دانلود رایگان مقاله انگلیسی قیمت های مسکن و ریسک اعتبار: شواهدی از ایالات متحده به همراه ترجمه فارسی
|عنوان فارسی مقاله||قیمت های مسکن و ریسک اعتبار: شواهدی از ایالات متحده|
|عنوان انگلیسی مقاله||House prices and credit risk: Evidence from the United States|
|رشته های مرتبط||علوم اقتصادی، مدیریت، بانکداری، مهندسی مالی و ریسک، مدیریت مالی و اقتصاد پول و بانکداری|
|کلمات کلیدی||ریسک اعتباری، وام ها ی خنث، بانک ها، قیمت ها ی مسکن، انواع وام، داده ها ی ترکیبی پویا|
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|نشریه||الزویر – Elsevier|
|مجله||مدلسازی اقتصادی – Economic Modelling|
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بخشی از ترجمه فارسی مقاله:
ا ین مقاله، به شیوه ها ی مختلف به کامل تر شدن منابع و مطالعات موجود کمک می کند. اولا، ما به طور ویژه به بررسی اثر نوسانات قیمت مسکن بر روی کیفیت پورتفولیوی وام ها د ر سطح بانک می پردا زیم. کار ها ی تجربی موجود بر نقش قیمت ها ی مسکن د ر بی ثبات سا زی سیستم بانکداری به طور کلی متمرکز هستند( رینهارت و ر اکوف ۲۰۰۸، بارل و همکار ان ۲۰۱۰)، د ر حالی که اثر قیمت ها ی مسکن بر روی کیفیت پورتفولیوی وام د ر تک تک بانک ها کم تر بررسی شده است. پان و وانگ(۲۰۱۳) د ر ر ابطه با ا ین بعد خاص ا ز تحلیل، به بررسی اثر ات رشد د ر امد بر روی ر ابطه بین قیمت ها ی مسکن و NPL می پردا زد. با ا ین حال، پان و وانک(۲۰۱۳)، تنها اثر غیر متقارن قیمت ها ی مسکن ر ا بر روی وام ها ی خنث د ر نظر گرفته است، د ر حالی که سایر عوامل موثر بر ریسک خطا می تواند اثر ات نامتقارن بر روی نرخ نکول داشته باشد. د ر ا ین مطالعه تجربی، اثر ات نامتقارن بالقوه عوامل ریسک اعتباری بر روی نرخ نکول با بررسی پویایی NPL د ر طی دوره ها ی زمانی مختلف بررسی می شود.
بخشی از مقاله انگلیسی:
The recent subprime mortgage crisis in the United States has demonstrated the key role that the housing market plays in destabilizing the financial system. From the late 1990s, there was a sharp increase in the subprime mortgages fuelled by low interest rates and lax lending standards. However, while the quality of banks’ loan portfolios was deteriorating by the constant growth of the subprime mortgages, the default rates remained artificially low due to the rapid house price appreciation. The booming house prices and low default rates encouraged banks to invest heavily in the real estate market, which eventually led to the creation of a speculative real estate bubble. The collapse of the real estate bubble exerted enormous pressure on the banks that were highly exposed to the real estate market. In particular, many banking institutions suffered from severe liquidity shortages due to a sharp increase in their nonperforming real estate loans. In fact, falling house prices undermined the value of real estate collaterals, which motivated many subprime mortgage borrowers to default on their loan repayments. Higher default rates, in turn, led to credit contraction and tightening of the lending standards in banks.
As a consequence, the housing demand substantially dropped, while the housing supply was increasing due to the rising number of real estate foreclosures. The imbalances between supply and demand further reduced house prices and exacerbated deteriorating credit market conditions, which severely affected the real economy and led to high default rates across all loan categories. Fig. 1 demonstrates the relationship between U.S. house prices, bank lending, and nonperforming loans (NPL). It appears that there is a close relationship between house prices, aggregate loan level, and aggregate NPL in the U.S. banking system. In other words, rising house prices are associated with increased lending and low default rates, while NPL increase substantially when house prices and real estate lending drop. In addition, Fig. 1 shows that NPL dynamics vary significantly across loan categories and bank types. More specifically, it appears that the impact of house price fluctuations is much higher on real estate loans, compared to other loan categories. It also emerges that, compared to savings institutions (SI), commercial banks (CB) suffer from higher loan losses in response to deteriorating market conditions. Against this background, it is clear that understanding how house prices affect the quality of loan portfolios is of crucial importance to financial institutions and regulators interested in maintaining financial stability. Accordingly, this study uses dynamic panel data models to empirically investigate the impact of house price fluctuations on the evolution of NPL across U.S. banks. The analysis is further extended by examining if this relationship varies across different loan categories and different types of banks.
This paper complements the existing literature in several ways. First, we specifically examine the impact of house price fluctuations on the quality of loan portfolios at bank-level. Available empirical works focus primarily on the role of house prices in destabilizing the banking system as a whole (see, e.g., Reinhart and Rogoff (2008); Barrell et al. (2010)), while the impact of house prices on the quality of loan portfolios in individual banks is less investigated. Closely related to this particular aspect of our analysis, Pan and Wang (2013) study the threshold effects of income growth on the relationship between house prices and NPL. However, Pan and Wang (2013) only consider the asymmetric impact of house prices on NPL, whereas other credit risk determinants may also have asymmetric effects on default rates. In this empirical study, we account for potential asymmetric effects of all credit risk determinants on default rates by investigating NPL dynamics during different time periods. Second, to the best of our knowledge, this is the first study that investigates how different loan categories are affected by house price movements. Using aggregate NPL to examine the relationship between house prices and the quality of loan portfolios may be challenged as the composition of loan portfolios varies widely across banking institutions (Louzis et al., 2012). In addition, it is evident in Fig. 1 that NPL dynamics vary substantially across different loan categories. Therefore, it is essential to investigate the sensitivity of different loan categories to house price fluctuations in order to develop an insight for financial regulators to provide better regulatory practices for individual banks with different loan portfolio compositions. Third, potential differences between determinants of NPL across different types of the U.S. depository institutions have remained undetected, despite their important regulatory implications. It is argued that a bank’s lending policies reflect its risk attitude, which in turn depends on its mission and institutional structure (see Salas and Saurina (2002)). Furthermore, as seen in Fig. 1, there are major differences between NPL dynamics of CB and SI over time. Therefore, this study adds to the existing credit risk literature by examining if the impact of house prices on the evolution of NPL varies across two types of depository institutions, namely CB and SI.1 Finally, another feature of this paper is that we assess the house price-credit risk nexus based on state-level data and during different macroeconomic conditions. It is argued that the dynamics of house prices vary widely both over time and across geographical regions (see, e.g., Mian and Sufi (2009); Holly et al., (2010)). In particular, despite the recent boom and bust cycle in U.S. national house prices, the patterns of house prices were non-uniform across states. While some states, such as California and Florida, experienced substantial changes in the house prices over both boom and bust periods, some states, such as Vermont and Montana, only underwent rapid house price appreciation, and some other states, such as Georgia and Michigan, only faced large declines over the bust period.
These substantial variations in regional house prices reflect differences in the housing market supply and demand, which in turn depend on demographic and socio-cultural factors, local economic conditions, regional regulations and jurisdictions, and local financial systems. Although these factors can contribute markedly to the diversity of credit risk within the United States, the impact of time and regional variations in house prices on the evolution of credit risk has been largely neglected by the literature. Therefore, we investigate the impact of state-level house price fluctuations on the evolution of NPL during different macroeconomic conditions. In essence, the empirical results reveal that house prices significantly affect the quality of banks’ loan portfolios. More specifically, there is a strong negative relationship between changes in house prices and evolution of NPL in individual banks, which supports the view that house prices can serve as a key macroprudential indicator (see, e.g., Davis and Zhu (2009); Barrell et al. (2010)). We also find that the impact of house prices on NPL is more pronounced during adverse economic conditions. In fact, we show that most bank-specific and systematic factors have asymmetric impact on loan losses during different economic conditions. This important finding complements the credit risk literature as similar studies examine the potential asymmetric effects of only one variable on default rates (see, e.g., Marcucci and Quagliariello (2009); Pan and Wang (2013)).
Furthermore, unlike prior studies in the banking literature, we show that the effects of house prices on loan losses vary significantly across different loan categories. More specifically, it is shown that falling house prices lead to higher loan losses in real estate loan portfolios, implying that banks with higher real estate lending may face greater financial constraints when house prices drop. Our empirical results also show that the impact of house prices varies among bank types. In particular, we show that CB are more sensitive to falling house prices although SI are traditionally mandated to concentrate on residential mortgages. It is also found that the impact of house prices on loan losses varies depending upon the quality of loan portfolios. In other words, lower quality loan portfolios are more sensitive to house price fluctuations. This particular finding supports the view that there is a circular relationship between house prices, bank lending behavior, and loan losses. Finally, we show that our key findings remain unchanged when we assess the robustness of our results by using different house price indicators, different econometric methodologies, and alternative model specifications. The remainder of this paper is organized as follows. Section 2 provides some theoretical background and highlights the hypotheses to be tested. In Section 3, the empirical models and estimation procedure are introduced. Section 4 describes the data, while Section 5 discusses the empirical results associated with each hypothesis. In Section 6 we report findings from further empirical checks. Section 7 concludes.