نوع مقاله : مقاله پژوهشی

نویسندگان

1 دکتری شهرسازی،گروه شهرسازی، دانشکده معماری و شهرسازی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

2 استادیار، گروه شهرسازی، دانشکده معماری و شهرسازی، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران.

3 دانشیار، گروه مهندسی نقشه‌برداری، دانشکده مهندسی عمران، دانشگاه تربیت دبیر شهید رجایی، تهران، ایران.

4 دانشیار،گروه اقتصاد، دانشگاه بوعلی‌سینا، همدان، ایران.

چکیده

خانوارها با تغییر شرایط زندگی، نیازهای متفاوتی پیدا می‌کنند که پاسخگویی به آن‌ها ضرورت جابجایی در شهر را بوجود می‌آورد که شامل تغییر در واحد مسکونی و بافت اجتماعی- اقتصادی ساکنین پیرامونی است. در این مقاله شناسایی الگوی انتخاب مسکن و تمایز ترجیحات خانوارها برمبنای نظریه ادوار زندگی تبیین می‌شود که چرخه عمومی تحولات زندگی و نیازهای مسکن برای تمامی خانوارها را بیان می‌کند. در توسعه نظریه مذکور، هر خانوار شهری به دسته‌های متمایزی با ویژگی‌های اجتماعی- اقتصادی تقسیم می‌شود که این تمایزها منشأ انتخاب‌های گوناگون مسکن است. شناسایی اثرات و ارتباط میان ویژگی‌های اجتماعی- اقتصادی خانوارها و ویژگی‌ همسایه‌ها امکان تبیین گفتمان‌ها و جریان‌های اصلی شهر را فراهم می‌کند؛ مانند همگنی محلات و جدایی‌گزینی فضایی. در این مقاله به انتخاب مسکن گروه‌های خاص در شهر تهران با تاکید بر بافت اجتماعی- اقتصادی محله انتخابی آن‌ها پرداخته شده است. بنابراین گروه‌های خانوار هدف پژوهش مهاجران به شهر تهران، متولدین خارج از تهران، کارگران ساده، خانوارهای تک‌سرپرست، دارای معلول و اجاره‌نشین هستند. مدلسازی انتخاب مسکن بصورت انتخاب گسسته و با استفاده از روش «رگرسیونی چند متغیره کمی» انجام شده است که امکان تحلیل متغیرهای متعدد وابسته را ممکن می‌کند. داده‌های ورودی مدل از سرشماری عمومی نفوس ‌و مسکن سال 1390 شهر تهران و تسهیلات شهری تهران از طرح تفصیلی اخذ شده است. متغیرهای گروه واحد مسکونی شامل متراژ زیربنا و پایداری بنا است و متغیرهای گروه بافت اجتماعی- اقتصادی ساکنین شامل نرخ باسوادی در گروه زنان، نرخ اشتغال زنان، نرخ حضور ساکنین دارای تحصیلات عالی، سرانه وسیله نقلیه شخصی و نرخ حضور دانشجویان است. یافته‌های پژوهش نشان می‌دهد که متغیرهای گروه «واحد مسکونی» بیشترین نقش را در انتخاب مسکن ایفا می‌کند و از میان متغیرهای بافت اجتماعی- اقتصادی خانوارها، تمامی متغیرها ارتباط معناداری دارند که نشان از روایی مدل دارد و دو متغیر «سرانه اتومبیل شخصی خانوار» و «نرخ باسوادی زنان» بیشترین درصد تبیین در این گروه را ایفا می‌کنند. در گروه «واحد مسکونی» شانس انتخاب واحدهای مسکونی با متراژ زیربنای کمتر و بناهای ناپایدار در میان گروه‌های خاص بیشتر است و همچنین شانس انتخاب بافت اجتماعی- اقتصادی پایین‌ترِ ساکنین افزایش می‌یابد و تنها استثنای این روند، گروه هدف مهاجران 5 سال گذشته است. یافته‌های کلی پژوهش نشان می‌دهد که برای گروه‌های خاص انتخاب واحد مسکونی مناسب ارجح‌تر از محله است و تنها برای گروه مهاجران 5 سال گذشته است که انتخاب بافت اجتماعی- اقتصادی محله در اولویت قرار می‌گیرد.

چکیده تصویری

بررسی الگوی انتخاب مسکن و محله در گروه‌های خاص شهر تهران؛ با مروری بر مدل‌های انتخاب مسکن

تازه های تحقیق

- سنجش تاثیر مدت زمان مهاجرت در انتخاب مسکن.
- استفاده از روش رگرسیون چندمتغیره برای انتخاب مسکن.
- بررسی گروه‌های خاص جامعه.
- بسط نظریه ادوار زندگی.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A Patterns of housing and neighborhood selection among special groups; A review of housing selection models in Tehran city

نویسندگان [English]

  • Reza Asadi 1
  • Atoosa Modiri 2
  • Farhad Hosseinali 3
  • Ali Akbar Gholizadeh 4

1 Ph.D. in Urban Studies, Faculty of Architecture and Urbanism, Tehran Central Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Faculty of Architecture and Urbanism, Tehran Central Branch, Islamic Azad University, Tehran, Iran.

3 Associate Professor, Department of Surveying Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.

4 Associate Professor, Faculty of Economic, Bu-Ali Sina University, Hamedan, Iran.

چکیده [English]

Extended Abstract
Background and Objectives: With changing living conditions, households develop diverse needs, prompting moves within the city that involve changes in residential units and the socio-economic environment of surrounding residents. In this paper, the identification of housing pattern selection and the differentiation of households’ preferences are explained based on the life course theory, which describes the general cycle of life changes and housing needs for all households. In the development of the mentioned theory, every urban household is divided into distinct categories with socio-economic characteristics, and these distinctions are the source of various housing choices. Identifying the effects and relationship between the socio-economic characteristics of households and the characteristics of neighbors explains the main discourses and currents of the city; such as the homogeneity of localities and spatial separation. In this paper, the choice of housing for certain groups in Tehran is discussed, emphasizing the socio-economic context of their chosen neighborhood. Therefore, the target household groups of the research are immigrants to Tehran, those born outside of Tehran, simple workers, single-parent households, individuals with disabilities and renters.
Methods: Housing choice modeling employs discrete choice methodology and utilizes “quantitative multivariate regression” techniques, allowing for the analysis of multiple dependent variables. The model’s input data is sourced from the 2013 general population and housing census of Tehran city, as well as urban facility data from the detailed plan. The variables pertaining to the residential unit group include infrastructure size and building sustainability. Socio-economic structure variables encompass the female literacy rate, female employment rate, higher education attendance rate, private vehicles per capita, and student attendance rate.
Findings: The research findings indicate that variables related to the “residential unit” group apply the most significant influence on housing choice. Additionally, all socio-economic variables demonstrate a significant relationship, validating the model’s accuracy. Notably, “personal car per capita” and “women’s literacy rate” contribute the highest percentage of explanation within this group. Among certain groups, there is a higher likelihood of selecting residential units with limited infrastructure and unstable buildings within the “residential unit” group. Similarly, the probability of choosing neighborhoods with lower socio-economic contexts increases. An exception to this trend is observed among immigrants who arrived within the last five years. Overall, the research suggests that, for certain groups, the priority lies in selecting a suitable residential unit over the neighborhood. However, for immigrants who have lived in the area for the past five years, prioritizing the socio-economic context of the neighborhood becomes more prevalent.
Conclusion: The research findings highlight that, within the housing selection model, the characteristics of the residential unit show a significantly greater influence compared to the socio-economic context of the neighborhood. This prioritization of housing unit characteristics over other housing attributes aligns with observations made in third-world countries (Coulomb, 1998; Jacob & Saved off, 1999; Koizumi & Asadi et al., 2021). Reasons cited include the lack of premeditated planning for urban development, ineffective zoning regulations, and challenges within city neighborhoods. Furthermore, the pattern of residential unit selection remains consistent across the research groups, with an increased likelihood of choosing units with limited infrastructure and greater building instability. It appears that, for all groups, selecting appropriate housing takes precedence over choosing an ideal living environment. Within the socio-economic context of neighborhoods, car ownership and the literacy rate of women emerge as significant indicators of socio-economic differentiation in urban environments. Car ownership serves as a tangible marker of household wealth and is closely aligned with the city’s wealth distribution pattern, making it the primary index for defining the residential environment. Following closely, the literacy rate among women emerges as the second most influential indicator, impacting all target groups studied in this research. This underscores the importance of women’s education and their role within household structures in shaping Tehran’s socio-economic landscape, ultimately influencing household decisions in selecting a neighborhood. In analyzing the selection pattern of the socio-economic environment for residence, it was observed that nearly all independent variables and household characteristics align. Notably, as the quality of socio-economic neighborhood characteristics decreases, the likelihood of selection by research groups increases. However, an exception is noted among recent immigrants within the last five years, suggesting a divergence in the pattern of selecting the socio-economic environment among households-an important finding of this study.

کلیدواژه‌ها [English]

  • Life Course Theory
  • Housing Choice Models
  • Separation Selection
  • Special Groups
  • Multivariate Regression Model

این مقاله برگرفته از رساله دکتری نویسنده نخست با عنوان «نقش کالای عمومی در انتخاب مسکن با استفاده از مدل عامل‌مبنا  (نمونه موردی شهر تهران)» می‌باشد که به راهنمایی نویسنده دوم و مشاوره نویسنده سوم و چهارم در دانشگاه آزاد اسلامی واحد تهران جنوب انجام گرفته است.

This article is derived from the first author`s doctoral thesis entitled “The role of public goods in residential location choice with agent based model (Case study: Tehran)”, supervised by the second author and advised by the third and fourth, at Islamic Azad University, Tehran Central Branch.

 

  1. Akbari N, Khoshakhlaq R, Mardiha S. Measurement and Valuation of Factors Affecting Housing Choice Using a Choice Experiment Method: Viewpoints of Households Living at Old Urban Textures of Isfahan. QJER 2013; 13 (3) :19-47.[in persian]
  2. Alonso, W. (1960). A model of the urban land market: location and densities of dwellings and businesses. University of Pennsylvania.
  3. Andrew, M., & Meen, G. (2006). Population structure and location choice: A study of London and South East England. Papers in Regional Science, 85(3), 401-419.
  4. Ardestani, B. M., O'Sullivan, D., & Davis, P. (2018). A multi-scaled agent-based model of residential segregation applied to a real metropolitan area. Computers, Environment and Urban Systems69, 1-16.
  5. Arentze, T. A., Borgers, A. W., Ma, L., & Timmermans, H. J. (2010). An agent-based heuristic method for generating land-use plans in urban planning. Environment and planning B: Planning and Design, 37(3), 463-482.
  6. Asadi R, Modiri A, Gholizadeh A, Hoseinali F.(2021). The impact of household characters on choosing a house: the housing unit and quality of access to urban facilities Case study: Tehran City. Haft Hesar J Environ Stud. 10 (38) :25-44(doi: 10.52547/hafthesar.10.38.4)[in persian]
  7. Bayoh, I., Irwin, E. G., & Haab, T. (2006). Determinants of residential location choice: How important are local public goods in attracting homeowners to central city locations?. Journal of Regional Science, 46(1), 97-120.
  8. Benenson, I. (2004). Review of Imitation in Animals and Artifacts.
  9. Bhat, C. R., & Guo, J. Y. (2007). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transportation Research Part B: Methodological, 41(5), 506-526.
  10. Bogart, W. T., & Cromwell, B. A. (2000). How much is a neighborhood school worth?. Journal of urban Economics, 47(2), 280-305.
  11. Boumeester, H. J. (2011). Traditional housing demand research. In The measurement and analysis of housing preference and choice (pp. 27-55). Springer, Dordrecht.
  12. Boumeester, H. J. F. M. (2004). Duurdere koopwoning en wooncarrière: Een modelmatige analyse van de vraagontwikkeling aan de bovenkant van de Nederlandse koopwoningmarkt.
  13. Chen, X. (2009). Residential Relocation Choice and Consequent Behavioral Change. City University of New York
  14. Clark, W. A., & Huang, Y. (2003). The life course and residential mobility in British housing markets. Environment and Planning A, 35(2), 323-339.
  15. Coulomb, R. (1989). Rental housing and the dynamics of urban growth in Mexico City. Housing and land in urban Mexico, 39 -50.
  16. Coulter, R. (2018). Parental background and housing outcomes in young adulthood. Housing Studies, 33(2), 201–223. https://doi.org/10.1080/02673037.2016.1208160
  17. Cui, C. (2020). Housing career disparities in urban China: A comparison between skilled migrants and locals in Nanjing. Urban Studies, 57(3), 546–562. https://doi.org/10.1177/0042098018800443
  18. Cui, C., Geertman, S., & Hooimeijer, P. (2016). Access to Homeownership in Urban China: A Comparison between Skilled Migrants and Skilled Locals in Nanjing. In Cities. 50.188-196
  19. Cui, J., Cui, C., Ronald, R., Yu, S., & Mu, X. (2021). The dynamics of gender in the intergenerational transmission of homeownership: A case study of young couples in Shanghai. Population, Space and Place, 27(6), 1–13. https://doi.org/10.1002/psp.2428
  20. Cupchik, G. C., Ritterfeld, U., & Levin, J. (2003). Incidental learning of features from interior living spaces. Journal of Environmental Psychology, 23(2), 189-197.
  21. De Palma, A., Kilani, M., & Lindsey, R. (2005). Congestion pricing on a road network: A study using the dynamic equilibrium simulator METROPOLIS. Transportation Research Part A: Policy and Practice, 39(7-9), 588-611.
  22. Docquier, F., & Rapoport, H. (2021). Skilled Migration: The Perspective of Developing Countries. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.625259
  23. Druta, O., Limpens, A., Pinkster, F. M., & Ronald, R. (2019). Early adulthood housing transitions in Amsterdam: Understanding dependence and independence between generations. Population, Space and Place, 25(2). https://doi.org/10.1002/psp.2196
  24. Elder Jr, G. H., & Shanahan, M. J. (2006). The life course and human development.
  25. Filipovič Hrast, M., Sendi, R., Hlebec, V., & Kerbler, B. (2019). Moving house and housing preferences in older age in Slovenia. Housing, Theory and Society, 36(1), 76-91.
  26. Friedman, J. (1981). A conditional logit model of the role of local public services in residential choice. Urban Studies, 18(3), 347-358.
  27. Greene, M., & de Dios Ortúzar, J. (2002). Willingness to pay for social housing attributes: a case study from Chile. International Planning Studies, 7(1), 55-87.
  28. Habib, M. A., & Miller, E. J. (2009). Reference-dependent residential location choice model within a relocation context. Transportation Research Record, 2133(1), 92-99.
  29. Hu, L. and L. Wang (2019). "Housing location choices of the poor: does access to jobs matter?" Housing Studies 34(10): 1721-1745.
  30. Huang, Q., Parker, D. C., Filatova, T., & Sun, S. (2014). A Review of Urban Residential Choice Models Using Agent-Based Modeling. Environment and Planning B: Planning and Design41(4), 661–689.
  31. Hunt, J. D. (2010). Stated preference examination of factors influencing residential attraction. In Residential Location Choice (pp. 21-59). Springer, Berlin, Heidelberg.
  32. Hunt, J. D., & Abraham, J. E. (2007). Influences on bicycle use. Transportation, 34(4), 453-470.
  33. Hurtubia, R., Gallay, O., & Bierlaire, M. (2010). Attributes of households, locations and real estate markets for land use modelling. SustainCity Deliverable, 2, 1-27.
  34. Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., & Collins, W. D. (2008). Radiative forcing by long‐lived greenhouse gases: Calculations with the AER radiative transfer models. Journal of Geophysical Research: Atmospheres, 113(D13).
  35. Jacobs, M., & Savedoff, W. D. (1999). There is More Than One Way to Get a House: Housing Strategies in Panama, Inter -American Development Bank, Office of the Chief Economist Documento de Trabajo . 392.
  36. Jacobs, M., & Savedoff, W. D. (1999). There's More Than One Way to Get a House: Housing Strategies in Panama.
  37. Jansen, S. J., Coolen, H. C., & Goetgeluk, R. W. (Eds.). (2011). The measurement and analysis of housing preference and choice. Springer Science & Business Media.
  38. Kauko, T. (2006). Expressions of housing consumer preferences: Proposition for a research agenda. Housing, Theory and Society, 23(2), 92-108.
  39. Kim, T. K., Horner, M. W., & Marans, R. W. (2005). Life cycle and environmental factors in selecting residential and job locations. Housing studies, 20(3), 457-473.
  40. Koizumi, N., & McCann, P. (2006). Living on a plot of land as a tenure choice: The case of Panama. Journal of Housing Economics , 15(4), 349 -371.
  41. Liu, Y., & Shen, J. (2017). Modelling skilled and less‐skilled interregional migrations in China, 2000–2005. Population, Space and Place, 23(4).
  42. Louviere, J., & Timmermans, H. (1990). Stated preference and choice models applied to recreation research: a review. Leisure Sciences, 12(1), 9-32.
  43. Lowry, I. S. (1964). A model of metropolis. Rand Corp Santa Monica Calif.
  44. Marois, G., Lord, S., & Morency, C. (2019). A mixed logit model analysis of residential choices of the young-elderly in the Montreal metropolitan area. Journal of Housing Economics, 44, 141-149.
  45. Mohammad Talei, Abbas Alimohammadi Sarab, Ali Shirzadi Babkan (2013). Simulating the effects of the urban transportation system on the location of the residence and the travel pattern of households. Ministry of Science, Research and Technology - Khwaja Nasiruddin Tusi University of Technology - Faculty of Mapping.
  46. Mohammadzadeh, P., and Ghanbari, A., and Nazimfar, R. (2014). Determining the factors influencing the location of residential units using the discrete choice model (case study: Tabriz city). Economics and Urban Management, 3(10), 95-110.[in persian]
  47. Mulder C. H., Lauster N. T., (2010). Housing and Family: an Introduction. Housing Studies. 25. 433–440
  48. Mulder, C. H. (1993). Migration Dynamics: A Life Course Approach. Amsterdam: Thesis Publishers.
  49. Mulliner, E., & Algrnas, M. (2018). Preferences for housing attributes in Saudi Arabia: A comparison between consumers' and property practitioners' views. Cities, 83, 152-164.
  50. Musterd, S., & Ostendorf, W. (2008). Integrated urban renewal in The Netherlands: a critical appraisal. Urban Research & Practice1(1), 78-92.
  51. Nechyba, T. J., & Strauss, R. P. (1998). Community choice and local public services: A discrete choice approach. Regional Science and Urban Economics, 28(1), 51-73.
  52. Opoku, R. A., & Abdul-Muhmin, A. G. (2010). Housing preferences and attribute importance among low-income consumers in Saudi Arabia. Habitat international, 34(2), 219-227.
  53. Pagliara, F., Preston, J., & Simmonds, D. (Eds.). (2010). Residential location choice: models and applications. Springer Science & Business Media.
  54. Pasha, H. A., & Butt, M. S. (1996). Demand for Housing Attributes in Developing Countries: A study of Pakistan. Urban Studies. 33(2). 1141–1154.
  55. Pinjari, A. R., Bhat, C. R., & Hensher, D. A. (2009). Residential self-selection effects in an activity time-use behavior model. Transportation Research Part B: Methodological, 43(7), 729-748.
  56. Pinjari, A. R., Pendyala, R. M., Bhat, C. R., & Waddell, P. A. (2011). Modeling the choice continuum: an integrated model of residential location, auto ownership, bicycle ownership, and commute tour mode choice decisions. Transportation, 38(6), 933-958.
  57. Rosen, H. S. and Fullerton, D. J. (1977). A Note on Local Tax Rates, Public Benefit Levels, and Property Values. Journal of Political Economy. 85. 433–440.
  58. Rossi, P. H. (1955). Why families move: A study in the social psychology of urban residential mobility. Free Press.
  59. Schirmer, P. M., & Axhausen, K. W. (2014). Quantifying the value of urban form: A hedonic rent price model on Zurich. In 14th Swiss Transport Research Conference (STRC 2014). 14th Swiss Transport Research Conference (STRC 2014).
  60. Shen, J., & Liu, Y. (2016). Skilled and less-skilled interregional migration in China: A comparative analysis of spatial patterns and the decision to migrate in 2000–2005. Habitat International, 57, 1-10.
  61. Sirgy, J., Grzeskowiak, S., & Su, C. (2005). Explaining Housing Preference and Choice: The Role of Self-congruity and Functional Congruity. Journal of Housing and the Built Environment. 20(4). 329–34.
  62. Smith, B., & Olaru, D. (2013). Lifecycle stages and residential location choice in the presence of latent preference heterogeneity. Environment and Planning A, 45(10), 2495-2514.
  63. Tan, T. H. (2011a). Measuring the Willingness to Pay for Houses in a Sustainable Neighborhood. International Journal of Environmental, Cultural, Economic and Social Sustainability. 7.1–12.
  64. Tan, T. H. (2011b). Neighborhood Preferences of House Buyers: The Case of Klang Valley, Malaysia. International Journal of Housing Markets and Analysis. 4(1). 58–69.
  65. Tan, T. H. (2012). Meeting first-time buyers’ housing needs and preferences in greater Kuala Lumpur. Cities, 29(6), 389-396.
  66. Teck‐Hong, T. (2011b). Neighborhood preferences of house buyers: the case of Klang Valley, Malaysia. International Journal of Housing Markets and Analysis.
  67. Tian, G., Ewing, R., Greene, W., (2014). Desire for Smart Growth: a Survey of Residential Preferences in the Salt Lake Region of Utah. Housing Policy Debate. 25(3). 446-462.
  68. Tolai, Navin, & Yari, Jalil. (2014). Investigating factors affecting the desire to migrate within the city in Tehran, emphasizing the feeling of spatial inequality. Social analysis of social order and inequality, 1390(2). [in persian]
  69. Traoré, S. (2019). Residential location choice in a developing country: What matter? A choice experiment application in Burkina Faso. Forest Policy and Economics, 102, 1-9.
  70. Tsou, K. W., Hung, Y. T., & Chang, Y. L. (2005). An accessibility-based integrated measure of relative spatial equity in urban public facilities. Cities, 22(6), 424-435.
  71. Yusuf, A., & Resosudarmo, B., (2009). Does Clean Air Matter in Developing Countries' Megacities? A Hedonic Price Analysis of the Jakarta Housing Market, Indonesia. Ecological Economics. 68(5). 1398–1407.
  72. Zhou, B., & Kockelman, K. M. (2008). Self-selection in Home Choice: Use of Treatment Effects in Evaluating Relationship between Built Environment and Travel Behavior. Transportation Research Record. 2077(1), 54-61.
  73. Zhou, J., & Musterd, S. (2018). Housing preferences and access to public rental housing among migrants in Chongqing, China. Habitat International, 79, 42-50.
  74. Zondag, B., & Pieters, M. (2005). Influence of accessibility on residential location choice. Transportation Research Record, 1902(1), 63-70.