Real Estate Investors and the Boom and Bust of the US Housing ...

Real Estate Investors and the Boom and Bust of the US Housing Market Wenli Liy

Zhenyu Gao

September 2012 (Preliminary and Comments Welcome)

Abstract This paper studies residential real estate investors and their relationship with local house price movement using several comprehensive micro data on mortgage application and performance. The paper makes two contributions to the growing literature on the recent boom and bust of the US housing market. First, using mortgage application data, we document the important role played by real estate investors. We show that the fraction of mortgage applications for investment homes rises signi…cantly during the house price run-up and falls sharply during the house price decline and the pattern is more pronounced for the bubble states (Arizona, Florida, and Nevada). More importantly, the majority of investment mortgage borrowers are prime instead of subprime borrowers and they are less likely to use risky mortgage contracts with adjustable-rate or interest-only than their subprime primary mortgage counterparts. Second, we …nd that while relative demand for investment housing responds to past house price changes up to 10 months, it contributes signi…cantly to changes in local house prices especially during the pre-crisis period. For the post-crisis period, we show that investors are more likely to default or being foreclosed on than primary home owners. We argue that this tendency deteriorated the housing bust. Keywords: Mortgage crisis, investment housing, house prices, default

We thank Wei Xiong and seminar particpants at the Federal Reserve Bank of Philadelphia and Princeton University for their comments. The views expressed here are those of the authors. They do not necessarily re‡ect those of the Federal Reserve Bank of Philadelphia or the Federal Reserve System. y Zhenyu Gao: Department of Economics, Princeton University. [email protected]. Wenli Li: Research Department, Federal Reserve Bank of Philadelphia. [email protected].

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1

Introduction

The dramatic house price movement of the last decade has led to an increasing literature that is devoted to the study of residential housing. Almost all of the studies, however, have focused on owner-occupied housing despite that over 14 percent of US households also own other residential properties.1 In this paper, we provide a comprehensive empirical description of the characteristics of these households and their activities (purchasing, loan performance, etc.) and contrast them with those of owner-occupied housing.2 We are particularly interested in the relationship between investment housing and local house prices during the recent housing cycle. Understanding this relationship is important for the design and implementation of policies aimed at reviving the current housing market and preventing future crisis. The key di¤erence between owner-occupied housing and investment housing is that while owner-occupied housing provides housing services to its owner and at the same time serves as an investment vehicle, investment housing functions mostly as an investment asset. Consequently, transaction and default cost (monetary cost, emotional cost, etc.) is lower for real estate investors than for owner-occupants. A direct implication is then the demand for investment housing is more price elastic than that for owneroccupied housing. I.e., real estate investors are more likely to buy and sell as house price changes and they are more likely to default on their mortgages when housing conditions deteriorate. Furthermore, they are likely to be price setters in local housing market. Our micro data come from several sources. The primary source is the Home Mortgage Disclosure Act (HMDA) which provides us with individual monthly mortgage application and origination information. Using HMDA, we show that at the national level there was a huge run-up in the fraction of mortgage applications for investment housing between 2000 and 2005. At the peak in 2005, the rate reached over 16 percent from its low of 6 percent in 2000. After 2005, however, the rate came down sharply while house prices continued to climb until the second half of 2006. We observe the same pattern with similar magnitude when we construct the ratio by the origination amount. For the states that have the most housing boom and the worst housing bust (Arizona, California, Florida, and Nevada), with the exception of California, the run-up and the subsequent decline in the relative 1

There are two types of nonowner-occupied residential properties, vacation or future retirement homes and investment homes whose owners intend to resell the property without the intention of living in the house. In both cases, the house may be rent out when the owners are not occupying the house. The line between the two categories, however, can be …ne as homeowners can easily turn vacation and retirment homes into investment homes. 2 Throughout the paper, we will abuse the notation and use nonowner-occupied housing and investment housing interchangeably.

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demand are more evident.3 At the peak, over one-fourth of the loan applications as well as loan originations are for investment housing. Only a small fraction of the borrowers for investment housing are subprime borrowers (less than 15 percent at the peak). This is consistent with the …ndings from the Survey of Consumer Finances (SCF) where we show that real estate investors tend to have higher income and more educated than primary homeowners. They also have lower mortgage loan-to-value ratios and overall deb-to-asset ratios. Furthermore, using information from LPS Applied Analytics, Inc. (LPS) and Corelogic Inc. (Corelogic), we show that, counter to conventional wisdom, real estate investors are actually less likely to use exotic mortgage products (adjustable rate mortgages, interest-only mortgages, etc.) than their subprime counterparts though they are more likely to use these products than their prime counterparts. Using instrumental variable approach, we show that while the relative demand of investment housing measured by the share of investment housing mortgage application in total application responds positively to past local house price movements at the zip code level up to 10 months, it contributes to local house price movements with both economic and statistical signi…cance especially during the pre-crisis period where a 10 percent increase in the relative demand leads to over 6 percent increase in the monthly house price growth rates. After the crisis, we show that investment home mortgages are much more likely to default especially those that are also subprime. This tendency combined with …ndings in the literature on foreclosure and house prices (Mian, Su…, and Trebbi 2010) suggest that investment housing deteriorated housing bust. Our paper contributes to the burgeoning literature that searches for an explanation for the recent boom-bust pattern in house prices. In particular, the paper is most closely related to Haughwout, Lee, Tracy Klaauw (2011) who are among the …rst to point out the important role played by real estate investors during the housing cycle.4 Our analysis extends Haughwout et al. (2011) along two important dimensions.5 First, 3

California is unique in the nation because of Proposition 13. Proposition 13, passed in 1978, established the base year value concept for property tax assessments. Under Proposition 13, the 19751976 …scal year serves as the original base year used in determining the assessment for real property. Thereafter, annual increases to the base year value are limited to the in‡ation rate, as measured by the California Consumer Price Index, or two percent, whichever is less. A new base year value, however, is established whenever a property has had a change in ownership or has been newly constructed. This proposition obviously is not conducive to real estate investors as they frequently buy and sell properties. Other states such as Florida and New York have adopted similar policies. However, they are far less restricting. 4 See Wheaton and Nechayev (2006). For industry note on investor behavior, see, for example, http://www.calculatedriskblog.com/2005/04/housing-speculation-is-key.html. 5 Instead of relying on households’self-reported occupancy type, Haughwout et al. (2011) back out housing occupancy type by counting the number of …rst liens held by households using credit bureau data. Their methodology allows them to overcome the potential underreporting bias of investment housing by owners. Indeed, the rate of investment housing demand by origination amount is about 10

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by using HMDA, we are able to observe investment housing demand directly (mortgage applications as captured by HMDA) in addition to mortgage originations at higher frequency and more comprehensively. Additionally, our study of both prime and subprime mortgage loan-level data allows us to reach a di¤erent conclusion concerning the riskiness of investment housing mortgage borrowers.6 These households are much more likely to be prime borrowers and they are less likely to use risky mortgage products than their subprime counterparts. Second and more importantly, we explore the empirical relationship between investment housing and local house prices and ask to what extent investment housing has contributed to the housing boom and deteriorated the housing bust. This additional analysis is crucial in helping us better understand the housing cycle and thus shed light on relevant policy debates. Besides Haughwout et al. (2011), another closely related paper is Robinson and Todd (2010) where they examine the role non-owner occupied properties played during the foreclosure crisis. Other papers that investigate speculative housing behavior include Barlevy and Fisher (2011), Bayer, Geissler, and Roberts (2011), Chinco and Mayer (2011), and Choi, Hong, and Shenkman (2011). Barlevy and Fisher (2011) describe a rational expectations model in which speculative bubbles in house prices can emerge and when they emerge, both speculators and lenders prefer interest-only mortgages. They test their theory using city level data. Bayer et al. (2011) examine the role of speculators and middlemen in Los Angeles and …nd that middlemen who buy and sell many houses operate equally during booms and busts, but that speculators who buy and sell a smaller number of houses appear to try unsuccessfully to time the market and are strongly associated with neighborhood price instability. Chinco and Mayer (2011) study the price impact of adding noise traders in the form of distant speculators to a …nancial market using unique transactions level data on US residential housing. They …nd that adding out of town speculators to a market causes excess house price appreciation and that out of town speculators likely earn lower returns than local purchasers. Choi, Hong, and Sheinkman (2011) develop and empirically test a speculation-based theory of home improvements. They …nd that improvements are increasing and convex in home prices. And the change in the recoup ratio (the ratio of resale value of improvements to construction costs) is negatively correlated with construction cost growth controlling for home price appreciation. percentage points higher in their data than in ours. One potential shortcoming of their approach is that there may be double counting for those households who are in the process of selling and buying houses and, therefore, may have two mortgages on their account during the transition. 6 Since Corelogic ABS data consists of subprime and alt-A borrowers only, the match between the credit bureau data and Corelogic conducted in Haughwout et al. (2011) does not capture investment housing activities among prime borrowers.

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Another strand of the literature, notably, Mian and Su… (2009), Keys, Mukherjee, Seru, and Vig (2009), Adelino, Gerardi, and Willen (2009), Jiang, Nelson, and Vytlacil (2010), and Elul (2011), focuses on subprime lending and mortgage securitization as the leading cause of the housing bubble. That literature has generally found that the expansion in mortgage credit to subprime borrowers is closely correlated with the increase in securitization of subprime mortgages and this increase in turn leads to poor performance of the securitized loans. Following up on this literature, Piskorski, Seru, and Vig (2010), and Agarwal, Amromin, Ben-David, Chomsisengphet, and Evano¤ (2011) later show that whether a delinquent loan is securitized or not may also a¤ect the ease of modifying it and hence of avoiding foreclosure. Finally, the paper also has important implications for the macro housing literature that studies issues such as house price determination, household portfolio choice, and the e¤ect of government involvement in the housing market. This literature has focused exclusively on the primary housing market.7 Put it simply, the only margin along which households adjust their housing is by moving from renting to owning or vice versa. Many primary home purchasers make “churn”moves from one house to another –hence a transaction may have little impact on market vacancy and the overall housing market. A purchase/sale by real estate investors by comparison can subtract or add more directly to vacancy and hence net housing supply. In other words, our research suggests that exclusion of investment housing may bias down the response of house prices to other shocks and households’ adjustment of consumption and portfolio in the presence of house price shocks. In our view, a housing model that allows for investment housing is perhaps a more appropriate framework for understanding house price dynamics and studying housing policy issues. The remainder of the paper is structured as follows. Section 2 develops a theoretical model of owner-occupied and investment housing demand and derives several model implications. Section 3 describes the data and provides initial empirical analysis of the residential real estate investors. Section 4 presents the empirical analysis with a focus on the relationship between investment housing demand and local house price dynamics. Section 5 concludes the paper. 7

To name a few of the papers in the literature, Flavin and Yamashita (2002), Cocco (2005), Yao and Zhang (2005), Li and Yao (2007), Chambers, Garriga, and Schlagenhauf (2009), Favilukis, Ludvigson, and Van Nieuwerburgh (2009), and Kiyotaki, Michaelides, and Nikolov (20011).

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2

A Simple of Theory of Owner-occupied and Investment Housing

We develop a simple model of housing demand that di¤erentiates between primary homes and investment homes in this section. The purpose is to sort out the di¤erent economic forces such as income, …nancial constraints, and expected house price changes on the relative demand of investment housing to primary housing and the feedback e¤ect of the relative demand on house prices. Derived model implications help guide our subsequent empirical analysis.

2.1

The Setup

Consider a household that lives for two periods and has a quasi-linear utility function, (1)

log c + (1

) log h + Ew;

where c represents non-housing consumption, h represents housing services derived from primary residence, w denotes liquid wealth at the second period, and 1 (0 1) is the housing preference parameter (weight). The timing of the events is as follows. Households start period 1 with income y1 and face house price p1 . The household then decides on consumption c, the amount of primary housing h, and the amount of investment housing s to purchase. We rule out short sales by restricting h; s 0. To purchase a house, the household has to put down a fraction (0 < < 1) of the house value as down payment. We do not allow for other forms of borrowing. Let r denote the risk free interest rate lenders have to o¤er to outside depositors that are not modeled here, rh and rs denote the mortgage rate lenders charge on primary housing and investment housing, respectively. Additionally, there is a risk management cost of ( 0) associated with each unit of loans made. We assume a competitive lending market. At the beginning of the second period, the household learns the new house price p2 as it decides whether to repay the mortgage debt or to walk away from the house by defaulting. If it repays the mortgage debt, it receives the remaining house equity. If it defaults, it su¤ers a loss of a proportional cost ch for primary housing and cs for investment housing. We assume that 0 < cs < ch < 1 to capture the additional cost (monetary as well as emotional) associated with defaulting on ones’primary residence. We denote the household’s default decision on its primary residence and investment housing by dh and ds , respectively, where dh (ds ) takes the value of 1 if the household defaults on its primary (investment) mortgages and 0 otherwise. Additionally, we assume 6

that selling one’s primary residence requires a cost that is proportional (0 < < 1) to the house value and normalize the selling cost for investment housing to 0. Again, this assumption is to capture the additional monetary cost one incurs when moving its family out of its primary residence as well as the emotional cost associated with having to leave one’s home. The household’s optimization problem can then be written as, max

f log c + (1

fh;s;a;dh ;ds g

(2)

s:t:

p1 (h + s) + c

y1 ;

w = (1

dh )[(1

)p2 h

rh (1

)p1 h]

+ (1

ds )[p2 s

rs (1

)p1 s]

ds cs p2 s;

(3) (4)

) log h + Ewg

h; s; c

dh ch p2 h

0; dh ; ds 2 f0; 1g;

where equation (2) is the …rst period budget constraint. Equation (3) is the second period budget constraint. The term (1 dh )[(1 )p2 h rh (1 )p1 h] is the home equity after repaying the debt when the household repays the debt on the primary house, and dh ch p2 h is the cost of defaulting on the primary mortgage. Similarly, (1 ds )[p2 s rs (1 )p1 s] is the home equity after repaying the debt when the household repays the debt on the investment house, and ds cs p2 s is the cost of defaulting on the investment mortgage. Lenders’break-even conditions on lending to the primary house and lending to the investment housing are as follows, (5)

(r + )(1

)p1 h = E[(1

dh )rh (1

)p1 h + dh p2 h];

(6)

(r + )(1

)p1 s = E[(1

ds )rs (1

)p1 s + ds p2 s]:

The left hand side of the equations represents the opportunity cost of making the mortgages while the right hand side the expected payo¤s.

2.2

Partial Equilibrium Solutions

In appendix A, we provide …rst order conditions for the problem outlined above. From the …rst order conditions, we obtain the following results immediately, Result 1 Everything else the same, relatively rich households purchase investment housing and the richer the household is, the more investment housing it purchases.

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Under the assumption that no default occurs for either the primary and investment 1 + E[p2 (r+ )(1 )p1 ] ; mortgages, we have if y1 Ep2 (7)

h=

1

; Ep2 y1 s= p1 E[p2

(8)

1 (r + )(1

)p1 ]

Ep2

;

and hence Result 2. Under the assumption that no default occurs for either primary or investment homes, the relative demand for investment housing decreases with the risk management cost but increases with the expected second period house price rate of appreciation E pp12 : When defaults do occur, under the assumption that ch > cs +

we have,

Result 3. Households are more likely to default on investment houses than primary houses holding everything else constant.

2.3

Endogenizing First-Period House Price

A simple way to endogenize the …rst period’s house price determination p1 is to assume that there is a …xed supply of housing, L, and a measure one of households with …rst period income y1 following the distribution F (y1 ). The market clearing condition is, Z

(h + s)dF (y1 ) = L:

y1

One can show that any factor that leads to higher housing demand in general would lead to higher …rst period price. Among those factors, as we have shown, improvement in …rst period income, risk management fees, and expected second house price appreciation rate would lead to disproportional increases in the demand in investment housing. Result 4. Higher relative demand for investment housing is associated with higher …rst period prices. In Appendix B, we provide a numerical example where we allow for default and endogenize …rst period house price. The prior results carry through. The intuition for these four results remains with several extensions of the model. For example, one can allow for dividend payment with investment housing in the …rst period or an additional investment opportunity, bond or stock, between the two periods. 8

3 3.1

Data and Descriptive Analysis Data Source

The data for the study come from four sources: Home Mortgage Disclosure Act (HMDA), Survey of Consumer Finances (SCF), LPS Applied Analytics, Inc. (LPS), and Corelogic Inc. (Corelogic). HMDA covers almost all mortgage applications as well as originations in US. It records each applicant’s …nal status (denied/approved/originated), purpose of borrowing (home purchase/re…nancing/home improvement), occupancy type (primary residence/second or investment homes), loan amount, race, sex, income, as well as lender institution.8 The Survey of Consumer Finances (SCF) is a triennial cross-sectional survey of US families except over 2007–2009 periods when the survey collected panel data. The data include information on families’balance sheets, pensions, income, and demographic characteristics. Households report their holdings of primary residential property and non-primary residential property separately. However, like HMDA, the survey does not distinguish between second and investment homes. Our prime mortgage sample comes from LPS which provides information from homeowners’mortgage applications concerning their …nancial situation, characteristics of the property, terms of the mortgage contract, and information about securitization, plus updates on whether homeowners paid in full or defaulted, whether lenders started foreclosure and whether the home was sold in foreclosure. LPS covers some two-thirds of installment-type loans in the residential mortgage servicing market. Our subprime mortgage sample comes from Corelogic which provides similar information as LPS. CoreLogic covers nearly all mortgages that were in non-agency subprime mortgage securitization. According to Ashcraft and Schuermann (2008, table 1), around 72 percent of all subprime mortgages issued during our period were included in non-agency securitization, making our sample fairly representative of all subprime mortgages. Both LPS and Corelogic are at the monthly frequency and distinguish between second home mortgages and investment home mortgages. Our zip code level house price indexes come from Corelogic. These price indexes are aggregated over all housing transactions, those with mortgages (prime as well as subprime) and those without. For the part of our analysis that uses HMDA, we study all purchase mortgages applied or originated since HMDA did not report on lien type before 2004. For the analysis using LPS and Corelogic, we focus on …rst-lien purchase mortgages to avoid double counting on properties. Due to data size, we only follow a 2 percent random sample of these mortgage loans over time, LPS as well as Corelogic, until they are 8 A lender who does not do business in any msa does not need to report (e.g., small community banks) to HMDA.

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repaid in full, go into default, or until the sample period ends which is October 2011 for both data sets. Note that our analysis includes all family types, one-to-four family dwelling as well as multifamily dwelling. Because one-to-four family dwelling accounted for over 95 percent of total mortgage applications and over 97 percent of second and investment home mortgage applications during our sample period, our results are not a¤ected much if we focus our analysis exclusively on one-to-four family units.

3.2

Relative Demand for Investment Housing

We measure relative demand in investment housing using two surveys, SCF measurement that is at three-year frequency and limited in coverage and geographic information but captures owner-occupied and investment housing that are not …nanced by mortgages, and HMDA measurement that is at monthly frequency and much more comprehensive but captures only demand …nanced by mortgages. According to Survey of Consumer Finances, from 1989 to 2007, the fraction of households that own their primary homes increased signi…cantly from 64 percent to 69 percent while the fraction of households that own other residential properties increased slightly from 13 percent to 14 percent. In terms of real asset value, however, nonowner-occupied housing increased by 250 percent, far stripping the increase of 192 percent in owneroccupied housing suggesting that there had been more demand for investment housing along the intensive margin than the extensive margin during the housing boom. Interestingly, by 2010, while the fraction of primary homeowners fell to 67 percent, the fraction of residential investors increased to over 14 percent after a dip in 2009. In terms of asset value, both property types experienced substantial declines, 23 percent for own-occupied properties and 22 percent for nonowner-occupied properties. To capture investment housing demand at higher frequency, we turn to HMDA to construct the following two measures: the fraction of total number of loan applications that are for investment housing and the fraction of total amount of loan applications that are for investment housing. We chart the two measures in …gure 1. For comparison, we also chart the real house price indexes provided by Corelogic. We use the headline consumer price index as the de‡ator. As can be seen, the relative demand for investment housing began to increase in 2000 and the increase accelerated at the end of 2003. At its peak, investment housing accounts for about 16 percent of total loan applications both in numbers and in dollar amount. What is more, the relative demand peaked in late 2005, one year ahead of the peak of real house price index. Finally, the relative demand for investment homes plateaued in 2009 along with house prices but ticked up substantially since early 2010 while house prices continued to move sideways.

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We chart the same information for the four states that had the most drastic house price changes during the housing cycle, Arizona, California, Florida, and Nevada, in …gure 2. With the exception of California, the relative demand for investment homes in all three other states goes up much faster and declines much more sharply than the country as a whole. For example, at the peak, investment housing applications account for close to 30 percent of total application both in terms of numbers and dollar amount for Florida. The timing of the peaks also varies by states. As is with the nation, relative demand for investment housing picked up in all four markets in early to mid 2010. Finally, we present histograms for the distribution of the relative demand for investment homes for the years 2000, 2005, and 2010 in …gure 3. Con…rming our earlier discussions, the distribution is more spread out and shifts to the right in 2005 relative to 2000. In particular, the share of zip codes with near zero investment housing demand is signi…cantly reduced. By 2010, however, even though we still see signi…cant mass at 20 percent or higher share of relative demand for investment housing. Compared to 2005, the fraction of zip codes with near zero relative investment housing demand shot up again albeit still below its 2000 level.

3.3

Real Estate Investors and Subprime Borrowers

To explore to what extent the phenomenon we have documented is part of the subprime phenomenon, i.e., whether real estate investors are just proxies for subprime borrowers, we estimate the fraction of investors that are subprime and the fraction of subprime borrowers that have purchased investment homes. We identify subprime borrowers in di¤erent ways depending on the data sets. To identify subprime borrowers in HMDA, we employ a commonly used methodology – US Department of Housing and Urban Development (HUD) listing – that classi…es lenders as generally making either prime or subprime loans.9 The left panel of …gure 4 depicts the fraction of subprime borrowers in total mortgage applications as well as the fraction of subprime borrowers in investment home mortgage applications. The right panel depicts the fraction of investment home mortgages borrowers in total mortgage applications and the fraction of investment mortgage borrowers in subprime mortgage applications. As can be seen, both the fraction of investment mortgage applications in total mortgages applications and the fraction of subprime mortgage applications in total mortgage applications increased between 2000 and 2005, more so for the fraction of subprime borrowers. But the fraction of subprime mortgage applications in investment 9

See: www.huduser.org/datasets/manu.html. The methodology, though imperfect, is widely used by, among others, the Federal Reserve and Harvard University’s Joint Center for Housing Studies. HUD stopped classifying lender types after 2005.

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home mortgage applications and the fraction of investment home mortgage applications in subprime mortgage applications stay ‡at between January 2004 and December 2005. Unlike HMDA, lenders identify subprime borrowers in LPS and Corelogic possibly using a combination of criteria including credit score, document type, loan-to-value ratio, etc. In the left panel of …gure 5, we chart the fraction of prime borrowers that purchased investment housing and second homes, respectively, in LPS for the country as a whole. The right panel charts the fraction of subprime as well as Alt-A borrowers that purchased investment housing and second homes, respectively, in Corelogic.10 For prime borrowers, what is striking is that the fractions of both investment home and second home mortgages were low at around 5 percent and 2.5 percent, respectively, in 2000. At the peak of 2005, however, the fraction shot up to close to over 10 percent for investment home borrowers and slightly over 5 percent for second home borrowers. By contrast, the fraction of subprime mortgages that are for investment housing and second homes ‡uctuates at round 10 percent during the same time period while the fraction of Alt-A mortgages that are for investment housing and second homes came down sharply between 2000 and 2002 before moving up again to its 2000 level. The subprime and alt-A market dried up during the second half of 2007. To summarize, the most increase in investment housing demand appears to have come from prime borrowers. Given that prime borrowers constitute the majority of mortgage originations even during the peak of the crisis, it is not surprising that the majority of investment housing mortgage demand at the peak of house prices is prime borrowers. To further substantiate the evidence we presented so far, we report the median income at application as well as origination for owner-occupants and real estate investors separately according to HMDA in table 1. We also report the median credit score for owner-occupants and real estate investors obtained from LPS for prime mortgages and Corelogic for subprime mortgages. As can be seen, real estate investors have higher income at both application and origination and higher credit scores at origination than owner-occupants. Finally, an examination of the recent SCF surveys (2001, 2004, 2007, 2009, and 2010) further reveals that owners of second and investment housing indeed have higher income and more educated than those who only own their primary residences. For example, in 2007, while 35 percent of primary home owners have 4 or more years of college almost 50 percent of nonprimary home owners have 4 or more years of college. 10

Alt-A mortgage borrowers, short for Alternative A-paper, typically have less than full documentation for their mortgage applications.

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3.4

Real Estate Investors and Mortgage Products

We have so far established that real estate investors are mostly prime borrowers. In this subsection, we investigate the type of mortgage products used by real estate investors such as the loan-to-value ratio (LTV) at origination, percent of adjustable rate mortgages (ARM), and the share of interest-only (IO) mortgages. We present the results in table 2. For both prime and subprime mortgages, median mortgage LTVs at origination are consistently higher for primary properties than for investment housing. This result may stem from the fact that lenders demand a higher down payment for investment mortgages than for primary mortgages if they view real estate investors riskier than owner-occupants despite that real estate investors have higher average income and average credit score. In terms of adjustable rate mortgages, for prime mortgage borrowers, though both types of borrowers have increased their use of adjustable mortgages, real estate investors are much more likely to use adjustable rate mortgages. For subprime mortgages, however, those who borrow for primary residences are always more likely to use adjustable mortgages than real estate investors though the latter increased their use of adjustable mortgages much more between 2004 and 2007. We observe a similar pattern for the use of interest-only mortgages. In summary, among the prime borrowers, real estate investors are more likely to use ARM and IO mortgages especially between 2000 and 2007 than other primary borrowers though their mortgage LTV tends to be lower. Among subprime borrowers, however, real estate investors are actually less likely to use ARM and IO mortgages than other subprime borrowers and their mortgage LTVs are also lower.

4 4.1 4.1.1

Regression Analysis Investment Housing and Local House Price Changes Empirical Speci…cation and Data Setup

From our theoretical model, we see that households’ relative demand for investment housing and local house price movements are inter-related. Past house price changes a¤ect relative demand for investment housing through their e¤ect on expectations and relative investment housing demand in the meantime drives local house price changes. We explore this relationship empirically in this subsection. In particular, we ask to what extent can local house price movements be explained by the relative demand of

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investment housing. To that end, we estimate the following equation, (9)

pi;t = fi + ft + qit +

X

j

pi;t

j

+ yi;t

1

+ "i;t ;

j=1;::;n

where subscript i stands for area and t for time. We include fi and ft as our explanatory variables to control for both time and area e¤ects.11 The relative demand by real estate investors is captured by qit . Terms pi;t j (j = 0; 1; 2; :::; n) represent local zip code level house price changes; yit 1 indicates area economic fundamentals such as lagged county level unemployment rate, lagged change in zip code level employment and payroll; and "i;t is the error term and is assumed to be iid and normally distributed. It is obvious that relative demand qit is endogenous and we employ the instrumental variable approach to address this issue. In particular, as a …rst step, we estimate the following regression, (10)

qit = fi0 + ft0 + xi;t +

X

0 j

pi;t

j

+ 0 yi;t

1

+

i;t ;

j=1;::;n

where xi;t , the fraction of employment that is in recreation and accommodation at the zip code level, is our instrument. i;t is the error term that is iid and normally distributed. The predicted value from this equation will be used in estimating equation (9) and standard errors are adjusted accordingly. We classify area by zip code and construct the relative demand for investment housing using HMDA between January 2000 and December 2010. We obtain changes in aggregate payroll and aggregate employment from the Census’ Zip Business Patterns. We use Corelogic zip code level house price index. Note that unlike Corelogic mortgage data, Corelogic house price index covers all housing transactions, with and without mortgages and regardless of mortgage types. Finally, we construct the fraction of employment in recreation and accommodation at the zip code level from the 2000 Census Survey to proxy for di¤erences in local amenities. We delete observations that are missing information on the above variables and are left with a sample with 6; 376 unique zip codes from 886 counties and 72; 0926 observations.12 Table 3 presents the summary statistics. All nominal variables are de‡ated by the overall Consumer Price Index. As can be seen, the relative demand for investment housing as measured by the fraction of mortgage applications for investment 11 In the analysis, it is not realistic to control for zip code level dummies given its number. We control for state dummies instead. 12 The loss of the majority of the observations is due to missing zip code house price index. This arises when there was not enough sales at that zip code at the month for Corelogic to construct a repeated-sale house price index.

14

homes has a wide range between 0 (e.g., Agawam City in Hapmden County, MA (zip 01001), Drexel Hill in Delaware County, PA (zip 19026), and Calhown in Gordon County, GA (zip 30701)) and 1 (e.g., Laughlin in Clark County, NV (zip 89029), Green Valley in Pima County, AZ (zip 85622), and Falmouth in Barnstable County, MA (02540)) across zip codes during the sample period. Interestingly, about half of the cases where the demand for housing comes entirely from investment housing occurred in late 2010 as real estate investors intensi…ed their bid for foreclosed houses. Similarly, the fraction of employment in recreation and accommodation also varies from 0 percent to over 69 percent according to the 2000 Census. In particular, Lumberton in Burlington county, NJ (zip 08048), Lareda Ranch in Orange County, CA (zip 92694), and Rancho Cordo in Sacramento County, CA (zip 95742) had zero employment in recreation and entertainment while Atlantic City in Atlantic County, NJ (zip 08205), Mesquite in Clark County, NV (zip 89027), and Laughlin in Clark County, NV (zip 89029) had over 50 of its employment in recreation and accommodation. There is also substantial heterogeneity over time and across zip codes in growth rate in payroll employment and total payrolls. Finally, during our sample period, house prices experienced both big rises and big declines with the maximum monthly net rate of appreciation being 14 percent and maximum net rate of depreciation being 16 percent. Before turning to our regression analysis, it is worth pointing out that our instrument, the fraction of employment in recreation and accommodation in 2000 at the zip code level, is highly positively correlated with the relative demand of investment housing with an overall correlation coe¢ cient of 0:4460. Its correlation with other explanatory variables, the zip code level aggregate payroll and aggregate employment growth rate, lagged zip code level house price growth rates, by comparison, is very weak with correlation coe¢ cients less than 0:0015. 4.1.2

Results

Table 4 reports our benchmark regression analysis where we proxy the relative demand by the fraction of mortgage application that are for investment housing and the sample spans from January 2000 to December 2010. We do not report the coe¢ cients on time and state dummies to save space. As can be seen, in the …rst stage our instrument, the fraction of workers in recreation and accommodations in 2000, has signi…cant explanatory power for the relative demand of investment housing. A 10 percentage point increase in the fraction leads to 13 percentage point increase in the relative demand. This is not surprising as the fraction of workers in recreation and accommodation accounts for di¤erences in amenities. Areas that have a higher fraction of such workers are areas that attract more tourists and thus are more likely to have vacation and investment housing. 15

The one-month lagged zip code level real aggregate payroll growth rate does not impact on the relative demand statistically signi…cantly, but zip code level employment growth rate contributes negatively to the relative demand. This result suggests that second and investment housing are purchased by households mostly outside the zip code and its immediate surrounding area where its labor force reside. Put it di¤erently, good local labor market leads to more primary housing buying and hence lower relative demand for investment housing in the area these workers reside. Another striking …nding is that relative demand responds positively to past house price appreciation up to 10 months. Local county level unemployment rates, on the other hand, do not a¤ect much of the relative demand. For the second stage analysis, we …nd that relative demand for investment housing contributes positively to changes in real house price index with a marginal e¤ect of 0:12. Speci…cally, a 10 percentage point increase in the share causes monthly real house price growth rate to go up by 1:2 percentage points, about 67 percent of the average monthly house price growth rate between January 2000 and December 2010. Turning to the other variables, we …nd that local aggregate employment growth rate and aggregate payroll growth all contribute positively to house price increases. Furthermore, past house price changes for the most part also drive current house price changes. Local unemployment rates, by comparison, are largely inconsequential after we control for other variations. Table 5 presents pre-crisis regression results. We …nd much larger positive e¤ects of lagged house price changes on relative demand for investment housing in the …rst stage and a much larger e¤ect of current relative demand on investment housing on house price growth rate. Speci…cally, the marginal e¤ects of relative demand on houses price changes increased by …ve fold. In other words, a 10 percentage point increase in the relative demand leads to an increase in growth rates of 6:1 percentage point, about 11 percent of the average monthly house price growth rate between January 2000 and December 2005. The e¤ects of other variables remain similar to the benchmark. We conduct additional robustness tests by including MSA level, lagged growth rates of real average annual rents come from surveys of “Class A” (top-quality) apartments by Reis, a commercial real estate information company. See Ambrose, Eichholtz, and Lindenthal (2012) for a comprehensive discussion of the impact of rents on local house prices. We lose a third of observations because of Reis’limited coverage. We conduct the whole sample analysis and report the results in table 6. The e¤ects of relative demand of investment on housing on house price changes are now slightly larger. The relative demand now responds less to past house price changes and only up to sever months. The lagged real rent growth rates a¤ect the relative demand for investment housing in two ways. On the one hand, the higher the rents, the more likely people will chose to 16

own their homes. On the other hand, people are also more likely to buy investment housing as the dividend payments are higher. Our analysis suggests that the …rst e¤ect dominate. We also …nd our results robust to an alternative de…nition of relative demand for investment housing, the fraction of mortgage application amount that is for investment housing as seen in table 7. Finally, anecdotal evidence suggests that many of the investment housing purchase after the crisis are cash transactions, hence, not captured by HMDA. However, these transactions occurred most recently. In other words, our 2010 measurement of investment housing demand may be biased downward. We conduct an additional analysis restricting our sample to be between January 2000 and December 2009, not surprisingly, the marginal e¤ect of relative investment housing demand on local house price changes, at 0:13; is now slightly larger. We do not report the regression analysis here to save space.

4.2

Mortgage Performance

Because investment housing does not provide direct housing service to its owners, our theory predicts that households are more likely to default on their mortgages on investment housing than on their primary mortgages. In this subsection, we use a 2 percent random sample of the LPS and Corelogic to test this theory for prime and subprime investment housing mortgages separately. We focus our sample period to from January 1996 to October 2011. In particular, we run the following probit regression dit = cons + !IN Vi + Xit +

it ;

where di is a dummy variable that takes a value of 1 if the mortgage is 90 days or more delinquent and 0 otherwise, IN Vi is an indicator for investment housing mortgages, and Xit include all the other controls including year and state …xed e¤ects, age of the loan and its square, mortgage loan-to-value ratio at origination; whether the mortgage has full documentation, whether the mortgage is of …xed rate, whether the mortgage is interest only, jumbo, or balloon. We restrict our attention to the …rst 90-day mortgage delinquency. In other words, we delete a mortgage from the data after it becomes 90-days delinquent from our sample. The results are reported in table 8. Holding everything else the same, for prime mortgages, being investment home raises the 90-day delinquent rate by 1 basis point, about 4 percent of the average default rate of prime mortgages during the period. The subprime mortgages, the increase is much larger – 16 basis points, close to 20 percent 17

of the average default rate of subprime mortgages. Most of the other variables have the expected signs for both prime and subprime mortgages, high leverage, jumbo mortgage (for prime mortgages as all subprime mortgages are jumbo loans), and balloon mortgage all increase mortgage default rates. By contrast, having full document, …xed-rate mortgage and high credit score at origination all reduce mortgage default rates. Loan age, interestingly, increases the default rates for prime mortgages but decreases the default rates for subprime mortgages. Finally, past local house price appreciation rate, local payroll growth, and local employment growth all reduce mortgage default rates.

5

Policy Implications and Conclusion

This paper makes two important contributions to the literature on the recent boom and bust of the US housing market. First, we document that investment housing played an important role in the recent housing boom and bust. Moreover, investment homes are more likely to be prime or near-prime borrowers than subprime borrowers and real estate investors do not appear to use exotic mortgage products more frequently than primary borrowers. Then, we study the relationship between the relative demand for real estate mortgages and local housing market, we show that while past local house price changes have signi…cantly a¤ected the relative demand for investment housing, the relative demand also drove the price movement especially during the pre-crisis period. According to our calculation, from 2000 to 2005, zip code level real house price growth shot up from an average of 0:39 percent at monthly frequency to 0:74 percent while the relative demand for investment housing went up from 0:072 to 0:143. Thus, of the 35 basis point increase, 4:3 (0:611 (0:143 0:072)) basis points or 12 percent were due to increases in the relative demand for investment housing. Although the drop in the relative demand contributed relatively little to the overall house price decline since the onset of the crisis directly, the indirect e¤ect through foreclosure is likely to be large (Mian, Su…, and Trebbi 2010). In 2000, the 90 days and more default rate for prime mortgages is a little under 2 percent and almost all of them came from primary mortgages as there were hardly any investment home prime mortgages at the time. In 2009, however, prime mortgage default rate climbed up to 9:3 percent, and 7:3 percent of the default mortgages are investment home mortgages. For subprime mortgages, in 2000 the default rate was about 5 percent and a little over 3 percent of them came from investment housing mortgages. In 2010, the default rate jumped up to close to 12:3 percent, and over 11 percent of them are investment home mortgages. In 2009, about 72 percent mortgage outstanding is primary according to LPS and Corelogic. Investment mortgages, thus, caused an increase in default and foreclosure rates of about 0:76 (0:093 18

0:073 0:72 + 0:123 (0:11 0:03) 0:28) percentage points, a 7:6 percent increase. According to Mian, Su…, and Trebbi (2010), this should have further lowered house price decline substantially (roughly another 2 percent if we use the -2:693 estimation coe¢ cient from their table 6). One caveat of our analysis is that we only capture the part of the relative demand for real estate investing …nanced by mortgages as many anecdotal evidence suggests over the last several years, many housing transactions especially investment housing transactions are bought during foreclosures, are all cash transactions. Furthermore, we cannot identify the “‡ippers” – those who bought and sold at high frequency. We intend to tackle these issues in a future research when housing transaction data become available to us.

19

Appendix A. First Order Conditions Let us start with the household’s default decision. Given that the household is risk-neutral in the second period with no additional income and that this is two-period mortgage contract which eliminates the option side of the mortgage default,13 it follows immediately that (11)

dh = 1 if rh (1

)p1 h > [p2 (1

(12)

ds = 1 if rs (1

)p1 s > (p2 + p2 cs )s:

) + p2 ch ]h;

In other words, the household will default on its mortgage, primary or investment housing, if the second period house value plus the default cost falls below the required mortgage payment. Note that in our model the default decisions and the mortgage rates are not functions of house sizes. We can rewrite the household problem as follows after some algebra, max

f log c + (1

) log h + Ef(1

fh;s;c 0;0 dh ;ds 1g

(13)

dh ch p2 h + (1

ds )[p2 s

dh )[(1

rs p1 (1

)p2 h )s]

rh (1

)p1 h]

ds cs p2 sgg:

From the …rst period’s budget constraint, we can replace investment housing demand s by c and h: Then, we obtain the following …rst-order conditions ( is the Lagrangian multiplier for s 0), (14) (15)

p1 1 + c h

+ Ef(1

dh )[(1

p1 + Ef(1 c

)p2

ds )[p2

(16)

rh (1

rs (1

)p1 ] )p1 ] (y1

13

dh ch p2 g = 0;

d s cs p 2 g + p1 h

= 0;

c) = 0;

For example, if the mortgage term were three periods instead of two, then borrowers facing a low house price in the second period can either default then or wait for a possible housing-market recovery in the third period. Introducing a third period, however, complicates the model substantially.

20

which can be simpli…ed as (17) h= =

Ef(1

ds )[p2

Efp2 [dh ch

rs (1

1 ds cs + (1

)p1 ]

d s cs p 2 g

dh ) ]g +

1 Ef(1

dh )[(1

)p2

rh (1

)p1 ]

d h ch p 2 g +

;

(18) s = maxf

y1 p1

c p1

h; 0g:

(19) c=

Ef(1

ds )[p2

p1 rs p1 (1

p1 where = 0 if Efp2 [dh ch(1 ds c)s +(1 solution to p1 h + c = y1 .

)]

d s cs p 2 g +

dh ) ]g

+

=

E[(1

p1 E[(1 ds cs )p2 (r+ )(1

ds cs )p2 )p1 ]

p1 (r + )(1 y1 . Otherwise,

)p1 ] + is the

Appendix B. A Numerical Example We provide a numerical example here to gain intuition of the situation when default does occur. We assume that = 0:50; = 0:20; = 0:10; = 0; ch = 0:15; cs = 0:03;and L = 10: The expected second period house price p2 is normally distributed in [0; p]; and the …rst period income y1 is normally distributed in [0:01; 1]: We report the simulation results in table 3 where we increase the upper bound of the second period house price expectation p from 3 to 14. As can be seen, for all the scenarios households are more likely to default on investment homes. As a result, the mortgage interest rate on investment homes are always higher. As we increase the second period house price expectation in the sense of …rst-order stochastic dominance by increasing p, households begin to spend more on investment housing by reducing both the non-housing consumption and consumption on primary housing. Since overall housing demand increases, the …rst period house price also increases monotonically.

21

References [1] Adelino, M., Gerardi, K., P. Willen. 2009. “Why Don’t Lenders Renegotiate More Home Mortgages? Redefaults, Self-Cures and Securitization,”Federal Reserve Bank of Atlanta Working Paper. 2009-17. [2] Agarwal, Sumit, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet, and Douglas D. Evano¤. 2011. “The Role of Securitization in Mortgage Renegotiation.” Journal of Financial Economics, 102(3), pp. 559-578. [3] Ambrose, Brent W., Piet Eichholtz, and Thies Lindenthal. 2012. “House Prices and Fundamentals: 355 Years of Evidence.”Manuscript. [4] Barlevy, Gadi, and Jonas D.M. Fisher. 2011. “Mortgage Choices and Housing Speculation.”Federal Reserve Bank of Chicago Working Paper June 15, 2011. [5] Bayer, Patrick, Christopher Geissler, and James W. Roberts. 2011. “Speculators and Middlemen: The Role of Filppers inthe Housing Market.” National Bureau of Economic Research Working Paper Series No. 16784. [6] Chambers, Mattew, Carlos Garriga, and Don Schlagenhauf. 2009. “Accounting for Changes in the Homeownership Rate.” International Economic Review 50(3), pp. 677-726. [7] Chinco, Alexander, and Christopher Mayer. 2011. “Noise Traders, Distant Speculators and Asset Bubbles in the Housing Market.”manuscript, Columbia University. [8] Choi, Hyun-Soo, Harrison Hong, and Jose Scheinkman. 2011. “Speculating on Home Improvements.”manuscript, Princeton University. [9] Cocco, Joao. 2005. “Portfolio Choice in the Presence of Housing.”Review of Financial Studies 18(2), pp. 535-567. [10] Elul, Ronel. 2011. “Securitization and Mortgage Default.”Federal Reserve Bank of Philadelphia Working Paper May, 2011. [11] Favilukis, Jack, Sydney Ludvigson, and Stin Van Favilukis. 2011. “Macroeconomic E¤ects of Housing Wealth, Housing Finance, and Limited Risk Sharing in General Equilibrium,”manuscript, New York University. [12] Flavin, Marjorie, and Takashi Yamashita. 2002. “Owner-Occupied Housing and the Composition of the Household Portfolio over the Life-cycle,” American Economic Review 92, 345-362. 22

[13] Haughwout, Abdrew, Donghoon Lee, Joseph Tracy, and Wilbert van der Klaauw. 2011. “Real Estate Investors, the Leverage Cycle, and the Housing Market Crisis.” Federal Reserve Bank of New York Sta¤ Report no. 514. [14] Jiang, Wei, Ashlyn Nelson, and Edward Vytlacil. 2010. “Securitization and Loan Performance: A Contrast of Ex Ante and Ex Post Relations in the Mortgage Market.”manuscript, Columbia University. [15] Keys, Benjamin, Tanmoy Mukherjee, Amit Seru, and Vikrant Vig. 2010. “Did Securitization Lead to Lax Screening? Evidence from Subprime Loans.” Quarterly Journal of Economics 125(1), pp.307-362. [16] Kiyotaki, Nobuhiro, Alex Michaelides, and Kalin Nikolov. 2011. “Winners and Losers in Housing Markets.” Journal of Money, Credit, and Banking 43(2-3), 255296. [17] Li, Wenli, and Rui Yao. 2007. “The Life-cycle E¤ects of House Price Changes.” Journal of Money, Credit, and Banking, pp. 1375-1409. [18] Mian, Atif, and Amir Su…. 2009. “The Consequences of Mortgage Credit Expansion: Evidence from the U.S. Mortgage Default Crisis.”Quarterly Journal of Economics 124(4), pp1449-1496. [19] Mian, Atif, Amir Su…, and Francesco Trebbi. 2010. “Foreclosures, House Prices, and the Real Economy.”NBER working paper 16685. [20] Piskorski, Tomasz, and Alexei Tchistyi. 2011. “Stochastic House Appreciation and Optimal Mortgage Lending.”Review of Financial Studies 24(5), pp.1407-1446. [21] Robbinson, Breck L, and Richard M. Todd. 2010. “The Role of Non-owner-occupied Homes in the Current Housing and Foreclosure Cycle.” Federal Reserve Bank of Richmond wp 10-11. [22] Wheaton, William C., and Gleb Nechayev. 2006. “Past Housing ‘Cycles’ and the Current Housing ‘Boom:’ What’s Di¤erent This Time?”Manuscript, MIT. [23] Yao, Rui, and Harold Zhang. 2005. “Optimal Consumption and Portfolio Choices with Risky Housing and Borrowing Constraints.” Review of Financial Studies 18, 197-239.

23

Table 1. Borrower Characteristics by Occupancy Type Med. Income at App.

Med. Income at Orig.

orig.

(data source: HMDA)

year

Primary

Investment

Primary

2000

25,803

42,961

2001

26,301

2002

Median Credit Score at Orig. Prime (LPS)

Subprime (Corelogic)

Investment

Primary

Investment

Primary

Investment

30,622

51,941

707

723

599

639

46,447

31,137

53,931

713

733

608

643

27,255

48,727

31,754

55,614

719

739

625

657

2003

27,652

51,286

32,204

58,424

720

741

636

672

2004

27,340

52,714

32,581

60,308

724

739

637

671

2005

28,187

54,604

33,288

63,673

719

740

635

670

2006

29,815

54,160

34,256

66,686

720

740

631

672

2007

30,316

59,394

33,599

66,398

720

750

630

663

2008

28,569

52,729

32,167

59,386

728

762

2009

26,701

51,271

30,762

57,729

729

776

2010

26,934

49,265

30,494

56,742

733

779

Note: Median income is de‡ated using overall consumer price index with 1980-1984=100. Table 2. Mortgage Products by Occupancy Type LTV (median)

Share of ARM (%)

Share of Interest Only (%)

Subprime

Prime

Subprime

Prime

orig.

Prime

year

Prim.

Inv.

Prim.

Inv.

Prim.

Inv.

Prim.

Inv.

Prim.

Inv.

Prim.

Inv.

2000

89.83

79.15

100.00

90.00

10.55

13.24

57.21

27.70

0.002

0.070

0.464

0.436

2001

89.74

79.27

100.00

95.00

5.98

8.23

55.49

36.20

0.002

0.017

0.383

0.591

2002

84.04

79.17

100.00

94.92

12.81

13.00

63.11

38.57

0.050

0.068

2.645

1.803

2003

80.00

79.23

100.00

94.96

17.72

21.39

68.86

43.01

0.815

1.532

10.344

7.634

2004

79.99

79.21

100.00

94.55

35.68

44.47

81.10

64.55

7.597

12.257

31.149

27.669

2005

79.78

78.96

100.00

95.00

36.44

49.44

81.10

72.31

18.953

25.726

39.782

35.991

2006

79.76

78.13

100.00

95.00

29.17

39.08

78.45

66.90

18.753

26.874

32.560

35.258

2007

80.00

78.31

100.00

90.00

12.62

19.72

68.93

60.95

13.989

21.302

39.876

41.158

2008

88.27

76.00

4.31

7.08

2.452

5.583

2009

90.01

74.62

1.63

3.67

0.256

1.244

2010

91.93

74.90

3.89

7.23

0.302

1.344

24

Subprime

Table 3. Sumary Statistics variable

mean

median

s.d.

min

max

relative demand for investment housing (application num)

0.113

0.083

0.112

0.000

1.000

relative demand for investment housing (application amt)

0.100

0.069

0.110

0.000

1.000

fraction of employment in recreation and accommodation

0.080

0.071

0.040

0.000

0.693

1-mon lagged zip net real aggregate payroll growth rate (%)

0.470

0.000

17.083

-100

6377

1-mon lagged zip net aggregate employment growth rate (%)

0.222

0.000

17.851

-100

3588

1-mon lagged county unemployment rate (%)

5.828

5.200

2.528

0.900

32.2

2-mon lagged county unemployment rate (%)

5.786

5.200

2.509

0.900

32.2

1-mon lagged net real house price growth rate ( %)

0.018

0.095

1.593

-16.93

14.48

2-mon lagged net real house price growth rate ( %)

0.027

0.104

1.594

-16.93

14.48

Note: we include twelvel lags of county unemployment rates and zip code level real house price index growth rates. To save space, we only report two here.

25

Table 4. Investment Housing Demand (num) and House Price Changes (January 2000 - December 2010) variable fraction of employ. in rec. and accom.

Relative Demand for Inv. Housing

Real HPI Changes

(…rst stage)

(second stage)

1.2600 (0.0033)

relative demand for inv. housing

0.1217 (0.0342)

-0.0000 (0.0000) lagged aggregate employment growth rate -0.0001 (0.0000)

0.0038 (0.0003)

lagged aggregate payroll growth rate

0.0022 (0.0003)

1-mon lagged zip real hpi growth rate

0.0012 (0.0001)

0.2841 (0.0011)

2-mon lagged zip real hpi growth rate

0.0008 (0.0000)

0.0022 (0.0012)

3-mon lagged zip real hpi growth rate

0.0009 (0.0001)

-0.0489 (0.0012)

4-mon lagged zip real hpi growth rate

0.0008 (0.0001)

0.0469 (0.0012)

5-mon lagged zip real hpi growth rate

0.0009 (0.0001)

0.0406 (0.0012)

6-mon lagged zip real hpi growth rate

0.0008 (0.0001)

0.0267 (0.0013)

7-mon lagged zip real hpi growth rate

0.0007 (0.0001)

0.0309 (0.0011)

8-mon lagged zip real hpi growth rate

0.0005 (0.0001)

0.0373 (0.0013)

9-mon lagged zip real hpi growth rate

0.0004 (0.0001)

0.0385 (0.0013)

10-mon lagged zip real hpi growth rate

0.0003 (0.0001)

0.0356 (0.0011)

11-mon lagged zip real hpi growth rate

0.00001 (0.0001)

0.0471 (0.0011)

12-mon lagged zip real hpi growth rate

-0.0001 (0.0001)

0.0529 (0.0011)

1-mon lagged county unemp. rate

-0.0002 (0.0003)

-0.0869 (0.0042)

2-mon lagged county unemp. rate

-0.0000 (0.0004)

0.0201 (0.0055)

3-mon lagged county unemp. rate

0.0001 (0.0004)

0.0115 (0.0055)

4-mon lagged county unemp. rate

0.0003 (0.0004)

0.0076 (0.0055)

5-mon lagged county unemp. rate

-0.0001 (0.0004)

0.0059 (0.0056)

6-mon lagged county unemp. rate

0.0019 (0.0004)

0.0018 (0.0056)

7-mon lagged county unemp. rate

0.0005 (0.0004)

-0.0152 (0.0056)

8-mon lagged county unemp. rate

0.0003 (0.0004)

-0.0119 (0.0055)

9-mon lagged county unemp. rate

0.0008 (0.0004)

0.0067 (0.0056)

10-mon lagged county unemp. rate

-0.0001 (0.0004)

-0.0097 (0.0055)

11-mon lagged county unemp. rate

0.0005 (0.0004)

0.0055 (0.0055)

12-mon lagged county unemp. rate

0.0005 (0.0003)

0.0068 (0.0042)

time dummies

yes

yes

state dummies

yes

yes

number of observations

720,926

720,926

R-sq

0.0934

0.4013

Note: *** indicates 1% signi…cance, ** indicates 5% signi…cance, and * indicates 10% signi…cance.

26

Table 5. Investment Housing Demand (num) and House Price Changes (January 2000 - December 2005) variable fraction of employ. in rec. and accom.

Relative Demand for Inv. Housing

Real HPI Changes

(…rst stage)

(second stage)

1.1949 (0.0041)

relative demand for inv. housing

0.6114 (0.0484)

-0.0001 (0.0000) lagged aggregate employment growth rate -0.0002 (0.0000)

0.0018 (0.0005)

lagged aggregate payroll growth rate

0.0019 (0.0006)

1-mon lagged zip real hpi growth rate

0.0027 (0.0001)

0.2265 (0.0017)

2-mon lagged zip real hpi growth rate

0.0021 (0.0001)

-0.0306 (0.0017)

3-mon lagged zip real hpi growth rate

0.0024 (0.0001)

-0.0702 (0.0017)

4-mon lagged zip real hpi growth rate

0.0024 (0.0001)

0.0310 (0.0017)

5-mon lagged zip real hpi growth rate

0.0023 (0.0001)

0.0250 (0.0017)

6-mon lagged zip real hpi growth rate

0.0023 (0.0001)

0.0160 (0.0017)

7-mon lagged zip real hpi growth rate

0.0022 (0.0001)

0.0252 (0.0011)

8-mon lagged zip real hpi growth rate

0.0019 (0.0001)

0.0285 (0.0013)

9-mon lagged zip real hpi growth rate

0.0019 (0.0001)

0.0284 (0.0013)

10-mon lagged zip real hpi growth rate

0.0017 (0.0001)

0.0276 (0.0011)

11-mon lagged zip real hpi growth rate

0.0014 (0.0001)

0.0401 (0.0011)

12-mon lagged zip real hpi growth rate

0.0017 (0.0001)

0.0482 (0.0011)

1-mon lagged county unemp. rate

0.0003 (0.0004)

-0.0265 (0.0042)

2-mon lagged county unemp. rate

-0.0001 (0.0005)

0.0157 (0.0073)

3-mon lagged county unemp. rate

0.0008 (0.0005)

-0.0046 (0.0072)

4-mon lagged county unemp. rate

0.0006 (0.0005)

0.0059 (0.0073)

5-mon lagged county unemp. rate

-0.0008 (0.0005)

0.0004 (0.0073)

6-mon lagged county unemp. rate

0.0023 (0.0005)

0.0249 (0.0074)

7-mon lagged county unemp. rate

0.0001 (0.0005)

-0.0271 (0.0074)

8-mon lagged county unemp. rate

0.0002 (0.0005)

-0.0184 (0.0073)

9-mon lagged county unemp. rate

0.0009 (0.0005)

-0.0028 (0.0073)

10-mon lagged county unemp. rate

0.0008 (0.0005)

0.0260 (0.0073)

11-mon lagged county unemp. rate

0.0004 (0.0005)

-0.0296 (0.0072)

12-mon lagged county unemp. rate

0.0002 (0.0004)

0.0227 (0.055)

time dummies

yes

yes

state dummies

yes

yes

number of observations

361,007

361,007

R-sq

0.0828

0.2236

Note: *** indicates 1% signi…cance, ** indicates 5% signi…cance, and * indicates 10% signi…cance.

27

Table 6. Investment Housing Demand (num) and House Price Changes (January 2000 - December 2010) (adding rent as control) variable fraction of employ. in rec. and accom.

Relative Demand for Inv. Housing

Real HPI Changes

(…rst stage)

(second stage)

1.2600 (0.0033)

relative demand for inv. housing

0.1248 (0.0598)

-0.0001 (0.0000) lagged aggregate employment growth rate -0.0002 (0.0000)

0.0032 (0.0006)

lagged local rent growth rate

-0.0003 (0.0001)

0.0170 (0.0015)

1-mon lagged zip real hpi growth rate

0.0009 (0.0001)

0.2843 (0.0015)

2-mon lagged zip real hpi growth rate

0.0007 (0.0000)

0.0091 (0.0015)

3-mon lagged zip real hpi growth rate

0.0006 (0.0001)

-0.0486 (0.0016)

4-mon lagged zip real hpi growth rate

0.0006 (0.0001)

0.0472 (0.0016)

5-mon lagged zip real hpi growth rate

0.0006 (0.0001)

0.0396 (0.0016)

6-mon lagged zip real hpi growth rate

0.0005 (0.0001)

0.0241 (0.0016)

7-mon lagged zip real hpi growth rate

0.0003 (0.0001)

0.0290 (0.0016)

8-mon lagged zip real hpi growth rate

0.0001 (0.0001)

0.0343 (0.0016)

9-mon lagged zip real hpi growth rate

0.0000 (0.0000)

0.0371 (0.0016)

10-mon lagged zip real hpi growth rate

0.0359 (0.0015)

11-mon lagged zip real hpi growth rate

-0.0000 (0.0001) -0.0003 (0.0001)

12-mon lagged zip real hpi growth rate

-0.0006 (0.0001)

0.0520 (0.0014)

1-mon lagged county unemp. rate

0.0004 (0.0004)

-0.0913 (0.0059)

2-mon lagged county unemp. rate

0.0004 (0.0005)

0.0247 (0.0078)

3-mon lagged county unemp. rate

-0.0002 (0.0005)

0.0030 (0.0078)

4-mon lagged county unemp. rate

0.0001 (0.0005)

0.0147 (0.0080)

5-mon lagged county unemp. rate

-0.0003 (0.0005)

0.0135 (0.0081)

6-mon lagged county unemp. rate

0.0019 (0.0005)

-0.0044 (0.0080)

7-mon lagged county unemp. rate

0.0001 (0.0005)

-0.0125 (0.0080)

8-mon lagged county unemp. rate

0.0005 (0.0005)

-0.0239 (0.0080)

9-mon lagged county unemp. rate

0.0005 (0.0005)

-0.0095 (0.0079)

10-mon lagged county unemp. rate

-0.0007 (0.0005)

0.0058 (0.0080)

11-mon lagged county unemp. rate

0.0008 (0.0005)

0.0245 (0.0080)

12-mon lagged county unemp. rate

0.0010 (0.0004)

0.0665 (0.0006)

time dummies

yes

yes

state dummies

yes

yes

number of observations

418,754

418,754

R-sq

0.0811

0.4194

lagged aggregate payroll growth rate

0.0017 (0.0005)

0.0469 (0.0015)

Note: *** indicates 1% signi…cance, ** indicates 5% signi…cance, and * indicates 10% signi…cance.

28

Table 7. Investment Housing Demand (amt) and House Price Changes (January 2000 - December 2010) variable fraction of employ. in rec. and accom.

Relative Demand for Inv. Housing

Real HPI Changes

(…rst stage)

(second stage)

1.3049 (0.0034)

relative demand for inv. housing

0.1175 (0.0330)

-0.0000 (0.0000) lagged aggregate employment growth rate -0.0001 (0.0000)

0.0038 (0.0004)

lagged aggregate payroll growth rate

0.0022 (0.0003)

1-mon lagged zip real hpi growth rate

0.0011 (0.0001)

0.2841 (0.0012)

2-mon lagged zip real hpi growth rate

0.0009 (0.0001)

0.0022 (0.0012)

3-mon lagged zip real hpi growth rate

0.0008 (0.0001)

-0.0489 (0.0012)

4-mon lagged zip real hpi growth rate

0.0009 (0.0001)

0.0469 (0.0012)

5-mon lagged zip real hpi growth rate

0.0010 (0.0001)

0.0406 (0.0012)

6-mon lagged zip real hpi growth rate

0.0009 (0.0001)

0.02267 (0.0013)

7-mon lagged zip real hpi growth rate

0.0008 (0.0001)

0.0309 (0.0013)

8-mon lagged zip real hpi growth rate

0.0007 (0.0001)

0.0373 (0.0012)

9-mon lagged zip real hpi growth rate

0.0006 (0.0001)

0.0385 (0.0012)

10-mon lagged zip real hpi growth rate

0.0006 (0.0001)

0.0356 (0.0012)

11-mon lagged zip real hpi growth rate

0.0004 (0.0001)

0.0471 (0.0012)

12-mon lagged zip real hpi growth rate

0.0003 (0.0001)

0.0529 (0.0012)

1-mon lagged county unemp. rate

-0.0006 (0.0003)

-0.0869 (0.0042)

2-mon lagged county unemp. rate

0.0004 (0.0004)

0.0201 (0.0055)

3-mon lagged county unemp. rate

0.0001 (0.0004)

0.0115 (0.0044)

4-mon lagged county unemp. rate

0.0002 (0.0004)

0.0076 (0.0055)

5-mon lagged county unemp. rate

-0.0001 (0.0004)

0.0059 (0.0056)

6-mon lagged county unemp. rate

0.0020 (0.0004)

0.0018 (0.0056)

7-mon lagged county unemp. rate

0.0001 (0.0004)

-0.0151 (0.0056)

8-mon lagged county unemp. rate

0.0004 (0.0004)

-0.0119 (0.0056)

9-mon lagged county unemp. rate

0.0008 (0.0005)

0.0067 (0.0057)

10-mon lagged county unemp. rate

-0.0000 (0.0004)

-0.0098 (0.0056)

11-mon lagged county unemp. rate

0.0002 (0.0004)

0.0056 (0.0056)

12-mon lagged county unemp. rate

0.0005 (0.0003)

0.0683 (0.0042)

time dummies

yes

yes

state dummies

yes

yes

number of observations

720,296

720,296

R-sq

0.0968

0.4013

Note: *** indicates 1% signi…cance, ** indicates 5% signi…cance, and * indicates 10% signi…cance.

29

Table 8. Mortgage Performance –Marginal E¤ects (dependent variable: 90-days or more deliq) variable

marginal e¤ects Prime Mortgages

Subprime Mortgages

whether investment housing

0.0001

0.0016 (0.0002)

loan age

1.39e-08 (6.95e-09)

-0.0001 (3.55e-06)

mortgage LTV

5.75e-07 (3.92e-08)

0.0004 (0.0000) 0.0296 (0.0020)

missing mortgage LTV credit score at origination

-3.19e-07 (1.85e-08)

-0.0001 (8.74e-07)

full document

-9.00e-06 (7.24e-07)

-0.0044 (0.0001)

…x-rate mortgage

-1.99e-5 (1.67e-06)

-0.0046 (0.0001)

interest-only mortgage

0.0006 (0.0001)

jumbo mortgage

1.43e-06 (1.05e-06)

balloon mortgage

1.5113e-04 (2.36e-05)

0.0045 (0.0003)

lagged local house price gr rate

-6.17e-05 (4.73e-06)

-0.0477 (0.0006)

lagged local payroll growth

-1.23e-05 (2.77e-06)

-0.0009 (0.0004)

lagged local employment rate

4.51e-06 (2.97e-06)

0.0002 (0.0004)

time dummies

yes

yes

state dummies

yes

yes

number of obs.

16,631,802

2,518,561

Pseudo R-sq

0.2767

0.1241

Note: *** indicates 1% sign…cance, ** indicates 5% signi…cance, and * indicates 10% signi…cance.

30

Table A.1 A Numerical Example

p

rh

rs

Edh

Eds

c

h

s

p1

1

1.3976

1.3984

0.4065

0.4146

0.2365

9.6988

0.3011

0.3818

2

1.2419

1.2422

0.2182

0.2224

0.0778

4.9556

5.0443

0.4611

3

1.1900

1.1902

0.1444

0.1472

0.0443

3.3203

6.6802

0.4778

4

1.1657

1.1658

0.1076

0.1097

0.0308

2.4946

7.5048

0.4846

5

1.1517

1.1518

0.0857

0.0874

0.0236

1.9972

8.0024

0.4882

6

1.1426

1.1427

0.0712

0.0725

0.0191

1.6651

8.3348

0.4905

7

1.1362

1.1363

0.0608

0.0620

0.0160

1.4276

8.5716

0.4920

8

1.1315

1.1315

0.0531

0.0542

0.0138

1.2493

8.7512

0.4931

9

1.1279

1.1279

0.0472

0.0481

0.0122

1.1106

8.8896

0.4939

10

1.1250

1.1250

0.0424

0.0432

0.0108

0.9997

9.0009

0.4946

11

1.1226

1.1227

0.0385

0.0393

0.0098

0.9088

9.0903

0.4952

12

1.1207

1.1207

0.0353

0.0359

0.0089

0.8331

9.1673

0.4955

13

1.1191

1.1191

0.0325

0.0332

0.0082

0.7691

9.2309

0.4959

14

1.1177

1.1177

0.0302

0.0308

0.0076

0.7142

9.2860

0.4962

31

200 100 120 140 160 180 house price index (Corelogic)

.16 share of investment home applications (%) .06 .08 .1 .12 .14

Jan2000

Jan2002

Jan2004

Jan2006 time

mortgage application # house price index (right axis)

Jan2008

Jan2010

mortgage application $

.05

.1

.15

.2

.25

.3

Figure 1. Share of Investment Home Mortgage Applications and House Price Index – US

Jan2000

Jan2002

Jan2004

Jan2006 time US CA NV

Jan2008

Jan2010

AZ FL

Source: HMDA

Figure 2. Share of Investment Housing Application Numbers –US and Selected States

32

Density 0 2 4 6 8101214

0

.2

.4

.6

.8

1

.6

.8

1

.6

.8

1

Density 0 2 4 6 8101214

year 2000

0

.2

.4

Density 0 2 4 6 8101214

year 2005

0

.2

.4 year 2010

Figure 3. Histograms of Shares of Investment Housing Mortgage Applications

33

0.25 share of investment mortgage apps (%) 0.05 0.10 0.15

0.20

0.25 0.20

Jan2002

Jan2004

0.00

share of subprime mortgage apps (%) 0.05 0.10 0.15 0.00

Jan2000

Dec2005

Jan2000

Jan2002

time of investment apps

Jan2004

Dec2005

time of all apps

of subprime apps

Source: HMDA

of all apps

Source: HMDA

0

.05

.05

.1

.1

share(%) .15

share(%) .15

.2

.2

.25

.25

.3

.3

Figure 4. Investment Housing and Subprime Mortgages (HMDA)

0

200001

200001 200201 200401 200601 200801 201001 201111 time Investment Homes

Second Homes

Source: LPS

200201

200401 time

200601

200708

Investment in Sub

2nd in Sub

Investment in AltA

2nd in AltA

Source: Corelogic

Figure 5. Investment Mortgage Shares of Prime Mortgages and Subprime Mortgages (LPS and Corelogic)

34