جستجوی مقالات مرتبط با کلیدواژه « Housing price » در نشریات گروه « مالی »

The dynamic relationship between stock prices and housing prices has been an important topic of discussion in the academic and professional literature. The impact of stock prices on housing prices can be examined through two substitution effects and the wealth effect. Real estate and stocks are both assets and can also be considered investment options. The connection between these two assets, housing and stocks, can be interpreted as a substitution effect. This implies that a high return on investment in the stock market may lead investors to divest from the housing market, resulting in a decrease in demand and housing prices. In this case, stock prices will harm housing prices. When housing is considered as a consumer good, the effect of wealth is more pronounced. An increase in income and wealth, including financial assets due to the increase in stock prices, has a positive effect on the total consumption expenditure, including housing costs. More specifically, because homeowners receive windfalls from stocks, the wealth effect of these windfalls increases the purchase of houses as a consumer good. In other words, the wealth effect establishes a positive correlation between stock prices and housing. This is because high returns in the stock market augment the overall wealth of homeowners, enhancing their capacity to invest in additional real estate. Therefore, the stock market index can have a positive or negative effect on housing prices. It is necessary to examine this relationship for each country in detail and based on statistical evidence. Accordingly, in the present study, the effect of the stock market index on housing prices in Iran is investigated.

To reach the research goals, data spanning from 1971 to 2020 is utilized. To test the research hypotheses, the NARDL method was used to identify possible asymmetries effect of positive and negative shocks of the stock price index on the housing price index. 

Findings show that in the short run, positive and negative shocks in the stock market index have no significant effect on the housing price index, but in the long term, positive shocks in the stock market index have a positive effect on the housing price index, while the effect of negative shocks on the housing price index is insignificant. 

According to the obtained results, due to the substantial impact of stock market fluctuations on housing prices in Iran, it is recommended that policymakers take the following into account when formulating stock market policies. First, in the policymaking process for the housing sector, where the anticipation of housing price trends is crucial, careful attention should be given to potential shifts in the stock market index. Next, a long-term boom in the stock market correlates with an increase in housing prices, while the impact of a stock market downturn on housing price increases is deemed insignificant. Consequently, policies promoting a stock market boom can be considered as a contributing factor to the rise in housing prices.

Forecasting the asset prices in any market is an inseparable and critical part of research on markets. Obtaining a credible prediction of an asset's potential future price provides valuable information for investors. Besides that, being confronted with real estate price shocks and price fluctuations in alternative markets, determining the best time for the investment may come up as a big challenge for any investor. Tracing the trend of housing price changes in Iran shows that the average housing price has had a general trend close to other prices and price indices; but the significant issue in this market is the various growth processes and dynamics, compared to changes in other economic indicators. This dynamic gets more complex when a variety of quantitative and qualitative data from diverse types and from several markets are added to the model. This diversity along with the unstructured, stochastic, and scattered datasets makes the implementation of the model so hard to do. The main goal of this paper is to build an Artificial Intelligence model with the most flexibility in the input part for the data type heterogeneity and the lowest error in the output part as the predicted price. All the implemented models showed a high accuracy on the real data extracted from the housing market.

Artificial Intelligence models have an enormous capacity to get a broad range of any kind of data and process and concurrently compile them to build a valid output. This characteristic of AI is very useful in financial models to increase the accuracy of output. Our model is based on the Recurrent Neural Networks. Due to its capability to preserve past information, the LSTM algorithm was implemented as a time series forecasting model.

In this study, using diverse types of official datasets, such as the Central Bank of Iran, we distinguish the influencing variables in the housing market and then we could predict the average housing price in Tehran. Our findings indicate that the average housing price demonstrates the strongest correlation with gold prices, foreign exchange rates, the Consumer Price Index, and market liquidity levels. Utilizing these indicators, predictions with very high accuracy were obtained.

Among the four different models of this research, the best prediction belonged to the multivariate stacked-LSTM model, which was empowered by the highly correlated macroeconomic variables. The validation of models was done by the MAPE Indicator. Furthermore, all the results confirmed that the LSTM algorithm was highly effective in utilizing over two decades of actual housing data from Tehran to forecast future prices.

Both on micro and macro scales, the exchange rate and its dynamics play a pivotal role as influential variables in the economy. They can affect most economic, social, and cultural variables through various channels and change their behaviors. Given the importance of the exchange rate as a key variable in the Iranian economy, extensive studies have been conducted on the exchange rate and the associated concerns regarding exchange rate volatility. The rate affects many of the government's economic policies. In addition, since housing costs constitute the main household expenses in Iran, problems and limitations in providing housing can have a significant impact on the spread of social dissatisfaction. The purpose of this study is to investigate the effect of exchange rate instability on the housing price index in Iran. Therefore, analyzing the relationship between the exchange rate market and the housing market can help policymakers and economic decision-makers adopt appropriate policies in the foreign exchange market and housing sector.

In this study, the GARCH method was initially used to estimate the volatility of the exchange rate. Next, to investigate the effect of exchange rate instability on the housing price index from 1991 to 2020, the GMM time-series method was employed. Due to the dynamic nature of the model in this study and the need to establish conditions for generalized moment estimators, the use of this method seems appropriate. The GMM method is used for linear time-series models to ensure the conditions of moment estimators and incidental properties.

The results showed that the volatility of the exchange rate has a negative and significant effect on housing prices. Also, it was found that the interruption of the housing price index, oil revenues, inflation rate, liquidity, and urbanization all exert a notable and positive influence on housing prices. Additionally, the study's findings revealed a significant and adverse effect of variables such as interest rate, gold market return, and stock market return on housing prices within Iran's economy. The results of the tests used demonstrated that the estimation model is in a suitable condition in terms of statistical indicators.

Exchange rate volatility is of the parameters affecting housing prices. Accordingly, this study concludes that exchange rate volatility harms the housing price index in Iran. It is recommended that the central bank adopt appropriate currency policies to reduce volatility in the currency market and its effect on parallel markets, including the housing market. The results of estimating the coefficients of control variables are consistent and in line with the theoretical and empirical literature. Variables such as inflation rate, liquidity, and urbanization have a positive and significant effect on the housing price index. With an increase in liquidity, the housing price index in the Iranian economy increases, indicating that expansionary monetary policies have significant effects on the housing market in Iran.

If the asset market is information-efficient and people behave rational, asset prices reflect available information about expected events. On the other hand, choosing the right policy is important in economic stability.Since traditional models have failed to identify correct and appropriate policies, using time varying methods, can select the policy according to the current situation.Based on Bayesian averaging method, the variable total government expenditures was determined as the most fragile variable affecting the asset prices. so, in the TVP-FAVAR model, the effect of this variable on the price of each asset in different time periods in MATLAB has been investigated.According to the results, expansionary fiscal policy, in the long run, has had a positive effect on the price of each asset. This effect on the exchange rate variable has intensified in recent years. It should be noted that based on the results in recent years, fiscal policy has had a negative impact on housing prices and the stock market and has reduced the price of these assets. Also, the fiscal policy shock in short run, on the stock market, in medium term, on the housing index and in long run, has the highest impact on the exchange rate.