外文文獻翻譯

1、浙江財經(jīng)學院本科生畢業(yè)論文（設計）外文文獻翻譯此處為論文中文題目，要求居中填寫主標題不超過24個漢字；可加副標題（副標題前加破折號），副標題與主標題間空一行的位置主標題：黑體，小二，居中副標題：楷體_gb2312，四號，居中閱后刪除此文本框。本 科 生 畢 業(yè) 論 文（設計）外 文 文 獻 翻 譯譯文一： 需求驅(qū)動因素和違約風險的住宅房屋貸款以印度為例譯文二：學生姓名 顏凱華學 號 0807100245指導教師 張奎燕二級學院 經(jīng)濟與國際貿(mào)易學院專業(yè)名稱 經(jīng)濟學班 級 08經(jīng)濟(2)2012年2月3需求驅(qū)動因素和違約風險的住宅房屋貸款以印度為例1 節(jié)選自bandyopadhyay, arind

2、am and saha，印度國家銀行管理協(xié)會，2009,2:23-52. 銀行在印度一直扮演著重要的角色,是提供信貸的住房部門,從而導致了總需求。此外,印度銀行也會對各種類型的貸款延長住宅性能。銀行在印度一直扮演著重要的角色,住房貸款是現(xiàn)在超過25%的貸款組合.而評估貸款的建議,銀行會關注定性和定量因素來評估他們的客戶，剖析客戶從而來知道他們的偏好和需求變化，預測幫助銀行更好地理解市場。它是抵押信貸迅速發(fā)展，房價已經(jīng)讓位于擔憂關于住房貸款的默認值?？焖贁U大信用提價的可能性，放松收入標準和信貸標準對于那些申請貸款或貸款給那些收入低的客戶是沒有安全的保證的。在整個行業(yè)變化的標準下可能會引發(fā)系統(tǒng)風險

3、。然而,在后期,在新抵押貸款的抵押物比率大幅上升,并100%明確的人就非常多。融資下降主要的原因是提前付款的違約增加。在美國， 艾利斯(2008)顯示貸款標準下降越多的地區(qū),經(jīng)歷了較大的信貸繁榮和房子價格的上漲。近年來美國的次貸危機對幾乎已經(jīng)殘廢的健康和財務系統(tǒng),發(fā)出一個明確的信號,表明應該進行合理的貸款。貸款應該繼續(xù)成為體系里健全的金融實踐的基礎。它也進一步揭示了如地表水資源般脆弱的財務機構由于互動房價下跌，房屋所有人的房屋產(chǎn)權信貸，作為住房價格已經(jīng)上漲了。當前的情況結束未來十年,產(chǎn)品增生,讓消費者能夠進入，從而財富貶值了。在這個過程中，他們欣賞馬克市場穩(wěn)定增加的貸款價值比。這些產(chǎn)品有潛在危

4、險，成為房價下跌的一個重要因素。實證研究表明，由于在印度房地產(chǎn)市場是稀缺的，因而也缺乏有關資料。本文主數(shù)據(jù)是通過找到的自然的住宅建筑的數(shù)據(jù)進行研究的。目前在印度的房地產(chǎn)市場中，需求是主要因素,假設把住宅建筑需求,與現(xiàn)有住房貸款的借款人所選擇的銀行和住房金融公司之間的關系聯(lián)系起來,了解借款人的特點和貸款參數(shù),如資產(chǎn)質(zhì)量、拖欠、期限的貸款、擔保價值等。研究怎么樣類型的家庭人口更容易拖欠付款。進而研究人口和就業(yè)等環(huán)境因素地位、家庭類型、收入水平、位置是如何影響違約風險的。我們同樣也研究之間的聯(lián)系,貸款犯罪和價值的比較性,進一步指出了抵押品的估值。為了達到以上目的,本研究使用了個人帳戶從銀行貸款的數(shù)據(jù)

5、水平和住房金融公司的宏觀經(jīng)濟信息。從各種二手資料收集到的各種因素來檢查印度住宅建筑需求和違約風險。我們用宏觀經(jīng)濟變量、貸款有關情境和區(qū)位因子作為解釋變量。大多數(shù)的實證研究對估計的房屋需求量以價格、收入和人口統(tǒng)計參數(shù)或在對數(shù)線性這一階段定價回歸分析。bartik epple,bajari和kahn（2003年)用超過54個國家的數(shù)據(jù)用線性對數(shù)回歸函數(shù)(log-linear)估計住房需求。 bajari和kahn(2003)估計享樂價格函數(shù)的分布參數(shù)預測功能。arimah(1992)用需求函數(shù)估計了在尼日利亞這個城市對一套住房的屬性。ibadan使用兩步法 (1974)。他們的研究結果顯示最重要的

6、需求因素有:收入、價格、家庭大小和戶主職業(yè)地位。除了收入與財富、其他特性(社會或人口)的家庭也會影響不同在住房的需求。家庭消費能力也影響住房需求的積極性。此外,自然的專業(yè)活動(雇員vs個體)和職業(yè)地位(退休vs活動)也會影響到住房需求。股票價值等財務信息(年齡、教育程度等)也可以解釋住房需求。在本文中,我們做了一個面板最小二乘法虛擬變量回歸方法(lsdv)。他們使用13487借款人賬戶數(shù)據(jù)學習收入、價格、年齡對住房的需求。在我們的微觀模型里,我們已經(jīng)思考用了房子面積(平方米)作為代理房屋需求。我們有設定了各種位置相關的變量,并研究了其對住宅單位的需求。李青蓉(2002)認為購買房地產(chǎn)的目的中存

7、在違約風險。如果借款人購買新房子的目的是個人投資，而不是自有居住。然后他們會把他們風險較小部分靠金融機構支付,金融機構的支付降低其初始股權。因此,當市場價格下跌時，金融機構經(jīng)濟效益大幅下降和惡化,經(jīng)常將財產(chǎn)主人遺棄，從而限制了他們的損失。這些補充(2003)依附在1998年的調(diào)查發(fā)現(xiàn)，通常用消費者表來明借款人財務金融資產(chǎn)。并使用其他非住宅來幫助在意想不到時期的付款期經(jīng)濟壓力。這符合事發(fā)現(xiàn)的事實,chinloy(1995年)發(fā)現(xiàn):在英國期1983年到1992年期間,犯罪伴隨著抵押物和收入的主要變化趨勢。其他的研究報告也發(fā)現(xiàn),信用分數(shù)同時代的經(jīng)濟條件、激勵結構均可影響貸款人的犯罪。（史密斯1998

8、年,巴庫華希特1999年,安布羅西和卡2009年。）另一個變量,支付與收入比率(或等同于每月分期付款的emi收入的比例),也是很重要的風險解釋。確切的研究證實，斯坦塞爾和米勒 (1976年)、 凡達爾(1978年) 和英格拉姆和弗雷澤 (1982年)，他們認為,支付與收入的比例越大,違約風險越大。威廉斯貝拉和肯克爾 （1974年）發(fā)現(xiàn)，一旦與入息比例的首期付款超過 30%，借款人往往有較高概率的違約值。菲里安、黃、奧德里奇 (1999)在他們的模擬模型中考慮到位置,人口和經(jīng)濟變量作為解釋變量。，由kau和keenan (1998年)，把它認為了是一個合理的默認值。決定例如在一個缺省值時才會發(fā)生

9、該房屋價值(資產(chǎn)凈值)低于該值。李會昌(2002)實驗證明,住宅回贖權的取消率是與地方經(jīng)濟多樣化存在著負相關。factors driving demand and default risk in residential housingindian evidence1 bandyopadhyay, arindam and saha,national institute of bank management,february,2009.banks in india have been playing an important role in providing credit to the hous

10、ing sector and thereby contributing to the aggregate demand in the sector. moreover, indian banks also extend various types of loans against residential housing properties. housing loan now constitutes more than 25 percent of the lending portfolios of individual banks.while evaluating the loan propo

11、sal, banks look at both qualitative and quantitative factors to evaluate the credit worthiness of their customers. profiling of customers and idea of their changing preference and demand projections would help banks to better understand the market. it is also pertinent to mention at this stage that

12、rapid growth in mortgage credit and house prices has given way to heightened concerns about housing loan defaults. ellis (2008) in us has shown that lending standards declined more in areas that experienced larger credit booms and house price increases.recent us mortgage crisis, which has virtually

13、crippled the health of the financial system, sends a clear signal that due-diligence in lending should continue to be the corner stone of sound banking practices. it has further revealed the vulnerability of the financial institutions due to interaction between falling housing prices and homeowners

14、home equity lines of credit.1 as housing prices have risen over the course of the current decade, products have proliferated that allow consumers to tap into the wealth created by home-price appreciation and in the process their mark to market loan to value ratio was steadily increasing. these produ

15、cts became potentially risky during periods of falling home prices.the empirical research on housing market in india is scarce due to the paucity of relevant data. the main objective of this paper is a) to find the nature of residential housing demand at present in india and estimate the major facto

16、rs that drive the residential housing demand, b) to correlate existing profile of housing loan borrowers of select banks and housing finance companies to understand the relationship between borrower characteristics and loan parameters such as asset quality, delinquencies, period of loans, collateral

17、 values etc. c) type of households in the population more prone to default on payments, d) how demographic and situational factors such as employment status, family type, income level,locations affect risk of default. in doing so, we also examine the linkage between loan delinquency and value of col

18、lateral to further pinpoint the importance of valuation in the housing sector.in order to accomplish the above objectives, present study uses individual account level loan data from banks and housing finance companies and macroeconomic information collected from various secondary sources to examine

19、the determinants of residential housing demand and default risk in india. apart from macroeconomic variables, loan related, situational and locational factors are used as explanatory variables.most of empirical studies on estimation of housing demand take price, income and demographic parameters eit

20、her in a log-linear regression or in a two-stage hedonic pricing regression method (rosen, 1974; brown and rosen, 1982; epple, 1987; bartik, 1987; bajari and kahn, 2003. tewari and parikh (1998 and 1999) have used ridge regressions function (log-linear) to estimate housing demand. bajari and kahn (2

21、003) estimated the hedonic price functions to predict the distribution of taste parameters as a function of demographics. arimah (1992) estimated demand functions for a set of housing attributes for the city of ibadan in nigeria using a two-step method like rosen (1974). their empirical results reve

22、al that the most important determinants of the demand attributes are: income, price, household size and the occupational status of the head of household. besides income and wealth, other characteristics (sociological or demographic) of the household may influence the difference in housing demands. t

23、he number of people in the household influences the consumption demand positively (more spacious housing). moreover, the nature of professional activity (employee vs. self-employed) and professional status (retired vs. in activity) also can affect housing demand. the stock value of financial informa

24、tion (proxied by age, education, etc.) may also explain housing demand. in this paper, we have performed a panel least squaredummy variable (lsdv) regression method using 13,487 borrower account data to study the effect of income, price, and age on housing demand. in our micro model, we have conside

25、red natural log of house area (in square meter) as proxy for housing demand. we havealso captured various location related variables, and studied their influence on demand for dwelling units. lee (2002) has identified the purpose of purchasing real estate property is one of the key determinants of d

26、efault risk. if the borrowers purchase new houses for the purpose of personal investment instead of owner-occupied housing, then they will transfer part of their risk to the financial institutions by paying smaller down payments and decreasing their initial equity commitment as much as possible. the

27、refore, when the market price of collateral falls sharply or economic performance becomes much worse, the property frequently will be abandoned by the owners thereby limiting their loss.getter (2003) complemented these finding by using the 1998 survey of consumer finances to show that borrowers use

28、other non-housing financial assets to help make payments during unexpected periods of financial stress. consistent with prior findings, chinloy (1995) found that in the united kingdom during the period 1983 through 1992, ltv and income were the primary covariates associated with delinquency. other reported studies have also found that credit scores, contemporaneous economic conditions, and the incentive structure of the lender all can impact delinquency (baku and smith 1998, calem and wachter 1999, ambrose and capone 2000).another variable, payment-t