Quarterly Journal of Quantitative Economics (JQE)

the effectiveness of macroprodential policies in the growth of credit and housing price by using a dynamic panel data model

https://doi.org/10.22055/jqe.2019.28074.2009
Volume 16, Issue 4 - Serial Number 63
Vol. 16, No. 4, Winter 2020
Winter 2020
Pages 163-201
Quarterly Journal of Quantitative Economics (JQE)

Document Type : Research Paper

Authors

zahra Afshari 1
َAvin Khezri 2

1 Professor of economics, Faculty of Social Sciences and Economics, AL Zahra University, Tehran, Iran.

2 PhD student of economy, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran

Abstract
In recent years, many countries have experienced boom-bust cycles in credit and asset prices, some of which resulted in severe financial crises. Given that housing prices in both advanced and emerging countries have been heavily influenced by housing credit, the decline in housing prices is similar in both groups of countries. In response to these cycles, authorities in many countries have used macro prudential policies as the frontline of defense against the financial instability risks. Macro prudential measures are, indeed, policy tools that have been used intensively in recent years to target suppliers or applicants of financial services (e.g., households and firms).
There is no consensus on the relationship between monetary stability and financial stability and their effects on each other. Some argue that while high-interest rates may control inflation (monetary stability), they are deemed to destroy banks’ balance sheets (financial instability). Another argument is that in the financial instability condition, the contraction monetary policy intensifies the likelihood of instability in the financial institutions. In turn, some believe that the financial policies adopted by monetary institutions, including the central bank, are the leading causes of financial instability. On the other hand, some believe that financial stability preservation is always one of the intrinsic duties of the central bank. In effect, the lack of cooperation between the macro prudential and monetary policy-maker authorities may lead to conflicting policies and, therefore non-optimal results. Moreover, monetary and macro prudential policies as complements to each other can guarantee monetary and financial stability at the same time. This paper studies the effect of
macro prudential policies and the interaction of monetary and macro prudential policies on the credit growth and housing prices using the generalized moment method. To that end, the first macro prudential tools index (including the capital requirements, loan-to-value ratios, and debt-to-income ratios) are constructed for the advanced and emerging economies over the period 2000 to 2014. Then, an aggregate index was constructed under two scenarios of tightening (contraction) and easing (expansion) actions. In effect, the produced aggregate index showed the state of macro prudential policy in each country. To construct the macro prudential tools, IMF (2011) data used in the study of Lim et al. (2011) and Shim et al. (2013) were considered. Following Eckinsky and Ramsey (2017), this paper used seven macro prudential tools (i.e., loan-to-value ratios, debt-to-income repayments and other housing sector tools, countercyclical capital requirements, loan-loss provision requirements, consumer loan limits, and credit growth limits), to construct the aggregate indicator for the macro prudential policy and sub-indices for housing and non-housing in the studied countries. The dummy variable was, in turn, used to construct the macro prudential tools. If the macro prudential tool was used or intensified in the desired season, the dummy variable was considered 1, and if the use of the macro prudential tool was reduced, the dummy variable was -1. Otherwise, if no macro prudential tool was used, the value was zero. The aggregate macro prudential policy index for each of the housing and non-housing sectors was considered as the sum of the dummy variables. In addition, by aggregating the dummy variables of the specific tools in each country, the non-housing and housing macro prudential policy indexes were constructed. The aggregate macro prudential index was also obtained from the sum of the housing and non-housing indexes.
The results showed that the macro prudential policy indexes (aggregate-housing) had no significant effect on the housing prices and credit growth. However, the simultaneous adoption of the macro prudential policies and monetary policies inhibited the growth of the credit and, consequently the rise in the housing prices. In turn, a comparison of the coefficients showed that the effect of these tools on the credit growth was greater than the housing price growth. This finding, consistent with the results of other research including Wenden Beach et al. (2012), Kotner and Shim (2012), Brno and Friedrich (2014), Bruno et al. (2017),
showed that the effectiveness of macro prudential policies in controlling the credit growth was more than lowering the housing prices. On the other hand, the interaction of monetary policies with the non-housing and housing macro prudential policies produced results which were more effective in controlling the credit and housing prices, respectively. In other words, the macro prudential policies that targeted the housing sector were more effective in controlling the housing prices. However, non-housing macro prudential policies were more effective in reducing the credit growth.

Keywords

housing prices
GMM
for macroprudential policies
Subjects
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  • Receive Date 31 December 2018
  • Revise Date 26 June 2019
  • Accept Date 01 October 2019
  • First Publish Date 20 February 2020
  • Publish Date 20 February 2020

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