Comparative Analysis of Spatial Impact of Living Social Overhead Capital on Housing Price by Residential type

Abstract

We examined 15,446 single-family detached houses and 2,494 apartment houses in Saha-gu, Busan Metropolitan City, to see whether the accessibility to social overhead capital (living SOC), influences housing prices spatially. Living SOC represents different types of essential facilities. We divided houses into single-family detached houses and apartment houses to perform a more accurate analysis. We used a geographically weighted regression (GWR) model for local-level analysis with a 200-m grid and 400-m grid as spatial units to consider spatial effects. The GWR model explained the price variation better than an ordinary least squares (OLS) model. The results provide a variety of implications to consider when establishing a business-and-action plan based on living SOC.

This is a preview of subscription content, access via your institution.

References

  1. Anselin L (1988) Spatial econometrics: Methods and models (Vol. 4). Springer, Heidelberg, Germany, 16–31, DOI: https://doi.org/10.1007/978-94-0157799-1

    Google Scholar 

  2. Anselin L (1999) The future of spatial analysis in the social sciences. Geographic Information Sciences 5(2):67–76. DOI: https://doi.org/10.1080/10824009909480516

    Google Scholar 

  3. Archer WR, Gatzlaff DH, Ling DC (1996) Measuring the importance of location in house price appreciation. Journal of Urban Economics 40:334–353, DOI: https://doi.org/10.1006/juec.1996.0036

    Google Scholar 

  4. Bae SY, Chung EC, Lee SY (2018) Effects of urban railway transportation services on housing prices: Case of apartments in gyeonggi province. Journal of the Korea Real Estate Analysts Association 24(3):85–98, DOI: https://doi.org/10.19172/KREAA.24.3.6 (in Korean)

    Google Scholar 

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

    Google Scholar 

  6. Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis 28(4):281–298, DOI: https://doi.org/10.1111/j.1538-4632.1996.tb00936.x

    Google Scholar 

  7. Brunsdon C, Fotheringham AS, Charlton M (2002) Geographically weighted summary statistics — A framework for localised exploratory data analysis. Computers, Environment and Urban Systems 26(6): 501–524, DOI: https://doi.org/10.1016/S0198-9715(01)00009-6

    MATH  Google Scholar 

  8. Can A (1992) Specification and estimation of hedonic house price models. Regional Science and Urban Economics 22(3):453–474, DOI: https://doi.org/10.1016/0166-0462(92)90039-4

    Google Scholar 

  9. Choi Y (1999) Perception and evaluation for residential environment conditions and public and neighborhood facilities between single detached unit dwellers and apartment dwellers. Journal of Korea Planning Association 34(2):79–91 (in Korean)

    Google Scholar 

  10. Choi CS (2019) A study on the relationship between house price and housing regulation policy. Asia-Pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology, The Convergent Research Society among Humanities, Sociology, Science, and Technology 9(9):1031–1040, DOI: https://doi.org/10.35873/ajmahs.2019.9.9.088 (in Korean)

    Google Scholar 

  11. Choi Y, Seo MJ, Oh SH (2019) The correlates between walkable environments and housing price using multi-level model. KSCE Journal of Civil Engineering 23(10):4516–4524, DOI: https://doi.org/10.1007/s12205-019-1894-0

    Google Scholar 

  12. Chun HJ, Park HS (2019) A study on the expected growth rate in housing prices in metropolitan areas of Korea. Korea Real Estate Academy Review 76:35–44

    Google Scholar 

  13. Crompton JL (2001) The impact of parks on property values: A review of the empirical evidence. Journal of Leisure Research 33(1):1–31, DOI: https://doi.org/10.1080/00222216.2001.11949928

    Google Scholar 

  14. Dwyer MC, Miller RW (1999) Using GIS to assess urban tree canopy benefits and surrounding greenspace distributions. Journal of Arboriculture 25:102–107, DOI: https://doi.org/10.5849/jof.11-052

    Google Scholar 

  15. Evans AW (1973) The economics of residential location. Macmillan, London, UK, 59–71

    Google Scholar 

  16. Fik T, Ling D, Mulligan G (2003) Modeling spatial variation in housing prices: A variable interactions approach. Real Estate Economics 31(4):623–646, DOI: https://doi.org/10.1046/j.1080-8620.2003.00079.x

    Google Scholar 

  17. Fotheringham AS, Brunsdon C, Charlton M (2003) Geographically weighted regression: The analysis of spatially varying relationships. John Wiley & Sons, Hoboken, NJ, USA, 1–26

    Google Scholar 

  18. Gao X, Asami Y (2001) The external effects of local attributes on living environment in detached residential blocks in Tokyo. Urban Studies 38(3):487–505, DOI: https://doi.org/10.1080/00420980120027465

    Google Scholar 

  19. Gleditsch KS, Ward MD (2008) Spatial regression models. SAGE Publications, Newbury Park, CA, USA, 8–10

    Google Scholar 

  20. Graves P, Linneman P (1979) Household migration: Theoretical and empirical results. Journal of Urban Economics 6(3):383–404, DOI: https://doi.org/10.1016/0094-1190(79)90038-X

    Google Scholar 

  21. Guo Y, Tang Q, Gong DY, Zhang Z (2017) Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sensing of Environment 198:140–149, DOI: https://doi.org/10.1016/j.rse.2017.06.001

    Google Scholar 

  22. Habib MA, Miller EJ (2008) Influence of transportation access and market dynamics on property values: Multilevel spatiotemporal models of housing price. Journal of the Transportation Research Board 2076(1):183–191, DOI: https://doi.org/10.3141/2076-20

    Google Scholar 

  23. Haider M, Miller EJ (2000) Effects of transportation infrastructure and location on residential real estate values: Application of spatial autoregressive techniques. Journal of the Transportation Research Board 1722(1):1–18, DOI: https://doi.org/10.3141/1722-01

    Google Scholar 

  24. Hui EC, Chau CK, Pun L, Law MY (2007) Measuring the neighboring and environmental effects on residential property value: Using spatial weighting matrix. Building and Environment 42(6):2333–2343, DOI: https://doi.org/10.1016/j.buildenv.2006.05.004

    Google Scholar 

  25. Jia S, Wang Y, Fan GZ (2018) Home-purchase limits and housing prices: Evidence from China. The Journal of Real Estate Finance and Economics 56(3):386–409, DOI: https://doi.org/10.1007/s11146-017-9615-2

    Google Scholar 

  26. Kim MY, Kim EJ (2019) Does the access to neighborhood environment affect multi-family housing prices?: Comparison between Gangnam 3 districts and gangbuk 3 districts in Seoul. Journal of The Korean Regional Development Association 31(2):229–250 (in Korean)

    Google Scholar 

  27. Kim HS, Lee GE, Lee JS, Choi Y (2019) Understanding the local impact of urban park plans and park typology on housing price: A case study of the Busan metropolitan region, Korea. Landscape and Urban Planning 184:1–11, DOI: https://doi.org/10.1016/j.landurbplan.2018.12.007

    Google Scholar 

  28. Kim BG, Ryu SK, Hong SJ (2016) The effect of medical service accessibility on the housing price — Focused on apartment complex in Gyeonggido, Korea. Korea Real Estate Academy Review 66:188–201 (in Korean)

    Google Scholar 

  29. Lee WM, Kim KM, Kim JS (2019) A study on the housing market of seoul districts in responses to housing policies. Journal of the Economic Geographical Society of Korea 22(4):555–575, DOI: https://doi.org/10.23841/egsk.2019.22.4.555 (in Korean)

    Google Scholar 

  30. Lee CM, Ryu KM, Choi K, Kim JY (2018) The dynamic effects of subway network expansion on housing rental prices using a repeat sales model. International Journal of Urban Sciences 22(4):529–545, DOI: https://doi.org/10.1080/12265934.2018.1487331

    Google Scholar 

  31. Lee KB, Suh JY (2019) A study on improvement of disclosure pricing confirmation using housing market and urban characteristics. Korea Real Estate Academy Review 79:118–134, DOI: https://doi.org/10.31303/krear.2019.79.118 (in Korean)

    Google Scholar 

  32. Li SM, Hou Q, Chen S, Zhou C (2010) Work, home, and market: The social transformation of housing space in Guangzhou, China. Urban Geography 31(4):434–452, DOI: https://doi.org/10.2747/0272-3638.31.4.434

    Google Scholar 

  33. Luttik J (2000) The value of trees, water and open space as reflected by house prices in the Netherlands. Landscape and Urban Planning 48(3–4):161–167, DOI: https://doi.org/10.1016/S0169-2046(00)00039-6

    Google Scholar 

  34. Mansfield C, Pattanayak SK, McDow W, McDonald R Halpin P (2005) Shades of Green: Measuring the value of urban forests in the housing market. Journal of Forest Economics 11(3):177–199, DOI: https://doi.org/10.1016/j.jfe.2005.08.002

    Google Scholar 

  35. Netusil NR (2013) Urban environmental amenities and property values: Does ownership matter? Land Use Policy 31:371–377, DOI: https://doi.org/10.1016/j.landusepol.2012.07.016

    Google Scholar 

  36. Openshaw S (1984) The modifiable areal unit problem concepts and techniques in modern geography. Geobooks, Norwich

    Google Scholar 

  37. Park S, Kim M, Baek J (2017a) Application of geographical and temporal weighted regression model to the determination of house price. Journal of the Korean Data and Information Science Society 28(1):173–183, DOI: https://doi.org/10.7465/jkdi.2017.28.1.173

    Google Scholar 

  38. Park JH, Lee DK, Park C, Kim HG, Jung TY, Kim S (2017b) Park accessibility impacts housing prices in Seoul. Sustainability 9(2): 185–198, DOI: https://doi.org/10.3390/su9020185

    Google Scholar 

  39. Richardson HW, Vipond J, Furbey RA (1974) Determinants of urban house prices. Urban Studies 11(2):189–199, DOI: https://doi.org/10.1080/00420987420080341

    Google Scholar 

  40. Rosen S (1974) Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy 82(1):34–55, DOI: https://doi.org/10.1086/260169

    Google Scholar 

  41. Rosiers FD, Dube J, Theriault M (2011) Do peer effects shape property values? Journal of Property Investment and Finance 29(4–5):510–528, DOI: https://doi.org/10.1108/14635781111150376

    Google Scholar 

  42. Sander HA, Haight RG (2012) Estimating the economic value of cultural ecosystem services in an urbanizing area using hedonic pricing. Journal of Environmental Management 113:194–205, DOI: https://doi.org/10.1016/j.jenvman.2012.08.031

    Google Scholar 

  43. Sander H, Polasky S, Haight RG (2010) The value of urban tree cover: A hedonic property price model in Ramsey and Dakota Counties, Minnesota, USA. Ecological Economics 69(8):1646–1656, DOI: https://doi.org/10.1016/j.ecolecon.2010.03.011

    Google Scholar 

  44. Seo K, Golub A, Kuby M (2014) Combined impacts of highways and light rail transit on residential property values: A spatial hedonic price model for Phoenix, Arizona. Journal of Transport Geography 41:53–62, DOI: https://doi.org/10.1016/j.jtrangeo.2014.08.003

    Google Scholar 

  45. Sunding DL, Swoboda AM (2010) Hedonic analysis with locally weighted regression: An application to the shadow cost of housing regulation in Southern California. Regional Science and Urban Economics 40(6):550–573, DOI: https://doi.org/10.1016/j.regsciurbeco.2010.07.002

    Google Scholar 

  46. Tian G, Wei YD, Li H (2017) Effects of accessibility and environmental health risk on housing prices: A case of Salt Lake County, Utah. Applied Geography 89:12–21, DOI: https://doi.org/10.1016/j.apgeog.2017.09.010

    Google Scholar 

  47. Wen H, Xiao Y, Hui EC, Zhang L (2018) Education quality, accessibility, and housing price: Does spatial heterogeneity exist in education capitalization? Habitat International 78:68–82, DOI: https://doi.org/10.1016/j.habitatint.2018.05.012

    Google Scholar 

  48. 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, DOI: https://doi.org/10.1016/j.habitatint.2017.03.010

    Google Scholar 

  49. Yuan F, Wei YD, Wu J (2020) Amenity effects of urban facilities on housing prices in China: Accessibility, scarcity, and urban spaces. Cities 96:102433, DOI: https://doi.org/10.1016/j.cities.2019.102433

    Google Scholar 

Download references

Acknowledgments

This work was supported by a 2-Year Research Grant of Pusan National University.

Author information

Affiliations

Authors

Corresponding author

Correspondence to JeJin Park.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Choi, Y., Jeung, I. & Park, J. Comparative Analysis of Spatial Impact of Living Social Overhead Capital on Housing Price by Residential type. KSCE J Civ Eng 25, 1056–1065 (2021). https://doi.org/10.1007/s12205-021-1250-z

Download citation

Keywords

  • Living SOC
  • Residential type
  • Housing price
  • Spatial effect
  • GWR model