scholarly journals Bank-Specific Shocks and House Price Growth in the U.S.

Author(s):  
Franziska Bremus ◽  
Thomas W. Krause ◽  
Felix Noth
Keyword(s):  
2020 ◽  
Vol 33 (11) ◽  
pp. 5288-5332 ◽  
Author(s):  
Vahid Saadi

Abstract This paper studies the role of the Community Reinvestment Act (CRA) in the U.S. housing boom-bust cycle. I find that enhanced CRA enforcement in 1998 increased the growth rate of mortgage lending by CRA-regulated banks to CRA-eligible census tracts. I show that during the boom period house price growth was higher in the eligible census tracts because of the shift in mortgage supply of regulated banks. Consequently, these census tracts experienced a worse housing bust. I find that CRA-induced mortgages were awarded to borrowers with lower FICO scores and were more frequently delinquent.


Author(s):  
TAKAAKI OHNISHI ◽  
TAKAYUKI MIZUNO ◽  
CHIHIRO SHIMIZU ◽  
TSUTOMU WATANABE

How can we detect real estate bubbles? In this paper, we propose making use of information on the cross-sectional dispersion of real estate prices. During bubble periods, prices tend to go up considerably for some properties, but less so for others, so that price inequality across properties increases. In other words, a key characteristic of real estate bubbles is not the rapid price hike itself but a rise in price dispersion. Given this, the purpose of this paper is to examine whether developments in the dispersion in real estate prices can be used to detect bubbles in property markets as they arise, using data from Japan and the U.S. First, we show that the land price distribution in Tokyo had a power-law tail during the bubble period in the late 1980s, while it was very close to a lognormal before and after the bubble period. Second, in the U.S. data we find that the tail of the house price distribution tends to be heavier in those states which experienced a housing bubble. We also provide evidence suggesting that the power-law tail observed during bubble periods arises due to the lack of price arbitrage across regions.


2009 ◽  
Vol 12 (3) ◽  
pp. 193-220
Author(s):  
Karol Jan Borowiecki ◽  

This paper studies the Swiss housing price determinants. The Swiss housing economy is reproduced by employing a macro- series from the last seventeen years and constructing a vector-autoregressive model. Conditional on a comparatively broad set of fundamental determinants considered, i.e. wealth, banking, demographic and real estate specific variables, the following findings are made: 1) real house price growth and construction activity dynamics are most sensitive to changes in population and construction prices, whereas real GDP, in contrary to common empirical findings in other countries, turns out to have only a minor impact in the short-term, 2) exogenous house price shocks have no long-term impacts on housing supply and vice versa, and 3) despite the recent substantial price increases, worries of overvaluation are unfounded. Furthermore, based on a self-constructed quality index, evidence is provided for a positive impact of quality improvements in supplied dwellings on house prices.


2010 ◽  
Vol 13 (1) ◽  
Author(s):  
Mark R. Trusheim ◽  
Murray L. Aitken ◽  
Ernst R. Berndt

While much has been written about the distinctions between biologics and small molecules in terms of their scientific, manufacturing and regulatory experiences, relatively little has been published comparing their clinical and commercial experiences. Employing a data base encompassing all 96 biologics and 212 small molecules newly launched in the U.S. between 1998Q1 and 2008Q4, we compare their downstream clinical and commercial characteristics. Substantial heterogeneity occurs across therapeutic classes. Biologics are more concentrated than small molecules in their therapeutic class composition, but have obtained FDA indication approvals in 13 of 15 classes. While average delays between FDA approval and first observed sales revenues are similar, biologics are twice as likely as small molecules to be Orphan Drugs, are slightly more likely to be designated FDA priority rather than standard review status, and gain slightly more supplemental indication approvals. Although 9.4% of new small molecules permanently exited the market for a variety of reasons, 7.3% of new biologics exited, but 26% of biologics had black box warnings compared to 20% of small molecules. Both biologics and small molecules take 21-22 quarters from launch to reach $100 million in real revenues. Small molecules have an initially more rapid uptake, but thereafter biologics’ mean revenues tend to be slightly greater than for small molecules. While launch prices for biologics are commonly perceived as being greater than for small molecules, price growth per standard unit is generally greater for small molecules than biologics, with rates of price growth increasing for small molecules in the first five years since launch, and decreasing thereafter. We conclude that the market dynamics of biologics differ substantially from those of small molecules, although therapeutic class composition plays a major role.


2015 ◽  
Vol 29 (24) ◽  
pp. 1550181 ◽  
Author(s):  
Hao Meng ◽  
Wen-Jie Xie ◽  
Wei-Xing Zhou

The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a marked fall during the global financial tsunami and China’s economy has also slowed down by about 2%–3% when measured in GDP. Nevertheless, the housing markets in diverse Chinese cities seemed to continue the almost nonstop mania for more than 10 years. However, the structure and dynamics of the Chinese housing market are less studied. Here, we perform an extensive study of the Chinese housing market by analyzing 10 representative key cities based on both linear and nonlinear econophysical and econometric methods. We identify a common collective driving force which accounts for 96.5% of the house price growth, indicating very high systemic risk in the Chinese housing market. The 10 key cities can be categorized into clubs and the house prices of the cities in the same club exhibit an evident convergence. These findings from different methods are basically consistent with each other. The identified city clubs are also consistent with the conventional classification of city tiers. The house prices of the first-tier cities grow the fastest and those of the third- and fourth-tier cities rise the slowest, which illustrates the possible presence of a ripple effect in the diffusion of house prices among different cities.


2020 ◽  
Vol 10 (2) ◽  
pp. 64-72
Author(s):  
Filip Ostrihoň

Relying on a recently published database of financial crises, this paper assesses an early warning model for predicting banking sector distress. The exercise employs discrete choice models and a signaling approach to evaluate the performance of an existing model based on credit-to-GDP change and real house price growth in regard to predominantly post-crisis data for EU and Visegrad Group countries. As such, unbalanced panel data for 27 EU countries, spanning with annual frequency at longest the period of 2003-2017, as well as unbalanced panel data for 4 Visegrad Group countries covering at most the period 2008Q1-2017Q4 with quarterly frequency were analyzed. The results are generally in line with other empirical research featuring the same model and indicate that the model retains most of its predictive capabilities even when currently available data are used. However, the analysis identifies that the indicator of real house price growth may not be as useful of a predictor of banking crises in more recent periods for EU countries, as it might have been before the 2008 financial and economic crisis. Consequently, a simpler univariate early warning indicator approach might be sufficient for banking sector risk monitoring and management in EU and Visegrad Group countries in regard to identifying periods of distress similar to those in 2008.


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