Evaluation of the factors influencing the housing safety awareness of residents in Shanghai

Autoři: Jin Ban aff001;  Longzhu Chen aff001
Působiště autorů: Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai, China aff001
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0227871


Shanghai has experienced rapid urbanization and has a serious housing aging problem. The situation of urban housing safety management needs to be strengthened. However, in China, housing safety management (HSM) is just in its beginning stage and it lacks thorough research. Housing safety awareness is one of the most significant aspects of housing safety management. Therefore, in order to investigate the housing safety awareness of Shanghai residents, this paper investigates the safety attitudes of residents living in housing of different ages using consulting questionnaires and Statistical Package for Social Science (SPSS) software. The results show that in Shanghai, the residents lack an understanding of housing management law, policy, and awareness of safety use and have low willingness to buy commercial insurance. Based on these results, the factors that affect the safety awareness of Shanghai residents are summarized as follows: (1) asymmetric information; (2) assessment of the safety status of the premises; and (3) differences in house users.

Klíčová slova:

Built structures – Factor analysis – Housing – Insurance – Questionnaires – Risk management – Surveys – Commercial law


1. Shanghai Municipal Housing Security and Building (SMHSB) (2018). Shanghai Statistical Yearbook. Shanghai. (In Chinese).

2. Wang X, Yang J, Lee C, Ji Z, and You S. Macro-level safety analysis of pedestrian crashes in Shanghai, China. Accident Analysis & Prevention. 2016, 96: 12–21. http://dx.doi.org/10.1016/j.aap.2016.07.028.

3. Mo X, Wang W. Review on a Practical Approach of Sustainable Urban Design Strategy in the Perspective of Conflict in Shanghai. International Review for Spatial Planning and Sustainable Development. 2014, 2(4): 44–53. http://dx.doi.org/10.14246/irspsd.2.4_44.

4. Yue W, Fan P, Wei YD and Qi J. Economic development, urban expansion, and sustainable development in Shanghai. Stochastic environmental research and risk assessment. 2014, 28(4):783–799. http://dx.doi.org/10.1007/s00477-012-0623-8.

5. Gao Y, Ma Y. What is absent from the current monitoring: Idleness of rural industrial land in suburban Shanghai. Habitat International. 2015, 49: 138–147. http://dx.doi.org/10.1016/j.habitatint.2015.05.019.

6. Cui DQ. Extend Building Life and Promote Building Energy Efficiency. Advanced Materials Research. Trans Tech Publications. 2015, 1073: 1239–1243. http://dx.doi.org/10.4028/www.scientific.net/AMR.1073-1076.1239.

7. Sun L, Yung EHK, Chan EHW, and Zhu D. Issues of NIMBY conflict management from the perspective of stakeholders: A case study in Shanghai. Habitat international. 2016, 53: 133–141. http://dx.doi.org/10.1016/j.habitatint.2015.11.013.

8. Wang X, Song Y, Yu R, and Schultz GG. Safety modeling of suburban arterials in Shanghai, China. Accident Analysis & Prevention. 2014, 70: 215–224. http://dx.doi.org/10.1016/j.aap.2014.04.005.

9. Chen LJ, Luo H. A BIM- based construction quality management model and its application. Automation in construction. 2014, 46: 64–73. http://dx.doi.org/10.1016/j.autcon.2014.05.009.

10. Mao Y, Huang H, Liu Y. Research on Government Supervision System of Housing Safety Appraisal Institution. 2017 7th International Conference on Education and Management (ICEM 2017). Atlantis Press, 2018. http://dx.doi.org/10.2991/icem-17.2018.160.

11. Yu D, Fang C. The dynamics of public safety in cities: A case study of Shanghai from 2010 to 2025. Habitat international. 2017, 69: 104–113. http://dx.doi.org/10.1016/j.habitatint.2017.09.007.

12. Jiao L.(2010). Study on Safety Management of Existing Buildings in Cities and Towns. (Master's thesis).

13. Chan FKS, Griffiths JA, Higgitt D, Xu S, Zhu F, and Tang YT, et al. “Sponge City” in China—A breakthrough of planning and flood risk management in the urban context. Land use policy. 2018, 76: 772–778. http://dx.doi.org/10.1016/j.landusepol.2018.03.005.

14. Paul JD, Buytaert W, Allen S, Ballesteros-Cánovas JA, Bhusal J, Cieslik K, et al. Citizen science for hydrological risk reduction and resilience building. Wiley Interdisciplinary Reviews: Water. 2018, 5(1): e1262. http://dx.doi.org/10.1002/wat2.1262.

15. Qian Q, Lin P. Safety risk management of underground engineering in China: Progress, challenges and strategies. Journal of Rock Mechanics and Geotechnical Engineering. 2016, 8(4): 423–442.http://dx.doi.org/10.1016/j.jrmge.2016.04.001.

16. Leung KW, Yau JH, and Roberds W. Challenges in applying landslide risk management to housing developments in Hong Kong. Landslide Risk Assessment. Routledge. 2018. 251–259. http://dx.doi.org/10.1201/9780203749524-15.

17. Shan L, Ann TW, and Wu Y. Strategies for risk management in urban–rural conflict: Two case studies of land acquisition in urbanising China. Habitat International. 2017, 59: 90–100. http://dx.doi.org/10.1016/j.habitatint.2016.11.009.

18. Yu T, Shen GQ, Shi Q, Lai X, Li CZ, and Xu K. Managing social risks at the housing demolition stage of urban redevelopment projects: A stakeholder-oriented study using social network analysis. International Journal of Project Management. 2017, 35(6): 925–941. http://dx.doi.org/10.1016/j.ijproman.2017.04.004.

19. Chan DWM, Hung HTW. An empirical survey of the perceived benefits of implementing the Mandatory Building Inspection Scheme (MBIS) in Hong Kong. Facilities. 2015, 33(5/6): 337–366. http://dx.doi.org/10.1108/F-09-2013-0066.

20. WM Chan D, TW Hung H, PC Chan A, and KK Lo T. Overview of the development and implementation of the mandatory building inspection scheme (MBIS) in Hong Kong. Built environment project and asset management. 2014, 4(1): 71–89. http://dx.doi.org/10.1108/BEPAM-07-2012-0040.

21. Li C Z, Hong J, Xue F, Shen GQ, Xu X, and Luo L. SWOT analysis and Internet of Things-enabled platform for prefabrication housing production in Hong Kong. Habitat International. 2016, 57: 74–87. http://dx.doi.org/10.1016/j.habitatint.2016.07.002.

22. He J, Wang S, Liu Y, Ma H, and Liu Q. Examining the relationship between urbanization and the eco-environment using a coupling analysis: Case study of Shanghai, China. Ecological Indicators. 2017, 77: 185–193. http://dx.doi.org/10.1016/j.ecolind.2017.01.017.

23. Liu J, Kuang W, Zhang Z, Xu X, Qin Y, Ning J et al. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s. Journal of Geographical Sciences. 2014, 24(2): 195–210. http://dx.doi.org/10.1007/s11442-014-1082-6.

24. Yung EHK, Chan EHW, Xu Y. Sustainable development and the rehabilitation of a historic urban district–Social sustainability in the case of Tianzifang in Shanghai. Sustainable Development. 2014, 22(2): 95–112. http://dx.doi.org/10.1002/sd.534.

25. Shanghai Institute of Real Estate Science(SIRES). (2017). General report on research and demonstration of safety technology and information system for housing safety management. Shanghai. (In Chinese).

26. Huang Y, Bao Y, Wang Y. Analysis of geo environmental hazards in urban underground space development in Shanghai. Natural hazards. 2015, 75(3): 2067–2079. http://dx.doi.org/10.1007/s11069-014-1414-y

27. Wang J, Wu JH, Zhang Y, and Wu E. Deep foundation pit engineering in Shanghai environmental safety problems. Journal of geotechnical engineering. 2006, 28 (supply): 1328–1331. (in Chinese).

28. Shanghai Municipal Commission of Construction and Transportation (SMCCT). (2006). “Shanghai municipal regulation for deep excavation engineering.”

29. Doling J, and Ronald R. (2014). Housing East Asia: socioeconomic and demographic challenges. Springer.

30. Schwartz A. F. (2014). Housing policy in the United States. Routledge.

31. McCrone G, and Stephens M. (2017). Housing policy in Britain and Europe. Routledge.

32. Pettersen C. (2017). Dynamic effects of the housing price in different city tiers in China on the Shanghai stock exchange. (Master's thesis) http://dx.doi.org/10.1108/02630809910291352.

33. Wu Y, Sharifi A, Yang P, Borjigin H, Murakami D, and Yamagata Y. Mapping building carbon emissions within local climate zones in Shanghai. Energy Procedia. 152 (2018): 815–822. http://dx.doi.org/10.1002/jae.2318.

34. Olubodun F, Mole T. Evaluation of defect influencing factors in public housing in the UK. Structural Survey. 1999, 17(3): 170–178. http://dx.doi.org/10.1108/02630809910291352.

35. Long H, Li T. The coupling characteristics and mechanism of farmland and rural housing land transition in China. Journal of Geographical Sciences. 2012, 22(3): 548–562. http://dx.doi.org/10.1007/s11442-012-0946-x.

36. Luciani M. Monetary policy and the housing market: A structural factor analysis. Journal of applied econometrics. 2015, 30(2): 199–218. http://dx.doi.org/10.1002/jae.2318.

37. Hayden J, Keegan M, Kardong- Edgren S. Reliability and validity testing of the Creighton Competency Evaluation Instrument for use in the NCSBN National Simulation Study. Nursing Education Perspectives. 2014, 35(4): 244–252. doi: 10.5480/13-1130.1 25158419

38. Chan DWM, Chan APC, Choi TNY. An empirical survey of the benefits of implementing pay for safety scheme (PFSS) in the Hong Kong construction industry. Journal of safety research. 2010, 41(5): 433–443. doi: 10.1016/j.jsr.2010.07.001 21059461

39. Chen F, Chen S. Injury severities of truck drivers in single-and multi-vehicle accidents on rural highways. Accident Analysis & Prevention. 2011, 43(5): 1677–1688. http://dx.doi.org/10.1016/j.aap.2011.03.026.

40. Chen F, Chen S, Ma X. Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of safety research. 2018, 65: 153–159. doi: 10.1016/j.jsr.2018.02.010 29776524

41. Wu Q, Chen F, Zhang G, Liu XC, Wang H, and Bogus SM. Mixed logit model-based driver injury severity investigations in single-and multi-vehicle crashes on rural two-lane highways. Accident Analysis & Prevention. 2014, 72: 105–115. http://dx.doi.org/10.1016/j.aap.2014.06.014.

42. Chan KW. Social construction of gender inequality in the housing system: Housing experience of women in Hong Kong. Routledge, 2019.

43. Gravetter FJ, and Wallnau LB. (2016). Statistics for the behavioral sciences. Cengage Learning.

44. Li CH. Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior research methods. 48.3 (2016): 936–949. http://dx.doi.org/10.3758/s13428-015-0619-7.

45. Bryman A, and Cramer D. Quantitative data analysis with SPSS for Windows: A guide for social scientists. Routledge, 1997.

46. Goretzko D, Pham TTH, and Bühner M. Bühner M. Exploratory factor analysis: Current use, methodological developments and recommendations for good practice. Current Psychology. 2019: 1–12. http://dx.doi.org/10.1007/s12144-019-00300-2.

47. Reio TG Jr, and Shuck B Exploratory factor analysis: implications for theory, research, and practice. Advances in Developing Human Resources. 2015, 17(1): 12–25. http://dx.doi.org/10.1177/1523422314559804.

48. Maniatis P. Investigating factors influencing consumer decision-making while choosing green products. Journal of Cleaner Production. 2016, 132: 215–228. http://dx.doi.org/10.1016/j.jclepro.2015.02.067.

49. Sakaluk JK, and Short SD. A methodological review of exploratory factor analysis in sexuality research: Used practices, best practices, and data analysis resources. The Journal of Sex Research. 2017, 54(1): 1–9.http://dx.doi.org/10.1080/00224499.2015.1137538.

50. Li CH. Confirmatory factor analysis with ordinal data: Comparing robust maximum likelihood and diagonally weighted least squares. Behavior research methods. 2016, 48(3): 936–949. doi: 10.3758/s13428-015-0619-7 26174714

51. Geldhof GJ, Preacher KJ, and Zyphur MJ. Reliability estimation in a multilevel confirmatory factor analysis framework. Psychological methods. 2014, 19(1): 72. doi: 10.1037/a0032138 23646988

52. Prudon P. Confirmatory factor analysis as a tool in research using questionnaires: A critique. Comprehensive Psychology 2015, 4: 03. CP. 4.10. http://dx.doi.org/10.2466/03.CP.4.10.

53. Horner RMW, El-Haram MA, and Munns AK. Building maintenance strategy: a new management approach. Journal of quality in maintenance engineering. 1997, 3(4): 273–280. http://dx.doi.org/10.1108/13552519710176881.

54. Will C, Schuller A. Understanding user acceptance factors of electric vehicle smart charging. Transportation Research Part C: Emerging Technologies. 2016, 71: 198–214. http://dx.doi.org/10.1016/j.trc.2016.07.006.

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