Effects Of Macro-control Policies On Housing Prices Based On Spatial Quantile Regression Model Associated With An Asymmetric And Time Varying Weights Matrix

In recent years,China's housing market has encounted some problems such as rapidly expanding housing credit and high ratio of real estate mortgage loans.The price risk of housing mortgage loans increases with the growth of its scale and ratio,and the eruption of this price risk can seriously damage financial institutions and system.Overheated real estate market is easy to creat housing price bubble in China,and housing mortgage loans coming from banks,credit unions or other financial institutions may confront credit and default risk,which may cause the imbalance of the allocation of resources and industrial structure.Moreover,if the housing price bubble erupts,the risk in housing market will transmit to financial sector and then induce financial crisis.This may lead to a continued decline in housing prices,consumption and investment,which probably results in economic deterioration.Consequently,rational housing price is the basic assurance of avoiding risks in property market.Housing price-to-income ratio is a measure of housing affordability and the overall performance of real estate market.In China's housing market,we find that the ratio of housing price-to-income is too high in most cities,where residents have insufficient housing affordability.Therefore,housing price-to-income ratio should be reduced to promote the healthy development of real estate market.The ratio can be reduced by raising incomes or controlling housing prices,and our government focuses on the latter.Actually,housing policies have been intensively enacted to cool China's overheated housing market since 2010.However,how to measure the effects of macro-control policies on housing prices? what characteristics do the influences have? and what suggestions could be brought? This study tries to answer these focused questions.We find that spatial autocorrelation exists in urban housing prices,and many studies demonstrate that neglecting this spatial autocorrelation may cause inefficient and biased estimations.Therefore,spatial econometric methodologies and models are employed to study the effects of macro-control policies on housing prices.However,classical spatial weights matrices based on geographical criteria are not appropriate to capture the spatial autocorrelation in housing prices.According to this,we propose an asymmetric and time varying spatial weights matrix and illustrate the advantage and robustness of this matrix using non-nest test.This new matrix can fully capture complicated factors of geography,economy and society to form spatial autocorrelation in urban housing prices,depict the asymmetry of housing price interactions between cities in reality,and appropriately change with time.In China,the development level of each city's housing market is different because of diverse resource allocation,economic development degree and population aggregation.Therefore,the effect of each macro-control policy on housing prices should vary across cities.However,in the citities with the similar level of housing development,the effects should be close.In order to capture these heterogeneous effects,we incorporate quantile regression into spatial econometric model,and 2SQR is used to estimate the spatial lag model selected by LM test to avoid endogeneity.Housing variables controlled by policies and data across 84 large and medium-size cities over the period 2010-2015 are used to investigate the effects of macro-control policies on housing prices.It is shown that the decrease of residential selling area makes housing prices rise faster in cities with higher housing prices,decreasing residential land price makes housing prices have a larger drop in cities with high housing prices,the positive effect of domestic loans on housing prices is greater in cities with lower housing prices,loan rate only has a significantly positive effect in cities with higher housing prices and the positive effect of housing development investment on housing prices is much stronger in cities with higher housing prices.Government should enact classifying macro-control policies according to each city's housing market.It is suggested that administrative policy should be combined with market and other policies especially in cities with higher housing prices,and it is not appropriate to use this policy in cities with lower housing prices.Vacant land and house tax is suggested to be collected and government should apply differential tax rates according to each city's housing prices to alleviate housing structural contradiction.Reducing the dependence on land finance of local government and establishing a centralized and unified land market are also helpful to decrease housing prices,in cities with higher property prices,government should not only care about land prices but also consider land properties while land is supplied.About monetary policy,government should guide private capital and give interest rate subsidy for housing loans combined with bank in cities with higher housing prices.