چکیده

در حال حاضر مسکن بعنوان بزرگ ترین چالش در کلان شهر تهران محسوب می شود که با بیشترین میزان افزایش نوسان قیمت روبه رو بوده است. براساس گزارش مرکز آمار ایران طی سالهای (1399-1394) در تهران حدود 28.675 واحد مسکونی پروانه ساختمانی صادر شده است که در این بین بر اساس تعداد معاملات انجام شده در تهران، منطقه پنج تهران بیشترین حجم معاملات خرید و فروش مسکن را به خود اختصاص داده است. ازاین رو هدف پژوهش شناسایی عوامل موثر بر قیمت مسکن در منطقه 5 شهر تهران می باشد. روش تحقیق مبتنی بر مطالعات کتابخانه ای و پیمایشی می باشد. از مطالعات کتابخانه ای برای تدوین مبانی نظری، پیشینه و شناسایی متغیرهای پژوهش استفاده گردید. سپس متغیرها در ابعاد (ساختاری، دسترسی و محیطی) تقسیم بندی گردیدند. در مرحله بعد با روش پیمایشی از بین 78 مشاورین املاک در منطقه 5 تهران پرسشگری صورت گرفت؛ از نرم افزارهای SPSS و EViews 10 در بخش تجزیه و تحلیل اطلاعات پرسشنامه استفاده گردیده است. نتایج پژوهش نشان داد در بین مؤلفه های فیزیکی ساختمان، متغیر زیربنا، استفاده از مصالح مرغوب، قرارگیری واحد در طبقات و از میان متغیرهای دسترسی فاصله تا فضای سبز و پارک، فاصله تا مراکز آموزشی، فاصله تا نزدیکترین ایستگاه حمل و نقل عمومی و در بین متغیرهای محیطی عرض کوچه و وضعیت ترافیکی آن عوامل مؤثر بر قیمت مسکن بوده است. افزون بر آن نتایج تحلیل آماری نشان می دهد که به ترتیب شاخص های ساختاری، دسترسی، محیطی بر قیمت مسکن به طور مستقیم تأثیرگذار بوده اند.

An Analysis of the Factors Affecting Housing Pricing Under Study: District 5 of Tehran

Introduction With the beginning of the land reform program in 1963 and with the collapse of the feudal system, a large wave of new population and labor force applying for land and housing went to the cities. As a result, construction activities and related economic activities intensified. The land, in economic activities, is sometimes produced as input and sometimes as a durable commodity. It creates attractiveness for people, and the demand for it increases in inflationary conditions. In these conditions, with the increase in demand, housing prices increase. Micro and macro factors that are effective in determining housing prices can be divided into two categories of external stimuli, such as the increase in the volume of money and liquidity, inflation, capital market, stock market, and in general, the factors that are rooted in macroeconomic and political activities. Internal drivers can be referred to as the price of land, the price of consumables, branches, and the cost of wages. In the neoclassical approach, land as an attractive input needs mechanisms to coordinate it according to demand and add to its capital value. According to economists such as Lancaster, Muth, and Rosen, the characteristics of goods are significant for consumers, and what is desirable for them is the quality of the product. Materials and Methods Considering the nature of the subject, the present research is of an applied type with a descriptive-analytical approach. The data collection tool was based on library and survey methods. Library studies were used to formulate theoretical foundations and background and identify research variables. In the next step, real estate consultants in Tehran Municipality’s Region five were questioned by the survey method using a researcher-made questionnaire. Then, the factors that affected housing prices in the study area during 2011-2021 were investigated. A multivariate regression analysis method was used using the Hedonic function to analyze the correlation between the variables. Furthermore, SPSS and EV views 10 were used to analyze the data. Results The analysis of the factors affecting housing prices in the Tehran Region five showed that out of 33 variables in structural, accessibility, and environmental dimensions, the coefficients of 28 variables were significant at the error level of 0.5. Among the physical variables affecting the price of housing, the floor area at 0.193, the building age at 0.166, the selection of suitable materials at 0.151, the location of the unit on the floors, especially the middle ones at 0.143, and the possibility of direct lighting (being towards the north or south direction of the housing) at 0.125 had a positive and significant effect on the housing price; for one unit of change in the mentioned variables, the housing price is increased. Among the accessibility variables, the distance between residential units to the nearest park and green space, that of residential units to the nearest educational centers, and that of residential units to the nearest public transportation station (metro, BRT, bus station, or taxi station) (-0.143, -0.124, -0.106, respectively) had a negative and significant effect on housing prices. The distance between residential units from the mentioned variables was effective in reducing housing prices. Among the environmental variables, the distance from narrow streets (-0.095) and the increase in traffic level (-0.087) decreased the housing price. Based on the one-sample t-test results, physical and structural indicators were the most important factors influencing the increase in housing prices, and accessibility and environmental components affected increasing housing prices, respectively. Pearson's parametric test results indicated a positive or direct correlation between structural indicators and housing prices. The coefficients obtained from this test showed that the correlation in structural, accessibility, and environmental indices were 0.659, 0.487, and 0.441, respectively. Moreover, the intensity of the influence of indices on housing prices using multivariable regression was (0.757). Conclusion Providing housing at a reasonable price is always considered a fundamental necessity in urban housing planning. From the point of view of microeconomics, housing is an immovable commodity, heterogeneous and dependent on a specific location, which, compared to others, is affected by various structural, spatial, and human variables in its valuation. This research analyzed the factors affecting housing prices in the Tehran Region five and indicated the way the micro factors increase housing prices. The hedonic function of housing for Tehran Region five was divided into structural, accessibility, and environmental dimensions to achieve the research goal. The results showed that among the mentioned components, the floor area, the year of construction, the materials of the housing, the location of the unit on the floors and the north-south direction of the housing, the distance of the residential units to the nearest park and green space, that of the residential units to the nearest educational center, that of residential units to the nearest public transportation station, alley and street width, and alley and street traffic conditions were significant variables in housing prices. The correlation test results showed a positive correlation between structural, accessibility, and environmental components on housing prices. In examining the rates of coefficients according to the regression test, it was found that the structural, accessibility, and environmental indicators directly influenced the housing price. Identifying the dimensions affecting housing prices can play a vital role in predicting and controlling housing prices, checking the quality of construction and living environment, and consumer preferences.

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