فهرست مطالب شهلا صمدی پور

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.

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 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 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.

Trade and climate change are some of the important factors affecting income inequality. With the globalization and trade interrelationships between countries, income inequality has been reduced to some extent. On the other hand, climate change as an important and current issue in the world has had great effects on economic growth and income inequality of countries. The purpose of this study is to analyze the relationship between trade and climate change and income inequality. To examine the asymmetric effects of trade and climate change on income inequality in 60 countries, the quantile panel regression model is used by 2010 to 2020 data. In addition, the Climate Change Performance Index for climate change and the Trade Openness Index for trade has been used. The results show that climate change over the years has increased income inequality in selected countries. Also, the results show that the third to seventh quantiles of income have been most affected by climate change. On the other hand, trade between countries has reduced the inequality of selected countries in different income quantiles.

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.

Globalization an undeniable issue with economic, political and social aspects. In other words, globalization is economic, political, social and cultural rights beyond national boundaries and has received major attention of economist and politician in the last few decades. This study aims to investigate the effects of economic globalization, political globalization and social globalization on economic growth in 74 developing countries which are divided into two groups of upper middle income and lower middle income using fixed effected method. The Indicators used for globalization are account restrictions and period studied (1970-2016) .The impact of the indicators on the overall globalization, economic globalization, social globalization and political globalization in developing countries in two groups with higher incomes and lower than average are positive and meaningful. Hence, this study confirms the positive effects of globalization in all aspects on economic growth in developing countries in two groups with higher incomes and lower than average.

Today, globalization is inescapable fact for all countries. Generally, there are two approaches related to globalization in economics; according to first approach globalization leads to stable economy in the developing countries, improving the economic welfare, decreasing of poverty and increasing the economic growth. In the second approach, this phenomenon leads to dispersing power and interests in these countries and as a result, increasing economic instability, poverty and economic vulnerability of these countries. In this study, the impacts of globalization on poverty in developing countries, upper and lower middle income, over the period 1990-2014 is investigated using panel data. The results show that economic, social, and political globalization and total index have negative and significant effect on increasing poverty in upper middle income countries, statistically. Furthermore, social and total index of globalization have positive and significant effect on increasing poverty in lower middle income countries while economic and political globalization do not have significant effect in these countries, statistically.