Abd-ol-Rasul Ghasemi; Elham Shadabfar
Abstract
With respect to the fact that housing satisfies a social need, in addition to the impact of economic factors, the social factors are alsoimportant in housing market. The broad economic and social developments in previous years require a study on their impacts on home ownership, so recognizing important ...
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With respect to the fact that housing satisfies a social need, in addition to the impact of economic factors, the social factors are alsoimportant in housing market. The broad economic and social developments in previous years require a study on their impacts on home ownership, so recognizing important factors is a good guide for planners and policy makers. Since people with higher education need to spend some money on their education and this education status increases the expectation of people with higher degrees with respect to their salaries and employment opportunities, therefore studying the impact of education level on the probability of home ownership and evaluating its trend in different time horizons has a critical importance. Since about twenty percent of higher education graduates of Iran do reside in Tehran province, this effect for Tehran province is stronger and of higher importance. In this study with emphasis on recognizing the effect of factors identified in theoretical basis, we have used logit and probit models to investigate the importance of some factors affecting the status of home ownership with emphasis on education level of head of households during the years 1997, 2002 and 2007. The results show that the socio-economic characteristics of households have a significant effect on housing ownership and the relationship between education level of head of household and home ownership is negative. Also the probability of home ownership in families with higher education decrease in the years under study.
Seyyed Safdar Hossini; Ali Eskandari Pour
Volume 12, Issue 46 , October 2012, , Pages 85-100
Abstract
Since many years ago, Iran has faced a macroeconomic problem of inflation. The problem of high inflation rate caused the economic growth of this country to slow down. As we all know, inflation is one of the major economic challenges that most of the countries in the world are facing, especially those ...
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Since many years ago, Iran has faced a macroeconomic problem of inflation. The problem of high inflation rate caused the economic growth of this country to slow down. As we all know, inflation is one of the major economic challenges that most of the countries in the world are facing, especially those in Asia including Iran. Therefore, forecasting the variable of inflation rate in Iranian economy becomes very important for the government to design effective economic strategies or monetary policies to combat any unexpected high inflation in this country. This paper studies a model of seasonal autoregressive integrated moving average to forecast inflation rates in the city of Tehran. Using monthly inflation data from March 2002 to February 2010 in Tehran, we find that ARIMA models can explain the behavior of actual data of inflation rate in Tehran in an acceptable way. Based on the selected model, we forecast the value of monthly inflation rate for six (6) months ahead in the city of Tehran that are out of the sample period (i.e. from March 2010 to August 2010). The observed inflation rate from March 2010 to August 2010 which was published by Tehran Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point inflation rate in Tehran August 2010.
Adel Berjesyan; Saeed Abedin Dour Koush
Volume 12, Issue 45 , July 2012, , Pages 55-74
Abstract
In this research, we try to specify the establishment location of private banks’ branches at the whole 22 areas of Tehran. We rank areas by using effective financial variables on bank services demand. For reaching this goal, we used Logit regression and numerical Taxonomi in a linear manner, and ...
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In this research, we try to specify the establishment location of private banks’ branches at the whole 22 areas of Tehran. We rank areas by using effective financial variables on bank services demand. For reaching this goal, we used Logit regression and numerical Taxonomi in a linear manner, and then by evaluating these two methods, we selected the best areas to establishing branches.
