فهرست مطالب aliakbar gholizadeh

The purpose of this paper is to analyze the effect of the heterogeneous behavior of investors in the housing sector on housing prices and subsequently on Iran's economic growth during the period 2001:3 to 2020:3. For this purpose, first, the effect of herding behavior and overconfidence of investors on housing prices and then the effect of housing prices on economic growth was estimated by smooth transition regression (STR) method. Then, by calculating the Sobel statistic, the mediating role of the housing price in the transmission of the behavior of investors in the housing sector on economic growth was tested. The results showed that the housing price and economic growth functions have a threshold and two regimes. The imitative behavior of a previous period is the transition variable of the housing price function with a threshold equal to 0.0051 percent. The housing price is also a variable of economic growth function transfer with a threshold equal to 0.0266 percent The estimated coefficients of the first and second regimes indicate the asymmetric behavior of investors on housing prices. The estimation of the economic growth function showed that housing prices had a negative effect on economic growth in the first and second regimes, but this effect was strengthened in the second regime. The calculation of Sobel's statistic also showed that the housing price had a mediating role in transferring the effect of herding behavior on economic growth, but it did not act as a mediator in transferring the effect of overconfidence on economic growth.

As a Labor-intensive industry, tourism helps to improve the economy through employment in tourist destinations, infrastructure development, maintaining financial resources, revitalizing historical and new tourist places, and better understanding of the world's people from each other. The mentioned factors can lead to higher economic growth of the host countries and increase investment in other economic sectors. The housing sector is one of the sectors affected by increased economic growth. Thus, this article aims to analyze the effect of foreign tourism development on housing prices in Iran from the first quarter of 2012 to the first quarter of 2021. For this purpose, the number of foreign tourists entering the country as an indicator for the development of tourism and the variables of dollar price, population, the number of highways under the protection of the Ministry of Roads and Urban Development, and the number of issued building permits were considered as control variables. The Autoregressive Distributed Lag (ARDL) model was used for estimation. The estimation results showed that the number of foreign tourists negatively affected Iran’s housing prices during the studied period. Dollar price, population, and the number of highways under the protection of the Ministry of Roads and Urban Development positively affect housing prices, and the number of building permits issued hurts housing prices.

The purpose of this research is to investigate the effect of exchange rate jump on the outflow of migration using the Synthetic Control Group approach during the period of 1980-2015 in Iran. For this purpose, based on this approach, the countries with real exchange rate stability were selected and among them, the weighted composition of the countries that had the most similarity in the situation before the real exchange rate jump of Iran was estimated. Therefore, two groups were formed, including the group of selected countries named artificial Iran and the other, real Iran. Finally, the results of the estimates of these two groups were compared and analyzed. The results indicate a 15% difference in the immigration process of artificial Iran and real Iran. The analysis of the results showed that the impact of the jump in the real exchange rate on the migration flow in Iran was positive. Based on the results, it is suggested that policymakers pay attention to the destructive effect of real exchange rate jumps on the flow of migration from the country and adopt appropriate economic policies to stabilize the real exchange rate.

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.

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.

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.

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.

Similar to other main markets in an economy the stock market is also affected by various shocks that can negatively or positively affect the stock market and subsequently the national economy. Despite the fact that the economies of the Middle East are generally dependent on oil revenues, the change in the views of their politicians in recent decades and moving towards the replacement of incomes from other economic activities, such as capital market incomes instead of traditional income channels, has caused to pay special attention to the impact of different economic variables on these markets. In the meantime, the way the stock markets of the Middle East economies respond to various shocks, especially the shocks of competing markets such as the gold market and the foreign exchange market, has gained so importance. Therefore, the aim of the present study is to investigate the response of stock market returns of selected Middle Eastern countries to the shocks of parallel markets such as the foreign exchange market and the gold market as two competing markets. The estimated panel vector autoregression (PVAR) approach was used to estimate the model and impulse response functions (IRF) to extract the effect of shocks in the period from 2010 to 2021 using monthly data. The results show the positive response of the Middle East stock markets to the currency shock and the gold price shock. Increase in the exchange rate and the gold price lead the returns of the stock markets in the Middle East economies to increase at the first month. Moreover, the results of the variance decomposition (VDC) reveal that the most explanation in the variation of the forecast error variance is caused by the gold price variable and the exchange rate stands as the second.

The purpose of this article is to analyze the effect of the heterogeneous behavior of investors in the housing sector on housing prices and inflation in Iran during the period 2001:3 - 2020:3. The threshold effects of behavioral variables on housing prices and the effect of housing prices on inflation using smooth transition regression method (STR) is estimated. Then, using Sobel's statistic. The results of the estimation of house prices show that Herding behavior has a positive effect on housing prices in the first regime and the second regime. Overconfidence in the first regime was ineffective on housing prices, but in the second regime, it had a positive and significant effect. The results of estimating the inflation model also indicated that there are two limit regimes for the inflation function. Housing prices had a positive effect on the inflation rate in the first and second regimes. The calculation of the Sobel statistic also indicates the confirmation of the transmission of the effect of herding behavior on inflation from the housing price channel, while the Sobel test did not confirm the transmission of the effect of overconfidence from the housing price channel on inflation

The relationship between portfolio diversification (stock portfolio) and performance in private banks is a question that private banks always face and is one of the daily and important issues that they must answer. The purpose of this research is to examine the relationship between portfolio diversity (stock portfolio) and the performance of selected private banks in Iran (including: Pasargad Banks, Karabharani Banks, Ekhtaz Novin Banks, Sina Banks and Parsian Banks). For the time period under study, it is 2016-2018. This research is applied in terms of research strategy, descriptive in terms of purpose, and comparative in terms of research approach. Also, this research is correlational in terms of investigation, organizational in terms of analysis, and cross-sectional in terms of time horizon. In this research, the generalized econometric method of moments (GMM) is used to estimate the models for analysis using Stata software. The results of the model estimation showed that there is a positive and significant relationship between bank-specific variables (asset return rate, interest rate). On the other hand, the results showed that there is no significant relationship between the amount of loans granted to the total assets of banks and the annual growth rate of assets. Also, based on the results of the research, the rate of return on the rights of the shareholders, the value of the shares to the total assets has a negative and significant effect on the diversification of the portfolio.

The hyper volatilities of the housing market and the reasons for the emergence of boom-and-bust cycles cannot be explained by traditional theories. Recent studies indicate that behavioral factors are responsible for market volatility in the financial and real estate sectors, making it impossible to comprehend housing market events if they are neglected. According to this new viewpoint, the main objective of this study is to investigate the influence of two major behavioral principles, namely herd behavior and overoptimism, on the Iranian housing market. The seasonal data of Iran’s housing market from 2004:4 to 2021:5 and the bounds testing approach to cointegration of Pesran et al. (2001) based on the ARDL model have been utilized in this research. Since herd behavior is a hidden unobservable variable, Hwang & Salmon’s (2004) method was employed to quantify it. The quantification of herd behavior in the housing market demonstrates observable changes over time, confirming the methodology employed to quantify it in this study. The results of the cointegration test demonstrate that the correct variables influencing house prices were chosen and that there is a cointegration relationship between the research variables. Also, the results of this study reveal that behavioral components are among the most important determinants of housing prices, and that both herd behavior and overoptimism have a positive and statistically significant effect on housing prices. In addition to behavioral considerations, the results of this study indicate that economic factors such as real volume of liquidity, real GDP, and informal market exchange rate also influence housing prices, with all three variables having a positive effect on real housing prices.

The purpose of this article is to analyze the effects of the asymmetric shocks of the covid-19 epidemic as well as the dollar price shocks on housing prices in Iran during the period of 2020:1-2023:4. In order to investigate the effects of dollar price shocks, the periods in which the dollar price increased (decreased) were identified as positive (negative) shocks and used in the model. Nonlinear Autoregressive Distributed Lag (NARDL) method was used to check the symmetry or asymmetry of the shocks of Covid-19 and the price of the dollar. The results indicate the asymmetry of the positive and negative shocks of Covid-19 in the short and long term. So that in the short term, the positive shocks of Covid-19 have a negative effect and the negative shocks of Covid-19 have a positive effect on the housing price index in Iran. The results of the long-term period indicate that the negative shock of covid-19 had a negative effect and the positive shock of covid-19 had a positive effect on the housing price index. the error correction coefficient also shows that in each period 41% of the imbalance is adjusted in the short term and moves towards its long term trend.

Factors such as expectations, lack of financial flexibility, uncertainties in the macroeconomic environment and other economic and non-economic indicators are influential in creating business cycles, none of which alone creates business cycles. The purpose of this study is to investigate the relationship between economic, financial, political and international risks with the economic business cycles of Iran during the period 2001-2009. To achieve this goal, the long-run trend will be separated from the cyclical GDP trend using the Hadrick-Prescott filter, and the data will be analyzed using the Structural Vector Distribution (SVAR) model. According to the results, the average of one business cycle in the Iranian economy is equal to 10 seasons, which are equal to 5.45 and 5 seasons for the recession and boom period, respectively. Economic risk of -0.354 percentage has immediate effects on the business cycles of the Iranian economy, which for financial risk, international risk and political risk, the figures show -0.315, -0.0031 and 0.0048 percentage, respectively. According to the Granger causality test, the two variables of economic risk and financial risk are the cause of business cycles in the Iranian economy, while political and international risks are not the cause of business cycles. Economic risks in the first period with an impact of about 6% have the most explanatory effect in creating business cycles of GDP, after which financial risk has the greatest impact on business cycles, on the other hand, political risks among the studied risks have the least impact on cycles.

Investigating the behavior of speculators is very important in explaining the phenomena of the Iranian housing market. Many of the undesirable phenomena that occur in the housing market are for speculation. In this research, time-varying effects of factors influencing speculation in Iran's housing market have been investigated using state-space models for the period 1991 to 2020. The speculation index is estimated by Roehner. The results show that the shock of the housing tax has reduced speculation in the housing market. In the short and long term, the stock market shock had a small and different effect on speculation in the housing market in different years, respectively. The effect of the unofficial real exchange rate shock on speculation in Iran's housing market was positive. In the short and long term, the effect of this shock was small and more, respectively. According to the results, the real interest rate shock had a positive effect in the first two periods and then a negative effect on speculation in the Iranian housing market. The shock caused by the real housing yield rate has different effects on speculation. Still, overall it has caused an increase in speculation in the Iranian housing market. Based on the results, the shock caused by speculation in the parallel markets of the housing market (currency and stock markets) has caused an increase in speculation in the housing market.

The status of income distribution is economically important because other macroeconomic variables, especially savings rates, affect the amount of investment and aggregate demand in different markets, and are politically a measure of government efficiency in attracting voters. The present study aims to investigate the macroeconomic variables affecting inequality in income distribution in the two groups of middle-income countries and high-income countries based on the International Monetary Fund classification. For this purpose, the annual data of economic complexity, scientific productivity, political risk, economic risk, and financial risk and the period 2019-2000 and the panel method have been used. The results show that in high-income countries, increasing economic complexity and scientific productivity reduces income inequality, while in middle-income countries, increasing scientific productivity reduces income inequality, but increasing economic complexity increases income inequality. Reducing political risk in both groups reduces income inequality; While reducing financial risk reduces income inequality in high-income countries, it increases income inequality in middle-income countries. The impact of economic risk on income inequality is also negligible in high-income countries, while in middle-income countries the impact of economic risk on income inequality is very strong, and reducing economic risk in this group of countries strongly reduces income inequality.

The purpose of this study is to measure and analyze the effect of housing assets on private sector consumption using time series data in Iran for 1991-2021. This study analyzes the relationship in the form of a autoregressive distributed lag (ARDL) econometric model using Eviews software. The final trend for short- and long-term consumption of permanent income, housing and financial assets is also obtained. It is 0.55 and 0.69 for permanent income and 0.56 and 0.70 for housing, respectively. The error correction model shows the adjustment of short-term disequilibrium with a coefficient of 0.48, which is a reasonable relative adjustment rate. Another result of this study is the positive insignificant effect of financial assets on private consumption in Iran

Housing is one of the most basic needs of every household that can play a special role in the macroeconomic and household dimensions, and the largest share of annual costs for an urban household is related to the cost of housing, which is provided in the form of rent and property. Real estate has a significant impact on economic growth and is therefore preferable to rental housing. There are several factors influencing the choice of households to buy or rent housing, and identifying these factors and their impact can help housing policy and planning and the welfare and economic level of society. In this study, using the logit model and using the budget data of urban households in Iran in 2019, the effect of economic and social factors of households and market variables on housing tenure choice has been investigated. The results show that permanent income, age, marital status, employment, and the level of education of the head of the household and the post-household increase the likelihood of home ownership

Households move in cities for better housing and access to better public goods services, but the public goods are not equally important for households and their spatial distribution is not uniform in the city. In order to investigate in detail, we divvied the public goods into two groups: net and gross and modeling the housing residential choice. The main purpose of the study is to measure the impact of this product among all households and high-income groups. Data were collected from Census (2016) and the neighborhood quality survey (2015-2016) of Tehran Municipality. We use agent-based model for investigate the behavior of households. The results showed that the gross public goods are effective in choosing the house of all households in the city. Among the variables in this group, for all households in the city, “access to the public transportation system” has the greatest impact, but this variable does not affect the choice of high-income households. The variable “access to educational space” also has the least impact among all households, but in the high-income group, this variable has the most impact. In the group of net public goods, all variables are effective in choosing the house of all households in the city, and the variable “security” has the greatest impact. Compared to the high-income group, except for “distance to educational space”, other variables do not have a significant effect on housing choice, which indicates the unbalanced spatial distribution of net public goods in the city and the homogeneity of spatial distribution in high-income households. Also, based on the research background, educational space in the high-income group is considered as a measure of neighborhoods quality.

The study of speculators' behavior is very important in explaining the phenomena of the Iranian housing market. Many of the unfavorable phenomena that occur in this market are the results of speculative activity in the housing market. Speculators enter the market at the beginning of the boom period and earn maximum profit of price increases, and as signs of recession appear, they quickly exit the market and invest in parallel markets such as banks, stocks, exchange and gold, which leads to fluctuations in housing prices. In this study, following the Roehner model and using the time varying parameter ordinary least squares method (TVP-OLS), the speculation index in the housing market during the period 1991 to 2020 was estimated, and then the impact of different shocks to stock market, exchange market, gold market, interest rates and housing tax on speculation in the housing market was investigated, and for estimating the effects of these shocks, the Markov-switching vector autoregressive (MSVAR) method was used. The results show that during the period under study, on average, 20% of the increase in housing prices was related to speculation, with the highest growth in 1997 at 320% and the lowest growth in 2005 at -23%. According to the results of the Markov-switching vector autoregressive model (MSVAR), speculation in the housing market had the highest response to the exchange and gold markets, and interest rates and the least response to the stock market and housing tax.

Speculation in the housing market is one of the main causes of fluctuations and shocks in the housing market that have destructive effects on the macroeconomic variables. In this study, the spatial pattern of speculation in the housing market of 22 districts of Tehran has been investigated. The research period was 2001 to 2019 and Nonlinear Least Squares (NLS) method was used to estimate the model. The results indicate that the motivation for speculation is the result of price expectations and price trend analysis of previous periods, which was reflected in the form of speculation demand in the market and had a significant impact on fluctuations in the housing market. Also, due to the low elasticity of housing supply, it can be concluded that increasing the speed of production and strengthening the supply side, can be a key factor in controlling market fluctuations by responding to the demand for speculation. The results show that the intensity of speculation in areas 1, 2, 3, 5 and 22 of Tehran is higher than other areas and by moving from north to south of Tehran, the intensity of speculation has decreased. According to the results of the research, the beginning of speculation in region 1 of Tehran started before 2001 in the housing market and for other regions of Tehran, the time of speculation started after 2001.

Over the past two decades, in developing countries, including Iran, with the increase in population and the trend of migration to cities, housing has become one of the acute problems of policy makers and planners. The main purpose of this study is to estimate the consumption demand for housing in Iran. In this study, to estimate the model, uses the autoregressive distributed lag (ARDL) method and the period is from 1991 to 2020. Findings show that population, permanent income and life expectancy have positive and significant effects and housing prices have a negative and significant effect on the consumption demand for housing. With an increase in population, permanent income and life expectancy in short run and long run, consumption demand for housing is increases. Housing prices have had a significant negative effect on consumption demand for housing in both short and long run. Increase in housing prices, reduces of consumption demand for housing. The error correction coefficient shows that in each period, 64% of the imbalance in the consumption demand for housing in Iran is adjusted and moves towards a long run value.

In terms of financing, penetration in global markets and emphasis on comparative advantage, attracting FDI play a key role in boosting economic growth, providing foreign exchange and increasing non-oil exports. In this study, the effect of determinants on foreign-owned firms survival is investigated by the Complementary Log-Log Model. To achieve the purpose, the future status of valid foreign investment licenses in 2007 until 2019 was followed. The results suggest that after JCPOA until withdrawal of US from the agreement, foreign-owned firms were less likely to leave Iran than during the sanctions. The results also show that with increasing in age and size of capital, the hazard of foreign-owned firms exit will decrease. Foreign-owned firms from Europe were less likely to leave than other investors, as well as foreign-owned firms which active in the industrial sectors and some provinces with more than three million people were less likely to leave.

The "life cycle" theory is the most important tool for understanding the relationship among characteristics of housing and households, which reflects the behavior of households in the city. The theory, households are defined based on characteristics such as household size, number of students, employment status, income, age of household, etc. Every household, based on what are their characters, has different priorities in housing choice. The purpose of this study is to analyze that characteristics of households are effective on housing choice, which was developed in two parts: quality of housing unit and access to urban facilities.

To measure the variables, the city of Tehran was selected as a case study and the research data were obtained from two sources; the data for households characters was obtained from the households expenditure-income survey which w implemented annually by the National Statistics Center and the locations of urban facilities were gained by urban development plan of Tehran.By using a univariate multi-nominal method and cluster analysis, effective characteristics in the housing selection model were identified from the two groups of characteristics of quality of "housing unit" and "access to urban facilities", effective variables in selecting "housing unit" are, area of hous unit, the type of structure building, variables in "access to urban facilities" included access to educational services and public transportation system, which created a total of 6 options for selecting residential units and 8 options for accessing urban facilities. Then, the relationship between household characteristics and housing selection was performed using the multivariate regression method in R software.

The analysis of the model revealed that residential units with an area of ​​60-90 square meters are the most attractive ones for households with students. Also, increasing the income decile of households, the tendency of households to live near educational spaces and distance from the public transportation system increases. Households with the employment status of a caregiver in a non-simple working group are more inclined to choose to live in houses close to the public transportation system than households with a caregiver, which indicates the difference in financial capacity between the two groups of households.

The relationship between students and the area of ​​residential units has been proven in research background, but in this study, a specific range for the unit area(60-90 square meters) is proved for households with students. However, the relationship was not proved in other areas of housing characters such as the situation of access to urban facilities.Another important conclusion has occurred in the relation between the group of variables "residence" and "household characteristics". Only the variables related to job and income are effective in choosing a place of residence and other social and demographic characteristics of households do not have a significant relationship with the household location that can be reflected in living conditions, lack of identity and prestige in the city's neighborhoods, social segregation in Tehran, incorrect policies in the field of public goods and inappropriate housing distribution conditions in this city.

The influence of gross domestic product (GPD) on housing prices has been investigated in numerous studies but the influence of human capital shock on housing prices is a new subject. This article, which is an adoption of Young & Pan (2020) work), tried to investigate this subject and analyze the findings. The statistical population of this research includes the provinces of Iran and a period of 2012 to 2017 has been chosen as the research sample, retrieved from existing economic data. Panel vector autoregressive models were employed in order to analyze data. Obtained results from regression models depict that human capital has impulse effects on housing prices through channels. In other words, boosting/promoting human capital will increase the demand for housing and prices. The results of impulse response function (IRF) depicted that response of human capital (housing price) to fluctuations in housing prices (human capital) and the response of production to fluctuations in human capital is higher than other variables responses. The results of variance analysis illustrate that the housing prices can be explained better by gross domestic product (18%) and human capital (1%), better than other variables. Hence, the growth of the housing market can have a positive influence on the economy. It should be noted that the influence of housing market growth (production) on production (housing market growth) is significantly greater than human capital.

Housing is one of the most important basic human needs and as the worthy asset of the household is the determining factor in economic success, formation of personal identity, and social status of people. Also, it can play an important role in macroeconomic and household dimensions and is provided in two forms: rent and real. Since home ownership is the biggest financial investment in the lifetime of many households, rental housing is preferred in decisions. On the other hand, the amount of rent in villages has increased significantly in the last few years. Several factors are effective in housing tenure choices (real or rent) that identification of these factors and their effectiveness can help to make policy, housing sector planning, and increasing the welfare and economic level of society. In this study, by applying the Probit model and using sample events related to the budget of rural households in 2020, housing tenure choice has been studied. The results show that permanent income, age, marital status, employment of the head of the household, and size of the family increase the probability of home ownership in the villages, and the level of education of the head of the household increases the probability of renting.

Housing is one of the most critical and expensive assets of human life, and it is a costly commodity that requires long-term planning. A major issue in the housing sector is affordability, especially for low-income families. In this paper, we combine the cross-sectional household income-expenditure microdata (generated by the Iranian Statistics Center) and create pseudo-panel data. We make cohorts from the repeated cross-sectional data and examine family behavior for 25 years. This study was conducted from 1991 to 2015 and covered seven metropolises including Tehran, Karaj, Mashhad, Isfahan, Tabriz, Shiraz, and Ahwaz. The target population of this study consisted of those who were born from 1930 to1989. Because rich people make more speculative investment in housing than their need, this behavior affects the housing market and low-income households through the intensification of income inequality. The empirical results demonstrate that the high per capita income inequality of households in metropolises has a negative and significant effect on residual income (i.e. non-housing expenses) and per capita living space. That is, higher income inequality is related to a lower residual income and a lower per capita living space. The results also show that permanent income has a positive and significant effect on housing affordability, and higher permanent income leads to an increase in residual income as well as an increase in per capita living space.

mersion of Non-current demands is one of the effects monetary activities of credit institutions. A phenomenon that has a negative impact on the resource and cost cycle of banks. Housing price changes is one of the most important factors affecting on delayed bank demands. The boom and stagnation of the housing sector, can be effective on the repayment ability of the received facility. In this study, using the model (Blundell and Bond, 1998), it is attempting to examine the effect of housing prices on nonperforming loans in the banking sector. For this purpose, a dynamic panel model was used for a sample of 15 banks during the period of 2005-2015 using generalized moments method. In this study, the growth rate of GDP without oil, the rate of growth of housing price changes and the unemployment rate is used as independent variables and the ratio of facilities to assets, the ratio of cost to income, the size of the bank and the proportion of facilities granted to the housing and housing sector to the total facility is used as the factors of inter banking. The results of this study shows that the growth rate of housing price changes and the growth rate of gross domestic product without oil has a negative and significant effect, and unemployment rate has a positive effect on the ratio of non-current demands to facilities.

Housing is one of the most important and most expensive assets of the human life. It is an expensive commodity that requires long-term planning to purchase it, so housing affordability in the household budget portfolio is one of the most important issues. In this paper, based on Zhang (2015) model and Matlack and Vigdor (2008) study and using a cross-sectional household income - expenditure microdata generated by the Iranian Statistics Center, we generate pseudo-panel data. We make cohorts by repeated cross sectional data and monitor family behavior for 25 years. This study was conducted for the period of 1991 to 2015 and includes seven metropolises: Tehran, Karaj, Mashhad, Isfahan, Tabriz, Shiraz and Ahwaz, which covers only urban areas. The target population studied in this study is those who were born between the years 1930-1989. The empirical results demonstrate that higher per capita income inequality of household within metropolises has a negative and significant effect on residual income (household non-housing expenses) and a positive and significant effect on the house price and rent. That is, higher income inequality is related to a lower residual income, higher rent-to-income ratio and higher housing-price-to-income ratio for low-income households.In particular, a rise in income inequalities, as increase in gap between high-income groups and low incomes, lead to a reduction in housing affordability of middle/low-income households, and the quality of use of these households is significantly reduced. The results also show that permanent income has a positive and significant effect on housing affordability, and higher permanent income leads to increase in residual income.