بررسی تأثیر عوارض شهرداری بر عرضه مسکن در مناطق دهگانه کلانشهر تبریز

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه تبریز، تبریز، ایران

2 استاد گروه اقتصاد، دانشکده اقتصاد و مدیریت، دانشگاه تبریز، تبریز، ایران

چکیده

بیان مسئله: بخش مسکن به منزله سهم بزرگی از اقتصاد و به عنوان یک موضوع کلیدی در کمک به توسعه اقتصادی از اهمیت به سزایی برخوردار است. درحال حاضر، مدیریت شهری در اکثر شهرهای ایران جهت تأمین نیازهای مالی خود به ناپایدارترین منبع درآمدی یعنی عوارض ساختمانی وابسته شده‌اند.
هدف: این پژوهش، عوامل تأثیرگذار بر عرضه مسکن در مناطق دهگانه کلانشهر تبریز را با تأکید بر عوارض شهرداری مرتبط با مسکن، مورد بررسی قرار می‌دهد .
روش: مطالعه حاضر به لحاظ هدف از نوع تحقیقات کاربردی و به لحاظ روش تجزیه و تحلیل از نوع تحقیقات ترکیبی است. روش ترکیبی یک رویکرد منطقی و قدرتمند است که از نقاط قوت هر دو روش کیفی (نظریه داده بنیاد) و کمی (معادلات ساختاری) استفاده می‌کند. دلیل اصلی استفاده از مدل‌سازی معادلات ساختاری (SEM) بعد از نظریه داده بنیاد (Grounded Theory)، تأیید و تعمیم آماری مدلی است که از داده‌های کیفی به دست آمده است.
یافته‌ها: در مرحله اول و رهیافت داده بنیاد، عوامل مؤثر بر عرضه مسکن شناسایی شده در پژوهش رضائی و بهشتی (1393) که شامل 68 مفهوم مستخرج از روش دلفی فازی با استفاده از مصاحبه و پرسشنامه می‌باشد، مورد تحلیل قرار گرفت. بعد از اجرای مراحل کدگذاری باز، محوری و انتخابی، مدل پارادیمی تدوین شد. براساس مدل پارادیمی مقوله‌ها تحت 6 دسته شامل مقولۀ مرکزی، شرایط علی، شرایط مداخله­گر، شرایط، راهبردها و پیامدها قرار گرفته‌اند.
نتیجه ­گیری: این تحقیق نشان داد که نوسانات بازار مسکن تبریز، ناشی از یک زنجیره علّی (شناسایی شده توسط GT) است که در آن عوامل مدیریتی و عوارض شهرداری، عرضه را تضعیف می‌کنند و این ضعف عرضه، نیروی اصلی افزایش قیمت‌ها است. مسئله اصلی، قیمت‌گذاری نیست، بلکه جریان ساخت و ساز است. سیاست‌های صرفاً کنترلی قیمت بدون رفع موانع عرضه (که GT آنها را شناسایی کرده)، محکوم به شکست است. نتایج این تحقیق، یک مدل نظری پیچیده برای مدیریت شهری است که ریشه در داده‌های کیفی محلی دارد و توصیه‌های آن نیازمند تغییرات ساختاری در نحوه اخذ عوارض و مدیریت فرآیند صدور مجوزها در سطح هر منطقه از شهر تبریز است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

The Impact of Municipal Taxes on Housing Supply in Tabriz Ten Metropolitan Districts

نویسندگان [English]

  • Khadijeh Rezaei 1
  • Mohammad Bagher Beheshti 2
1 Ph.D Student, Department of Economics, Faculty of Economics and Management, Tabriz University, Tabriz, Iran
2 Professor, Department of Economics, Faculty of Economics and Management, Tabriz University, Tabriz, Iran,
چکیده [English]

Introduction: The housing sector, as a major component of the economy and a key factor in supporting economic development, has significant importance. Currently, urban management in most Iranian cities has become dependent on the most unstable source of revenue—construction fees—to meet its financial needs. Housing-related charges, especially in the past decade and in the country’s major cities, are regarded as an influential factor affecting both the price and supply of housing. In recent years, due to the rapid expansion of Tabriz city, Tabriz Municipality divided the city into ten independent districts to provide proper urban services to citizens. These ten districts of the Tabriz metropolis possess diverse features and capacities in terms of area, population, urban fabric, and other aspects, which significantly influence the housing supply within each district. Therefore, the main challenge concerning the varying housing supply across Tabriz’s ten districts lies in examining the factors that affect housing supply. This study examines the factors influencing housing supply by the ten districts of Tabriz metropolis, with an emphasis on municipal charges related to housing. Ultimately, it evaluates a model based on the factors affecting how municipal fees impact the housing supply
Methodology: This research investigates the factors influencing housing supply in the ten districts of Tabriz Metropolis, with an emphasis on housing-related municipal fees. In this regard, the study intends to answer three main questions:

Can a model be extracted that significantly describes the factors influencing housing supply in the ten districts of Tabriz Metropolis?
Which factors have the greatest impact on housing supply?
What is the effect of improper housing supply on housing prices?"
Results: The present study is applied in terms of its objective and mixed-methods in terms of its analytical approach. The mixed-methods approach is a logical and powerful framework that utilizes the strengths of both qualitative (Grounded Theory) and quantitative (Structural Equation Modeling) methods. The main reason for using Structural Equation Modeling (SEM) after Grounded Theory (GT) is to statistically validate and generalize the model derived from the qualitative data.To achieve the research objective, adopting the Grounded Theory approach, a model was developed based on the factors affecting housing supply. The developed model includes the core category, causal conditions, intervening conditions, contextual conditions, strategies, and consequences. Finally, the constructed model was tested using Structural Equation Modeling.The statistical population under review includes experts in Tabriz urban management. Given the limited size of the population, an attempt was made to include all individuals as the sample, totaling 156 people, surveyed during the period from Dey 1401 to Tir 1402 (approximately January 2023 to June 2023). The distributed questionnaire comprised 68 questions based on the categories validated and extracted in the qualitative phase, using a Likert scale. The SmartPLS software was used to evaluate the data obtained from the questionnaire.
Discussion: The questionnaire was designed with 68 questions in the form of a Likert scale, whose items comprised categories mentioned in Table 1. After ensuring the validity and reliability of the questionnaire, the questionnaire was distributed among the samples, and after collection, the data were entered into SmartPLS software for evaluation.To examine the extent to which each of the research constructs aligns with the indicators used to measure them, Confirmatory Factor Analysis (CFA) was employed.
Conclusion: This research indicates that the fluctuations in the Tabriz housing market are caused by a causal chain (identified via GT) in which managerial factors and municipal fees undermine supply, and this weakened supply is the primary force behind price increases. The core issue is the construction flow, not just pricing. Policies based solely on price control, without resolving the supply barriers (identified by GT), are destined to fail.The results provide a complex theoretical model for urban management rooted in local qualitative data. Its recommendations necessitate structural changes in how fees are collected and how the permit issuance process is managed at the level of each district in Tabriz. The key operational policy is that price management must be achieved through improving supply (such as system integration, reducing unnecessary stringent regulations, etc.), and planning must be regional and non-uniform to account for the inherent differences across the ten districts. The crucial point highlighted in the conclusion is that a uniform policy is inefficient due to the intrinsic differences in housing characteristics across the ten districts, meaning success demands adopting tailored regional approaches for each area.In the current study, a questionnaire was used to measure the variables.

کلیدواژه‌ها [English]

  • municipal taxes
  • housing supply
  • districts of Tabriz metropolis
  • Grounded Theory
  • Structural Equation Modeling
Andrews, D., Sánchez, A. C., & Johansson, Å. (2011). Housing Markets and Structural Policies in OECD Countries. OECD Economics Department Working Papers, 836. Doi: 10.1787/5kgk8t2k9vf3-en
Abbott, R., & Bogenschneider, B. (2018). Should Robots Pay Taxes: Tax Policy in the Age of Automation. Harv. L. & Pol'y Rev., 12, 145. Doa:10.2139/ssrn.2932483
Akingbade, A., Navarra, D., Zevenbergen, J., & Georgiadou, Y. (2012). The Impact of Electronic Land Administration on Urban Housing Development: The Case Study of the Federal Capital Territory of Nigeria. Habitat International, 36 (2), 324-332. Doi: 10.1016/j.habitatint.2011.10.008
Abbaszadeh, A. D., Fani, A., & Samimi, M. R. (2011). Familiarity with income and methods of increasing it in municipalities. Municipalities and Village Administrations Organization (Iran), Tehran. (in Persian)
Aram, A. & setudeh, H. (2024). Developing a conceptual model to investigate the factors affecting housing prices. Eighth international Congress on Development of Technological Infrastructure in Civil Engineering, Architecture and Urban of IRAN. (in Persian)
Chen, Z., & Wang, C. (2021). Effects of Intervention Policies on Speculation in Housing Market: Evidence from China. Journal of Management Science and Engineering, 7(2), 233-242. Doi: 10.1016/j.jmse.2021.08.002
DiPasquale, D., & Wheaton, M. (1994). Housing Market Dynamics and the Future of Housing Prices. Journal of Urban Economics, 35, 1-27. Doi: 10.1006/juec.1994.1001
Fullerton, D., Leicester, A., & Smith, S. (2008). National Bureau of Economic Research, Environmental Taxes, w14197. URL: http://www.nber.org/papers/w14197
Hong, Y., & Li, Y. (2019). Housing Prices and Investor Sentiment Dynamics: Evidence from China sing a Wavelet Approach. Finance Research Letters, 35, 101-300. Doi: 10.1016/j.frl.2019.09.015
Haghroosta, S., ‪Rafieian, M., & Zebardast, E. (2022). Identification and analysis of housing submarkets in Tehran metropolis, Housing and Rural Environment. 41(179), 33-46. (in Persian) Doi:  %2010.22034/41.179.33
Hoyle, R. H. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications , 1–15 Sage Publications, Inc.
Jafari, F., & sherizadeh, A. (2019). Identifying the Key Factors Effective on the House price of Tabriz With Cross Impact Analysis method. Journal of Geography and Planning, 23(67), 67-89. (in Persian) URL: https://geoplanning.tabrizu.ac.ir/article_8751.html?lang=en
Khakpour, B., & Samadi, R. (2014). Analysis and Evaluation of Factors Affecting Land and Housing Prices In District No. 3 of Mashhad City. Geography and Territorial Spatial Arrangement4(13), 21-38. (in Persian) Doi: 10.22111/gaij.2014.1771
Mehregan, N., Sahabi, B., & tartar, M. (2015). The Effects of Municipal Tolls on Housing Price: A Case Study of Tehran. Iranian Economic Development Analyses3(2), 129-154. (in Persian) Doi: 10.22051/edp.2016.2527
Mortazavi, F., & Hosseini Nourzad, S. H. (2024). Extracting Key Economic Indicators Affecting Housing Prices in the Metropolises of Iran Using the Fuzzy Delphi Method, bagh-e nazar, 21(136), 29-38. (in Persian) Doi:10.22034/bagh.2024.454160.5595
Negintaji, Z., & Sinaee, S. A. (2024). The impact of urban development taxes on private sector investment in housing; A case study of Tehran city. Journal of economics and regional development, 21(136), 33-42. (in Persian) Doi: 10.22067/erd.2023.82454.1182
Otrok, C., & Terrones, M. E. (2017). Global House Price Fluctuations: Synchronization and Determinants. In NBER International Seminar on Macroeconomics, 9(1), 119-166.
Rezaei, kh. & Beheshti, M. B. (2024). Analyzing the impact of municipal taxes on housing supply in the ten districts of Tabriz metropolis, Journal of Urban Space and Social Life, 3(9), 21-39. (in Persian) URL:https://journals.tabrizu.ac.ir/article_18476.html?lang=en
Rauf, M. A., & Weber, O. (2022). Housing Sustainability: The Effects of Speculation and Poverty Taxes on House Prices within and beyond the Jurisdiction, Sustainability, 14, 7496, 1-19. Doi: 10.3390/su14127496
Salo, S. (1994). Modelling the Finnish Housing Market. Economic Modelling, 11(2), 250-265. Doi: 10.1016/0264-9993(94)90022-1
Tahamipour Zarandi, M., Aghaali, E., Tahmasebi, Z., & Habibollahi, M. (2022). Study of factors affecting housing prices in Tehran. Tehran International Conference on Investment Opportunities papers, Tehran. (in Persian)
Wang,  Y., & Jiang,  Y. (2016). An  empirical analysis of factors affecting the  housing  price in Shanghal. Asian Journal of Economic Modelling, 4(2), 104-111. Doi: 10.18488/journal.8/2016.4.2/8.2.104.111
Wang, zh., & Zhang, Q. (2014). Fundamental factors in the housing market of chain. Journal of Housing Economics,25, 53-61. Doi: 10.1016/j.jhe.2014.04.001
Wenli, Li. & Edison, G. Yu. (2022). Real Estate Taxes and Home Value: Evidence from TCJA. Review of Economic Dynamics, 43, 125-151. Doi: 10.1016/j.red.2021.02.003            
Weston, R., & Gore, P. A., Jr. (2006). A Brief Guide to Structural Equation Modeling. The Counseling Psychologist, 34(5), 719–751. Doi: 10.1177/0011000006286345