MANIPULATION OF COLLATERAL VALUES BY ...

MANIPULATION OF COLLATERAL VALUES BY BORROWERS AND INTERMEDIARIES Itzhak Ben-David∗ Job Market Paper January 2008

ABSTRACT The paper examines an agency problem in which borrowers and intermediaries in the housing market inflate appraisals and overstate transaction prices. The purpose of these manipulations is to mislead lenders about the value of the collateral and thus improve borrowers’ financing terms. The first part examines whether appraisers distort property valuations in the interest of the financial firm that hires them. In a sample of mortgages issued by a large mortgage firm, all appraisals are equal to or higher than transaction prices. Appraisals are especially high for low-quality borrowers and hard-to-value properties. The second part explores whether home buyers collude with sellers to inflate sales prices. Consistent with such behavior, financially constrained borrowers are attracted to seller hints about cashback and appear to pay higher prices. The foreclosure rate of properties with manipulated prices is higher, although lenders do not use this information for pricing. Evidence of manipulation is stronger for low income borrowers, for a small set of real estate agents, when information about valuations is poor, and when monitoring by loan buyers is weak. Overall, the results are consistent with the hypothesis that manipulation of collateral values arises when its returns are highest and when it is difficult to detect. ∗

Graduate School of Business, University of Chicago. I would like to thank my dissertation committee: Toby Moskowitz (Chairman), Erik Hurst, Steve Levitt, and Dick Thaler for their guidance, challenges, and support. I also thank John Birge, Peter Cassell, Hui Chen, Francesca Cornelli, Doug Diamond, Steven Drucker, Alex Edmans, Raife Giovinazzo, Michael Goffman, Uri Gneezy, Steve Kaplan, Roni Kisin, Michael LaCour-Little, Adair Morse, Joe Pagliari, Josh Rauh, Darren Roulstone, Gideon Saar, Aner Sela, Amit Seru, Per Str¨ omberg, Amir Sufi, Chad Syverson, Elu von Thadden, and seminar participants at the Financial Intermediation Conference at the University of Mannheim and the University of Chicago for helpful comments and suggestions. I learned a great deal about industry practices from Jon Goodman and Nashani Naidoo (real estate lawyers), Xinxin Wang (Federal Home Loan Bank), Pamela Crowley (www.MortgageFraudWatchList.org), Fredda Berman, Dominick Spalla, and Ray Wolverton (Cook County officers), John Carlson, George Hatch, and Mary McKenna (appraisers), Travis Galdieri (loan officer), and from four real estate agents and a loan officer who requested to remain anonymous. An Illinoisbased financial firm kindly provided proprietary appraisal data. Merle Erickson and Toby Moskowitz supported the purchase of the Cook County data sets. Correspondence to: Itzhak Ben-David, Graduate School of Business, University of Chicago, Chicago IL 60637-1511, E-mail: [email protected].

I. Introduction In collateralized lending, lenders usually rely on asset valuations when deciding loan terms.1 For example, in many cases lenders determine loan limits as percentage of collateralized assets, and quote interest rates that reflect their risk exposure depending on the valuation of the asset in place (Benmelech and Bergman 2007). In these situations, borrowers have an incentive to attenuate valuations upwards in order to extend loan limits and reduce interest rates, while intermediaries have an incentive to assist them, in order to capture transaction fees. But while there is some anecdotal evidence about manipulation activity in loan contracts, there is no systematic evidence of such practices, their determinants, and their implications. This paper empirically explores the practice of overstating asset valuations in the residential real estate market. This market does indeed have characteristics that could promote manipulation activity. First, prices have a large idiosyncratic component which aggravates the information asymmetry problem (Garmaise and Moskowitz 2004). Second, financial institutions offer loans with extremely high leverage ratios (often above 90% loan-to-value). At these leverage ratios the risk premium that lenders charge is sensitive to asset valuation, and therefore even small manipulations are very valuable. Third, the market is decentralized and run by intermediaries who mediate both realty transactions and financing arrangements. Intermediaries may intensify manipulation activity in order to capture transaction fees. The paper focuses on two methods used to attenuate collateral values that were featured in the press and in practitioner literature: Appraisal Bias and Cashback Transactions. Appraisal bias occurs when appraisers are pressured by financial firms2 to inflate the appraisal values that support mortgage applications. In cashback transactions, buyers and sellers collude to inflate contract prices in order to mislead lenders about the value of the collateral, thereby expanding loan limits or reducing interest rates. 1

Perhaps the primary reason why a large fraction of loan contracts are supported by collateralized assets (Coco 2000) is to reduce moral hazard problems due to information asymmetry (e.g., Stiglitz and Weiss 1981, Wette 1983, Bester 1987, Berger and Udell 1990, Sharpe 1990, Manove, Padilla, and Pagano 2001). 2 Financial firms bear no direct risk of borrower default since all mortgages are sold to loan buyers.

1

The main results of paper suggest that patterns of manipulation can be traced in both appraisal and transaction data. While manipulation leads to higher default rates among borrowers, the evidence indicates that lenders do not succeed in charging adequately higher interest rates on suspected transactions. Evidence for manipulation is stronger when the manipulation has greater benefits and a lower risk of detection. Furthermore, the results suggest that a small set of intermediaries take an active role in promoting manipulation. The first part of the paper tests whether appraisers distort valuations in favor of the intermediaries who hire them in return for future assignments (Smith 2002, Aaron 2006a, Coon 2006, Louis and Crenson 2007, Harney 2007). By providing high appraisal values, appraisers improve the value of mortgages in the secondary market and thus increase the likelihood of transaction completion.3 For the purpose of this test, I examine a sample of 311 residential appraisals originated between 2003 and 2005 by one of the largest financial firms operating in Illinois. Sample appraisal values seems to be biased, since all properties are appraised either at the transaction price or above it.4 The ratio of appraisal-to-price is higher for highly leveraged transactions, especially for properties in low income areas and those that are hard to value. The results are consistent with the hypothesis that appraisers hired by this financial firm distort their valuations when it is beneficial to do so and when the bias is hard for outsiders to detect. The second part of the paper explores whether cashback transactions are prevalent in the transaction data. In a cashback transaction, the buyer and the seller agree to overstate the transaction price in order to mislead the lender about the true value of the collateral. If the misrepresentation succeeds, i.e., the lender’s appraiser confirms that the price reflects market valuation, the buyer can borrow a larger amount based on the same collateralized asset or reduce the cost of funds. 3 In a recent allegation against one of the largest mortgage originators, Washington Mutual, New York Attorney General Andrew Cuomo argues that “Our expanding investigation into the mortgage industry has uncovered that Washington Mutual improperly pressured appraisers to provide inflated values that best served the lender’s interest. Knowing this, Fannie Mae and Freddie Mac cannot afford to continue buying Washington Mutual mortgages unless they are sure these loans are based on reliable and independent appraisals.” (Gutierrez 2007). 4 The rate of incomplete or rejected applications for this lender during these years is about 10%.

2

The sample in this part consists of about 740,000 residential transactions made between the years 1995 and 2007 in Illinois. In order to identify cashback transactions in the data, I outline the conditions under which cashback transactions are most likely to take place. First, cashback transactions are beneficial only for highly leveraged borrowers whose loanto-value ratio is within the range in which interest rates are sensitive to the value of the underlying asset. Second, I argue that prices are more likely to be inflated when sellers hint about their willingness to make transfers to buyers.5 Indeed, highly leveraged borrowers are twice as likely to engage in transactions in which sellers advertise that they will consider cashback or credit at closing. Third, I argue that cashback is more likely to take place when transaction prices are high (since they include a cashback component). In fact, highly leveraged borrowers are nearly twice as likely to pay the full listing price or above. The manipulation of collateral values has real effects. I find that buyers in transactions where manipulation is suspected pay higher prices for their homes, and are more likely to default on their debt. Nevertheless, a subsample of interest rates charged indicates that lenders do not fully differentiate between manipulating borrowers to honest ones. Furthermore, I find evidence for price momentum due to manipulation; in particular, current prices are higher in geographical areas in which manipulation activity was intense in the past. Evidence for manipulation is stronger when the manipulation is more beneficial to the manipulators. In particular, homes in low income areas and homes that remain longer on the market show significantly stronger signs of price manipulation. For buyers and sellers of these homes, manipulation might be the only way to complete the transaction: financially constrained buyers in low income areas might not have enough equity for a down payment, and sellers who are pressed to sell need to attract buyers. I investigate the role of real estate agents and industry professionals as promoters of manipulation, as industry reports and the popular press have claimed (Committee on Financial Services 2004, Roberts 2006). I find that evidence for manipulation is stronger when a single agent represents both transacting parties, and when sellers are industry professionals (e.g., real estate agents, developers, investors). Also, I find that the identity and 5

Some sellers advertise “Let’s talk about cashback at closing!!!,” “$10,000 back with full price,” “Buy this home with no money down!,” and “Seller is open to creative financing.”

3

history of real estate agents explain the cashback indicators well. Specifically, transactions that are suspected of manipulation are persistently conducted by a small group of agents, about 10% to 15%. This evidence is consistent with the hypothesis that manipulation is confined to a small community of intermediaries. Also, I document that manipulation indicators are stronger in consequences in which the detection of manipulation is hard, in particular, when information uncertainty is greater. I exploit a variation in the timing of tax valuation reassessments by the County Tax Assessor (often used by appraisers as a price benchmark (Fannie Mae 2005)) and find that manipulation indicators are stronger in townships for which reassessment information has not been released yet. This result is consistent with the idea that manipulation thrives in areas with poor price information. Finally, I find that manipulation is stronger when agency frictions are higher. There is some evidence that manipulation indicators are stronger for mortgage originators who sell all their mortgages to loan buyers (“financial firms”) than they are for loan originators who are likely to retain mortgages in their portfolios (“banks”). Moreover, in a subsample of traded mortgages, tighter monitoring by loan buyers (proxied by the closeness of loans to the jumbo loan cutoff, controlling for mortgage size) is associated with weaker evidence for cashback transactions. This evidence corroborates the hypothesis that monitoring by loan buyers resolves some of the moral hazard issues in loan trading (Hellwig 2005). The paper proceeds as follows. Section II provides background information about the primary and secondary mortgage markets, and it reviews the market practices of cashback transactions and appraisal bias and derives empirical predictions. In Section III the data sets used in the study are described. Section IV examines the evidence for appraisal bias, and it explores the circumstances in which such bias arises. Cashback transactions are characterized in Section V; it studies the behavior of manipulation indicators with respect to transaction characteristics, the incentives of buyers, sellers, and loan originators, the involvement of industry professionals, information uncertainty, and monitoring by loan buyers. Section VI concludes.

4

II. Background and Hypotheses A. Primary and Secondary Markets for Residential Mortgages A.1. The Mortgage Origination Process The market for residential mortgages in the U.S. has dramatically expanded in the last decade. The dollar volume of originated mortgages has increased from $0.6 trillion in 1995 to $2.8 trillion in 2004 (Inside Mortgage Finance 2005). Unlike commercial loans, which vary in size and are often tailored to fit borrower needs, residential mortgages are small, standardized, and typically secured by simple and relatively transparent assets. Due to its relative simplicity, the secondary market for residential mortgages is one of the most developed markets for asset-backed securities. In 2004, about 56% of originated mortgages were securitized and sold in the secondary market, while a further sizeable fraction of mortgages is traded between private parties without being securitized (Inside Mortgage Finance 2005). In this market there are two primary types of mortgage originators: financial firms and banks.6 “Financial firms” are intermediaries that are in the business of originating mortgages and selling them in the secondary market to loan buyers. “Banks” are lenders who originate loans and keep them as long-term investments in their portfolios. Financial firms and bank differ not only in in their business models, but also in the compensation that they offer to their loan officers. The Bureau of Labor Statistics (2006) reports that most loan officers are compensated according to the loan volume that they generate.7 Interviews with loan officers suggest that, in general, compensation of loan officers is compatible with the incentives of their organization, i.e., compensation in financial firms tends to be purely on commission, while banks usually offer fixed-salary compensation. 6

I conform to the terminology of Minton, Sanders, and Strahan (2004), who make the distinction between financial firms and banks and present evidence for their behavior with respect to loan trading. 7 “The form of compensation for loan officers varies. Most are paid a commission that is based on the number of loans they originate. In this way, commissions are used to motivate loan officers to bring in more loans. Some institutions pay only salaries, while others pay their loan officers a salary plus a commission or bonus based on the number of loans originated. Banks and other lenders sometimes offer their loan officers free checking privileges and somewhat lower interest rates on personal loans.” (Bureau of Labor Statistics 2006).

5

The procedure of financing residential real estate transactions is fairly standard. When a potential borrower applies for a mortgage he provides documentation about the property (e.g., sales contract) and about his credit-worthiness (e.g., a proof of income, proof of availability of funds for down payment). Next, the mortgage originator inquires about the borrower at credit agencies and hires an appraiser to certify the value of the property. Once the mortgage is issued, it will be kept on the lender’s books if the lender is a bank, or sold on the secondary market if the lender is a financial firm. Most residential mortgages originated by financial firms are bought by governmentsponsored entities (GSEs), primarily Fannie Mae and Freddie Mac (Cao 2005). The GSEs purchase mortgages, package them in pools, and securitize them in the secondary market. Mortgage-based securities are mostly purchased by institutional investors such as banks, hedge funds, and pension funds. All GSEs and many lenders require borrowers with loan-tovalue higher than 80% to purchase private mortgage insurance (PMI),8 which dramatically increases the cost of these mortgages (as in Figure 2a), ultimately creating an incentive to overstate collateral values.

A.2. Agency and Moral Hazard Since financial firms sell their mortgages to third parties, there is a concern that they would exploit their informational advantage and shirk on screening borrowers (Gorton and Pennacchi 1989, Gorton and Pennacchi 1995, Gorton and Winton 2002). In the secondary market for mortgages, such moral hazard problems are addressed in several ways. First, loan buyers usually reserve the right to return mortgages to originators in case of early default (6 to 12 months). Second, loan buyers often charge financial firms a “guarantee fee” based on the quality of past originations (Cao 2005). Third, loan originators who shirk on screening incur reputational costs in the long term.9 8

This insurance covers a fraction (20% to 40%) of the losses to the mortgage owner in case of borrower default (Canner, Passmore, and Mittal 1994). 9 Note, however, that risk-sharing arrangements that are common in other secondary loan markets (Gan and Mayer 2006, Sufi 2007) and that are considered incentive aligning (Gorton and Pennacchi 1995) are not as common in the secondary market for residential mortgages.

6

To minimize the moral hazard problem embedded in loan trading, loan buyers do not rely on the judgement of financial firms to determine the quality of mortgages. Rather, loan buyers demand that financial firms supply hard information (Petersen 2004) about the quality of borrowers from credit agencies and valuations of collateralized assets by professional appraisers.10 Yet, even within this narrow task, loan officers maintain some discretion that could attenuate mortgage approval decisions by loan buyers.

B. Manipulation Method I: Distorting Appraisal Valuations One way in which loan officers can improve the likelihood of mortgages being approved is by putting pressure on appraisers to increase their valuations. In return, loan officers promise future assignments (Smith 2002, Callahan 2005, Cao 2005, Aaron 2006a, Coon 2006, Hagerty and Simon 2006, Louis and Crenson 2007). Harney (2007) reports that in a survey of 1,200 appraisers conducted in October 2006, 90% of appraisers reported that they have been subject to pressure to raise appraisal values by mortgage brokers, realty agents, lenders, and individual home sellers. In the previous survey, in 2003, only 55% of appraisers reported that they had been under such pressure.11 Appraisers may respond to the pressure perhaps because they have low bargaining power and can cooperate at relatively low risk. Appraisers cannot compete well on prices because the hiring decision is made by loan officers while borrowers pay the appraisal fees. Hence, many appraisers find it more effective to surrender to loan officer pressure so they can receive assignments in the future. Furthermore, biasing appraisals is not very risky because there is a substantial idiosyncratic component to home valuations. 10

In many institutions, the official title of loan officers is “loan collection officers,” reflecting their actual task (Bureau of Labor Statistics 2006). 11 As part of the background investigation, one appraiser from California commented “I now have a private appraisal practice, and there still are occasions when a financial firm or loan officer will call me and say: if you can make this deal work, I will have more work for you than you can handle.” Also, many appraisers who signed the appraisers’ petition protesting the pressures they experience from lenders (http://appraiserspetition.com/index.htm) write that most of the pressure comes from commissionpaid loan officers who often condition future assignments with achieving certain appraisal values. Some appraisers say that they were ‘black-listed’ because they did not deliver the right values. Among the comments: “I have lost clients for NOT hitting a number,” “Appraisers are like pawns in some financial firm’s game. If they don’t get what they want, they blacklist you,” “Appraisals need to be ordered by someone without a vested interest in the value,” “This is the single largest problem that faces the appraisal industry today.”

7

To demonstrate the leeway that appraisers have in their valuations, Congressman Gary G. Miller of California reported on his experience at the U.S. Department of Housing and Urban Development in a session of the Committee on Financial Services on the effects of mortgage fraud on the lending industry Committee on Financial Services (2004): “. . . we refer to the MAI12 appraisals as Made As Instructed. And it is very, very simple for an individual to buy a $210,000 home, have a connection with a broker or an appraiser, and an appraiser will come back with an appraisal for $235,000 that might be inflated $25,000. But that inflation on your part is very, very, very hard to prove. Because an aggressive appraiser can justify most anything they want within 10 percent. If they are really creative, I have seen it to exceed 10 percent very easily; and for you to come back and say they committed fraud is very difficult.”

C. Manipulation Method II: Cashback Transactions C.1. Motivation for Cashback Transactions The second manipulation method is based on buyers colluding with sellers to overstate the sales contract price.13 The intention is that lenders and appraisers fix on the inflated sales price when valuing the collateralized property.14 To inflate the transaction price, a buyer needs the seller’s cooperation to increase the sales contract beyond the agreed-on price and to coordinate a way to remit the difference in prices back to the buyer.15 Once 12

Originally: Member of the Appraisal Institute. This scheme is well-known to practitioners in the real estate business, and has been covered several times in the media; a partial list includes Gendler (1998), Goodman (2002), Simpson (2004), Conrad (2005), Jackson (2005), Aaron (2006b), Coombes (2006), Goodman (2006), Olinger (2006), Lloyd (2006), Roberts (2006), Tong (2006), Carr (2007), Lahart (2007), Reagor (2007), and Hagerty and Corkery (2007). 14 After the buyer submits his mortgage application, the lender sends over his appraiser to confirm that the property’s valuation matches the sales price. Although appraisers are required to value properties as if they were purchased with cash, and to eliminate any seller concession (Fannie Mae 2005, Fannie Mae 2007), they may have difficulty identifying whether prices are inflated due to the idiosyncratic nature of properties and because the inflated amounts are relatively small (3% to 15%, according to background interviews). 15 Some mortgage programs allow seller concessions in which sellers pay transaction-related costs that normally would be paid by buyers, as long as these concessions are properly disclosed to the lender and are within the lender’s guidelines. Typically, the amount of allowed transfers declines with leverage, so that borrowers put up enough equity of their own. For example, in conventional mortgages, Fannie Mae allows seller concessions of up to 6% of the transaction price as long as the buyer puts at least 10% of his own equity, 3% if the buyer puts at least 5% in down payment, and 2% if the he puts up to 3% in down payment. Importantly, appraisers are required to evaluate homes net of seller concessions or creative 13

8

the mortgage is issued and the seller receives the full (inflated) sales price, he reimburses the difference between the true price and the inflated price back to the buyer, usually in cash. The end result is that the buyer borrows a greater fraction of the true price than the lender intended to lend. There are virtually no legal consequences to engaging in cashback transactions for buyers and sellers as long as manipulations are sporadic. Specifically, legal authorities are concerned only with organized fraud (“fraud for profit”),16 and not with the small and occasional falsifications made by homeowners (“fraud for property/housing”), the type of manipulations studied here.17

C.2. Example To illustrate the mechanism by which cashback transactions operate, consider an example based on an actual property that was offered for sale on the south side of Chicago in 2006 and which was visited by the author as part of the preparatory study. A developer offers condos for $235,000. When potential buyers inquire about the property, the developer proposes to increase the price to $255,000 and return $20,000 in cash to the buyer at the closing table. The seller also proposes to document his liability for cashback in a separate contract, which will not be disclosed to the lender.18 Once the buyer’s lender receives the sales contract for $255,000, he would send in his appraiser to confirm that the price reflects the market value of the property. In this specific financing, as if the transaction were in cash. Non-allowed (even if properly disclosed) concessions should lower the appraiser’s valuation dollar-for-dollar (Fannie Mae 2005). 16 Such fraud often involves borrowing based on falsified documentation. Fraudsters usually borrow large amounts based on greatly overappraised properties and default immediately and intentionally (Committee on Financial Services 2004). 17 Chris Swecker, Assistant Director for Criminal Investigations in the Federal Bureau of Investigation, testifies that “. . . I mentioned the two types of frauds, fraud for housing, fraud for profit. Most of our efforts are focused on the fraud-for-profit type of violation. We are looking for something that is more systemic than just an unwitting individual borrower who has been caught up in a situation. Maybe they went along with it because they were unsophisticated, but that is not our focus at all. We don’t have the resources to engage in that type of single transaction investigation. Our focus is clearly on the insiders and the schemes.” (Committee on Financial Services 2004). 18 In the negotiations process, the author managed to negotiate the “price” upwards to $269,900 (15% above the true transaction price), the price that the real estate agent thought would be the upper limit that the appraiser be able to approve.

9

property, a single financial firm financed the other condos in the building (supposedly with similar “price” arrangements), hence it would have been easy for an appraiser to justify the inflated price given the recent historical transactions. With a price increase of $20,000 and a 95% mortgage, the deal can be executed with no down payment. With a typical mortgage of 95% loan-to-value, the buyer can borrow $242,250, paying an interest rate which reflects the risk on a 95% loan-to-value mortgage ($242,250/$255,000), rather than that of a 103.1% loan-to-value ratio ($242,250/$235,000). In this example, the mortgage itself covers the transaction price and 2.5% transaction costs, and even leaves some cash at hand. In short, a buyer can purchase this property with no equity invested. To estimate the maximum direct wealth transfer of manipulation in this example, suppose that the borrower pays an annual percentage rate of 7.1% on the 95% loan-to-value mortgage (as in Figure 2a), and that the lender cannot distinguish between honest and cheating borrowers. In reality, however, the mortgage is as risky as a 103% loan-to-value mortgage. Hence, the loss to the lender is the difference in the interest he could receive on a 103% loan-to-value mortgage and the interest he currently receives. Since loans with 103% leverage do not normally exist, the interest rate on this hypothetical loan needs to be estimated. The marginal amount of loan-to-value (from 95% to 103%) is, for practical purposes, an unsecured mortgage, therefore its cost should be close to the cost of unsecured consumer credit, in the region of 13% to 16%.19 Hence, the total interest lost is the difference between the opportunity interest rate (suppose 15%) and the current interest rate (7.1%) for the inflated portion ($20,000×95%), i.e., around $1,500 per year.20 19

Source: http://www.federalreserve.gov/releases/g19/ This analysis leaves out the second order effects of tax allowance and transaction costs (lawyer fees, agent fees etc.). Note also that price manipulation does not affect property taxes in Illinois, since taxes are calculated according to general price levels and not according to specific prices (www.cookcountyassessor.com). 20

10

C.3. Why are Cashback Transactions Sustainable in Equilibrium? Since buyers cannot enforce cashback side-agreements in court (they are illegal), there is a concern that cashback arrangements may not be sustainable in equilibrium due to the holdup problem. In theory, sellers could refuse to return the cash at the last moment (illegal cashback is not enforceable) and force buyers to purchase the property at the inflated sales price (sales contract could be enforceable). A simple analysis shows that as long as the earnest money21 is sufficiently low, cashback transactions are internally consistent. If sellers turn away from their commitment for cashback, buyers could decide to pull out with a maximal loss of their earnest money. Hence whenever earnest money is low, sellers will fulfill their obligation ex post, even though there is no external enforcement mechanism that forces them to do so.22

D. Empirical Predictions D.1. Who Gains and Who Loses from Manipulation? When prices are overstated outside observers may believe that the level of fundamental prices has appreciated (Conrad 2005, Tong 2006, Lahart 2007). Aaron (2006a) reports that one of the issues that appraisers face is the need to have their valuations supported by the prices of neighboring properties, although they cannot determine whether or not these prices are inflated (either with or without the lenders’ permission). In addition to appraisers, market participants, sellers and buyers, also often rely on recent transactions to 21 Earnest money is a good faith deposit that is usually submitted with a price offer. Background interviews suggest earnest money is typically 5% to 7% of the price in Cook County, but could be be as little as $500 in low income neighborhoods or when the market is slow. 22 A real estate attorney commented about the closing process of transactions with cashback and about the possibility of sellers holding up buyers: “Buyer can pull out anytime and all that will happen is that he will lose earnest money. How is cashback enforced? At the closing, buyer and his attorney sit on one side of the table. Seller and his attorney sit on the other side. Buyer and his attorney literally sit with the checks. The other side is given copies. Seller and his attorney literally sit with deed and keys. The other side gets a copy of the deed. When all paperwork is done, then buyer’s attorney hands over checks and seller’s attorney hands over deed and keys. . . . if there is cashback, then a check will be written from the seller’s attorney’s account and given to the other side along with the deed and keys. [Can the seller withdraw from his commitment for a cashback?] Yes, seller could decide not to go through. But if he doesn’t, then he walks away from the big fat check that he sold his house for because he didn’t want to give the buyer is $15K cash back. Who would do that??”

11

gauge market prices. As a result, property prices are expected to increase in neighborhoods in which price manipulation is common.

Prediction 1 (Price Momentum) Prices are higher in areas in which prices were manipulated in the past.

Whether or not manipulation is efficient depends on whether it has consequences on borrower default, and whether lenders and loan buyers can price the risk accordingly. On one end of the spectrum, there is a possibility that borrowers manipulate collateral values, but in equilibrium, lenders and loan buyers are aware of the risk and adjust their interest rates to compensate for the elevated risk. In this case, the manipulation of collateral values is similar to the Miller and Rock (1985) signal-jamming model in which all firms use dividends to signal their future prospects, but the market discounts the information and pricing remains efficient. This equilibrium also resembles the manipulation of earnings equilibrium in Stein (1989): all firms manipulate earnings; however, market prices remain efficient since investors are aware of the manipulation and therefore discount it. At the other side of the spectrum, the lending market is inefficient: manipulation has real effects on borrower default, but lenders and loan buyers are either not aware of the problem, or they cannot price it correctly. If they are not mindful of the extent of manipulation, they will suffer losses.23 Alternatively if lenders are aware of the problem in collateral values, and impose higher interest rates on risk groups, it is possible that honest borrowers effectively subsidize the gains of cheating borrowers, and that lenders remain largely unharmed. Finally, there is a possibility that the manipulation could even be Pareto improving, i.e., the manipulation is “good corruption”. This may happen if, for example, the PMI that is imposed by regulators is suboptimal. In this case, borrowers contract around the inefficient rules and both borrowers and lenders may achieve better outcome. Given that lenders and loan buyers lobby against manipulations (e.g., Committee on Financial Services 2004) and invest resources to curb it (Fannie Mae 2006a, Fannie Mae 23

Supporting this view, Corkery (2007) reports that fraud is a main driver of the foreclosure wave in 2007.

12

2006b), it is likely that they are aware of the phenomenon, but are unable to perfectly control it with pricing. Prediction 2 (Consequences of Manipulation) Properties of borrowers who manipulate collateral values are more likely to be foreclosed. However mortgages on these transactions are not paying higher interest rates. Since manipulation is costly, it should be more prevalent where it is needed the most. Specifically, the benefit from manipulation is higher for certain populations, e.g., low income populations, sellers who are under pressure to sell, and intermediaries who can generate more fees from manipulation. Prediction 3 (Return on Manipulation) The manipulation of collateral values is more common when the return on manipulation is high.

D.2. The Role of Real Estate Agents and Industry Insiders Anecdotal evidence suggests that some industry insiders and a potentially small group of real estate agents play a key role in promoting manipulations (Committee on Financial Services 2004, Fannie Mae 2006a, Anonymous 2007). Transaction intermediaries, i.e., real estate agents, lawyers and loan officers, usually earn fees that are contingent on success, and therefore have the incentive to generate transaction volume (Rutherford, Springer, and Yavas 2005, Levitt and Syverson 2005, Bureau of Labor Statistics 2006, New-York Attorney General 2007).24 By helping to manipulate collateral values, they increase the likelihood of transaction completion and hence secure their fees.25 This behavior is more likely to take place when industry insiders are involved to a greater than usual degree, for example, when the sellers are industry professionals. 24

Further support for this argument is in LaCour-Little and Chun (1999), who find that mortgages originated by third party originators are more likely to be prepaid, and in Gan and Mayer (2006) who find that commercial loans are more likely to default when they have been sold by the loan originators. 25 William Matthews, Vice President and General Manager of Mortgage Asset Research Institute, Inc. testifies that “. . . One [type of mortgage fraud] is commission fraud. This is where one or more industry professionals misrepresent information in a loan transaction in order to receive a commission. Commission fraud is a more common practice in the industry and is a concern to mortgage lenders. It can result in harm not only to consumers but to lenders as well.” (Committee on Financial Services 2004).

13

A related prediction concerns the diffusion of manipulation in the industry. Since manipulation is illegal in practice, it is likely to be concentrated within a confined circle of intermediaries. Prediction 4 (Industry Insiders) Cashback transactions are more likely: (1) when industry insiders are involved, and (2) within a small group of intermediaries.

D.3. Likelihood of Detection Besides the benefits of manipulation, there are potentially costly consequences if manipulation is discovered by lenders or loan buyers. In practice, lenders and loan buyers attempt to curb distortions in valuations and manipulations, possibly indicating that they cannot use discriminating pricing efficiently enough. For example, they operate to increase awareness of manipulations,26 and perform random audit procedures to verify loan details and the source of the funds used in transactions (e.g., Office of General Inspector General 2005). Interviews indicate that appraisals are audited by loan buyers on a random basis. The interviews also indicate that the likelihood of audits is higher when there is a high rate of borrower defaults within a pool of assets that were originated by the same financial firm or assessed by the same appraiser. Loan buyers also monitor mortgage performance over time, and impose sanctions on intermediaries and appraisers who are suspected of misconduct.27 Appraisers and loan originators are not the only ones who may suffer the consequences of the detection of manipulation; real estate agents and the transacting parties also bear these costs. For example, real estate agents lose opportunity fees if a manipulated transaction with which they are involved is detected. Buyers in situations where manipulations have been detected may need to find other lenders, and potentially other properties. Likewise, sellers may need to find alternative buyers. 26

For example, GSEs like Fannie Mae and Freddie Mac issue guidelines to loan originators instructing them on how to identify mortgage fraud and artificial price inflation (Fannie Mae 2006b). 27 For instance, the Federal Housing Administration (FHA) regularly monitors the loan default rate and imposes sanctions on financial firms and appraisers in cases of high default rates. Between 1997 and 2004, the FHA stopped working with 397 appraisers (out of 25,000 FHA-authorized appraisers) and 261 branches of loan originators (Committee on Financial Services 2004).28

14

Hence the extent to which market participants are willing to engage in manipulation should be negatively related to the likelihood of detection.

Prediction 5 (Hard to Detect) Manipulation is more likely to arise when it is less likely to be detected.

D.4. Monitoring by Loan Buyers One solution to the agency problem in intermediated lending is monitoring by loan buyers. Loan officers in financial firms are exposed to moral hazard when originating mortgages since they are more often compensated according to the loan volume generated and because they suffer the consequences of borrower default only indirectly. Therefore, loan officers in financial firms may prefer to turn a blind eye to cashback transactions as long as their firms receive their flat origination fee. Loan buyers can reduce the extent of manipulation by further exerting monitoring efforts. For example, they can randomly audit mortgage applications by verifying the information that loan originators provide. When monitoring efforts are high, the expected costs from manipulation are high, and therefore less manipulation is expected.

Prediction 6 (Monitoring by Loan Buyers) Manipulation is less common when monitoring by loan buyers is tighter.

III. Data A. Appraisal Data The tests for distorted appraisals are performed with a unique data set of appraisals performed for one of the largest financial firms in Illinois that specializes in originating residential mortgages and serves borrowers from the entire income spectrum (“the Mortgage Originator”). The Mortgage Originator sells all originated mortgages in the secondary market, either to large institutions such as banks or to the GSEs. Loan officers who work 15

for the Mortgage Originator generate their own borrower leads and are compensated proportionately to the mortgage volume that they originate. The culture in the organization is such that loan officers are publicly rewarded when they complete large transactions or when they achieve a high number of transactions. The sample contains appraisals of completed transactions. Specifically, to construct the sample, appraisals were randomly drawn from the Mortgage Originator’s database, based on a query for completed mortgages financing home purchases in Chicago between the years 2003 and 2005. The fact that only completed applications are included should not cause a material bias because, over the period, only 9% of applications were withdrawn by applicants and less than 1% of mortgage applications were rejected by the Mortgage Originator (primarily due to poor credit history).29 The data set contains 311 appraisal reports. Each observation includes information about the purchase price, the appraised value, and details of the three comparable assets (“comps”) selected by the appraiser. I calculate leverage ratios by matching the appraisal data set to the mortgage file provided by the Recorder of Deeds. In addition I match income statistics for 2004 based on zip code-level data obtained from the Internal Revenue Service (IRS).30 Table I, Panel A provides summary statistics for the sample. The average purchase price is about $334,600, and the average appraisal is about $338,200. 73% of the borrowers borrow above 80% of the transaction price; the average leverage is 89.1%.

B. Transaction Data The tests for cashback transactions use several sources of information. The main data set is the Multiple Listings Service of Northern Illinois (MLS), which includes all property listings and transactions that were recorded through registered real estate agents in Cook County, Dupage County, Kane County, Kendal County, Lake County, McHenry County, 29

These figures are based on the Mortgage Originator’s mortgage-level reports to the Home Mortgage Disclosure Act (HMDA) database about mortgage origination for 2003 to 2005 (http://www.ffiec.gov/hmda/). 30 Source: http://www.irs.gov/taxstats/indtaxstats/

16

and Will County between January 1995 and the October 2007. The database contains detailed information about home characteristics, sellers’ and buyers’ agents, listing prices, transaction prices, and time on the market. In addition, this data set includes data about mortgages, including mortgage amounts, lenders, and interest rates. In addition to the mortgage data available in the MLS database, I use also mortgage data and deeds data provided by the Cook County Recorder of Deeds. The mortgage database contains information about loan sizes and registered lenders. Matching between databases is based on the property identification number (PIN), closing date, and sale prices. This additional data is used to ensure the quality of the MLS data. To limit the effects of outliers, transactions with prices below $30,000 or above $7,000,000 are excluded (similar restrictions were applied to a similar data set in Levitt and Syverson 2005). Also, transactions with no matched mortgages or with leverage above the market’s normal lending terms (above 103% or below 25% loan-to-value) are eliminated. Likewise, transactions which closed below 50% or above 200% of the listing price, and properties that have been on the market more than two years are excluded; these observations are likely to reflect data errors rather than real transactions. The final sample contains 740,408 completed transactions with mortgage data. Three additional data sets are used in the analysis. First, I compile a township-based schedule of tax reassessments by Cook County Tax Assessor from the Assessor’s website31 and from archival news resources. The schedule contains the dates since 1997 in which the County Tax Assessor publishes valuation reassessment in each of the 38 townships in the County. The Assessor’s valuations are used in the study because appraisers often rely on them when preparing appraisals. Second, I match the data with time-series data about loan cutoff levels provided by Fannie Mae (“jumbo loan cutoff” levels).32 This time-series is used in the study to identify shocks to the monitoring ability of the GSEs. Third, I match the data with IRS zip code-level average income data. Table I presents summary statistics for the main data set used in the study. The mean transaction price is $246,400, and the median transaction is $192,000 (Panel D). Figure 2b 31 32

Source: http://www.cookcountyassessor.com Source: http://www.efanniemae.com/sf/refmaterials/loanlimits/

17

presents the cumulative distribution of transactions as a function of the leverage. From the chart, the most popular leverage ratios are 80%, 90%, 95%, 97%, 98%, 99%, and 100%. Panel B presents some time-series statistics. Over the sample period, average leverage has risen from 84.8% to 86.7%.

IV. Empirical Analysis: Appraisal Bias This part of the paper tests whether appraisals are biased and explores the determinants of the bias. A simple univariate examination of the appraisal data set reveals that sample appraisals are biased. All 311 properties were at least appraised at the transaction price: 36% of appraisals exactly match transaction prices, and the rest were appraised at higherthan-transaction prices. As discussed in Section III.A, the sample suffers from a mild sample selection bias (less than 1% of mortgages were rejected, mostly on credit issues, and 9% of applications were withdrawn by applicants). It is implausible, however, that the selection would generate such an extreme bias in appraisals.33 Nevertheless, the sample is based on mortgages originated by a single lender and the appraisals were conducted by a small set of appraisers; results should be interpreted with this in mind. The analysis shows that appraisals are especially biased for risky mortgages. A univariate analysis shows that the average ratio of Appraisal /Price is 101.0% and that it is higher (101.6%) for highly leveraged borrowers (Figure 1). This observation is confirmed in a multivariate analysis. In Table II, Appraisal /Price is regressed on a high leverage indicator (Leverage > 80 %) and on size, time, and township controls. The high leverage variable in columns (1) and (2) indicates that appraisals are higher by about 0.6%, on average, for highly leveraged borrowers. Column (3) shows that the bias in appraisals for highly leveraged borrowers exists in fact only for borrowers in low income zip codes. Specifically, when highly leveraged borrowers buy properties in low income neighborhoods, their properties are appraised higher, by 0.8% 33

The in-sample average appraisal-to-price ratio is 101.0%. If appraisals are unbiased, than the rejected/uncompleted applications should have an average appraisal-to-price of about 90%, which seems unlikely given that these are arm’s length transactions and that less than a mere fifth of the rejections are on valuation grounds. Most of the rejections are due to poor credit history.

18

on average. This result is consistent with the hypothesis that appraisals are especially biased for mortgages in which the collateral is an important input in the valuation of the mortgage claim (Prediction (3)). Consistent with Prediction (5), appraisals are also biased in cases where the bias is hard to detect by loan buyers. Columns (4) and (5) test whether appraisals of properties that are hard-to-value are more severely biased. In particular, I compute the dispersion of prices for comparable assets as the price of the largest comparable property minus the price of the smallest one, scaled by the transaction price of the property evaluated. The results show that properties of highly leveraged borrowers are also hard to value; they are appraised higher by about 1% on average. Overall, the results in Table II support the hypothesis that appraisers in the sample bias valuations in keeping with the interests of the lender who hires them. Appraisals are more biased when collateral values are more important for the valuation of mortgages in the secondary market, and when the bias is hard to detect.

V. Empirical Analysis: Cashback Transactions This section focuses on identifying cashback transactions in the transaction data. In a cashback transaction, the transaction price is inflated, and in turn the seller transfers cash or goods back to the buyer (the “cashback” component). Since the cashback component is unobservable, empirical identification relies on observed transaction characteristics only. The identification process is developed in two stages. First, I identify indicators for the transactions of financially constrained borrowers and for transactions that are likely to involve cashback transfers. Then, I explore whether these indicators have stronger effects in situations which offer the greatest rewards for manipulation, and which are simultaneously unaffected by financial constraints.

19

A. Financial Constraints and Cashback Transactions A.1. High Leverage is Necessary for Cashback Transactions The manipulation of collateral values is only productive for financially constrained buyers. The reason for this is that mortgage interest rates in the U.S. are only sensitive to the value of the collateral when the ratio of loan-to-value exceeds 80%. This pattern is illustrated in a real mortgage annual percentage rate quote presented in Figure 2a. Below 80%, loanto-value rates are practically flat.34 Beyond 80% loan-to-value, rates increase steeply with leverage, since the GSEs requires borrowers to pay private mortgage insurance (PMI) on mortgages above 80% leverage.35

A.2. Indicator I: Sellers’ Cashback Hints In a small number of MLS listing records, sellers explicitly hint that they are willing to negotiate cashback deals. For example, one seller writes “. . . $8,000 cashback to buyer at closing with full price offer.” In this case, a cashback transfer is likely to have taken place, because the property was sold for its full asking price, $80,000, and was financed with an $80,000 mortgage. In another case, a seller offers “. . . no money down. . . $10,000 under-appraised. . . ” Also in this case, cashback transfer is likely to have taken place. The seller asks for $159,000 but the sale price is $170,000. The buyer borrowed $161,500 (95% of the sale price). If the true price were $159,000, the mortgage amount is sufficient to cover the full price and most of the transaction costs, i.e., almost no money down on behalf of the buyer is required, as promised. 34

At very low leverage, mortgage rates slope downwards due to the amortization of set-up costs. Background interviews with loan officers indicate that the upward sloping rate structure is similar for piggyback mortgages, i.e., when high-leverage mortgages are split into first and second mortgages so that borrowers do not need to pay the PMI. 35

20

In many other cases, however, the intentions of sellers are less clear cut. For instance, some listings promise that “seller will consider creative financing”, or “seller will give credit at closing”.36 To investigate this evidence in a systematic manner, I build a dictionary of about 150 common word strings that are likely to be associated with transfers from sellers to buyers but are less common in other contexts. These transfers can be direct (i.e., cash), or indirect (i.e., the seller pays to a third party on behalf of the buyer).37 A transaction that includes any of the keywords is flagged with the dummy variable Cashback Hint. Overall, 2.9% of the sample transactions are flagged. Of course, many sellers who include these word strings do not necessarily intend to engage in any illegal activity whatsoever. Nevertheless, on average, flagged listings should be more susceptible to manipulative transfers than otherwise. Next I examine whether cashback hints actually attract financially constrained buyers. Figure 3a plots the percentage of transactions in which sellers hint about cashback as a function of buyer leverage. Below 80% leverage, where mortgage rates are not a function of collateral values, only 2.0% of the transactions include cashback words. Above 80% leverage, the fraction of transactions with cashback hints steadily increases: at 95% leverage, the fraction of cashback transactions increases to 3.0%, and at 100% leverage it increases to 4.8%. A similar association appears in the regression framework, in Table III, columns (1) and (2).38 The association between seller cashback hints and buyer leverage increases with 36

As further evidence that cashback transfers are illegal, in a small number of announcements sellers deliberately clarify that they will not transfer cash back to buyers, or pay closing costs beyond the allowed amount. 37 Examples of the most common strings include: seller will give ALLOWANCE/CREDIT, FHA APPRAISAL on file, seller will give REBATE, seller includes home and appliances PROTECTION plan, this home can be purchased with NO MONEY down, seller MAY / IS WILLING TO / HELP, SPECIAL ASSESSMENT to be paid by seller, CLOSING COSTS are covered by seller, seller will give XX00 CREDIT / CASH, seller will consider CREATIVE FINANCING. 38 The regression framework used in this table and throughout the paper is OLS. Although the dependent variables in most regressions are binary indicators (multiplied by 100), a linear regression framework can accommodate interactions on the righthand side, where non-linear frameworks (such as probit and logit) may produce inconsistent coefficients (Ai and Norton 2003).

21

buyer leverage (column (1)), although it declines as controls and fixed effects are added (column (2)).39

A.3. Indicator II: Paying the Full Listing Price (or Above) By construction, transactions with cashback components have higher prices relative to the same economic transactions without cash transfers. As a yardstick for this fundamental value, I propose the last listing (asking) price. There are two reasons why transactions that close at or above the listing price are more likely to be manipulated. First, the listing price, or just above it, is the highest price that buyers can push for without risking the deal or sharing too much of the manipulation surplus with sellers. Buyers who try to manipulate prices attempt to push them upwards because any additional increment to the price means that their cashback transfer is higher. However, prices cannot be too high lest they raise red flags to outside observers. Second, since the asking price is a focal price for negotiations, buyers can make “innocent” offers that contain illegal cashback components. For example, a buyer may offer to pay the full asking price conditional on the seller paying for some “needed decorations” as cash at closing. High prices accompanied by high leverage are consistent with cashback, but could also result from buyer financial constraints without any manipulation. Recent evidence suggests that financially constrained buyers have been willing to pay high prices for goods when down payments are small (Adams, Einav, and Levin 2007). Thus, a relation between prices and leverage could result because borrowers are financially constrained and not necessarily because they manipulate prices. At this stage, I characterize the association between prices and leverage without being able to distinguish between explanations. Table I, Panel B presents time-series trends of the percentage of transactions in which buyers pay the full price (or above). The time-series patterns of this variable are similar to the time-series of cashback hints. The fraction of low leveraged buyers who pay at least 39

Property controls include: logged price, logged number of bedrooms, logged number of garages, logged number of bathrooms, logged time between the listing date and contract date, and logged property age. Time fixed effects are calender quarterly indicators. Location fixed effects are at the township level (e.g., there are 38 townships in the Cook County). Many regressions use interactions of townships and calendar quarters as fixed effects.

22

the full listing price increases from 6.2% in 1995 to 17.8% in 2005 and declined to 8.9% in 2007. The rate of change for highly leveraged buyers is steeper: in 1995, 10.9% of highly leveraged buyers paid at least the full listing price; in 2005, over a quarter (27.9%) of highly leveraged buyers paid at least the full listing price. This figure did not decline by much when the market slowed down in 2007 (20.1%). In short, transactions with prices that equal, or exceed, the listing price are good candidates for being cashback transactions, and therefore are flagged with an indicator variable Paid100 . To assess whether borrowers who pay at least the full listing price also tend to be highly leveraged, I plot the fraction of transactions that close at the last listing price as a function of the buyers’ leverage (Figure 3b, solid black line). Below 80% leverage, the fraction of transactions that close at the listing price is almost constant at around 13%. Between leverage levels of 80% to 95%, the fraction increases to about 16%. At extreme leverage levels, above 95%, the fraction of transactions that close at or above the listing price dramatically increases, and peaks at 37.2% at 98% loan-to-value. These results are confirmed in a regression framework in Table III, Panel A, columns (3) to (5), where the dependent variable is Paid100 . The likelihood of paying at least the full listing price increases with leverage. After controlling for property characteristics, location, and time, borrowers are 3.8% more likely to pay at least the full listing price, or above, if their leverage is between 90% and 97%, and they are 13.6% more likely to do so if their leverage is higher than 97%. These are economically significant figures given that the dependent variable has a mean of 18.5%. Columns (3) to (5) provide more insight into the relationship between the cashback indicators. In particular, columns (4) and (5) show that buyers who pay the full listing price are also more likely to respond to cashback hints. Furthermore, in column (5), buyers’ agents fixed effects are introduced. The fixed effects increase the adjusted R2 from 0.11 in column (4) to 0.22 in column (5) and eliminate the association between paying the full listing price and cashback hints of highly leveraged borrowers. This evidence supports the hypothesis that real estate agents have an important role in promoting cashback transactions. Also, these fixed effects reduce the association between leverage and the likelihood of paying at least the full price by 30%.

23

If cashback transactions are approximated either by cashback hint or by paying at least the full listing price, then the joint signal (i.e., transactions that close at the listing price and include a cashback hint) should produce even stronger results. In columns (6) and (7), I examine the association between the interaction Paid100 × Cashback Hint and indicators for leverage brackets. The mean of the dependent variable is 0.76%, and therefore the marginal effect of the extreme leverage indicator is 0.96/0.76 = 1.22 (column (6)), which is higher than the effect in column (1) (2.09/2.86 = 0.73) or the effect in column (3) (17.27/19.51 = 0.89).

B. Economic Magnitude To understand the magnitude of the potential loss due to cashback transactions, consider Table III. First I use columns (1) and (3) to approximate for the lower and upper bounds for the manipulation rate in the sample. The coefficients presented in column (1) are estimates of the percentage of highly leveraged transactions which are likely to respond to cashback hints offered by sellers. Arguably, this percentage is the lower bound of the number of manipulated transactions. The regressions in columns (3) and (4) are of Paid100 . Since paying the full listing price and taking high leverage could result from reasons other than manipulation, I propose to use column (4) as a benchmark for the upper bound of the fraction of manipulated transactions. In this regression, there are fixed effects of location interacted with time, which potentially absorb much of the variation related to alternative motives for the relation between high prices and leverage. Using the coefficients in columns (1) and (3), and the fraction of transactions in each leverage bracket we can compute the fraction of transactions that are likely to be distorted across the sample. The calculation yields that manipulated transactions have a lower bound of 0.7% for all transactions (1.2% for all transactions with leverage above 80%), and an upper bound of 3.6% for all transactions (5.9% for all transactions with leverage above 80%). To calculate the loss in dollar terms, further assumptions are required. Suppose that the lost interest rate is 8%, as estimated in Section II.C.2. Additionally suppose that, on

24

average, prices are inflated by 5%. With these parameters, the lower bound of the annual interest loss is $5.2m, and the upper bound is $26.1m. This calculation assumes that cashback indicators are not priced in interest rates (this assumption is tested in the next section). Hence, the potential damage due to manipulation is in the ballpark of $10m-$20m a year in this sample. If this sample is representative of the U.S., then the annual national loss in recent years is about 15 to 20 times greater, i.e., $150m-$400m.

C. Returns, Price Momentum, Foreclosure, and Interest Rates To investigate further whether and how Cashback Hint and Paid100 are related to financial constraints and potentially to cashback transactions, I study the association of these variables with property and mortgage performance in Table IV. Specifically, I examine whether transactions that are suspected of being cashback transactions have higher prices relative to a hedonic model. Also, I explore whether cashback transactions are related to higher rate of default (proxied by foreclosures), and whether lenders use differential pricing for manipulating borrowers. First, I examine property performance, i.e., whether buyers who are more likely to participate in manipulation report higher prices for their properties. The prediction is that if prices are inflated, then transactions should exhibit high returns to their sellers but low returns to buyers (in the following transaction).40 Property performance is measured for properties that were sold at least twice during the sample period41 as the difference in price percentile within transaction months between the previous transaction price and the current price for the same property. By using percentiles, prices are adjusted for the current level of prices, and thus allow for the comparison of transaction prices over time. A price change of one percentile is equivalent to a change of about 1.8% in the dollar amount for properties in the central part of the distribution. 40

Although such evidence would be consistent with price manipulation, it could be also consistent with poor decision-making on the part of financially constrained buyers. For example, financially constrained buyers may need to pay a premium above market prices because they pose a credit risk to sellers, or they may be tempted to pay high prices when sellers offer even very small amounts of cashback. 41 This analysis also includes transactions which were recorded at the Recorder of Deeds but not in the MLS database.

25

The results show that transactions that are flagged as potentially manipulated are executed at excessive prices. Adding up the relevant coefficients in Table IV, column (1), shows that highly leveraged buyers who pay the full list price or above overpay by 3.6% percent over the original price that the sellers paid (adjusted for the current price levels). Column (2) presents evidence that these buyers lose up to 1.6% when selling the properties they bought. Highly leveraged buyers who respond to cashback hints also overpay by up to 3.4%, and, when selling, lose, in dollar terms, up to 1.0% more than cash buyers do. The results in column (2) are likely to be understated because homeowners tend not to sell their homes if they depreciate in value (Genesove and Mayer 2001). Columns (3) and (4) examine whether current home prices are higher in areas in which intense manipulation took place in previous periods (Prediction (1)). To test this hypothesis, for each township-quarter in the sample I compute the fraction of transactions in the previous four quarters in which buyers borrowed above 80% and which have one of the manipulation indicators. The independent variable in the regressions is the percentile rank of each transaction in the universe of transactions that were signed on the same calendar month. The results in column (4) show that an increase of one standard deviation in the intensity of cashback hints (0.02) translates to an average increase of 0.36 percentile (about 0.7% in dollar terms) in property prices. Similarly, a shift of one standard deviation in the historical intensity of high leverage and in paying the full listing price increases current prices by 0.37 percentile, on average. This result supports the claim of Second, I examine whether borrowers who are identified as potentially manipulating prices are more likely to default on their debt, and whether this risk is correctly priced by lenders. The regression results are presented in Table V. The dependent variables in columns (1) to (3) are indicator variables as to whether properties were foreclosed by the first, third, or fifth year of occupancy, respectively.42 The results indicate that properties for which highly leveraged buyers responded to cashback hints or paid at least the full listing price or above are more likely to be foreclosed. For example, a property of a borrower with 98% leverage who responds to a cashback hint and pays the full listing price is twice as likely to be foreclosed compared with the sample mean. This result corroborates Ong, Neo, 42

Foreclosures are identified in the data according to whether they were assigned by the court to a third party at a later date.

26

and Spieler (1988), who find that foreclosure rates are higher when buyers pay more for properties, and Adams, Einav, and Levin (2007) who find that subprime car buyers are more likely to pay more and default more often. Interestingly, the effect of high leverage on the foreclosure rate is not as high as the effects of the manipulation indicators. Only in the long horizon, five years, is the effect of high leverage comparable in magnitude to the effect of manipulation indicators. Column (4) tests whether manipulation indicators are priced in mortgage interest rates. The sample used consists of 67,099 mortgages in which lenders voluntarily report interest rates to the Recorder of Deeds and Mortgages.43 The results show that interest rates increase with leverage and are higher by up to 24 basis points in cases in which buyers pay the full listing price or above. However, lenders do not charge high interest rates to the risk group that defaults the most: the group of highly leveraged borrowers who potentially engage in price manipulation. Thus, consistent with Prediction (2), lenders do not fully charge higher interest rates in cashback transactions.

D. Productivity of Cashback Transactions Prediction (3) proposes that manipulation is more likely when it is more beneficial. Table VI examines some of the determinants of cashback hints and paying the full listing price. The dependent variables are the manipulation indicators. In particular, there are two relevant variables for this prediction. First the table shows that highly leveraged transactions of properties in low income neighborhoods are more likely to be classified as suspicious in terms of cashback. For people in these neighborhoods, cashback is often an essential component to becoming a homeowner. Second properties that linger on the market and are eventually sold to highly leveraged buyers are more likely to be suspected of cashback. Sellers of these properties are likely to be more pressed to sell, and therefore are more willing to engage in cashback transactions. In sum, these examples show that cashback transactions arise in transactions where the benefits for the transacting parties are greater. 43

Most data was provided by lenders after 2003.

27

E. The Role of Real Estate Agents and Industry Insiders Prediction (4) suggests that some real estate professionals and real estate agents play an important role in organizing cashback transactions. Some evidence for the role of real estate agents is in Table III, column (5), where the identity of real estate agents explains manipulation indicators far better than other property characteristics or than geographical and time fixed effects. This section more closely examines the behavior of real estate professionals. In an initial series of tests, I explore whether transactions are likely to display a stronger relationship between manipulation indicators and leverage when real estate agents have greater incentives to manipulate prices and when industry insiders sell their own properties. In a second series of tests, I investigate whether agents’ behavior persists over time with respect to the behavior related to manipulation. First I examine whether leveraged buyers are more likely to respond to cashback hints or to pay at least the full listing price when industry insiders are involved more intensely in the transaction. Transactions in which industry professionals are more involved are transactions in which the same agent represents both the seller and the buyer (11.7% of transactions), transactions in which sellers are agents (4.4% of transactions), and transactions in which sellers are investors or developers (16.9% of transactions). The motivation to engage in manipulation is higher when a single real estate agent is involved, primarily because the risk of detection is lower and the benefits from success are higher. The motivation is also higher when sellers are professionals because professional sellers are more attuned to buyers’ financing needs and are more familiar with the mechanics of cashback transactions. Table VI shows that cashback transactions are more likely to take place when industry insiders are involved. In columns (1) to (3), most results indicate that highly leveraged buyers are more likely to respond to cashback hints and to pay at least the full listing price when the transaction is mediated by a single agent or when the seller is a professional. Interestingly, the strongest effect of industry professionals on the association between manipulation indicators and leverage is when the sellers are agents themselves.

28

Second I test whether manipulations of collateral values, being unlawful, are confined to a small circle of agents. To some extent this is an examination of the distribution of the coefficients of agent fixed effects in Table III, column (5). The test is based on ranking agents according to their likelihood to engage in cashback transactions. For each agent in the sample, I create two sub-samples: one that contains all transactions in which the agent represents buyers, and the other contains transactions in which he represents sellers. Within each subsample, I compute the fraction of highly leveraged transactions in which the buyer pays at least the full listing price. This fraction is a proxy for the involvement of the agent in cashback transactions.44 Then I match the cashback involvement proxy back to the agents in each transaction. To avoid a mechanical correlation later in the regressions, buyers’ agents are matched to their proxies calculated on the basis of transactions in which they were sellers’ agents, and sellers’ agents are matched to their proxies calculated in samples in which they were buyers’ agents. Next agents are ranked into deciles according to their cashback involvement proxy. Agents who frequently participate in such transactions are ranked in higher deciles. If highly leveraged buyers respond to cashback hints or pay at least the full listing prices spontaneously, then the agent’s involvement in other deals should be irrelevant. However if buyers and seller are induced to participate in cashback transactions by agents, then their history of participating in such transactions should matter. Table VII presents regressions where the dependent variables are the manipulation indicators, and the explanatory variables are decile indicators interacted with extreme leverage (above 97%). In addition to the usual property and transaction controls, regressions on this table include time fixed effects and township fixed effects. In addition I add agent fixed effects to ensure that the manipulation indicators are correlated with transactions of highly leveraged buyers, and do not capture agents’ unobserved characteristics (such as making their clients pay high prices or respond to cashback hints). The distribution of the coefficients on the decile variables is highly skewed towards the high deciles. When agents in the top three deciles facilitate transactions, the correlation 44

To reduce noise, this proxy is calculated only for agents who were involved in at least 10 highly leveraged transactions.

29

between manipulation indicators and leverage dramatically increases, especially in columns (2) and (3). In the first seven deciles, the coefficients are virtually zero, there is no “agenthistory” effect. However, starting from the 8th , but especially in the 9th and 10th deciles, agents’ participation in suspicious transactions matters in whether leveraged buyers respond to cashback hints and in whether they pay at least the full listing price. To illustrate the economic effect, highly leveraged buyers who work with an agent who is intensely involved in cashback transactions have a higher likelihood of paying at least the full listing price by 6.9% (the unconditional mean of paying the full listing price or more in this subsample is 18.2%). This result is strong because agent history is computed using transactions in which agents were hired by the other party. Consider an agent from the 10th decile who often represents sellers who sell their properties to highly leveraged buyers and receive at least the full listing price. Controlling for the agent’s characteristics, in transactions in which the agent represents highly leveraged buyers, the buyers are more likely to pay at least the full listing price. Furthermore column (4) shows that clients of agents who often participate in suspicious transactions are more likely to pay strictly more than the listing price. Columns (4) and (5) test whether borrowers who use the services of agents who often engage in cashback transactions are more likely to default, and whether they pay higher interest rates on their mortgages. Column (4) shows that foreclosure rates are higher in properties in which buyers’ agents have richer history of cashback transactions. In contrast column (5) shows that interest rates for these buyers are not higher. Overall the results in Tables VI and VII are consistent with the claim that cashback transactions are more prevalent when industry professionals are involved. Furthermore the results indicate that only a small group of real estate agents (10% to 15%)45 potentially take an active role in cashback transactions. 45 This estimation is based on the results that identify 2 to 3 deciles of agents with suspicious histories, and based on the fact that about 60% of agents did not have the minimal number of transactions required for the test (at least 10 highly leveraged transactions).

30

F. Information Uncertainty and Manipulation Activity Prediction (5) postulates that manipulation is more common when it is harder to detect. I examine how uncertainty about valuations may affect the association between high leverage and manipulation indicators. Uncertainty about valuations is essential for manipulating collateral values using the cashback method; without uncertainty, appraisers could easily reveal misrepresentations. One source of information that appraisers rely on when preparing their appraisals is the property valuation assessments published periodically by the County Tax Assessor (Fannie Mae 2005). Because valuation assessments are revised every few years, the assessments create discontinuous changes in the information available to appraisers. In Cook County each district (there are 3 districts) is assessed during a different calendar year, and each township (there are 38 townships) is assessed during a different calendar month over the three-year cycle.46 Figure 4 presents a map of Cook County with the tax districts (each a different color) and the boundaries of the different townships. I limit the transaction sample to properties that are sold within one year before or after their township’s reassessment dates. The variable Post Tax Reassessment measures the time (in years) since from the most recent reassessment date of the township’s properties until transaction closing. In this setting it is expected that following tax assessments, the manipulation rate is lower. This hypothesis is tested in Table VIII. The test is a diff-in-diff test in which we control for both time and township, and test the effect of information event around the time of announcement. The dependent variables in the regressions are the manipulation indicators. The explanatory variables are high leverage indicators and post-tax reassessment dummies, interactions with leverage, property controls, and township and calendar quarter fixed effects. Columns (1) and (3) show that although Post Tax Reassessment does not have a strong effect on low leverage borrowers’ propensity to respond to cashback hints or to pay at least the full listing price, it reduces highly leveraged borrowers’ propensity to engage in these transactions. The effect is economically and statistically significant in all columns. To illustrate the effect, the rate of cashback hints for highly leveraged borrowers 46 The schedule of reassessments is published two years in advance. Further information about the triennial assessment cycle is available on http://www.cookcountyassessor.com/ccao/cycle.html.

31

is higher by 0.7% (= 0.23 × 3) just before the next reassessment date compared to just after the reassessment date. Similarly the likelihood of paying the full listing price, or above, is higher by 4.5% for highly leveraged borrowers just before reassessment dates compared to just after reassessment dates.

G. Monitoring by Loan Buyers Cashback transactions are predicted to be more prevalent when lenders are exposed to moral hazard problems, i.e., when they sell the mortgages that they originate (Prediction (6)). Lenders in the data set can be classified into two main categories– banks and financial firms– according to their registered names.47 Table VI tests whether evidence for manipulation is stronger for financial firms. In column (1) there is no evidence that attraction to cashback hints is stronger for borrowers from financial firms. In column (2), however, the likelihood of paying the full listing price or above is higher for highly leveraged borrowers of financial firm. This mixed evidence is also consistent with the financial constraints explanation, since financial firms may attract financially constrained borrowers (as indicated in Table I, Panel D).48 One way to disentangle the financial constraints hypothesis from the manipulation explanation is to examine how monitoring by loan buyers affects the association between manipulation indicators and leverage. Tighter monitoring should reduce manipulation, but should not affect the purchasing patterns of financially constrained borrowers. The empirical strategy therefore focuses on the tension between financial constraints and the tightness of monitoring with respect to mortgage size. As mortgage size decreases, the financial constraints of borrowers intensify, and at the same time, the unconditional likelihood of monitoring decreases due to the fixed costs of auditing. However, financial constraints and monitoring by loan buyers do not always comove in tandem. While financial 47 Lenders are tagged as banks if their names include strings like BANK, BK, or B&T (31.9% of observations) and they are classified as financial firms either if their names include words like LTD, LP, LOAN, LENDING, or FINANCIAL, or if they register their mortgages for trading (MORTGAGE ELECTRONIC REGISTRATION SYSTEM) (68.1% of observations). 48 Financial firms may be able to offer better terms to financially constrained borrowers than banks do because, since they off-load mortgages to loan buyers, they bear less or no default risk on average.

32

constraints are continuous over time,49 the tightness of monitoring changes in a discontinuous manner over time, depending, for example, on the number of mortgage applications submitted. In this test I focus on mortgages that are bought by the GSEs. I use the maximal amount that the GSEs lend per property (“jumbo loan cutoff”) as a factor that constrains the number of submitted mortgage applications. While the market price level changes continuously over time, the GSEs modify the jumbo loan cutoff level only once a year. Therefore, the jumbo cutoff level does not track market price levels perfectly (Figure 5).50 Hence, changes in the cutoff level shift the number of eligible mortgage applications and, as a result, the effectiveness of monitoring. Yet these shifts do not affect the propensity to engage in manipulation through the channel of financial constraints. I argue that, ceteris paribus, mortgages that are closer to the jumbo cutoff level would be monitored more closely by the GSEs, and therefore should exhibit lower manipulation levels.51 To test this prediction, I construct two size variables for mortgages. One variable measures the size of mortgages relative to the size of the median asset Mortgage Sizei = Mortgagei . Median Assett

The second variable measures the size of mortgages relative to the jumbo cutoff

level: Monitoringi =

Mortgagei . Jumbo Cutofft

The prediction is that Monitoringi reduces the associ-

ation between the manipulation indicators and leverage, while Mortgage Sizei intensifies this relationship. To ensure that the regressions capture the effect of monitoring and not other effects, I apply several restrictions to the data. First, I limit the sample to transactions in which only one mortgage was taken, and in which the leverage does not exceed 97%. This restriction 49 To illustrate, the general level of financial constraints of borrowers who borrow $100,000 should be more or less the same today and a month from now. 50 For instance, in mid-2000 the jumbo cutoff level was at the 90th percentile of the price level in Cook County, while in the beginning of 2002, the loan cutoff level matched the 95th percentile. These discrepancies are exacerbated by annual discontinuous adjustments to the jumbo cutoff level. 51 To make this argument, I make several implicit assumptions. First, I assume that the GSEs do not have capacity constraints and that they purchase all mortgages that match their criteria, such as location, house characteristics, credit quality, and mortgage size criteria. Second, I assume that the propensity to manipulate prices depends primarily on home and borrower characteristics, such as desired home size and available equity. Third, I assume that the propensity to manipulate collateral values is not related to changes in the maximum mortgage level that the major loan buyers are willing to purchase, especially for loans that are smaller than this maximum level. Fourth, I assume that mortgage auditing by Fannie Mae and Freddie Mac is performed randomly (perhaps within risk groups), and involves a fixed cost.

33

increases the likelihood that the final purchaser is either Fannie Mae and Freddie Mac, and not some other sub-prime lender. Second, I exclude mortgages with Monitoring > 0.97. This restriction addresses the concern that high quality borrowers cluster around the jumbo cutoff, in order to benefit from the attractive interest rates offered by the GSEs.52 Finally, to avoid multicollinearity issue due to the high correlation between Mortgage Size and Monitoring, I orthogonalize them by regressing Monitoring on Mortgage Size and using the regression residual as the variable of interest, Monitoring (residual ). Table IX presents the regression results. Columns (1) to (3) show some evidence that the association between high leverage and manipulation indicators declines as mortgages are closer to the jumbo loan cutoff level, even after controlling for mortgage size. The coefficients of Monitoring (residual) take the right sign, although they are not always statistically significant. Columns (4) and (5) provide further evidence that Monitoring (residual) does indeed capture the monitoring effect. In these regressions, the dependent variables are foreclosure indicators in the first three and five years, respectively. The regression shows that, controlling for mortgage size, foreclosure rates are lower for highly leveraged properties that are closer to the jumbo cutoff and are therefore exposed to tighter monitoring than the rates are for highly leveraged properties that are further away from the cutoff; these properties are monitored less. Overall the results in this section are consistent with the hypothesis that moral hazard in loan-selling generates an incentive for intermediaries to shirk or to turn a blind eye to (or even support) the manipulation of collateral values. Moreover, the analysis indicates that monitoring by loan buyers can reduce the moral hazard problem, since the cost of shirking is higher when monitoring is tighter.

VI. Conclusion The paper presents evidence consistent with the manipulation activity of borrowers and intermediaries that is targeted to mislead lenders about the quality of the assets financed. 52 Loutskina and Strahan (2005) show that the likelihood of mortgages being accepted right at the jumbo cutoff is significantly higher.

34

Manipulations of collateral values help borrowers to achieve better financing terms, and intermediaries to expand their business. Both practices presented in the paper (appraisal bias and cashback transactions) happen due to asymmetric information and misaligned incentives between borrowers, financial intermediaries, and investors (loan buyers). The paper presents evidence that manipulation has real effects on home prices and on borrower default, yet lenders do not use differential pricing accordingly. Furthermore, manipulation is most likely to take place when it is productive and difficult to detect. Manipulation activity is difficult to identify empirically because the main driver of manipulation is buyers’ financial constraints, which by itself produce many empirical predictions similar to those of manipulation. The paper attempts to disentangle the two effects by examining signals that are unique to manipulation (e.g., cashback hints), and by investigating the consequences under which these signals are more likely to relate to transactions of financially constrained borrowers. The main findings are that evidence for manipulation is stronger when information about prices is poor, when a small group of industry insiders is involved in transactions, and when monitoring by loan buyers is poor. The policy implication derived from the paper relates to the agency problem in mortgage origination. In particular, agency conflicts of appraisers are a main reason why manipulation of collateral values can exist. In the current state of affairs, all parties involved in the mortgage origination process are interested in generating deal volume and therefore benefit from high appraisal valuations. In turn, appraisers often have the incentive to cooperate with mortgage originators in order to be rehired in the future. Resolving some of the agency issues around the mortgage origination process (as recently was proposed by the Federal Reserve in changes to Regulation Z) could possibly decrease appraisal bias and align incentives of mortgage originators and appraisers with those of investors in the secondary market.

35

References Aaron, Bob, 2006a, Appraisal Buzz interview with Kathy Coon, Appraisal Buzz July 2006. , 2006b, Mortgage fraud persists with cash-back schemes, Toronto Star 10 December 2006. Adams, William, Liran Einav, and Jonathan Levin, 2007, Liquidity constraints and imperfect information in subprime lending, Stanford University working paper. Ai, Chunrong, and Edward C. Norton, 2003, Interaction terms in logit and probit models, Economic Letters 80, 123–129. Anonymous, 2007, Cash back at closing scheme gets broker, agent, and 4 others convicted, Originator Times 11 April 2007. Benmelech, Efraim, and Nittai Bergman, 2007, Collateral pricing, Harvard and MIT Working Paper. Berger, Allen N., and Gregory F. Udell, 1990, Collateral, loan quality and bank risk, Journal of Monetary Economics 25, 21–42. Bester, Helmut, 1987, The role of collateral in credit markets with imperfect information, European Economic Review 31, 887–899. Bureau of Labor Statistics, U.S. Department of Labor, 2006, Loan officers, Occupational Outlook Handbook, 2006-07 Edition, http://www.bls.gov/oco/ocos018.htm. Callahan, David, 2005, Home insecurity: How widespread appraisal fraud puts homeowners at risk, Demos: A network for ideas and action. Canner, Glenn B., Wayne Passmore, and Monisha Mittal, 1994, Private mortgage insurance, Federal Reserve Bulletin October, 883–899. Cao, Menglin, 2005, Essays on information asymmetry in the U.S. residential mortgage market: Incentives and estimations, University of Maryland Working Paper. Carr, Anthony, 2007, Seller subsidies legal at settlement, cash back to buyer is not, Realty Times 12 January 2007. Coco, Giuseppe, 2000, On the use of collateral, Journal of Economic Surveys 14, 191–214. Committee on Financial Services, 2004, Mortgage fraud and its impact on mortgage lenders, Hearing before the Subcommittee on Housing and Community Opportunity of the Committee on Financial Services U.S. House of Representatives (second session), Serial No. 108-116. Conrad, Debbie, 2005, Appraisal fraud: When property values are over-inflated, Wisconsin Real Estate Magazine June 2005. Coombes, Andrea, 2006, Borrowers discover that home is where the mortgage fraud is, Wall Street Journal 11 April 2006. Coon, Kathy, 2006, Us vs. them: The fight against fraud starts with you, National Mortgage Broker Magazine September 2006.

36

Corkery, Michael, 2007, Fraud seen as a driver in wave of foreclosures, Wall Street Journal 21 December 2007. Fannie Mae, 2005, Uniform residential appraisal report (form 1004), Standard appraisal form. , 2006a, Mortgage fraud update, April-November 2006. , 2006b, Originating quality loans, http://www.efanniemae.com/lc/publications/pdf/qabestpractices.pdf. , 2007, Guide to underwriting with desktop underwriters, Memorandum. Gan, Yingjin Hila, and Christopher Mayer, 2006, Agency conflicts, asset substitution, and securitization, Working Paper. Garmaise, Mark J., and Tobias J. Moskowitz, 2004, Confronting information asymmetries: Evidence from real estate markets, Review of Financial Studies 17, 405–437. Gendler, Neal, 1998, Phantom down payments; ’Numbers game’ hides truth from lenders, Star Tribune (Minneapolis, MN) p. 9A 14 July 1998. Genesove, David, and Christopher Mayer, 2001, Loss aversion and seller behavior: Evidence from the housing market, Quarterly Journal of Economics 116, 1233–1260. Goodman, Jonathan A., 2002, How to commit loan fraud, http://www.frascona.com/resource/jag902loanfraud.htm. , 2006, How to commit more loan fraud, http://www.frascona.com/resource/jag206loanfraud.htm. Gorton, Gary B., and George G. Pennacchi, 1989, Are loan sales really off-balance sheet?, Journal of Accounting, Auditing and Finance 4, 125–145. , 1995, Banks and loan sales: Marketing nonmarketable assets, Journal of Monetary Economics 35, 389–411. Gorton, Gary B., and Andrew Winton, 2002, Financial intermediation, NBER Working Paper. Gutierrez, Carl, 2007, Washington Mutual: Things are worse, Forbes 7 November 2007. Hagerty, James R., and Michael Corkery, 2007, How hidden incentives distort home prices, Wall Street Journal 19 December 2007. Hagerty, James R., and Ruth Simon, 2006, New headache for Americans: Inflated home appraisals, Wall Street Journal 22 July 2006. Harney, Kenneth R., 2007, Appraisers say pressure on them to fudge values is up sharply, Realty Times 5 February 2007. Hellwig, Martin, 2005, Market discipline, information processing, and corporate governance, Max Planck Institute for Research on Collective Goods Bonn, Working Paper 2005/19. Inside Mortgage Finance, 2005, Mortgage market statistical annual, http:www.imfpubs.com.

37

Jackson, David, 2005, ’If you are still making money selling drugs, you are an informant or about to be busted: Mortgage fraud is the thing to do now.’, Chicago Tribune 5 November 2005. LaCour-Little, Michael, and Georgory H Chun, 1999, Third party originators and mortgage prepayment risk: An agency problem?, Journal of Real-Estate Research 17, 55–70. Lahart, Justin, 2007, Housing bubble: Toil and trouble follows pattern, Wall Street Journal 5 March 2007. Levitt, Steven D., and Chad Syverson, 2005, Market distortions when agents are better informed: The value of information in real estate transactions, University of Chicago Working Paper. Lloyd, Carol, 2006, Mortgage fraud – the worst crime no one’s heard of, San Francisco Chronicle 29 October 2006. Louis, Brian, and Sharon L. Crenson, 2007, Ohio sues real estate firms for pressuring appraisers, Bloomberg.com 7 June 2007. Loutskina, Elena, and Philip E. Strahan, 2005, Securitization and the declining impact of bank finance on loan supply: Evidence from mortgage acceptance rates, Working Paper, Boston College. Manove, Michael, A. Jorge Padilla, and Marco Pagano, 2001, Collateral versus project screening: A model of lazy banks, The RAND Journal of Economics 32, 726–744. Miller, Merton H., and Kevin Rock, 1985, Dividend policy under asymmetric information, Journal of Finance 40, 1031–1051. Minton, Bernadette, Anthony B. Sanders, and Philip E. Strahan, 2004, Securitization by banks and finance companies: Efficient financial contracting or regulatory arbitrage?, Ohio State University and Boston College Working Paper. New-York Attorney General, 2007, Lawsuit of the State of New-York against First American Corporation and First American eAppraisIT, http://www.oag.state.ny.us/. Office of General Inspector General, 2005, Audit report: Washington Mutual Bank did not follow HUD regulations when underwriting six loans, Audit Report Number 2005-KC-1009, http://www.hud.gov/oig/ig571009.pdf. Olinger, David, 2006, Steal of a deal, Denver Post 29 October 2006. Ong, Seow Eng, Poh Har Neo, and Andrew C. Spieler, 1988, Price premium and foreclosure risk, Real-Estate Economics 34, 211242. Petersen, Mitchell A., 2004, Information: Hard and soft, Working Paper. Reagor, Catherine, 2007, State targets mortgage fraud, The Arizona Republic 23 January 2007. Roberts, Ralph, 2006, Cash back at closing: Appealing arrangement or sinister scam, Realty Times 2 May 2006. Rutherford, R. C., T. M. Springer, and A. Yavas, 2005, Conflicts between principals and agents: Evidence from residential brokerage, Journal of Financial Economics 76, 627–665.

38

Sharpe, Steven A., 1990, Asymmetric information, bank lending and implicit contracts: A stylized model of customer relationships, Journal of Finance 45, 1069–1087. Simpson, David, 2004, Dekalb neighbors fight ’flippers’; Mortgage fraud led to higher taxes and empty, foreclosed homes, The Atlanta Journal-Constitution p. 1A 15 May 2004. Smith, Steve, 2002, Predatory lending, mortgage fraud, and client pressures, The Appraisal Journal pp. 200–213 April 2002. Stein, Jeremy, 1989, Overreactions in the options market, Journal of Finance 44, 1011–1022. Stiglitz, Joseph E., and Andrew Weiss, 1981, Credit rationing in markets with imperfect information, American Economic Review 71, 393–410. Sufi, Amir, 2007, Information asymmetry and financing arrangements: Evidence from syndicated loans, Journal of Finance 62. Tong, Vinnee, 2006, Home seller incentives may be inflating prices, Chicago Tribune 26 October 2006. Wette, Hildegard C., 1983, Collateral in credit rationing in markets with imperfect information: Note, American Economic Review 73, 442–445.

39

Appendix A: Variable Definitions A. Appraisals Data Set log(Price)

Logged transaction price (in $’000).

Appraisal /Price (%)

The ratio of appraisal value to transaction price in %.

log(Avg. Annual Income)

The logged average income in 2004 for the zip code of the property, based on IRS data.

Leverage > 80 %

Leverage indicator that indicates whether the buyer borrowed more than 80% loan-to-price.

Low Income

Indicator variable as to whether the property is located in a zip code that is in the lower half of the zip code income distribution. Based on average zip code-level income statistics of the IRS for 2004.

High Comps Dispersion

Indicator variable as to whether the appraiser did not find good comparable properties. For each transaction the Dispersion is calculated as the highest comparable property minus the lowest comparable property, scaled by the transaction value. The indicator variable receives the value 1 if Dispersion is higher than the median.

B. Transactions Data Set Agent Decile X

Indicator of whether the buyer’s agent is ranked at decile X for the variable Past Participation. Past Participation measures the fraction of transactions in which real estate agents participated in the last three years as sellers agents and in which buyers paid at least the full listing price and borrowed more than 80% of the transaction price. This variable is calculated only for real estate agents who had more than 5 transactions as sellers’ agents in the previous three years.

Cashback Hint

Cashback indicator indicates whether the seller advertised a hint about cashback or a transfer.

Foreclosed by X year

Indicator variable as to whether the property was foreclosed in the first X years following the transaction.

Lender is Financial Firm

Indicator variable as to whether a lender is a “bank” and likely to hold the mortgages it originates (= 0), or is a “financial firm” and likely to sell the mortgages it originates (= 1).

X % < Leverage ≤ Y %

Indicator variables as to whether loan-to-price is higher than X and lower than or equal to Y .

log(Average Income)

Logged average income at zip-code level according to the IRS.

Low /Med /High Income

Income brackets, based on IRS zip-code level data.

40

Monitoring (residual )

Residual of a regression in which the mortgage amount scaled by the jumbo cutoff level is regressed on Mortgage Size.

Mortgage Size

The mortgage amount scaled by the median house price that was financed in the same month.

Post Tax Reassessment

Time elapsed (in years) since the last tax reassessment date in the township.

log(Price)

Logged transaction price.

Property Controls

Controls variables for: logged price, logged time on the market (in days) between listing and contract, logged number of bathrooms, logged number of half-bathrooms, logged number of bedrooms, logged number of garages, logged age, type of walls indicator, new-status indicator (whether the property is under construction, etc.), and transaction contract month dummies.

Seller is Agent

Indicator variable as to whether the seller himself is a real estate agent.

Seller is Professional

Indicator variable as to whether the seller is a non-homeowner for tax purposes or whether the property is new.

Single Agent

Indicator variable as to whether the same agent represents the buyer and seller in a transaction.

log(Time on the Market) Logged number of days between listing and contract plus one. Price percentile

Percentile rank of the transaction price in the universe of transactions that were signed on the same month.

∆price percentile

Change in the Price percentile for the same property between the current transaction and the last transaction of the property, or between the next transaction of the property and the current transaction.

Historical Cashback Hint The fraction of transactions in the township in the previous Intensity four quarters That, out of all highly leveraged (above 80%) transactions, included a Cashback Hint. Historical Paid100 Intensity

The fraction of transactions in the township in the previous four quarters in which buyers paid the full listing price out of all highly leveraged (above 80%) transactions.

41

Table I

Summary Statistics The table presents descriptive statistics. Panel A presents summary statistics for the appraisals data set. Variables denoted with † are rounded in order to maintain confidentiality for the data provided (minimum and medians of transaction price and appraisals are rounded to the nearest $10k; maximum values are rounded to the nearest $100k; maximum Appraisal/P rice ratio is rounded to the nearest 5%; maximum Leverage is rounded to the nearest percentage point). Panels B through D presents summary statistics for the MLS data set. Panel B presents summary statistics for the transactions data set broken up by year of closing. Panel C presents summary statistics broken up by the type of lender. Panel D presents pooled summary statistics for the transactions data set.

Panel A: Summary statistics (Appraisals Sample) Variable Transaction price ($0 000 ) Appraisal ($0 000 ) Appraisal /Price (%) Leverage (%) Leverage > 80 % Average Annual Income ($0 000 )

Obs 311 311 311 311 311 311

Mean 335 338 101.0 89.1 0.73 63

Std Dev 225 227 2.0 11.3 0.44 43

Min 70† 70† 100.0 21.3 0 21

Median 280† 280† 100.3 90.0 1 45

Max 2100† 2100† 118† 102.4 1 373

Panel B: Summary statistics, by year of closing (Transactions Sample) Cashback # days from Hint (%) Paid100 (%) listing to Average Leverage Leverage Year Obs contract Leverage ≤ 80% > 80% ≤ 80% > 80% 1995-1997 111265 56.4 84.8 2.1 2.8 6.2 10.9 1998-2000 149835 42.3 85.1 1.9 2.8 13.9 20.3 2001-2002 121301 34.7 85.1 1.7 3.1 16.4 26.7 2003-2004 153802 42.5 85.1 1.9 3.3 14.9 27.1 2005-2006 160462 52.1 85.3 2.6 3.9 17.8 27.9 2007 43743 75.4 86.7 3.1 5.3 8.9 20.1 Panel C: Summary statistics, by lender type (Transactions Sample) Financial Financial Banks Firms Others Banks Firms Others Mean statistics Income % of observations % observations 36.1 56.8 7.1 Low 25.9 33.3 30.0 Transaction price ($) 278797 226814 238638 Medium 39.9 40.2 41.2 Leverage (%) 82.3 87.1 84.7 High 34.2 26.5 28.8 Leverage > 80 %(× 100 ) 54.6 66.7 59.8 Total 100.0 100.0 100.0 Paid100 16.0 21.6 20.3 Cashback Hint 2.5 3.1 3.2 Cashback Hint × Paid100 0.5 0.9 0.9

42

Table I: Summary Statistics (Cont.)

Panel D: Descriptive statistics (Transactions Sample) Variable Age (years) #baths #bedrooms #cargarages Time on the Market (days) Transaction price/Listing price Transaction price ($0 000 ) Leverage (%) Leverage > 80 % 80 % < Leverage ≤ 90 % 90 % < Leverage ≤ 97 % Leverage > 97 % Paid100 (%) Cashback Hint (%) Cashback Hint × Paid100 (%) ∆ price percentile pre transaction ∆ price percentile post transaction Price percentile Interestrate Historical Cashback Hint Intensity Historical Paid100 Intensity Foreclosed by 1 st year (%) Foreclosed by 3 rd year (%) Foreclosed by 5 th year (%) log(Average Income) log(Time on the Market) log(price) Post Tax Reassessment Lender is Financial Firm Monitoring (residual ) Mortgage Size Single Agent Seller is Agent Seller is Professional

Obs 740408 740408 740408 740408 740408 740408 740408 740408 740408 740408 740408 740408 740408 740408 740408 268784 163490 740408 67153 725855 725855 696665 536203 382401 714502 740408 740408 440802 692238 236521 236521 708000 740408 740408

43

Mean 26.9 1.69 3.00 1.65 47.28 97.15 246.4 85.2 0.62 0.19 0.19 0.22 19.52 2.86 0.76 4.41 2.42 51.51 6.56 0.03 0.21 0.79 0.82 1.04 10.80 3.23 12.21 2.25 0.61 0.00 0.80 0.12 0.06 0.42

Std Dev 20.8 0.82 1.31 0.79 57.50 4.08 209.3 15.1 0.49 0.40 0.39 0.42 39.63 16.67 8.70 17.69 17.81 27.82 1.73 0.02 0.11 8.88 9.01 10.14 0.48 1.24 0.60 1.01 0.49 0.16 0.30 0.33 0.23 0.49

Min -5 0 0 0 0 50 30 25.5 0 0 0 0 0 0 0 -96.5 -99.3 0.1 0.3 0.00 0.00 0 0 0 9.68 0.00 10.32 0.00 0 -0.98 0.05 0 0 0

Median 26 2 3 2 27 97.4 192 90.0 1 0 0 0 0 0 0 1.9 1.6 51.9 6.5 0.02 0.21 0 0 0 10.74 3.33 12.17 2.21 1 -0.01 0.78 0 0 0

Max 100 9 9 9 730 200 6800 103.5 1 1 1 1 100 100 100 99.5 97.6 99.9 14.8 0.13 0.55 100 100 100 13.63 6.59 15.73 4.00 1 0.69 2.95 1 1 1

Table II

Property Appraisals and Leverage The table presents regressions of Appraisal/P rice on property and borrower characteristics. The sample is based on 311 appraisal reports obtained from a large financial firm in Illinois. Variable definitions are provided in Appendix A. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

Leverage > 80 %

(1) 0.59*** (0.17)

× Low Income × High Comps Dispersion

Appraisal /Price (%) (2) (3) (4) 0.55*** 0.10 0.05 (0.18) (0.15) (0.16) 0.74*** (0.25) 1.06*** (0.30)

Low Income

-0.20 (0.19)

High Comps Dispersion log(Price)

Year fixed effects Township fixed effects Observations Adj. R2

311 0.02

44

(5) -0.33 (0.24) 0.67*** (0.22) 0.98*** (0.31)

-0.39** (0.18) 0.01 (0.29)

0.28 (0.77) -0.37** (0.17) 0.08 (0.26)

-0.01 (0.30)

0.08 (0.23)

Yes Yes

Yes

Yes Yes

Yes Yes

311 0.10

311 0.05

311 0.12

311 0.13

Table III

Cashback Hints, Paying the Full Listing Price (or Above) and High Leverage The table presents baseline regressions. Variable definitions are provided in Appendix A. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

80 % < Leverage ≤ 90 %

% Obs 19.4

90 % < Leverage ≤ 97 %

19.3

Leverage > 97 %

22.4

Cashback Hint Cashback Hint (%) Paid100 (%) × Paid100 (%) (1) (2) (3) (4) (5) (6) (7) 0.29*** -0.04 1.92*** 0.44** 0.08 0.00 -0.03 (0.09) (0.05) (0.37) (0.17) (0.17) (0.03) (0.02) 0.98*** 0.12** 6.33*** 3.48*** 2.01*** 0.17*** 0.09*** (0.16) (0.06) (0.76) (0.27) (0.19) (0.03) (0.03) 2.09*** 0.58*** 17.27*** 12.62*** 8.06*** 0.93*** 0.65*** (0.25) (0.12) (1.24) (0.58) (0.49) (0.09) (0.06)

Cashback Hint × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 % × Leverage > 97 %

Property controls Township × Quarter fixed effects Township fixed effects Quarter fixed effects Buyer’s agent fixed effects Observations Adj. R2

Yes Yes

2.28*** (0.68) -0.30 (0.93) 0.88 (0.71) 2.60*** (0.85)

2.26*** (0.56) -1.30 (0.93) -0.61 (0.75) 0.87 (0.77)

Yes Yes

Yes

Yes Yes

Yes Yes Yes 740408 0.00

739790 0.02

45

740408 0.03

739790 708224 739790 0.11 0.22 0.01

739790 0.01

Table IV

Property Performance and Cashback Transactions The table presents regressions of asset and mortgage performance. Variable definitions are provided in Appendix A. A change of one price percentile is equivalent to a change of approximately 1.8% in house prices. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

Cashback Hint × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 % × Leverage > 97 %

Paid100 × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 % × Leverage > 97 %

80 % < Leverage ≤ 90 % 90 % < Leverage ≤ 97 % Leverage > 97 %

∆ price percentile pre − transaction post − transaction (1) (2) 1.31*** -0.14 (0.45) (0.54) -0.39 -1.41* (0.50) (0.78) 0.02 -0.87 (0.52) (0.66) 0.04 -1.38** (0.46) (0.67) 0.66*** (0.21) -0.45** (0.22) 0.14 (0.28) 0.82*** (0.18)

0.87** (0.33) -0.70** (0.32) -1.41*** (0.38) -2.78*** (0.42)

-0.74*** (0.20) -0.02 (0.15) -0.14 (0.18) 0.08 (0.20)

0.35*** (0.11) 0.53*** (0.17) 0.52 (0.34)

0.50*** (0.16) 0.49** (0.19) 1.04** (0.50)

0.34*** (0.10) -1.12*** (0.20) -2.76*** (0.29)

Historical Cashback Hint Intensity

72.86** 17.82** (28.86) (7.86) 7.19 3.38** (7.04) (1.59)

Historical Paid100 Intensity

Property controls Township × Quarter fixed effects Township fixed effects Quarter fixed effects Observations Adj. R2

Price percentile (3) (4) -0.57*** (0.16) -0.47* (0.26) -0.64** (0.28) -0.10 (0.24)

Yes Yes

268564 0.12

46

Yes Yes

163337 0.23

Yes Yes Yes

Yes Yes

725855 0.23

725264 0.92

Table V

Mortgage Performance and Cashback Transactions The table presents regressions of asset and mortgage performance. Variable definitions are provided in Appendix A. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

Cashback Hint × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 % × Leverage > 97 %

Paid100 × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 % × Leverage > 97 %

80 % < Leverage ≤ 90 % 90 % < Leverage ≤ 97 % Leverage > 97 %

Property controls Township × Quarter fixed effects Observations Adj. R2

1 st year (%) (1) -0.12 (0.14) 0.38 (0.25) 0.56** (0.23) 0.62** (0.28)

Foreclosed by . . . 3 rd year (%) 5 th year (%) (2) (3) -0.18 -0.21 (0.13) (0.15) 0.49* 0.64* (0.28) (0.35) 0.55** 0.67** (0.25) (0.31) 0.93*** 1.04*** (0.27) (0.33)

Interest rate (4) 0.11*** (0.04) 0.19** (0.07) 0.06 (0.06) 0.03 (0.06)

0.04 (0.05) 0.00 (0.08) 0.13 (0.08) 0.23** (0.10)

0.04 (0.06) -0.01 (0.10) 0.19 (0.12) 0.33*** (0.11)

0.04 (0.04) 0.06 (0.09) 0.21 (0.13) 0.88*** (0.17)

0.23*** (0.03) 0.20*** (0.04) 0.24*** (0.03) -0.01 (0.05)

-0.04 (0.03) -0.09** (0.04) 0.06* (0.04)

-0.05 (0.04) -0.06 (0.04) 0.20*** (0.05)

-0.05 (0.05) -0.03 (0.05) 0.76*** (0.12)

0.58*** (0.03) 0.53*** (0.03) 0.92*** (0.04)

Yes Yes

Yes Yes

Yes Yes

Yes Yes

696100 0.01

535825 0.01

382123 0.01

67099 0.42

47

Table VI

Productivity of Cashback Transactions The table presents evidence on manipulation levels across property and transaction characteristics. Variable definitions are provided in Appendix A. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

80 % < Leverage ≤ 90 % 90 % < Leverage ≤ 97 % Leverage > 97 % Leverage > 80 % × Single Agent × Seller is Agent × Seller is Professional × log(Average Income) × log(Time on the Market) × Lender is Financial Firm

Single Agent Seller is Agent Seller is Professional log(Average Income) log(Time on the Market) Lender is Financial Firm

Property controls Township × Quarter fixed effects Observations Adj. R2

Cashback Hint (%) (1) 0.01 (0.23) 0.17 (0.21) 0.55** (0.25)

Paid100 (%) (2) 0.61 (0.72) 3.55*** (0.77) 12.13*** (1.04)

Cashback Hint × Paid100 (%) (3) 0.06 (0.11) 0.16 (0.11) 0.68*** (0.13)

0.55*** (0.18) 1.23*** (0.29) 0.11 (0.12) -0.10*** (0.03) 0.23*** (0.04) 0.13 (0.09)

-0.83** (0.34) 0.05 (0.75) 0.12 (0.25) -0.37*** (0.09) 0.71*** (0.12) 2.68*** (0.29)

0.19*** (0.07) 0.65*** (0.11) 0.10** (0.05) -0.06*** (0.02) 0.11*** (0.02) 0.18*** (0.04)

0.17 (0.11) 0.54*** (0.20) 0.36*** (0.10) -0.53** (0.23) 0.38*** (0.03) 0.01 (0.06)

-0.69* (0.38) 0.19 (0.56) 3.34*** (0.29) -5.61*** (1.19) -4.67*** (0.23) 0.82*** (0.28)

0.09** (0.03) 0.01 (0.06) 0.18*** (0.03) -0.41*** (0.11) 0.00 (0.01) 0.08** (0.03)

Yes Yes

Yes Yes

Yes Yes

638618 0.02

638618 0.11

638618 0.02

48

Table VII

Agent Transaction History and Cashback Transactions The table presents evidence about the relation between manipulation indicators and agent’s transaction history. Variable definitions are provided in Appendix A. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

Agents: 80 % < Leverage ≤ 90 % 90 % < Leverage ≤ 97 % Leverage > 97 % Leverage > 97 % × Agent decile 2 × Agent decile 3 × Agent decile 4 × Agent decile 5 × Agent decile 6 × Agent decile 7 × Agent decile 8 × Agent decile 9 × Agent decile 10

Property controls Township fixed effects Quarter fixed effects Buyer’s agent fixed effects Seller’s agent fixed effects Observations Adj. R2

Cashback Hint (%) (1) Sellers’ -0.07 (0.05) 0.01 (0.06) -0.16 (0.16)

Paid100 (%) (2) Buyers’ 0.16 (0.19) 1.97*** (0.19) 4.55*** (0.70)

Cashback Hint × Paid100 (%) (3) Buyers’ -0.03 (0.03) 0.01 (0.03) -0.05 (0.11)

Foreclosed by 5 th year (%) (4) Buyers’ -0.04 (0.05) 0.04 (0.05) -0.10 (0.18)

Interest rate (5) Buyers’ 0.49*** (0.04) 0.41*** (0.03) 0.59*** (0.15)

-0.15 (0.39) -0.10 (0.22) -0.08 (0.19) 0.30 (0.25) 0.29 (0.25) 0.36* (0.19) 0.53* (0.28) 0.88*** (0.22) 0.40 (0.31)

-1.65 (1.04) 0.43 (0.75) 0.43 (0.81) 1.51* (0.87) 2.66*** (0.79) 2.92*** (0.69) 3.86*** (0.79) 5.36*** (0.68) 6.44*** (0.71)

-0.15 (0.15) 0.15 (0.17) 0.00 (0.14) 0.21 (0.15) 0.42** (0.17) 0.40*** (0.14) 0.41** (0.16) 0.61*** (0.19) 0.70*** (0.13)

0.31 (0.25) 0.86*** (0.27) 0.82*** (0.23) 0.33 (0.24) 0.94*** (0.22) 0.44* (0.23) 1.01*** (0.24) 1.38*** (0.28) 1.35*** (0.25)

0.10 (0.21) 0.20 (0.22) 0.18 (0.14) 0.29 (0.20) 0.33** (0.16) 0.32* (0.16) 0.35** (0.17) 0.00 (0.15) 0.04 (0.14)

Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

Yes Yes Yes Yes

411742 0.17

411742 0.04

232272 0.06

30698 0.61

Yes 504203 0.11

49

Table VIII

Cashback Transactions Around Tax Reassessment Dates The table presents evidence on manipulation levels around tax reassessment dates. Variable definitions are provided in Appendix A. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

Post Tax Assessment × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 % × Leverage > 97 %

80 % < Leverage ≤ 90 % 90 % < Leverage ≤ 97 % Leverage > 97 %

Property controls Township fixed effects Quarter fixed effects Observations Adj. R2

Cashback Hint (%) (1) -0.14*** (0.05) 0.00 (0.08) -0.02 (0.08) 0.23** (0.10)

Paid100 (%) (2) -0.04 (0.41) -0.31 (0.22) 1.08*** (0.18) 1.48*** (0.23)

Cashback Hint × Paid100 (%) (3) -0.05 (0.03) -0.02 (0.04) 0.06* (0.04) 0.14*** (0.05)

-0.02 (0.20) 0.22 (0.22) 0.24 (0.24)

1.27** (0.59) 1.59*** (0.49) 9.61*** (0.93)

0.04 (0.09) -0.02 (0.10) 0.42*** (0.12)

Yes Yes Yes

Yes Yes Yes

Yes Yes Yes

440537 0.02

440537 0.09

440537 0.01

50

Table IX

Loan-Buyer Monitoring and Cashback Transactions The table presents tests of whether monitoring by loan buyers decreases manipulation rates. Samples are restricted to mortgages that were originated by financial firms, are below 98% leverage, and are below 97% of the jumbo loan cutoff level. Samples in columns (4) and (5) are restricted to transactions closed prior to 2007 and 2005, respectively. Variable definitions are provided in Appendix A. All regressions are OLS regressions. *, **, *** denote two-tailed significance at the 10%, 5%, and 1% levels respectively. Standard errors are clustered at the township level.

Monitoring (residual ) × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 %

Mortgage Size × 80 % < Leverage ≤ 90 % × 90 % < Leverage ≤ 97 %

80 % < Leverage ≤ 90 % 90 % < Leverage ≤ 97 %

Property controls Township × Quarter fixed effects Observations Adj. R2

Cashback Cashback Hint Hint (%) Paid100 (%) × Paid100 (%) (1) (2) (3) 0.08 6.65** 0.71** (0.72) (3.01) (0.31) -1.60** -8.64*** -1.24*** (0.61) (2.10) (0.32) -2.15*** -10.50*** -1.07*** (0.78) (2.63) (0.32)

Foreclosed by . . . 3 rd year (%) 5 th year (%) (4) (5) 1.59** 1.17** (0.63) (0.58) -0.79** -0.82* (0.34) (0.46) -0.45 -0.97** (0.29) (0.37)

-0.37 (0.33) 0.08 (0.31) -0.62* (0.35)

-1.05 (0.83) -1.68** (0.82) -3.39*** (1.04)

-0.32*** (0.11) 0.10 (0.16) -0.04 (0.23)

-0.30* (0.16) -0.41* (0.22) -0.18 (0.16)

-0.37* (0.18) -0.41* (0.22) -0.13 (0.18)

0.23 (0.28) 0.74** (0.30)

3.36*** (0.74) 7.84*** (1.06)

0.05 (0.14) 0.24 (0.20)

0.34* (0.18) 0.13 (0.14)

0.41** (0.19) 0.22 (0.16)

Yes Yes

Yes Yes

Yes Yes

Yes Yes

Yes Yes

236396 0.02

236396 0.10

236396 0.02

183394 0.01

131399 0.02

51

100

105

Appraisal / Price (%) 110 115

120

Figure 1. Appraisal/P rice Ratio and Leverage

20

40

60 Leverage = Loan / Price (%)

80

100

Distribution of Appraisal/P rice ratio as a function of buyer’s leverage. The sample covers a random sample of 311 transactions that were financed by an Illinois-based lender between 2003 and 2005. The size of the marks is proportional to the size of mortgage clusters.

52

Figure 2. Mortgage Rates and Leverage Cumulative Distribution

Bank's Quote: Annual Percentage Rate (%)

Figure 2a. Quoted Mortgage Rates

7.1% 7.0% 6.9% 6.8% 6.7% 6.6% 6.5% 10

20

30

40

50

60

70

80

90

100

Leverage (Loan-to-Value) (%)

Quoted mortgage rates (annual percentage rate) for a 30-year mortgage from the First National Bank of Chicago. The quote is for a $400,000 newly-purchased single family home in Chicago. The leverage sought is computed as the fraction of the loan requested out of the purchase price. The base interest rate is 6.5% fixed over the period. Source: First National Bank of Chicago website (http://www.fnbgreatbanc.com). Figure 2b. Cumulative Leverage Distribution 100%

Cumulative distribution

80%

60%

40%

20%

0% 25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Leverage (%) = Mortgage / Price

The figure presents a cumulative distribution of sample transactions as a function of the leverage in the transaction. Leverage is defined as the sum of mortgages scaled by transaction price. 53

Figure 3. Indicators of Cashback Transactions

Figure 3a. Cashback Hints

Cashback Hints by Sellers (% transactions)

5%

4%

3%

2%

1%

0% 40

45

50

55

60

65

70

75

80

85

90

95

100

Leverage (%) = Mortgage / Price

Percentage of transactions in which sellers offer cashback arrangements as a function of buyers’ leverage. Leverage is defined as the sum of mortgages scaled by transaction price. Dash lines represent 2 standard error bounds. Figure 3b. Paying the Full Listing Price (or Above) Buyers paying the full listing price or above (% transactions)

40%

30%

20%

10%

0% 40

45

50

55

60

65

70

75

80

85

90

95

100

Leverage (%) = Mortgage / Price

Percentage of transactions in which buyers paid at least the full listing price. Leverage is defined as the sum of mortgages scaled by transaction price. Dashed lines represent 2 standard error bounds. 54

come to the Cook County Assessor's Virtual Office Figure 4. Triennial Assessment Districts in the Cook County, Illinois

Map of the townships and districts in the Cook County. Filling colors indicate the year in the triennial cycle in which townships are reassessed. Township are reassessed at different months within each year. Assessment years: Pink: 1997, 2000, 2003. Cyan: 1998, 2001, 2004. Yellow: 1999, 2002, 2005. Source: http://www.cookcountyassessor.com

55

Page 1

Figure 5. Jumbo Loan Cutoff

Jumbo loan cutoff ($, bold) and 1st mortgage size distribution ($)

600,000 Jumbo loan cutoff

500,000

400,000 p95

300,000

200,000

p90 p75 p50 p25 p10

100,000

0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Date

Time-series of jumbo loan cutoff levels and 1st mortgage size-distribution. Source: http://www.efanniemae.com/sf/refmaterials/loanlimits/

56