تحلیل فضایی ارزش مبادله ای مسکن شهری (مورد مطالعه: منطقه ۱۰ تهران) (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
مسکن یکی از عناصر مهم در برآورده کردن نیازهای زیستی، اقتصادی و اجتماعی هر خانوار است. رشد شهرنشینی و افزایش جمعیت شهرها، مهاجرت از روستا به شهر، استهلاک ساختمان های قدیمی و تخریب آنها، کوچک شدن بُعد خانوارها و مسائلی از این قبیل تأمین مسکن را به یکی از مشکلات بیشتر شهرها (تهران-بمبئی) به خصوص در کشورهای جهان سوم ازجمله کشور ایران و هندوستان تبدیل کرده است. در پژوهش حاضر به ارزش مبادله ای مسکن در منطقه 10 تهران توجه شده است. پژوهش حاضر از نظر هدف، کاربردی و از نظر روش، توصیفی-تحلیلی است که به روش کمّی انجام شده است. برای بررسی های مرتبط با پژوهش حاضر از روش های رگرسیون و موران استفاده شده که در آن قیمت مسکن (ویلایی، آپارتمانی) به عنوان متغیر وابسته و تعداد 14 شاخص به عنوان متغیرهای مستقل تحقیق در نظر گرفته شده است. در این پژوهش برای تحلیل های فضایی از نرم افزار Gis Arc استفاده شده است. باتوجه به نتایج تحلیل رگرسیون چند متغیره در بین عوامل اثرگذار اصلی بر نوسان قیمت، عامل درآمد با 17 درصد، عامل تراکم با 77 درصد، قدمت سازه با 63 درصد و نوع سازه با 31 درصد به ترتیب بیشترین تاثیر معکوس را بر نوسان های قیمت مسکن در منطقه 10 تهران دارند. دسترسی به فضای درمانی بیشترین تأثیر معکوس را بر تعیین قیمت مسکن در جنوب منطقه 10 دارد؛ در حالی که شاخص دسترسی به مراکز تجاری بیشترین تأثیر مستقیم را بر تبیین ارزش مبادلاتی مسکن در منطقه 10 تهران داشته است؛ از این رو بیشترین تمرکز لکه های داغ (بیشترین مبادله مسکن) در قسمت های شمالی و برخی از قسمت های مرکزی منطقه 10 تهران دیده می شود.Spatial Analysis of Urban Housing Exchange Values (Case Study: 10th District of Tehran)
Housing is a fundamental component in fulfilling the biological, economic, and social requirements of every household. The rapid urbanization, burgeoning urban populations, rural-to-urban migration, degradation and demolition of aging structures, diminishing household sizes, and housing supply challenges are prevalent issues in many cities, particularly in developing nations, such as Iran and India. This study focused on the housing exchange values in the 10th district of Tehran. The research was purpose-driven and employed a descriptive-analytical approach with a quantitative methodology. Regression and Moran methods were utilized for the investigation with housing prices for villas and apartments as dependent variables and 14 indicators as independent research variables. Spatial analysis was conducted using ArcGIS software. The results of the multivariate regression analysis indicated that household income had a direct impact of 17% among economic factors, while density had a direct effect of 77% among social factors. Additionally, structure age and type accounted for 63% and 31% of the most significant negative impact on housing price fluctuations in the 10th district of Tehran, respectively. Access to medical facilities had the most adverse effect on housing prices in the southern part of District 10, whereas access to commercial centers had the most substantial direct impact on housing exchange values in the district. Consequently, the highest concentration of hot spots indicating the highest housing exchange was observed in the northern and central parts of the 10th district of Tehran.Keywords: Exchange Value, Housing, Future Changes, Space Trap, 10th district of TehranIntroductionIn recent years, the housing sector of Iran as a developing country has experienced significant fluctuations, resulting in widespread adverse effects on households, other economic sectors, and even the banking system. Consequently, the impact of monetary policy on the performance of housing market has become a primary concern for policymakers, necessitating appropriate responses from monetary and financial authorities due to the sector's pivotal role in the country. Housing and its market have been the subject of extensive urban studies, particularly in urban economics, over the past 4 decades. The municipality of District 10 of Tehran has undergone rapid physical and economic expansion in the last two decades accompanied by increased investment in construction. Urban management initiatives have further exacerbated this situation, leading to unbalanced spatial developments at the regional level. Weaknesses in the housing sector within the municipality of the 10th district of Tehran include the housing supply-demand gap, particularly among low-income groups, a high number of households requiring assistance, a low proportion of durable residential units, a high prevalence of non-professional constructions, and the greater profitability of land transactions compared to housing construction. Additionally, there has been a surge in trading activities, a disproportionate increase in land prices compared to construction, and an intensified expansion in recent years. Therefore, this research conducted a spatial analysis of urban housing exchange values over a 1-year period (2022). By examining the spatial analysis of housing prices and exchange values, variations in income, culture, urban infrastructures, and perceived social and environmental comfort across different areas and localities could be observed. Consequently, the findings of this study could offer valuable insights for urban planners, managers, and housing investors. Materials and MethodsThis study was applied in its purpose and employed a descriptive-analytical method using a quantitative approach. Multivariable regression analysis was utilized to explore the relationship between factors influencing the exchange value of housing from both consumer and producer perspectives, identifying key determinants affecting housing prices. The research findings were presented through descriptive tables, explanatory tables, and numerous maps created by the researcher. The results highlighted the components and factors with the greatest and least impact on the land and housing market in the neighborhoods of District 10 of Tehran. The study drew on documentary sources, statistical data, censuses, official information from relevant organizations, internal and external information databases, as well as comprehensive and detailed urban development maps and plans to collect theoretical foundations and extract data. Given the significance of direct observation in geographical research, the researchers’ observational method and familiarity with the study area served as the primary research tools supplemented by a researcher-designed questionnaire. The questionnaire primarily focused on gathering land and housing prices from real estate consultants for 2022. The research methodology integrated both quantitative and qualitative approaches. The theoretical framework was developed based on the subject under study and the study employed statistical description and analysis, image processing, and analysis using spatial databases and geographic information systems. Additionally, Excel, SPSS, and ArcGIS software were utilized for data preparation and processing. Research FindingsOne of the most notable and distinctive aspects of this study in comparison to others was the spatial analysis of housing prices in District 10. This involved the integration of geographic information system software and quantitative models, such as hot and cold spot models, Moran, Vansim, and vector map conversion to raster. These were recalculated at the level of study units (statistical blocks of the 10th district of Tehran) revealing the spatial analysis of independent (economic, social, and physical) variables. Despite the highest density of apartment buildings being in the neighborhoods of Hashemi, South Karun, South Salisbil, and North Salisbil in Tehran's 10th district, it appeared that the value of multi-story buildings in the neighborhood of South Zanjan was more influenced by land prices, household income, and employment rates. Three spatial autocorrelation patterns (clustered, scattered, and random) were observed with Moran's coefficient indicating a clustered pattern in relation to the price of villa buildings at the level of North Salisbil, South Karun, and South Karun localities. The economic factors influencing housing prices in 2022 at the level of residential blocks in Tehran's 10th district included land prices, household income, mortgages, and rents. The regression analysis conducted in Excel software revealed a 0.64% correlation between economic indicators and housing prices. The highest correlation between socio-cultural indicators and housing prices in the southern areas of Tehran's 10th district was observed in Imam Khomeini, Hashemi, Karoun, and Kimel streets, the beginning of Nematullah Shah Parori Street, and the end of Malik Ashtar Street. According to Moran's spatial autocorrelation model, the coefficient for the correlation of the indicators of access to uses and services and housing prices was 0.050658%. The highest correlation to all factors was observed in District 10 (north of the region) corresponding to Karun and North Salisbil localities. Access to urban services, military uses, and facilities like airport had led to a significant decrease in prices, while access to commercial, medical, and educational centers had a positive effect on increasing housing prices. Discussion of Results & ConclusionHousing stands as a fundamental necessity within any society and fluctuations within this sector exert a profound impact on the broader economy. Therefore, precise forecasting of housing prices holds significant importance for homeowners, investors, and other stakeholders in the housing sector. The presence of a reliable method for predicting housing prices can enhance efficiency of the housing market. The surge in urban population over recent decades coupled with limited access to resources, services, and essential infrastructure has transformed housing into a scarce and invaluable commodity. The housing predicament is a consequence of the industrial revolution and subsequent developments leading to a detachment of its exchange value from its consumption value. Its significance has continued to escalate across diverse societies. This study leveraged spatial descriptions and analyses available in geographic information systems (ArcGIS software) complemented by statistical analyses conducted using Excel and SPSS software.
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