/10.1016/j.jenvman.2022.115672
Tawfeeq Najah, F., Fakhri Khalaf Abdullah, S., & Ameen Abdulkareem, T. (2023). Urban land use changes: Effect of green urban spaces transformation on urban heat islands in Baghdad. Alexandria Engineering Journal, 66, 555–571. https://doi.org/10.1016/j.aej.2022.11.005
Tiebout, C. M. (1956). A pure theory of local expenditures. Journal of Political Economy, 64(5), 416–424. https://doi.org/10.1086/257839
Wang, J., & Lee, C. L. (2022). The value of air quality in housing markets: A comparative study of housing sale and rental markets in China. Energy Policy, 160, Article 112601. https://doi.org/10.1016/j.enpol.2021.112601
Wen, H. Z., Xiao, Y., & Zhang, L. (2017). Spatial effect of river landscape on housing price: An empirical study on the Grand Canal in Hangzhou, China. Habitat International, 63, 34–44. https://doi.org/10.1016/j.habitatint.2017.03.007
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., Li, S., Hui, E. C. M., Jia, S., & Cui, W. (2021). Purchase motivation, landscape preference, and housing prices: Quantile hedonic analysis in Guangzhou, China. Journal of Urban Planning and Development, 147(3), Article 04021033. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000734
Wen, H., Xiao, Y., & Hui, E. C. M. (2019). Quantile effect of educational facilities on housing price: Do homebuyers of higher-priced housing pay more for educational resources? Cities, 90, 100–112. https://doi.org/10.1016/j.cities.2019.01.019
Worku, G. B. (2017). House price drivers in Dubai: Nonlinearity and heterogeneity. International Journal of Housing Markets and Analysis, 10(3), 384–409. https://doi.org/10.1108/IJHMA-06-2016-0048
Wu, J., Wang, M., Li, W., Peng, J., & Huang, L. (2015). Impact of urban green space on residential housing prices: Case study in Shenzhen. Journal of Urban Planning and Development, 141(4), Article 05014023. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000241
Xiao, Y., Hui, E. C. M., & 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
Xu, T., & Zhang, M. (2016). Influences of regional development conditions on the railway transit access premium: Evidence from Wuhan City. Urban Problems, 9, 48–57.
Zemo, K. H., Panduro, T. E., & Termansen, M. (2019). Impact of biogas plants on rural residential property values and implications for local acceptance. Energy Policy, 129, 1121–1131. https://doi.org/10.1016/j.enpol.2019.03.008