Real estate investments and financial stability: evidence from regional commercial banks in China

Zhang, Dayong; Cai, Jing; Liu, Jia; Kutan, Ali M.

doi:10.1080/1351847x.2016.1154083

Cited by 56 publications

(47 citation statements)

References 52 publications

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“…The purpose of this study is to examine whether fluctuations in real estate prices affect bank lending decisions and performance. To do this, we estimate Model (1). We construct a fixed effects model to estimate Model (1).…”

Section: Methodsmentioning

confidence: 99%

“…Che et al [7] argue that bank loans play a crucial role in increasing property prices in China; as banks increase loans, property prices rise. Zhang et al [1] also investigated the Chinese property market. They suggest that regional banks' profits stem from the rapid increase in real estate investment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…To assess the impact of real estate price fluctuations on a bank's performance, we choose testing variables based on the literature as follows. The most widely used variable to measure bank performance is NPL [1,2,[24][25][26][27]. ROA is also a very popular measure to assess bank performance [28][29][30][31][32][33][34][35].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Understanding the price fluctuations in the real estate market is of importance because fluctuations in real estate prices have a significant impact on the soundness and profitability of the banking sector. One extreme case of the impact can be seen in the recent financial crisis caused by defaults on subprime mortgage loans in the United States [1][2][3]. In 2007, a significant drop in the United States housing prices caused subprime mortgage loans to start defaulting, and it eventually became a reason for a global financial crisis.…”

Section: Introductionmentioning

confidence: 99%

“…There is no way for a bank to force a loan repayment unless the borrower provides collateral [6]. Even if the borrower fails to repay the loan, if the value of the real estate rises, the recovery rate is very high [1]. Thus, real estate is mainly used as collateral [7]; banks determine the interest rate of the loan according to the value of the collateral assets such as quality, liquidity, and price volatility provided as collateral.…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Housing Prices on Bank Performance in Korea

Kim

Park

2019

Sustainability

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This study analyzes the relationship between housing prices and bank performance in Korea. To this end, using lending growth, return on assets, and non-performing loans as a performance measure, we estimate fixed-effects models for each measure. Major empirical results are summarized as follows. First, fluctuations in housing prices affect the banks’ lending decisions. If housing prices rise, banks tend to increase the volume of loans. Second, fluctuations in housing prices affect the quality of assets owned by banks. Banks’ asset soundness will improve in the case of a rise in housing prices. Third, fluctuations in housing prices have a greater impact on bank profitability when the real estate market goes bust. Our study suggests that in Korea, one of the emerging markets, there is a positive relationship between changes in housing prices and banks’ performance. In particular, banks’ profitability and soundness could be significantly hampered by a drop in housing prices. Therefore, it is necessary to be wary of excessively expanding real estate loans during the period of real estate booms.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Effect of Housing Prices on Bank Performance in Korea

Kim

Park

2019

Sustainability

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Systemic risk in the Chinese financial system: A copula‐based network approach

Zhang

et al. 2020

Int. J Fin Econ

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This article examines the systemic risk in the Chinese financial system by combining copula and graph theory approaches. We start from identifying the interdependence amongst financial institutions based on static and dynamic copula functions. A minimum spanning tree is then generated to analyse the network structure in the financial system in both static and dynamic contexts. Evidence from both static and dynamic analysis verifies the systemic importance of Industrial Bank Co., Ltd. Some others, Shanghai Pudong Development Bank and Huaxia Bank Co., Limited, occasionally show systemic importance in the dynamic setting. We further examine the stability of the network structure and find that whilst the dependence structure exhibits time‐varying patterns, certain relationships appear to be more stable than others. Our findings hold important policy and investment implications.

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Measuring systemic risk and dependence structure between real estates and banking sectors in China using a CoVaR‐copula method

Cao

2020

Int. J Fin Econ

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The aim of this paper is to study the dependence structure between the real estate and the banking sectors in China. Various time‐varying symmetric and asymmetric copula functions of the elliptical and Archimedean families are used to model the underlying dependence structure. Furthermore, it analyses risk spillover effects between these two sectors by quantifying three risk measures, namely, the value at risk (VaR), the conditional value at risk (CoVaR) and the delta conditional value at risk (ΔCoVaR). Over the period from January 2005 to March 2019, the empirical results show that there exists evidence of significant and symmetric dependence structure between these two sectors and Student's t copulas best capture this dependence. Moreover, we find that there are significant risk spillover effects from the real estate to the banking sectors, and vice versa. By considering different periods of financial distress, the empirical results show that risk spillover effects during the 2015–2016 Chinese stock market turbulence period are much significant than that during the 2007–2008 global financial crisis. Furthermore, the statistical tests show that the real estate sector contributes significantly to systemic risk of the banking sector, and vice versa. Finally, some useful implications are summarized for investors and policymakers.

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Real estate investments and financial stability: evidence from regional commercial banks in China

Cited by 56 publications

References 52 publications

The Effect of Housing Prices on Bank Performance in Korea

The Effect of Housing Prices on Bank Performance in Korea

Systemic risk in the Chinese financial system: A copula‐based network approach

Measuring systemic risk and dependence structure between real estates and banking sectors in China using a CoVaR‐copula method

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