top of page

こもれび Heart LINK クラブのグループ

Hunter Montgomery
Hunter Montgomery

UPDATED Crack CFD 2005 Key

NVDRS began collecting data in 2003 with seven states participating (Alaska, Maryland, Massachusetts, New Jersey, Oregon, South Carolina, and Virginia); six states joined in 2004 (Colorado, Georgia, North Carolina, Oklahoma, Rhode Island, and Wisconsin), four more joined in 2005 (California, Kentucky, New Mexico, and Utah), and two joined in 2010 (Michigan and Ohio), for a total of 19 states. CDC provides funding for state participation and anticipates that NVDRS will eventually expand to include all 50 states, the District of Columbia (DC), and U.S. territories (59).

crack CFD 2005 key

Speculative borrowing in residential real estate has been cited as a contributing factor to the subprime mortgage crisis.[88] During 2006, 22% of homes purchased (1.65 million units) were for investment purposes, with an additional 14% (1.07 million units) purchased as vacation homes. During 2005, these figures were 28% and 12%, respectively. In other words, a record level of nearly 40% of homes purchased were not intended as primary residences. David Lereah, National Association of Realtors's chief economist at the time, stated that the 2006 decline in investment buying was expected: "Speculators left the market in 2006, which caused investment sales to fall much faster than the primary market."[89]

Housing prices nearly doubled between 2000 and 2006, a vastly different trend from the historical appreciation at roughly the rate of inflation. While homes had not traditionally been treated as investments subject to speculation, this behavior changed during the housing boom. Media widely reported condominiums being purchased while under construction, then being "flipped" (sold) for a profit without the seller ever having lived in them.[90] Some mortgage companies identified risks inherent in this activity as early as 2005, after identifying investors assuming highly leveraged positions in multiple properties.[91]

In addition to considering higher-risk borrowers, lenders had offered progressively riskier loan options and borrowing incentives. In 2005, the median down payment for first-time home buyers was 2%, with 43% of those buyers making no down payment whatsoever.[102] By comparison, China has down payment requirements that exceed 20%, with higher amounts for non-primary residences.[103]

Types of mortgages became more risky as well. The interest-only adjustable-rate mortgage (ARM) allowed the homeowner to pay only the interest (not principal) of the mortgage during an initial "teaser" period. Even looser was the "payment option" loan, in which the homeowner has the option to make monthly payments that do not even cover the interest for the first two- or three-year initial period of the loan. Nearly one in 10 mortgage borrowers in 2005 and 2006 took out these "option ARM" loans,[72] and an estimated one-third of ARMs originated between 2004 and 2006 had "teaser" rates below 4%. After the initial period, monthly payments might double[98] or even triple.[105]

The Financial Crisis Inquiry Commission reported in January 2011 that many mortgage lenders took eager borrowers' qualifications on faith, often with a "willful disregard" for a borrower's ability to pay. Nearly 25% of all mortgages made in the first half of 2005 were "interest-only" loans. During the same year, 68% of "option ARM" loans originated by Countrywide Financial and Washington Mutual had low- or no-documentation requirements.[72]

By October 2007, approximately 16% of subprime adjustable-rate mortgages (ARM) were either 90-days delinquent or the lender had begun foreclosure proceedings, roughly triple the rate of 2005.[121] By January 2008, the delinquency rate had risen to 21%[122] and by May 2008 it was 25%.[123]

Fannie Mae and Freddie Mac are government sponsored enterprises (GSE) that purchase mortgages, buy and sell mortgage-backed securities (MBS), and guarantee nearly half of the mortgages in the U.S. A variety of political and competitive pressures resulted in the GSEs ramping up their purchase and guarantee of risky mortgages in 2005 and 2006, justas the housing market was peaking.[275][276] Fannie and Freddie were both under political pressure to expand purchases of higher-risk affordable housing mortgage types, and under significant competitive pressure from large investment banks and mortgage lenders.[277]

Several studies by the Government Accountability Office (GAO), Harvard Joint Center for Housing Studies, the Federal Housing Finance Agency, and several academic institutions summarized by economist Mike Konczal of the Roosevelt Institute, indicate Fannie and Freddie were not to blame for the crisis.[281] A 2011 statistical comparisons of regions of the US which were subject to GSE regulations with regions that were not, done by the Federal Reserve, found that GSEs played no significant role in the subprime crisis.[282] In 2008, David Goldstein and Kevin G. Hall reported that more than 84% of the subprime mortgages came from private lending institutions in 2006, and the share of subprime loans insured by Fannie Mae and Freddie Mac decreased as the bubble got bigger (from a high of insuring 48% to insuring 24% of all subprime loans in 2006).[283] In 2008, another source found estimates by some analysts that Fannie's share of the subprime mortgage-backed securities market dropped from a peak of 44% in 2003 to 22% in 2005, before rising to 33% in 2007.[277]

In 2005, Ben Bernanke addressed the implications of the United States's high and rising current account deficit, resulting from U.S. investment exceeding its savings, or imports exceeding exports.[315] Between 1996 and 2004, the U.S. current account deficit increased by $650 billion, from 1.5% to 5.8% of GDP. The U.S. attracted a great deal of foreign investment, mainly from the emerging economies in Asia and oil-exporting nations. The balance of payments identity requires that a country (such as the U.S.) running a current account deficit also have a capital account (investment) surplus of the same amount. Foreign investors had these funds to lend, either because they had very high personal savings rates (as high as 40% in China), or because of high oil prices.

Members of US minority groups received a disproportionate number of subprime mortgages, and so have experienced a disproportionate level of the resulting foreclosures.[349][350][351] A study commissioned by the ACLU on the long-term consequences of these discriminatory lending practices found that the housing crisis will likely widen the black-white wealth gap for the next generation.[352] Recent research shows that complex mortgages were chosen by prime borrowers with high income levels seeking to purchase expensive houses relative to their incomes. Borrowers with complex mortgages experienced substantially higher default rates than borrowers with traditional mortgages with similar characteristics.[353] The crisis had a devastating effect on the U.S. auto industry. New vehicle sales, which peaked at 17 million in 2005, recovered to only 12 million by 2010.[354]

(W)hile the unemployment rate for those over 34 peaked at about 8 percent, the unemployment rate among those between the ages of 18 and 34 peaked at 14 percent in 2010 and remains elevated, despite substantial improvement; delinquency rates on student loans have risen several percentage points since the Great Recession and even into the recovery; and the homeownership rate among young adults has dropped from a peak of 43 percent in 2005 to 37 percent in 2013 concurrent with a large increase in the share living with their parents.[463]

Recently, with the development of machine learning classified as deep learning inspired by structure of the brain called artificial neural networks (ANN) [45], many algorithms have been proposed to perform object detection and image classification tasks. ANN is employed to solve many civil engineering problems [46,47,48,49,50]. Gao and Mosalam in [51] applied the transfer learning to detect damage images with structural method, and this method can reduce the computational cost by using the pre-trained neural network model. Meanwhile, the author needs to fine the neural network to perform the crack detection. Local patch information was employed to inspect crack information by convolutional neural networks (CNN) in [52]. In CrackNet [53], the algorithm improved pixel-perfect accuracy based on CNN by discarding pooling layers. In CrackNet-R [54], a recurrent neural network (RNN) is deployed to perform automatic crack detection on asphalt road. Cha et al. [55] adopted a sliding windows based on CNN to scan and detect road crack. Fan et al. in [56] proposed a structured prediction method to detect crack pixels with CNN. The small structured pixel images (27 27 pixels) was input into the neural network, which may generate overload for the computer memory. Ensemble network is proposed to perform crack detection and measure pavement cracks generated in road pavement [57]. Maeda et al. on [58] adopted object detection network architecture to detect crack images, and the network architecture can be transferred to a smartphone to perform road crack detection. Cha et al. used the Faster-RCNN to inspect road cracks [59]. Yang et al. in [60] adopted a fully convolutional network (FCN) to inspect road pavement cracks at pixel level, which can perform crack detection by end-to-end training. Li et al. in [61] employed the you-only-look-once v3 (YOLOv3)-Lite method to inspect the aircraft structures, and the depth wise separable convolution and feature pyramid were adopted to design the network architecture and joined the low- and high-resolution for crack detection. Jenkins et al. presented an encoder-decoder architecture to perform road crack detection, and the function of the encoder and decoder layers are used to reduce the size of input image to generate lower level feature maps, and obtain the resolution of the input data with up-sampling, respectively [62]. Tisuchiya et al. proposed a data augmentation method based on YOLOv3 to perform crack detection, which can increase the accuracy effectively [63].




bottom of page