University of IsfahanUrban Economics2588-48673220181222An Analysis of the Effects of Monetary Policy Shocks on the Housing Sector: (A DSGE Model)An Analysis of the Effects of Monetary Policy Shocks on the Housing Sector: (A DSGE Model)1182363610.22108/ue.2019.111991.1070FAHosseinPanahiProfessor, Department of Development Economics and Planning, Faculty of Economics and Management, University of Tabriz, Tabriz, IranDavoudBehboudiProfessor, Department of Development Economics and Planning, Faculty of Economics and Management, University of Tabriz, Tabriz, IranHosseinAsgharpourProfessor, Department of Economic Sciences, Faculty of Economics and Management, University of Tabriz, Tabriz, IranNajmehKeshtkaranPh.D. Student, Department of Development Economics and Planning, Faculty of Economics and Management, University of Tabriz, Tabriz, IranJournal Article20180903The present study is designed to analyze the effects of monetary policy' shocks on the prices and supply of the housing sector in Iran. In doing so, a DSGE model including the households, housing sector, banks, firms, government and the central bank is used. Monetary policy is modeled according to the Taylor rule and the reaction of interest rate to inflation volatilities and relative house rent. In order to estimate the parameters of the model, Bayesian method was applied to the quarterly data for the time period between 1989 and 2016. After analyzing the estimation results using MCMC, Gelman–Brooks and prior–posterior distribution functions comparison, contractionary monetary policy' shocks has been analyzed in the form of increase in interest rate. The results show that an increase in interest rate will reduce the housing supply and price index by 3% and 2%, respectively. Moreover, housing sector responses to the policies will result in a reduction in consumption, production, inflation, exchange rate and real money balances variables.
<strong>JEL Classification: </strong>E31, E32, E42, E51.The present study is designed to analyze the effects of monetary policy' shocks on the prices and supply of the housing sector in Iran. In doing so, a DSGE model including the households, housing sector, banks, firms, government and the central bank is used. Monetary policy is modeled according to the Taylor rule and the reaction of interest rate to inflation volatilities and relative house rent. In order to estimate the parameters of the model, Bayesian method was applied to the quarterly data for the time period between 1989 and 2016. After analyzing the estimation results using MCMC, Gelman–Brooks and prior–posterior distribution functions comparison, contractionary monetary policy' shocks has been analyzed in the form of increase in interest rate. The results show that an increase in interest rate will reduce the housing supply and price index by 3% and 2%, respectively. Moreover, housing sector responses to the policies will result in a reduction in consumption, production, inflation, exchange rate and real money balances variables.
<strong>JEL Classification: </strong>E31, E32, E42, E51.https://ue.ui.ac.ir/article_23636_020531ca4ba6b63a5f8ac1d50ec0ab89.pdfUniversity of IsfahanUrban Economics2588-48673220181222Spatial analysis of housing price using geographically weighted regression (A case study in District 2 of Tehran Metropolitan City, Iran)Spatial analysis of housing price using geographically weighted regression (A case study in District 2 of Tehran Metropolitan City, Iran)19382366210.22108/ue.2018.109447.1056FAHamidrezaSaremiAssistant Professor, Department of Urban Planning, Faculty of Art, Tarbiat Modares University, Tehran, IranMohammadHeydariM.A. student, Department of Urban Planning, Faculty of Art, Tarbiat Modares University, Tehran, IranFatemehAghaeiM.A. student, Department of Urbanism, Faculty of Art and Architecture, Shiraz University, Shiraz, IranJournal Article20180329Housing 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 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.https://ue.ui.ac.ir/article_23662_3de5e45afe744cf805e182ff71da2d73.pdfUniversity of IsfahanUrban Economics2588-48673220181222Spatial Analysis of Poverty Areas (Case Study: GhaemshahrCity)Spatial Analysis of Poverty Areas (Case Study: GhaemshahrCity)39562432410.22108/ue.2019.113324.1088FAMahmoodArvinPh. D student of Geography and Urban Planning, Faculty of Geography, University of Tehran, Tehran, IranAminFarajiAssistance Professor, Faculty of Management and Accounting, College of Farabi, University of Tehran, Tehran, IranShahramBazrafkanM.A of Geography and Urban Planning, Faculty of Humanities,Tarbiat Modares University, Tehran, IranJournal Article20181014geographers and urban planners are using tools in order to identify the poverty areas in the cities to mitigate the social and spatial consequences of that. The purpose of this applied research is to identify and analyze the poverty areas in Ghaemshahr city using descriptive-analysis method. The dominant approach for identifying poverty areas is spatial analysis. In this research, 25 indicators were extracted from the statistical block data of 1390 census. Grey Analysis Method was used to weight research Indicators and for overlapping layers researchers used the Fuzzy method in ArcGIS software. To analyze the spatial distribution of poverty areas spatial statistics were applied. The results illustrated that the central areas of the city around the main squares are in a good condition regarding research indicators and the highest levels of urban poverty is recognized in neighborhoods 15, 16, 17 and 20 which are located in the south and north-east. According to the classification of poverty in the city, the poverty level of the most deprived class was 6.66% and poverty in the deprived, middle, affluent and very affluent class were calculated 19.08, 32.90, 23.32 and 18.02 respectively. Urban poverty clustering has been confirmed using the Moran model; spatial patterns of clustering have been identified using the Hot Spot Analysis.geographers and urban planners are using tools in order to identify the poverty areas in the cities to mitigate the social and spatial consequences of that. The purpose of this applied research is to identify and analyze the poverty areas in Ghaemshahr city using descriptive-analysis method. The dominant approach for identifying poverty areas is spatial analysis. In this research, 25 indicators were extracted from the statistical block data of 1390 census. Grey Analysis Method was used to weight research Indicators and for overlapping layers researchers used the Fuzzy method in ArcGIS software. To analyze the spatial distribution of poverty areas spatial statistics were applied. The results illustrated that the central areas of the city around the main squares are in a good condition regarding research indicators and the highest levels of urban poverty is recognized in neighborhoods 15, 16, 17 and 20 which are located in the south and north-east. According to the classification of poverty in the city, the poverty level of the most deprived class was 6.66% and poverty in the deprived, middle, affluent and very affluent class were calculated 19.08, 32.90, 23.32 and 18.02 respectively. Urban poverty clustering has been confirmed using the Moran model; spatial patterns of clustering have been identified using the Hot Spot Analysis.https://ue.ui.ac.ir/article_24324_b347efc1905ae9fae0eaa54897a113ff.pdfUniversity of IsfahanUrban Economics2588-48673220181222An Analysis of the Effect of Subsidy Reform Induced'''' Energy Price'''' Risk on the Budget of Urban Households in IranAn Analysis of the Effect of Subsidy Reform Induced'''' Energy Price'''' Risk on the Budget of Urban Households in Iran57782432510.22108/ue.2019.109928.1057FAZeinolabedinSadeghiAssociate Professor, Department of Economics, Shahid Bahonar University of Kerman, , Kerman, IranHamedehMohammad ShiraziM.A of Energy Economics, Department of Economics, Shahid Bahonar University of Kerman, Kerman, IranAlirezaShakibaeAssociate Professor, Department of Economics, Shahid Bahonar University of Kerman, , Kerman, IranJournal Article20180821This paper estimates the impact of energy price'' risk on the budget of urban households in Iran due to the subsidy reform by estimating the expenditure'' risks for nine parts of Iranian household expenditures. Moreover, the changes in the risks for different income deciles and the income and cost effects of changes in expenditures are calculated during the period of 1989- 2013. In this research, the energy expenditures of sample households are modeled as a simple function, in order to evaluate the welfare effects of energy price'' structural reform. So, energy expenditures for sample households are a function of the cost (price) of energy. Then, by estimating the risk and standard deviation of energy expenditures, the effect of subsidy reform on the households'' welfare can be analyzed. The results showed that the risk for the energy sector from 0.062 percent since 2009, one year before the execution of law on subsidy reform, reached 0.072 percent in 2011. The risk for food, clothing and footwear was not changed that much. The risk for all sectors, however, decreased in 2012, due to a sharp rise in inflation rate from 12.4 to 21.5 percent between 2010 and 2011. Such an increase, to some extent, has undermined the increase in energy prices due to the subsidy reform. The risk for lower-income deciles has been increased in the beginning years of implementation of subsidy reform, but the risk of high income deciles has been either fixed or slightly changed. It should be noted that in 2011, due to the increase in inflation rate, the risk for low-income households decreased to some extent. Finally, the income and cost effects of changes in energy expenditures have put the household energy expenditures at risk and the risk for all households of the society, especially low-income households, has been increased.This paper estimates the impact of energy price'' risk on the budget of urban households in Iran due to the subsidy reform by estimating the expenditure'' risks for nine parts of Iranian household expenditures. Moreover, the changes in the risks for different income deciles and the income and cost effects of changes in expenditures are calculated during the period of 1989- 2013. In this research, the energy expenditures of sample households are modeled as a simple function, in order to evaluate the welfare effects of energy price'' structural reform. So, energy expenditures for sample households are a function of the cost (price) of energy. Then, by estimating the risk and standard deviation of energy expenditures, the effect of subsidy reform on the households'' welfare can be analyzed. The results showed that the risk for the energy sector from 0.062 percent since 2009, one year before the execution of law on subsidy reform, reached 0.072 percent in 2011. The risk for food, clothing and footwear was not changed that much. The risk for all sectors, however, decreased in 2012, due to a sharp rise in inflation rate from 12.4 to 21.5 percent between 2010 and 2011. Such an increase, to some extent, has undermined the increase in energy prices due to the subsidy reform. The risk for lower-income deciles has been increased in the beginning years of implementation of subsidy reform, but the risk of high income deciles has been either fixed or slightly changed. It should be noted that in 2011, due to the increase in inflation rate, the risk for low-income households decreased to some extent. Finally, the income and cost effects of changes in energy expenditures have put the household energy expenditures at risk and the risk for all households of the society, especially low-income households, has been increased.https://ue.ui.ac.ir/article_24325_76d441cd70c4b0fd62f1ce2b82a43a35.pdfUniversity of IsfahanUrban Economics2588-48673220180924Land Use Evaluation with an Emphasis on Increasing Societal Security in Residential Neighborhoods: (The Case: Fatemieh Neighborhood of Mashhad)Land Use Evaluation with an Emphasis on Increasing Societal Security in Residential Neighborhoods: (The Case: Fatemieh Neighborhood of Mashhad)79962432910.22108/ue.2019.112571.1077FAMohammad RezaMabhootAssistant Professor, Faculty of Architecture and Urbanism, Khavaran institute of Higher Education, Mashhad, IranMahdiMadahiAssistant Professor, Faculty of Architecture and Urbanism, Khavaran institute of Higher Education, Mashhad, IranAmirrezaRezayee GorganiMaster student of Urban Planning, Faculty of Architecture and Urbanism, Khavaran Institute of Higher Education, Mashhad, IranJournal Article20180818Security has always been an important issue throughout history. Regarding the importance of the subject, the effect of land use in residential neighborhoods on societal security and crime prevention has been analyzed in this study using descriptive-analysis method. Data collection has been conducted through studying library resources, documentation, and field surveys. Our sample size was calculated 383 using PASS software. Finally, the data were analyzed by SPSS software using Mann-Whitney, Spearman correlation coefficients and Friedman tests. The VIKOR model was used for evaluating the options. The results illustrated that societal security in Fatemiyeh neighborhood is low and land uses affect the security level. There are also incompatible land uses around and inside the neighborhood that have affected societal security. Among existing land uses, religious, educational and health uses have had the greatest impact on improving societal security in Fatemiyeh neighborhood. Finally, some suggestions for improving societal security in order to optimize land use are proposed.Security has always been an important issue throughout history. Regarding the importance of the subject, the effect of land use in residential neighborhoods on societal security and crime prevention has been analyzed in this study using descriptive-analysis method. Data collection has been conducted through studying library resources, documentation, and field surveys. Our sample size was calculated 383 using PASS software. Finally, the data were analyzed by SPSS software using Mann-Whitney, Spearman correlation coefficients and Friedman tests. The VIKOR model was used for evaluating the options. The results illustrated that societal security in Fatemiyeh neighborhood is low and land uses affect the security level. There are also incompatible land uses around and inside the neighborhood that have affected societal security. Among existing land uses, religious, educational and health uses have had the greatest impact on improving societal security in Fatemiyeh neighborhood. Finally, some suggestions for improving societal security in order to optimize land use are proposed.https://ue.ui.ac.ir/article_24329_3b2d0de36a78c1151af9e033503a6d5a.pdfUniversity of IsfahanUrban Economics2588-48673220181222Upgrading Slum Textures of Bushehr city (An Application of CDS Approach)Upgrading Slum Textures of Bushehr city (An Application of CDS Approach)971182433210.22108/ue.2019.115880.1107FARozitaMoayedfarAssistant Professor, Economic Department, University of Isfahan, Isfahan, IranZahraJamshidianMA in Economics, Economic Department, University of Isfahan, Isfahan, IranShekoofehFarahmandAssociate Professor, Economic Department, University of Isfahan, Isfahan, IranJournal Article20190305Slum textures are one of the inevitable consequences of city expansion, which can be the source of many undesirable economic, social and structural problems. Choosing an appropriate urban management strategy can play a crucial role in improving urban structure in its different areas including the slums. The aim of this research is to determine the priorities in upgrading the slum texture of Bushehr city with an emphasis on the level of livability and urban good governance, using AHP method. Therefore, according to experts opinions the priorities of livability and urban good governance sub-indicators and the priorities of slum texture neighborhoods for upgrading were determined. The sampling method was non-random and purposive. According to the results, in the slum neighborhoods, sub-indicators of livability including health, quality of economic indicators (job satisfaction and income level) and the sub-indicators of urban good governance including the rule of law and corruption control are in priority, respectively. The priority of the seven slum neighborhoods of Bushehr for upgrading is respectively as follows: The Third Tangak, The Second Tangak, The First Tangak, Saretol, Rayani, Rishehr and Emamzadeh.Slum textures are one of the inevitable consequences of city expansion, which can be the source of many undesirable economic, social and structural problems. Choosing an appropriate urban management strategy can play a crucial role in improving urban structure in its different areas including the slums. The aim of this research is to determine the priorities in upgrading the slum texture of Bushehr city with an emphasis on the level of livability and urban good governance, using AHP method. Therefore, according to experts opinions the priorities of livability and urban good governance sub-indicators and the priorities of slum texture neighborhoods for upgrading were determined. The sampling method was non-random and purposive. According to the results, in the slum neighborhoods, sub-indicators of livability including health, quality of economic indicators (job satisfaction and income level) and the sub-indicators of urban good governance including the rule of law and corruption control are in priority, respectively. The priority of the seven slum neighborhoods of Bushehr for upgrading is respectively as follows: The Third Tangak, The Second Tangak, The First Tangak, Saretol, Rayani, Rishehr and Emamzadeh.https://ue.ui.ac.ir/article_24332_0ee2919a4557f502be841b9190dc3893.pdf