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James D Thomas - One of the best experts on this subject based on the ideXlab platform.
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impact of wall Constraint on velocity distribution in proximal Flow convergence zone implications for color doppler quantification of mitral regurgitation
Journal of the American College of Cardiology, 1996Co-Authors: Pieter M Vandervoort, Neil L Greenberg, Kimerly A Powell, Brian P Griffin, James D ThomasAbstract:Abstract Objectives. This study sought to evaluate the effect of proximal Flow Constraint induced by the left ventricular wall on the accuracy of calculated Flow rates and to assess a possible correction factor to adjust the proximal convergence angle. We further defined under which hydrodynamic and geometric conditions it is necessary to apply the corrected convergence angle. Background. The proximal Flow convergence method has been proposed as a new approach to quantify valvular regurgitation. However, significant overestimation of the calculated regurgitant Flow rate has been reported, particularly in patients with mitral valve prolapse and severe mitral regurgitation. Methods. We used an in vitro Flow model and induced various degrees of proximal Flow Constraint. The accuracy of the proposed convergence angle formula, α = π + 2 tan−1d/r (d = wall distance; r = isovelocity radius) was tested in vitro and in a three-dimensional numerical simulation. Results. With a constraining wall near the orifice, overestimation of regurgitant Flow rates was noted and was most significant with the constraining wall positioned closest to the orifice (calculated Flow rate [Qc]/true Flow rate [Qc] = 1.85 ± 0.55 [mean ± SD]). These findings were similar to the results of the numerical simulation. Applying the correction factor nearly completely eliminated the overestimation of the calculated Flow rates (cQc), with cQc/Qo= 1.13 ± 0.25. Conclusions. In the presence of a constraining wall, significant overestimation of calculated Flow rates is observed when hemispheric symmetry of the Flow field is assumed. In this situation, it is necessary to apply the corrected convergence angle formula to improve the accuracy of the proximal Flow convergence method.
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quantification of mitral regurgitation by the proximal convergence method using transesophageal echocardiography clinical validation of a geometric correction for proximal Flow Constraint
Circulation, 1995Co-Authors: Pieter M Vandervoort, Brian P Griffin, Dominic Y Leung, William J Stewart, Delos M Cosgrove, James D ThomasAbstract:Background Proximal Flow convergence is a promising method to quantify mitral regurgitation but may overestimate Flow when the Flow field is constrained. This has not been investigated clinically, nor has a correction factor been validated. Methods and Results Eighty-five patients were studied intraoperatively with transesophageal echocardiography and divided into two groups: central convergence (no constraining wall) and eccentric convergence (at least one constraining wall). Regurgitant stroke volume (RSV) and orifice area (ROA) were calculated by ROA=2π r2 Va/Vp and RSV=ROA×VTIcw, where r and va are the radius and velocity of the aliasing contour and vp and VTIcw are the peak and integral of regurgitant velocity. In eccentric convergence patients, convergence angle (α) was measured from two-dimensional Doppler color Flow maps, and ROA and RSV were corrected by multiplying by α/180. For reference, RSV was the difference between thermodilution and pulsed Doppler stroke volumes. In central convergence pat...
Qinghui Tang - One of the best experts on this subject based on the ideXlab platform.
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short term optimal hydrothermal scheduling problem considering power Flow Constraint
Congress on Evolutionary Computation, 2015Co-Authors: Jingrui Zhang, Shuang Lin, Xiangxiang Zeng, Qinghui TangAbstract:Short-term optimal hydrothermal scheduling problem is one of the most popular research issues in power systems optimization. A novel mathematical model of the short-term optimal hydrothermal scheduling is proposed in this paper. This model aims at minimizing the total fuel cost of the thermal generating units while satisfying the various Constraints such as power balance, water balance, transmission network and other system's Constraints. A modified differential evolution algorithm is also introduced to solve the short-term optimal hydrothermal scheduling problem. In the proposed approach, an operation of migration and a self-adaptive mechanism are presented to improve the searching efficiency. Moreover, four Constraint handling rules are proposed to handle the complex Constraints of short-term optimal hydrothermal scheduling problem. An IEEE nine buses test system is applied to verify the proposed mathematic model and algorithm. The numerical results show the feasibility and efficiency of the proposed approach to the short-term optimal hydrothermal scheduling problem.
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short term optimal hydrothermal scheduling with power Flow Constraint
Chinese Control and Decision Conference, 2015Co-Authors: Shuang Lin, Jian Huang, Jingrui Zhang, Qinghui Tang, Weixia QiuAbstract:Short-term optimal hydrothermal scheduling (STOHS) problem is one of the most popular research issues in power system optimization. A novel mathematical model of the short-term hydrothermal scheduling is proposed in this paper. This model aims at minimizing the total fuel cost of the thermal generating units while satisfying the various Constraints such as power balance, water balance, transmission network and other system's Constraints. A modified particle swarm optimization algorithm which employs a migration operation and inertia weight decreasing strategy is presented to solve this optimization problem. Five Constraint handling rules are integrated to handle the various Constraints of the problem. An IEEE nine buses test system is applied to verify the proposed model and algorithm. The numerical results show the feasibility and efficiency of the proposed approach to the STOHS problem.
Donghoon Lee - One of the best experts on this subject based on the ideXlab platform.
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A Service of zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics Real estate investors, the leverage cycle, and the housing market crisis Real Estate Investors, the Leverage Cycle, and the Housing Market Crisis Real Estate
2020Co-Authors: Andrew Haughwout, Donghoon Lee, Joseph Tracy, Wilbert Van Der KlaauwAbstract:Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Abstract We explore a mostly undocumented but important dimension of the housing market crisis: the role played by real estate investors. Using unique credit-report data, we document large increases in the share of purchases, and subsequently delinquencies, by real estate investors. In states that experienced the largest housing booms and busts, at the peak of the market almost half of purchase mortgage originations were associated with investors. In part by apparently misreporting their intentions to occupy the property, investors took on more leverage, contributing to higher rates of default. Our findings have important implications for policies designed to address the consequences and recurrence of housing market bubbles. The U.S. economy is still recovering from the financial crisis that began in the fall of 2007. The collapse of house prices across many markets was a precipitating factor in the financial crisis and adverse feedback effects between financial markets and the real economy led to the most severe recession in the post-war period. Extraordinary interventions by fiscal and monetary authorities both in the U.S. and abroad were required in order to prevent a complete collapse of global markets and the potential onset of another great depression. Attention has shifted from containing the financial crisis to examining its causes and designing policies to limit both the likelihood and the severity of a similar crisis in the future. Given the central role that housing played as a catalyst to the crisis, it is important to better understand the determinants of the dynamics of house prices and of subsequent mortgage defaults over this recent cycle. While house prices were rising in many parts of the country over the period leading up to the crisis, these increases were particularly pronounced in four states -Arizona, California, Florida and Nevada (the "bubble" states). This rapid run-up and then crash in house prices exacted a terrible cost to homeowners, financial firms and to the economy. Current estimates are that around 23 percent of active mortgages are "under water" in that the balance on the mortgage exceeds the current value of the 2 California is a bit of an exception in that it appears that average house prices have stabilized at a level 50 percent higher than in 2000. 2 house. As of 2010 Q4, nearly 2.8 million homes have gone through foreclosure, and another 2 million homes are in the process of foreclosure. 4 Serious delinquencies continue to add new homes to the foreclosure pipeline over time. Nationally distress sales represent around half of all repeat-sale transactions. These distress sales continue to exert downward pressure on house prices making it more difficult for housing markets to recover. A focus on residential mortgage finance in order to understand what the determinants were of the house price and mortgage default dynamics generated over the recent cycle would inform efforts to enhance financial stability. A more robust system of residential mortgage finance should aim to limit the degree to which house prices rise and fall over a credit cycle. Reducing the amplitude of the house price swings will limit the potential for collateral damage created by housing markets for the real economy. Related Literature Given that housing is a durable asset, periods of rising prices are indicative of increasing demand for housing. 5 One strand of the literature on housing demand focuses on the determinants that affect the "user cost" of housing. 6 The user cost of housing (UC) is the annual Flow cost to the owner per dollar of house price, taking into account after-tax financing costs, property taxes and insurance, maintenance and depreciation costs and the expected risk-adjusted return to owning the house. The value of the housing service Flow is proxied by the annual rent (R). If we assume that there is arbitrage between owned and rental housing, then the annual rent should equate to the price of housing (P) times the user-cost. 3 http://www.corelogic.com/About-Us/News/New-CoreLogic-Data-Shows-23-Percent-of-BorrowersUnderwater-with-$750-Billion-Dollars-of-Negative-Equity.aspx 4 http://www.ots.treas.gov/_files/490069.pdf 5 That is, with the exception of natural disasters and periods of armed conflict, the supply of housing in a market cannot contract significantly over a short period of time to drive up house prices. 6 See where r m is the mortgage financing rate, τ describes the tax environment, δ the depreciation rate on housing net of that offset by maintenance expenditures, g e the risk adjusted expected return to housing, and Y is the average income. This framework suggests several possible candidates for explaining the rise in house prices in the early to mid-2000s. A rise in income in a housing market will increase area rental rates to a degree that reflects the elasticity of supply of rental housing in that local market. Higher rents will translate into higher house prices by a factor given by the reciprocal of the user-cost in that market. As a consequence, house prices will vary more with changes in rents in markets with low user-costs of housing. Lower financing costs for housing reduces the user-cost of housing which would lead to higher prices holding rents constant. However, if some of the benefits of lower financing costs to landlords are passed on to renters, then the impact of lower mortgage rates on house prices will be attenuated. The Bush tax cuts were enacted during this period which lowered marginal tax rates. These lower marginal tax rates would raise the user-cost by reducing the benefit from the mortgage interest deduction. These lower marginal tax rates would have led to lower house prices, all else the same, with the magnitude of the reduction reflecting in part expectations over whether the tax cuts would be made permanent. While income, monetary policy and tax rates each underwent some changes in the first half of the 2000s, the term in the user-cost that has received the most attention in trying to explain the house price boom is the expected return to housing, g e . The higher the risk-adjusted expected return, the lower the user-cost and the higher house prices will be in a market. As Himmelberg et al 5 2004 to 6.14 in December 2006, so that any further declines in the user-cost was not being driven by lower financing costs during this period. 10 Glaeser et al (2010) argue that the empirical connection between mortgage rates and house prices is not strong enough to explain the dynamics of house prices during the housing boom. On a conceptual level, they argue that the impact of any shift in housing demand on house prices depends on the housing supply elasticity in that market. For markets with inelastic housing supply, increases in housing demand will mainly result in higher house prices instead of increased production of new homes. In contrast, in housing markets with elastic housing supply, increases in housing demand will mainly result in the production of new homes. House prices in these markets are determined by the cost of building a new home. 11 Furthermore, they argue that expected future mortgage rates are important in addition to the current mortgage rate. If mortgage rates are expected to rise, then the effect of a low current mortgage rate on house prices will be attenuated. This argument can be captured in the user-cost arbitrage condition shown earlier by factoring the expected rise in financing costs into the expected house price appreciation term. 12 Credit conditions enter into the standard user-cost formulation solely through the mortgage interest rate. However, a second important aspect is the required downpayment by the borrower. The interest rate and the required downpayment reflect the two underwriting Constraints on a borrower when bidding on a property. The minimum downpayment percentage is also referred to as the "collateral rate" on the mortgage. 13 For a given mortgage balance, the mortgage interest rate impacts the monthly payment that the borrower will have to make. Underwriting standards will 10 Some authors pointed to the rise in price-rent ratios as likely to be followed by a reduction in subsequent price growth 6 stipulate a maximum that the sum of the annual mortgage payments in addition to the taxes and insurance on the property can be as a fraction of the borrower's income. 14 We will refer to this as the "cash-Flow Constraint". A lender will also require the borrower(s) to make a minimum downpayment. The ratio of the downpayment to the sale or appraised value of the house determines the origination loan-to-value ratio (LTV). We refer to this as the "downpayment Constraint." The maximum that a borrower may bid on a house will depend on which of these two Constraints first becomes binding given the underwriting standards in use at the time. The mortgage interest rate and the collateral rate are jointly determined in a credit market (see There is an additional channel, not necessarily captured by changes in the average origination LTV and not explicitly addressed by 7 possibly rental income. Alternatively, in computing the DTI ratio, debts other than the mortgage loan under consideration may be ignored or incorporated differently. As shown in We can incorporate changes in underwriting standards into the user-cost framework. , , , , , where LTV M is the maximum allowed origination loan-to-value ratio, s captures other prevailing underwriting standards at the time of the home purchase such as DTI and documentation, and f captures how changes in the degree of leverage and documentation impact house prices holding constant the user-cost. Finally, there is a potentially important amplification effect of leverage on house prices which is not fully captured in our augmented user-cost arbitrage conditions. Geanakoplos This distribution of buyers in terms of their opinions about the future value of housing can generate an amplification mechanism for house price dynamics. In normal times, optimistic buyers are infra-marginal participants in the housing market. At the prevailing house prices they would like to purchase additional housing but are prevented from doing so because the cash-Flow Constraint or the downpayment Constraint is binding. However, during the early phase of a housing boom, lenders may reduce the required downpayment percentage on new mortgages and begin to relax other underwriting standards due to the strong performance of house prices and low delinquency rates. These actions enable the optimistic buyers to purchase additional housing. The increasing leverage allowed in the market, then, begins to shift the composition of new purchase transactions in the market toward more optimistic buyers who are willing to bid higher prices for houses. This is an additional channel by which higher leverage can amplify the upward pressure on house prices. Geanakoplos describes this dynamic as the upswing phase of a "leverage cycle". Can increasing leverage help to explain the acceleration in house prices from 2004 to 2006? For leverage to have played an important role we need to establish at least two things. First, we need to show that leverage was increasing over these three years. Second, we need to demonstrate that the composition of purchasing activity was shifting toward more optimistic buyers. 15 There are two observations that can be drawn from There are several reasons to expect credit conditions to have particularly affected investor activity in 15 Our finding of increases in the median nonprime CLTV at origination is consistent with that of Mayer and Pence (2009). We use our matched sample, which represents a random sample of all LP loans as described below, for this table. 16 The Glaeser et al (2010) data indicate that the 90 th percentile was at 100 going back to 1998. 17 This is consistent with trends reported by Geanakoplos (2009, chart 1) for the average downpayment as a proportion of the purchase price. Among the 50% lowest downpayment ratios for subprime and ALT-A borrowers (based on CoreLogic data), he found a decrease in the average downpayment from 13% in the first quarter of 2000, to a low point of 2.7% in the second quarter of 2006. 10 the buildup of the housing boom. In discussing these, we will distinguish between three different types of buyers in a housing market: buyers who want to live in the house (owner-occupiers), investors who want to keep the house as vacation or future retirement home or who want to rent the property and then resell at a future date (buy and hold), and investors who want to resell the property without living in or renting the house (buy and flip). The first reason to expect a role for investors in bidding up prices concerns the impact of the previously discussed increase in average origination LTVs. For a given mortgage interest rate, reducing the required downpayment percentage can allow a borrower to bid more aggressively for a property, but this is especially so for investors. The easiest way to see the impact of variation in the allowed LTV on the maximum bid is to take the case of a "buy and flip" borrower. As an illustration, consider an investor who has $50,000 to invest in real estate. This money must cover the downpayment as well as the mortgage payments, property taxes and home insurance during the expected holding period. For simplicity, we assume that the house is financed with a 30-year fixedrate mortgage with an interest rate of 5.5 percent. We assume that annual property taxes, insurance payments and any required maintenance expenditures equate to 2 percent of the house value. The investor will not be renting out the property during the time until resale. We consider two cases: in the first the investor plans to be able to finance the purchase for up to three years, and in the second the investor plans to be able to finance the project for up to two years. The relationship between the allowed level of leverage as indicated by the origination LTV and the maximum bid is shown in A third channel affecting real estate investors concerns the use of second liens on existing mortgages to facilitate the down payment and meeting of loan requirements for purchasing additional investment properties. As documented in earlier work by 12 mortgages, cash-out refinances, second mortgages and home equity lines of credit. In fact, on average for each 1% increase in home prices, homeowners increased their mortgage debt by 1%, so that proportionally their equity share in their homes actually remained relatively constant until the end of 2006. Equity extraction may have been especially attractive to optimistic, but cashconstrained investors, by allowing them to use these funds to make downpayments on purchases of additional homes. Accordingly, we expect the combined LTV on existing mortgages to have increased for investors during the period in which they purchased additional properties. Finally, we refer again to the amplification effects that result from shifts in the market toward more optimistic buyers. In the next section of the paper we explore the Geanakoplos hypothesis. We identify optimistic buyers as investors, and especially the "buy and flip" investors. We document the role of this class of investors over the past credit cycle both nationally as well in four boom states. We explore the extent to which the investor share of purchase transactions changes over the credit cycle. These changes are decomposed into both the extensive margin -more investors enter the market -and the intensive margin -existing investors increase the size of their portfolio of residential real estate exposures. We also examine the default behavior of investors as compared to owner-occupant borrowers. The final section of the paper discusses implications of our findings for current policy work on improving financial stability. Investors and the Leverage Cycle If Geanakoplos' description of the dynamics of the leverage cycle is applicable to the housing boom-bust cycle of the 2000s, we would expect to see changes in the characteristics of leveraged buyers of residential real estate over the period. In this section, we provide descriptive evidence of some major changes in the observable characteristics of mortgagors between 2000 and 2010. 13 While there has been some anecdotal evidence supporting the idea that investors played an important role in the boom, careful analysis of this issue has been impeded by lack of appropriate data. 20 For investors, the benefits of living in a house are immaterial to the decision of whether or not to keep making the mortgage payment, making default a less costly decision for investors than for owner-occupants. Of course, lenders are well aware of this difference, and typically require mortgagors to declare whether they will live in the collateral property, charging higher interest rates and requiring higher downpayments from those who acknowledge that they will not, ceteris paribus. But the interest rate penalty and limitations on leverage discourage borrowers from declaring their intention to live elsewhere, and self-reported "occupancy status" is thus considered a particularly unreliable piece of data. 21 Fitch We bring two distinct kinds of data to the analysis of this important question. Our primary source is the FRBNY Consumer Credit Panel (CCP) which comprises an anonymous and nationally representative 5% random sample of US individuals with credit files and all of the household members of those 5%. 22 In all, the data set includes files for more than 15% of the population, or 20 See, for example, http://www.metrotrends.org/commentary/mortgage-lending.cfm 21 Early defaults are defined to be defaults that occur within the first year. 22 The FRBNY CCP panel is based on Equifax credit report data. Lee and van der Klaauw (2010) provides further details on the data set. The analyses reported in this paper are solely based on the representative random sample and do not include the additional household members sampled. 14 approximately 37 million individuals in each quarter from 1999-2011Q1. 23 The FRBNY CCP data allow us to overcome some of the difficulties with self-reported occupancy status. Unlike loan-level data, which focus on individual debt contracts and the information used in underwriting them, credit reports are designed to give lenders (and potential lenders) dynamic credit information on individual borrowers, including the types and amounts of debt they have outstanding at any point in time. Our panel allows us to track individual borrowers over time, through refinances and moves, where at each point in time we observe all outstanding mortgage loans and non-mortgage debts. We can use this information to separate mortgage borrowers based on how many distinct first-lien mortgage accounts appear on their credit reports. Since each property can secure at most a single first-lien mortgage, the number of such mortgages on a borrower's credit report is a reliable, non-self reported, indicator of the minimum number of properties a given individual has borrowed against. 24 This kind of information about individual borrowers is not available in loan-level data sets and thus the FRBNY CCP data provide a unique perspective into important questions about who is originating new mortgages at any point in time, as well as their subsequent behavior
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Real Estate Investors, the Leverage Cycle, and the Housing Market Crisis," Federal Reserve Bank of
2011Co-Authors: Andrew Haughwout, Donghoon Lee, Joseph Tracy, Wilbert Van Der KlaauwAbstract:Abstract We explore a mostly undocumented but important dimension of the housing market cycle: the role played by real estate investors. Using unique credit report data, we document large increases in the investor share of purchases and subsequent delinquencies. In states that experienced the largest housing boom-bust cycles, at the peak investors were associated with almost half of purchase mortgage originations. In part through apparently misreporting their intentions to occupy the property, investors took more leverage, contributing to higher default rates. Our findings have important implications for the design of policies to address the consequences and recurrence of housing market bubbles. 2 The U.S. economy is still recovering from the financial crisis that began in the fall of 2007. The collapse of house prices across many markets was a precipitating factor in the financial crisis and adverse feedback effects between financial markets and the real economy led to the most severe recession in the post-war period. Extraordinary interventions by fiscal and monetary authorities both in the U.S. and abroad were required in order to prevent a complete collapse of global markets and the potential onset of another great depression. Attention has shifted from containing the financial crisis to examining its causes and designing policies to limit both the likelihood and the severity of a similar crisis in the future. Given the central role that housing played as a catalyst to the crisis, it is important to better understand the determinants of the dynamics of house prices and of subsequent mortgage defaults over this recent cycle. While house prices were rising in many parts of the country over the period leading up to the crisis, these increases were particularly pronounced in four states -Arizona, California, Florida and Nevada (the "bubble" states). This rapid run-up and then crash in house prices exacted a terrible cost to homeowners, financial firms and to the economy. Current estimates are that around 23 percent of active mortgages are "under water" in that the balance on the mortgage exceeds the current value of the 2 California is a bit of an exception in that it appears that average house prices have stabilized at a level 50 percent higher than in 2000. As of 2010 Q4, nearly 2.8 million homes have gone through foreclosure, and another 2 million homes are in the process of foreclosure. 4 Serious delinquencies continue to add new homes to the foreclosure pipeline over time. Nationally distress sales represent around half of all repeat-sale transactions. These distress sales continue to exert downward pressure on house prices making it more difficult for housing markets to recover. A focus on residential mortgage finance in order to understand what the determinants were of the house price and mortgage default dynamics generated over the recent cycle would inform efforts to enhance financial stability. A more robust system of residential mortgage finance should aim to limit the degree to which house prices rise and fall over a credit cycle. Reducing the amplitude of the house price swings will limit the potential for collateral damage created by housing markets for the real economy. Related Literature Given that housing is a durable asset, periods of rising prices are indicative of increasing demand for housing. 5 One strand of the literature on housing demand focuses on the determinants that affect the "user cost" of housing. 6 The user cost of housing (UC) is the annual Flow cost to the owner per dollar of house price, taking into account after-tax financing costs, property taxes and insurance, maintenance and depreciation costs and the expected risk-adjusted return to owning the house. The value of the housing service Flow is proxied by the annual rent (R). If we assume that there is arbitrage between owned and rental housing, then the annual rent should equate to the price of housing (P) times the user-cost. 3 http://www.corelogic.com/About-Us/News/New-CoreLogic-Data-Shows-23-Percent-of-BorrowersUnderwater-with-$750-Billion-Dollars-of-Negative-Equity.aspx 4 http://www.ots.treas.gov/_files/490069.pdf 5 That is, with the exception of natural disasters and periods of armed conflict, the supply of housing in a market cannot contract significantly over a short period of time to drive up house prices. 6 See where r m is the mortgage financing rate, τ describes the tax environment, δ the depreciation rate on housing net of that offset by maintenance expenditures, g e the risk adjusted expected return to housing, and Y is the average income. This framework suggests several possible candidates for explaining the rise in house prices in the early to mid-2000s. A rise in income in a housing market will increase area rental rates to a degree that reflects the elasticity of supply of rental housing in that local market. Higher rents will translate into higher house prices by a factor given by the reciprocal of the user-cost in that market. As a consequence, house prices will vary more with changes in rents in markets with low user-costs of housing. Lower financing costs for housing reduces the user-cost of housing which would lead to higher prices holding rents constant. However, if some of the benefits of lower financing costs to landlords are passed on to renters, then the impact of lower mortgage rates on house prices will be attenuated. The Bush tax cuts were enacted during this period which lowered marginal tax rates. These lower marginal tax rates would raise the user-cost by reducing the benefit from the mortgage interest deduction. These lower marginal tax rates would have led to lower house prices, all else the same, with the magnitude of the reduction reflecting in part expectations over whether the tax cuts would be made permanent. While income, monetary policy and tax rates each underwent some changes in the first half of the 2000s, the term in the user-cost that has received the most attention in trying to explain the house price boom is the expected return to housing, g e . The higher the risk-adjusted expected return, the lower the user-cost and the higher house prices will be in a market. As Himmelberg et al (2005) explain, the sensitivity of house prices to house price expectations increases with the degree to which house prices are expected to rise. The expected return to housing is the only forward-looking aspect to the user-cost of housing framework. The arbitrage condition listed above has a potential self-fulfilling characteristic. If owners expect house prices to rise in the future, then the user-cost of housing will fall and, given a constant rent, the value of houses will rise. important to note that the average 30-year fixed-rate mortgage rate increased from 5.74 in January 9 Rents would not be expected to rise since the value of the current Flow of housing services has not changed. 6 2004 to 6.14 in December 2006, so that any further declines in the user-cost was not being driven by lower financing costs during this period. 10 Glaeser et al (2010) argue that the empirical connection between mortgage rates and house prices is not strong enough to explain the dynamics of house prices during the housing boom. On a conceptual level, they argue that the impact of any shift in housing demand on house prices depends on the housing supply elasticity in that market. For markets with inelastic housing supply, increases in housing demand will mainly result in higher house prices instead of increased production of new homes. In contrast, in housing markets with elastic housing supply, increases in housing demand will mainly result in the production of new homes. House prices in these markets are determined by the cost of building a new home. 11 Furthermore, they argue that expected future mortgage rates are important in addition to the current mortgage rate. If mortgage rates are expected to rise, then the effect of a low current mortgage rate on house prices will be attenuated. This argument can be captured in the user-cost arbitrage condition shown earlier by factoring the expected rise in financing costs into the expected house price appreciation term. 12 Credit conditions enter into the standard user-cost formulation solely through the mortgage interest rate. However, a second important aspect is the required downpayment by the borrower. The interest rate and the required downpayment reflect the two underwriting Constraints on a borrower when bidding on a property. The minimum downpayment percentage is also referred to as the "collateral rate" on the mortgage. 13 For a given mortgage balance, the mortgage interest rate impacts the monthly payment that the borrower will have to make. Underwriting standards will 10 Some authors pointed to the rise in price-rent ratios as likely to be followed by a reduction in subsequent price growth 7 stipulate a maximum that the sum of the annual mortgage payments in addition to the taxes and insurance on the property can be as a fraction of the borrower's income. 14 We will refer to this as the "cash-Flow Constraint". A lender will also require the borrower(s) to make a minimum downpayment. The ratio of the downpayment to the sale or appraised value of the house determines the origination loan-to-value ratio (LTV). We refer to this as the "downpayment Constraint." The maximum that a borrower may bid on a house will depend on which of these two Constraints first becomes binding given the underwriting standards in use at the time. The mortgage interest rate and the collateral rate are jointly determined in a credit market (see There is an additional channel, not necessarily captured by changes in the average origination LTV and not explicitly addressed by 8 possibly rental income. Alternatively, in computing the DTI ratio, debts other than the mortgage loan under consideration may be ignored or incorporated differently. As shown in , , , , , where LTV M is the maximum allowed origination loan-to-value ratio, s captures other prevailing underwriting standards at the time of the home purchase such as DTI and documentation, and f captures how changes in the degree of leverage and documentation impact house prices holding constant the user-cost. Finally, there is a potentially important amplification effect of leverage on house prices which is not fully captured in our augmented user-cost arbitrage conditions. Geanakoplos This distribution of buyers in terms of their opinions about the future value of housing can generate an amplification mechanism for house price dynamics. In normal times, optimistic buyers are infra-marginal participants in the housing market. At the prevailing house prices they would like to purchase additional housing but are prevented from doing so because the cash-Flow Constraint or the downpayment Constraint is binding. However, during the early phase of a housing boom, lenders may reduce the required downpayment percentage on new mortgages and begin to relax other underwriting standards due to the strong performance of house prices and low delinquency rates. These actions enable the optimistic buyers to purchase additional housing. The increasing leverage allowed in the market, then, begins to shift the composition of new purchase transactions in the market toward more optimistic buyers who are willing to bid higher prices for houses. This is an additional channel by which higher leverage can amplify the upward pressure on house prices. Geanakoplos describes this dynamic as the upswing phase of a "leverage cycle". Can increasing leverage help to explain the acceleration in house prices from 2004 to 2006? For leverage to have played an important role we need to establish at least two things. First, we need to show that leverage was increasing over these three years. Second, we need to demonstrate that the composition of purchasing activity was shifting toward more optimistic buyers. 15 There are two observations that can be drawn from There are several reasons to expect credit conditions to have particularly affected investor activity in 15 Our finding of increases in the median nonprime CLTV at origination is consistent with that of Mayer and Pence (2009). We use our matched sample, which represents a random sample of all LP loans as described below, for this table. 16 The 11 the buildup of the housing boom. In discussing these, we will distinguish between three different types of buyers in a housing market: buyers who want to live in the house (owner-occupiers), investors who want to keep the house as vacation or future retirement home or who want to rent the property and then resell at a future date (buy and hold), and investors who want to resell the property without living in or renting the house (buy and flip). The first reason to expect a role for investors in bidding up prices concerns the impact of the previously discussed increase in average origination LTVs. For a given mortgage interest rate, reducing the required downpayment percentage can allow a borrower to bid more aggressively for a property, but this is especially so for investors. The easiest way to see the impact of variation in the allowed LTV on the maximum bid is to take the case of a "buy and flip" borrower. As an illustration, consider an investor who has $50,000 to invest in real estate. This money must cover the downpayment as well as the mortgage payments, property taxes and home insurance during the expected holding period. For simplicity, we assume that the house is financed with a 30-year fixedrate mortgage with an interest rate of 5.5 percent. We assume that annual property taxes, insurance payments and any required maintenance expenditures equate to 2 percent of the house value. The investor will not be renting out the property during the time until resale. We consider two cases: in the first the investor plans to be able to finance the purchase for up to three years, and in the second the investor plans to be able to finance the project for up to two years. The relationship between the allowed level of leverage as indicated by the origination LTV and the maximum bid is shown in A third channel affecting real estate investors concerns the use of second liens on existing mortgages to facilitate the down payment and meeting of loan requirements for purchasing additional investment properties. As documented in earlier work by 13 mortgages, cash-out refinances, second mortgages and home equity lines of credit. In fact, on average for each 1% increase in home prices, homeowners increased their mortgage debt by 1%, so that proportionally their equity share in their homes actually remained relatively constant until the end of 2006. Equity extraction may have been especially attractive to optimistic, but cashconstrained investors, by allowing them to use these funds to make downpayments on purchases of additional homes. Accordingly, we expect the combined LTV on existing mortgages to have increased for investors during the period in which they purchased additional properties. Finally, we refer again to the amplification effects that result from shifts in the market toward more optimistic buyers. In the next section of the paper we explore the Geanakoplos hypothesis. We identify optimistic buyers as investors, and especially the "buy and flip" investors. We document the role of this class of investors over the past credit cycle both nationally as well in four boom states. We explore the extent to which the investor share of purchase transactions changes over the credit cycle. These changes are decomposed into both the extensive margin -more investors enter the market -and the intensive margin -existing investors increase the size of their portfolio of residential real estate exposures. We also examine the default behavior of investors as compared to owner-occupant borrowers. The final section of the paper discusses implications of our findings for current policy work on improving financial stability. Investors and the Leverage Cycle If Geanakoplos' description of the dynamics of the leverage cycle is applicable to the housing boom-bust cycle of the 2000s, we would expect to see changes in the characteristics of leveraged buyers of residential real estate over the period. In this section, we provide descriptive evidence of some major changes in the observable characteristics of mortgagors between 2000 and 2010. 14 While there has been some anecdotal evidence supporting the idea that investors played an important role in the boom, careful analysis of this issue has been impeded by lack of appropriate data. 20 For investors, the benefits of living in a house are immaterial to the decision of whether or not to keep making the mortgage payment, making default a less costly decision for investors than for owner-occupants. Of course, lenders are well aware of this difference, and typically require mortgagors to declare whether they will live in the collateral property, charging higher interest rates and requiring higher downpayments from those who acknowledge that they will not, ceteris paribus. But the interest rate penalty and limitations on leverage discourage borrowers from declaring their intention to live elsewhere, and self-reported "occupancy status" is thus considered a particularly unreliable piece of data. 21 Fitch We bring two distinct kinds of data to the analysis of this important question. Our primary source is the FRBNY Consumer Credit Panel (CCP) which comprises an anonymous and nationally representative 5% random sample of US individuals with credit files and all of the household members of those 5%. 22 In all, the data set includes files for more than 15% of the population, or 20 See, for example, http://www.metrotrends.org/commentary/mortgage-lending.cfm 21 Early defaults are defined to be defaults that occur within the first year. 22 The FRBNY CCP panel is based on Equifax credit report data. Lee and van der Klaauw (2010) provides further details on the data set. The analyses reported in this paper are solely based on the representative random sample and do not include the additional household members sampled. 15 approximately 37 million individuals in each quarter from 1999-2011Q1. 23 The FRBNY CCP data allow us to overcome some of the difficulties with self-reported occupancy status. Unlike loan-level data, which focus on individual debt contracts and the information used in underwriting them, credit reports are designed to give lenders (and potential lenders) dynamic credit information on individual borrowers, including the types and amounts of debt they have outstanding at any point in time. Our panel allows us to track individual borrowers over time, through refinances and moves, where at each point in time we observe all outstanding mortgage loans and non-mortgage debts. We can use this information to separate mortgage borrowers based on how many distinct first-lien mortgage accounts appear on their credit reports. Since each property can secure at most a single first-lien mortgage, the number of such mortgages on a borrower's credit report is a reliable, non-self reported, indicator of the minimum number of properties a given individual has borrowed against. 24 This kind of information about individual borrowers is not available in loan-level data sets and thus the FRBNY CCP data provide a unique perspective into important questions about who is originating new mortgages at any point in time, as well as their subsequent behavior
Wilbert Van Der Klaauw - One of the best experts on this subject based on the ideXlab platform.
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A Service of zbw Leibniz-Informationszentrum Wirtschaft Leibniz Information Centre for Economics Real estate investors, the leverage cycle, and the housing market crisis Real Estate Investors, the Leverage Cycle, and the Housing Market Crisis Real Estate
2020Co-Authors: Andrew Haughwout, Donghoon Lee, Joseph Tracy, Wilbert Van Der KlaauwAbstract:Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may Abstract We explore a mostly undocumented but important dimension of the housing market crisis: the role played by real estate investors. Using unique credit-report data, we document large increases in the share of purchases, and subsequently delinquencies, by real estate investors. In states that experienced the largest housing booms and busts, at the peak of the market almost half of purchase mortgage originations were associated with investors. In part by apparently misreporting their intentions to occupy the property, investors took on more leverage, contributing to higher rates of default. Our findings have important implications for policies designed to address the consequences and recurrence of housing market bubbles. The U.S. economy is still recovering from the financial crisis that began in the fall of 2007. The collapse of house prices across many markets was a precipitating factor in the financial crisis and adverse feedback effects between financial markets and the real economy led to the most severe recession in the post-war period. Extraordinary interventions by fiscal and monetary authorities both in the U.S. and abroad were required in order to prevent a complete collapse of global markets and the potential onset of another great depression. Attention has shifted from containing the financial crisis to examining its causes and designing policies to limit both the likelihood and the severity of a similar crisis in the future. Given the central role that housing played as a catalyst to the crisis, it is important to better understand the determinants of the dynamics of house prices and of subsequent mortgage defaults over this recent cycle. While house prices were rising in many parts of the country over the period leading up to the crisis, these increases were particularly pronounced in four states -Arizona, California, Florida and Nevada (the "bubble" states). This rapid run-up and then crash in house prices exacted a terrible cost to homeowners, financial firms and to the economy. Current estimates are that around 23 percent of active mortgages are "under water" in that the balance on the mortgage exceeds the current value of the 2 California is a bit of an exception in that it appears that average house prices have stabilized at a level 50 percent higher than in 2000. 2 house. As of 2010 Q4, nearly 2.8 million homes have gone through foreclosure, and another 2 million homes are in the process of foreclosure. 4 Serious delinquencies continue to add new homes to the foreclosure pipeline over time. Nationally distress sales represent around half of all repeat-sale transactions. These distress sales continue to exert downward pressure on house prices making it more difficult for housing markets to recover. A focus on residential mortgage finance in order to understand what the determinants were of the house price and mortgage default dynamics generated over the recent cycle would inform efforts to enhance financial stability. A more robust system of residential mortgage finance should aim to limit the degree to which house prices rise and fall over a credit cycle. Reducing the amplitude of the house price swings will limit the potential for collateral damage created by housing markets for the real economy. Related Literature Given that housing is a durable asset, periods of rising prices are indicative of increasing demand for housing. 5 One strand of the literature on housing demand focuses on the determinants that affect the "user cost" of housing. 6 The user cost of housing (UC) is the annual Flow cost to the owner per dollar of house price, taking into account after-tax financing costs, property taxes and insurance, maintenance and depreciation costs and the expected risk-adjusted return to owning the house. The value of the housing service Flow is proxied by the annual rent (R). If we assume that there is arbitrage between owned and rental housing, then the annual rent should equate to the price of housing (P) times the user-cost. 3 http://www.corelogic.com/About-Us/News/New-CoreLogic-Data-Shows-23-Percent-of-BorrowersUnderwater-with-$750-Billion-Dollars-of-Negative-Equity.aspx 4 http://www.ots.treas.gov/_files/490069.pdf 5 That is, with the exception of natural disasters and periods of armed conflict, the supply of housing in a market cannot contract significantly over a short period of time to drive up house prices. 6 See where r m is the mortgage financing rate, τ describes the tax environment, δ the depreciation rate on housing net of that offset by maintenance expenditures, g e the risk adjusted expected return to housing, and Y is the average income. This framework suggests several possible candidates for explaining the rise in house prices in the early to mid-2000s. A rise in income in a housing market will increase area rental rates to a degree that reflects the elasticity of supply of rental housing in that local market. Higher rents will translate into higher house prices by a factor given by the reciprocal of the user-cost in that market. As a consequence, house prices will vary more with changes in rents in markets with low user-costs of housing. Lower financing costs for housing reduces the user-cost of housing which would lead to higher prices holding rents constant. However, if some of the benefits of lower financing costs to landlords are passed on to renters, then the impact of lower mortgage rates on house prices will be attenuated. The Bush tax cuts were enacted during this period which lowered marginal tax rates. These lower marginal tax rates would raise the user-cost by reducing the benefit from the mortgage interest deduction. These lower marginal tax rates would have led to lower house prices, all else the same, with the magnitude of the reduction reflecting in part expectations over whether the tax cuts would be made permanent. While income, monetary policy and tax rates each underwent some changes in the first half of the 2000s, the term in the user-cost that has received the most attention in trying to explain the house price boom is the expected return to housing, g e . The higher the risk-adjusted expected return, the lower the user-cost and the higher house prices will be in a market. As Himmelberg et al 5 2004 to 6.14 in December 2006, so that any further declines in the user-cost was not being driven by lower financing costs during this period. 10 Glaeser et al (2010) argue that the empirical connection between mortgage rates and house prices is not strong enough to explain the dynamics of house prices during the housing boom. On a conceptual level, they argue that the impact of any shift in housing demand on house prices depends on the housing supply elasticity in that market. For markets with inelastic housing supply, increases in housing demand will mainly result in higher house prices instead of increased production of new homes. In contrast, in housing markets with elastic housing supply, increases in housing demand will mainly result in the production of new homes. House prices in these markets are determined by the cost of building a new home. 11 Furthermore, they argue that expected future mortgage rates are important in addition to the current mortgage rate. If mortgage rates are expected to rise, then the effect of a low current mortgage rate on house prices will be attenuated. This argument can be captured in the user-cost arbitrage condition shown earlier by factoring the expected rise in financing costs into the expected house price appreciation term. 12 Credit conditions enter into the standard user-cost formulation solely through the mortgage interest rate. However, a second important aspect is the required downpayment by the borrower. The interest rate and the required downpayment reflect the two underwriting Constraints on a borrower when bidding on a property. The minimum downpayment percentage is also referred to as the "collateral rate" on the mortgage. 13 For a given mortgage balance, the mortgage interest rate impacts the monthly payment that the borrower will have to make. Underwriting standards will 10 Some authors pointed to the rise in price-rent ratios as likely to be followed by a reduction in subsequent price growth 6 stipulate a maximum that the sum of the annual mortgage payments in addition to the taxes and insurance on the property can be as a fraction of the borrower's income. 14 We will refer to this as the "cash-Flow Constraint". A lender will also require the borrower(s) to make a minimum downpayment. The ratio of the downpayment to the sale or appraised value of the house determines the origination loan-to-value ratio (LTV). We refer to this as the "downpayment Constraint." The maximum that a borrower may bid on a house will depend on which of these two Constraints first becomes binding given the underwriting standards in use at the time. The mortgage interest rate and the collateral rate are jointly determined in a credit market (see There is an additional channel, not necessarily captured by changes in the average origination LTV and not explicitly addressed by 7 possibly rental income. Alternatively, in computing the DTI ratio, debts other than the mortgage loan under consideration may be ignored or incorporated differently. As shown in We can incorporate changes in underwriting standards into the user-cost framework. , , , , , where LTV M is the maximum allowed origination loan-to-value ratio, s captures other prevailing underwriting standards at the time of the home purchase such as DTI and documentation, and f captures how changes in the degree of leverage and documentation impact house prices holding constant the user-cost. Finally, there is a potentially important amplification effect of leverage on house prices which is not fully captured in our augmented user-cost arbitrage conditions. Geanakoplos This distribution of buyers in terms of their opinions about the future value of housing can generate an amplification mechanism for house price dynamics. In normal times, optimistic buyers are infra-marginal participants in the housing market. At the prevailing house prices they would like to purchase additional housing but are prevented from doing so because the cash-Flow Constraint or the downpayment Constraint is binding. However, during the early phase of a housing boom, lenders may reduce the required downpayment percentage on new mortgages and begin to relax other underwriting standards due to the strong performance of house prices and low delinquency rates. These actions enable the optimistic buyers to purchase additional housing. The increasing leverage allowed in the market, then, begins to shift the composition of new purchase transactions in the market toward more optimistic buyers who are willing to bid higher prices for houses. This is an additional channel by which higher leverage can amplify the upward pressure on house prices. Geanakoplos describes this dynamic as the upswing phase of a "leverage cycle". Can increasing leverage help to explain the acceleration in house prices from 2004 to 2006? For leverage to have played an important role we need to establish at least two things. First, we need to show that leverage was increasing over these three years. Second, we need to demonstrate that the composition of purchasing activity was shifting toward more optimistic buyers. 15 There are two observations that can be drawn from There are several reasons to expect credit conditions to have particularly affected investor activity in 15 Our finding of increases in the median nonprime CLTV at origination is consistent with that of Mayer and Pence (2009). We use our matched sample, which represents a random sample of all LP loans as described below, for this table. 16 The Glaeser et al (2010) data indicate that the 90 th percentile was at 100 going back to 1998. 17 This is consistent with trends reported by Geanakoplos (2009, chart 1) for the average downpayment as a proportion of the purchase price. Among the 50% lowest downpayment ratios for subprime and ALT-A borrowers (based on CoreLogic data), he found a decrease in the average downpayment from 13% in the first quarter of 2000, to a low point of 2.7% in the second quarter of 2006. 10 the buildup of the housing boom. In discussing these, we will distinguish between three different types of buyers in a housing market: buyers who want to live in the house (owner-occupiers), investors who want to keep the house as vacation or future retirement home or who want to rent the property and then resell at a future date (buy and hold), and investors who want to resell the property without living in or renting the house (buy and flip). The first reason to expect a role for investors in bidding up prices concerns the impact of the previously discussed increase in average origination LTVs. For a given mortgage interest rate, reducing the required downpayment percentage can allow a borrower to bid more aggressively for a property, but this is especially so for investors. The easiest way to see the impact of variation in the allowed LTV on the maximum bid is to take the case of a "buy and flip" borrower. As an illustration, consider an investor who has $50,000 to invest in real estate. This money must cover the downpayment as well as the mortgage payments, property taxes and home insurance during the expected holding period. For simplicity, we assume that the house is financed with a 30-year fixedrate mortgage with an interest rate of 5.5 percent. We assume that annual property taxes, insurance payments and any required maintenance expenditures equate to 2 percent of the house value. The investor will not be renting out the property during the time until resale. We consider two cases: in the first the investor plans to be able to finance the purchase for up to three years, and in the second the investor plans to be able to finance the project for up to two years. The relationship between the allowed level of leverage as indicated by the origination LTV and the maximum bid is shown in A third channel affecting real estate investors concerns the use of second liens on existing mortgages to facilitate the down payment and meeting of loan requirements for purchasing additional investment properties. As documented in earlier work by 12 mortgages, cash-out refinances, second mortgages and home equity lines of credit. In fact, on average for each 1% increase in home prices, homeowners increased their mortgage debt by 1%, so that proportionally their equity share in their homes actually remained relatively constant until the end of 2006. Equity extraction may have been especially attractive to optimistic, but cashconstrained investors, by allowing them to use these funds to make downpayments on purchases of additional homes. Accordingly, we expect the combined LTV on existing mortgages to have increased for investors during the period in which they purchased additional properties. Finally, we refer again to the amplification effects that result from shifts in the market toward more optimistic buyers. In the next section of the paper we explore the Geanakoplos hypothesis. We identify optimistic buyers as investors, and especially the "buy and flip" investors. We document the role of this class of investors over the past credit cycle both nationally as well in four boom states. We explore the extent to which the investor share of purchase transactions changes over the credit cycle. These changes are decomposed into both the extensive margin -more investors enter the market -and the intensive margin -existing investors increase the size of their portfolio of residential real estate exposures. We also examine the default behavior of investors as compared to owner-occupant borrowers. The final section of the paper discusses implications of our findings for current policy work on improving financial stability. Investors and the Leverage Cycle If Geanakoplos' description of the dynamics of the leverage cycle is applicable to the housing boom-bust cycle of the 2000s, we would expect to see changes in the characteristics of leveraged buyers of residential real estate over the period. In this section, we provide descriptive evidence of some major changes in the observable characteristics of mortgagors between 2000 and 2010. 13 While there has been some anecdotal evidence supporting the idea that investors played an important role in the boom, careful analysis of this issue has been impeded by lack of appropriate data. 20 For investors, the benefits of living in a house are immaterial to the decision of whether or not to keep making the mortgage payment, making default a less costly decision for investors than for owner-occupants. Of course, lenders are well aware of this difference, and typically require mortgagors to declare whether they will live in the collateral property, charging higher interest rates and requiring higher downpayments from those who acknowledge that they will not, ceteris paribus. But the interest rate penalty and limitations on leverage discourage borrowers from declaring their intention to live elsewhere, and self-reported "occupancy status" is thus considered a particularly unreliable piece of data. 21 Fitch We bring two distinct kinds of data to the analysis of this important question. Our primary source is the FRBNY Consumer Credit Panel (CCP) which comprises an anonymous and nationally representative 5% random sample of US individuals with credit files and all of the household members of those 5%. 22 In all, the data set includes files for more than 15% of the population, or 20 See, for example, http://www.metrotrends.org/commentary/mortgage-lending.cfm 21 Early defaults are defined to be defaults that occur within the first year. 22 The FRBNY CCP panel is based on Equifax credit report data. Lee and van der Klaauw (2010) provides further details on the data set. The analyses reported in this paper are solely based on the representative random sample and do not include the additional household members sampled. 14 approximately 37 million individuals in each quarter from 1999-2011Q1. 23 The FRBNY CCP data allow us to overcome some of the difficulties with self-reported occupancy status. Unlike loan-level data, which focus on individual debt contracts and the information used in underwriting them, credit reports are designed to give lenders (and potential lenders) dynamic credit information on individual borrowers, including the types and amounts of debt they have outstanding at any point in time. Our panel allows us to track individual borrowers over time, through refinances and moves, where at each point in time we observe all outstanding mortgage loans and non-mortgage debts. We can use this information to separate mortgage borrowers based on how many distinct first-lien mortgage accounts appear on their credit reports. Since each property can secure at most a single first-lien mortgage, the number of such mortgages on a borrower's credit report is a reliable, non-self reported, indicator of the minimum number of properties a given individual has borrowed against. 24 This kind of information about individual borrowers is not available in loan-level data sets and thus the FRBNY CCP data provide a unique perspective into important questions about who is originating new mortgages at any point in time, as well as their subsequent behavior
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Real Estate Investors, the Leverage Cycle, and the Housing Market Crisis," Federal Reserve Bank of
2011Co-Authors: Andrew Haughwout, Donghoon Lee, Joseph Tracy, Wilbert Van Der KlaauwAbstract:Abstract We explore a mostly undocumented but important dimension of the housing market cycle: the role played by real estate investors. Using unique credit report data, we document large increases in the investor share of purchases and subsequent delinquencies. In states that experienced the largest housing boom-bust cycles, at the peak investors were associated with almost half of purchase mortgage originations. In part through apparently misreporting their intentions to occupy the property, investors took more leverage, contributing to higher default rates. Our findings have important implications for the design of policies to address the consequences and recurrence of housing market bubbles. 2 The U.S. economy is still recovering from the financial crisis that began in the fall of 2007. The collapse of house prices across many markets was a precipitating factor in the financial crisis and adverse feedback effects between financial markets and the real economy led to the most severe recession in the post-war period. Extraordinary interventions by fiscal and monetary authorities both in the U.S. and abroad were required in order to prevent a complete collapse of global markets and the potential onset of another great depression. Attention has shifted from containing the financial crisis to examining its causes and designing policies to limit both the likelihood and the severity of a similar crisis in the future. Given the central role that housing played as a catalyst to the crisis, it is important to better understand the determinants of the dynamics of house prices and of subsequent mortgage defaults over this recent cycle. While house prices were rising in many parts of the country over the period leading up to the crisis, these increases were particularly pronounced in four states -Arizona, California, Florida and Nevada (the "bubble" states). This rapid run-up and then crash in house prices exacted a terrible cost to homeowners, financial firms and to the economy. Current estimates are that around 23 percent of active mortgages are "under water" in that the balance on the mortgage exceeds the current value of the 2 California is a bit of an exception in that it appears that average house prices have stabilized at a level 50 percent higher than in 2000. As of 2010 Q4, nearly 2.8 million homes have gone through foreclosure, and another 2 million homes are in the process of foreclosure. 4 Serious delinquencies continue to add new homes to the foreclosure pipeline over time. Nationally distress sales represent around half of all repeat-sale transactions. These distress sales continue to exert downward pressure on house prices making it more difficult for housing markets to recover. A focus on residential mortgage finance in order to understand what the determinants were of the house price and mortgage default dynamics generated over the recent cycle would inform efforts to enhance financial stability. A more robust system of residential mortgage finance should aim to limit the degree to which house prices rise and fall over a credit cycle. Reducing the amplitude of the house price swings will limit the potential for collateral damage created by housing markets for the real economy. Related Literature Given that housing is a durable asset, periods of rising prices are indicative of increasing demand for housing. 5 One strand of the literature on housing demand focuses on the determinants that affect the "user cost" of housing. 6 The user cost of housing (UC) is the annual Flow cost to the owner per dollar of house price, taking into account after-tax financing costs, property taxes and insurance, maintenance and depreciation costs and the expected risk-adjusted return to owning the house. The value of the housing service Flow is proxied by the annual rent (R). If we assume that there is arbitrage between owned and rental housing, then the annual rent should equate to the price of housing (P) times the user-cost. 3 http://www.corelogic.com/About-Us/News/New-CoreLogic-Data-Shows-23-Percent-of-BorrowersUnderwater-with-$750-Billion-Dollars-of-Negative-Equity.aspx 4 http://www.ots.treas.gov/_files/490069.pdf 5 That is, with the exception of natural disasters and periods of armed conflict, the supply of housing in a market cannot contract significantly over a short period of time to drive up house prices. 6 See where r m is the mortgage financing rate, τ describes the tax environment, δ the depreciation rate on housing net of that offset by maintenance expenditures, g e the risk adjusted expected return to housing, and Y is the average income. This framework suggests several possible candidates for explaining the rise in house prices in the early to mid-2000s. A rise in income in a housing market will increase area rental rates to a degree that reflects the elasticity of supply of rental housing in that local market. Higher rents will translate into higher house prices by a factor given by the reciprocal of the user-cost in that market. As a consequence, house prices will vary more with changes in rents in markets with low user-costs of housing. Lower financing costs for housing reduces the user-cost of housing which would lead to higher prices holding rents constant. However, if some of the benefits of lower financing costs to landlords are passed on to renters, then the impact of lower mortgage rates on house prices will be attenuated. The Bush tax cuts were enacted during this period which lowered marginal tax rates. These lower marginal tax rates would raise the user-cost by reducing the benefit from the mortgage interest deduction. These lower marginal tax rates would have led to lower house prices, all else the same, with the magnitude of the reduction reflecting in part expectations over whether the tax cuts would be made permanent. While income, monetary policy and tax rates each underwent some changes in the first half of the 2000s, the term in the user-cost that has received the most attention in trying to explain the house price boom is the expected return to housing, g e . The higher the risk-adjusted expected return, the lower the user-cost and the higher house prices will be in a market. As Himmelberg et al (2005) explain, the sensitivity of house prices to house price expectations increases with the degree to which house prices are expected to rise. The expected return to housing is the only forward-looking aspect to the user-cost of housing framework. The arbitrage condition listed above has a potential self-fulfilling characteristic. If owners expect house prices to rise in the future, then the user-cost of housing will fall and, given a constant rent, the value of houses will rise. important to note that the average 30-year fixed-rate mortgage rate increased from 5.74 in January 9 Rents would not be expected to rise since the value of the current Flow of housing services has not changed. 6 2004 to 6.14 in December 2006, so that any further declines in the user-cost was not being driven by lower financing costs during this period. 10 Glaeser et al (2010) argue that the empirical connection between mortgage rates and house prices is not strong enough to explain the dynamics of house prices during the housing boom. On a conceptual level, they argue that the impact of any shift in housing demand on house prices depends on the housing supply elasticity in that market. For markets with inelastic housing supply, increases in housing demand will mainly result in higher house prices instead of increased production of new homes. In contrast, in housing markets with elastic housing supply, increases in housing demand will mainly result in the production of new homes. House prices in these markets are determined by the cost of building a new home. 11 Furthermore, they argue that expected future mortgage rates are important in addition to the current mortgage rate. If mortgage rates are expected to rise, then the effect of a low current mortgage rate on house prices will be attenuated. This argument can be captured in the user-cost arbitrage condition shown earlier by factoring the expected rise in financing costs into the expected house price appreciation term. 12 Credit conditions enter into the standard user-cost formulation solely through the mortgage interest rate. However, a second important aspect is the required downpayment by the borrower. The interest rate and the required downpayment reflect the two underwriting Constraints on a borrower when bidding on a property. The minimum downpayment percentage is also referred to as the "collateral rate" on the mortgage. 13 For a given mortgage balance, the mortgage interest rate impacts the monthly payment that the borrower will have to make. Underwriting standards will 10 Some authors pointed to the rise in price-rent ratios as likely to be followed by a reduction in subsequent price growth 7 stipulate a maximum that the sum of the annual mortgage payments in addition to the taxes and insurance on the property can be as a fraction of the borrower's income. 14 We will refer to this as the "cash-Flow Constraint". A lender will also require the borrower(s) to make a minimum downpayment. The ratio of the downpayment to the sale or appraised value of the house determines the origination loan-to-value ratio (LTV). We refer to this as the "downpayment Constraint." The maximum that a borrower may bid on a house will depend on which of these two Constraints first becomes binding given the underwriting standards in use at the time. The mortgage interest rate and the collateral rate are jointly determined in a credit market (see There is an additional channel, not necessarily captured by changes in the average origination LTV and not explicitly addressed by 8 possibly rental income. Alternatively, in computing the DTI ratio, debts other than the mortgage loan under consideration may be ignored or incorporated differently. As shown in , , , , , where LTV M is the maximum allowed origination loan-to-value ratio, s captures other prevailing underwriting standards at the time of the home purchase such as DTI and documentation, and f captures how changes in the degree of leverage and documentation impact house prices holding constant the user-cost. Finally, there is a potentially important amplification effect of leverage on house prices which is not fully captured in our augmented user-cost arbitrage conditions. Geanakoplos This distribution of buyers in terms of their opinions about the future value of housing can generate an amplification mechanism for house price dynamics. In normal times, optimistic buyers are infra-marginal participants in the housing market. At the prevailing house prices they would like to purchase additional housing but are prevented from doing so because the cash-Flow Constraint or the downpayment Constraint is binding. However, during the early phase of a housing boom, lenders may reduce the required downpayment percentage on new mortgages and begin to relax other underwriting standards due to the strong performance of house prices and low delinquency rates. These actions enable the optimistic buyers to purchase additional housing. The increasing leverage allowed in the market, then, begins to shift the composition of new purchase transactions in the market toward more optimistic buyers who are willing to bid higher prices for houses. This is an additional channel by which higher leverage can amplify the upward pressure on house prices. Geanakoplos describes this dynamic as the upswing phase of a "leverage cycle". Can increasing leverage help to explain the acceleration in house prices from 2004 to 2006? For leverage to have played an important role we need to establish at least two things. First, we need to show that leverage was increasing over these three years. Second, we need to demonstrate that the composition of purchasing activity was shifting toward more optimistic buyers. 15 There are two observations that can be drawn from There are several reasons to expect credit conditions to have particularly affected investor activity in 15 Our finding of increases in the median nonprime CLTV at origination is consistent with that of Mayer and Pence (2009). We use our matched sample, which represents a random sample of all LP loans as described below, for this table. 16 The 11 the buildup of the housing boom. In discussing these, we will distinguish between three different types of buyers in a housing market: buyers who want to live in the house (owner-occupiers), investors who want to keep the house as vacation or future retirement home or who want to rent the property and then resell at a future date (buy and hold), and investors who want to resell the property without living in or renting the house (buy and flip). The first reason to expect a role for investors in bidding up prices concerns the impact of the previously discussed increase in average origination LTVs. For a given mortgage interest rate, reducing the required downpayment percentage can allow a borrower to bid more aggressively for a property, but this is especially so for investors. The easiest way to see the impact of variation in the allowed LTV on the maximum bid is to take the case of a "buy and flip" borrower. As an illustration, consider an investor who has $50,000 to invest in real estate. This money must cover the downpayment as well as the mortgage payments, property taxes and home insurance during the expected holding period. For simplicity, we assume that the house is financed with a 30-year fixedrate mortgage with an interest rate of 5.5 percent. We assume that annual property taxes, insurance payments and any required maintenance expenditures equate to 2 percent of the house value. The investor will not be renting out the property during the time until resale. We consider two cases: in the first the investor plans to be able to finance the purchase for up to three years, and in the second the investor plans to be able to finance the project for up to two years. The relationship between the allowed level of leverage as indicated by the origination LTV and the maximum bid is shown in A third channel affecting real estate investors concerns the use of second liens on existing mortgages to facilitate the down payment and meeting of loan requirements for purchasing additional investment properties. As documented in earlier work by 13 mortgages, cash-out refinances, second mortgages and home equity lines of credit. In fact, on average for each 1% increase in home prices, homeowners increased their mortgage debt by 1%, so that proportionally their equity share in their homes actually remained relatively constant until the end of 2006. Equity extraction may have been especially attractive to optimistic, but cashconstrained investors, by allowing them to use these funds to make downpayments on purchases of additional homes. Accordingly, we expect the combined LTV on existing mortgages to have increased for investors during the period in which they purchased additional properties. Finally, we refer again to the amplification effects that result from shifts in the market toward more optimistic buyers. In the next section of the paper we explore the Geanakoplos hypothesis. We identify optimistic buyers as investors, and especially the "buy and flip" investors. We document the role of this class of investors over the past credit cycle both nationally as well in four boom states. We explore the extent to which the investor share of purchase transactions changes over the credit cycle. These changes are decomposed into both the extensive margin -more investors enter the market -and the intensive margin -existing investors increase the size of their portfolio of residential real estate exposures. We also examine the default behavior of investors as compared to owner-occupant borrowers. The final section of the paper discusses implications of our findings for current policy work on improving financial stability. Investors and the Leverage Cycle If Geanakoplos' description of the dynamics of the leverage cycle is applicable to the housing boom-bust cycle of the 2000s, we would expect to see changes in the characteristics of leveraged buyers of residential real estate over the period. In this section, we provide descriptive evidence of some major changes in the observable characteristics of mortgagors between 2000 and 2010. 14 While there has been some anecdotal evidence supporting the idea that investors played an important role in the boom, careful analysis of this issue has been impeded by lack of appropriate data. 20 For investors, the benefits of living in a house are immaterial to the decision of whether or not to keep making the mortgage payment, making default a less costly decision for investors than for owner-occupants. Of course, lenders are well aware of this difference, and typically require mortgagors to declare whether they will live in the collateral property, charging higher interest rates and requiring higher downpayments from those who acknowledge that they will not, ceteris paribus. But the interest rate penalty and limitations on leverage discourage borrowers from declaring their intention to live elsewhere, and self-reported "occupancy status" is thus considered a particularly unreliable piece of data. 21 Fitch We bring two distinct kinds of data to the analysis of this important question. Our primary source is the FRBNY Consumer Credit Panel (CCP) which comprises an anonymous and nationally representative 5% random sample of US individuals with credit files and all of the household members of those 5%. 22 In all, the data set includes files for more than 15% of the population, or 20 See, for example, http://www.metrotrends.org/commentary/mortgage-lending.cfm 21 Early defaults are defined to be defaults that occur within the first year. 22 The FRBNY CCP panel is based on Equifax credit report data. Lee and van der Klaauw (2010) provides further details on the data set. The analyses reported in this paper are solely based on the representative random sample and do not include the additional household members sampled. 15 approximately 37 million individuals in each quarter from 1999-2011Q1. 23 The FRBNY CCP data allow us to overcome some of the difficulties with self-reported occupancy status. Unlike loan-level data, which focus on individual debt contracts and the information used in underwriting them, credit reports are designed to give lenders (and potential lenders) dynamic credit information on individual borrowers, including the types and amounts of debt they have outstanding at any point in time. Our panel allows us to track individual borrowers over time, through refinances and moves, where at each point in time we observe all outstanding mortgage loans and non-mortgage debts. We can use this information to separate mortgage borrowers based on how many distinct first-lien mortgage accounts appear on their credit reports. Since each property can secure at most a single first-lien mortgage, the number of such mortgages on a borrower's credit report is a reliable, non-self reported, indicator of the minimum number of properties a given individual has borrowed against. 24 This kind of information about individual borrowers is not available in loan-level data sets and thus the FRBNY CCP data provide a unique perspective into important questions about who is originating new mortgages at any point in time, as well as their subsequent behavior
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impact of wall Constraint on velocity distribution in proximal Flow convergence zone implications for color doppler quantification of mitral regurgitation
Journal of the American College of Cardiology, 1996Co-Authors: Pieter M Vandervoort, Neil L Greenberg, Kimerly A Powell, Brian P Griffin, James D ThomasAbstract:Abstract Objectives. This study sought to evaluate the effect of proximal Flow Constraint induced by the left ventricular wall on the accuracy of calculated Flow rates and to assess a possible correction factor to adjust the proximal convergence angle. We further defined under which hydrodynamic and geometric conditions it is necessary to apply the corrected convergence angle. Background. The proximal Flow convergence method has been proposed as a new approach to quantify valvular regurgitation. However, significant overestimation of the calculated regurgitant Flow rate has been reported, particularly in patients with mitral valve prolapse and severe mitral regurgitation. Methods. We used an in vitro Flow model and induced various degrees of proximal Flow Constraint. The accuracy of the proposed convergence angle formula, α = π + 2 tan−1d/r (d = wall distance; r = isovelocity radius) was tested in vitro and in a three-dimensional numerical simulation. Results. With a constraining wall near the orifice, overestimation of regurgitant Flow rates was noted and was most significant with the constraining wall positioned closest to the orifice (calculated Flow rate [Qc]/true Flow rate [Qc] = 1.85 ± 0.55 [mean ± SD]). These findings were similar to the results of the numerical simulation. Applying the correction factor nearly completely eliminated the overestimation of the calculated Flow rates (cQc), with cQc/Qo= 1.13 ± 0.25. Conclusions. In the presence of a constraining wall, significant overestimation of calculated Flow rates is observed when hemispheric symmetry of the Flow field is assumed. In this situation, it is necessary to apply the corrected convergence angle formula to improve the accuracy of the proximal Flow convergence method.
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quantification of mitral regurgitation by the proximal convergence method using transesophageal echocardiography clinical validation of a geometric correction for proximal Flow Constraint
Circulation, 1995Co-Authors: Pieter M Vandervoort, Brian P Griffin, Dominic Y Leung, William J Stewart, Delos M Cosgrove, James D ThomasAbstract:Background Proximal Flow convergence is a promising method to quantify mitral regurgitation but may overestimate Flow when the Flow field is constrained. This has not been investigated clinically, nor has a correction factor been validated. Methods and Results Eighty-five patients were studied intraoperatively with transesophageal echocardiography and divided into two groups: central convergence (no constraining wall) and eccentric convergence (at least one constraining wall). Regurgitant stroke volume (RSV) and orifice area (ROA) were calculated by ROA=2π r2 Va/Vp and RSV=ROA×VTIcw, where r and va are the radius and velocity of the aliasing contour and vp and VTIcw are the peak and integral of regurgitant velocity. In eccentric convergence patients, convergence angle (α) was measured from two-dimensional Doppler color Flow maps, and ROA and RSV were corrected by multiplying by α/180. For reference, RSV was the difference between thermodilution and pulsed Doppler stroke volumes. In central convergence pat...