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Can Housing Prices Be an Alternative to a Census-based Deprivation Index? An Evaluation Based on Multilevel Modeling

주택가격이 센서스에 기반한 박탈지수의 대안이 될 수 있는가?: 다수준 모델에 기반한 평가

  • Sohn, Chul (Department of Urban Planning and Real Estate, Gangneung-Wonju National University) ;
  • Nakaya, Tomoki (Graduate School of Envirnmental Studies, Tohoku University)
  • 손철 (강릉원주대학교 도시계획부동산학과) ;
  • 나카야 토모키 (도호쿠대학교 환경학연구대학원)
  • Received : 2018.10.04
  • Accepted : 2018.11.22
  • Published : 2018.12.10

Abstract

We conducted this research to examine how well regional housing prices are suited to use as an alternative to conventional census-based regional deprivation indices in health and medical geography studies. To examine the relative performance of mean regional housing prices compared to conventional census-based regional deprivation indices, we compared several multilevel logistic regression models, where the first level was individuals and the second was health districts in the Seoul Metropolitan Area (SMA) in Korea, for the sake of adjusting the regional clustering tendency of unknown factors. In these models, we predicted two dichotomous variables that represented individuals' after-lunch tooth brushing behavior and use of dental floss by individual characteristics and regional indices. Then, we compared the relative predictive performance of the models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results from the estimations showed that mean regional housing prices and census-based deprivation indices were correlated with the two types of dental health behavior in a statistical sense. The results also revealed that the model with mean regional housing prices showed smaller AIC and BIC compared with other models with conventional census-based deprivation indices. These results imply that it is possible for housing prices summarized using aerial units to be used as an alternative to conventional census-based deprivation indices when the census variables employed cannot properly reflect the characteristics of the aerial units.

본 연구에서는 건강에 대한 공간적 연구에서 통상적으로 사용되는 센서스에 기반한 지역 박탈지수의 대안으로 지역 주택가격이 사용될 수 있는지 평가하였다. 평가를 위해 개인을 1수준으로, 수도권의 보건소 구역을 2수준으로 하는 다수준 로지스틱 모델이 추정되었다. 다수준 모델에는 개인의 점심식사후 칫솔질과 치간실 사용을 설명하기 위한 개인수준의 변수들과 보건소 구역을 대표하는 사회적 박탈지수 및 지역주택가격 수준이 포함되었다. 추정된 모델들의 설명력은 Akaike Information Criterion (AIC)와 Bayesian Information Criterion (BIC)를 이용하여 평가되었다. 모델의 추정결과는 사회적 박탈지수 및 지역 주택가격이 모두 개인의 치아관리 행동을 설명하는 데 기여하나 지역 주택가격을 사용한 모델의 AIC 및 BIC가 통상적인 센서스 기반 지역 박탈지수를 사용한 경우 보다 낮은 것을 보여 주었다. 본 연구결과는 센서스에 기반한 박탈지수를 생성하는 데 사용된 센서스 변수가 시점의 차이 등의 이유로 적절하지 않을 경우 지역 주택가격이 지역의 사회경제적 수준을 대표하기 위해 대안적으로 사용될 수 있음을 보여준다.

Keywords

References

  1. Kim CS, Han SY, Kim CW. 2013. The relationship between regional socioeconomic position and oral health behavior: A multilevel approach analysis. Journal of Korean Academy of Oral Health. 37(4):208-215.
  2. Sohn C. 2013. The Use of Housing Price As a Neighborhood Indicator for Socio-Economic Status and the Neighborhood Health Studies. Journal of Korea Spatial Information Society. 21(6):81-89. https://doi.org/10.12672/ksis.2013.21.6.081
  3. Aveyard, P., Manaseki, S., and Chambers, J. 2002. The relationship between mean birth weight and poverty using the Townsend deprivation score and the Super Profile classification system. Public Health. 116(6):308-314. https://doi.org/10.1038/sj.ph.1900872
  4. Can, A. 1992. Specification and estimation of hedonic housing price models. Regional Science and Urban Economics. 22(3):453-474. https://doi.org/10.1016/0166-0462(92)90039-4
  5. Coffee, Neil T., Lockwood, T., Hugo, G., Paquet, c.,Howard, N. J. and Daniel, M. 2013. Relative Residential Property Value as a Socio-Economic Status Indicator for Health Research. International journal of health geographics. p. 12-22.
  6. Drewnowski, A., D. Rehm, C. and Solet, D. 2007. Disparities in Obesity rates: analysis by ZIP code area. Social Science & Medicine. 65(12): 2458-2463. https://doi.org/10.1016/j.socscimed.2007.07.001
  7. Kiel, K.A., and Zabel, J.E. 2008. Location, location, location: The 3L Approach to house price determination. Journal of Housing Economics. 17(2):175-190. https://doi.org/10.1016/j.jhe.2007.12.002
  8. Macintyre, S., Ellaway, A. and Cummins, S. 2002. Place effects on health: How can we conceptualise, operationalise and measure them?. Social Science & Medicine. 55(1): 125-139. https://doi.org/10.1016/S0277-9536(01)00214-3
  9. Myers, C.K. 2004. Discrimination and neighborhood effects: understanding racial differentials in US housing prices. Journal of Urban Economics. 56(2):279-302. https://doi.org/10.1016/j.jue.2004.03.006
  10. Rehm, C. D., Moudon, A. V., Hurvitz, P. M. and Drewnowski, A. 2012. Residential property values are associated with obesity among women in king county, WA, USA. Social Science & Medicine. 75(3): 491-495. https://doi.org/10.1016/j.socscimed.2012.03.041
  11. Liu, X. 2015. Applied Ordinal Logistic Regression Using Stata. SAGE Publications, Inc.

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