Hodjatollah Mirzaei; Narges Razban; Teymor Mohamadi; Habib Morovat
Abstract
Housing price shocks of one region may spread to the housing market of neighboring regions or geographical areas bounded by political border and lead to the formation of price shocks in shock-receiving areas. The housing policies may not be effective when implemented regionally and separately ...
Read More
Housing price shocks of one region may spread to the housing market of neighboring regions or geographical areas bounded by political border and lead to the formation of price shocks in shock-receiving areas. The housing policies may not be effective when implemented regionally and separately if there is a confirmed network connection between the housing markets of regions. Price shocks to a housing market spreads with a delayto interconnected housing markets, ultimately resulting in the diffusion of the price shock across the entire of the housing network. This researchaims to investigate the housing network between selected cities (centers of the country's provinces) using the VAR model and Forecast Error Variance Decomposition (FEVD). The results of this research confirm the existence of a network connection between the housing markets of the country's provinces, and unlike previous studies, the results show that it is not only the city of Tehran that spreads price shocks to other regions, but also cities such as Karaj, Shiraz, and Arak spread price shocks to other cities. In addition, the results suggest that the recent price jump, since 2019 has significantly increased the density of the housing network in the country. Based on this, price shocks are expected to be distributed more quickly throughout the country.
Introduction
In addition to the fact that economic characteristics, macroeconomic policies, and external factors affect housing prices, housing price shocks in neighboring geographical areas also spread to housing prices in each region and can lead to the formation of price changes in the price-accepting region. Therefore, it is essential to investigate the network connection between the housing markets of the geographical regions within a country. This research aims to explore the network connections and dynamics between housing markets in provincial centers, as well as the relationships between all pairs of centers to form a comprehensive housing market network for the country. Specifically, the study seeks to identify: (a) the centers of the provinces whose housing price disturbances are most contagious to other provinces and (b) the centers of the provinces that are most affected by the housing price disturbances of other provinces should be identified.
Methods and Material
The study utilized data from the Statistics Center spanning period from 2009 to 2011.
The research methodology employed the vector autoregression (VAR) model. To address the identification problem in the model, the centers of the provinces were classified into four groups:
1: Tehran, Alborz, Mazandaran, Isfahan, Gilan, Khorasan-Razavi, Qom, Qazvin and East Azerbaijan.
2: Fars, Khuzestan, Golestan, Hormozgan, Bushehr, Zanjan and Hamedan.
3: Semnan, Yazd, Lorestan, North Khorasan, Kerman, South Khorasan, Kohgiluyeh and Boir Ahmad, Markazi and Kurdistan.
4: West Azerbaijan, Ardabil, Ilam, Kermanshah, Sistan and Baluchistan, and Chaharmahal and Bakhtiari.
In the network connection approach proposed Diebold and Yilmaz (2014), the vector autoregression model or VAR has been used.
In a country with three geographical regions A, B and C:
(1)
The VAR system of equations has three equations for housing prices in areas A, B, and C. The housing price in each region such as A at the current time (t) is a function of the price of the same region in previous periods ( ), and the price of other regions in previous periods ( and ) (k=1, 2, .. K). The effectiveness of the price of region A from the price of the same region and regions B and C in the previous periods are measured by β-11k, β-21k, and β-31k coefficients, respectively. The number of optimal breaks in equation (1) is determined by the Schwartz criterion.
In order to form a network connection and to check the amount of shock propagation from region i to j, variance analysis of prediction error is used. In this regard, Diebold and Yilmaz (2014) introduced four indicators:
(1)Shock received from others: The shock received by each region from other regions
FC=
(2)Shock sent to others: The shock sent by each region to other regions
OC=
(3)Total connections per network: average total shock per region
TC=
(4)Net communication or NC: the net shock sent by any region to other regions
NC=
Correlation between regions based on variance analysis
Shock received from other areas
areas
Shock sent to other areas
Results and Discussion
The reliability test of Becker et al. (2007) was conducted for all provinces, which was found to be significant in all cases.
Based on the results of VAR model and variance analysis:
First group: Isfahan and Qom are the biggest receivers and Mashhad is the weakest recipient. Karaj and Tehran are the biggest senders of shocks and Qom and Isfahan are the weakest senders.
Second group: Gorgan and Hamadan are the most important and Bushehr is the weakest recipient. Shiraz and Zanjan are the most important shock transmitters , while Bandar Abbas and Bushehr are the weakest.
Third group: Semnan and Sanandaj are the most important shock receiver, and Bojnord is the weakest receiver; Arak and Yazd are the most important sender of shocks and Sanandaj is the weakest sender .
Fourth group: Ardabil and Kermanshah are the most important senders and receivers of price shocks, respectively. The calculation of the total communication index in the housing network shows that the first group has the densest and the fourth group has the thinnest housing network.
In order to investigate the evolution of the housing network (changes in density over time), the Galtan's regression logic was used indicating an increase in the density of the housing network in the centers of the provinces over time.
Conclusion
The dynamics of real housing price changes demonstrate three distinct patterns.. During the years 2009 to 2012, the price of most centers decreased and remained almost constant from 2012 to 2018, and then all the centers had a sharp price increase. As a result:
(1) Karaj, Tehran, Shiraz, Arak and Ardabil sent the most price shocks;
(2) Isfahan, Gorgan, Semnan and Kermanshah received the most price shocks,
(3) the strongest housing network was observed between the cities of Mashhad, Sari, Qom and Tabriz, Isfahan, Karaj, Tehran, Qazvin and Rasht and
(4) the housing network among the provincial centers has increased during the years (2009 to 2010).
In times when the living conditions in the cities that are significant senders the shock become difficult, other cities within the network have become centers of population attraction and can change their roles. Consequently, it is advisable for housing market policies to consider the mutual influence between city prices. By doing so, when market price jumps occur, the extent of shock transmission from these driving centers can be somewhat controlled, thereby mitigating market excitement and excessive growth in prices. As an example, the policies on the supply side can be such that the supply in the shock-sending areas is facilitated. Preventive measures such as supporting the supply of semi-finished units, facilitating the conditions for issuing permits, facilitating access to construction loans, activating pre-sale tools, etc., should be adopted in leading areas so that when price jumps occur, shocks will be sent from these regions to other regions at a slower rate. it is advisable for housing market policies to consider the mutual influence between city prices. By doing so, when market price jumps occur, the extent of shock transmission from these driving centers can be somewhat controlled, thereby mitigating market excitement and excessive growth in prices.
Acknowledgment
In the end, we would like to express our gratitude to Dr. Nasser Khiabani, Dr. Ali Nasiri-Aghdam, Dr. Mirhossein Mousavi, and Dr. Taleblo, for their invaluable contributions to this paper.
Ali Asghar Salem; Habib Morovat; Atefeh Heidary Milani; Masoumeh Azizkhani
Abstract
Over the last decade, there has been a clear increase in ICT expenditures by households, both in value and as a proportion of total expenditure. Such a trend, however, has not affected all households in the same way. This study analyzes the socio-economic determinants of urban household expenditures ...
Read More
Over the last decade, there has been a clear increase in ICT expenditures by households, both in value and as a proportion of total expenditure. Such a trend, however, has not affected all households in the same way. This study analyzes the socio-economic determinants of urban household expenditures on ICT goods and services in Iran in the year 2019 based on microdata from the Household Budget Surveys. To achieve this, we have applied Heckman's two-stage model, aiming to identify the determinants affecting the likelihood of spending on ICT and the amount spent. Our analysis further dissects ICT spending into its constituent components, including IT goods, IT services, communication goods, and communication services. Based on the results, per capita income has a significant and positive effect both on the probability of spending and the level of per capita ICT expenditures. Moreover, households led by male heads are more likely to engage in ICT spending, although their actual expenditures are lower. Households with larger sizes and households which have a married head are more likely to spend on ICT. The impact of education level and age of the household’s head on both the probability to use and the amount spent is positive and significant. There is a negative quadratic relationship between the age of the household’s head and both the probability of spending and the level of per capita ICT expenditures. Furthermore, consumption economies of scale exist in ICT expenditures. The likelihood of spending on all types of ICT components is positively influenced by per capita income, size of the household, education and age of the household’s head. Concerning the level of per capita expenditures on IT goods and communication goods and services, they are higher for households who have higher per capita income. There is a negative quadratic relationship between the age of the household’s head and both the probability of spending and the level of communication services expenditures. Households with highly educated heads tend to allocate higher budgets to communication goods and services. Lastly, consumption economies of scale are observed in IT goods, IT services, and communication services.
Ali Asqhar Salem; Habib Morovat; Reza Bakhtiarinejad
Abstract
Nowadays, Information and Communications Technology is growing rapidly due to the considerable increase in using knowledge-based theories in all countries, especially in developing economies such as Iran. As a non-competitive technology with unlimited use capacity, Information and Communications ...
Read More
Nowadays, Information and Communications Technology is growing rapidly due to the considerable increase in using knowledge-based theories in all countries, especially in developing economies such as Iran. As a non-competitive technology with unlimited use capacity, Information and Communications Technology entry in the general application and social life shows its potential to affect social welfare. This study will evaluate the impact of Information and Communications Technology on Sen's Social Welfare Index in Iranian provinces using data from 2011 to 2016. The paper uses Feasible Generalized Least Squares method to capture variance heteroscedasticities and cross-section correlations. The results indicate that Information and Communications Technology has a significant and positive effect on Iranian social welfare. Moreover, variables such as industrialization, government spending, and urbanization have a substantial and positive impact on social welfare. The inflation rate, on the other hand, has a significant and negative effect.
Habib Morovat; Ali Asghar Salem; mahboobeh khadem
Abstract
Tourism Industry has important role in the job creating and economic development. Because of it, identifying effective factors on foreign tourism demand and attractiveness of tourism destinations is important. We used panel data of incoming tourist and characteristics of tourism destinations in 147 countries ...
Read More
Tourism Industry has important role in the job creating and economic development. Because of it, identifying effective factors on foreign tourism demand and attractiveness of tourism destinations is important. We used panel data of incoming tourist and characteristics of tourism destinations in 147 countries between years 2007 to 2015 to examine which factors have significant effect on foreign tourism demand. We used three aggregate indices to identify characteristics of tourism destinations that are regulatory framework, business environment and infrastructure, and human, cultural and natural resources. Our results show that business environment, tourism infrastructures such as air and ground transportation infrastructure and price competitiveness have significant effects on foreign tourism demand. We have also used thirteen more detailed indices of tourism destination characteristics and show that safety and security (such as the costliness of common crime and violence as well as terrorism, and the extent to which police services can be relied upon for protection from crime) , natural and cultural resources (such as cultural heritage and nationally protected areas) and price competitiveness indices have a significant effect on foreign tourism demand.
Somayeh Shahhosseini; Ali Faridzad; Ali Faridzad
Abstract
Some researchers consider trade liberalization as a factor for increasing the quality of the environment. However, others argue that trade liberalization leads to some countries specialize in the production of pollution-intensive, energy-intensive or capital-intensive goods and therefore the quality ...
Read More
Some researchers consider trade liberalization as a factor for increasing the quality of the environment. However, others argue that trade liberalization leads to some countries specialize in the production of pollution-intensive, energy-intensive or capital-intensive goods and therefore the quality of the environment is reduced. Since theory offers grounds for both positive and negative relationship between trade and the environment, the issue must be settled through empirical analysis. In this context, the main question here is how international trade affects quality of environment in oil exporting countries? For this purpose, in the form of composition, scale and technical effects, we construct a panel data analysis to identify the relationship between international trade and carbon emission of selected oil exporting countries during 1990–2011. Our results suggest that there is positive relationship between trade openness and carbon emission and thus, pollution heaven hypothesis for these countries is not rejected. Also, the relationship between per capita output and carbon emission is N-shape and scale effect is negative. Furthermore, increasing comparative advantage and foreign direct investment leads to a reduction in oil exporting countries’ carbon emissions; which respectively indicate scale and technical effects of the international trade to be negative.
Ali Faridzad; Habib Morovvat
Volume 15, Issue 58 , October 2015, , Pages 1-36
Abstract
Resilience economy as a new subject is considered for Iranian economic policies since 2013. Applying mainstream economic theories for solving economic issues in Iran is the main concern of general policies on resilience economy. Accordingly, evaluation of vulnerability of economic sectors regarding to ...
Read More
Resilience economy as a new subject is considered for Iranian economic policies since 2013. Applying mainstream economic theories for solving economic issues in Iran is the main concern of general policies on resilience economy. Accordingly, evaluation of vulnerability of economic sectors regarding to international sanctions can identify the vulnerable sectors with the purpose of planning and executing economic resilience policies. In this study, intermediate import decomposition is used for identifying import dependence of sectors and the method of mixed variable input-output model based on constrained supply approach is applied regarding the year 2011 input-output table which is aggregated for 9 sectors. Results show that first, regarding to the constraines of supply based on import dependence of each sector, the sectors of industry and mine sector, water, electricity and natural gas distribution services and construction are the most vulnerable sectors and second, the economic sectors which have more shares in Iran aggregate import aren’t more vulnerable necessarily.
