Hojjat Izadkhasti, Abbas Arab Mazar, Mahboubeh Refahi,
Volume 12, Issue 45 (11-2021)
Abstract
Rental housing has been affected by housing prices in different periods and the growth of housing prices has reduced the purchasing power of housing applicants and increased the percentage of rented households. Therefore, any recession and boom in the housing sector has a direct impact on the housing rental market, and planning to control the rental market will not be achieved without considering the housing market. In this regard, the purpose of this study is to investigate the factors affecting housing rent based on two groups included large, small and medium cities in Iran using the Generalized moment method (GMM) in the period (2008-2018). The results show that housing rental prices in the previous period, housing prices, land leverage and real per capita income of urban households had the most positive impact on housing rents in both large and small and medium cities. Also, the impact of housing prices and rental prices in the previous period has been greater in large cities. Also, Housing bank facilities, the number of urban marriages and the real interest rate were other variables affecting the rental price of housing in urban areas.
Saeed Kianpoor, Reza Shamsollahi, Jafar Zarin,
Volume 15, Issue 57 (12-2025)
Abstract
Objective: The aim of this research is to investigate the dynamic and nonlinear dependence between housing market fluctuations and the returns of construction companies on the Tehran Stock Exchange.
Materials and Methods: The data used include construction service returns, land price returns, inflation, exchange rate returns, stock index returns, industrial production returns, and rental returns in the period 1991 to 2023 using T-GARCH, Copula-GARCH, and DCC-GARCH.
Results: The results indicate the existence of strong and nonlinear dependencies between the returns of construction services and housing market variables, especially the returns of land prices and rents. The T-GARCH model showed a high fit (R-squared=0.969) and confirmed that past shocks have a significant impact on current fluctuations. The Copula-GARCH model confirmed the nonlinear dependencies with an average correlation coefficient of 0.31, while the rolling correlation analysis in the DCC-GARCH model indicated dynamic changes in dependencies in different economic periods. The Kendall-Tao correlations in boom (0.928) and recession (0.923) periods also showed a small but significant difference in the intensity of dependencies. The sensitivity analysis showed that changes in industrial production have a significant impact on the returns of construction services.
Conclusion: These findings are useful for investors and policymakers in risk management and setting economic policies in the Iranian housing market. |
