Housing Economics

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.

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 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.

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

Housing is one of the most essential needs of a household, which it can have an important role in dimensions of macroeconomics and household, being provided in both rented and ownership types. Several factors can have effect on households choosing to buy or rent,identification of these factors and amount of effect can help to make policy and housing planning. Logit and probit models are used for this purpose and the results obtained from the estimation model are compared to determine the best model. Used data are cost and income data of urban households 93. two criteria AIC and BIC have been used to determine the best model among the two predicted models of Logit and Probit. Based on these two criteria, a model with the highest likelihood and minimum BIC and AIC is selected as the optimal model. Accordingly, by comparing two models of Logit and Probit, the logit model has the maximum likelihood and minimum BIC and AIC and is selected as the best model. for choosing the best and most significant model, we use the LR statistic between the permanent income logit models and the current income logit model and assume that the model with the highest LR statistic has less bias and the best model, but in these two models (model Permanent income logit and current income logit model) Because of the equality of degrees of freedom of both models (degree of freedom 8), there is no possibility of carrying out the LR test and LR CHI ^ 2 is used to determine the best model. By comparing the value of the LR CHI ^ 2 statistic in two permanent income logit models and the current income logit model, the permanent income logit model has a higher LR CHI-2 statistic and the model has a higher degree of certainty. Following the two permanent income logit models and the current income logit model, the logit model is selected with a permanent income and the interpretation of the model results is based on the logit model with the permanent income and the effective factors on housing tenure choice are defined: permanent and current income, age, gender and education, occupied household head and size, Based on the permanent logit model, all independent variables of the model have a positive effect on the probability of ownership of the housing and all the estimated coefficients in the model, except for the household status quotient ratio, are significant but the focus of this study is on household's income and education and The results show that income and education of the head of family has positive significant impact on housing tenure choice. The results of the marginal impact assessment show that the increase of 10 million rials in the household's permanent income increases the probability of ownership of housing by 76.1% in this research. due to the use of cross-sectional data, the explanation of the type of housing tenure has been considered. Because of the lack of policy variables in household income-expenditure data, the possibility of providing policy recommendations is limited. Nevertheless, the results can provide the basis for providing policy recommendations that may be directly or indirectly related to the results of the research. The results show that the most important and effective factor influencing the increase in the share of civil housing is permanent income; in other words, one of the main tenants of low-income groups and special groups is that they do not have permanent occupation and income, and therefore policies affecting the permanence of this job At the same time, the groups will have a decisive impact on their housework.