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Influence of Railroad Infrastructure on Residential Property Prices on the Example of Kórnik Municipality

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The study's primary objective was to analyse how railroad infrastructure affects residential property prices on the example of the municipality of Kórnik. The analysis includes 737 transactions, which were concluded in 2019-2021. Input data forming the basis of the study were obtained from the District Surveying and Cartographic Documentation Centre in Poznań. The most important part of the analyses conducted was performed using the Ordinary Least Squares (OLS) method and the Geographically Weighted Regression (GWR) method. The analysis helped to identify the attributes that had the most significant impact on price. The statistical tools showed that a railroad line in the vicinity of residential properties negatively affected transaction prices. A unique role in the study is also played by spatial analyses, whose priority is to increase transparency in describing phenomena occurring within the real estate market.
Rocznik
Tom
Strony
54--73
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
  • Department of Land Improvement, Environmental Development and Spatial Management, Poznań University of Life Sciences, Poland
autor
  • Department of Land Improvement, Environmental Development and Spatial Management, Poznań University of Life Sciences, Poland
Bibliografia
  • Hayashi, Y., Ram, K.S., Bharule, S. (2020). Evaluation of Transport Infrastructure Impacts and their Implications for Quality of Life. Handbook on High-Speed Rail and Quality of Life, 527.
  • Podawca, K. & Staniszewski, R. (2019). Impact of Changes of the Permissible Railway Noise Levels. Rocznik Ochrona Środowiska, 21(2), 1378-1392.
  • Eckersten, S., Balfors, B., Gunnarsson-Östling, U. (2021). Challenges and opportunities in early stage planning of transport infrastructure projects: environmental aspects in the strategic choice of measures approach. Sustainability, 13(3), 1295. DOI: 10.3390/su13031295
  • Vijay, R., Sharma, A., Chakrabarti, T., Gupta, R. (2015). Assessment of honking impact on traffic noise in urban traffic environment of Nagpur, India. Journal of environmental health science and engineering, 13(1), 1-10. DOI: 10.1186/s40201-015-0164-4
  • Liang, J., Koo, K.M., Lee, C.L. (2021). Transportation infrastructure improvement and real estate value: impact of level crossing removal project on housing prices. Transportation, 48(6), 2969-3011. DOI: 10.1007/s11116-020-10157-1
  • Yang, L., Chau, K.W., Szeto, W.Y., Cui, X., Wang, X. (2020). Accessibility to transit, by transit, and property prices: Spatially varying relationships. Transportation Research Part D: Transport and Environment, 85, 102387. DOI: 10.1016/j.trd.2020.102387
  • Andersson, H., Jonsson, L., Ögren, M. (2010). Property prices and exposure to multiple noise sources: Hedonic regression with road and railway noise. Environmental and resource economics, 45(1), 73-89. DOI: 10.1007/s10640-009-9306-4
  • Berawi, M.A., Miraj, P., Saroji, G., Sari, M. (2020). Impact of rail transit station proximity to commercial property prices: utilising big data in urban real estate. Journal of Big Data, 7(1), 1-17. DOI: 10.1186/s40537-020-00348-z
  • Yang, L., Chen, Y., Xu, N., Zhao, R., Chau, K. W., Hong, S. (2020). Place-varying impacts of urban rail transit on property prices in Shenzhen, China: Insights for value capture. Sustainable Cities and Society, 58, 102140. DOI: 10.1016/j.scs.2020.102140
  • Cohen, J. P. & Brown, M. (2017). Does a new rail rapid transit line announcement affect various commercial property prices differently? Regional Science and Urban Economics, 66, 74-90. DOI: 10.1016/j.regsciurbeco.2017.05.006
  • Debrezion, G., Pels, E., Rietveld, P. (2011). The impact of rail transport on real estate prices: an empirical analysis of the Dutch housing market. Urban Studies, 48(5), 997-1015. DOI: 10.1177/0042098010371395
  • Behrends, S. (2012). The significance of the urban context for the sustainability performance of intermodal road-rail transport. Procedia-Social and Behavioral Sciences, 54, 375-386. DOI: 10.1016/j.sbspro.2012.09.757
  • Zielińska, E. (2017). Analiza rynku usług kolejowych w Polsce. Autobusy: technika, eksploatacja, systemy transportowe, 18.
  • 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), 74. DOI: 10.3390/ijgi11020074
  • Batóg, J., Foryś, I., Gaca, R., Głuszak, M., Konowalczuk, J. (2019). Investigating the impact of airport noise and land use restrictions on house prices: Evidence from selected regional airports in Poland. Sustainability, 11(2), 412. DOI: 10.3390/su11020412
  • Lieske, S.N., van den Nouwelant, R., Han, H., Pettit, C. (2018). Modelling value uplift on future transport infrastructure. In Real Estate and GIS, 80-98. Routledge.
  • Liang, J., Koo, K.M., Lee, C.L. (2021). Transportation infrastructure improvement and real estate value: impact of level crossing removal project on housing prices. Transportation, 48(6), 2969-3011. DOI: 10.1007/s11116-020-10157-1
  • Colwell, P.F., & Dilmore, G. (1999). Who was first? An examination of an early hedonic study. Land Economics, 620-626. DOI: 10.2307/3147070
  • Coulson, E. (2008). Monograph on Hedonic Estimation and Housing Markets; Penn State University: State College, PA, USA.
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  • Hutcheson, G. (2019). GLM models and OLS regression.
  • 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. DOI: 10.1080/24694452.2018.1470925
  • Tomal, M. (2020). Modelling housing rents using spatial autoregressive geographically weighted regression: A case study in Cracow, Poland. ISPRS International Journal of Geo-Information, 9(6), 346. DOI: 10.3390/ijgi9060346
  • Brunsdon, C., Fotheringham, A.S., Charlton, M.E. (1996). Geographically weighted regression: a method for exploring spatial nonstationarity. Geographical analysis, 28(4), 281-298. DOI: 10.1111/j.1538-4632.1996.tb00936.x
  • Fotheringham, A.S., Brunsdon, C., Charlton, M. (2003). Geographically weighted regression: the analysis of spatially varying relationships. John Wiley & Sons.
  • Lin, C.H. & Wen, T.H. (2011). Using geographically weighted regression (GWR) to explore spatial varying relationships of immature mosquitoes and human densities with the incidence of dengue. International journal of environmental research and public health, 8(7), 2798-2815. DOI: 10.3390/ijerph8072798
  • Yang, W. (2014). An extension of geographically weighted regression with flexible bandwidths (Doctoral dissertation, University of St Andrews).
  • Blanco, J.C. & Flindell, I. (2011). Property prices in urban areas affected by road traffic noise. Applied Acoustics, 72(4), 133-141. DOI: 10.1016/j.apacoust.2010.11.004
  • Brandt, S. & Maennig, W. (2012). The impact of rail access on condominium prices in Hamburg. Transportation, 39(5), 997-1017. DOI: 10.1007/s11116-011-9379-0
  • Cellmer, R. (2011). Spatial analysis of the effect of noise on the prices and value of residential real estates. Geomatics and Environmental Engineering, 5(4), 13-28.
  • Cellmer, R., Bełej, M., Konowalczuk, J. (2019). Impact of a vicinity of airport on the prices of single-family houses with the use of geospatial analysis. ISPRS International Journal of Geo-Information, 8(11), 471. DOI: 10.3390/ijgi8110471
  • Getis, A. (2007). Reflections on spatial autocorrelation. Regional Science and Urban Economics, 37(4), 491-496. DOI: 10.1016/j.regsciurbeco.2007.04.005
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  • Manasa, J., Gupta, R., Narahari, N. S. (2020). Machine learning based predicting house prices using regression techniques. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), 624-630. IEEE. DOI: 10.1109/ ICIMIA48430.2020.9074952
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ae480d66-df9e-433c-879c-17e6e24258bc
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