Credit Constraints,Housing Price Fluctuations And Macroeconomic Growth

As an important pillar industry of the national economy,the real estate industry plays an important role in promoting economic growth.Besides,large fluctuations in housing prices will also have an impact on macroeconomic stability.Real estate industry is a capital-intensive industry,and its healthy development is inseparable from the support of the financial industry.Both real estate development and real estate consumption are affected by the credit environment.Historically,every boom of housing price has been supported by a loose credit policy behind them.Many global financial crises are closely related to the sharp decline of housing prices.In particular,the impact of the US “subprime mortgage crisis” in 2008 lasted for a long time,spread a wide range,and had a serious impact on the economy.This also attracted scholars’ attention on the relationship of credit constraints,housing price fluctuations and economic growth.The research ideas of this paper originate from several problems raised by China’s real estate and macroeconomic development.One is how to effectively explain the internal transmission mechanism of housing prices,credit resource allocation and macroeconomic fluctuations;whether real estate has a crowding effect on the real economy.The other is how to explore the coordination effect of credit constraints,housing price fluctuations,macroprudential policies and monetary policies;what are the characteristics of the optimal combination of macroprudential policy and monetary policy.The third is how to interpret the nonlinear characteristics of credit constraints,monetary policies and housing prices.The fourth is how to effectively measure the spatial linkage and spillover effect of credit constraints,housing price fluctuations and economic growth.This paper attempts to explore these issues in depth.Firstly,this paper focuses on the analysis of financial frictions,housing prices,credit resources allocation,and macroeconomic fluctuations in China.By analyzing the sample data of China in 2008-2017,this paper finds that the ratio of real estate output to GDP has increased,while the ratio of industrial output to GDP has decreased.The prosperity of real estate promotes the increase of the proportion of real estate loans,and the decrease of the proportion of real economy loans;more credit resources are allocated to real estate,while real economy financing is squeezed.The empirical analysis based on SVAR model shows that the increase of real estate loans will cause the increase of real estate industry GDP,but at the same time cause the decline of industrial GDP;the rise in residential land prices will drive the increase of real estate loans.Credit resource allocation is an important factor in explaining macroeconomic structural changes and financial market stability,while financial friction is a key element in explaining the allocation of credit resources.In view of the factual characteristics of real estate and macroeconomics,considering the factors such as land and financial friction,a multi-sector DSGE model is established,which includes banking sector,real estate sector,real economy sector and local government.The parameters were calibrated and estimated by Bayesian method by using sample data from China.Then the internal transmission mechanism of financial friction,housing price and credit resource allocation is analyzed.It explains why the prosperity of the real estate market superimposed by the land market division of local governments will cause excessive bank credit to flow into the real estate market,thus crowding out the real economy investment,leading to the transition from real economy to virtual economy.In addition,by comparing the differences between the benchmark model and the non-banking model,the credit constraint exogenous counterfactual model,the land unified counterfactual model,the credit constraint relaxation model,the different characteristics of credit resource allocation and macroeconomic fluctuations are systematically analyzed.The results show that the heterogeneous interaction of amplification mechanism between the leverage of the banking sector and the financial friction of the real estate sector and the real economy sector has promoted the more allocation of credit resources to the real estate sector;the real estate boom has a crowding effect on the real economy;technological innovation can effectively drive the sustained and healthy development of the macroeconomy.The comparison between the benchmark model and the non-banking model found that the financial friction mechanism of the banking sector amplifies the scope of the transition of credit resource allocation from real economy to virtual economy and increases the fluctuations of macroeconomy.Under the framework of unified land prices,it has not changed the fact that real estate has a crowding out effect on the real economy in the medium and long term.When the mortgage mechanism is closed,the macroeconomic fluctuations are less than the fluctuations of the benchmark model.The looser the credit market environment,the greater the crowding out effect of real estate on the real economy sector.Secondly,this paper focuses on the coordination effect of credit constraints,housing price fluctuations,macroprudence and monetary policies.Considering three monetary policy rules and two endogenous macroprudential policies which are credit constraints(loan to value,LTV)for household and counter-cyclical capital(capital requirement ratios,CRR)for bank,this paper establishes a DSGE model based on the housing market.Through welfare analysis of different combinations of macroprudential rules and monetary policy rules,this paper identifies the optimal policy combinations,discusses the characteristics of the fluctuations of the main macroeconomic variables and welfare changes,and analyzes the impact mechanism and coordination effect between macroprudential policies and monetary policies.Finaly this paper makes an empirical test with the sample data of China.The results of the study show that whatever the monetary policy rules,the introduction of macroprudential tools has improved the level of social welfare.When prudent monetary policy is adopted and LTV or CRR macroprudential rules are introduced,welfare improvement is relatively small.The central bank can partially complete the work of macroprudential supervision institutions through proper parameter combination of prudent monetary policy rules;but this will also make the central bank take too many factors into account in the formulation of monetary policy,which will affect the effect of policy implementation.The optimal monetary policy rule combined with LTV macroprudence is prudent monetary policy rule.The optimal monetary policy rule combined with CRR macroprudence is the standard Taylor monetary policy rule.In the optimal "two pillars" framework of monetary policies and macroprudential rules,the main objective of monetary policy is to stabilize prices,and the macroprudential mechanism is aimed at the bank’s CRR macroprudential policy;this combination can effectively promote the stability of the real estate market,financial market and macroeconomy,while maximizing the improvement of social welfare.The empirical data in China shows that there is a significant positive correlation between real estate loans and housing prices,economic growth;there is a long-term cointegration relationship between housing prices,output and interest rates,inflation.Through the macroprudence on counter-cyclical capital of credit scale,the stability of financial markets and macroeconomy can be effectively promoted.Thirdly,this paper focuses on the asymmetry impact of credit constraints and monetary policies on housing prices.The macroeconomic complexity,monetary policy adjustments and intervention of real estate regulatory policies have increased the uncertainty of housing price fluctuations.The fluctuations of house price show the characteristics of structural change;therefore,it is necessary to establish a non-linear model to describe the asymmetry impact of monetary policies and credit constraints on housing prices under different conditions.This paper analyzes the transmission mechanism of monetary policy,credit constraints and housing prices.Combining with mechanism analysis and considering credit constraints,this paper establishes a partial equilibrium model including households and banking sectors,explains the non-linear characteristics of the impact of monetary policy and credit constraints on house prices from a theoretical perspective.Furthermore,this paper makes an empirical analysis based on MS-VAR model by selecting sample data of China.The results show that when monetary policy adjustments and interest rates change,the equilibrium point moves,which makes the sensitivity of housing prices to credit constraints to change.Under different equilibrium states,the endogenous interaction among monetary policy,credit constraints and housing prices is not constant,but shows non-linear features.There are two types of regimes in China: large fluctuations and small fluctuations.In the two regimes,the increase in loan growth rate will cause housing prices to rise,and the tightening monetary policy and interest rate increase will cause housing prices to fall.In the period of large fluctuations of housing prices,the use of interest rate instruments has a better regulating effect than credit;in the period of small fluctuations of housing prices,the use of credit instruments has better regulating effect than interest rates.Fourthly,this paper focuses on the spatial spillover effect and the spatial linkage of China’s credit constraints,housing price fluctuations,economic growth.By selecting China’s provincial panel data,the spatial dependence and clustering characteristics among China’s regions are tested by Moran index and LISA cluster graph.Then,considering the factors of geographical distance,economic distance and asymmetry factors,five spatial weight matrices are constructed such as binary neighbor weight,negative exponential distance weight,economic distance weight,universal gravitational weight and asymmetric influence weight.Meanwhile,five kinds of credit constraints are considered,which include narrow credit constraints,three generalized credit constraints and credit constraint structure indicators;spatial lag models(SLM)and spatial error models(SEM)are used to estimate;and optimal spatial econometric models with different factors and different weights are determined.The spatial linkage of credit constraints,house price fluctuations and macroeconomy is analyzed.Further,under the control of spillover effects of housing prices and credit constraints,the direct and indirect effects of credit constraints and housing prices are empirically analyzed based on the spatial Durbin models.Finally,the endogenous test is carried out by using GMM estimation of SLM;the robustness analysis is also carried out.The research results show that there is significant positive spatial correlation in the regional economic development,and the spatial dependence plays a significant role in promoting the economic development.The direction and significance of spatial spillover effects are consistent under different models and weights;the spatial weight which considers the factors of geographical distance,economic distance and asymmetric factors is the best.Generalized credit constraints,which include the scale of financial development,the level of financial development and the efficiency of financial development,have no significant impact on regional economic growth;only narrow credit constraints have a certain impact on regional economic growth;these reflect that the rapid development of China’s financial industry has not played a significant positive role in economic growth.From the perspective of credit structure,short term credits have a significant negative correlation with economic growth,while medium and long term credits have no significant correlation with economic growth.In addition to promoting the economic growth of the region,the rise of housing prices will also have a pulling effect on the economic growth of other areas,with a significant spillover effect.