قیمت مسکن

Introduction :

Housing is the main challenge related to urbanisation, so it is both the driver of urban growth and its result. On the other hand, housing has always been considered one of the basic needs in urban societies, which defines the quality of life and well-being of the citizens of every nation. It has always been very important in the life of urban societies. The innovation of the current research can be proposed in the sense that in the research carried out so far, the variables affecting housing prices have been modelled linearly or spatially and simultaneously with the help of regression models or spatial correlation tests. This is even though many variables influence the housing price, especially the social characteristics of the place of residence, economic characteristics on a macro and micro scale, neighbourhoods, and accessibility, which have a moderating and controlling role on the housing price. They say that this issue has not been addressed in the reviewed research. Tabriz metropolis, the sixth metropolis in the country, is facing many problems, such as a high population concentration and high land and housing prices. In the last two decades, the increasing growth of the urban population in the metropolis of Tabriz has significantly changed the housing price equations in this city. Therefore, this article, while examining the factors that influence the final price of housing in Tabriz metropolis, seeks to investigate the mediating role of influencing variables on the final housing price in Tabriz metropolis. For this purpose, by checking the theoretical framework of the research as well as examining the background and theories related to pricing in the housing sector, the selection of the most important components and sub-components influencing the price of housing in the metropolis of Tabriz will be made. In the next step, a certain number of real estate consultants in Tabriz metropolis will be questioned, and then the data will be analysed in the SPSS software environment using various statistical tests. The similarities and differences between the current research and the previous studies will be examined, as well as the conclusions from the research.

The current research is in the category of applied research. In terms of methodology, it is included in the category of correlational research in which regression methods are used to investigate the mediating role of economic, social, and accessibility variables in estimating The final price of housing in Tabriz metropolis. The studied population included all the entrepreneurs of the Tabriz metropolis, 55 cases of which were selected from real estate consultants who were ready to cooperate using the snowball method. A researcher-made questionnaire was used to collect data, and its face validity and reliability were calculated through Cronbach’s alpha 2 test. This questionnaire included four physical, economic, social, and access variables and had 39 items. After questioning, the data were entered into the SPSS 26 software environment, and the pre-tests of Kolmogorov Smirnoff, Shapiro Wilke, Durbon Watson, Tolerance, and VIF were used to determine the statistical tests and also verify the validity of the used tests. It was taken advantage of. In this way, normal distribution pre-tests (Kolmogrove-Smirnov and Shapiro-Wilk) were used to determine the statistical tests, and Durbon-Watson, VIF, and Tolerance pre-tests were used to verify the correctness of the regression model. After the pre-tests, the main tests were conducted as follows: First, using stepwise regression, the number of items was reduced from 39 items to 18 items, and then the inter-regression method was used to investigate the mediating role of the mediating variables of the research. Finally, Sobel’s test confirmed the control and mediation role of mediating variables.

After reviewing the literature and background of the research, the most important criteria and sub-criteria influencing determining the final price of housing were identified: The physical-physical criteria (13 sub-criteria), economic (9 sub-criteria), social (10 sub-criteria) and access and proximity (7 sub-criteria) were chosen as the four main criteria of this research. Then, in the next step, using step-by-step regression, a deeper investigation of the relationship between the dependent variable of the research (housing price) and the independent variables of the research (physical-physical, economic, social, and access) was done. At first, by entering the dependent variable (housing price) and 39 other sub-criteria of the research as independent variables, only 18 items are known to be effective on the dependent variable of housing price, which have been entered into the model in the order of influence, as well as 21 other sub-criteria. They were removed from the model. The first variable entered into the stepwise multivariate regression equation is the distance of the residential unit from the nearest medical-sanitary use. The second variable that has played a very important role in determining the final price of housing in Tabriz metropolis is the level of education of the residents. After the residents’ education level variable, 15 variables that have the most influence in determining the housing price in Tabriz metropolis are related to bank interest rate, building size, household size, price of construction materials, renovation fees, and age. The building, the distance of the residential unit from the nearest public transportation stations, the number of newly built units, the type of skeleton (structure), the good reputation of the citizens, the age of the head of the household, investment, the facade of the building, the distance of the residential unit from the nearest green space and Park and finally the distance of the residential unit from the nearest religious place. Multiple linear regression was used to determine the effect of each independent variable on the final housing price in the Tabriz metropolis. Also, the Durbon-Watson test was used to verify the validity of the presented model. According to the results, social factors are more important in predicting the “final housing price” variable. Therefore, it can be said that determining the final price of housing in the Tabriz metropolis is more influenced by social factors than any other variable. After ensuring there is no correlation between the influential mediating variables in determining the final housing price in the Tabriz metropolis, Sobel’s test was used to investigate the mediating role of each mediating variable. In general, it can be said that the mediating role of economic, social, and accessibility variables in increasing the influence of the independent variable (physical factor) in determining the final housing price (dependent variable) in the Tabriz metropolis is evident. Among the mediating variables, the social variable (6.998) has the greatest effect in determining the final housing price in the Tabriz metropolis. After that, access (4.667) and economic (2.736) mediating variables are ranked second and third, respectively.

The results obtained from the multiple regression indicated that the physical-physical factor only explains 48% of the changes in the final housing price in the metropolis of Tabriz, and the rest (52%) are related to economic, social, and access factors. In general, it can be said that in connection with determining the final price of housing in the Tabriz metropolis, the physical factor (independent variable) is not the only determining factor, and social, access, and economic factors are mediating and mediating factors in the increase The influence of the physical factor in determining the final housing price (dependent variable) in Tabriz metropolis has a significant role, among the influential factors, the most influential factors are related to social factors (Beta = 0.435), physical-physical factors (Beta = 0.302) ), access (Beta = 0.290) and finally economic (Beta = 0.272). To put it better, According to the respondents, social characteristics are more influential in determining the final price of housing in Tabriz metropolis compared to other characteristics. In the final step, Sobel’s test was used to confirm the controlling and mediating role of mediating and mediating research variables (economic, social, and access). The obtained results indicate the confirmation of the mediating role of the mentioned variables in increasing the influence of the independent variable (physical factor) in determining the final housing price (dependent variable) in Tabriz metropolis and also the significant correspondence of the results obtained in The Sobel test with the results obtained from the multiple regression is evident in such a way that among the mediating variables, the social variable (6.998) has the most influence in determining the final housing price in Tabriz metropolis, and then the order of the mediating variables. (4.667) and economic (2.736) are in the second and third ranks. In a better way, it can be said that the social bases of residents in Tabriz metropolis, as well as the good reputation of citizens, have a significant effect compared to other factors (physical-physical, economic, and accessibility) in determining the final price of housing in Tabriz metropolis.

This paper analyzed factors affecting housing prices in Iranian metropolitan with a spatial model.For this purpose, the spatial auto-regression (SAR) method with panel data approach has been used to explore the existence of the internal relationship between economic variables of different regions on housing prices in these regions and the coefficients of economic variables affecting housing prices related to 429 counties of Iran from 2011 to 2021. Finally, it examines how likely it is that housing prices lead to regional economic changes and financial crises. Estimates show a significant degree of heterogeneity among counties. As expected, the net spatial coefficient estimates (simultaneous and lagged) are mostly positive and indicate a high degree of spillover effects of housing price changes to neighborhood regions. By generalizing the results of this paper to the capital characteristics of house, it also confirmed that house is an asset. This article is of great help to credit providers and insurance organizations and financial funds to diversify assets

Housing is a critical economic sector, with its growth benefiting households. However, rising prices in recent years have diminished household welfare. Using provincial-level data (2011–2021) and a spatial econometric approach, this study analyzed the factors driving housing price increases. The findings revealed that urbanization and economic growth positively influenced housing prices, while financial development had negative spillover effects, underscoring the role of traders in price determination. Although industrialization had no direct effect, its spillover effects were found to be positive, highlighting the service sector’s influence. The study confirmed the housing price divergence and recommended enforcing tax laws on vacant properties and increasing financing in the housing sector to improve Iran’s housing market.

The housing sector is one of the important economic sectors that, in addition to consumer demand, also faces demand from speculators due to its high capital return rate and low risk level. Speculators, motivated by the desire to profit from future price increases, refrain from offering their houses for sale, resulting in a housing vacancy. The presence of vacant houses reduces the housing supply and can lead to the formation of a housing price bubble. Imposing taxes on vacant houses is one of the government's tools to address this issue. The aim of this study is to examine the impact of taxes on vacant houses on the housing price bubble in the city of Shiraz.

In this research, an agent-based model is used, considering four active agents in the housing market: sellers, buyers (including sellers and buyers with personal consumption and speculative motivations), developer, and real estate agencies, to investigate the dynamic processes of the housing market. To forecast the housing prices in Shiraz over an eight-year period, statistics and information by the beginning of 2022 have been incorporated into the model, and three different percentages of speculative buyers, including 30%, 50%, and 70% of the total buyers, along with different tax rates of 10%, 15%, 20%, and 25% have been considered.

The results of the research show that by applying a tax rate of 10%, if 70% of buyers are speculators, the highest growth rate of the housing price bubble was observed; that the decreasing growth rate was equal to 18%, that is, with application of tax on empty houses, the housing price bubble of Shiraz city in 2022 to the end of 2031 decreased by almost 19%, and after that the application of the tax rate of 15% in these conditions was approximately 17% which reduced the housing price bubble. But when the number of regular buyers is more than speculative buyers (30% of buyers are speculative), the application of different tax rates on vacant houses shows the least reduction effect on the housing price bubble. Therefore, when 70% of the buyers in the market are present in the market with the motive of personal consumption, the number of transactions is low. Since ordinary buyers will re-enter the market with a slight probability, and the majority of transactions are made by the 30% of buyers who are speculative, so applying the tax on vacant houses in the first year will cause a number of speculater to leave the market and the number of transactions will be less than before the tax was applied. In fact, mobilisation of the current stock of housing due to the tax may not have been high enough to affect prices which is consistent with Sego (2019).
Furthermore, the results indicate that increasing the tax rate on vacant houses does not necessarily lead to a further reduction of housing price bubble. When more than half of the housing market is in the hands of speculators, their power in transactions would be greater, and the increase in tax rate in the form of an increase in price will intensify the housing price bubble which could mean more transactions between traders. In fact, traders add tax to the price of the property, and increasing tax rates, in return worsens the bubble. So, here selecting the optimal tax rate becomes critically important. When less than half of the housing market is in the hands of speculators, the power of speculators will decrease as a result, which leads to further weakening of the price bubble. However, to a lesser extent when more than half of the market is in the hands of speculators, the price bubble will decrease.

The research results indicate that the implementation of different tax rates, despite varying numbers of speculators, can lead to a reduction in the housing price bubble in the city of Shiraz, although the effectiveness may vary under different conditions. Moreover, it can create an appropriate income for the government, which can reduce the class gap by allocating and optimally directing the resulting resources towards the supply of housing for low-income groups. But the government should be careful in choosing the tax rate. It is necessary to set the tax rate on empty houses in such a way that renting the house or offering it in the market is more economical than keeping it empty by traders. In addition to the tax rate, choosing the tax base is also crucial. As mentioned in the text of the research, some countries consider the value of the property as the tax base instead of the rental income, or a fixed annual tax is collected

The purpose of this research is to investigate the convergence of housing prices in the provinces of Iran using the Markov switching nonlinear unit root test along with the generalized Dickey - Fuller unit root test between 1993 and 2021. The results of the Dickey-Fuller test show that housing prices do not converge, while the Markov switching nonlinear unit root test confirms the validity of this theory in some periods of Iran's economy. According to the results obtained in the boom regime (housing price increase conditions), there is convergence in housing prices, but there is no convergence in the recession regime (housing price decrease conditions). The result obtained from the nonlinear test seems to be expected.This case could be considered as temporary due to inflationary conditions and high inflation expectations, the decrease in housing prices, and there is an expectation of a housing price increase in a short time, and therefore prices do not decrease rapidly. According to the results of the research, the use of policy tools specific to each region and province can provide better management in controlling the fluctuations of the country's housing market. The adoption of complementary policies to stabilize housing prices may also be appropriate

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 housing sector in Iran is considered one of the most important economic sectors, so that around 20-30% of the country's capital formation was allocated to this sector in the last decade. Price vibratilitys are one of the most important economic challenges of this sector. Examining the conditions governing the economy in the past years shows that the conditions of recession and inflation governing the country's economy have created one of the most difficult periods for the country, and in the meantime, the housing sector, as one of the economic sub-sectors, has not been protected from the Adverse effects of the conditions governing the macro economy. And it has experienced relatively long periods of recession and relatively high inflation before that. On the other hand, one of the basic problems that banks and financial and credit institutions are dealing with today is the problem of non-current claims and uncollected facilities. In addition to reducing the profitability of banks, this issue causes the slow circulation of liquidity in the economy, the lack of allocation of credits to productive requests on time, in the fields of industry, mining, trade and agriculture, and ultimately economic stagnation. One of the most important factors affecting the non-current claims of the banking system can be the business cycles and periods of the housing sector, since the civil partnership facilities of banks have a significant share, the booms and busts of the housing and non-residential buildings sectors can affect the ability to repay the received facilities. be influential. The boom in the housing sector and investors in this sector will help repay loans. While the stagnation of the housing sector and its expansion will reduce the willingness and ability of investors to repay loans; Therefore, examining the cycles of the housing sector as one of the factors affecting non-current claims is necessary and necessary. Also, it seems that the increase in demand is not the only factor affecting housing prices and the unfavorable conditions of other economic sectors of the country make the housing sector attractive to all investors (banks and non-banks). Considering the importance of the issue and considering that one of the effects and consequences of the monetary activities of credit institutions is the emergence of non-current claims that have negative effects on the cycle of resources and expenses of banks and can also affect housing prices; Therefore, the main goal of this research is to answer the question that to what extent does the investment of banks in real estate affect the price of housing? In this research, using the TVP-DMA method, the most important factors affecting housing prices have been identified. To examine a forecasting model or to choose the best model among different models for a time series, an index is needed to make the necessary decision regarding the acceptance or rejection of the forecasting model. In this study, two standard indices of the Mean Squared Forecast Error (MSFE) and the Mean Absolute Forecast Error (MAFE) are used. Based on the results, the most important variables affecting housing prices in Iran's economy are inflation variables; exchange rate; liquidity; Economic Growth; Bank payment facilities for housing; Overdue claims and increase in bank debt; the amount of fixed assets of banks; Land price index in Tehran; sanctions index; population; housing tax; Urbanization coefficient and price index of construction materials. Based on the results of inflation variables; exchange rate; liquidity; Bank payment facilities for housing; the amount of fixed assets of banks; Land price index in Tehran; sanctions index; population; Urbanization coefficient and price index of construction materials have a positive effect and economic growth variables; Overdue claims and increase in bank debt and housing tax have a negative effect on housing prices. Based on these results, inflation with the longest period of influence is the most important variable affecting housing prices in Iran. Based on the results, the following suggestions are made to improve the situation in the housing sector:Tax policies: According to the current conditions, the most effective way to deal with housing speculation is to apply effective taxes in the field of housing and real estate. These taxes can be placed in 5 areas: real estate capital gains tax, real estate transfer tax, annual real estate tax, real estate rental income tax, and real estate construction and sale income tax.Pending claims policies: Considering the results of the research and the significance of the effect of housing prices on non-current claims of banks in the period under review, it seems that policymakers and supervisors should consider housing prices as a key indicator affecting the stability of banks. In this context, it is desirable to provide a framework to control the relationship between housing prices, bank loans and loan losses.Facilitation policies: as a policy solution arising from the results and findings of this research and considering the existence of potential accumulated demand in the housing market and the existing imbalance, it is suggested to prevent the stimulation of potential demand and the increase of housing prices, the share of bank facilities granted for the purchase housing will be reduced and on the other hand, the share of bank facilities granted for housing construction will increase and then the facilities allocated for housing construction will be converted into long-term contracts for installment sales and rent as a condition of possession. It is worth mentioning that the interest rate of bank facilities can also be effective on housing prices from the cost aspect; Therefore, in the housing market, both supply and demand factors must be monitored and controlled.

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.

Introduction Housing price fluctuations in recent years in the country have had extensive negative effects on households and the performance of various economic sectors, these fluctuations have always had negative effects on production, investment, employment, and other economic variables directly and indirectly. Therefore, considering the basic effects of housing prices in big cities on the well-being of households and different economic sectors, as well as its effects in creating boom and bust periods in the housing market, the necessity of research in this matter is evident.The analysis of factors and components influencing the determination of housing prices and their correct management prevents fluctuations in the housing market and significantly increases the success rate of housing construction programs and desirable housing plans. It can be said that controlling housing price fluctuations requires knowing the factors affecting them. High housing prices seriously affect the quality of life of urban residents, especially the lower class of society. Today, due to continuous urbanization and social and economic development in Tabriz city, the problem of housing prices as a stable social problem should still be addressed and improved. In this regard, the current research aims to analyze the factors influencing housing prices from the perspective of Tabriz residents. Factors affecting housing prices are organized by several variables specified in previous studies. According to the mentioned contents, the hypothesis is obtained that various factors are effective on the housing price according to the residents of the Cherandab neighborhood of Tabriz city. Analyzing the influencing factors and components in determining housing prices and their proper management in the neighborhoods of Tabriz can prevent fluctuations in the housing market to some extent. It can be said that controlling housing price fluctuations in urban areas requires knowing the factors affecting it, and one of these studies is from the perspective of urban residents. High housing prices seriously affect the quality of life of urban residents, especially the lower class of society.Materials and MethodsThe research method in this research is descriptive-analytical, and in terms of its purpose, it is considered part of applied research, and in terms of gathering information, it is survey research. The statistical population of 37,532 people in this research is the residents of the Charandab neighborhood in Tabriz City. According to Cochran’s formula, the sample size was 380 people. The samples were selected randomly. In this research, a questionnaire tool is used to collect data. After the above steps, data classification was done by analyzing structural equations using SPSS and Amos software. Cronbach’s alpha was used for the reliability of the questions. For data analysis, a structural equation modeling approach is used using Spss (version 19), Mplus (version 7.4), and Amos (version 22) software. Structural equation modeling (SEM) is one of the most original methods of analysis and multivariate analysis, which is used to examine the complex relationships between manifest and latent variables. It consists of two parts: the measurement model and the structural model. In this research, at first, using SPSS software, exploratory factor analysis was applied with the application of principal components extraction and varimax rotation, to discover the structure of factors influencing housing prices from the point of view of the residents of Cherandab neighborhood in Tabriz. Then, using the Mplus software, the confirmatory factor analysis method was used to confirm the exploratory factor structure and the fit indices were used to evaluate the fit of the final model obtained from the structural equation modeling approach. Confirmatory factor analysis has been used in this model because some of the observed data cannot be measured directly but have similar patterns in their values. This model was also used to evaluate the structure. This type of factor analysis tests the degree of conformity between the theoretical structure and the experimental structure of the research. Exploratory factor analysis has been used to reduce the number of variables and identify the main factors that are effective in determining housing prices.FindingsBased on the results of this study, it has been proven that residential satisfaction factors (HS) and household income (HI) have a significant relationship with housing prices. Because p values are less than 0.05, meanwhile, housing policy (HP) has no practical significance for house prices. The findings showed that among the three investigated components: satisfaction with housing and household income have an effect on the price of housing with a regression weight of 0.799 and 0.558 in the Cherandab neighborhood of Tabriz city, but housing policy with a regression weight of -0.252. It does not affect the housing price in any way. Among the following indicators, home buyers pay more attention to the security of the neighborhood, the level of crime in the neighborhood, the community of the neighborhood, and the internal and external structure of the houses. Providing housing at a reasonable price is always considered the most important necessity in urban housing planning. From the point of view of microeconomics, housing is a type of non-movable, heterogeneous, and location-dependent commodity. Compared to other goods, it is affected by various structural, spatial, and human variables in its valuation.ConclusionSeveral internal and external factors with direct or indirect relative effects have been associated with housing affordability. The findings show that residential satisfaction and household income have a significant effect on house prices in the Cherandab neighborhood. For this reason, residential satisfaction can be considered an important influencing factor on the price of houses in the Cherandab neighborhood of Tabriz city, that is, if the builders and sellers intend to have an excellent house structure, facilities, and environment for buyers in the Cherandab neighborhood. provide Tabriz, they should, first of all, consider the satisfaction of housing. In addition, household income also affects the price of houses in the Cherandab neighborhood through the commitment of the buyer to providing the money needed to buy a house. Despite the lack of direct influence of housing policy factors on determining the price of houses in the Cherandab neighborhood of Tabriz, it should still be given serious attention by the main actors of the construction industry. For example, the policymakers should make sure that the builders and sellers provide a suitable, safe, comfortable, and accessible housing environment for every family along with proper security, privacy, and health facilities. This study can be used as a home-buying guide.

Housing, as an asset and shelter after food and clothing, is the most basic human need, which is very important for the life and survival of the individual as well as for the society. Changes in housing prices, on the one hand, as the main asset in households' portfolios, will lead to changes in individual wealth over time, and on the other hand, fluctuations in housing prices, especially their rapid increases, are among the threats that governments aim to provide access to. People face challenges in housing. This issue has become more important especially in recent decades with population growth, urbanization and the formation of new families. Air pollution as one of the most important environmental concerns of this century and the result of increasing population and urbanization and housing construction threatens the lives of millions of people in developing countries as well as in developed and industrialized countries. Therefore, considering the importance of air pollution and its impact on housing and housing prices, this research tries to use the panel data technique of the effect of air pollution on housing prices in developed countries (including: Norway, Switzerland, Australia, Iceland, Denmark, America , Japan and the Czech Republic) and developing countries (including: Turkey, Mexico, Brazil, China, Colombia, South Africa, Indonesia, India) during the period of time (2010-2017) and then this review for Iran using self-explanatory vector technique with extended non-linear intervals (NARDL) and during the years (1375-1996).

As mentioned in the introduction, the purpose of this research is to analyze the impact of air pollution and some economic variables on housing prices in developing countries, developed countries and Iran. For this purpose, the following model has been used:Which in this model: Logarithm of housing price in year t.Logarithm of air pollution index in year t.Logarithm of per capita income in year t.Logarithm of liquidity in year t.Logarithm of the price index of consumer goods and services in year t.Logarithm of exchange rate in year t.Interest rate in year t.It is important to mention two points here: First, the index considered for air pollution is the amount of suspended particles PM2.5. Second; The base year is 2010 for developed and developing countries and 2010 for Iran. The desired statistical information, which is in the form of a time series, was collected through the information portal of the World Bank, the International Monetary Fund, the Organization for Economic Cooperation and Development, the Central Bank of Iran, the Statistical Center of Iran, and the energy balance sheet.

The impact of air pollution on housing prices in developed and developing countries:Due to the short period of time (due to the limited access to information) and the invalidity of the results of the unit root test in these conditions, in the first step, the problem of heterogeneity of units was investigated using the F-test statistic of Limer. Considering that the probability value of the F test for both groups of developed and developing countries is less than 0.01, therefore, Panel Data technique is used to estimate the model in both groups of countries. As the next step to examine the correlation between disturbance components and explanatory variables (i.e. choosing between the fixed effects method and the random effects method), the Hausman test was performed, and the results of this test indicated a probability value smaller than 0.01 for both groups of countries. Investigating and applying the fixed effects method.The results of model estimation are as follows:In developed countries, air pollution leads to a decrease in housing prices by 30%. But in developing countries, there is no significant relationship between air pollution and housing prices. The effect of per capita income on housing prices is positive and significant in both groups of countries under investigation. The effect of liquidity on housing prices is positive and significant in developed countries, but the effect of liquidity on housing prices is not significant in developing countries. Considering the relative stability of the consumer goods and services price index in developed countries, this index has no effect on housing prices in the aforementioned countries. In developing countries, the effect of the price index of consumer goods and services on the price of housing is positive and significant at the rate of 48%.The stability of the exchange rate in developed countries has caused this variable to have no significant effect on housing prices. But in developing countries, an increase in the exchange rate causes a 4% decrease in housing prices. The increase in interest rate causes the housing price to increase by 0.5% in developed countries and decrease by 0.2% in developing countries.The impact of air pollution on housing prices in Iran:As the first step, the variables used in the model were tested for reliability. The results of the ADF unit root test showed that all the variables investigated in this research were I(1) and this means that the vector self-explanatory model with wide ARDL intervals is a suitable method for analyzing the short-term and long-term behavior of the variables in this research. Then, in order to check the classical assumptions, relevant diagnostic tests were used, and the obtained results indicated that the model does not have any problems in terms of the classical assumptions, that is, there is no autocorrelation and heterogeneity of variance, and the distribution of disturbance components is normal.The results of model estimation are as follows:The effect of negative and positive air pollution shocks on housing prices is significant, which means that the decrease in air pollution leads to an increase in housing rent and an increase in the level of air pollution leads to a decrease in the housing rent index in Iran. An increase in per capita income leads to an increase in housing rent index. The variable coefficient of liquidity logarithm is negative and meaningless. An increase in the price index of consumer goods and services and the exchange rate will lead to an increase in housing rent. The results of the model estimation indicate the insignificant effect of the interest rate of this variable on the housing rental index in the long term, but in the short term, with the increase in the interest rate, the demand for housing rent and consequently the rent will increase.

Based on the results obtained in this study, the effect of air pollution on housing prices was negative and significant in developed countries, while this effect was insignificant in developing countries. The results of the model estimation in Iran show that air pollution has a significant effect on housing rent.According to the obtained results, the following suggestions are presented in order to control housing prices:Stability of prices and prevention of excessive growth of the price index of consumer goods and services through control of liquidity and exchange rate.Exchange rate management: Since the fluctuations and instability of the exchange rate can leave destructive effects in the economy of the countries, therefore, the forecasting and use of tools that enable the economy of the countries to face the fluctuations of the exchange rate should be considered.

Investigating the behavior of agents in the housing sector, including investors, households and developers, is very important. A large part of housing price fluctuations is caused by the activities of these agent, especially the investors in this sector, and these housing price fluctuations influence other economic sectors because the housing sector has a lot of backward and forward relationships with other sectors. This research aims to analysis of the behavior of agent in the housing sector in the forming of the equilibrium pattern of housing prices in Iran using the mathematical optimization method for the period of 1991 to 2022. The results show that there is a positive relationship between the equilibrium price of housing with the construction cost of each residential unit, household income and investors' expectations of housing prices. Also, there is a direct relationship between the volume of trading of investors and their expectations of housing prices. When the expectations of housing prices are in the range of zero to 1.71 and more than 1.71, investors will be in the position of selling and buying housing, respectively. Hence, the relationship between the equilibrium price of housing and the volume of trading of investors with the risk component of investors is positive, and if the equilibrium price of housing is more (less than) 2.33, investors take a selling (buying) position. On the other hand, if the investors are in the low range of zero according to the amount of risk and volume of trading of investors, then the investors are in the selling position and if they are in the upper range of zero, the investors are in the buying position.

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 aim of this article is to assess the effects of the real exchange rate on housing prices in 31 provinces of Iran by of the static and dynamic spatial panel model framework over the period of 2011 to 2020. The findings indicate a positive effect of the real exchange rate on housing prices in the provinces of Iran, which is consistent with the research conducted in this area. In addition, per capita income has a negative effect on housing prices, indicating the non-substitutability of food and non-food expenses in the household expenditure basket and the need for significant liquidity to afford housing. This finding is in line with the results of some other studies. Evaluating the spillover effects of housing prices in the provinces of Iran using static and dynamic spatial panel models demonstrates that a change in housing prices in one province affects housing prices in neighboring provinces. This result confirms the ability of housing shocks to spread at the national level. Furthermore, the evidence shows that the long-term spillover effect of the real exchange rate on housing prices is unstable, while in the short term, this effect is positive. Therefore, the overall effect of the real exchange rate on housing prices is positive in both the short and long term, and stability in the foreign exchange market will contribute to improving the housing market conditions.

The housing sector has always played a crucial role in the economy, with its fluctuations exerting significant effects on various economies. Prior to the 2007 financial crisis, standard models were commonly employed to explain price changes. These models assumed that agents were rational and well-informed, disregarding factors like irrationality and the heterogeneity of individuals that could contribute to such crises. However, agent-based models offer a different perspective, viewing the economy as a complex system with heterogeneous agents possessing limited information, engaging in interactions with each other. As a result, this study aims to evaluate a spatial agent-based model, specifically developed to analyze the housing market in Tehran. The simulation's results over an eleven-year period revealed that the growing demand from young households with limited savings for residential units under 100 square meters significantly drove up the prices of these particular units, outpacing other residential properties. Moreover, the findings indicated the higher growth of housing prices in the central areas of the city, primarily triggered by the influx of young households into these regions, seeking investment opportunities.

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.

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.

With the beginning of the land reform program in 1963 and with the collapse of the feudal system, a large wave of new population and labor force applying for land and housing went to the cities. As a result, construction activities and related economic activities intensified. The land, in economic activities, is sometimes produced as input and sometimes as a durable commodity. It creates attractiveness for people, and the demand for it increases in inflationary conditions. In these conditions, with the increase in demand, housing prices increase. Micro and macro factors that are effective in determining housing prices can be divided into two categories of external stimuli, such as the increase in the volume of money and liquidity, inflation, capital market, stock market, and in general, the factors that are rooted in macroeconomic and political activities. Internal drivers can be referred to as the price of land, the price of consumables, branches, and the cost of wages. In the neoclassical approach, land as an attractive input needs mechanisms to coordinate it according to demand and add to its capital value. According to economists such as Lancaster, Muth, and Rosen, the characteristics of goods are significant for consumers, and what is desirable for them is the quality of the product.

Considering the nature of the subject, the present research is of an applied type with a descriptive-analytical approach. The data collection tool was based on library and survey methods. Library studies were used to formulate theoretical foundations and background and identify research variables. In the next step, real estate consultants in Tehran Municipality’s Region five were questioned by the survey method using a researcher-made questionnaire. Then, the factors that affected housing prices in the study area during 2011-2021 were investigated. A multivariate regression analysis method was used using the Hedonic function to analyze the correlation between the variables. Furthermore, SPSS and EV
views 10 were used to analyze the data.

The analysis of the factors affecting housing prices in the Tehran Region five showed that out of 33 variables in structural, accessibility, and environmental dimensions, the coefficients of 28 variables were significant at the error level of 0.5. Among the physical variables affecting the price of housing, the floor area at 0.193, the building age at 0.166, the selection of suitable materials at 0.151, the location of the unit on the floors, especially the middle ones at 0.143, and the possibility of direct lighting (being towards the north or south direction of the housing) at 0.125 had a positive and significant effect on the housing price; for one unit of change in the mentioned variables, the housing price is increased. Among the accessibility variables, the distance between residential units to the nearest park and green space, that of residential units to the nearest educational centers, and that of residential units to the nearest public transportation station (metro, BRT, bus station, or taxi station) (-0.143, -0.124, -0.106, respectively) had a negative and significant effect on housing prices. The distance between residential units from the mentioned variables was effective in reducing housing prices. Among the environmental variables, the distance from narrow streets (-0.095) and the increase in traffic level (-0.087) decreased the housing price. Based on the one-sample t-test results, physical and structural indicators were the most important factors influencing the increase in housing prices, and accessibility and environmental components affected increasing housing prices, respectively. Pearson's parametric test results indicated a positive or direct correlation between structural indicators and housing prices. The coefficients obtained from this test showed that the correlation in structural, accessibility, and environmental indices were 0.659, 0.487, and 0.441, respectively. Moreover, the intensity of the influence of indices on housing prices using multivariable regression was (0.757).

Providing housing at a reasonable price is always considered a fundamental necessity in urban housing planning. From the point of view of microeconomics, housing is an immovable commodity, heterogeneous and dependent on a specific location, which, compared to others, is affected by various structural, spatial, and human variables in its valuation. This research analyzed the factors affecting housing prices in the Tehran Region five and indicated the way the micro factors increase housing prices. The hedonic function of housing for Tehran Region five was divided into structural, accessibility, and environmental dimensions to achieve the research goal. The results showed that among the mentioned components, the floor area, the year of construction, the materials of the housing, the location of the unit on the floors and the north-south direction of the housing, the distance of the residential units to the nearest park and green space, that of the residential units to the nearest educational center, that of residential units to the nearest public transportation station, alley and street width, and alley and street traffic conditions were significant variables in housing prices. The correlation test results showed a positive correlation between structural, accessibility, and environmental components on housing prices. In examining the rates of coefficients according to the regression test, it was found that the structural, accessibility, and environmental indicators directly influenced the housing price. Identifying the dimensions affecting housing prices can play a vital role in predicting and controlling housing prices, checking the quality of construction and living environment, and consumer preferences.

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.

Housing price fluctuations are a very important issue in the housing economy and are affected by different factors. One of these factors is the impact of environmental variables. Therefore, in deciding to buy a house, there is a hidden market due to considering the quality of the environment around the property. The purpose of this research is to investigate the effect of environmental services of the Farahzad river ecosystem on housing prices and to evaluate the social-economical revitalization of this river. For this purpose, 386 questionnaires were used and the hedonic method, which represents the regression of non-fundamental qualitative factors on housing prices, was used in the form of estimating three regression models to achieve the goal. The results showed that the average willingness to pay for the restoration is equal to 611,000 rials per month. The results of all three regression models indicate the importance of environmental variables on housing prices, so that in the first model, 22.6%, in the second model, 17.1%, and in the last model, they cover 16.5% of the price changes. Also, the results showed that the revitalization of this river is not justified from the point of view of economic evaluation at the regional level, considering its costs, but it is justified at the level of Tehran city, considering the benefits and the generalization of willingness to pay. The information of this study can be useful for the management of housing price fluctuations as well as planning for the revitalization of urban gorges.