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The effect of house prices on bank risk: empirical evidence from Hungary
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Housing market is important from a macroprudential perspective because it has a strong effect on the banking sector. Changes in real estate prices may affect the level of bank risk through household mortgage lending, however, the literature has no clear conclusion on this impact mechanism. Using a bank-level database containing quarterly data from 1998 to 2016 we estimated dynamic fixed-effects panel models to examine how bank risk is influenced by housing prices via mortgage lending in the Hungarian banking system. According to the results (1) higher house prices lead to higher bank risk, (2) the higher the share of mortgage loans at a bank, the stronger the positive effect of house prices on bank risk. In the period following the onset of the crisis a much stronger positive relationship could be observed between house prices and bank risk than before the crisis. Using the house price gap which measures the deviation of house prices from their fundamental value we provide empirical evidence that the deviation hypothesis was stronger for Hungary. This suggests that both banks and households tend to undertake excessive risks during a housing market boom, which can be mitigated by macroprudential policy instruments.
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... Koetter and Pohosyan (2010) find that, in the case of the German real estate market, changes in housing prices are negative to bank instability, but not related to changes in price levels. Banai and Vágó (2018) find that rising housing prices increase bank risk and that the higher the proportion of mortgage loan, the greater the impact. Kok and Ismail (2019) argue that the higher the housing price, the more stable the bank is, but if the housing price exceeds a certain level, the impact of the housing price on bank stability becomes negative. ...
This study analyzes the effect of housing prices on banking performance in Korea. The findings of the study reveal that the impact of housing price changes on banking performance was different in the commercial banks, regional banks and specialized banks. This seems to be due to the different characteristics of each bank. The three types of banks in Korea have different functions, and thus the impact of changes in housing prices on their respective banking performance is different. Therefore, it would be desirable for Korea’s financial regulatory authorities to draw up policies and perform its supervisory functions in consideration of these points.
The literature primarily focuses on the effect of changes on property prices in terms of macrovariables and monetary aggregates. Only a few studies have taken into account bank characteristics when considering the effects of real estate market trends on bank lending policies and performance, and there is no study that controls for the type of bank or loan purpose. The paper studies the linkage between property market trends and bank risk exposure. We test for any significant difference of real estate banks with respect to other banks and the different roles of the real estate market trend in explaining changes in bank risk exposure. The empirical evidence demonstrates that real estate banks are not always riskier than other banks, and specialized banks are less sensitive to real estate market trends than other banks.
The risks of household lending are still a major issue in Hungarian banking. The proportion of non-performing loans is rising continuously. We constructed a model to find those factors which have significant effect on the probability of default of households' mortgages. We also used this model to calibrate the optimal level of household mortgages' payment-to-income ratios, which is important from a regulatory point of view. Our results show that the denomination of the loan and the indebtedness of the household are crucial factors in the performance of the loan. We also show that loans contracted via agents are riskier than others. The results carry two important messages from a regulatory perspective. Prescribing the same payment-to-income (PTI) ratios for HUF and FX loans may be unnecessarily restrictive for the former and excessively permissive for the latter. The uniform regulation of households with different income levels may also lead to undesired anomalies.
When a model for panel data includes lagged dependent explanatory variables, then the habitual estimation procedures are asymptotically valid only when the number of observations in the time dimension (T) gets large. Usually, however, such datasets have substantial sample size in the cross-section dimension (N), whereas T is often a single-digit number. Results on the asymptotic bias (N → ∞) in this situation have been published a decade ago, but, hence far, analytic small sample assessments of the actual bias have not been presented. Here we derive a formula for the bias of the Least-Squares Dummy Variable (LSDV) estimator which has a approximation error. In a simulation study this is found to be remarkably accurate. Due to the small variance of the LSDV estimator, which is usually much smaller than the variance of consistent (Generalized) Method of Moments estimators, a very efficient procedure results when we remove the bias from the LSDV estimator. The simulations contain results for a particular operational corrected LSDV estimation procedure which in many situations proves to be (much) more efficient than various instrumental variable type estimators.
We compare the different existing approaches to the construction of time-varying Z-score measures, plus an additional alternative one, using a panel of banks for the G20 group of countries covering the period 1992–2009. We examine which ways of estimating the moments used in these different approaches best fit the data, using a simple root mean squared error criterion. Our results are supportive of our alternative time-varying Z-score measure: it uses mean and standard deviation estimates of the return on assets calculated over full samples combined with current values of the capital-asset ratio, and is thus straightforward to implement.
This paper presents a statistical analysis of time series regression models for longitudinal data with and without lagged dependent variables under a variety of assumptions about the initial conditions of the processes being analyzed. The analysis demonstrates how the asymptotic properties of estimators of longitudinal models are critically dependent on the manner in which samples become large: by expanding the number of observations per person, holding the number of people fixed, or by expanding the number of persons, holding the number of observations per person fixed. The paper demonstrates which parameters can and cannot be identified from data produced by different sampling plans.
The relative magnitudes are compared of successive terms in a higher-order asymptotic expansion of the bias of the LSDV estimator in dynamic panels. We find that the leading term accounts for the major part of the actual bias in small samples. This implies that bias correction procedures can be based on relatively simple bias approximation formulas.
This paper extends the LSDV bias approximations in [Bun, B.J.G., Kiviet, J.F. 2003. On the diminishing returns of higher order terms in asymptotic expansions of bias. Economic letters, 79, 145-152.] to unbalanced panels. The approximations are obtained by modifying the within operator to accommodate the dynamic selection rule. They are accurate, with higher order terms bringing only decreasing improvements to the approximations. This removes an important cause for limited applicability of bias corrected LSDV estimators.
Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, and compared to non-linear GMM. The importance of these results is illustrated in an application to the estimation of a labour demand model using company panel data.
This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized
method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our
GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation
in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables.
We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying
restrictions and Hausman specification tests.