전체 대출 거래 시스템

전체 대출.

'전체 대출'이란 무엇입니까?

전체 융자는 대출자가 차용자에게 발행 한 하나의 주거용 또는 상업용 모기지이며 증권화되지 않았습니다. 전체 대출 대출 기관은 일반적으로 2 차 모기지 시장에서 전체 대출을 Fannie Mae와 같은 구매자에게 판매합니다. 대출 기관이 전체 대출을 판매하는 한 가지 이유는 위험을 줄이는 데 있습니다. 15 년 또는 30 년 동안 모기지를 지니고 차용자가 돈을 갚을 수 있기를 바라는 대신 대출 기관은 즉시 교장을 돌려받을 수 있습니다.

속보 '전체 론'

대출 기관은 더 이상 판매하는 전체 대출에 대한이자를받지 않지만 추가 대출을 위해 현금을 얻습니다. 대출 기관이 추가 모기지를 마감하면 차용자가 지불 한 대출 수수료, 포인트 및 기타 마감 비용에서 돈을 벌습니다. 이 유동성은 또한 차용인이 모기지를 쉽게 얻을 수있게합니다. 패니 매 (Fannie Mae)는 한 번에 하나씩 전체 대출을 구매할 예정이지만 일부 다른 2 차 시장 단체는 전체 대출 풀만 살 것입니다. 대출 풀은 다양한 대출 조건 및 신용 점수와 같이 위험 특성이 다른 대출을 포함하는 한 위험을 줄일 수 있습니다. Fannie Mae는 구매하는 전체 대출이 특정 적격성 및 인수 기준을 충족하도록 요구함으로써 위험을 줄입니다.

모기지 거래에 오신 것을 환영합니다.

귀하의 전체 대출 컨시어지.

전체 대출의 원산지, 제조, 구매 또는 판매?

특수화 된 하이 터치 서비스.

혼돈을 깨끗하게 전환.

대출 창고의 수명.

Mortgage Trade는 조직이 전체 대출을 구매, 판매 및 분석하는 방식을 개선하는 데 중점을 둡니다. 우리는 국내 최대 은행과의 광범위한 경험을 통해 최고의 솔루션 인 Clean Room을 구축했습니다. 우리의 솔루션과 맞춤형 서비스는 프로세싱, 인수 및 대출 제공의 품질을 향상시켜 부가가치를 창출합니다. 또한 KBYO / TRID 준수를위한 평가 및 전체 대출을 확인하기위한 특수 도구가 있습니다.

우리가 너를 도울 방법.

우리 소프트웨어는 기관 (은행, 중개인, 투자자)이 대출을 거래 할 수있는 속도를 높입니다.

투명도.

대출 파일을 순수한 구조화 된 데이터로 변환하여 각 개별 대출의 투명성을 높입니다.

위험 회피.

각 대출 및 대출 풀에 세계적 수준의 분석을 적용하여 오류 위험을 줄입니다.

통신.

우리의 협업 시스템은 거래 상대방 간의 불일치를 쉽게 해결할 수 있습니다.

금융 산업을위한 MIAC 분석 솔루션.

주택 자금 대출은 Fannie Mae, Freddie Mac 또는 Ginnie Mae (FHA / VA)가 제공하는 프로그램 및 Agency가 아닌 기관의 두 가지 범주 중 하나에 속합니다. 2015 년 말 현재 대행사 대출은 신규 출자의 약 90 %입니다. 연방 준비 제도 이사회 (FRB)에 따르면, 그 해의 현저한 모기지 부채는 약 13 조 5 천억 달러에 달했다. 이와는 대조적으로 사기업 (비 기관) 시장은 1 조 달러가 넘는 균형을 이루고 있습니다. 비 기관 시장의 중요성은 전체 대출 실행을 통해 일반적으로 판매되는 비 기관용 프로그램이 대금업자가 경쟁 업체와 차별화 할 수있는 틈새 시장을 찾을 수있는 경우가 많기 때문에 시장 점유율에 비례합니다.

대행사 대 비 기관 대부.

Fannie Mae, Freddie Mac 및 FHA / VA는 그들이 구매할 대출을 정의하는 지침을 발행합니다. 은행, 모기지 회사 및 기타 창안자는 이러한 지침을 충족하는 대출을 생성하여 소비자가 가계 대출을위한 여러 출처에 액세스 할 수 있도록합니다. 제작자는 요금, 가격 및 서비스에서 경쟁하면서 Agencies에서 제공하는 표준화 된 금융 기관에 일관되게 액세스 할 수 있습니다. 이는 모기지 대출을위한 자금 조달의 알려진 출처를 제공함으로써 시장에 유동성을 창출합니다.

대행사가 구매하지 않은 대출을 포함하는 비 기관 시장은 다른 계층의 소비자에게 서비스를 제공하며, 시장 이후 비 기관 모기지에 대한 자본 출자가 급격히 감소함에 따라 주택 복구에서 벗어난 사람들이 많습니다 이러한 고객은 신용 기록 부족, 자영업 수입, 일관성없는 고용 기록, 제외 된 부동산 유형 또는 대출 잔액을 포함하여 여러 가지 이유로 기관 대출을받을 자격이 없습니다. 비 기관 대출은 일반적으로 은행이 대차 대조표 ( "포트폴리오", 즉 용어집, 포트폴리오 대여)에 보관할 때 유래합니다. 이 추가 위험이 포트폴리오 대금업자 / 투자자에게 가치가 있기 위해서는 일반적으로 대행사 보고서를 통해 50-150bp의 이자율이 적용됩니다. 2015 년 말 현재, 장기간 많은 투자자들이 우세한 저금리를 받아 들일만큼 낮기 때문에 이러한 고수익 용지에 대한 다양한 금융 기관에 대한 수요가 증가하고 있습니다.

대행사 대출을위한 자금 지원.

13 조 달러 모기지 시장의 대다수는 위의 기관 목록에 의해 발행 된 모기지 채권에 의해 자금을 조달받습니다. 미국 정부는 소비자를 위해 모기지에 자금을 공급하는 데 사용되는 채권 및 증권화를 통해 모기지 부채를 발행합니다. 이러한 도구는 공개 시장에서 거래되며 기관과 개인이 신용 위험을 실질적으로 제거하여 미국 정부의 보증을 받아 주택 시장에 투자 할 수있게합니다.

비 기관 대출 자금.

비 기관 대출을위한 자금 원천은 주로 ​​생명 보험 회사, 헤지 펀드, 리츠 (REITS), 포트폴리오 수익률을 추구하는 대형 은행, 증권화 및 일부 다른 유형의 보험 회사의 사적, 비정부 기금으로 구성됩니다. 2010 년 중반의 저금리 환경은 순이자 마진 (NIM : 은행이 대출에서 얻는 이익)을 압박하고 예금주 및 기타 사람들이 추가 수입원을 찾도록 강요했습니다.

이 환경은 생명 보험 시장에서 특히 수익률이 높은 비 기관용 모기지를 흥미롭게 만듭니다. 역사적으로, 이 자본은 가장 높은 유동성을 가진 가장 낮은 위험 투자를 위해 예비되었습니다. 그러나 우울한 이자율로 인한 수입 감소는 기본 보안 계좌의 시장 가치에 대한 타격과 결합되어 보험 회사가 수익률을 쫓아 가게합니다. 대행사 대출은 비 대행사 대부보다 높은 수율을 가지고 있지 않습니다. 따라서 수익률을 추구하는 보험 회사는 비 기관 대출을 선호하는 경향이 있습니다.

헤지 펀드는 수익률 및 현금 흐름에 대한 대출을 구매하거나, 중개 시장에서 판매 할 대출금을 집계하고 증권화합니다. 헤지 펀드는 야간 창구 나 다른 정부 자금에 접근 할 수 없으므로 전통적인 은행보다 자금 비용이 높습니다. 따라서, 그들의 주요 자본 출처는 사설 기금, 기관 및 개인 투자자입니다. 이 투자자들은 비 기관 모기지에 더 높은 이자율을 혼합하는 레버리지를 통해 얻는 더 높은 수익률을 요구합니다. 그들의 자본을 레버리지함으로써, 4.5 %의 이자율을 담보로하는 담보 대출은 두 자리 수표로 예약 할 수 있습니다.

헤지 펀드는 또한 증권화 및 매각을위한 대출 (종종 특파원 관계를 통한)을 기점으로하고 있습니다. 증권화 과정에서 부채는 등급이 매겨지고, 전체 원천과 비교하여 더 많은 투자가 이루어집니다. 이러한 상품은 선급금과 신용 위험이 모두 상쇄되므로 더 높은 프리미엄으로 판매됩니다. 이를 통해 투자자는 채무 구조와 위험 / 수익 프로파일을 일치시키려는 신용 위험 및 현금 흐름 특성만으로 전체 증권화의 특정 부분 (트렌치)을 구매할 수 있습니다.

헤지 펀드는 Dodd-Frank가 규제하지 않기 때문에 Agencies가 제공하지 않을 고객 계층에 빌려줄 수 있습니다. 자영업, 신용 손상, 하한 지불 제한, 부동산 유형 또는 여러 가지 요인으로 인해이 투자자는 소비자와 주택 시장에 대한 중요한 요구를 충족시킵니다. 대출 시장의 확대와 맞물려 주택 시장의 개선으로 인해 비 기관 시장에서의 대출에 대한 신용 제한이 완화 되었기 때문에 회복의 대부분이 이루어 졌음이 분명합니다.

시장에는 주택 모기지 REIT가 제한되어 있습니다. 세금 구조 때문에 REIT는 부채와는 반대로 부동산에 중점을 둡니다. 기존의 주거용 REIT는 주로 신규 대출을 창출하는 것과 달리 중개 시장에서 대출을 구매하여 시장에 참여합니다. 리츠 (REIT) 모델은 오직 구매하는 펀드와 유사합니다.

비 기관 대부의 유형.

2010 년 중반에 Mortgage Lenders는 신용 곡선을 훨씬 밑도는 신제품을 모색하고 있었으며 이전에는 금지 된 위험 기반 가격 조정을 초래했을 수있는 손상을 허용했습니다. 특히 대형 커뮤니티 은행은 포트폴리오 담보 대출을 LTV (Loan-to-Value) 비율을 높이고 FICO를 낮추고 대안적인 의사 유형을 인수했습니다. 일반적으로 세 가지 기준 중 하나만 대출 기관을 만드는 것과 다릅니다. 이 말을하는 또 다른 방법은 포트폴리오 대출 기관이 과도한 위험 계층화를 피하기 위해서입니다. 은행은 여전히 ​​규제 당국이 대출을하는 이유를 정당화 할 수 있어야하며 "관계"는 받아 들일 수있는 대답이 아닙니다. 이런 형태의 공동체 은행 대출이 더욱 보편화됨에 따라 이들 대출의 2 차 시장이 확대되기 시작했다. 다른 은행들은 소매 대출과 관련된 오버 헤드를 피할 수 있기 때문에 이러한 대출에 대해 프리미엄을 지불 할 의향이 있으며, 대출의 높은 수익률은 (인식되거나 실제적인) 증분 신용 위험에 대한 적절한 보상입니다.

비 기관 대출을위한 인수.

비 기관 대출 (Non-Agency Loans)은 보험 인수 프로세스의보다 큰 유연성과 맞춤화를 허용합니다. 보험 인수 기준은 투명해야하지만, 중등 시장은 은행이 다양한 범위의 차용자에게 서비스를 제공 할 수있게합니다.

담보 대출.

집주인과 대형 부동산 소유자의 요구에 부응하기 위해 때로는 기금이 전체 주택 포트폴리오에 빌려주기도합니다. 이 담보 대출은 일반적으로 최대 LTV가 65 % 인 100 개의 자산으로 확보됩니다. 이점은 하나의 대출로 지불금을 모으는 비용 효율성입니다. 차용인은 일반적으로 전문 매니저로서 이론적으로는 하나의 부동산보다 더 많은 지분이 있기 때문에 채무 불이행 가능성이 적습니다. 또한 현금 흐름의 다양 화를 통해 추가적인 보안이 제공됩니다. 일부 부동산이 비어있는 경우 나머지 임대 부동산에 의해 자금이 제공됩니다. 대부는 전형적으로 5 년에서 10 년 임기를 특징으로하며이자 만 또는 20 년 고정 이자율입니다. 현금 흐름은 상업 대출과 유사한 방식으로 지불되며 부채 상환을 지원할 능력을 결정하기 위해 계산 된 부채 서비스 보장 비율이 적용됩니다.

신용도가 낮은 차용자.

FICO 점수가 낮은 차용자, 파산, 처분 및 모기지 금리를 포함한 최근의 불리한 신용 사건은 여전히 ​​지역 은행에서 대출이 필요하다는 아이디어와 함께 커뮤니티 은행으로부터 모기지를 얻을 수 있습니다. 예를 들어, 우리 회사는 완벽한 파산 후 지불 기록을 가진 13 장 파산에서 1 년인 차용자를위한 주거용, 소유주가 저당 잡히는 모기지를 창출 할 은행 고객을두고 있습니다. 반면, 에이전시 지침에서는 퇴원 후 4 년의 임금 기록이 필요합니다. 그 논리는 만약 차용자가 제때에 수탁자 지불액을 지불하고 신용을 재개하고 모든 채무가 빚이 탕감되는 제 7 장에 반하여 지불 되었다면 차용자는 채무자가 적시에. 우리 고객은 이것이 매우 위험하다고 판단하고 현재까지는 디폴트를 가지고 있지 않습니다.

자영업자.

중개인 가이드 라인을 충족시키지 못한 자영업자 대출은 2010 년 중반까지 시장 붕괴 이후 사실상 존재하지 않았는데, 이는 중등 시장이 식욕을 나타 내기 시작했을 때입니다. 대출 기준은 LTV, FICO 등과 같은 기관 가이드 라인과 일치합니다. 융자 기관이 차용자의 소득에 대한 이해와 상환 능력 결정을위한 계산에 어떻게 유연하게 차별화 될 수있는 기회가 있습니다. 이러한 계산에는 비즈니스 및 개인 현금 흐름으로 소득 확인, 비즈니스 소유권 이해, 차용자 산업 전반에 대한 이해가 포함됩니다. 자영업자를위한 보험 담보 대출은 각 대출 및 차용자에 대한 깊은 이해가 필요합니다. 다시 한번, 대리점 인수에서 하나의 편차를 갖는 것이 허용됩니다. 신용 및 담보가 허용되는 한, 채권자는 수입에 대한 위험 또는 상환 능력을 관리 할 수 ​​있습니다.

비은행 금융 기관은 투자 부동산에 대해 LTV보다 높은 대출을 창출하고 있습니다. 이러한 모기지의 경우 훨씬 높은 FICO 점수와 전체 소득 증빙 서류 및 상당한 준비금이 필요합니다. 차용인은 주거용 부동산보다 투자 부동산에 대한 채무 불이행 가능성이 높으며 임대 주택의 거주자는 주택을 유지할 가능성이 적습니다. 따라서 은행은 시장성있는 조건에서 유질 처분 된 부동산을 회수 할 가능성이 적습니다.

대행사 및 비 기관 대부금의 가격 책정.

비 기관 모기지를 발행하는 자금은 대출이 미국 정부에 의해 보험이 부과되지 않으며 기본 기간 동안 발생한 손실은 대출을 소유 한 투자자가 직접 태어난 것이므로 LTV가 더 낮아야합니다. NPL (Non-Performing Loan) 비용에는 이연이자, 기본이자, 변호사 비용, 법원 수수료, 유지 보수 비용 등이 포함됩니다. 따라서 재산의 형평은 배제를위한 모든 수수료와 소매 시장에서 재산을 재판매하는 것과 관련된 거래 비용을 충당 할만큼 충분해야합니다. 또는 판매자는 불량 자산을 전문으로하는 다른 펀드에 대량 판매를 통해 자산을 처분 할 수 있습니다. 2010 년 중반에이 NPL의 시장 가격은 부동산 가격의 65 % 이하였습니다. 이 수준은 NPL이 일반적으로 부동산 가치의 30 ~ 45 %에 판매 된 2007-2008 년 시장 축소의 초기 단계부터 NPL의 시장 가치가 크게 증가한 것으로 나타났습니다. 은행은 일반적으로 부실 채권 등 비 적립 자산을 장부에 보유하지 않습니다. 2 차 시장은 NPL을 매입하여 은행의 자본 확충을 가능하게합니다.

자산의 가격은 투자와 관련된 위험에 대해 상대적으로 투명한 지표입니다. 모기지 보험, 정부 보증 또는 자본 보조가 없기 때문에 위험 및 보상을 정상화하려는 시도에서 중등 시장에서 요구하는 수익률을 높일 수 있습니다. 다시 말하면, 투자자는 Agencies가 빌려주지 않는 사람에게 빌려주기 위해 자신의 가치가있게하기 위해 투자에서 얻는 것이 무엇일까요? 이 프리미엄은 대부가 신용 곡선에 얼마나 멀리 있는지의 함수입니다. 인지 된 리스크가 높을수록 투자자가 대출에 필요한 수익률이 높아집니다. 2015 년 말 현재이 프리미엄은 50 ~ 150bps입니다. 2000 년대 초반 / 중반기에는 대금업자가 궁극적으로 성과를 내지 못하는 많은 대출을 창출 했으므로 보험료는 650 bps까지 높아질 수있었습니다.

수익률은 시장의 위험 인식과 관련이 있습니다. 이것은 시장이 높은 위험 프리미엄을 부과하는 대출을 안전하게 창출 할 전문 지식을 보유한 포트폴리오 투자자에게 기회를 창출합니다. 이것은 포트폴리오에서 더 높은 수익을 올릴 수있는 기회를 제공 할뿐만 아니라 대출이 충분히 조미되고 대출 기관이 대출의 실제 성과를 보여줄 수있게되면 프리미엄으로 수익을 창출 할 수있는 기회가 될 수 있습니다. 2 차 시장.

대출 금액은 LTV, Doc 유형, FICO 등 모든 부분의 합계입니다. 어느 한 요소가 보상 요인이 될 수 있으며, 어느 하나가 딜레시브 킬러가 될 수 있습니다. 포트폴리오 공간에서는 일반적으로 다른 두 개가 보상 할만큼 충분히 강하다고 가정하고 한 장애를 수용하는 유연성을 가질 수 있습니다. 이것은 포트폴리오 대출의 기본 테마입니다 : 모기지 인수에 보상 요인을 사용할 수있는 능력. 2000 년대에는 BNC, Countrywide, New Century, First Franklin 및 Fremont와 같은 대금업자가 성공적으로 투자 부동산에 대한 100 % 자금 조달 또는 소유 자산에 대한 100 % 자금 조달을 580 건 또는 560 건 FICO, 오늘날의 환경에서 일반적으로 융통성이없는 신용 범위.

사례 연구.

대형 중서부 은행은 일반적으로 자영업자이며 세금 환급은 현금 영수증을 반영하지 않는 채무자가 집중되어있는 주요 캘리포니아시에 발자국을 가지고 있습니다. 이 은행은 도시의 매우 높은 부동산 가치를 비롯하여 실제 은행 예금이 세금 환급보다 대출 기관이 기대하는 것보다 훨씬 더 강력한 현금 흐름을 보여주기 때문에 차용자와 시장을 이해할 시간이 필요했습니다. 차용자의 현금 흐름에 대한 깊은 이해, 탁월한 부동산 가치, 최대 LTV 65 점, 가중 평균 LTV 61 점, 강력한 신용 점수의 결합으로 인해 은행은 신용 위험으로 인해 높은 수준의 편안함을 누릴 수있었습니다.

은행은 30 일간의 연체료 지불을하지 않고이 고객에게 수억 달러의 대출을 제공 할 수있는 일련의 지침을 만들었습니다. MIAC는 중개 시장에서이 대출의 구매자를 확인할 수있었습니다. 구매자는 전체 이야기가 전달되면 인수 및 대출 특성에 익숙해 질 수 있었고 결과적으로 우리는 은행으로부터 전체 대출을 구매할 투자자와의 거래 관계를 발전시킬 수있었습니다. 이 시장의 창설은 은행의 유동성을 향상시키고, 대출이 시장성이 있다는 은행 감독관에게 보여 주었고, 구매자에게 시장 수익률 이상의 대출 포트폴리오를 제공했습니다.

대출 경향.

2000 년대 패니 메이 (Fannie Mae)는 주택 소유자의 기회를 넓히기 위해 "빈곤 한"지역에서 차용자에게 대출을 확대 할 것을 정치인이 의무화했다. 내 커뮤니티와 같이 100 % LTV를 허용하고 경우에 따라 100 % 융자를 허용하는 대출이 생성되었습니다.

보 존되지 않은 지역은 왜 이러한 상황이 존재하는지에 관계없이 저소득 및 주택 소유율이 낮은 경향이있는 지리적 영역으로 정의됩니다.

자유 주의적 부채 - 소득 비율과 신용 요구 조건을 직접 결합하면 불안정성을 가장 적게 줄 수있는 이웃 지역의 부도율이 높아집니다. 부도율이 가속화되고 재산 가치가 침식되고 많은 지역의 악화가 가속화되기 시작했습니다.

정치적 압력이 커지면서 대출 기관은 더 많은 차용자, 특히 현재 기관 시장에서 서비스를 제공하지 않는 차용인에게 주택 담보 대출을 제공해야합니다. 우리는 FHA가 Sub Prime 시장과 경쟁하기 위해 정부에 의해 강요당한 것처럼 2000 년대 초반에 이런 일이 일어나는 것을 보았습니다. 정부 기관은 서브 프라임 대출 기관으로부터 사업을 복구하려는 시도에서보다 적은 선급금 또는 선금 없음으로 대출을 시작했습니다. 2015 년에 대행사는 100 % 자금 조달과 판매자 지불 비용을 다시 도입하여 차용자가 자신의 주머니에서 재정적으로 거의 또는 전혀 기여하지 않는 주택을 구입할 수 있도록했습니다.

기관 지침은 일반적으로 포트폴리오 대출 프로그램의 출발점이기 때문에 전체 대출 시장과 관련이 있습니다. 대출 기관은 더 많은 차용자와 더 높은 수익률을 끌어낼 수 있기를 바라면서 한 기관의 제품을 받아 신용 스펙트럼에서 한 가지 이상의 기준을 조금 더 높일 것입니다. 이는 연방 정부가 보장하는 것보다 약간 위험하다는 것입니다. 마지막 크레딧주기에서 Agencies는 가이드 라인을 완화하고 민간 및 공공 부문이 신용 경쟁에 나선 것은 재난을위한 처방입니다.

2 차 시장에서 이것이 의미하는 바는 수익률이 높아질수록 위험을 이해하고 시장 앞에서 빠져 나오기에 충분히 정교한 기금이 적시에 나왔다고 가정 할 때 유리하게 이익이된다는 것입니다. 우리는 2007-2008 년의 최근 모기지 산업이 위축되어 사후 적으로 이익을 얻었습니다. 현재 거의 제로에 가까운 금리로 인해 전통적인 투자로 펀드가 투자자에게 이익을 창출하는 것이 어려워졌습니다. 포트폴리오 관리자는 2010 년 중반에 시장에서 사용 가능한 모기지가 허용 가능한 위험 / 보상 관계를 제공하고 이러한 대출을 계속 구매할 용의가 있다고 판단했습니다. 은행은 다양한 이유로 대출을 계속해서 창출 할 것이며, 그 중 가장 중요한 것은 이익을 위해 대출을 구매할 적극적인 2 차 시장입니다.

비 기관용 틈새 제품.

외국 국민, 개인 세금 번호 (ITN) 융자 및 사회 보장 번호가없는 차용자에 대한 융자는 매우 구체적인 차용자의 하위 집합입니다. 미국 이외의 시민, Resident Aliens 등의 대출을 저지하고자하는 은행은 매우 구체적입니다. 이러한 대출은 전통적인 포트폴리오 대출보다 훨씬 안전한 위치에서 시작됩니다. 일반적으로 LTV는 최대 65 %이며, 문서 유형은 일반적으로 전체 문서 또는 문서화 된 보안 측정을 제공하는 일종의 자산 고갈 대부입니다. 궁극적으로 이러한 차용자의 성격을 이해하면 보안은 부동산에 있으며 부동산 처분을 통해 전체를 만들 가능성이 있습니다.

차용자는 일반적으로 휴스턴에있는 석유 산업과 같이 매우 특정한 산업에 속해 있습니다. 휴스턴에서는 포트폴리오 대출 업체가 이러한 대출을 창출하고이 인구를 위해 봉사하면서 추가적인 관계 비즈니스를 얻을 수 있습니다. 이 전략은 일반적인 모국 출신의 사람들이 자주 찾는 휴가 지역에서 계속됩니다. 예를 들어, 플로리다 주 마이애미에는 외국인이 소유 한 두 번째 집과 휴가 콘도가 집중되어 있습니다. 이러한 모기지를 창출 한 은행은 일반적으로 75bps 수율 프리미엄을 얻습니다.

또한 비 소비자 대출을 창출하는 대출 시장의 일부가 훨씬 작습니다. 이러한 대출은 소비자 금융 보호국 지침에서 면제됩니다. 비교적 규제가없는 시장은 일반적으로보다 정교하고 투자 도구와 같은 대출을 사용하는 매우 구체적인 고객에게 서비스를 제공합니다. 여기에는 12 개월 이내에 부동산을 재판매 할 목적으로 개조 또는 개조 사업에 참여하는 투자자가 포함됩니다. 투자 수익은 이러한 차용을 이러한 차용자에게 실질적인 도구로 만드는 요인입니다. 이 대출은 주거에 대해 발행되지 않습니다. 이러한 대출의 수익률은 전통적인 모기지 시장을 통해 일반적으로 500-800bps입니다.

보조 시장에서의 거래는 어떻게 생겼습니까?

최상의 실행은 일반적으로 담보물 및 부채에 익숙한 거래 상대방으로부터 발생하며 위의 시장 가격으로 구매하고자합니다. 이는 보유 자산의 다변화 압력이나 현재 포트폴리오의 수익률 부족 또는 마진에 따라 기관의 대출 포트폴리오의 전반적인 구성이 개선 될 것이라는 믿음 때문일 ​​수 있습니다.

즉, 상당한 비용 효율성은 두 당사자 사이의 유동 약정을 수립함으로써 만들어 질 수있다. 구매자와 판매자가 법률 문서를 협상하는 자원 집약적 인 과정을 거친 후에는 서로간에 미래 사업을하는 것이 더 쉽습니다. 정식으로 초안을 작성하거나, 보다 일반적으로는 임시 관계로 계약을 체결 할 수 있으며, 당사자들이 적절한 조건으로 적절하게 자주 거래 할 것이라는 상호 이해가 있습니다. 두 당사자가 거래에서 이익을 얻고 따라서 미래 거래와의 관계를 계속 유지할 동기가 있다고 생각합니다. 시장이 어느 당사자로부터 멀어지면 시장이 유리할 때까지 거래를 포기할 수있는 유연성이 있습니다.

대량 판매의 경우, 판매자는 "페이드"위험이 가장 적고 가장 효율적이거나 가장 가까운 마감 시간 인 가장 가까운 가격 및 보증 조합을 얻으려고합니다. 입찰자 풀에서 경험이 많은 구매자는 종종 최상의 실행을 제공합니다. 그것은 최고의 구매자가 최고의 구매자라는 말은 아닙니다. 무역 잠김의 의도로 상기 시장 입찰을 제출할 입찰자로 알려져 있습니다. 그런 다음 입찰자는 판매자를 다시 거래하거나, 대출을 중개하거나, 거래를 완료하기 위해 기금을 모으거나, 완전히 사라지려고 시도합니다. 이 중 재 거래가 가장 일반적인 불리한 결과입니다. 입찰자는 파일을 더 깊이 파고 그들이 생각한 바가 아니라는 것을 깨닫게됩니다. 모든 입찰자가 동일한 정보를 가지고 있기 때문에 이것은 일반적으로 판매자의 잘못이 아닙니다. 이 예제의 입찰자는 개별 지형, 대출 유형, 조미료 또는 그들이 생각한 것과 다른 담보 여부와 상관없이 개별 수영장의 뉘앙스를 이해하는 경험이 없었습니다. 이것은 많은 기업들이 많은 관련 경험없이 거래를 쫓아 시장을 침수 시켰기 때문에 2000 년대 후반에 특히 보편화되었습니다. 이 사건의 빈도는 판매자가 바보 취급을 당하고 구매자가이 거래를 종료한다고 생각했던 것처럼 쉬운 일이 아니란 것을 알면서 공간에서 빠져 나올 때 시장을 불안정하게 만들었습니다.

비 기관 대출을위한 2 차 시장.

담보 대출은 모기지 담보부 증권 (MBS) 또는 전체 대출로 판매됩니다. MBS의 구매자는 모기지 론으로 뒷받침되는 보안을 구매하고 있습니다. 전체 대출 구매자는 실제 대출, 메모 및 모기지를 구매합니다. 전체 대출은 더 많은 수익을 낼 가능성이 있지만, 구매자는 대출, 서비스 유지 보수를 판매하지 않는 한 서비스를 제공하고 실사를 수행 할 인프라가 있어야합니다.

일부 덜 정교한 전체 대출 구매자는 풀에 번호를 던져서 효과를 내기를 희망합니다. 스펙트럼의 다른 끝에서, 구매자는 입찰을 제출하기 전에 대출에 대해 거의 완전한 실사를 수행합니다. 가장 좋은 가장 정확한 입찰가를 가질 수 있기 때문에 두 가지 방법 모두를 줄일 수 있지만 입찰하는 사람들은 더 높은 입찰가를 제공 할 수있는보다 넓은 오류 마진을 갖습니다. 미납 원금의 74 %에서 92 %에 이르는 대출 풀에 대해 입찰가를 보는 것은 드문 일이 아닙니다.

전체 대부 거래의 대다수는 Aggregators (Wells Fargo 또는 Chase와 같은 은행, Agencies에 직접 판매 할 수 있음) 또는 Agencies (Quicken과 같은)의 대규모 원조 업체에서 매일 또는 대량으로 제공합니다. 이 상품이 제공하는 더 높은 수익률을 추구하는 다른 기관, 기금, 은행 등에 판매하려는 비 기관 대출을 창출하는 모기지 은행 및 소매 또는 상업 은행 형태의 추가 출처 채널이 있습니다. 이러한 비 기관별 거래는 거래 규모에 따라 다릅니다. 대형 커뮤니티 뱅크가 모기지가 필요한 펀드 나 은행에이 제품을 매월 10-15 밀리미터의 돈으로 원천 징수하는 것은 드문 일이 아니지만이 필요를 채울 수있는 창구가 없습니다.

함께 거래를 퍼팅.

최고의 거래 상대방 (구매자 또는 판매자)을 식별하는 데 가장 적합한 회사는 시장 활동에 가장 많이 노출 된 회사입니다. 여기에는 대행사 거래, 가격 결정 실행, 포트폴리오 평가 및 포트폴리오 헤징이 포함됩니다. 사업 활동의 폭이 넓어 대부분의 창업자, 펀드, 은행 및 포트폴리오 회사와 자주 접촉하는 MIAC만큼의 활동을하는 회사와 협력하는 것이 중요합니다.

귀하가 함께 일하는 회사는 주어진 거래에 대해 최고의 실행을 식별하고 참여시키는 데 필요한 시장 정보를 수집해야합니다. 여기에는 매수와 매도 측면에서 기업의 비즈니스 모델과 가격 요구 사항 및 투자자의 "식욕"에 대한 친밀한 이해가 포함됩니다. 일부 거래자는 기껏해야 투자자의 목표에 대해 이해하기 쉽습니다. 이해의 깊이가 부족한 경우가 많으므로 (많은 수의 자격을 갖추지 않은 바이어에게 보여지는) 널리 퍼진 판매로 인해 많은 심각한 자금으로 피할 수있는 거래가됩니다.

최상의 실행은 가격 및시기면 에서뿐만 아니라 포트폴리오의 뉘앙스에서도 판매자의 요구 사항을 완전히 이해했기 때문에 발생합니다. 거래하는 거래업자는이를 이해하고 무역에 중점을두고 소량의 심각한 구매자와 거래를 일치시켜 매우 높은 보증 등급으로 마감 할 수있는 페이드가없는 입찰을해야합니다.

똑같이 중요한 것은 현재의 시장을 이해하는 것입니다. 타이밍 고려 사항, 구매자의 거래 집중력에 영향을 미치는 외부 이벤트 및 기타 시장 고려 사항이 있습니다. 2015 년 말과 같은 연방 준비 제도 이사회 (Federal Reserve policymaking)의 전환점은 "Fed가 할 일"에 대한 투자자의 의견에 따라 입찰 수준이 크게 달라질 수 있음을 보여줍니다. 대량 시장을 "폭발"시키는 것은 거의 불가능합니다. 어떤 인터넷 기반 기업들이 현재 진행중인 거래를 희생시키면서 미래의 거래를위한 연락 창구를 늘리기 위해 어떤 일을 할 것인가하는 것입니다. 최고의 거래는 인터넷이 아닌 관계와 경험을 통해 실행됩니다. 전화 나 회의로 많은 것을 성취해야합니다.

보조 시장 풀 크기.

대형 풀은 전형적으로 전형적인 10 달러 거래와 다른 잠재 고객을 끌어들입니다. 대출 거래와 관련된 특정 고정 비용이 있습니다. 예를 들어, 법률 부서는 거래가 단일 대출인지 100 개 대출인지에 관계없이 동일한 문서를 작성합니다. 구매자가 실사와 종결을위한 인력 시간을 계획 할 때 존재하는 일정 규모의 경제가 있습니다. 이것은 투자 할 자본이있는 구매자에게 더 큰 거래를 매력적으로 만듭니다. 대형 거래 시장 참가자는 일반적으로 트랜잭션을 수행 할 때보다 정교하고 경험이 풍부하며 시장에 대한 정확한 이해와 가격 책정을하는 경향이 있습니다.

크기는 또한 강력한 구매자가 판매자에게 특히 매력적인 거래를 산출 할 수있는 경매에 참여하지 못하게 할 수 있습니다. 이 경우 가끔 판매자의 풀 조각이이 입찰자에게 판매 될 수 있으며 나머지는 다른 당사자가 다시 입찰 할 수 있습니다. 이 전략으로 가장 바람직한 자산이 새겨 져 있기 때문에 이전의 높은 입찰자가 무역에서 굴절 할 위험이 있습니다. 또는 다수의 거래 당사자와의 거래를 보증하기에 충분한 스프레드가없는 경우 "모두 또는 없음"판매가 실행됩니다. 이 경우 판매자는 여러 거래 상대방의 전체 대출 상인 또는 중개인을 통해 입찰가를 자주 청구합니다. 최종 입찰 프로세스는 잠재적 인 구매자에게 최후의 최우수 실행 제안으로 풀에서 우승 할 수있는 마지막 기회를 제공합니다.

여러 파트너와 풀을 거래함으로써 거래 상대방 다변화에 약간의 가치가 있습니다. 그러나 위에 논의 된 바와 같이 거래 비용이 명백히 증가합니다. 내 경력에는 계약이 잘못되어 전혀 예상 할 수없는 이유로 거래되지 않은 경우가 있습니다. 구매자가 여러 명 있었다면 거래는 한쪽이나 다른 쪽에서 끝났을 것입니다.

Brendan Teeley, Whole Loan Sales & amp; 무역, MIAC 자본 시장.

MIAC Perspectives & # 8211; 2016 년 여름.

전체 대출 실행.

이 포스트를 공유하십시오.

최근 게시물.

카테고리.

자산 판매 & amp; 자문.

5 억 7 천만 달러의 에이전시 서비스 제공.

$ 339 백만 기관 제공 서비스.

최근 뉴스.

MIAC Perspectives & # 8211; 2017 년 가을.

전체 대출 실행 업데이트.

MIAC 이벤트.

MIAC Analytics Secondary Market Webinar Series: Sell Side Trading Tool On Demand Webinar.

이벤트 세부 정보.

Register now to receive the.

이벤트 세부 정보.

Register now to receive the materials from our free Secondary Solutions Webinar Series:

Best execution between bid tapes, AOT/DT, cash/ratesheet, MBS executions when selling loans How to create commitments and allocate loans to commitments automatically How to enable bulk bidding capability, which includes selection of eligible loans, automated generation of bulk bid files, incorporation of investor eligibility criteria, and automated of bulk bids Direct integration to FNMA Cash Window for fast, simple and secure executions.

How can your institution utilize these new features?

Tina Freeman, CPA, Managing Director of MIAC’s Secondary Solutions Group , and Bradley Eskridge, SVP, Secondary Solutions Group , will present an overview of MarketShield’s already robust best-execution functionality which includes MBS, Direct/AOT trades, Co-Issue Contracts, servicing released/retained decisions, as well as user-customizable delivery timelines, warehouse carry, delivery fees and execution bias.

The webinar has ended. You can request a personalized demo by completing the form below:

Year Around Event (2017) EST.

Organizer.

자세히 알아보십시오.

30 dec - 31 dec 30 1:00 am dec 31 IMN's 2nd Annual Mortgage Notes & NPL/RPL Forum (East) January 30 - 31 | Fort Lauderdale, FL.

이벤트 세부 정보.

Meet with MIAC's.

이벤트 세부 정보.

Contact us to schedule a meeting.

Ritz Carlton Fort Lauderdale.

1 North Fort Lauderdale Beach Boulevard.

Fort Lauderdale, Florida 33304.

30 (Saturday) 1:00 am - 31 (Sunday) 1:00 am ET.

Ritz Carlton Fort Lauderdale.

1 North Fort Lauderdale Beach Boulevard, Fort Lauderdale, Florida 33304.

자세히 알아보십시오.

21 mar - 23 mar 21 8:00 pm mar 23 MIAC's 9th Annual Secured Financing Conference March 21 - 23 | Scottsdale, AZ.

이벤트 세부 정보.

MIAC's 9th Annual.

이벤트 세부 정보.

MIAC’s 9 th Annual Secured Financing Conference.

On March 21 st – 23 rd , MIAC Analytics will host its 9 th Annual Secured Financing Conference at the luxury Phoenician Hotel.

The event is well attended by Secured Finance professionals representing a myriad of banks and institutions from across the country every year: MIAC’s Perspective is unique.

The Secured Financing Conference is part of a series of events MIAC hosts annually for Clients. MIAC offers solutions to financial institutions for risk management, asset/liability management, asset valuation and brokerage.

Agenda Topics.

Modeling Borrower Behavior: Understanding How Borrower Analytics Has Evolved to Improve Portfolio Performance.

Residential Whole Loan Market Update: Current Trends and Annual Forecast.

Preparing for CECL: Overview, Implementation & Best Practices.

Historical Loan Performance Data Mining and Similar Cohort Analysis.

Commercial Whole Loan Market Update: Current Trends and Annual Forecast.

6000 East Camelback Road.

Scottsdale, Arizona 85251.

MIAC Guest Room Rates Available.

Marketing Analyst, Capital Markets Group.

(212) 233 -1250 x225.

21 (Wednesday) 8:00 pm - 23 (Friday) 12:00 pm MST.

RELATED APPLICATION.

This application is a divisional of U. S. patent application Ser. No. 12/533,315, filed Jul. 31, 2009, which claims the benefit of U. S. Provisional Patent Application No. 61/191,011, filed Sep. 3, 2009, both of which are hereby fully incorporated herein by reference.

TECHNICAL FIELD.

The present invention relates generally to systems and methods for optimizing loan trading and more specifically to computerized systems and computer implemented methods for optimizing packages of whole loans for execution into bonds or sale as whole loan packages.

배경.

Financial institutions, such as investment banks, buy loans and loan portfolios from banks or loan originators primarily to securitize the loans into bonds and then sell the bonds to investors. These bonds are considered asset-backed securities as they are collateralized by the assets of the loans. Many types of loans can be securitized into bonds, including residential mortgages, commercial mortgages, automobile loans, and credit card receivables.

A variety of bond structures can be created from a population of loans, each structure having characteristics and constraints that need to be accounted for in order to maximize the profit that a financial institution can realize by securitizing the loans into bonds. The optimal grouping or pooling of loans into bonds for a given bond structure and a given loan population can depend on the characteristics of each loan in the population. Furthermore, the bond pool or execution coupon that an individual loan executes into can depend on the bond pool or best execution of each other loan in the population. As the typical loan population considered for securitizing into bonds is very large (e. g. 10,000 loans or more), determining an optimal pooling of loans for securitizing into bonds can be challenging.

Accordingly, what is needed are systems and methods for optimizing the packaging of a population of loans into bonds for a given bond structure.

The invention provides computerized systems and computer implemented methods for optimizing fixed rate whole loan trading for a population of whole loans.

An aspect of the present invention provides a system for optimizing fixed rate whole loan trading. This system includes a computing system that includes a software application including one or more modules operable to develop a model for determining a securitization strategy for a population of whole loans, the securitization strategy including bonds; and operable to process the model until an optimal securitization strategy for the population of whole loans is found; and a user interface for receiving user input for the one or more modules and for outputting the optimal securitization strategy, the user interface being in communication with the software application.

Another aspect of the present invention provides a computer-implemented method for determining an optimal execution bond coupon for each loan in a group of loans in a senior/subordinate bond structure. The method includes creating a model comprising an objective function representing a total market value of the senior/subordinate bond structure for the loans. Further, the method includes maximizing the objective function to maximize the total market value of the senior/subordinate bond structure.

Another aspect of the invention provides a computer-implemented method for optimally pooling loans into pass through bond pools. The method includes selecting a population of loans. Further, for each loan of the selection population of loans, the method includes determining an optimal execution of each loan by a buy up or a buy down of guarantee fee. Further, the method includes determining one or more pools for which each loan is eligible. In addition, the method includes building a model based on at least one constraint for at least one determined pool; and allocating loans to the one or more pass through bond pools.

Another aspect of the invention provides a system including a memory that has a set of instructions for allocating a portion of a group of loans to a loan package. Further, the system includes a computer coupled to the memory. Upon execution of the set of instructions the computer determines which of the loans meet one or more constraints of the loan package. In addition, the computer determines a market price of each of the loans based on a securitization model. Further, the computer can model an objective function to determine which loans in the group of loans that meets the one or more constraints are least profitable for securitization in the securitization model; and allocate the loans that meets the one or more constraints and are least profitable for securitization into the loan package.

Another aspect of the present invention provides a method for optimizing fixed rate whole loan trading. This method includes the steps of selecting a population of loans; selecting one or more loans that meet a constraint of a bid; determining a price of each loan that meets the constraint based on a securitized model; determining whether to use an efficient model to select which of the one or more loans are least favorable to be securitized. Further, if the efficient model is used, the method includes selecting which of the pone or more loans are least favorable to be securitized by minimum dollar value of spread.

Another aspect of the present invention provides a system for optimally pooling excess coupon resulting from securitizing loans. The system includes and network and a computer communicable coupled to the network. Further, the computer creates a model corresponding to excess coupon bond pools and an unallocated pool, each excess coupon bond pool including at least one constraint; and processes the model to allocate each of the loans into either an excess coupon bond pool or into the unallocated pool in order to maximize the total market value of the excess coupon that gets allocated to the excess coupon bond pools.

These and other aspects, features and embodiments of the invention will become apparent to a person of ordinary skill in the art upon consideration of the following detailed description of illustrated embodiments exemplifying the best mode for carrying out the invention as presently perceived.

BRIEF DESCRIPTION OF THE DRAWINGS.

For a more complete understanding of the exemplary embodiments of the present invention and the advantages thereof, reference is now made to the following description, in conjunction with the accompanying figures briefly described as follows.

FIG. 1 is a block diagram depicting a system for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention.

FIG. 2 is a flow chart depicting a method for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention.

FIG. 3 is a flow chart depicting a method for determining a securitization strategy for a population of loans in accordance with one exemplary embodiment of the present invention.

FIG. 4 is a flow chart depicting a method for packaging a population of loans into a senior/subordinate structure in accordance with one exemplary embodiment of the present invention.

FIG. 5 is a flow chart depicting a method for packaging a population of loans into a senior/subordinate structure in accordance with one exemplary embodiment of the present invention.

FIG. 6 is a flow chart depicting a method for packaging a population of loans into pass through bonds in accordance with one exemplary embodiment of the present invention.

FIG. 7 is a flow chart depicting a method for packaging whole loans in accordance with one exemplary embodiment of the present invention.

FIG. 8 is a flow chart depicting a method for pooling excess coupon in accordance with one exemplary embodiment of the present invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS.

The invention provides computer-based systems and methods for optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a senior/subordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized. Although the exemplary embodiments of the invention are discussed in terms of whole loans (particularly fixed rate residential mortgages), aspects of the invention can also be applied to trading other types of loans and assets, such as variable rate loans and revolving debts.

The invention can comprise a computer program that embodies the functions described herein and illustrated in the appended flow charts. However, it should be apparent that there could be many different ways of implementing the invention in computer programming, and the invention should not be construed as limited to any one set of computer program instructions. Further, a skilled programmer would be able to write such a computer program to implement an embodiment of the disclosed invention based on the flow charts and associated description in the application text. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use the invention. The inventive functionality of the claimed computer program will be explained in more detail in the following description read in conjunction with the figures illustrating the program flow. Further, it will be appreciated to those skilled in the art that one or more of the stages described may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems.

Turning now to the drawings, in which like numerals represent like elements throughout the figures, aspects of the exemplary embodiments will be described in detail. FIG. 1 is a block diagram depicting a system 100 for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. Referring to FIG. 1, the system 100 includes a computing system 110 connected to a distributed network 140 . The computing system 110 may be a personal computer connected to the distributed network 140 . The computing system 110 can include one or more applications, such as loan trading optimizer application 120 . This exemplary loan trading optimizer 120 includes four modules 121 - 124 that can operate individually or interact with each other to provide an optimal packaging of loans into one or more bond structures and whole loan packages.

A senior/subordinate module 121 distributes loans into a senior/subordinate bond structure with bonds having different credit ratings and different net coupon values. As will be discussed in more detail with reference to FIGS. 4-5, the senior/subordinate module 121 distributes the loans into bonds having a AAA rating, subordinate bonds with lower credit ratings, and, depending on the loans and the coupon values of the AAA bonds and the subordinate bonds, interest only bonds and principal only bonds.

A pass-thru module 122 distributes loans into pass through bonds guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The pass-thru module 122 optimally pools the loans into To Be Announced (TBA) pass through securities based on a variety of constraints. The pass-thru module 122 is discussed in more detail below with reference to FIG. 6.

A whole loan module 123 allocates loans to meet bids for loan portfolios meeting specific requirements and constraints of the bid. The whole loan module 123 can interact with either the senior/subordinate module 121 or the pass-thru module 122 to allocate loans that meet the requirements of the bids but are less favorable to be securitized. The whole loan module 123 is discussed below in more detail with reference to FIG. 7.

An excess coupon module 124 distributes excess coupons of securitized loans into different bond tranches or pools. The excess coupon module 124 can pool excess coupons resulting from senior/subordinate bond structure created by the senior/subordinate module 121 and/or excess coupons resulting from pass through securities created by the pass-thru module 122 . The excess coupon module 124 is discussed below in more detail with reference to FIG. 8.

Users can enter information into a user interface 115 of the computing system 110 . This information can include a type of bond structure to optimize, constraints associated with bond structures and bond pools, information associated with loan bids, and any other information required by the loan trading optimizer 120 . After the information is received by the user interface 115 , the information is stored in a data storage unit 125 , which can be a software database or other memory structure. Users can also select a population of loans to consider for optimization by way of the user interface 115 . The loans can be stored in a database stored on or coupled to the computing system 110 or at a data source 150 connected to the distributed network 140 . The user interface 115 can also output to a user the bond packages and whole loan packages determined by the loan trading optimizer 120 .

The loan trading optimizer 120 can communicate with multiple data sources 150 by way of the distributed network 140 . For example, the loan trading optimizer 120 can communicate with a data source 150 to determine Fannie Mae TBA prices and another data source 150 to determine U. S. Treasury prices. In another example, the loan trading optimizer 120 can communicate with a data source 150 to access information associated with bids for whole loan packages. The distributed network 140 may be a local area network (LAN), wide area network (WAN), the Internet or other type of network.

FIG. 2 is a flow chart depicting a method 200 for optimizing fixed rate whole loan trading in accordance with one exemplary embodiment of the present invention. Referring to FIGS. 1 and 2, at step 205 , the user interface 115 receives input from a user. This user input is used by the loan trading optimizer 120 to determine the bond structure that should be optimized for a population of loans. For example, if the user desires to find the optimal pooling of loans for pass through bonds, the user can input the constraints for each bond pool. Examples of constraints for pass through bond pools include constraints on loan balances, total number of loans for a pool, and total loan balance for a pool.

At step 210 , a population of loans is selected for optimization. The population of loans can be selected from loans stored in a loan database stored on or coupled to the computing system 110 or from a database at a data source 150 connected to the distributed network 140 . The population of loans can include loans currently owned by the user (e. g. investment bank) of the loan trading optimizer 120 and/or loans that are up for bid by another bank, loan originator, or other institution. For example, a user may employ the loan trading optimizer 120 to find the maximum market value of a loan portfolio currently for sale in order to determine an optimal bid for the loan portfolio. Additionally, a user can select the population of loans by specifying certain criteria, such as maximum loan balance, location of the loans, and FICO score.

At step 215 , the loan trading optimizer 120 determines a securitization strategy for the population of loans selected in step 210 . Depending upon the user inputs received in step 205 , the loan trading optimizer 120 employs one or more of the senior/subordinate module 121 , the pass-thru module 122 , and the whole loan module 123 to determine the securitization strategy for the population of loans. Step 215 is discussed in more detail with reference to FIGS. 3-7.

At step 220 , the loan trading optimizer 120 determines whether the securitization strategy returned at step 215 is of sufficiently high quality. In this exemplary embodiment, the loan trading optimizer 120 iterates the step of determining a securitization strategy for the population of loans until either an optimal solution is found or the user determines that the securitization strategy is of sufficiently high quality. In order for the user to determine if the securitization strategy if of sufficient high quality, the loan trading optimizer 120 can output the results to the user by way of the user interface 115 . The loan trading optimizer 120 can output these results based on a number of iterations of step 215 (e. g. every 100 iterations) or when a certain level of quality is found. The user interface 115 can then receive input from the user indicating whether the securitization strategy is of sufficient high quality. If the securitization strategy is of sufficient high quality or optimal, the method 200 proceeds to step 225 . Otherwise, the method 200 returns to step 215 .

In one exemplary embodiment, quality is measured in terms of the total dollar value of the population of loans. For example, the user may desire to sell a population of loans for at least ten million dollars in order to bid on the loans. The user can set a threshold for the loan trading optimizer 120 to only return a solution that meets this threshold or a solution that is the optimal solution if the optimal solution is below this threshold.

At step 225 , the excess coupon module 124 of the loan trading optimizer 120 can pool any excess coupon resulting from the securitization strategy determined in step 215 . This step is optional and is discussed below in more detail with reference to FIG. 8.

At step 230 , the loan trading optimizer 120 communicates the final securitization strategy to the user interface 115 for outputting to a user. The user interface 115 can display the final securitization strategy and optionally other possible securitization strategies with similar quality levels.

FIG. 3 is a flow chart depicting a method 215 for determining a securitization strategy for a population of loans in accordance with one exemplary embodiment of the present invention. Referring to FIGS. 1 and 3, at step 305 , the loan trading optimizer 120 determines which models to use for determining the securitization strategies. In this exemplary embodiment, the loan trading optimizer 120 includes a senior/subordinate module 121 , a pass-thru module 122 , and a whole loan module 123 . Each of the modules 121 - 123 can build and process a model for determining an optimal packaging of loans as discussed below. The loan trading optimizer 120 determines which modules 121 - 123 to use based on the input received from the user in step 205 of FIG. 2. For example, the user may specify that only a senior/subordinate structure should be optimized for the population of loans. Alternatively, if the user has entered bid information for a portfolio of whole loans, the loan trading optimizer 120 can execute the whole loan module 123 with the senior/subordinate module 121 and/or the pass-thru module 122 to determine which of the loans meet the requirements of the bid and are least favorable for securitization. Additionally, a user may specify that both an optimal senior/subordinate bond structure and an optimal pooling of pass through bonds should be determined for the population of loans.

If the user selected that a senior/subordinate bond structure should be optimized, the method 215 proceeds to step 310 . At step 310 , the senior/subordinate module 121 develops a model for packaging the population of loans into a senior/subordinate bond structure and processes the model to determine an optimal senior/subordinate bond structure for the loan population. Step 310 is discussed in more detail with reference to FIGS. 4 and 5. After the senior/subordinate structure is determined, the method 215 proceeds to step 220 (FIG. 2).

If the user selected that the population of loans should be optimally pooled into pass through bonds, the method 215 proceeds to step 315 . At step 315 , the pass-thru module 122 develops a model for pooling the population of loans into multiple bond pools and processes the model to determine an optimal pooling for the loan population. Step 315 is discussed in more detail with reference to FIG. 6. After the pooling is determined, the method 215 proceeds to step 220 (FIG. 2).

If the user selected that whole loans should be allocated to a package of whole loans to be sold, the method 215 proceeds to step 320 . At step 320 , the whole loan module 123 develops a model for allocating whole loans that meet certain constraints and are less favorable to be securitized into a whole loan package and processes the model to determine which loans are best suited for the whole loan package. Step 320 is discussed in more detail with reference to FIG. 7. After the whole loan package is determined, the method 215 proceeds to step 220 (FIG. 2).

FIG. 4 is a flow chart depicting a method 310 for packaging a population of loans into a senior/subordinate bond structure in accordance with one exemplary embodiment of the present invention. As briefly discussed above with reference to FIG. 1, a senior/subordinate bond structure is a structure where bonds with different credit ratings are created. Typically, the senior/subordinate bond structure includes a senior tranche of bonds having a AAA or similar credit rating and a subordinate tranche of bonds having a lower credit rating. The senior tranche is protected from a certain level of loss by the subordinate tranche as the subordinate tranche incurs the first losses that may occur. The senior trance can be sold to investors desiring a more conservative investment having a lower yield, while the subordinated tranche can be sold to investors willing to take on more risk for a higher yield. For the purpose of this application, a AAA rated bond refers to a bond in the senior tranche, but not necessarily a bond having a credit rating of AAA.

Additionally, interest only (IO) and principal only (PO) bonds may be created in a senior/subordinate structure. An IO bond is created when the net coupon of a loan is more than the coupon of the bond in which the loan executes. Thus, the difference in the loan coupon and the bond coupon creates an interest only cash flow. Similarly, when the loan coupon is less than the bond coupon, a PO bond is created which receives only principal payments.

Referring to FIGS. 1 and 4, at step 405 , the senior/subordinate module 121 determines the bond coupons that are available for executing the loans into. The senior/subordinate module 121 may obtain the available bond coupons from a data source 150 or may receive the available bond coupons from the user by way of the user interface 115 in step 205 of FIG. 2. For example, the user may desire to execute the loans into bonds having coupon values between 4.5% and 7.0%.

At step 410 , the senior/subordinate module 121 selects a first bond coupon value from the range of available bond coupon values. This first coupon value can be the lowest bond coupon value, the highest coupon value, or any other bond coupon value in the range of available bond coupon values.

At step 415 , the senior/subordinate module 121 determines the execution price of each loan in the population of loans at the selected coupon value. Each loan in the population of loans is structured as a bond. The cash flow of each loan is distributed into symbolic AAA and subordinate bonds, and depending on the coupon of the loan and the selected bond coupon, an IO or PO bond. The principal payment and interest cash flows of each loan is generated in each period accounting for loan characteristics of the loan, such as IO period, balloon terms, and prepayment characteristics. The cash flow generated in each period is distributed to all bonds that the loan executes taking into account shifting interest rules that govern the distribution of prepayments between the AAA and the subordinate bonds in each period. The proportion in which the principal payments are distributed depends on the subordination levels of the AAA and the subordinate bonds. The subordination levels are a function of the loan attributes and are supplied by rating agencies for each loan through an Application Program Interface (API) coupled to the computing device 110 . Prepayments are first distributed pro rata to the PO bond and then between the AAA and the subordinate bonds based on the shifting interest rules. Any remaining prepayment is distributed proportionally among all the subordinate bonds. The interest payment for each of the bonds is a direct function of the coupon value for the bond.

After the cash flows of each of the bonds for each of the loans have been generated, the present value of these cash flows is determined. For fixed rate loans, the AAA bonds can be priced as a spread to the To Be Announced (TBA) bond prices. However, the subordinate bond cash flows are discounted by a spread to the U. S. Treasury Yield Curve. The IO and PO bonds are priced using the Trust IO and PO prices. Finally, the price of the AAA bond, the subordinate bonds, and the IO or PO bond is combined proportionally for each loan based on the bond sizes to get the final bond price for each loan. This final bond price is the price of the loan executing into the bond given the selected coupon value of the bond.

At step 420 , the senior/subordinate module 121 determines if there are more bond coupon values in the range of available bond coupon values. If there are more bond coupon values, the method 310 proceeds to step 425 . Otherwise, the method 310 proceeds to step 430 .

At step 425 , the next bond coupon value in the range of available bond coupon values is selected. In one exemplary embodiment, the senior/subordinate module 121 can increment from the previous selected bond coupon value (e. g. 0.5% increments) to determine the next bond coupon value. In an alternative embodiment, the senior/subordinate module 121 can progress through a fixed list of bond coupon values. For example, the user may select specific bond coupon values to execute the loans into, such as only 4.0%, 5.0%, and 6.0%. After the next bond coupon value is selected, the method 310 returns to step 415 to determine the execution price of each loan in the population of loans at the new coupon value.

At step 430 , the senior/subordinate module 121 determines, for each loan in the population of loans, which bond coupon value yielded the highest final bond price for that particular loan.

At step 435 , the senior/subordinate module 121 groups the loans according to the bond coupon value that yielded the highest final bond price for each loan. For example, if the available bond coupon values are 4.0%, 5.0%, and 6.0%, each loan that has a highest final bond price at 4.0% are grouped together, while each loan that has a highest final bond price at 5.0% are grouped together, and each loan that has a final bond price at 6.0% are grouped together. After step 435 is complete, the method proceeds to step 220 (FIG. 2).

In the embodiment of FIG. 4, the subordinate bonds for each loan execute at the same bond coupon value as the corresponding AAA bond. For example, if a first loan of 6.25% best executes into a bond having a coupon value of 6.0%, then a AAA bond of 6.0% and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 6.0% is created. If a second loan of 5.375% best executes into a bond having a coupon value of 5.0%, then a AAA bond of 5.0% and a subordinate bond that is priced at U. S. Treasury spreads specified for execution coupon 5.0% is created. This creates two AAA bonds and two subordinate bonds at two different coupon values.

Typically, when loans are packaged in a senior/subordinate bond structure, multiple AAA bonds with multiple coupon values are created with a common set of subordinate bonds that back all of the AAA bonds. This set of subordinate bonds is priced at the weighted average (WA) execution coupon of all of the AAA bonds created for the loan package. Pricing the subordinate bonds at the WA execution coupon implies that the spread to the benchmark U. S. Treasury curve, which is a function of the bond rating and the execution coupon of the subordinate bond, has to be chosen appropriately. In order to know the WA execution coupon of all the AAA bonds for the population of loans, the best execution coupon for each loan in the population of loans has to be known. In order to know the best execution coupon of each loan, the loan has to be priced at different bond coupon values and the AAA and subordinate bonds created at those coupons also have to be priced. However, the subordinate bond cash flows are discounted with spreads to the U. S. Treasury, with spreads taken at the WA best execution coupon which is still unknown. This creates a circular dependency as the best execution of each loan in the population of loans now depends on all the other loans in the population.

FIG. 5 is a flow chart depicting a method 500 for packaging a population of loans into a senior/subordinate structure in accordance with one exemplary embodiment of the present invention. The method 500 is an alternative method to that of method 310 of FIG. 4, accounting for pricing subordinate bonds at the WA execution coupon and provides a solution to the circular dependency discussed above.

The WA execution coupon for a population of loans can be calculated by:

In Equation [1], x ij is a binary variable with a value of either “0” or “1,” whereby a value of “1” indicates that the i th loan is optimally executing at the j th execution coupon value. The parameters d 0 to d j represent the j execution coupon values. For example, the coupons values could range from 4.5% to 7.0%. Finally, the parameter b i represents the balance of the i th loan.

If q o to q j are the weights of the j execution coupons, then:

where q 0 to q 1 are special ordered sets of type two, which implies that at most two are non-zero and the two non-zero weights are adjacent.

Let Pa ij be the price of the AAA bond when loan i executes at coupon j. Next, let Ps ij be the overall price of all of the subordinate bonds combined when loan i executes at coupon j. Finally, let Pio ij and Ppo ij be the prices of the IO and PO bonds respectively when loan i executes at coupon j.

The AAA bond prices and the IO and PO bond price components of loan i executing at coupon j are linear functions of x ij . The AAA priced as a spread to the TBA is a function of the execution coupon of the AAA bond and the IO/PO prices are a lookup based on collateral attributes of the loan. However, pricing the subordinate bonds is complicated because the subordinate cash flows are discounted at the WA execution coupon.

Let P i be a matrix of size j*j that contains the prices of the subordinate bonds. The (m, n) entry of the matrix represents the price of the subordinate cash flows when the cash flow of loan i is generated assuming that loan i executes at the m th coupon and is discounted using subordinate spreads for the n th coupon. Subordinate spreads to the U. S. Treasury are a function of the execution coupon and any product definition, such as the size (e. g. Jumbo/Conforming), maturity (e. g. 15/30 years), etc. The price of the subordinate bond of the i th loan can be written as:

which is a non linear expression as the equation contains a product of q and x ij , both of which are variables in this equation.

FIG. 5 provides a method 500 for overcoming this non-linearity. Referring to FIG. 5, at step 505 , the senior/subordinate module 121 determines the optimal execution price for each loan in the population of loans independent of the WA execution coupon. In one exemplary embodiment, the senior/subordinate module 121 employs the method 310 of FIG. 4 to find the optimal execution price for each loan.

At step 510 , the senior/subordinate module 121 determines the WA execution coupon corresponding to the optimal execution price for each loan. This WA execution coupon can be found using Equation [1] above.

At step 515 , the senior/subordinate module 121 determines the weights (i. e. q 0 −q j ) of each execution coupon for the WA execution coupon found in step 510 . These weights can be found using Equation [3] above.

At step 520 , the senior/subordinate module 121 builds a model including an objective function to determine the optimal execution coupon for each loan to maximize the total market value of all of the bonds in the senior/subordinate structure. The expression of the objective function contains i*j terms, where the ij term represents the market value of executing the i th loan at the j th execution coupon. After inserting the values of the weights of the execution coupons (i. e. q's) into the expression for subordinate bond price (Equation [4]), only two of the terms will be non-zero for the sub-price of the i th loan executing at the j th execution coupon.

As the method 200 of FIG. 2 iterates step 215 , different WA execution coupons can be used to maximize the objective function. The iterations can begin with the WA execution coupon found in step 510 and the senior/subordinate module 121 can search around this WA execution coupon until either the optimal solution is found or the user decides that a solution of sufficient high quality is found in step 220 of FIG. 2. In other words, the senior/subordinate module 121 searches for an optimal solution by guessing several values of the WA execution coupon around an initial estimate of the optimal execution coupon. After a final solution is found by the senior/subordinate module 121 , the loans can be grouped based on the coupon values for each loan in the final solution to the objective function.

In some instances, one of the undesirable effects of the senior/subordinate bond structure is the creation of IO and/or PO bonds, which may not trade as rich as AAA bonds. In some exemplary embodiments, the senior/subordinate module 121 can ameliorate this issue by considering a loan as two pseudo loans. For example, a loan having a net rate of 6.125% and a balance of $100,000 can be considered equivalent to two loans of balance b1 and b2 and coupons 6% and 6.5% such that the following conditions are satisfied:

The first condition conserves the original balance, while the second condition is to set the WA coupon of the two pseudo loans to equal the net rate of the original loan. Solving these equations for b1 and b2, we find that b1=75,000 and b2=25,000. These two loans, when executed at 6.0% and 6.5% bond coupons respectively, avoids the creation of either an IO bond or a PO bond.

Although in the above example two adjacent half point coupons were used to create the two pseudo loans, two coupons from any of the half point bond coupons that are being used to create the bonds can be used. For example, if only bond coupons from 4.5% to 7.0% are being used to create the bonds, there would be fifteen combinations to consider (6C2=15). In some cases, the best solution is not to split the loan into two adjacent half point bond coupons. For example, this split may not be optimal if the AAA spreads at the two adjacent half point coupons are far higher than the ones that are not adjacent to the net balance of the loan.

The senior/subordinate module 121 can construct a linear program or linear objective function to determine the optimal split into pseudo loans. The output of the linear program is the optimal splitting of the original loan into pseudo loans such that the overall execution of the loan is maximized, subject to no IO bond or PO bond creation. For each loan i, let variable x ij indicate the balance of loan i allocated to the jth half point coupon, subject to the constraint that the sum of over x ij for all j equals to the balance of loan i and the WA coupon expressed as a function of the x ij 's equals to the net coupon of loan i, similar to Equation [6] above. Let the execution coupons be r 0 to r n . Thus, this equation becomes:

where b i is the balance of loan i and c i is the net coupon of loan i. The price of loan i executing at coupon j is the sum of the price of the AAA bond and the subordinate bonds. No IO or PO bonds are created when the coupons are split. The senior/subordinate module 121 calculates the price of the AAA bond as a spread to the TBA, where the spread is a function of the execution coupon j. In one embodiment, the senior/subordinate module 121 also calculates the price of the subordinate bond as a spread to the TBA for simplification of the problem. Cash flows are not generated as the split of the balances to different execution coupons is not yet known. The senior/subordinate module 121 combines the price of the subordinate bond and the AAA bond in proportion to the subordination level of loan i, which can be input by a user in step 205 of FIG. 2 or input by an API. At this point, the senior/subordinate module 121 has calculated the price of loan i (P ij ) for each execution coupon j. To determine the optimal splitting of the original loan into pseudo loans, the senior/subordinate module 121 creates the following objective function and works to maximize this objective function:

Equation [8] is a simple linear program with two constraints and can be solved optimally. The solution gives the optimal split of the loan into at most two coupons and thus, a bond can be structured without creating any IO or PO bonds. The user can determine if the bond should be split or not based on the optimal execution and other business considerations.

FIG. 6 is a flow chart depicting a method 315 for packaging a population of loans into pass through bonds in accordance with one exemplary embodiment of the present invention. A pass through bond is a fixed income security backed by a package of loans or other assets. Typically, as briefly discussed above with reference to FIG. 1, a pass through bond is guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The government agency guarantees the pass through bond in exchange for a guarantee fee (Gfee). The Gfee can be an input provided by the agencies for a specific set of loans or can be specified as a set of rules based on collateral characteristics. Regardless of how the Gfee is obtained, the Gfee for a loan set is known.

When loans are securitized as a pass through bond, one has the option to buy up or buy down the Gfee in exchange for an equivalent fee to the agencies. Buying up the Gfee reduces the net coupon and thus the price of the bond as well. This upfront buy up fee is exchanged in lieu of the increased Gfee coupon. Similarly, buying down the Gfee reduces the Gfee and increases the net coupon and therefore increases the bond price. An upfront fee is paid to the agencies to compensate for the reduced Gfee.

The Fannie Mae and Freddie Mac agencies typically provide buy up and buy down grids each month. Referring to FIG. 1, these grids can be stored in a data source 150 or in the data storage unit 125 for access by the pass-thru module 122 of the loan trading optimizer 120 . If the Gfee is bought up or bought down, an excess coupon is created. The amount of buy up or buy down of Gfee can vary based on collateral attributes of the loan and can also be subject to a minimum and maximum limit.

Referring now to FIGS. 1 and 6, at step 605 , the pass-thru module 122 determines the optimal execution of each loan by buy up or buy down of the Gfee. In one exemplary embodiment, the optimal execution of each loan is determined by finding the overall price of the loan for each available buy up and buy down of the Gfee. Typically, a Gfee can be bought up or down in increments of 1/100 th of a basis point. The pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy up with a step size of 1/100 th of a basis point. Similarly, the pass-thru module 122 implements a loop for each loan from the minimum to the maximum Gfee buy down with a step size of 1/100 th of a basis point. In each iteration, the amount of Gfee buy up or buy down is added to the current net rate of the loan. From this modified net rate of the loan, the TBA coupon is determined as the closest half point coupon lower than or equal to the modified net rate. The excess coupon is equal to the modified net rate of the TBA coupon and the price of the excess coupon is a lookup in the agency grid. The fee for the buy up or buy down is also a lookup in the agency grid. The price of the TBA coupon is a lookup from the TBA price curve. When the Gfee is bought up, the cost is added to the overall price and when the Gfee is bought down, the cost is subtracted from the overall price. The pass-thru module 122 determines the overall price of execution for the loan at each iteration and determines the optimal execution for the loan as the execution coupon of the TBA for which the overall price is maximized. This overall cost is the combination of the price of the TBA coupon, the price of the excess coupon, and the cost of the Gfee (added if buy up, subtracted if buy down).

At step 610 , the pass-thru module 122 determines which TBA pools each loan is eligible for. Pooling loans into TBA bonds is a complex process with many constraints on pooling. Furthermore, different pools of loans have pool payups based on collateral characteristics. For example, low loan balance pools could prepay slower and thus may trade richer. Also, loan pools with geographic concentration known to prepay faster may trade cheaper and thus have a negative pool payup. Thus, pooling optimally taking into account both the constraints and the pool payups can lead to profitable execution that may not be captured otherwise.

Each of the TBA pools for which a loan can be allocated has a set of pool eligibility rules and a pool payup or paydown. Non-limiting examples of pools can be a low loan balance pool (e. g. loan balances less than $80K), a medium loan balance pool (e. g. loan balance between $80K and $150K), a high loan balance pool (e. g. loan balances above $150K), a prepay penalty loan pool, and an interest only loan pool. For a loan to be allocated to a specific pool by the pass-thru module 122 , the loan has to satisfy both the eligibility rules of the pool and also best execute at the execution coupon for that pool.

The pass-thru module 122 applies the eligibility rules of the TBA bond pools to the loans to determine the TBA bond pools for which each loan is eligible. The pass-thru module 122 can utilize pool priorities to arbitrate between multiple pools if a loan is eligible for more than one pool. If a loan is eligible to be pooled into a higher and lower priority pool, the pass-thru module 122 allocates the loan to the higher priority pool. However, if a loan is eligible for multiple pools having the same priority, the pass-thru module 122 can allocate the loan into either of the pools having the same priority.

At step 615 , the pass-thru module 122 builds a model for allocating the loans into TBA pools based on the constraints of each TBA bond pool. Let x ij be a binary variable with a value of “1” or “0” which has a value of “1” when loan i is allocated to TBA bond pool j. The total loan balance and loan count constraints of the TBA pools are linear functions of the x ij variables. The objective function for this model is also a linear combination of the market values of each loan. The primary problem in this model is that the given loan population selected in step 210 of FIG. 2 may not be sufficient to allocate all TBA loan pools, as some of the pools may not have loans to satisfy the balance and count constraints or the loans may not be eligible for those pools. In such cases, it is desirable for the pools to have the constraints when applicable. If there are some pools for which there are not enough loans in the population of loans to form a pool, then such pools are not subjected to the specified constraints while the other pools are. However, it is not possible to know a-priori which pools do not have enough loans to satisfy the constraints. Thus, the model employs conditional constraints to allow constraints to be applicable to only those pools which are allocated.

The pooling model is modified to allow for some loans to not be allocated to any pool. This non-allocation will ensure that the model is always solvable and is similar to introducing a slack variable in linear programming. Thus, for each loan in the population of loans, there is an additional binary variable representing the “unallocated pool” into which the loan can be allocated. Those loans allocated to the unallocated pool are given a zero cost/market value, thus encouraging the pass-thru module 122 to allocate as many loans as possible.

The next step in building this pooling model is to introduce p binary variables for the p possible TBA pools. A value of “1” indicates that this pool is allocated with loans satisfying the pool constraints and a value of “0” indicates that this pool is not allocated. These variables are used to convert simple linear constraints into conditional constraints.

Each constraint of each pool is converted to conditional constraints for the pooling model. To detail this conversion, a maximum loan count constraint is considered for pool P. Let x 1 to x n be binary variable where x i are the loans eligible for pool P. Next, let x 1 + . . . +x n =U, where U equals the total number of loans in pool P. Finally, let w be the binary variable to indicate if pool P is allocated. The user constraint for maximum loan count is specified as U≦K, where K is given by the user. In order to impose this constraint conditionally, this constraint is transformed to the following two constraints: U≦K w U≦M w.

where M is a constant such that the sum of all x i 's is bounded by M. Consider both the cases when pool P is allocated (w=1) and when pool P is not allocated (w=0) below: w=1: U≦K (required) U≦M (redundant) w=0: U≦0 U≦0.

The only way for U≦0 would be when all the x i 's are “0” and thus, pool P will be unallocated.

Other constraints, such as minimum count, minimum balance, maximum balance, average balance, and weighted average constraints can be transformed similarly for the pooling model. After all of the constraints are transformed to conditional constraints, the pooling model is ready to handle constraints conditionally.

At step 620 , the pass-thru module 122 executes the pooling model to allocate the loans into TBA pools. After the pass-thru module 122 executes the model for one iteration, the method 315 proceeds to step 220 (FIG. 2). As the method 200 of FIG. 2 iterates step 215 , different TBA pool allocations are produced by the pass-thru module 122 until either the optimal TBA pool allocation is found or until the user decides that a solution of sufficient high quality is found in step 220 (FIG. 2).

FIG. 7 is a flow chart depicting a method 320 for packaging whole loans in accordance with one exemplary embodiment of the present invention. The method 320 identifies an optimal package of loans meeting a set of constraints given by a customer or investor. In this embodiment, the loan package is optimized by determining which loans, among the population of loans that meet the constraints, are least favorable to be securitized. Although the method 320 of FIG. 7 is discussed in terms of the senior/subordinate bond structure, other bonds structures or models can be used.

Referring to FIG. 7, at step 705 , the whole loan module 123 determines which loans in the population of loans meets constraints of a bid for whole loans. Investment banks and other financial institutions receive bids for whole loans meeting specific requirements. These requirements can be entered into the user interface 115 at step 205 of FIG. 2 and/or stored in the data storage unit 125 or a data source 150 . The constraints can include requirements that the loans must satisfy, such as, for example, minimum and maximum balance of the total loan package, constraints on the weighted average coupon, credit ratings of the recipients of the loans (e. g. FICO score), and loan-to-value (LTV) ratio. The constraints can also include location based constraints, such as no more than 10% of the loan population be from Florida and no zip code should have more than 5% of the loan population.

After the whole loan module 123 selects the loans that meet the constraints, at step 710 , the whole loan module 123 determines the price of each loan that meets the constraints based on a securitization module. For example, the price of the loans may be calculated based on the senior/subordinate structure discussed above with reference to FIGS. 4 and 5.

At step 715 , the whole loan module 123 determines whether to use an efficient model to select loans least favorable to be securitized by minimizing the dollar value of the spread of execution of the loans based on a securitization model or a less efficient model to select loans least favorable to be securitized by minimizing the spread of execution of the loans based on a securitization model. In one exemplary embodiment, this determination can be based on the total number of loans in the population or chosen by a user. If the whole loan module 123 determines to use the efficient model, the method 320 proceeds to step 725 . Otherwise, the method 320 proceeds to step 720 .

At step 720 , the whole loan module 123 selects loans that are least favorable to be securitized by minimizing the spread of execution of the loans based on the senior/subordinate bond structure. The whole loan module 123 builds a model to select a subset of the loans that meet the constraints such that the WA price of the loans of this subset net of the TBA price of the WA coupon of this subset is minimized. The TBA price of the WA coupon of the subset is typically higher as the TBA typically has a better credit quality and hence the metric chosen will have a negative value. The objective function that needs to be minimized is given by:

In Equation [9], x 1 to x n are binary variables with a value of either “0” or “1”, whereby a value of “1” indicates that the loan is allocated and “0” otherwise. The variables b 1 to b n are the balances of the loans and p 1 to p n are the prices of the loans as determined in step 710 . The variables q 1 to q m are the weights for each of the half point coupons and px 1 to px m are the TBA prices for the half point coupons. The weights are special ordered sets of type two, which as discussed above, implies that at most two are non-zero and the two non-zero weights are adjacent. Thus, the expression (q 1 px 1 + . . . +q m px m ) is the price of the WA coupon of the allocated loans.

The weights (q 1 - q m ) are subject to the constraints:

where the c i 's are the net coupons on the loans and the r i 's are the half point coupons of the TBA curve.

An illustrated weighted average constraint (Kwac) on the coupon could be:

Let y 0 =M/(x 1 b 1 + . . . +x n b n ) and y j =x j y 0 where M is a scaling constant to keep the model scaled sensibly. Rewriting the equations, the objective function to minimize is:

Multiplying Equation [16] by y 0 yields:

Additionally, other constraints for loan balance and ratio balance can similarly be transformed into linear constraints. In this exemplary embodiment, the y's are real numbers and the y's should be equal to y 0 when that loan is allocated, else the y should equal “0.” This requirement can be enforced by adding additional constraints and variables:

The equations above are analyzed when z i is set to “1” and z i is set to “0” and which shows that y i will be y 0 or zero within a tolerance of eps. Eps is a model specific constant and is suitably small to account for lack of numerical precision in a binary variable. The tolerance eps is utilized in this model as although binary variables are supposed to be “0” or “1,” the binary variables suffer from precision issues and thus, the model should accommodate numerical difficulties. The source of this precision issue is the way y 0 has been defined. The denominator of y 0 =M/(x 1 b 1 + . . . +x n b n ) is essentially the sum of the balances of all loans in the pool, which can be a very large number resulting in a small y 0 .

After building the model, the whole loan module 123 minimizes the objective function in Equation [13] with each iteration of step 215 of FIG. 2 while maintaining the constraints of the subsequent equations [17]-[ 21 ]. The loans that are allocated into the whole loan package are the loans that meet the constraints of the bid and have a y value equal to y 0 . After step 720 is completed, the method 320 proceeds to step 220 (FIG. 2).

At step 725 , the whole loan module 123 selects loans that are least favorable to be securitized by minimizing the dollar value of the spread of execution of the loans based on the senior/subordinate bond structure. Thus, the difference of the market value of the allocated loans and the notional market value of the loan pool using the price of the WA execution coupon is minimized. The objective function that needs to be minimized for this model is given by:

Rewriting Equation [22] yields:

which gets transformed to:

After building the model, the whole loan module 123 minimizes the objective function in Equation [24] with each iteration of step 215 of FIG. 2 while maintaining the constraints of the subsequent equations [25]-[29]. The loans that are allocated into the whole loan package are the loans that meet the constraints of the bid and have a y value equal to y 0 . After step 725 is completed, the method 320 proceeds to step 220 of FIG. 2.

FIG. 8 is a flow chart depicting a method 225 for pooling excess coupon in accordance with one exemplary embodiment of the present invention. The excess coupon module 124 can pool the excess coupon of securitized loans into different tranches or pools. The excess coupon module 124 can take a large population of loans (e. g. 100 thousand or more), each with some excess coupon, and pool the loans into different pools, each pool with a different coupon and specified eligibility rules. Each of the pools can also have a minimum balance constraint. Pools that are created with equal contribution of excess coupon from every loan that is contributing to that pool typically trades richer than pools that have a dispersion in the contribution of excess from different loans. Therefore, it is profitable to create homogeneous pools.

Referring to FIG. 8, at step 805 , the excess coupon module 124 converts the pool constraints into conditional constraints as some of the pools defined in this excess coupon model may not have loans to satisfy the pool constraints. This conversion is similar to the conversion of constraints discussed above with reference to FIG. 6.

At step 810 , the excess coupon module 124 builds a model to determine the optimal pooling for the excess coupons. Let x ij be the contribution of excess coupon from loan i to pool j. Unlike the pooling model in FIG. 6 above, this variable is not a binary variable. However, an unallocated pool is added to the set of user defined pools which enables the pass-thru module 122 to always solve the model and produce partial allocations. The first constraint of this excess coupon model is the conservation of excess coupon allocated among all the pools for each loan. Any loan that does not get allocated to a user defined pool is placed in the unallocated pool, and thus the unallocated pool is also included in the conservation constraint. In this embodiment, the unallocated pool does not have any other constraint. The objective function of this excess coupon model is to maximize the total market value of the excess that gets allocated. Unallocated excess coupon is assigned a zero market value and thus the solver tries to minimize the unallocated excess coupon. In this model, the excess coupon module 124 tries to create the maximum possible pools with equal excess contribution. Any leftover excess from all the loans can be lumped into a single pool and a WA coupon pool can be created from this pool.

An aspect of this excess coupon model is to enforce equality of the excess coupon that gets allocated from a loan to a pool. Furthermore, it is not necessary that all loans allocate excess to a given pool. Thus, the equality of excess is enforced only among loans that have a non-zero contribution of excess to this pool.

Let xp 0 to xp p be p real variables that indicate the amount of excess in each pool. Also, let w ij be a binary variable that indicates if loan i is contributing excess to pool. For each eligible loan i, for pool j, the following constraints are added:

When w ij =1 (loan does not contribute excess to this pool), then:

Thus, x ij is equal to x pj , the excess coupon of the pool. When w ij =0, then:

When M is chosen to be the maximum excess coupon of all loans in the allocation, the expression xp j −M is negative. Thus, from x ij ≦0 and that all excess coupons have to be zero or positive, this implies that x ij =0 when w ij =0.

This excess coupon model can be difficult to solve because of its complexity level. In order to reduce the complexity, the excess coupon module 124 employs dimensionality reduction. The first step of this process is to identify the pools into which a loan can be allocated. Eligibility filters in this excess coupon model specify the mapping of the collateral attributes of the loans to the coupons of the pools that the attributes can go into. For example, loans with a net coupon between 4.375% and 5.125% can go into pools of 4.5% or 5.0%. Unlike the pooling model discussed above with reference to FIG. 6, there are no pool priorities.

At step 815 , the excess coupon module 124 identifies the pool into which a given loan can be allocated based on the collateral attributes of the loan and independent of the pool execution coupon. This gives a one to one mapping between the loans and the pools.

At step 820 , the excess coupon module 124 collapses all loans having the same excess coupon within a given pool definition into a single loan. This approach can significantly reduce the number of loans in the loan population.

After the population of loans is reduced, the excess coupon module 124 maximizes the objective function at step 825 . The excess coupon module 124 can iteratively determine solutions to the objective function until an optimal solution is found or until a user decides that a solution of sufficient high quality is found.

One of ordinary skill in the art would appreciate that the present invention provides computer-based systems and methods for optimizing fixed rate whole loan trading. Specifically, the invention provides computer-based systems and methods for optimally packaging a population of whole loans into bonds in either a senior/subordinate bond structure or into pools of pass through securities guaranteed by a government agency. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized.

Although specific embodiments of the invention have been described above in detail, the description is merely for purposes of illustration. It should be appreciated, therefore, that many aspects of the invention were described above by way of example only and are not intended as required or essential elements of the invention unless explicitly stated otherwise. Various modifications of, and equivalent steps corresponding to, the disclosed aspects of the exemplary embodiments, in addition to those described above, can be made by a person of ordinary skill in the art, having the benefit of this disclosure, without departing from the spirit and scope of the invention defined in the following claims, the scope of which is to be accorded the broadest interpretation so as to encompass such modifications and equivalent structures.