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The currency
Swazi emalengeni and South African rands are linked currencies and you can spend South African rands in Swaziland without any problems.
Remember to ask for your change in rands as emalengeni are worthless in South Africa where they are not accepted.
Incidentally, the one emalengeni (that's a contradiction in grammatical terms because emalengeni is plural, but I can't remember the singular) coin is very similar to the British pound in weight and size so if you have pound coins with you be careful not to confuse them. That could be expensive. Yes, the locals know and don't say anything.Related to:- Backpacking
- Eco-Tourism
- Budget Travel
King Mswati III
King Mswati III is the King of Swaziland. You will see pictures of him all throughout the country - on billboards, in offices, and in building lobbies. He became king at 18 after the death of his father, King Sobhuza II, in the 1980s. He is an absolute monarch, but in Swazi tradition rules in conjunction with his mother. Polygamy is customary for Swazi kings. King Mswati has 14 wives and numerous children.
Related to:- Road Trip
A Swazi Legend
A Swazi legend relates the romantic tale of a beautiful maiden and her handsome warrior.
For her hand he had to present her with the skin of a leopard which he had to hunt for on the rugged Gobolondlo Mountain.
Misfortune befell her suitor and he was seized by witches who inhabited the mountainside and transformed into a white flower, condemned for his trespassing to bloom and die among the mountain grasses.
When after many days the warrior had still not returned, the lamenting maiden sat on the river's edge where her tears flowed to form the Phophonyane waterfalls, which still flow today.Related to:- Historical Travel
Swaziland shows roads in very good shape !!
This is a very different panorama shown by Mozambique, Swaziland's neighbour country that endured a 17-year civil war and now needs its major infrastructures to be rebuilt.Related to:- Road Trip
The "Candle"
The Swazi Candle factory is actually located in a converted farmhouse! To quote from their literature: 'The concept involves the cloaking of candle wax with a colourful intricately designed shell. Each candle is hand-formed, the white wax is gently warmed, crafted into the basic shape which can be a large ball, an egg, mushroom, owl, tortoise, or larger bull, rhino or elephant. All of the craftsmen and women go through a training period when they begin with the easier shapes. Under the watchful eye of the senior candlemakers, the apprentices are coached towards making the more intricate shapes. Swazi Candles is a fun place to visit (I can confirm this!), generally the candlemakers like to work to music and there is a feeling of one large family working together here'. The photo shows our easily transported and relatively simple candle, compared to a TV controller!
Related to:- Family Travel
- Road Trip
The Umhalanga or "Reed Dance" is the second most important ceremony and the most colourful. It is celebrated in late August or early September. This ceremony attracts young girls from all over the country and it is an opportunity for the King to choose new brides. The girls wear short beaded skirts decorated with fringes and buttons; together with anklets, bracelets and necklaces, and colourful sashes. It is a very very interesting fest to see.
More details in Umhlanga Travelogue.Related to:- Music
- Festivals
- Arts and Culture
There are two traditional fests in the country:
The Incwala and the Umhlanga.
The Incwala or "first fruits" ceremony is held in December is the most sacred of all Swazi ceremonies. Every year, the Swazi astrologers decide the exact date for the fest.
More details in Incwala Travelogue -
It is a traditional dance that requires to be fit. It is a foot stamping dance, vigorous in style. It is performed by men with spectacular rhythm. You can see these dances in many places. I saw them in the Milwane Wildlife Sanctuary and again in the Mantenga Cultural Village, but shows are held in many lodges around the country.
Related to:- Music
- Festivals
- Arts and Culture
Have Candy & Move Out
Wherever we stopped in both Swaziland and SA to take pictures of the passing scenery or villages, it seemed that people very quickly materialized out of nowhere. The children have come to realize that there are "sweets" to be had from these passerbys! It is not long before your car is surrounded so be prepared to take your photo and move along as quickly as you can. Photo of some village huts in the dry southern areas of Swaziland - the country was noticeably poorer than SA..
The monarchy
Each year there is a huge festival in September when the king receives all the maidens of the country. Then he chooses a fiancée (not every year, but even so... quite often!), who he will wed the following year. It is of course, a great honour for the girl.
Hundreds of Swahili girls dance for the king. Unmarried maidens do not cover their upper body but decorate themselves with colourful bead jewelry. You can see pictures of that on my South Africa page.
A very foreign concept to us Europeans.
The festival is exclusively for the Swahili. Foreigners are not allowed to attend. Or very rarely so. -
We stumbled on this little batik factory (run by a Dutch woman). Batik is very common in Africa but it did come from Indonesia, although the patterns are much less intricate than in Indonesia. We bought some cushion covers for the new couch. Except we haven't got the new couch yet, because we went to South Africa!
Zulu Kraal
Much less touristic than in South Africa (Hluhluwe) was the Zulu kraal in Swaziland. The "chief" Albert started talking about our Dutch football hero Gullit, the moment he realised we were from the Netherlands.
Entering a kraal asks for some etiquette. The woman always enters first on the left side, because there might be an enemy inside to stab the person entering. The brave Zulu warrior enters on the right side after the woman!
He can have more than one wife, and obviously a lot of competition goes on between the wives who will poison each other to become the favoured first wife! So the child of the wife that has prepared the meal must taste it first.
I guess that's mostly history now... -
MANZINI : AN HISTORICAL SKETCH by JAMES HALL, a resident since 1988
===The colonial government setting up operations in the country chose the middleveld for the first administrative capital. ===They bought out a store owned by Arthur Bremer on the Mzimnene (Umzimunene) River, and called the place Bremersdorp in his honour (a condition of sale, actually).
===whilst much has happened since the 1880's, ===colonial and imperial history is well condensed
by James Hall===The years of change have not altered Bremersdorp/Manzini at all: it is still a place, as it was in the beginning, where everybody knows your name, amongst the now 70 000 population..
for the full article, go to http://www.mzcitycouncil.org.sz/themanzinistory.html -
Here's some Siswati basic...
Here's some Siswati basic words :
mage : mother
babe : father
sisi : sister
yebo : yes, hi (eg when you say hi to an adult woman, you'd say 'yebo mage')
nicela : please (the c is a click of the tongue)
The local people really enjoy it when you try to say a couple of words in their language. -
Women usually dance Inlamu, a rhythmic and vigorous dance too. Women dress colourful dresses (mahiya), many with the Swazi flag or King's face.
Related to:- Arts and Culture
- Music
- 1
Swaziland Hotels
Reillys Rock Hilltop Lodge
Mbabane, Swaziland
Satisfaction: Very Good
Good for: Families
Royal Swazi Sun (Private Bag Ezulwini.)
Private Bag Ezulwini
Satisfaction: Excellent
Good for: Solo
Royal Swazi Sun
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text:
Is there a way of Winning on the Pokies?
This article is written especially for regular players of Poker machines – or "Pokies" as they are known in Australia – and their quest to Win on Pokies. Includes tips on how to Win on Pokies, and potential winning strategy. [ VIDEOS – Pokies Win ] … Read More →

subset: commoncrawl


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De delicado diseño, estos aretes de diamante y aguamarina resaltan aguamarinas celestes rodeadas por micropavé de diamantes brillantes, creando así un elegante regalo.
Consulte nuestra sección de educación para obtener datos sobre nuestras joyas con gemas.
These earrings are beautifully crafted, delicate and attractive - catch the light perfectly. Certainly, they appear larger in the images on line than in real life, but Blue Nile provides the dimensions on line for reference, so there was no surprise when they arrived. Most importantly, my wife was thrilled with them, and she is very discerning when it comes to jewelry. We did well Blue Nile!
Thomas, Green Cove Springs, FL – December 2014
The diamond border does not sparkle which is disappointing compared to past purchases
Darren, Melbourne, Victoria – December 2014
They are a lot bigger than I anticipated but they look beautiful- they aquamarine looks great against the micropave diamond backdrop.
Anonymous, Durham, NC – February 2014


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Traditions and Custom
Last updated by david at 2014/12/22
The Origin of Chinese Surnames
In China, when two people meet for the first time, they always ask each other: "What's your surname?" >> See more details on the origin of Chinese surnames
Family Tree
"Family tree" (or "pedigree chart") is a special chart representing family relationships and important events of family members in a conventional tree structure. >> See more details on Chinese Family Tree
Weddings in China
Chinese weddings were, and still are very important ceremonies as the Chinese regard marriage as one of the three most important events (the others being birth and death) in a person's life. >> more details
China is a country steeped in ancient customs and traditions of which the native people are proud. To many people in the west countries, the word 'China' typically conjure up thoughts of dragons, giant pandas, the color, the number 8 and the distinctive calligraphs, all of which are traditional to this country.
China and the whole Europe share similar-size land area. With such a large area, customs are always different in different parts. In addition, due to the fact that this country is one of the existing oldest cultures, traditions have grown over centuries with unique festivals, customs, arts and cuisines.
Chinese New Year (or Spring Festival) is the most important festival celebrated all over the country. It is the first day of the whole year in Chinese lunar calendar. It's hard to miss the good fortune or happiness messages hung in red from every doorway and the smell of feasts being cooked as families gather at this time.
As a legend goes, a monster, named Xi, always finds food in villages for its lack of food in winter. Villagers are afraid of this monster, so they come up with a way to frighten and expel Xi by lighting bamboos and paste red paper on their doors. Xi fears the sound of lighted bamboos and red color, so it runs away.
People successfully chase away Xi. The day people expelling Xi, people call it Chu Xi which is the Chinese of New Year's Eve. (Chu in English means expelling or killing.). Nowadays, people still kindle bamboos in New Year's Eve, but just for celebrating the coming of New Year.
Chinese calligraph has played a significant role in Chinese art. Dating back over 3,000 years, characters were pictures of objects representing their meaning.
The Oracles is the origin of Chinese characters. According to Chinese mythology, Cang Jie invented Chinese characters. Findings show that the earliest characters are the Oracles which were engraved on animals' bones or tortoises' shells.
Master Wang Hsi-chih (303—361 A.D.) was an outstanding representative of Chinese calligraphy. Many people facsimile his works until today
Through all these years evolution, characters have evolved into the words we used today.
The food is one of the traditional cultures in China which is famous around the world. From Roast Duck and Noodles with Bean Paste in Beijing to dim sum in Hong Kong, no one would deny that the kinds of Chinese cuisines are various. It's not uncommon to hear people greeting each other by asking "Have you eaten yet?" Many travelers are aware but unsure of all the etiquette traditions but most locals will be tolerant of the odd mistake; a smile and following what your fellow diners do will get you through.
China is one of the oldest countries with long history and abundant cultures. Enjoying a trip to China, every visitor would have their own special experience. It would be advisable to prepare a little bit before any trips. China Travel can help you tailor-making your China trip, worry-free, save time and money!
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text: Jeff, Thanx for the response. I cobbled together something out of rmp2cpio.sh and cpio which let me get the logrotate stuff extracted from the widely-available logrotate rpms. Would it be worth writing a quick hint about this? I'd be willing to do that. - Mark mark at arcabama.com -----Original Message----- From: blfs-support-bounces at linuxfromscratch.org [mailto:blfs-support-bounces at linuxfromscratch.org] On Behalf Of Jeff Harris Sent: Monday, February 23, 2004 11:12 AM To: BLFS Support List Subject: Re: tar for rpm? [Logrotate] On Feb 22, 2004, "blfs-support-bounces at linuxfromscratch.org" claimed that: |I want to install logrotate on my LFS system, but I can only seem to find |it in RPM format. I've used rpm before, no problem, but... I can't seem |to find tar'd source code for rpm itself, all the sites I've checked have |it as an rpm itself. |[SNIP] |- Mark If you're only looking for a logrotate, and don't really need to add rpm, I found a logrotate shell script that seems to work pretty well for my needs. The "missing logrotate" seems to be a FAQ that doesn't have a common answer. Maybe a smattering of answers can be added to one of the books. The script I found is at http://iain.cx/src/logrotate/logrotate -- Registered Linux user #304026. | 52FC 20BD 025A 8C13 5FC6 gpg key: B0890FED | 68C6 9CF9 46C2 B089 0FED http://pgp.mit.edu:11371/pks/lookup?op=get&search=0xB0890FED Responses to this message should conform to RFC 1855. ======================================================================== A: Because it messes up the conversation. Q: Why is top posting bad? A: Top posting. Q: What's the most annoying thing on mailing lists? -- http://linuxfromscratch.org/mailman/listinfo/blfs-support FAQ: http://www.linuxfromscratch.org/blfs/faq.html Unsubscribe: See the above information page __________ NOD32 1.629 (20040220) Information __________ This message was checked by NOD32 antivirus system. http://www.nod32.com


text:
Height: | 5-10 |
Weight: | 170 |
Year: | 2010 |
Hometown: | Portland, Maine |
High School: | Deering |
Position: | IF/OF |
2008: Started 11 of 15 games played.... Concluded his season with eight hits, four of which were RBIs.... Went 3-4 in the first game of a doubleheader against Elms College in mid March. 2007: Started 19 times.... Concluded his season with a total of 22 hits.... 11 of which were RBIs.... In April, Linscott went three for four and drove in a total of three runs against Colby college. High School: 2006 - Southern Maine Athletic Association All-Star Team.... Captain of the Deering H.S. baseball team.... 2005 - Named to the Frozen Ropes Maine College Prospect Players fall team.... Second place team, State of Maine American Legion Tournament.... Named to the Western Maine Underclass All-Star team.... Telegram League Champs.... Deering High team ranked 20th in the nation by Baseball America and No. 2 in the nation by Easton Sports.... 2004 - Member of the American Legion National and State Champs (Nova Seafood) |


text:
Hi Kristin,
I'm happy to be following your blog now! :)
You can find my blog here: http://www.mommyisgreen.net/
I am following you now! I also liked you on Facebook. I would love it if you'd do the same for me! http://www.southernblessings.net/
Im following you!
I'm a follower...can't wait to read more. We're actually gonna go see Yo Gabba Gabba soon. Can't wait!
Please check out my blog:
For all of you avid readers out there, today is World Book Day. What's your favorite book?
Join the conversation to win a free Starbucks gift card. And, see yesterday's winner!
Mom Bloggers for Social Good is a global coalition of 2000+ mom bloggers who currently span over twenty countries and who care about spreading the good news about the amazing work non-profit organizations and NGOs are doing around the world. Want to make a difference? Please join us!
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text:
Debian Testing Looking for Security Staff
The free Debian Linux distribution is looking for Security staff for its "testing" branch.
As Nico Golde from the Testing Security Team writes in a message posted on a mailing list, currently just one or two developers are keeping an eye on security in the testing branch. The situation has become more aggravated because one of the team members has been hit by a computer defect.
The Testing Security team aims to achieve a reasonable measure of security for all users who declined the "testing" branch of the Debian distribution on their computers. Typical users include developers who prefer more recent software, but also end users.
Golde emphasizes bent you do not need to be a registered Debian developer to help the security team. The only basic requirements are some working knowledge of security mechanisms and vulnerabilities, familiarity with the Subversion version control system and of course enough time. A security issue occurs every two to three days. In order to find the vulnerability of team traces new input in the CVE Security directory and in Debian's Bugtracker. The Debian Security Tracker is used to trace vulnerabilities. An introduction to this tool is provided by the "Narrative Introduction" in the Subversion repository.
If you also have programming experience and know-how to build Debian packages, your contribution will definitely be welcome: after all, the Security team creates updated packages with fixes for vulnerabilities.
If you fulfill at least some of these requirements and are interested in an interesting task, you can contact the Testing Security Team via its mailing list. As an alternative contact you can also use the IRC channel "#debian-security" on irc.oftc.net.
Issue 172/2015
Buy this issue as a PDF
Old Vulnerabilities Are Kept Alive Through Bad Configuration
HP's annual Cyber Risk report offers a bleak look at the state of IT.
Linus Torvalds Announces Linux 4.0
But what do the big numbers really mean?
Microsoft Frees CoreCLR
.NET Core execution engine is the basis for cross-platform .NET implementations.
New Trojan Attacks Linux Servers
The Xnote trojan hides itself on the target system and will launch a variety of attacks on command.
Cisco Releases Annual Security Report
Spammers go low-volume, and 90% of IE browsers are unpatched.
Zero Day Exploits Target Flash
Adobe scrambles to release patches for vulnerable Flash Player.
Intel Unveils Compute Stick
Four-inch-long computer on a stick lets you boot a full Linux system from any HDMI display device.
Obama Proposes Personal Data Notification Law
New statute would require companies to report break-ins to consumers.
TGXf Project Warns of File Transfer through Screen Pixels
Weird data transfer technique avoids all standard security measures.
Industry Giants Announce a Fix for the Password Mess
FIDO alliance declares the beginning of the end for old-style login authentication.


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Played a few games today and I dont seem to be gaining My IP rewards.... Please help
Senior Member
Try re-logging, i have the same problem where the ip at the top-right doesn't update after a game.
Yep that fixe it..... well not really Riot please lookinto this, minor inconveinence thank you
Jesse Perring
Thanks for reporting. Is it just the top right corner that isn't updating? If you go into Summoner profile or the store, do you see different amounts?
when i went to the store before restarting it would allow me to purchase items that i had enough IP point for but it did not show up in the upper right coner there... hope this helps


text: The Role of Market and Regulatory Discipline in Mortgage Lender Failures: Bank versus Non-bank Failures in the Subprime Crisis Zsuzsa R. Huszár * and Wei Yu † Very preliminary: Please do not quote without permission First draft: March 2011 Abstract We provide a unique analysis of the failures of non-bank lenders in comparison with bank lenders to examine the role of market and regulatory discipline in the subprime mortgage crisis. We find that for non-bank lenders, the market discipline instead of promoting market stability rather impair it. First, lenders with high concentration of refinance and minority loans are more prone to fail despite that the information on these potentially aggressive lending practices has been public for years prior to the crisis. Second, competition increases risk taking and the probability of failures because lenders in competitive environment with finite number of qualified borrowers likely increase lending to less qualified borrowers to maximize revenues. More importantly, the similarities in bank and non-bank lenders' failures cast doubt on the importance of bank regulatory disciplines. Focusing on the bank lender subsample, we find that regulatory oversight, such as the capital reserve requirements and loan reserve ratios were generally inadequate. Since the higher loan reserves are associated with higher probability of failures, banks likely took on risky loans for higher profits while increased reserves which provided insufficient protection. Overall, we find that liquid asset ratios had the greatest economic impact in reducing the probability of failures suggesting that in addition to strengthening the existing regulatory requirements more emphasis should be put on alternative risk management tools such as liquid asset ratios. Keywords: non-bank failures, bank failures, mortgage market, subprime lenders, market discipline, banking regulation. * Zsuzsa R. Huszár is from the Finance Department at the National University of Singapore (NUS). Mochtar Riady Building, 15 Kent Ridge Drive, Singapore 119245. Huszár is also affiliated with the Risk Management Institute (RMI) and the Institute of Real Estate Studies (IRES) at NUS. Email: email@example.com. Tel: +(65) 6516 8017, Fax: +(65) 6779 2083 † Wei Yu is from the Finance, Real Estate and Law Department at the College of Business Administration, California State Polytechnic University, Pomona, CA. 3801 West Temple Avenue, Pomona, California 91768. Email: email@example.com The authors gratefully acknowledge the comments of Sumit Agarwal, David C. Ling, and Alexander Ljungqvist, Wenlan Qian and Weina Zhang. In Watters vs. Wachovia, the Court ruled that the Office of the Comptroller of the Currency ("OCC") is permitted exclusively to regulate mortgage companies that operate as subsidiaries of federally-chartered banks. In recognizing the OCC's exclusive regulatory authority, the Court's decision effectively strips state regulators of their ability to enforce existing consumer protection laws and hold bank mortgage lenders to the same standards for licensing, education, and criminal background checks as mortgage brokers and other state-chartered entities. US Supreme Court: Vol. 550. Docket No. 05-1342. 1. Introduction Banking crisis, clustered bank failures, periodically occur all over the world where a handful of financial institutions emerge as phoenix from the ashes and benefit by overtaking the weaker ones. In the 1980s, during the Saving and Loan Association (S&L) crisis, many heavily regulated financial institutions failed as they lost competitive advantage to commercial banks while commercial banks gained the market shares of thrifts. Despite many differences between the S&L and the recent crises, two important similarities are the overexposure to real estate and the near collapse of an industry segment. In the 1980s, thrifts almost disappeared, while in the recent crisis, non-bank subprime mortgage lenders and mortgage brokers seem to wane. Among others, Mian and Sufi (2009) and Mian, Sufi, and Trebbi (2010) examine the causes of the recent financial crisis, such as disintermediation, securitization, and the cooling of the real estate market. They provide important insights about the geographic distribution of the crisis by examining the geographic variation in the real estate price run-up and credit expansion, but provide limited empirical insight about the cross-sectional differences in performance among the different types of lenders. Some banks, such as Bank of America and Wells Fargo fared well enough to benefit via "successful" acquisitions, while others have been acquired or are still in the recovery process. 1 Similar situation can be observed in Australia, where the four big commercial 1 Belratti and Stulz (2010) examine the cross-sectional variation in bank performance to reveal why some bank performed well in the crisis. 2 banks significantly increased their market shares as risk-averse clients moved back from non- bank lenders to the safety of the Big Four despite higher interest rates. Another strand of studies examine bank failures with the objective to provide guidelines for establishing better early warning systems about bank failures (especially clustered events) and aid regulatory supervision. Cole and Wu (2010) propose a simple dynamic hazard model with time-varying covariates as a bank failure early warning model, by showing that the model significantly outperforms regulatory probit models with and without the macroeconomic variables. Boyd, De Nicoló, and Jalal (2010), testing Boyd, De Nicoló, and Jalal's (2009) banking model, find that with increased competition the probability of bank failures decline, but warn regulators not to over-emphasize competition as a self-regulatory tool because competition tends to increase loan-to-asset ratios. As the media and academics focus on the commercial bank failures, the failures of hundreds (if not thousands) of smaller non-bank lenders went relatively un-noticed after the big failures Ameriquest Mortgage Co, and New Century Financial Corp. 2 But, the role of non-bank lenders cannot be ignored as they provided a major portion of the subprime, now troubled, loans. 3 These non-bank lenders are especially important because with the use of mortgage brokers they heavily promoted disintermediation and potentially contributed to the loosening of credit standards. Also, these lenders, without deposit base as a source of founding, heavily relied on securitizations by non Government Sponsored Enterprises (GSEs) and involved other financial institutions (e.g., investment banks) in their lending business. According to Iyer and 2 In general, we refer to non-bank lenders as non-depository institutions, financial institutions that are not under the regulatory oversight of FDIC. 3 These two subprime lenders originated more than 160 billion in loans in a couple of year prior to 2008. The two other important subprime lenders, Countrywide and Indymac can be considered banks as they operate banking units and engage in deposit taking with FDIC guarantee. 3 Peydró (2010) these links between the affected financial institutions were likely crucial in the spread of the crisis. In this study, we aim to contribute to the literature on bank failures and provide the first comprehensive analysis of non-bank mortgage lenders failures in comparison with bank failures. Specifically, we examine the role of market and regulatory discipline in conjunction with lending practices in explaining the recent large number of lender failures. Generally, in the unavailability of financial data on non-bank lenders hinders a comprehensive analysis of non-bank mortgage lenders. We address this issue, using lending characteristics (i.e., geographical concentration, pricing, and securitization) and loan issuance (market size measure) information from the Home Mortgage Disclosure Act (HMDA) raw Loan Application Register (LAR) file. It is important to note that the recent financial crisis is term of time frame is quite different than the S&L crisis, where institutions failed over a course of several years. In our study, since the high concentration of lender failures around 2009 does not support any duration or risk hazard model, we use standard logistics model in predicting lender failures. The governmental intervention, via bad loan purchase and interest rate reductions, also contaminate the data, making it uninformative to distinguish between two failures: the one in June 2009 from the one March 2010. Thus, we treat all failures in the recent crisis as one event after 2007, and use public information (on lending practices and from financial statements) from 2003 to 2007 in the models. Our findings suggest that lending practices play a key role in lender failure. Interestingly, despite the significant differences in institutional details and regulatory requirements, bank and non-bank lenders are driven by the same factors. For both types of lenders, higher concentration of high credit quality borrower and low average loan amount are associated with significantly 4 lower probability of failure. In case of non-bank lenders, competition and transparency are expected to aid the self-regulation of the new industry but proved to be harmful instead. The higher concentration of competing lenders in the area is associated with higher probability of failure. Possibly some lenders relax their lending standards to attract enough borrowers for their business survival in strong competitive environment where the limited number of qualified borrowers do not provide enough revenues for all lenders. Transparency on lending practices is found to be under-utilized, as the evidence of aggressive lending to minorities already surfaced before the crises, which could have been used to identify problem institutions well in advance. Concerning bank lenders, we do not find evidence in support of the beneficial roles of regulations. Regulation, instead of being useful, tends to be rather harmful as the higher concentration of minority borrowers and high loan-to-income loans significantly increases the probability of failure. The high concentration of minority loans may have been the result of banks' attempt to support under-represented borrowers and areas according to the 1977 Community Reinvestment Act (CRA) and the Clinton and the G.W. Bush Administrations' National Homeownership Strategy. Bank regulatory requirements, such as capital and loan reserves, are generally uninformative in explaining the failures. Interestingly, the probability of bank failures increased with the loan reserve ratios suggesting that banks knowingly accumulated risk but the regulatory cushion was insufficient. Overall, we show that at the institution level, the failures of bank and non-bank lenders in the recent financial crisis can be explained by similar factors, the low concentration of good credit quality borrowers, and the high concentration of minority borrowers and refinance loans. Neither the open market competition for non-bank lenders, nor bank regulations for bank lenders were found to facilitate survival. Moreover, the risk management tools at the bank level rather 5 encouraged risk taking because banks were more likely to issue large quantity of risky loans while accumulating loan reserves on the side, perceiving the risk to be insured. Our finding that the liquid asset ratio is the most efficient tool in lowering risk of insolvency, suggests that regulators may need to review existing regulatory risk management tools and focus on new measures instead of strengthening the older ones. 2. Background 2. 1 Recent Development of the US Subprime Mortgage Market Since the mid 1990s, subprime lending became a rapidly growing segment of the mortgage market because it provided opportunities for homeownership to those who previously could not qualify due to discrimination or due to a record of poor credit history. However, this expanded access to mortgage loans came with a higher price (Fortowsky and LaCour-Little, 2002; Brent, 2007), thus making subprime lending also known as high-cost lending (Pennington- Cross 2006). 4 Today, many argue that regulatory changes resulting in loosening of regulations played an important role in the development and the collapse of the market (Mian and Sufi, 2008). For example, the 1982 Alternative Mortgage Transaction Parity Act (AMTPA) allowed the introduction of adjustable-rate mortgages, balloon payments, and interest-only mortgages. The 1986 Tax Reform Act (TRA) allowed interest deductions on mortgages and thus made mortgage debt cheaper to consumers and also encouraged homeowners to do cash-out refinancing by renewing their loans (Pennington-Cross 2006). Lastly, the 1998 Homeowners Protection Act (HPA) required the homebuyers with loans higher than 80% loan-to-value ratio to pay for extra insurance to reduce the risk of the loan. 4 For detailed review on the evaluation of the US subprime mortgage market read Courchane, Surette, and Zorn, (2004) and Chomsisengphet and Pennington-Cross (2006). 6 In the new millennium, the booming economy and the increasing real estate prices created increasing demand that the lenders were more than ready to meet, especially as the relaxed regulations allowed them to charge higher rates and fees at lower cost with disintermediation, with reliance on mortgage brokerages. In addition, the lenders seemingly passed off most of the risk by selling off loans in the secondary market and continued to leverage their positions and "pump" credit into the market. Although in hindsight it is hard to argue in support of the subprime mortgage market, it did provide more than 5 million home purchase subprime loans, including more than 1 million loans first time home purchases (mostly to previously under represented borrowers). But total of about 10 million new subprime loans included loans for refinancing, investments and speculation that are difficult to view in positive lights (Jaffe, 2008). 2.2 Mortgage Market Participants and Regulations Recently, the retail mortgage market became increasingly heterogeneous. New financial institutions, such as mortgage companies and finance companies entered the market, besides the traditional bank lenders such as commercial banks, saving and loan associations, and credit unions. Banks are required to fulfill Basel II requirements, and annually evaluated based on CAMELS criteria, as well as CRA guidelines. 5 The non-bank lenders, finance companies, mortgage companies, and merchant banks, subsidiaries of industrial firms and/or foreign banks, are less regulated. In addition, these lenders are also relatively opaque because their financial 5 According to CRA, commercial banks and savings associations are expected to meet the needs of borrowers in all segments of their communities, including low- and moderate-income neighborhoods. The objective of CRA is to reduce discriminatory lending practices against low-income neighborhoods, known as redlining. 7 information is not readily available in the market, making it difficult for lenders and investors to understand the risk these financial institutions were taking. 6 Despite the economic importance of these non-bank lenders in the US market, there is limited research on the lending practices of these financial institutions. One notable exception is Ambrose's (2003) study, which compares bank and non-bank lenders in the commercial mortgage market and shows theoretically and empirically that bank and non-bank lenders use different lending strategies. While banks use interest rates in allocating credit, non-banks adjust credit allocation with the use of the loan-to-value ratio. Therefore, the borrower clientele is likely to be different for those of banks and non-banks, because clients with a need for large loans may incur the higher rates with a bank loan. These findings are consistent with that of the US, UK and Australian residential mortgage market prior to 2007, where borrowers in search of lower rates (at least lower initial rates) chose non-bank lenders. Bank lenders are regulated at the state or the federal level and had to follow international rules while the non-bank lenders were mostly self-regulated. In a perfectly competitive and transparent market environment, competition would eliminate the bad lenders that aggressively target protected borrowers and engage in unethical business practices. 7 Despite high degree of transparency ensured by the equal lending opportunities act and HMDA, the information on lending practices was neither available timely nor well organized. Thus, market discipline via investors monitoring and competition was ineffective. Until today, the lending practices of the different subgroups of lenders, such as investment banks, mortgage companies, finance companies, thrifts and banks are relatively unexplored. The likely reason is the limited financial 6 In the aftermath of the crisis, the obtaining reliable financial information on the non-bank, mostly subprime lenders, is a challenge. 7 Protected Borrowers are minorities and/or low-income borrowers who are classified as protected borrowers under the Fair Housing Act (Federal Reserve, 2006) and were found to be especially targeted by aggressive lenders, because of the generally lower level of financial education (Hill and Kozup, 2007) 8 information at the institution level for the non-public lenders, at least in the US market, hinders comprehensive empirical studies. 2.3 Bank failures Despite the relative infrequency of bank crises around a world, a significant research has focused on the topic because of the macro economical importance of this sector. Especially, the spread of bank failures, so called contagion effect, concerns regulators. Initially two competing hypotheses emerged: pure panic and information-based contagion. Aharony and Swary (1996) is study provides additional evidence consistent with the latter hypothesis. More recently, Iyer and Peydró (2010) further explore the informational hypothesis and reveal that interbank linkages with failed bank are important source of contagion especially involving weak banks. They suggest that regulators pay more attention to interbank links in the future to reduce the contagion effect, and to evade similar situation as in the recent crisis. Interestingly, the role of market discipline, as monitory tool has attracted much attention at the end of 1980s, when the FDIC proposed the use of subordinated notes and debentures (SND) as a way of increase market discipline and augment regulatory discipline on banks and banking organizations. Avery, Belton and Goldberg (1988) find that the potential for market discipline is weak at best, as the pricing signals that bankers receive from the public subordinated debt market appear to be at odds with the directions desired by regulators. While Hannan and Hanweck (1988) find that the market prices the risk as the CD rates are higher for banks with more variable income. Another stand of studies focuses on establishing early warning system for regulators. Whalen (1991) propose a proportional hazard model of bank failures using bank data including 170 failures from 1985 to 1990. Using typical financial ratios and local economic condition 9 measures his model identifies both failed and non-failed banks with high accuracy. Cole and Gunther (1995) use a split-population survival-time model to distinguish factors that result in the ultimate failure from those that influence the survival time of failing banks. A few measures of bank's financial condition, such as capital reserve, troubled assets, and net income, are important in explaining the timing of bank failure, while other variables in bank failure models, such as measures of bank liquidity, are not associated with the time to failure. More recently, Cole and Wu (2010) introduce a dynamic hazard model with time-varying covariates as an early warning model for bank failures, which significantly outperform simple probit models (often used by regulators) with and without the macroeconomic variables. 2.4 The Fall of Retail Mortgage Lenders: Regulation, Transparency, and Competition While there is significant heterogeneity across national bank crises, the main causes are high credit growth, a negative GDP growth and a high real interest rate. In the recent financial crisis, the triggers were the high credit growth and the increasing interest rates in conjunction with the cooling of the real estate market. In 2006-2007 the "surprising" cooling of the residential house market and the increase in interest rates made refinancing more difficult, while the negative equity loans encouraged even non-financially constrained borrows to strategically default on their loans. For the over-leveraged mortgage companies that heavily relied on external funding (e.g., Asset backed commercial paper market), the soaring mortgage delinquencies quickly resulted in liquidity problems. As the quality of mortgage backed securities declined, the asset backed commercial paper market dried up and making the issuance of new securitized products difficult thereby cutting funding for non-bank lenders. The financing difficulty and the short term liquidity problems were crucial for many lenders that could not cope with these difficulties and failed. While the 10 Fed and the Treasury with the support of the Administration made attempts to stabilize the financial market after the critical 2008 failures (e.g., Lehman Brother and Bearn Stern), there was too much focus on the surface liquidity. Regulators have not realized that banks will be less willing to lend to each other and the 2008 and 2009 stimulus bills were unsuccessful in tempering the general economic downturn. 3. Hypotheses In the recent financial crisis, a system wide shock affected countless financial institutions, commercial banks, non-bank lenders. Insurance companies, including investment banks were also affected because of their indirect involvement in the subprime mortgage market via securitized investment products. For banks, capital reserve ratios and CAMELS ratings should have provided warning signs about the overexposure to real estate market risk, and the potential risk for bank failure, while for non-banks, the open market competition was expected to resolve any inefficiency with the help of transparency. This study aims to contribute to existing bank failure literature by examining the factors that contributed to the failure of lenders (bank and non-banks) during and after the subprime crisis. More specifically, we categorize our lenders into two groups: (1) Bank and (2) Non-bank lenders, where banks are primarily defined as depository institutions. The comprehensive analysis of the two different types of lenders in the recent mortgage crisis allows us to answer various important research questions. First, we focus on non-bank lenders, where in the absence of financial information we can examine only the role of aggressive lending, competition and transparency in failures with lending information. We examine the relationship between aggressive lending, bank 11 concentration and probability of failure, to shed light on the efficacy of self regulation, with the following two hypotheses H1A: The probability of non-bank failure is higher in competitive environment H1B: The probability of non-bank failure is higher with higher concentration of minority and refinance loans. If self-regulation is effective, banks in more competitive environment are less likely to fail. Earlier studies find that subprime lending concentrates in low income and minority neighborhoods (Calem, Gillen, Wachter, 2004) where the potentially high cost loans are not necessarily aiding the revitalization of the neighborhoods. A more recent study by Williams and Bond (2007) shows that new subprime loans from traditional lenders had some positive effect in decreasing segregation, but the loans from new subprime lenders, often specializing in minority and low income borrowers rather increased segregation. Thus, the subprime lenders did not seem to achieve the social benefit that regulators hoped they would. Since the information is public on minority and low income borrower concentration, in an efficient market, the past public information should be reflected in the value of the institution (especially for public firms) which is unlikely to predict failures. Second, we examine the role of regulations by comparing the failures of banks and non- banks. If regulations to some extent were efficient then the role of aggressive lending in bank failures should be less pronounced. H2: The relationship between failure and aggressive lending proxies, such as the concentration in minority and low income borrowers is economically less important for banks because of banking regulations (besides the market discipline). 12 But, even if we find that the banks are also more likely to collapse when they have high concentration of minority and low income borrowers, we still cannot conclude that these institutions are actively engaged in aggressive lending. Unlike in the case of non-banks, banks may have high concentration of the underrepresented risky borrower to encourage first time home ownership in line with the Administration's initiative. Lastly, focusing on banks, the authorities, through the regulations on bank activities, capital adequacy, annual supervision (Barth 2004), should have noted that banks excessively increased their real estate risk. Thus, we examine the effectiveness by testing whether low capital reserve or loan reserves were signaling future insolvency. Lastly, we examine whether liquidity ratios were effective warning signals as some countries, in addition to Basel II requirements also enforce higher liquid asset ratios to alleviate the risk of insolvency as a result of liquidity problems which was critical in the recent crisis. 8 H3A: Capital reserve ratios and loan reserve ratios are insignificant in predicting lender's failure H3B: High liquid asset ratios can significantly reduce the probability of bank failures (as these ratios are in place in countries that have been affected by the 1997 Asian Financial Crisis). 4. Data 4.1 Identification of Failed Mortgage lenders 8 For example, the banking regulation in Singapore requires the maintenance of up to 18% liquid asset ratio and a minimum of 3% cash balance. 13 We collect information on all failed residential lenders from Jan 1, 2007 till 31 Oct 2010, including banks and non-banks. Failed banks are primarily identified from the Federal Deposit Insurance Corporation (FDIC) website under the section of 'Failed Bank List' for the corresponding sample period of our research. 9 Information on failed non-banks lenders, such as mortgage firms, or subsidiaries of investment companies is very restricted as these financial institutions are not linked to FDIC, or OCC. 10 With web query and the use of 'The Mortgage Lender Implode-O-Meter' website, we identified about 300 non-bank institutions with location. 11 Our research focuses on studying mortgage lending practices during the subprime crisis, thus it is crucial that the failed financial institutions in our sample are actively involved in the mortgage lending business. To identify the respective mortgage lenders, we decided to work with the Home Mortgage Disclosure Act (HMDA) Lender File. Most lending institutions with offices in metropolitan areas are required by the Home Mortgage Disclosure Act of 1975 to disclose information with regards to their lending activities. Annually, the HMDA data contains over 8,000 lenders, accounting for at least 80% of the loans extended in the U.S. (Avery 2005). We match the hand collected default sample of 731 failed lenders (banks and non-bank lenders) with the HMDA dataset by (1) Lender name, (2) City and (3) State, which results in a match of 517 lenders with unique IDs. We include lenders that are among the 8,886 unique lenders that reported to HMDA in 2006 before the onset of the crisis. We use manual name matching and ensure that the failed institution was the same as that in HMDA by checking the location variables. The allocation of a unique HMDA ID code also allows us to account for overlaps that might occur, because of the double counting subsidiaries and the parent company 9 The Federal Deposit Insurance Corporation, FDIC.http://www.fdic.gov/(accessed Nov 5, 2010). 10 Interesting that one of the most organized sources of information about non-bank financial is available via Home Mortgage Disclosure Act's LAR database. 11 The Mortgage Lender Implode-O-Meter. http://ml-implode.com/index.html (accessed Nov 5, 2010). 14 (bank). In addition, we remove all foreign banks, or subsidiaries of foreign banks, but keep all subsidiaries or American banks that did not fail, classifying these failures differently in the empirical analysis. The HMDA agency codes allow us to identify the regulatory oversights for the institutions. All financial institutions which are regulated by the (1) Office of the Comptroller of the Currency (OCC), (2) the Federal Reserve Board (FRB), (3) Federal Deposit Insurance Corporation (FDIC) or the (4) Office of Thrift Supervision (OTS) are identified as banks. All others which are not regulated by the aforementioned four authorities are identified as non- banks. Overall we identify 517 failed lenders with valid lending information, including 344 bank and 253 non-bank lenders. In addition, using the HMDA performance data, we also identify 2,240 bank lenders with valid HMDA and banking information and 3,215 non-banks with valid HMDAID and lending information. Lastly, for banks (failed and non-failed bank lenders), we obtain financial statement data from Bankscope to control the bank health and profitability. 4.2 Description of Variables Using HMDA annual reports for each lender, we calculate a series of variables that capture their exposure to the lending market to specific states and some variables that reflect potentially aggressive lending behavior. We create a number of variables to capture lending practices, such as total loan application, total loan origination, annual approval rates, and concentration of refinance and repurchase loans. We attempt to measure the degree of responsible lending practices with geographical loan concentration, minority loan concentration, and the concentration of high-cost, high-rate, and HOEPA loans. 12 We also calculate the change 12 HOEPA loans are the loans that are required to be disclosed as high cost loans according to the Home Ownership and Equity Protection Act Amendments (HOEPA). In general, first (and second) lien loans with rates 8 (10) percentage point higher than that of comparable treasures are required to be disclosed. 15 in these variables over the period of 2003 to 2007, to capture whether lenders change their lending practices in terms of becoming more (less) diversified or more (less) exposed to minorities. Full list of lending practices variables, applicable to all bank and non-bank lenders, is included in Panel A of Table 1. In addition, we obtain financial information such as balance sheet and cash flow statement for all banks through Bankscope. 13 Specifically, we collect information about the bank's asset, equity, liquid asset, off-balance-sheet items (OBS), net income, total deposits and total loans. To assess banks financial stability, we obtain information on total capital and tier one capital reserve ratio, total loan reserve, and loan to equity ratio, loan to earning asset ratio, and total problem loans to liquid asset ratio. In addition to the annual averages, we also calculate trends, that is, the change in deposit base, liquid assets among others. Change in the bank financial status could provide important warning signal for future funding difficulty, or liquidity problem, and for the ultimate failure. The complete list of Bankscope variables are described in Panel B of Table 1. [Table 1 about here] 5. Empirical Analysis 5.1 Summary Statistics Panels A and B of Table 2 display the summary statistics for failed 249 bank and 329 non-bank lenders, respectively. The summary statistics suggest that failed bank and non-bank residential mortgage lenders are comparable in their lending activities. Since both types of lenders issue more refinance than purchase loans, the majority of loans was not to promote first time homeownership as many regulators argued. 13 Bankscope is accessed via Wharton Research Data Service (WRDS). 16 About 22-24% of the borrowers from areas with high FICO scores while the average loan amounts is about 2.2-2.8 times of the annual incomes. The somewhat higher loan-to-income ratios for bank lenders suggest that banks are more inclined to issue loans to lower income areas, or to lower income borrowers. One notable exception is the bank lenders' relatively heavy exposure to minority areas (51 versus 40%), potentially in response to governmental initiatives to promote minorities housing. More interestingly, while the average number of applications is significantly more (35 thousand versus 27 thousands) for banks than non-banks, the issued amounts are relatively comparable (63 versus 53 million). Because non-banks lenders not only have higher origination rates, 65% versus 50%, but also issue larger loans. [Table 2 about here] The time trends are also similar and consistent with the media reports following the crisis. Both types of institutions steadily increased loan originations and focused more on minority neighborhood. More importantly, the percentage borrowers from high credit quality areas declined. Interestingly, the approval rates have also declined over time, likely as the result of generally lower credit quality borrowers. For the still active bank and non-bank lenders, the trends are quite different in Table 3. For these lenders, there is no significant increase in minority borrowers or decline in the credit quality. [Table 3 about here] The lenders successfully surviving the crisis were generally less exposed to minorities and lower income borrowers, reflected by the lower Pctminorty and Loan-to-income variables in Panels A and B of Table 3. More importantly, these institutions are much less exposed to the residential mortgage market reflected by the significantly lower application numbers and the total issuance amount (Amount in Mill). For these institutions, the high origination rates are 17 unlikely to reflect aggressive lending or lower lending standards because of the high concentration of loans in high credit quality areas (captured by high PctgoodFICO). [Table 4 about here] While for non-bank lenders we are unable to collect reliable financial information, for bank lenders we are able to compare important bank risk management measures across failed and non-failed bank lenders. Consistent with Beltratti and Stulz (2010), we show that the banks that successfully survived were significantly less profitable from 2003 to 2007 in terms of net income (NI) and net profits (Profit). But these profits were declining with the onset of the mortgage crises and the declines in real estate prices. The over-exposure of these banks to the mortgage market was quite clear, as the total problem loans relative to capital reserves (Tprobloans_cap) and liquid asset (Tprobloans_liqAt) are about .23 and .47 for the failed institutions already before 2007. For the non-failed banks, the Tprobloans_cap and Tprobloans_liqAt measures are negligible at about .03 and .19. 5.2 The Role of Lending Practices in Bank and Non-bank Lender Failures At first glance, the logistic regression analyses for predicting the failure of mortgage lenders does not reveal anything surprising. The probability of failure is negatively related with higher concentration for borrowers from good FICO score areas and lower loan-to-income ratios. The higher percentage of subprime highrate loans and refinance loans tend to increase the probability of failure. For banks, the higher concentration of borrowers with minority areas is detrimental, as a 1% increase in Pctminority increases the probability of failure by 1.8%. Banks were strongly encouraged to promote minority homeownership; and thus, regulators and political forces were likely partly responsible for the higher minority lending and the subsequent negative consequences. 18 [Table 5 about here] Since overall the coefficient estimates and the reported odds ratios are similar (economically and statistically not significantly different), the question arises whether banking regulations were at all significant. 14 We also examine one aspect of self regulation, competition, and find that lenders in competitive environment are more likely to fail. The 1.214 odds ratio on the HighComp variable for non-bank lenders suggest that lenders that on average operate in areas where the completion was stronger than the median level of completion are about 20% more likely to fail. Intuitively, in the presence of inelastic supply of good credit quality borrowers, the stronger competition restricts some lenders to attract economically profitable number of new borrowers with good credit quality. Thus, as lenders are more likely to relax lending standards and increase risk taking in high competitive markets, the foundation of open economy and the benefits of competitions are at question. In Table 6, in addition to the past average lending practices, we also consider the role of time trends. Specifically, we examine whether increased loan amount, loan origination and approval rates that reflect relaxation in lending standards predict lenders failure. Although odds ratios on the change in loan amount (Chngloanamount) and loan origination (Chngloanorig) are positive and statistically significant, the economic significance is negligible in comparison with the effect of the loan-to-income ratio or the concentration of good credit quality borrowers (PctgoodFICO). But, the "abnormally" large positive odds ratio of the change in approval rates variable (Chngpctapproval) clearly shows that most failures are concentrated among institutions that significantly increased the approval rates from 2003 to 2007. [Table 6 about here] 14 Beltratti and Stulz (2010) raise a similar question in examining the role of regulations across countries in conjunction with bank performance. They find that on average bank regulatory indices are uncorrelated with bank risk measures thereby casting serious doubt on the efficiency of banking regulations. 19 5.3 The Role of Financial Performance and Bank Management in Bank Failures The comparable magnitude of bank and non-bank residential mortgage lenders' failures (about 300 in absolute and 10% in relative terms) suggest that regulations were ineffective. If bank regulations were effective in reducing excessive risk taking or ensuring suitable capital reserve, then bank failures should be less prevalent than non-bank failures. In addition, the results in Tables 5 and 6 also suggest that similar lending practices were the driving forces of bank and non-bank failures. But, based on these findings we cannot assertively conclude that regulations were ineffective because we may have overlooked a number of non-bank failures. To address the selection problem, we examine the role of regulations in a subsample of bank lenders by focusing on specific regulatory requirements such as loan reserves and capital reserve ratios. [Table 7 about here] In general, consistent with the results from Tables 5 and 6, Table 7 shows that banks are less likely to fail with lower minority lending concentration, lower concentration of refinance loans and lower loan-to-income ratios. More importantly, as we examine the role of financial information, we find that traditional banking with higher deposit ratios (deposit relative to total bank assets) significantly reduces the probability of failure (1% increase in deposits relative to total assets reduces the probability of failure by 2.5%.). Naturally the higher percentage of problem loans and off balance sheet items are associated with significantly higher default probability. Overall, considering the role of regulations, we find that although the higher capital reserve ratio slightly reduces the probability of failure, the loan reserves and liquid asset reserves are ineffective in evading failures. Especially the high odds ratios on the loan reserve ratios are 20 alarming because instead of providing insurance (security cushion) for loan losses, the loan reserves seem to encourage even more risk taking. Putting our findings in the context of proposed and new regulatory changes, we are suggesting more reliance on new measures, such as increased liquidity. Liquidity ratios could be implemented in conjunction with different asset and liability types. The traditional liquidity ratio, as percentage of deposits needs to be revised as banks are less and less reliant on deposits. In response to the crisis, President Barack Obama proposed drastic financial reforms that manifested in 2010 as the Dodd–Frank Wall Street Reform and Consumer Protection Act. Naturally, the Act emphasizes the role of regulations, especially aiming to out regulatory oversight―consolidate bank regulatory agencies and create new oversight council to evaluate systemic risk. In addition, the Act promotes increased transparency to identify risks at institutional level which potentially may cause system wide shocks. However, the planned increased oversight and transparency may not be beneficial without clear objectives; as already, prior to the crisis, staggering amount of lending information was available in the system which was hard to process. 5.4 Robustness Analysis In robustness analysis, we have used alternative samples. First, we excluded Washington Mutual and its subsidiaries and replicated the analysis from section 5. In addition, we also considered alternative classification where a financial institution is considered depository institution if the deposits account for non-negligible amount of the assets (using 5 and 8% cutoffs). This latter robustness test is crucial to address misclassification issues, such as the case of Indymac, where the lender is clearly a non-traditional subprime lender despite that it owns a commercial bank and can take deposits. 21 6. Conclusion Since 2008 hundreds of banks filed for bankruptcy, have been acquired, or merged with FDIC assistance. In addition, hundreds of non-bank lenders (e.g., now infamous subprime lenders such as New Century) have also failed resulting in a near collapse of the subprime lending market segment. With the failures of these non-bank lending institutions, large commercial banks continued to increase their market size and now a world-wide phenomenon is that a handful of large institutions dominate the lending market. Examining the failures of non- bank lenders in conjunction with bank failures provide a unique opportunity to shed light on the effectiveness of market and regulatory disciplines in the mortgage market. In this study, we aim to contribute to the literature on bank and non-bank lenders failures by focusing on institutional details (e.g., bank management and lending practices) in explaining the cross-sectional variation in lenders' failures in the aftermath of the recent financial crisis. Like other studies on the recent mortgage crisis, we are unable to compare the risk management practices and financial information of bank and non-bank lenders in the absence of reliable financial data for the non-bank lenders. To address this shortcoming, we use information on mortgage lending practices from HMDA in our empirical analysis. Specifically, we focus on the role of market discipline and aggressive lending practices in examining the non-bank failures. By contrasting the role of potentially aggressive lending practices in bank versus non-bank lender failures, we provide new insight about the role of regulatory discipline. Our findings suggest that lending practices, such as sound lending standards, play a key role in reducing the probability of lender failures. In general, the higher concentration of high credit quality borrowers and low average loan amount are associated with lower probability of 22 failure. For non-banks we find that self-regulation through transparency and competition was not only ineffective but rather harmful. First, we find that lenders overly exposed to refinance and minority loans are more likely to fail. These strong relationships between the potentially aggressive lending practices and failure are especially alarming since the public has been informed about these lending practices for years prior to the crisis. Second, the competitive environment did not promote better lending practices but rather more risk taking and increased the probability of failures. The presence of high concentration of lenders, when the number of qualified borrowers is limited, is likely to encourage lenders to relax lending standards in an attempt to issue more loans for greater revenues. Interestingly, despite the significant differences in institutional details and regulatory requirements, the failures of bank and non-bank lenders are driven by similar factors. At the first glance, the comparable failure rates across the bank and non-bank lenders already suggests that bank regulations were inefficient. Focusing on the bank subsample in examining the role of regulatory credit risk measures, the capital reserve requirement provides only weak protection, while the loan reserve ratio is entirely ineffectively. The strong positive relationship between failure and loan reserve suggests that banks knowingly take risk increase reserves but the higher level of reserves are still insufficient. We find that the higher liquid asset ratios provided the best tool to reduce the probability of failures. Thus, we recommend that regulators consider new alternative risk measures instead of strengthening the relatively inefficient existing regulatory requirements. 23 7. References Aharony, J. and Swary, I., (1996). Additional Evidence on the Information-Based Contagion Effects of Bank Failures, Journal of Banking & Finance 20 (1), 57-69. Ambrose, B. 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LaCour-Little, M., (2001). A Note on Identification of Discrimination in Mortgage Lending. Real Estate Economics 29 (2), 329-335. 25 Mian, A. R., and Sufi, A., (2009). The Consequences of Mortgage Credit Expansion: Evidence from the 2007 Mortgage Default Crisis. Quarterly Journal of Economics 124 (4), 1449-1496. Mian, A. R., Trebbi. F., and Sufi, A., (2010). The Political Economy of the U.S. Mortgage Default Crisis. American Economic Review 100 (5), 1967-98. Poole, W., (2010). Causes and Consequences of the Financial Crisis of 2007-2009, Harvard Journal of Law & Public Policy 33 (2), 421-441. Smith, B. C., (2007). The Subprime Mortgage Market, a Review and Compilation of Research and Commentary. Hoyt Institute Working paper. Williams, R., and Bond, C., (2007). Residential Segregation and the Transformation of Home Mortgage Lending. Social Forces 86 (2), 671-698. Whalen, G., (1991). A Proportional Hazards Model of Bank Failure: An Examination of Its Usefulness as an Early Warning Tool. Economic Review, Federal Reserve Bank of Cleveland, 21-31. 26 Table 1. Variable descriptions In general, all variables are calculated as the time series average of the annual number from 2003 to 2007. For example, the application variable is average of the annual total applications from 2003 to 2007. The change variables are calculated as the time series average of the annual changes to capture time trend. For example, the Chngamount is the average in the five year annual changes in the total issued loan amount from 2003 to 2007. Panel A. Lending variables based on HMDA dataset Variable name Description of variables Failure Dummy variable that takes on the value one for failed lenders (banks and non-banks) , the lender is unable to operate any longer in the residential market. Application Total number of loan applications Originate Total number of originated loans Pctoriginate Approval rate in percentage, approved loans relative to total loan application Loan-to-income Average loan-to-income ratio, excluding high cost loans, such as HOEPA and spread- reportable loans Pctminority Percentage of total loan application by minorities PctgoodFICO Percentage of total loan application with high credit score, as the percentage of loans from census tracts where the median credit score is in the top quartile relative to total loan applications Avgamount Annual average of the approved loan amount Amount Annual total loan amount issued by the lender Pctpurchase Percentage of loan application for purchase purposes, as the ratio of purchase applications relative to total loan applications Pctrefinance Percentage of loans application for refinance purposes, as the ratio of refinance applications relative to total loan applications HHI Banks' Herfindahl-Hirschman Index (HHI), which is the average of all census tracts, where the lender has issued loans High_HPI Dummy variable which takes on the value one if more than half of the originations are in areas with high property prices. Areas with high property prices are identified as those with HPIs from the top quartile. Concentration Geographic lending concentration measure, calculated as sum of squares of the total lending (number of loans) per state Highrate Annual average of total number of spread reportable loans HOEPA Annual average of total number of HOEPA loans Chngamount Annual average of the increase (or decrease) in the total amount of loan issued from 2003 to 2007 Chngloanorig Annual average of the increase (or decrease) in the total number of loan originated from 2003 to 2007 Chngpctapproval Annual average of the increase (or decrease) in approval rates from 2003 to 2007 Chngpctminority Annual average percentage of loan application by minorities from 2003 to 2007 ChnggoodFICO Annual average change in the percentage of loans form census tract with high FICO scores, FICO scores from top quartiles 27 Panel B. Financial variables from Bankscope for bank lenders Variable name Bankscope data Description of variables (in Mill) Tasset (in $bill) data5670 Total Assets (in Bill) Totalcap data2135 Total capital reserve Tier1cap data2130 Total tier one capital Equity data2055 Total common equity (in Bill) Liability (in $bill) data2060 - data2055 Total liability(in Bill) Total earning assets relative to total Earningasset/liability data2010 / liability liabilities Total off balance sheet items relative to total OBS_cap data2065 capital Liqat data2075 Total liquid assets NI data2115 Net income Profit data2105 Profit NI_liab data2115/data5670 Net income relative to total liabilities Totalloans data5330 Total Loans TotalfixAt data5660 Total fixed assets Tdeposit_loan data6080/data5330 Total deposits relative to total loans Tprobloan_cap data2150/data2135 Total problem loans relative to capital Tprobloans_liqAt data5240/ data2075 Total problem loans relative to liquid assets Loanres_loan data2070/ data5330 Total loan reserves relative to total loans (data2130- ChngT1ratio Percentage change in tier one capital reserve lag(data2130))/lag(data2130) (data2140- ChngTotalcap Percentage change in total capital reserve lag(data2140))/lag(data2140) ChngLiqAT (data2075- Percentage change in total liquid assets lag(data2075))/lag(data2075) (data2055- lag(data2055)) ChngEq Percentage change in total common equity /lag(data2055) (data2065- Percentage change in off balance sheet ChngOBS lag(data2065))/lag(data2135) assets relative to total capital ChngProfit_cap (data2105- Average change profit relative to common lag(data2105))/lag(data2135) equity (data2115- Average change net income relative to total ChngNI_cap lag(data2115))/lag(data2135) loans (data2070- lag(data2070))/lag( Average change in total loan reserves ChngLoanRes_loans data5330) relative to total capital ChngTasset (data5670- Average change in total assets scaled by lag(data5670))/lag(data5670) lagged total assets (data5330- Average change in total loans relative to Chngtotaloans_cap lag(data5330))/lag(data2135) total capital (data2150- Average change in total problem loans ChngTprobloans_cap lag(data2150))/lag(data2135) relative to total capital 28 Table 2. Summary statistics of mortgage lending activities for failed bank and non-banks. The detailed description of the variables are shown in Panel A of Table 1. Panel A. Failed banks Variables N MIN MAX MEAN STD Application 249 4.500 2431891.500 35135.071 162639.856 Pctoriginate 249 0.000 1.000 0.494 0.232 Loan-to-income 248 0.638 86.021 2.789 5.385 Pctminority 249 0.000 1.000 0.519 0.204 Pctgoodfico 249 0.000 0.800 0.222 0.127 Avgamount ('000) 249 33.312 9573.753 220.134 601.006 Amount (Bill) 249 0.004 4732.701 63.792 313.734 Pctpurchase 249 0.000 1.000 0.404 0.254 Pctrefinance 249 0.000 1.000 0.554 0.232 HHI 249 235.556 1533.397 350.721 108.666 High_HPI 249 0.000 1.000 0.525 0.476 Concentration 247 414.114 10000.000 4882.047 3523.814 Highrate 249 0.000 9.000 3.180 2.033 HOEPA 249 0.000 8.667 1.167 1.597 Chngamount (in mill) 223 -8.807 171.265 2.710 12.485 Chngloanorig 223 -35538.000 276605.333 5096.777 22026.970 Chngpctapproval 223 -0.972 0.747 -0.070 0.178 Chngpctminority 223 -0.556 0.914 0.057 0.171 ChnggoodFICO 223 -0.667 0.433 -0.017 0.116 Panel B. Failed non-banks Variables N MIN MAX MEAN STD Application 329 1.500 2431891.500 27142.123 168356.432 Pctoriginate 329 0.045 1.000 0.656 0.252 Loan-to-income 327 0.331 24.961 2.239 1.709 Pctminority 329 0.000 1.000 0.393 0.226 PctgoodFICO 329 0.000 0.922 0.242 0.186 Avgamount ('000) 329 33.312 35004.445 381.216 1986.979 Amount (Bill) 329 0.003 4732.701 53.354 340.129 Pctpurchase 329 0.000 1.000 0.412 0.231 Pctrefinance 329 0.000 1.000 0.467 0.221 HHI 329 222.196 1967.938 389.288 167.534 High_HPI 329 0.000 1.000 0.330 0.460 Concentration 329 397.048 10000.000 7992.232 2860.919 Highrate 329 0.000 9.000 2.779 2.057 HOEPA 329 0.000 7.000 0.784 1.370 Chngamount (in mill) 308 -2.402 171.265 2.239 13.142 Chngloanorig 308 -5623.333 276605.333 4392.027 24855.874 Chngpctapproval 308 -0.939 0.747 -0.028 0.149 Chngpctminority 308 -0.750 0.889 0.036 0.179 ChnggoodFICO 308 -0.667 0.500 -0.013 0.141 29 Table 3. Summary statistics of HMDA variables for match (non-failed) banks and non-banks The detailed variable description is in Panel A of Table 1. Non-failed (active) banks Non-failed (still active) non-banks Variables N MIN MAX MEAN STD N MIN MAX MEAN STD Application 2240 1.000 146091.75 816.835 4685.682 3215 1.000 1287758. 2749.048 31748.302 Pctoriginate 2240 0.006 1.000 0.791 0.147 3215 0.000 1.000 0.751 0.224 Loan-to-income 2238 0.141 31.374 1.757 1.068 3209 0.112 29.151 1.951 1.040 Pctminority 2240 0.000 1.000 0.242 0.206 3215 0.000 1.000 0.360 0.250 PctgoodFICO 2240 0.000 1.000 0.303 0.285 3215 0.000 1.000 0.259 0.218 Avgamount ('000) 2240 11.406 4694.989 164.942 205.832 3215 5.317 15982.78 148.327 365.562 Amount (bill) 2240 0.000 13.005 0.109 0.566 3215 0.000 207.055 0.446 5.098 Pctpurchase 2240 0.000 1.000 0.424 0.155 3215 0.000 1.000 0.358 0.269 Pctrefinance 2240 0.000 0.953 0.403 0.146 3215 0.000 1.000 0.467 0.231 HHI 2240 0.000 2099.517 416.636 187.489 3215 0.000 2505.969 359.056 136.657 High_HPI 2240 0.000 1.000 0.204 0.398 3215 0.000 1.000 0.381 0.476 Concentration 2240 580.954 10000.000 9008.233 1543.376 3154 358.374 10000.00 8311.468 2362.765 Highrate 2240 0.000 9.000 2.449 1.865 3215 0.000 9.000 1.605 1.828 HOEPA 2240 0.000 7.667 0.468 1.041 3215 0.000 8.000 0.253 0.764 Chngamount (in mill) 2159 -75.027 5.602 -0.128 2.385 2914 -82.235 31.920 -0.263 3.943 Chngloanorig 2159 -142237.333 14902.333 -224.284 4005.847 2914 -27501.000 79202.33 -593.505 8888.673 Chngpctapproval 2159 -0.859 0.786 0.004 0.104 2914 -0.929 1.000 0.001 0.163 Chngpctminority 2159 -0.925 0.938 -0.012 0.134 2914 -1.000 1.000 0.007 0.164 ChnggoodFICO 2159 -0.979 0.862 0.009 0.147 2914 -0.881 0.731 -0.001 0.133 30 Table 4. Summary statistics of balance sheet information for failed and non-failed banks The detailed discretion of the variables is shown in Panel B of Table1. Failed (inactive) banks Non-failed (active) banks Variables N MIN MAX MEAN STD N MIN MAX MEAN STD Tasset (in bill) 267 0.000 0.485 0.009 0.053 2240 0.000 0.088 0.001 0.004 Totalcap (in mill) 264 1.645 48683.388 890.537 5281.968 2238 1.930 10474.928 85.771 462.418 Tier1cap (in mill) 266 4.975 477.071 14.588 31.600 2238 7.153 86.675 14.755 7.010 Equity (in mill) 267 1.916 56778.500 998.658 6027.316 2240 2.270 8425.375 93.490 499.440 Liability (in bill) 267 0.000 0.431 0.008 0.047 2240 0.000 0.080 0.001 0.004 Earningasset_liab 267 0.788 50.110 1.215 3.017 2240 0.690 1.932 1.021 0.054 OBS_cap 263 -27.182 41.486 2.077 3.905 2238 0.000 262.189 1.555 6.056 liqAT 267 1.781 60515.500 855.240 5277.740 2240 0.911 21979.375 55.796 523.882 NI (in mill) 267 -150.924 6896.042 91.790 653.583 2240 -101.196 781.654 6.972 40.075 Profit (in mill) 267 -116.029 9177.000 131.147 879.833 2240 -126.889 1166.354 10.225 59.493 NI_liab 267 -0.052 36.541 0.138 2.236 2240 -0.081 0.381 0.009 0.011 Totaloans 267 0.000 319395.917 5774.676 33285.149 2240 6.866 64732.829 574.229 2855.614 TotalfixAt 267 0.000 6081.938 98.650 592.719 2240 0.039 947.683 11.421 43.855 Tdeposit_loanratio 266 -0.384 3.893 1.144 0.367 2239 0.485 93.037 1.373 2.023 Tprobloan_cap 264 -8.033 42.639 0.229 2.674 2238 -0.010 1.776 0.026 0.058 Tprobloans_liqAt 267 0.000 4.639 0.475 0.554 2240 0.000 10.198 0.191 0.331 Loanres_loan 266 -0.140 0.087 0.016 0.012 2240 0.001 0.111 0.013 0.006 ChngT1ratio 262 -0.947 6.491 -0.033 0.524 2238 -7.671 5.172 0.023 0.275 ChngTotalcap 262 -0.938 68.465 0.725 4.643 2238 -4.672 91.399 0.426 2.485 ChngLiqAT 265 -0.659 69.314 1.727 5.646 2240 -0.973 449.640 1.274 14.296 ChngEq 265 -1.872 68.407 0.762 4.775 2240 -5.934 99.540 0.526 3.253 ChngOBS 259 -1.000 411.263 7.582 37.780 2237 -1.000 2099.727 4.789 54.596 ChngProfit_cap 264 -335.640 17.786 -1.156 20.700 2239 -3.342 6.478 0.074 0.369 ChngNI_cap 264 -220.990 10.959 -0.779 13.626 2239 -3.295 3.820 0.052 0.247 ChngLoanRes_loan 264 -0.062 0.275 0.009 0.025 2240 -0.043 48.866 0.053 1.292 ChngTasset 265 -0.856 146.488 1.059 9.070 2240 -0.974 234.780 0.705 5.877 Chngtotaloans_cap 264 -6.866 238.246 5.564 22.086 2239 -13.993 733.508 3.080 18.880 ChngTprobloans_cap 263 -0.601 2.478 0.047 0.190 2239 -1.526 3.845 0.006 0.123 31 Table 5. Probability of failure analysis of bank and non-bank lenders The dependent variable failure takes on the value 1 for lenders that failed by August 2010, zero otherwise. The explanatory variables capture the time series average lending practices based on HMDA data for 2003 to 2007. Pctoriginate is the ratio of origination relative to loan application in percentage. Logamount is the natural logarithm of the average loan amount. Pctorg*Logamount is an interaction variable of Pctoriginate and Logamount variables. PctgoodFICO is the percentage of total loan application from census tracts with Average FICO scores above the median. Pctminority is the percentage of loan applications by minorities. Pctrefinance is the percentage of loan application for refinance purposes. Loan-to-income is the average loan-to-income ratio for all loans by lenders. HighComp is a dummy variable that takes on the value 1 for lenders operating in highly competitive environment, where the Concentration measure is below the median. Highrate is the annual average of total number of spread reportable loans. High_HPI is the dummy variable which takes on the value one if more than half of the originations are in areas with high property prices. The 1 percent, 5 percent and 10 percent significance levels are denoted with ***, **, and *, respectively. To save space the intercepts are not shown. Non-banks Banks odds ratio odds ratio odds ratio odds ratio (1) (2) (1) (2) Pctoriginate 0.918* 0.918** 1.067 1.068 (-1.859) (-1.987) (1.401) (1.412) Logamount 1.158 1.143 1.827** 1.800** (0.937) (0.941) (2.365) (2.293) Pctorg*Logamount 1.005 1.005 0.993* 0.993* (1.388) (1.452) (-1.664) (-1.673) PctgoodFICO 0.985 0.986 0.987* 0.988* (-1.466) (-1.262) (-1.924) (-1.928) Pctminority 0.998 1.000 1.018*** 1.018*** (-0.477) (0.026) (5.186) (4.223) Pctrefinance 1.010*** 1.010*** 1.013 1.014 (4.012) (4.435) (1.413) (1.514) Loan-to-income 1.001 0.999 1.175*** 1.179*** (0.162) (-0.265) (3.607) (3.666) Focus 0.812*** 0.794*** 0.807 0.800 (-2.897) (-2.819) (-1.228) (-1.295) HighComp 1.160 1.214* 1.356 1.383 (1.355) (1.656) (1.569) (1.552) Highrate 1.235*** 1.075*** (4.577) (2.583) High_HPI 1.026 1.190 (0.671) (1.166) Observations 3394 3394 2562 2562 pseudo R-squared 0.390 0.404 0.182 0.184 32 Table 6. Probability of failure analysis of bank and non-bank lenders with time trends The dependent variable failure takes on the value 1 for lenders that failed by August 2010, zero otherwise. The explanatory variables capture the time series average lending practices based on HMDA data for 2003 to 2007. Pctoriginate is the ratio of origination relative to loan application in percentage. Logamount is the natural logarithm of the average loan amount. Pctorg*Logamount is an interaction variable of Pctoriginate and Logamount variables. PctgoodFICO is the percentage of total loan application from census tracts with Average FICO scores above the median. Pctminority is the percentage of loan applications by minorities. Pctrefinance is the percentage of loan application for refinance purposes. Loan-to-income is the average loan-to-income ratio for all loans by lenders. HighComp is a dummy variable that takes on the value 1 for lenders operating in highly competitive environment, where the Concentration measure is below the median. Highrate is the annual average of total number of spread reportable loans. High_HPI is the dummy variable which takes on the value one if more than half of the originations are in areas with high property prices. Chngloanamount is the average annual change in the total loan amount by the lender from 2003 to 2007. Chngpctapproval is the average annual percentage change in approval rates. Robust z statistics are reported in parentheses. The 1 percent, 5 percent and 10 percent significance levels are denoted with ***, **, and *, respectively. To save space the intercepts are not shown. Non-banks Banks odds ratio odds ratio odds ratio odds ratio (1) (2) (1) (2) Pctoriginate 0.891** 0.878** 1.069 1.056 (-2.434) (-2.453) -1.483 (1.144) Logamount 0.973 0.942 1.796** 1.732** (-0.157) (-0.302) -2.37 (2.233) Pctorg*Logamount 1.007* 1.008* 0.993* 0.994 -1.952 (1.900) (-1.728) (-1.571) PctgoodFICO 0.977*** 0.977*** 0.986** 0.986** (-3.841) (-3.907) (-2.096) (-2.188) Pctminority 0.998 0.998 1.017*** 1.017*** (-0.374) (-0.518) -3.396 (3.512) Pctrefinance 1.012*** 1.012*** 1.013 1.012 -8.062 (8.188) -1.214 (1.292) Loan-to-income 1.004* 1.003 1.158*** 1.154*** -1.892 (1.245) -3.096 (2.998) Focus 0.770*** 0.755*** 0.775* 0.739* (-5.142) (-6.201) (-1.803) (-1.936) HighComp 1.211 1.310 1.35 1.363 -1.29 (1.344) -1.534 (1.472) Highrate 1.202*** 1.183*** 1.045 1.044 -4.845 (4.083) -1.358 (1.338) High_HPI 0.997 1.028 1.094 1.087 (-0.027) (0.290) -0.581 (0.548) Chngloanamount 1.038 1.053 1.000*** 1.000*** -1.393 (1.531) -3.465 (3.695) Chngpctapproval 5.963*** 6.382** (6.129) (2.397) Observations 3086 3086 2461 2461 pseudo R-squared 0.437 0.444 0.186 0.193 33 Table 7. Probability of failure analysis of bank lenders with financial information The dependent variable is the failure dummy in a logistic regression. The lending characteristics variables are defined in Tables 5 and 6. Tier1capratio is the total tier1 capital ratio in percentage. NI_asset is the ratio of Net income to earning assets. Probloanratio is the ratio of total problem loans to total loans. Depratio is the ratio of deposits to total assets for the bank. LogtotalfixAt is the natural logarithm of total fixed assets. Tprobloan_cap is the total problem loans relative to the total capital reserve. OBS_cap is the total off balance sheet items relative to total capital reserve. Loanres_loan is the total loan reserves relative to the total loans. Tprobloans_liqAt is the total problem loans relative to total liquid assets. Robust z-statistics are reported in parentheses. The 1 percent, 5 percent and 10 percent significance levels are denoted with ***, **, and *, respectively. To save space the intercepts are not shown. Odds ratio Odds ratio Odds ratio Odds ratio Odds ratio (1) (2) (3) (4) (5) Pctoriginate 1.089*** 1.131*** 1.124*** 1.134*** 1.123*** (2.659) (5.446) (3.597) (4.406) (4.504) Logamount 1.964*** 2.782*** 2.875*** 3.112*** 2.590*** (5.934) (15.263) (6.458) (10.516) (9.993) Pctorg*Logamount 0.992*** 0.988*** 0.989*** 0.988*** 0.989*** (-2.811) (-5.203) (-3.488) (-4.227) (-4.312) PctgoodFICO 0.989*** 0.988*** 0.986*** 0.988*** 0.986*** (-2.663) (-2.966) (-3.981) (-4.347) (-5.382) Pctminority 1.018*** 1.010** 1.015*** 1.014*** 1.013*** (4.413) (2.569) (3.771) (3.402) (3.284) Pctrefinance 1.008 1.017*** 1.012* 1.012** 1.013** (1.568) (2.685) (1.821) (2.145) (2.300) Loan-to-income 1.156*** 1.068** 1.097** 1.088** 1.098*** (2.916) (2.455) (2.205) (2.288) (2.596) High_HPI 1.307*** 1.398** 1.246* 1.339*** 1.280*** (2.595) (2.484) (1.696) (3.350) (2.816) Tier1capratio 0.892* 0.890*** 0.857*** 0.865*** 0.869*** (-1.934) (-2.780) (-2.921) (-3.536) (-3.191) NI_asset 0.539*** 0.389*** 0.432*** 0.428*** (-6.301) (-7.715) (-7.915) (-8.137) Deposit_ratio 0.985*** (-2.716) Probloanratio 1.728*** (10.655) LogtotalfixAt 1.057 (0.420) Tprobloan_cap 1.266*** 1.262*** 1.048 (2.881) (4.650) (0.948) OBS_cap 1.042** (2.125) Loanres_loan 2.052*** 1.827*** (7.505) (5.954) Tprobloans_liqAt 3.139*** (4.819) Observations 2487 2487 2451 2452 2452 pseudo R-squared 0.147 0.328 0.261 0.282 0.295 34 Appendix – Table 1 The extract shows some of the largest bank and non-bank lenders as of 2007, and the amount of high cost loans that these institutions issued. The assets for Banks (depository institutions) are reported in million USD, while the assets for non-banks reflect the minimum asset requirement (defined by the states). * reflects subprime lenders with at least one depository facility therefore considered banks in this classification, although these institutions are widely viewed as non-banks. Non-banks High-cost loans Asset Banks High-cost Loans Asset New Century Mortgage Corp. 193,04 10,000 Countrywide Home Loans * 210,688 175.09 WMC Mortgage Co. 142,16 31,000 National City Bank 189,415 69.48 Option One Mortgage Corp 118,43 10,000 Fremont Investment & Loan 140,290 11.32 Argent Mortgage Co. 107,53 10,000 Wells Fargo Bank, NA 110,770 403.26 American Home Mortgage 78,936 10,000 Long Beach Mortgage Co. 95,427 330.71 Accredited Home Lenders, 75,931 10,000 Decision One Mortgage 73,379 3.39 Homecoming Financial 62,730 10,000 Beneficial Company LLC 67,303 4.02 Novastar Mortgage, Inc. 43,733 10,000 Indymac Bank, FSB* 62,790 20.33 Wilmington Finance, Inc. 41,212 10,000 Equifirst Corp.* 62,685 81.07 35 Appendix – Table 2 FailD takes on the value one for lenders that fail (closed down, merged, or acquired) by August 2010, zero otherwise. Pctoriginate is the ratio of origination relative to loan application in percentage. Logamount is the natural logarithm of the average loan amount. Orig*Amount is an interaction variable of Pctoriginate and Logamount variables. PctgoodFICO is the percentage of total loan application from census tracts with Average FICO scores above the median. Pctminority is the percentage of loan applications by minorities. Pctrefinance is the percentage of loan application for refinance purposes. Loan-to-income is the average loan-to-income ratio for all loans by the specific lender. HighComp is a dummy variable that takes on the value 1 for lenders operating in highly competitive environment, where the Concentration measure is below the median. Highrate is the annual average of total number of spread reportable loans. High_HPI is the dummy variable which takes on the value one if more than half of the originations are in areas with high property prices. The pair-wise correlation coefficient estimates are reported with the corresponding p-values in italic. Panel A. Correlation analysis results for non-bank residential lenders Orig* Loan-to- Faild Pctoriginate Logamount amount PctgoodFICO Pctminority Pctrefinance income Focus HighComp Highrate High_HPI Faild 1.000 Pctoriginate -0.283 1.000 0.000 Logamount 0.405 -0.359 1.000 0.000 0.000 Orig*amount -0.088 0.782 0.253 1.000 0.000 0.000 0.000 PctgoodFICO -0.045 0.194 0.098 0.272 1.000 0.008 0.000 0.000 0.000 Pctminority 0.164 -0.325 0.244 -0.201 -0.241 1.000 0.000 0.000 0.000 0.000 0.000 Pctrefinance 0.097 -0.025 0.054 -0.034 0.068 -0.003 1.000 0.000 0.148 0.002 0.045 0.000 0.880 Loan-to- 0.122 -0.195 0.272 -0.027 -0.001 0.179 0.069 1.000 income 0.000 0.000 0.000 0.110 0.946 0.000 0.000 Focus -0.377 0.323 -0.551 0.014 0.021 -0.199 -0.068 -0.141 1.000 0.000 0.000 0.000 0.420 0.230 0.000 0.000 0.000 HighComp -0.068 0.049 -0.106 0.012 0.046 -0.044 -0.014 -0.018 0.101 1.000 0.000 0.004 0.000 0.479 0.007 0.010 0.429 0.289 0.000 Highrate 0.216 -0.072 0.360 0.146 -0.024 0.043 -0.027 0.071 -0.177 -0.081 1.000 0.000 0.000 0.000 0.000 0.167 0.012 0.113 0.000 0.000 0.000 High_HPI 0.078 -0.199 0.198 -0.055 0.007 0.386 0.060 0.187 -0.045 0.072 0.028 1.000 0.000 0.000 0.000 0.001 0.667 0.000 0.000 0.000 0.008 0.000 0.102 36 Panel B. Correlation analysis results for bank residential lenders Orig* Loan-to- Faild Pctoriginate Logamount amount PctgoodFICO Pctminority pctrefinance income Focus Highcomp Highrate High_HPI Faild 1.000 Pctoriginate -0.266 1.000 0.000 Logamount 0.276 -0.371 1.000 0.000 0.000 Orig*amount -0.116 0.737 0.323 1.000 0.000 0.000 0.000 PctgoodFICO -0.074 0.131 0.110 0.208 1.000 0.000 0.000 0.000 0.000 Pctminority 0.234 -0.298 0.120 -0.233 -0.153 1.000 0.000 0.000 0.000 0.000 0.000 Pctrefinance 0.135 -0.112 0.335 0.096 0.226 -0.170 1.000 0.000 0.000 0.000 0.000 0.000 0.000 Loan-to- 0.136 -0.075 0.160 0.045 0.141 0.214 0.092 1.000 income 0.000 0.000 0.000 0.022 0.000 0.000 0.000 Focus -0.221 0.249 -0.448 -0.010 0.010 -0.183 -0.073 -0.042 1.000 0.000 0.000 0.000 0.619 0.622 0.000 0.000 0.032 HighComp -0.029 0.052 -0.079 0.007 0.135 -0.031 0.037 0.030 0.055 1.000 0.138 0.008 0.000 0.708 0.000 0.115 0.062 0.134 0.005 Highrate 0.058 -0.055 0.113 0.018 -0.207 -0.137 0.015 -0.111 -0.069 -0.172 1.000 0.003 0.005 0.000 0.360 0.000 0.000 0.442 0.000 0.000 0.000 High_HPI 0.103 -0.164 0.114 -0.083 0.038 0.334 -0.064 0.163 -0.041 0.054 -0.154 1.000 0.000 0.000 0.000 0.000 0.057 0.000 0.001 0.000 0.040 0.007 0.000 37


text:
Alinta declares gas price rise will cost it dearly
PUBLISHED: 30 Apr 2012 14:38:00 | UPDATED: 01 May 2012 05:19:37PUBLISHED: 30 Apr 2012 PRINT EDITION: 01 May 2012Alinta Energy claims it will be forced to sell gas at a loss after failing to convince the West Australian government to let it raise gas prices by 12 per cent.
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Companies | Alinta Ltd |
People | Colin Barnett, Peter Collier |
Topics | Energy & Utilities, Politics /WA Politics |


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The DC Ward Beanie is a hat that is 100% acrylic/cotton and can be used for the outdoors. This hat is especially useful if you are living in regions that have extremely hard winters. This hat covers the full head and ears, so those areas are not subject to the extreme elements. And the visor of the Ward Beanie protects the eyes from getting snow in them, so they can focus on what is ahead.


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Washington Redskins Gear
Washington Redskins Ladies
Washington Redskins Ladies T-Shirts
Washington Redskins Ladies T-Shirts Short Sleeved
(180 items)
Product ID: 1404136
Main Content
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Show off your Redskins pride in style. This Morgan tee from Touch by Alyssa Milano features "Washington Redskins" lettering surrounding a team logo printed on a shirt made of team-spirited colors. This tri-blend shirt will have you strutting your 'Skins fanaticism in pure comfort!
- 55% Polyester/35% Cotton/10% Nylon
- Distressed printed graphics
- Tagless collar
- Two-tone design with team color stripes
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