جستجوی مقالات مرتبط با کلیدواژه « housing prices » در نشریات گروه « اقتصاد »

Various dimensions of housing heterogeneity have gained relative popularity in recent years. The most essential aspect of housing heterogeneity is a set of differences including technical, governance, socio-economic, and ecological differences of each residential unit. The origin of these distinctions is an objective matter that is regarded as an essential aspect of the research framework, but is frequently overlooked in managerial decision-making. In the scientific community, the social, technical, and economic dimensions have received the most attention, whereas the role of the behavioral characteristics of investors in housing prices has received scant attention. Focusing on the aspects of behavioral economics theory, the present study analyzes the heterogeneity of the behavior of housing investors, as well as the internal and external factors influencing housing prices and their effects on inflation in Iran from 2011 to 2020.

The primary objective of this article is to evaluate the effects of heterogeneous behavior of housing market investors on housing prices and the effects of heterogeneous behavior of housing market investors on inflation via housing prices. The following equation is used to determine the price of a house:POH=f1(A1. A2.A3) (1) In Equation 1, POH  represents the expense of housing, A1  is a vector of exogenousfactors influencing housing prices, A2  is a vector of exogenous factors influencing housing prices, and A3  is a vector of investor behavioral variables in the housing sector.Overoptimism and herding effect are considered to be two behavioral variables of housing sector investors:HBHt=1Tt=1T|et-em|  (2)OCHt=QtSt   (3)In equation 2, HBHt  represents the herding effect of investors in housing sector, et  represents the housing return at time t, and em  represents the average return of housing market. In equation 3, OCHt  represents overoptimism, Qt  represents the number of building permits issued, and St  represents the quantity of residential unit investment.Inflation is also viewed as a function of housing prices and other macroeconomic factors according to the equation below:INFR=f2(POH.B)  (4)In equation 5, INFR  represents inflation rate and B is a vector of independent variables influencing inflation.

During the period 2011-2020, land price, population growth rate, liquidity, and herding effect had a positive significant effect on housing prices in Iran, according to estimates. Conversely, the number of residential units constructed and the exchange rate has had a negative significant impact on housing prices in Iran; while, the variables of interest rate, per capita income growth rate, and overoptimism had nonsignificant effect on housing prices. Regarding the factors influencing inflation, the data also indicates that the housing price, exchange rate, and liquidity had a positive significant effect on Iran’s inflation rate between 2011 and 2020. In contrast, population growth and per capita income growth had a significant negative impact on inflation; while the interest rate had a negative but nonsignificant impact on Iran’s inflation rate over the period under review. Due to the nonsignificance of the effect of overoptimism on housing prices in the seemingly unrelated regression (SUR) model, it can be concluded that housing prices do not mediate the effect of overoptimism on inflation. Due to the significance of herding effect on housing prices, however, the mediating effect of housing prices and herding effect on inflation is confirmed.
Discussion and

In this article, SUR was used to analyze the effects of behavioral and non-behavioral factors on housing prices and inflation in Iran from 2011 to 2020. The following results were obtained:• An increase of 1% in internal factors affecting housing prices, such as land prices, the number of completed construction units, population growth rate, and per capita income, have resulted in respective increases of 1.19, -1.36, 0.59, and -0.015 in the Iranian housing costs.An increase of 1% in the behavioral factor of herding effect has resulted in a change of 0.77% in housing prices in Iran.A 1% increase in housing prices, currency prices, and liquidity has resulted in an inflation rate increase of 0.18%, 0.92%, and 0.17% in Iran, respectively. A 1% increase in the population growth rate and the per capita income growth rate has caused a decrease of 1.53% and 0.141% in inflation rate, respectively.Through housing prices, the behavior of investors in the housing sector can indirectly influence the inflation rate. Considering the positive impacts of herding effect on housing prices and housing prices on inflation rate, it can be concluded that herding effect has a positiveimpact on inflation rate.In accordance with the stated findings, the following policy recommendations are provided to prevent the rise in housing prices and inflation:Considering the positive impact of herding effect on the housing price and, consequently, the inflation rate, it is necessary to take measures to control and reduce emotional and irrational behavior of investors in housing sector. Since the internal factors of land price and population growth rate have a positive effect on the housing price, while the number of completed construction units and per capita income have a negative effect on the housing price, it is recommended that government provide unused governmental lands and remove obstacles to complete half-finished buildings that have been halted for legal reasons, and assist in supplying more housing to reduce its price. In addition, government should help control housing demand and reduce demand pressure by adopting population control policies and establishing suitable working, health, and educational conditions for the villagers, to diminish immigration level.

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.

During the last two decades, housing price fluctuations in some countries including Iran have been a main challenge of the housing market and the country's economy. In one period, there was a significant increase in housing prices and, in another period, it decreased or stabilized. Relatively high and widespread, it governs the price of housing, as a result of which significant developments have occurred in the housing sector and in the entire economy. In new theories, housing prices can fluctuate over time, and housing price fluctuations can be divided into two important categories. First, minor fluctuations result from market structure based on fundamentals. The housing market is based on the housing supply and demand conditions and the endogenous factors of the housing sector. Hence, the gradual and slow changes in the housing price over time are caused by the basic and underlying factors of the housing market and through changes in the total cost. Housing production changes housing prices. Second, housing cyclical shocks or impulses, are the exogenous factors that create cyclical shocks in the housing sector, and the monetary policy's effect on asset prices, including real estate and housing, is determined. The capital market, household asset portfolio composition and macroeconomic variables are among them.

We assume thatis the probability space,  is a filter created by Brownian  and Poisson process  with  is intensity. We also assume that Brownian process, Poisson process  and price jump  are independent of one another.  housing prices are based on time . In the Black-Scholes model (BSM), housing prices at time t are modeled by the following geometric Brownian process:where  is the average and  standard deviation of housing prices. In the jump diffusion model (JDM), housing prices are calculated by the following equation:where  is the expected growth rate,  is the turbulence of the Brownian process, and  is the housing price at time t and before the jump.

In this research, using GEM algorithm, the five parameters of jump diffusion model were estimated and then two parameters of Black-Scholes model were estimated using the maximum likelihood method. Next, the simulation of the future housing price was done based on the Monte-Carlo method. The simulation was done in 100,000 repetitions, and then the best model was selected. The housing price was simulated based on the real price, so that the price at time t could be calculated with its next monthly price, i.e. t+1. This method was repeated until the last data. In this research, many models were simulated with random numbers generated for housing prices to get the best model with the least error. In three cases of 6 months, 12 months and 24 months, housing prices were simulated and predicted. One way to calculate the accuracy of the model was based on the confidence interval with the assumption of normal approximation. One way to check the stability of the obtained coefficients of the models was to repeat the simulation with different random numbers and calculate the average performance of each model. In this research, in order to avoid bringing a large number of estimated models, 25 models with the best performance and the least error, and among these 25 models, the best models were identified.The results of the models show that, in most of the provincial centers of Iran, the jump diffusion model yields better results than the Black-Scholes model. Also, in some provincial centers, the 6-month performance is better, and, in some others, 12-month or 24-month performance is better. On the other hand, some provincial centers perform better in 6 months, 12 months and 24 months. The results of the average jump frequency in the centers of the provinces of Iran in the housing market show that, for most of the provinces, the average jump frequency is a high number, which indicates high fluctuations and the high impact of internal and external shocks in the Iranian housing market.

Conclusions and policy implications: 

Accurate modeling of the pricing of various assets, including the housing market, as well as its fluctuations, has always been one of the concerns of researchers and policymakers. Therefore, this research aimed at the comparative analysis of housing prices using Black-Scholes asset pricing models and jump diffusion in the provincial centers of Iran. This study used the monthly housing price data in the provincial centers of Iran for a period from March 2009 to March 2023. In addition, through the GEM algorithm, the jump diffusion model and the maximum likelihood method, the Black-Scholes model was fulfilled, and then the future housing prices in the centers of the provinces of Iran were simulated by the Monte Carlo method. The research results show that, in most provinces of Iran, the jump diffusion model has better and more accurate results than the Black-Scholes model in 6, 12 and 24 months of performance. It is worth mentioning that, in some provincial centers, the results of the Black-Scholes model were better than the jump diffusion model. According to the results of the average jump frequency, it is clear that the highest and lowest average jump frequencies belong to Khorasan Razavi and Kohgiluyeh-Boyer Ahmad Provinces with values of 0.58 and 0.09, respectively.

To understand the behavior of housing prices in the country it is necessary to investigate the spatial interactions of interconnected regional markets. The discussion on housing prices highlights the potential spatial heterogeneity and cross-sectional dependence in different regions. It is important to pay attention to the source of cross-sectional dependencies to better understand the dynamics of housing prices in different regions. Cross-sectional dependence can be caused either by the role of space in economic processes or by common shocks that affect the entire economy, such as the oil shock. Using a spatio-temporal housing price diffusion model, the study found that the Tehran region, as the center of economic development and oil revenues, was a dominant region, and that shocks to Tehran were propagated contemporaneously and spatially to other regions. The results show that in modeling the spatial diffusion of housing prices, financial proximity is more important than geographic proximity, and regions with stronger financial links with Tehran will be most affected by shocks to this region in the long run.

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 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.

One of the factors influencing these price jumps is the speculative behavior of banks in the housing market; The purpose of this article is to investigate the effect of commercial behavior of banks, including commercial, specialized, privatized and private, on housing prices in 22 districts of Tehran. Therefore, the necessary estimates have been made by a data panel model during the period 1392 to 1398 and using monthly battery variables.The behavior of banks in the model is examined using the variables of surplus deposits, banks' debt to the central bank and non-current facilities. The overall results show that the speculative performance of non-governmental banks on housing prices is much higher than that of state-owned banks. The results of the effect of the model variables show that the elasticity of housing prices to the surplus of deposits in the privatized and private banks have the highest coefficient, respectively. The highest elasticity of housing prices to the debt of banks to the central bank is related to specialized banks, which, of course, the high ranking of specialized banks due to the defined duties of this type of banks, which is not naturally comparable to other banks; But private and privatized banks are next. ; But private and privatized banks are next. The elasticity of housing prices to non-current facilities in commercial and specialized banks has the highest rank, and private and privatized banks are in the next ranks.

Consideration of the importance of the role of housing in the economy, especially in metropolitan areas such as Tehran, the analysis of house pricing and identifying the factors affecting housing prices is very importance. A noteworthy point in the study of housing price changes in the metropolis of Tehran is that the rate of price growth in different areas of Tehran has not been the same. Experience shows that price changes start in one district and then spread to other districts. Therefore, we should consider proximity and spatial dependence of housing prices in urban distrists.

THEORETICAL FRAMEWORK:

In the analysis of housing price data, the spatial dependence between observations must be taken into consideration. Accordingly, many experimental studies have shown strong evidence of spatial dependence in the housing market between urban areas. Spatial dependence on housing prices is also referred to as the wave effect, implying that housing prices in a district cause changes in neighboring districts' prices. Behavioral economics is a theoretical basis that can express the phenomenon of spatial dependence between urban districts. Regarding the theories of behavioral economics, nearby urban areas have the same culture, history, environment, and policies. Other phenomena that express the spatial dependence in the housing market of urban districts include migration theory, capital transfer, arbitrage, and spatial patterns.

 A critical issue in studies using spatial econometric techniques is the choice of the type of spatial model. Depending on the type of spatial interaction, we will encounter a variety of spatial models. To select spatial models, we can first consider the general spatial model and with the relevant tests to ensure the existence of the type of spatial interaction factor. Then, the appropriate type of model can be selected. According to Elhorst (2014), using the spatial lag fixed-effect model, we have modeled the housing prices in 22 districts of Tehran city in this study. Some variables affecting housing prices are related to the demand side of the housing market, and some are related to the supply side. Here, the explanatory variables in the model are the annual population growth rate of the districts, and the annual growth rate of the real disposable per capita income, which shows the demand side influencing the housing price, as well as the number of building permits and the annual unemployment rate as the supply-side variables that affect housing prices. The dependent variable is the districts' average annual real growth rate of housing prices.

According to the spatial specification results, the dynamic SAR model has been selected as the appropriate model. The estimated coefficient of the spatial lag of the dependent variable with a time lag is 0.033, which is statistically significant and indicates a positive effect. It implies that the spatial lag of the dependent variable with a time lag has influenced the growth rate of housing prices in the 22 districts of Tehran. Among the explanatory variables affecting the growth rate of housing prices in the 22 districts of Tehran, it is observed that, except for the unemployment rate variable, the other variables have a significant effect on the growth rate of housing prices in Tehran districts. The analysis of the local Moran scatterplot demonstrates that the spatial correlation of the housing price growth rate in the northern districts of Tehran is different from the southern districts.

 CONCLUSIONS & SUGGESTIONS:

Among the explanatory variables affecting the growth rate of housing prices in Tehran districts, except for the unemployment rate, other variables have statistically significant impacts. Two variables of the growth rate of income per capita and population growth rate have positive effects. However, the number of building permits has negatively influenced housing prices' growth rate. The statistically significant estimated coefficient of spatial lag of the dependent variable in the model implies the spatial effects of the housing price growth rate. All the necessary tests indicate that the null hypothesis, which indicates the lack of spatial autocorrelation, has been rejected, and a spatial correlation among the housing price growth rates of districts has been confirmed. The local Moran scatterplot illustrates that the Spatial correlation of housing price growth rate in the northern districts of Tehran is entirely different from the southern areas of Tehran. It is recommended that urban policymakers should not ignore the spatial relationship between housing prices in 22 districts of Tehran. Also, it is recommended to the policymakers, due to the different spatial correlation rate of the housing price growth rate in the southern areas of Tehran city compared to the northern districts of the city, for each of the northern and southern areas of Tehran, they should make different policies.

The issue of housing prices and its sharp rise has always been one of the most challenging issues for households and the government. On the other hand, municipalities, especially large municipalities, consider urban development as one of their main missions. What effect this investment in infrastructure and development projects will have on housing prices is important not only at the local level but also at the national level. The effect of investments in urban development plans on housing prices in Tehran is the subject of this study. The main purpose of this study is to investigate the effect of investment in infrastructure and urban services on increasing housing prices in 22 districts of Tehran. In other words, this study seeks to answer the scientific question of how much the difference in changes in relative housing prices in the 22 districts of Tehran is affected by this factor. Method used in the research The hedonic method and the variables used are three groups of variables that affect the price of housing in the city, which include physical or intrinsic variables of housing, environmental variables of housing and space variable.Due to the existence of spatial self-correlation and the results of Moran 1 test, the hedonic model has been estimated using the space factor. The results show that the effect of environmental variables is more than intrinsic variables and the variable of the share of the road network of the total area of ​​neighborhoods has the greatest impact on the price of residential units in Tehran.

This study seeks to measure the degree of housing speculation in selected provinces of Iran with a spatial econometric approach, and for that porpuse, the real value time series data on land prices, housing prices, GDP and rents in 20 provinces during the period (1385-1396) is used. After examining the effects of these variables on the housing prices of the selected provinces, a fixed-effect spatial autoregression model was estimated and the degree of housing speculation in the provinces was calculated based on the gap between the average actual and the estimated prices (intrinsic value). The results indicate that housing rent and land prices have a positive effect while GDP has a negative effect on housing prices. The housing prices gap was significant for all provinces which based on that, then, the degree of speculation of the provinces was calculated. The spatial and contiguity effects of provinces on their neighbors were estimated as well, based on provinces' neighborhoods and their degree of speculation.

The analysis of data on housing prices and their growth rates, typical of spatial data, is undoubtedly affected by spatial dependence. This means that housing prices and their growth in one district are affected by housing prices in neighboring districts. However, the other issue regarding housing prices in different districts is the heterogeneity of spatial dependence. This suggests that there are differences in the spatial dependence of housing prices in urban districts over time so that the spatial effects in urban districts when prices rise are different from the effects when prices fall. The theoretical analysis of this phenomenon is based on the theories of behavioral economics. In this study, the heterogeneity of spatial dependence of housing price growth rates was investigated for 22 districts of Tehran. Using a dynamic fixed effects spatial panel model, the effects of determinants on growth rates were estimated for 22 districts of Tehran. The test results indicate the existence of nonlinear relationships in the model. Therefore, the model was estimated by a smooth transition panel regression model with a transfer function in which the spillover rate of housing price growth rates from neighboring counties was determined as a transfer variable. The results show that the coefficient of spatial dependence for housing price growth rates is higher in boom times than in recession for Tehran districts. Thus, it can be concluded that there is heterogeneity in the spatial dependence of housing price growth rates in the urban districts of Tehran over time.JEL Classification: C31, C33, R31.

The purpose of this paper is to analyze the dynamics of club convergence of housing prices in different areas of Tehran. For this purpose, the log t regression test method and price data in two time periods of no price jump (2012-2018) and the occurrence of housing price jump (2019-2021) were used.  Based on the results of the log t-test, in these two time periods, the null hypothesis that there is convergence in the whole sample is not rejected. Also, the existence of club convergence in each of the periods was confirmed. Accordingly, in the period 2012-2018, the regions of Tehran form three clubs, but in the period 2019-2021, price behavior in the regions of Tehran changed and all regions of Tehran became one club and converged into a state of equilibrium. According to the results of the research, it can be said that in case of intensification of convergence, housing market policies will become more difficult than before to manage this market.

In recent years, many countries have experienced boom-bust cycles in credit and asset prices, some of which resulted in severe financial crises. Given that housing prices in both advanced and emerging countries have been heavily influenced by housing credit, the decline in housing prices is similar in both groups of countries. In response to these cycles, authorities in many countries have used macro prudential policies as the frontline of defense against the financial instability risks. Macro prudential measures are, indeed, policy tools that have been used intensively in recent years to target suppliers or applicants of financial services (e.g., households and firms). There is no consensus on the relationship between monetary stability and financial stability and their effects on each other. Some argue that while high-interest rates may control inflation (monetary stability), they are deemed to destroy banks’ balance sheets (financial instability). Another argument is that in the financial instability condition, the contraction monetary policy intensifies the likelihood of instability in the financial institutions. In turn, some believe that the financial policies adopted by monetary institutions, including the central bank, are the leading causes of financial instability. On the other hand, some believe that financial stability preservation is always one of the intrinsic duties of the central bank. In effect, the lack of cooperation between the macro prudential and monetary policy-maker authorities may lead to conflicting policies and, therefore non-optimal results. Moreover, monetary and macro prudential policies as complements to each other can guarantee monetary and financial stability at the same time. This paper studies the effect of macro prudential policies and the interaction of monetary and macro prudential policies on the credit growth and housing prices using the generalized moment method. To that end, the first macro prudential tools index (including the capital requirements, loan-to-value ratios, and debt-to-income ratios) are constructed for the advanced and emerging economies over the period 2000 to 2014. Then, an aggregate index was constructed under two scenarios of tightening (contraction) and easing (expansion) actions. In effect, the produced aggregate index showed the state of macro prudential policy in each country. To construct the macro prudential tools, IMF (2011) data used in the study of Lim et al. (2011) and Shim et al. (2013) were considered. Following Eckinsky and Ramsey (2017), this paper used seven macro prudential tools (i.e., loan-to-value ratios, debt-to-income repayments and other housing sector tools, countercyclical capital requirements, loan-loss provision requirements, consumer loan limits, and credit growth limits), to construct the aggregate indicator for the macro prudential policy and sub-indices for housing and non-housing in the studied countries. The dummy variable was, in turn, used to construct the macro prudential tools. If the macro prudential tool was used or intensified in the desired season, the dummy variable was considered 1, and if the use of the macro prudential tool was reduced, the dummy variable was -1. Otherwise, if no macro prudential tool was used, the value was zero. The aggregate macro prudential policy index for each of the housing and non-housing sectors was considered as the sum of the dummy variables. In addition, by aggregating the dummy variables of the specific tools in each country, the non-housing and housing macro prudential policy indexes were constructed. The aggregate macro prudential index was also obtained from the sum of the housing and non-housing indexes. The results showed that the macro prudential policy indexes (aggregate-housing) had no significant effect on the housing prices and credit growth. However, the simultaneous adoption of the macro prudential policies and monetary policies inhibited the growth of the credit and, consequently the rise in the housing prices. In turn, a comparison of the coefficients showed that the effect of these tools on the credit growth was greater than the housing price growth. This finding, consistent with the results of other research including Wenden Beach et al. (2012), Kotner and Shim (2012), Brno and Friedrich (2014), Bruno et al. (2017), showed that the effectiveness of macro prudential policies in controlling the credit growth was more than lowering the housing prices. On the other hand, the interaction of monetary policies with the non-housing and housing macro prudential policies produced results which were more effective in controlling the credit and housing prices, respectively. In other words, the macro prudential policies that targeted the housing sector were more effective in controlling the housing prices. However, non-housing macro prudential policies were more effective in reducing the credit growth.

The purpose of this study is to investigate the effect of uncertainty in inflation, Bank finance, bank interest rates, liquidity, stock prices, price index and GDP on housing prices in Iran. In order to achieve this goal, seasonal data has been used during the period 1991 to 2013. EGARCH pattern (1,1) as an estimation of AR (4) residues for inflation is used as a substitute for inflation uncertainty measurement, and a short-term model and long-term relationships between research variables have been set. The results of short-term model and long-run pattern show that uncertainty regarding inflation, interest rate, liquidity, GDP and national income have a positive and significant effect on housing prices. Indeed, stock prices and housing finance have a negative and significant effect on Housing prices. It is noteworthy to state that there are different sensitivities to housing prices in most variables, such as household income per capita, liquidity, and stock price index in the long term and short-term; so that, according to the theory, the elasticity of house prices relative to household income per capita, the volume of money and the stock price index in the long run is more than short-term.

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.

This paper studies the relationship between housing prices and business cycles in Iran. Since housing has a dual nature, that is, both private and capital nature, it can play an important role in investment costs and economic growth and incite other manufacturing sectors in the country. In this paper, housing prices and business cycles have been used to measure housing as a collateral, which is included in corporate credit constraints as well as a shock based on observations in housing price fluctuations. In order to investigate the relationship between housing prices, investment and economic fluctuations in Iran, seasonal data for the period 1991-2016 was used. To evaluate this dynamic, a dynamic stochastic general equilibrium model has been used. The results show a movement between housing prices and business investments influenced by the dynamics of housing prices in the macroeconomic. The results also indicate that the inclusion of housing prices as a collateral could be a factor in increasing the asset value of firms and, consequently, borrowings and future investments that lead to a move between housing prices and Investment and economic fluctuations in the country.

dentifying Tehran's housing market plays an important role in policing the country's urban housing sector and helping timely and effective decision-making. This study analyzes the market and forecasts of housing prices up to 1405 in Tehran using the dynamic system method. In this study, a causal model is first developed to study the dynamics of Tehran housing and then a Stock-flow model is designed to simulate a causal model. The model results predict an increase in housing prices by exponential growth pattern up to 1405. As land prices rise, housing prices rise exponentially. Hous-ing prices also follow capital demand and rise exponentially. By controlling capital de-mand for housing, one can reduce fluctuations in housing demand, which reduce hous-ing prices. After validating the model, demand and supply scenarios are designed

Housing is one of the most important bases for providing households' biological, economic, and social needs, and regarding the high volatility in supply and demand, a decent planning is required to pro-mote the quality of the residential environment. Housing prices are amongst those housing indices that could not be completely controlled by planners, but using spatial analysis of housing market performance and promoting the efficiency of plans and providing strategies and policies for housing plannings can improve the cotrolablity of the prices. The fluctuations in housing prices are one of the main urban management' challenges facing Tehran Metropolitan City. So, this paper is trying to investigate the spatial distribution of housing prices and to identify its determinants. One of the reasons for choosing district 2 of Tehran as the case in this research is the geographical spread of this district, which covers the center to the most northern urban areas of Tehran, and consequently includes a veraity of building types, residential patterns and housing prices. The recorded sales data in the Real Estate Market System, for apartment buildings in the district sold during Shahrivar and Mehr 1396, were used and applied in OLS and GWR regression techniques for modeling and analyzing the housing prices. In addition to identifying the variables affecting the housing price, the results indicate the utility of the GWR in comparison with the OLS technique in explaining the housing prices.

Housing price swings have always been under the spotlight for policy-makers and academics. Financial accelerator mechanism (developed by Bernanke and Gertler, 1999) can provide some explanation to these fluctuations. With focusing on the concept of financial accelerator, this paper sheds light on the long- and short-run correlation of housing prices and credit in Iran. We applied a Structural Vector Error Correction Model (SVECM) to housing and credit market over period 1988q2-2015q1. Our findings confirm the existence of a cointegrated relationship between credit and housing prices. In a long-run perspective, the causation goes from credit to housing prices. However, in the short-run we find an existence of contemporaneous bi-directional dependence between housing prices and credit. In general, we find the evidence of housing collateral effect in housing and credit markets in Iran. However, this role is small and limited compared to the same role in countries with developed financial and mortgage markets.