外文文獻(xiàn)翻譯

1、此處為論文中文題目，要求居中填寫主標(biāo)題不超過24個(gè)漢字；可加副標(biāo)題（副標(biāo)題前加破折號(hào)），副標(biāo)題與主標(biāo)題間空一行的位置主標(biāo)題：黑體，小二，居中副標(biāo)題：楷體_gb2312，四號(hào)，居中閱后刪除此文本框。本 科 生 畢 業(yè) 論 文（設(shè)計(jì)）外 文 文 獻(xiàn) 翻 譯譯文一： 需求驅(qū)動(dòng)因素和違約風(fēng)險(xiǎn)的住宅房屋貸款以印度為例譯文二：學(xué)生姓名 顏凱華學(xué) 號(hào) 0807100245指導(dǎo)教師 張奎燕二級(jí)學(xué)院 經(jīng)濟(jì)與國際貿(mào)易學(xué)院專業(yè)名稱 經(jīng)濟(jì)學(xué)班 級(jí) 08經(jīng)濟(jì)(2)2012年2月需求驅(qū)動(dòng)因素和違約風(fēng)險(xiǎn)的住宅房屋貸款以印度為例1 節(jié)選自bandyopadhyay, arindam and saha，印度國家銀行管理協(xié)會(huì)，2

2、009,2:23-52. 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(dǎo)致了總需求。此外,印度銀行也會(huì)對(duì)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(píng)估貸款的建議,銀行會(huì)關(guān)注定性和定量因素來評(píng)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(yù)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(jià)已經(jīng)讓位于擔(dān)憂關(guān)于住房貸款的默認(rèn)值。快速擴(kuò)大信用提價(jià)的可能性，放松收入標(biāo)準(zhǔn)和信貸標(biāo)準(zhǔn)對(duì)于那些申請(qǐng)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(gè)行業(yè)變化的標(biāo)準(zhǔn)下可能會(huì)引發(fā)系統(tǒng)風(fēng)險(xiǎn)。然而,在后期,在新抵押貸款的抵押物比率大幅上升

3、,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(biāo)準(zhǔn)下降越多的地區(qū),經(jīng)歷了較大的信貸繁榮和房子價(jià)格的上漲。近年來美國的次貸危機(jī)對(duì)幾乎已經(jīng)殘廢的健康和財(cái)務(wù)系統(tǒng),發(fā)出一個(gè)明確的信號(hào),表明應(yīng)該進(jìn)行合理的貸款。貸款應(yīng)該繼續(xù)成為體系里健全的金融實(shí)踐的基礎(chǔ)。它也進(jìn)一步揭示了如地表水資源般脆弱的財(cái)務(wù)機(jī)構(gòu)由于互動(dòng)房價(jià)下跌，房屋所有人的房屋產(chǎn)權(quán)信貸，作為住房價(jià)格已經(jīng)上漲了。當(dāng)前的情況結(jié)束未來十年,產(chǎn)品增生,讓消費(fèi)者能夠進(jìn)入，從而財(cái)富貶值了。在這個(gè)過程中，他們欣賞馬克市場穩(wěn)定增加的貸款價(jià)值比。這些產(chǎn)品有潛在危險(xiǎn)，成為房價(jià)下跌的一個(gè)重要因素。實(shí)證研究表明，由

4、于在印度房地產(chǎn)市場是稀缺的，因而也缺乏有關(guān)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(jìn)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(shè)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(guān)系聯(lián)系起來,了解借款人的特點(diǎn)和貸款參數(shù),如資產(chǎn)質(zhì)量、拖欠、期限的貸款、擔(dān)保價(jià)值等。研究怎么樣類型的家庭人口更容易拖欠付款。進(jìn)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(fēng)險(xiǎn)的。我們同樣也研究之間的聯(lián)系,貸款犯罪和價(jià)值的比較性,進(jìn)一步指出了抵押品的估值。為了達(dá)到以上目的,本研究使用了個(gè)人帳戶從銀行貸款的數(shù)據(jù)水平和住房金融公司的宏觀經(jīng)濟(jì)信息。從各種二手資料

5、收集到的各種因素來檢查印度住宅建筑需求和違約風(fēng)險(xiǎn)。我們用宏觀經(jīng)濟(jì)變量、貸款有關(guān)情境和區(qū)位因子作為解釋變量。大多數(shù)的實(shí)證研究對(duì)估計(jì)的房屋需求量以價(jià)格、收入和人口統(tǒng)計(jì)參數(shù)或在對(duì)數(shù)線性這一階段定價(jià)回歸分析。bartik epple,bajari和kahn（2003年)用超過54個(gè)國家的數(shù)據(jù)用線性對(duì)數(shù)回歸函數(shù)(log-linear)估計(jì)住房需求。 bajari和kahn(2003)估計(jì)享樂價(jià)格函數(shù)的分布參數(shù)預(yù)測功能。arimah(1992)用需求函數(shù)估計(jì)了在尼日利亞這個(gè)城市對(duì)一套住房的屬性。ibadan使用兩步法 (1974)。他們的研究結(jié)果顯示最重要的需求因素有:收入、價(jià)格、家庭大小和戶主職業(yè)地位。

6、除了收入與財(cái)富、其他特性(社會(huì)或人口)的家庭也會(huì)影響不同在住房的需求。家庭消費(fèi)能力也影響住房需求的積極性。此外,自然的專業(yè)活動(dòng)(雇員vs個(gè)體)和職業(yè)地位(退休vs活動(dòng))也會(huì)影響到住房需求。股票價(jià)值等財(cái)務(wù)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(gè)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(xué)習(xí)收入、價(jià)格、年齡對(duì)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(shè)定了各種位置相關(guān)的變量,并研究了其對(duì)住宅單位的需求。李青蓉(2002)認(rèn)為購買房地產(chǎn)的目的中存在違約風(fēng)險(xiǎn)。如果借款人購買新房子的目的是個(gè)人投資

7、，而不是自有居住。然后他們會(huì)把他們風(fēng)險(xiǎn)較小部分靠金融機(jī)構(gòu)支付,金融機(jī)構(gòu)的支付降低其初始股權(quán)。因此,當(dāng)市場價(jià)格下跌時(shí)，金融機(jī)構(gòu)經(jīng)濟(jì)效益大幅下降和惡化,經(jīng)常將財(cái)產(chǎn)主人遺棄，從而限制了他們的損失。這些補(bǔ)充(2003)依附在1998年的調(diào)查發(fā)現(xiàn)，通常用消費(fèi)者表來明借款人財(cái)務(wù)金融資產(chǎn)。并使用其他非住宅來幫助在意想不到時(shí)期的付款期經(jīng)濟(jì)壓力。這符合事發(fā)現(xiàn)的事實(shí),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(bào)告也發(fā)現(xiàn),信用分?jǐn)?shù)同時(shí)代的經(jīng)濟(jì)條件、激勵(lì)結(jié)構(gòu)均可影響貸款人的犯罪。（史密斯1998年,巴庫華希特1999年,安布羅西和卡2009年

8、。）另一個(gè)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(fēng)險(xiǎn)解釋。確切的研究證實(shí)，斯坦塞爾和米勒 (1976年)、 凡達(dá)爾(1978年) 和英格拉姆和弗雷澤 (1982年)，他們認(rèn)為,支付與收入的比例越大,違約風(fēng)險(xiǎn)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(jì)變量作為解釋變量。，由kau和keenan (1998年)，把它認(rèn)為了是一個(gè)合理的默認(rèn)值。決定例如在一個(gè)缺省值時(shí)才會(huì)發(fā)生該房屋價(jià)值(資產(chǎn)凈值)低于該值。李會(huì)昌(2002

9、)實(shí)驗(yàn)證明,住宅回贖權(quán)的取消率是與地方經(jīng)濟(jì)多樣化存在著負(fù)相關(guān)。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 housing sector and thereby c

10、ontributing 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 proposal, banks look at both

11、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 rapid growth in mortgage

12、 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 crippled the health of t

13、he 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 home equity lines of cre

14、dit.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 products became potentially r

15、isky 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 factors that drive the reside

16、ntial 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 values etc. c) type of

17、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 collateral to further pinpo

18、int 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 the determinants of resi

19、dential 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 either in a log-linear regr

20、ession 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 (2003) estimated the hedon

21、ic 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 reveal that the most importa

22、nt 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. the number of people in t

23、he 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 information (proxied by age, ed

24、ucation, 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 considered natural log of house

25、 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 default risk. if the borr

26、owers 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. therefore, when the market

27、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 other non-housing financ

28、ial 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-to-