Evidence from Mortgage Market Segmentation

Credit Supply and House Prices: Evidence

from Mortgage Market Segmentation

Manuel Adelino

Dartmouth College

Antoinette Schoar

MIT and NBER

Felipe Severino

MIT

April 17, 2011

Preliminary and incomplete.

Please do not cite or circulate without the authors’ permission.

Abstract

Government-sponsored enterprises (GSEs) can only purchase or securitize mort-

gages with a balance below a given cutoff, a limit known as the conforming loan limit.

In this paper we use changes in these limits from one year to the next to identify the

effect of credit supply on different measures of house valuation. We consider houses

that transact just below a threshold price that can be financed at 80 percent with a

conforming loan and transactions just above this threshold. Transactions that cannot

be financed at a full 80 percent with conforming loans are associated with lower value

per square foot and lower prices after we control for a rich set of house characteristics.

The results are stronger in the first half of our sample (1998-2001) when other forms

of financing such as second liens were less common and when the spread between in-

terest rates on conforming loans and jumbo loans was higher. Our estimates point

to a strong effect of the availability of financing on house prices, namely a reduction

in house price conditional on house characteristics of 1,400 dollars for a 2,000-3,000

dollar reduction in the mortgage obtained.

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1 Introduction

House prices in the United States increased two-fold in nominal terms between the beginning

of 2000 and the end of 20061. While there was some cross-sectional variation in the pace

of appreciation across different cities (Miami, FL prices rose by 180 percent whereas those

in Atlanta, GA rose by just 34 percent), most of the country shared this sharp increase

in prices. Over the same time period mortgage rates fell by 25% (from 8.2% to 6.1% for

conventional 30-year fixed rate mortgages) and were accompanied by a perceived change in

credit standards and the introduction of new mortgage products such as subprime mortgages

and other innovations that made credit more widely available. While there have been many

explanations for the movement in house prices, a number of observers have pointed to

easy access to credit as the central force fueling this boom (Favilukis, Ludvigson and Van

Nieuwerburgh, 2010; Hubbard and Mayer, 2008; Khandani, Lo and Merton, 2009). Similarly

the nationwide reversal in house price growth also coincided with the slowdown in housing

credit. However, on the other side of the debate are proponents of the view that credit

conditions are not a driver of house price appreciations but a symptom of it. For example,

in recent work Glaeser, Gottlieb and Gyourko (2010) argue that cheap credit alone cannot

explain the house price boom and bust and that other forces are likely to have been at play.

Perhaps the most important difficulty in settling the debate on the importance of credit

availability for house price growth is to establish the direction of causality: On the one

hand easier (or cheaper) credit might reduce borrower financing constraints and increase

total demand for housing, which in turn would lead to higher prices. But on the other

hand, credit conditions might be responding to expectations of stronger housing demand

and as a consequence higher house prices. Under this latter scenario cheaper credit is not

the driver of house price increases but a byproduct of increased demand for housing, since

housing as collateral becomes more valuable. The existing literature has had limited success

at separating these two (likely coexisting) effects.

In this paper we identify one instance in which we can separately identify the credit

channel and observe whether credit market conditions feed through to house prices and the

housing choices that borrowers make. We use the changes in the conforming loan limit

(CLL) from one year to the next as an instrument for the availability of credit for houses

that transact at prices close to what can be financed using conforming loans. The con-

forming loan limit defines the maximum loan balance of a mortgage that can be purchased

or securitized by Fannie Mae or Freddie Mac and thus benefit from lower interest rates.

The difference in interest rates between conforming loans and jumbo loans (those that are

1Increase in the Case-Shiller 20-city composite index.

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above the conforming limit) is on average 15-50 basis points (McKenzie, 2002; Ambrose,

LaCour-Little and Sanders, 2004; Sherlund, 2008). Conforming loan limits are set by the

administration each year based on the previous year’s limit plus the October to October

change in national median house price. 2 Since the change is based on the countrywide

average appreciation in house prices it is exogenous to an individual geography. In addition,

since house price levels differ across different parts of the United States, the limit change

affects different parts of the housing stock differently across areas. These features allow

us to cleanly identify the effect of easier access to credit due to increases in the loan sizes

supported by the conforming loan limit, from changes in the overall trend in house price

appreciation.

Our identification rests on the assumption that government support (via Fannie Mae

and Freddie Mac) for conforming loans provided easier access to credit for a wide range of

borrowers and in addition also reduced the cost of credit for conforming loans relative to

jumbo loans. To show that indeed CLL matters for credit access, we look at the distribution

of loan sizes and LTVs around the CLL. We define house transactions that can be supported

by conforming loans by dividing the conforming loan limit by 0.8. Houses with a price just

below one year’s conforming loan limit divided by 0.8 can be purchased using a conforming

loan without going over a loan-to-value (LTV) of 0.8, whereas those that transact just above

a price of CLL/0.8 can no longer be financed at 80 percent with a conforming loan.3 At this

price threshold, instead of financing 80 percent of their purchase with a conforming loan,

borrowers either finance their purchase at 80 percent using a jumbo loan (i.e. a loan above

the CLL) or they take out a mortgage at or below CLL and end up with an LTV below 0.8.

As we indicate before, jumbo loans are associated with higher interest rates, whereas an

LTV below 80 percent means having to either use savings or alternative forms of financing

to make up the difference to that amount.

Our analysis confirms that the CLL appears to have a strong impact on house transac-

tions, especially in the first part of the sample. While the norm in the mortgage market

during this time period is to borrow at an LTV of exactly 0.8 (between 37 and 60 percent

of transactions over time), many borrowers (about 30 to 45 percent) end up with an LTV

below 80 percent for houses that transact just above CLL/0.8 because they take out a mort-

gage that is (almost exactly) at the conforming loan limit (we define “just above” as being

2The administration sets goals for single family, two, three and four family houses, as well as for secondloans. The conforming loan limit for 2010 was USD 417,000 for single family houses in most regions inthe US (higher limits applied to “high cost” areas). The Office of Federal Housing Enterprise Oversightwas responsible for setting these limits between 1992 and 2008 and since 2008 this responsibility has beentransferred to the Federal Housing Finance Agency.

380 percent loan to value ratios are widely used in the industry as an important threshold for first lienmortgages. Just above 80 percent the pricing and availability of loans changes very significantly.

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up to USD 5,000 above the threshold of CLL/0.8). This choice of financing is virtually

inexistent (about 5 percent of borrowers) at this price level both before and after the limit

is in effect, i.e. very few people choose to have an LTV of 79 or 78 percent, except when

they are close to the threshold of the conforming loan limit. Borrowers who end up with

these slightly lower LTV ratios would have chosen to get 80 percent first lien loans, had it

not been for the conforming loan limit. Borrowers may choose to use a conforming loan

either because they are excluded from the jumbo loan market (due to poor credit history,

for example), or because a conforming loan carried a lower interest rate than a a slightly

bigger jumbo loan. Therefore, these borrowers were de facto constrained in their choice of

financing relative to borrowers who bought houses at a price just below CLL/0.8 (up to

USD 5,000 below the threshold of CLL/0.8). To verify the importance of the CLL, we look

at the same transaction sizes in the same area for the subsequent year when the effective

CLL has gone up. Given that we are looking very locally around the conforming loan limit

divided by 0.8, all the transactions we consider are eligible to be financed at 80 percent using

mortgages below the new limit. As a consequence, we see very few find buyers choosing

LTVs below 0.8 in this transaction range. These results confirm that the CLL constitutes an

important determinant of access to finance. Interestingly however, we find that after 2001

the CLL became less binding and we see more borrowers now are at an LTV of 0.8 with

proportionally fewer deviations from this LTV even around the CLL. This suggests that

access to the jumbo loan market became more widely available and thus our identification

strategy should work less well after 2002.

We then determine the impact of the CLL on house prices close to the threshold of

CLL/0.8 by estimating differences in differences regressions of houses just above the thresh-

old relative to houses just below using the year where the conforming loan limit is in effect

and the subsequent year. The intuition behind this estimation strategy is that transactions

which fall just above the CLL in a given year are unobtainable to borrowers who cannot

get a jumbo loan and thus their prices have been bid up less relative to the underlying

fundamentals of the house. Therefore a change in the CLL these transactions should affect

these houses most strongly since it allows borrowers who previously were not able to get

financing to enter this segment of the market. We use three different dependent variables

to capture the cheapness of a property : (1) the value per square foot; (2) the residuals

of house prices from a hedonic regression using a large set of controls for the underlying

characteristics of the house, and (3) The residuals of the value per square foot from similar

hedonic regressions. 4 We then construct averages of the cross sectional coefficients as in

4We run the hedonic regressions by year and by metropolitan statistical area (of which we have 10)and we use the set of controls available from deeds registry data, which includes common variables such

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Fama and MacBeth (1973).

We find that transactions for the “constrained” group of borrowers, i.e. those with

transaction values above CLL/0.8 (within USD 5,000 of this limit), were made at 2.4 dollars

lower value per square foot than those for the unconstrained group (for a mean value per

square foot of 237 dollars). This difference remains almost unchanged after we control for

quality, which suggests that credit constraints feed through to prices directly, rather than

changing the type of house (at least along observable characteristics) chosen by borrowers.

For the same time period, we estimate that “constrained” borrowers paid USD 2,318 less

for a house of similar quality than unconstrained borrowers (by sticking with a conforming

loan, borrowers obtained a USD 2,000-3000 smaller first lien loan than what would yield an

LTV of 80 percent). Thus we see that an increase in the CLL leads to a significant increase

in the prices of properties that are newly able to access this form of credit, relative to the

houses that werebelow the limit and thus could be bought with a conforming loan before.

But it is important to note that this effect becomes small and insignificant after 2002. This

is exactly the period when jumbo loans and second lien mortgages were much more widely

available and thus the CLL was less important.

We conjecture that the channel by which easier access to credit affects prices is via

increased competition. We compare deals that could have been bought with a deal value

just at CLL/0.8 versus those that were just above and thus out of reach for many borrowers.

As discussed above the data suggests that this CLL is binding for a large fraction of people

who are thus upwardly constrained in how much they can bid. Houses that were just above

this threshold therefore cannot be reached with CLL/0.8 and therefore have different values

per sqft. But that means there is less demand just above the CLL/0.8 threshold. It is still

in the sellers interest to sell, since there are not enough unconstrained people to sell to at

this price.

One concern about our identification approach could be that borrowers who choose an

LTV of below 0.8 are just intrinsically different (or more conservative) than other borrowers

and therefore also display different bidding behavior. If that were the case, a similar number

of conservative borrowers should be present in the years before and after the conforming loan

limit is in effect (as well as for transactions below CLL/0.8), but this is not what we see in the

data. Still, the difference in prices between the “constrained” and “unconstrained” groups

could be due to different characteristics of the borrowers in each of these groups, rather than

due to the financial constraints. One such characteristic is wealth – the borrowers in the

“constrained” group could be wealthier and thus able to afford a smaller loan, whereas those

as number of rooms and number of bedrooms, but also detail on the type of heating, architectural type,building type, among many others (we discuss these controls in more detail in Section 2.2).

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in the “unconstrained” group could not afford the smaller loan. At least two features of our

analysis make this an unlikely explanation of the results: first, it is unclear why wealthier

borrowers should pay less for a similar house than poorer borrowers (except if we assume

additionally that wealth is correlated with other characteristics like business savvy, for

example); second, and more importantly, it is unlikely that a difference of USD 2,000-3,000

in down payment says much about differences in wealth or other underlying characteristics.

By using transactions that are, on average, within USD 5,000 of each other and loans that

would be (had they all accounted for 80 percent of house value) USD 4,000 apart, we are

comparing essentially the same class of houses within zip codes and borrowers that, at least

on their observable choices, are very similar. We are not relying on a comparison between

significantly different houses or “rich” versus “poor” borrowers.

Overall, our findings suggest that an exogenous change in the ease of access to credit

due to the increase in the conforming loan limit had significant effects on the pricing of

properties that were previously just above this threshold. While we can only estimate a

local effect around the CLL, this presents a first test of the exogenous effect of cheaper

mortgage loans on housing prices.

2 Data and Methodology

2.1 House Transactions

The dataset we use in this paper contains all the changes of ownership of residential proper-

ties available in deeds and assessors records and it is provided by DataQuick. Our dataset

spans 11 years, from 1998 to 2008, and contains all transactions recorded on the deeds

records for seventy-four counties in ten metropolitan areas (MSA). The metropolitan areas

in the data are Boston, Chicago, DC, Denver, Las Vegas, Los Angeles, Miami, New York,

San Diego and San Francisco.

Each observation in the data contains the date of the transaction, the amount for which

a house was sold, the size of the first mortgage, together with an extensive set of variables

about the property itself. The set of characteristics included in the data are: interior square

feet, lot size, number of bedrooms, number of bathrooms, total rooms, house age, type of

house single family house or condo –, renovation status and date. Additional characteris-

tics include the availability of a fireplace, parking, the style of the building architectural

and structural-, the type of construction, exterior material, heating and cooling, heating

and cooling mechanism, type of roof, view, attic, basement, and garage. Importantly, the

property address is also included in the data.

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In order to clean the raw data received from Dataquick, we perform the following mod-

ifications to the data:

Outliers or missing observations

• Drop records with missing transaction value, house size or zip code. Merge MSA

classification extracted from the census bureau definition using FIPS5 unique code

identifier by county.

• Drop record if house size is smaller than 500 square feet and transactions values smaller

than three thousands and greater than one million and two hundred thousand dollars.

• Remove observations up to first percentile and above the ninety ninth percentile for

the value per square feet variable.

Inconsistent observations across categories

• Drop transactions for which the first loan amount is greater than the transaction value.

• Change second lien amount to missing if the first loan amount is equal to the second

loan amount, or if second loan amount is greater than the transaction value.

• Change second lien amount to missing if combined loan to value (CLT) is greater than

two and loan to value (LTV) is equal to one.

• Change to missing if house age, calculated using transaction year minus built year is

smaller than zero.

Company owned observations

• If the company flag field is populated or if the buyer or seller names contain LLC,

CORP or LTD.

Duplicate transactions

• Drop observations that are duplicates based on transaction value, dates and buyer-

seller information, as well as all the property characteristics.

• Remove duplicate information for which no seller information is available.

5FIPS county code is a five-digit Federal Information Processing Standard (FIPS) code which uniquelyidentifies counties and county equivalents in the United States, certain U.S. possessions, and certain freelyassociated states. The first two digits are the FIPS state code and the last three are the county code withinthe state or possession.

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• If person A sells to B and B sells to C in the same date, we keep the most recent

transaction.

The final sample contains 4.7 millions observation that are summarized in Tables 1 and

2. The average transaction value in our sample is 298 thousand dollars with a standard

deviation of 122 thousand dollars, and an average loan to value of 0.81. The average value

per square foot is 203 dollars per square foot with a standard deviation of 96 dollars per

square foot.

The whole sample is distributed in 10 Metropolitan Statistical Areas (MSAs). Panel A

of Table 2 shows that San Francisco is the metropolitan area with the highest valuation with

an average house price of 370 thousand dollars. Denver and Las Vegas represent the areas

with the lowest valuation with an average of 238 thousand dollars. When we compare values

per square foot we get a similar picture, namely San Francisco is the area with the highest

valuation with an average of 277 dollars per square foot and Las Vegas is the area with the

lowest valuation with an average of 132 dollars per square foot. Table 2 Panel B shows the

evolution of prices through time. Here we see the increase in house prices from an average

of 236 thousand dollars in 1998 to a peak of 352 thousand dollars in 2006. The increase

in prices is linked to an increase in volatility, in particular the standard deviation of the

transactions increased from 100 thousand dollars in 1998 to 124 thousand dollars in 2006.

This pattern is consistent with documented behavior of house prices on previous studies

(Stein, 1995). A similar pattern can be observed for the value per square foot measure,

where standard deviation is 138 dollars per square foot in 1998 and increases to an average

of 262 dollars per square foot in 2006. Finally, it is worth noting that the loan to value

average is stable both across MSAs and through the years, being consistently around 0.8.

2.2 Hedonic Regression

The first result we obtain in Table 6 is that transactions just above the conforming loan

limit have a lower value per square foot than those just below the CLL in the year that the

CLL is in effect. This difference has at least two interpretations - lower quality of the houses

in the first group or, alternatively, lower prices conditional on house quality. In order to

distinguish these two explanations, we estimate hedonic regressions of value per square foot

and house price on a number of house characteristics and estimate the residuals for each

of the two variables (which we denote by (LHSi)). Specifically, we estimate the following

regressions by MSA and by year:

LHSi,j,t = γ0 + ΓXi +monthi + zipcodei + εi

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By estimating these regressions by year and by MSA we allow the coefficients on the

characteristics to vary along these two dimensions. We also use month indicator variables

to account for seasonality in the housing market, as well as zip code fixed effects. The set of

controls Xi includes: interior square feet (linearly, squared and cubed), lot size, bedrooms,

bathrooms, total rooms, house age (linearly and squared), type of house, an indicator for

whether the house was renovated, an indicator for fireplace and parking, indicators for style

of building (architectural style and structural style), and additional indicators for type of

construction, exterior material, heating and cooling, heating and cooling mechanism, type

of roof, view, attic, basement, and garage. This is the same set of controls used in Campbell,

Giglio and Pathak (2010).

The estimated R2 of each of these regressions (80 in total for each left-hand side variable

– 10 MSAs in 8 years) is approximately 40-60% for transaction value and 50-70% for value

per square feet. While interior square feet, lot size and age are included as continuous

variables, all the other controls are included as indicator variables. For example, bedrooms

are divided into four categories: one bedroom, two bedrooms, three bedrooms and more

than four bedrooms.

2.3 Empirical Approach

2.3.1 Identification Strategy

The sample for our main regressions is made up of houses that transact in a tight band

around each years conforming loan limit divided by 0.8, as well as houses in the subsequent

year with prices in the same range. Specifically we look at houses within plus or minus

USD 5,000 from this value. Furthermore, we restrict our attention to houses within this

group that are bought with a first loan that is approximately 80% of the house value (which

implies that the loan is either slightly above or slightly below the conforming loan limit).

Within this restricted sample we define two groups of transactions: houses below the

threshold of CLL/0.8 that have prices that fall between USD CLL/0.8-5000 and CLL/0.8

and houses above that threshold that transact bewteen CLL/0.8 and CLL/0.8+5000. By

construction, in the year that the conforming loan limit is in effect, houses above the thresh-

old of CLL/0.8 cannot be financed at 80 percent using a conforming loan, whereas in the

following year they can (because in all cases between 1998 and 2005 the limit increases

enough from year to year to cover 80 percent of these transactions). Houses below this

threshold can be financed at 80 percent in all years using a conforming loan.

We also limit our attention to houses bought with loans within USD 4,000 from the

conforming loan limit itself. This means that the LTV for all these transactions is within

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about two percentage points of 80 percent. As we can see in Table 5, in the year in which the

CLL is in effect over 90 percent of the houses below CLL/0.8 in our sample are bought with

an LTV of exactly 80 percent, whereas for houses above this boundary a much lower fraction

reaches 80 percent (which for these transactions means using a jumbo loan). Many of the

transactions above CLL/0.8 are financed using a conforming loan, which means having an

LTV of 78-79.5 percent. On average, this means that these borrowers are taking out a

USD 1,000-3,300 smaller first mortgage than houses of similar prices that are financed at

80 percent (last row of Table5).

The construction of these groups is best understood through an example. We take the

year 2000 as our benchmark year. In 2000, the conforming loan limit (CLL) for single family

houses was USD 252,700. The corresponding threshold that we use for transaction values

is 315,875 (252,700/0.8). For this year, houses in our sample above this threshold have a

transaction value between USD 315,875 and USD (315,875 + 5,000) = 320,875. On the other

hand, houses below the threshold are those with a transaction price between USD (315,875 -

5,000) = 310,875 and USD 315,875 (those that transact at exactly USD 315,875 are included

in this second group). The group below the threshold is limited further to carrying loans

between CLL-8,000 and CLL, whereas those in the group above the threshold can use loans

between the conforming loan limit plus USD 4,000 and CLL-USD 4,000. For the purposes

of the regressions, we track these two groups of houses from 2000 to 2001, where 2000 is the

year in which the CLL is in effect and 2001 is the year where all these transactions could

be bought using a conforming loan at a full 80 percent LTV (the CLL changed to USD

275,000, so the threshold of CLL/0.8 was now USD 343,750).

One important assumption in our analysis is that borrowers in the group above CLL/0.8

that end up with an LTV < 0.8 in the year that the CLL was in force would have chosen a

higher loan amount if they could. We argue that this is the case because the choice of an

LTV of 78-79.5 percent is very infrequent anywhere else in the distribution of transactions

outside of this grop of transactions that are sligtly above CLL/0.8. In fact, looking at the

“yellow” group in figures 4 and 5 we see that the mass of borrowers choosing an LTV just

below 0.8 is virtually nonexistent before the CLL comes into play and almost disappears

right after the CLL is lifted in the subsequent year. These borrowers have a lower LTV

because they cannot get a higher one at the same price, or because they are excluded from

the jumbo market altogether. Whatever the reason, this is the group of borrowers that we

refer to as “constrained” in their set of options in terms of credit.

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2.3.2 Empirical Specification

Our main regressions consider the effect of the constraint imposed by the conforming loan

limit on the valuation of transactions made just above the threshold of CLL/0.8. We run

differences in differences regressions year by year with one indicator variable for houses

priced above CLL/0.8, another indicator for the year in which the CLL is in effect and an

interaction of these two indicator variables. The sample includes houses within a USD 5,000

band around the conforming loan limit in the year in which the limit is in force and in the

subsequent year.

V aluation measurei = β0 + β11AboveCLL/0.8 + β21Y ear CLL + β31AboveCLL/0.8×cll + εi

We estimate this regression for each year between 1998 and 2005. We cannot include

2006 and 2007 in our estimates because the conforming loan limit did not change after 2006

(house prices dropped and the administration left the limit unchanged). After we obtain

β1, β2 and β3 for all 8 years (1998-2005) we estimate Fama MacBeth averages of these

coefficients and obtain the standard errors of this average estimate by using the standard

deviation of the estimated coefficients. We include ZIP code fixed effects in all year-by-year

regressions.

3 Access to Credit, House Choice and House Prices

3.1 Regression Results

We present our main results in Table 6. This table presents Fama-MacBeth coefficients from

the year-by-year regressions that we show in detail in Table 7. The coefficient of interest in

Table 6, Panel A shows that for the whole sample, houses above the threshold of CLL/0.8

transacted at a value per square foot that was lower by about USD 1.7 in the year that the

CLL was in effect. The results are stronger for the first half of the sample, where the point

estimate is USD -2.4 per square foot for this set of transactions.

In Panels B and C we use the residuals from the regressions we described in Section 2.2

as the dependent variable to account for differences in quality between houses. The results

are qualitatively and quantitatively very similar to the ones we present in Panel A. In Panel

B we are using the residuals of a regression of house price on a set of characteristics and

we find that those residuals are lower by USD 1,400 for houses above CLL/0.8 when the

CLL binds. This suggests that transactions that cannot be financed at 80 percent with

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conforming loans are made at lower prices even after we control for a rich set of house

characteristics. A similar conclusion can be drawn from Panel C, where the point estimate

is that the value per square foot after we control hor house quality is lower by about USD

1.5.

For both the measures that account for house quality, and similarly to what happens

with value per square foot, the constraint imposed by the conforming loan limit is stronger

in the first half of the sample than in the second half. One possible explanation for this

is that borrowers had easier access to second lien loans after 2002 and used more of this

type of financing in the second half of our sample (this broad pattern is visible in Figure

3). Another possibility is that the fact that more borrowers use jumbo loans

When we use a wider band around the threshold of CLL/0.8 of USD 10,000 instead of

USD 5,000 as in our base specifications, the results are directionally the same but somewhat

weaker in terms of statistical significance. Table 8 shows that the point estimates are

all closer to zero, although they remain statistically significant for all three measures of

valuation in the first half of the sample. Specifically, for the first half of the sample values per

square foot are lower by USD 2.3, house prices conditional on quality are lower by USD 1,470

and value per square foot conditional on quality is lower by USD 1.9. It is not surprising that

our results become weaker when we select a wider band of transactions around CLL/0.8,

given that our identification strategy becomes weaker. In fact, the maximum additional

downpayment in the sample of transactions within USD 5,000 of CLL/0.8 due to sticking

with a conforming loan is USD 4,000, whereas that increases to USD 8,000 when we look

at transactions within USD 10,000 of CLL/0.8. While many of these borrowers will still be

comparable, one can argue that larger differences in downpayment can be correlated with

other differences across individuals and thus not reflect just finacing constraints. If we were

to expand the band further around CLL/0.8 this problem would become more severe and

our identification strategy would ultimately no longer be valid.

3.2 Placebo Tests

One possible explanation for the results that we find in the three panels of Table 6 is that

houses above the CLL/0.8 and below CLL/0.8 are on different trends, and the coefficient

on the interaction between “Above CLL/0.8” and “Year CLL” is picking up those different

trends . In order to test whether the effect that we find is indeed the product of the

conforming loan limits and not due to different trends, we run the same regressions described

in Section 2.3.2 for “placebo” loan limits. We do this by shifting the conforming loan limit in

negative USD 5,000 steps from the true value each year to CLL-100,000. We run regressions

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year-by-year and produce Fama MacBeth coefficients for each of the 20 alternative “placebo”

values for the CLL. The results from this exercise are shown in Figure 6.

The Figures show that the coefficients of interest we obtain for all three dependent

variables (values per square foot, residuals from the transaction amounts and residuals of

values per square foot) are the lowest of all obtained with the 20 “placebo” trials. The

coefficients we obtain are statistically different from the placebo means (where we assume

the 20 trials are independent trials), which indicates that the coefficients we obtain are not

obtained by pure chance.

4 Conclusion

In this paper we use the changes in the conforming loan limit to identify one setting in

which conditions in the credit market affect house prices directly. By looking locally around

the maximum price of a house that can be financed at 80 percent with a conforming loan,

we estimate that borrowers that are “constrained” by the loan limit pay on average USD

1,400 less for a similar quality house than those that are not constrained. This result is

stronger in the earlier part of our sample when borrowers were less likely to have access to

other forms of financing such as second liens and when the interest rate differential between

jumbo loans and conforming loans was larger.

While we can only estimate a local effect around the CLL, this presents a first test of

the exogenous effect of cheaper mortgage loans on house prices. We do not address the issue

of whether credit conditions can fully account for the increase in house prices of 2000-2006,

but we show that those credit conditions matter the formation of prices. Our results are not

consistent with credit market conditions purely responding to housing demand, but rather

point to an effect in the housing market from pure credit supply forces.

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References

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[2] Campbell, J.Y., Giglio, S. and Pathak, P. (2010). Forced Sales and House Prices.American Economic Review, Forthcoming.

[3] Fama, E. F. and MacBeth, J. D. (1973). Risk, Return, and Equilibrium: EmpiricalTests. The Journal of Political Economy, Vol. 81, No. 3, 607-636.

[4] Favilukis,J., Ludvigson, S.C. and Van Nieuwerburgh S. (2010). The MacroeconomicEffects of Housing Wealth, Housing Finance, and Limited Risk-Sharing in GeneralEquilibrium. NBER Working Paper, No. 15988.

[5] Glaeser, E. L, Gottleb, J. and Gyourko, J. (2010). Can Cheap Credit Explain theHousing Boom. NBER Working Paper, No. 16230.

[6] Khandani, A. E., Lo, A.W. and Merton, R.C. (2009). Systemic Risk and the RefinancingRatchet Effect. NBER Working Paper, No. 15362.

[7] McKenzie, J.A. (2008). House Prices, Interest Rates, and the Mortgage Market Melt-down. Columbia Business School Working Paper.

[8] McKenzie, J.A. (2002). A Reconsideration of the Jumbo/Non-jumbo Mortgage RateDifferential. The Journal of Real Estate Finance and Economics, Vol. 25, No. 2-3,197-213.

[9] Sherlund, S.M. (2008). The Jumbo-Conforming Spread: A Semiparametric Approach.Finance and Economics Discussion Series Working Paper, 2008-01.

[10] Stein, J.C. (1995). Prices and Trading Volume in the Housing Market: A Model withDown-Payment Effects. he Quarterly Journal of Economics. Vol. 100, No. 2, 379-406

14

Table 1: Summary Statistics Whole Sample

Panel A. House Characteristics. N=4,752,214Mean Std. Dev. 5 pctile Median 95 pctile

Transaction Value (1000 usd) 298.72 122.45 142.50 275.00 536.10Loan to value 0.81 0.15 0.51 0.80 0.99House Size (sqft) 1,603 630 840 1,463 2,846Lot Size (sqft) 8,669 15,299 0 5,998 27,038Number of rooms 6.54 1.69 4.00 6.00 10.00Number of bedrooms 2.94 0.86 2.00 3.00 4.00Number of bathrooms 1.92 1.00 0.00 2.00 3.00House age (years) 34.56 26.35 1.00 30.00 84.00

Panel B. House Valuation. N=4,752,214Mean Std. Dev. 5 pctile Median 95 pctile

Value per sqft (USD/Sqft) 203.21 96.42 88.80 180.47 393.74Value per sqft residual (USD/Sqft 0.00 43.60 -64.52 -1.34 69.81Transaction value residual (USD) 0 53,690 -79,703 -751 86,768

Note: Panel A shows the descriptive statistics for all transactions in ourdata from 1998 to 2008. The data was extracted from deeds records byDataquick. Panel B shows the different valuation measures we use in theregression analysis. Value per sqft is the transaction amount divided by thesize of the house measured in square feet. Both the residual measures areobtained from hedonic regressions run by year and by metropolitan areaof value per sqft and transaction value on a set of detailed house charac-teristics. We give more information on the construction of the residuals inSection 2, Data and Methodology.

15

Table 2: Summary Statistics For Whole Sample By Geography and Year

Panel A. Geographic Distribution

MSA Transaction Value Value per sqft Loan to Value

N Obs Mean Std. Dev Mean Std. Dev Mean Std. Dev

Boston 332,791 309.98 113.03 216.34 86.75 0.78 0.16Chicago 405,725 258.31 106.53 174.00 67.92 0.81 0.15DC 557,312 307.22 118.58 200.36 91.61 0.83 0.14Denver 416,826 238.42 91.61 156.47 50.38 0.84 0.15Las Vegas 280,192 238.16 96.03 132.10 46.23 0.82 0.14Los Angeles 988,823 321.70 127.51 235.86 106.66 0.81 0.13Miami 610,156 253.30 107.06 153.71 65.03 0.81 0.14New York 499,782 337.48 121.30 226.22 97.51 0.78 0.17San Diego 302,206 332.05 123.01 230.93 95.85 0.80 0.14San Francisco 358,401 370.07 123.97 277.25 110.43 0.78 0.13

Total 4,752,214 298.72 122.45 203.21 96.42 0.81 0.15

Panel B. Distribution By Year

Year Transaction Value Value per sqft Loan to Value


1998 156,729 236.85 100.53 138.20 53.11 0.81 0.151999 418,980 242.42 103.15 143.51 56.14 0.81 0.152000 431,831 252.22 107.49 154.34 63.98 0.81 0.162001 449,992 258.37 107.17 161.40 65.20 0.82 0.152002 495,545 275.47 112.55 177.20 73.63 0.81 0.152003 518,138 294.09 116.43 196.85 81.91 0.81 0.152004 630,352 320.33 120.84 225.65 95.43 0.79 0.142005 567,804 344.59 123.74 253.26 106.45 0.78 0.132006 434,905 352.65 124.02 262.69 109.34 0.79 0.132007 337,265 347.97 123.63 253.64 106.18 0.82 0.152008 310,673 317.13 119.91 222.15 95.34 0.84 0.15

Total 4,752,214 298.72 122.45 203.21 96.42 0.81 0.15

Note: This table uses all the deed registry data on house transactions for 10MSAs. Panel A shows the mean and standard deviation by city of (i)houseprice, (ii) value per sqft and (iii) loan to value. Panel B the mean andstandard deviation by year for the same three variables.

16

Table 3: Summary Statistics Restricted Sample

Panel A. House Characteristics. N=62260Mean Std. Dev. 5 pctile Median 95 pctile

Transaction Value (1000 usd) 376.71 54.33 285.00 380.00 450.00Loan to value 0.80 0.00 0.79 0.80 0.80House Size (sqft) 1,809 657 989 1,676 3,058Lot Size (sqft) 10,719 15,967 0 7,000 33,638Number of rooms 7.14 1.59 5.00 7.00 10.00Number of bedrooms 3.27 0.77 2.00 3.00 5.00Number of bathrooms 2.06 0.99 0.00 2.00 4.00House age (years) 38.11 26.45 1.00 38.00 83.00

Panel B. House Valuation. N=62260Mean Std. Dev. 5 pctile Median 95 pctile

Value per sqft (USD/Sqft) 238.31 98.79 110.27 220.37 420.42Value per sqft residual (USD/Sqft 6.24 45.79 -63.63 3.65 83.29Transaction value residual (USD) 3,657 44,493 -69,152 4,348 73,248

Note: Panel A shows the descriptive statistics for the transactions in ourdata that we use for the regressions from 1998 to 2008. The data for ourregressions includes only transactions that occur within USD 5,000 fromeach year’s conforming loan limit divided by 0.8, as well as transactions inthe same band in the subsequent year. Panel B shows the different valuationmeasures we use in the regression analysis. Value per sqft is the transactionamount divided by the size of the house measured in square feet. Both theresidual measures are obtained from hedonic regressions run by year and bymetropolitan area of value per sqft and transaction value on a set of detailedhouse characteristics. We give more information on the construction of theresiduals in Section 2, Data and Methodology.

17

Table 4: Summary Statistics For Restricted Sample By Geography and Year

Panel A. Geographic Distribution

MSA Transaction Value Value per sqft Loan to Value


Boston 4,702 366.55 53.25 217.60 71.43 0.80 0.00Chicago 2,863 364.63 55.17 200.82 81.71 0.80 0.00DC 8,296 381.51 53.68 211.39 93.00 0.80 0.00Denver 2,970 356.61 51.50 163.56 57.54 0.80 0.00Las Vegas 1,484 396.95 46.46 155.43 43.57 0.80 0.00Los Angeles 17,647 374.43 53.94 266.46 104.21 0.80 0.00Miami 3,674 383.19 54.59 170.36 65.12 0.80 0.00New York 6,591 405.48 48.18 255.57 86.99 0.80 0.00San Diego 6,269 375.94 52.08 253.43 101.74 0.80 0.00San Francisco 7,764 364.34 53.70 279.19 96.27 0.80 0.00

Total 62,260 376.71 54.33 238.31 98.79 0.80 0.00

Panel B. Distribution By Year

Year Transaction Value Value per sqft Loan to Value


1998 1,228 283.17 2.93 153.04 50.27 0.80 0.001999 5,996 291.53 9.30 160.62 53.05 0.80 0.002000 5,942 308.33 8.43 170.94 59.60 0.80 0.002001 6,434 329.20 13.43 183.55 61.72 0.80 0.002002 7,141 359.58 17.13 208.66 71.23 0.80 0.002003 8,287 388.32 12.93 237.21 77.71 0.80 0.002004 11,018 409.04 8.26 273.45 88.43 0.80 0.002005 11,039 434.18 15.82 303.40 100.89 0.80 0.002006 5,175 448.84 2.43 323.04 109.50 0.80 0.00Total 62,260 376.71 54.33 238.31 98.79 0.80 0.00

Note: This table uses the data on house transactions for 10 MSAs used inour regressions, i.e. only transactions within a USD 5,000 distance fromeach year’s conforming loan limit divided by 0.8, as well as transactionswithin that band in the subsequent year. Panel A shows the mean andstandard deviation by city of (i)house price, (ii) value per sqft and (iii) loanto value. Panel B the mean and standard deviation by year for the samethree variables.

18

Table 5: Summary Statistics for the Regression Sample in the Year in which CLL is inEffect

1998 1999 2000 2001 2002 2003 2004 2005

“Below CLL/0.8”Av. Price 280,537 299,010 313,987 340,511 374,154 400,150 415,010 446,295Share LTV=0.8 0.92 0.96 0.95 0.94 0.96 0.98 0.98 0.97Loan Difference 3,458 4,038 4,554 2,473 3,921 3,612 3,960 3,061

“Above CLL/0.8”Av. Price 285,816 303,700 319,055 345,819 379,131 405,605 419,842 450,448Share LTV=0.8 0.23 0.29 0.34 0.14 0.23 0.31 0.53 0.63Loan Difference 1,721 3,324 2,950 1,801 2,928 2,172 2,532 963

Note: For the two groups of transactions included in our analysis, this tableshows the mean transaction price in the year the CLL is in effect (first row),the share of loans with LTV=0.8 in the year that the CLL is in effect (secondrow), as well as the difference between the average mortgage taken out byborrowers with LTV=0.8 relative to those with mortgages below 80 percent(third row), also for the year in which the conforming loan limit is in effect.“Cheap” refers to transactions at a price between CLL/0.8 and CLL/0.8-5,000. “Expensive” includes transactions at a price between CLL/0.8 andCLL/0.8+5,000.

19

Table 6: Effect of CLL on Alternative Valuation Measures (Fama-McBeth)

Panel A: Value Per Square Foot

All years 1998-2001 2002-2005

Above CLL/0.8 0.951 1.730 0.171(0.752) (1.025) (1.089)

Year CLL -26.681 -15.911 -37.451(5.249) (3.069) (6.469)

Above CLL/0.8 x -1.673 -2.415 -0.931Year CLL (0.958) (1.672) (1.057)

No. Obs. 62,260 23,195 39,065

Panel B: Transaction Value Residual from Hedonic Regressions

All years 1998-2001 2002-2005

Above CLL/0.8 2,264.9 2,778.2 1,751.5(605.6) (853.1) (899.0)

Year CLL 12,125.3 9,946.8 14,303.7(1,742.2) (1,445.2) (2,985.2)

Above CLL/0.8 x -1,432.3 -2,318.3 -546.3Year CLL (698.3) (1,238.1) (468.4)

No. Obs. 60,797 22,383 38,414

Panel C: Value Per Square Foot Residual from Hedonic Regressions

All years 1998-2001 2002-2005

Above CLL/0.8 1.336 2.237 0.435(0.717) (0.820) (1.089)

Year CLL 3.056 4.338 1.774(0.654) (0.553) (0.771)

Above CLL/0.8 x -1.520 -2.580 -0.460Year CLL (0.780) (1.359) (0.495)

No. Obs. 60,835 22,403 38,432

Note: Table shows Fama McBeth coefficients computed from year by yearregressions that use three alternative measures of valuation as the dependentvariable in each of the three panels. Expensive refers to transactions up toUSD 5000 above the conforming loan limit divided by 0.8 and Year CLL isthe year in which the conforming loan limit is in effect.

20

Table 7: Effect of CLL on Alternative Valuation Measures


1998 1999 2000 2001 2002 2003 2004 2005

Above CLL/0.8 1.663 3.412 2.980 -1.134 0.759 1.547 1.432 -3.055(0.665) (1.784) (1.029) (2.960) (1.378) (2.351) (2.224) (3.412)

Year CLL -8.636 -17.032 -14.521 -23.455 -31.731 -51.898 -43.640 -22.534(1.045) (3.068) (2.418) (4.630) (6.348) (7.306) (5.399) (10.669)

Above CLL/0.8 x -4.699 -4.297 -3.171 2.508 -0.644 1.332 -3.780 -0.632Year CLL (1.778) (2.344) (2.127) (3.539) (3.454) (2.599) (3.473) (3.049)

No. Obs. 4,369 5,991 6,160 6,675 8,035 9,877 10,142 11,011R2 0.665 0.650 0.666 0.674 0.657 0.609 0.621 0.615


1998 1999 2000 2001 2002 2003 2004 2005

Above CLL/0.8 2,885.3 4,884.7 2,629.2 713.8 1,171.4 4,153.4 1,823.9 -142.8(1,429.9) (1,345.4) (1,032.2) (1,878.6) (1,169.1) (2,150.3) (1,138.3) (2,075.2)

Year CLL 6,243.5 13,136.0 9,481.0 10,926.8 10,657.3 20,743.4 17,819.9 7,994.4(3,343.3) (3,858.8) (2,596.5) (1,410.1) (2,254.5) (4,667.4) (4,497.6) (4,045.6)

Above CLL/0.8 x -3,555.0 -4,208.4 -2,804.9 1,294.9 573.0 -1,348.5 -1,290.6 -119.0Year CLL (3,338.5) (1,894.5) (2,856.9) (1,966.4) (2,545.1) (2,207.6) (1,772.4) (1,521.8)

No. Obs. 4,258 5,763 5,922 6,440 7,819 9,723 10,014 10,858R2 0.466 0.440 0.440 0.411 0.441 0.451 0.463 0.479


1998 1999 2000 2001 2002 2003 2004 2005

Above CLL/0.8 1.655 4.620 1.798 0.874 0.143 3.120 0.663 -2.188(1.282) (1.415) (0.709) (2.344) (1.432) (2.098) (1.589) (2.851)

Year CLL 4.143 5.896 4.028 3.282 0.799 3.194 2.979 0.124(2.482) (2.676) (1.591) (1.254) (1.417) (3.164) (3.613) (2.169)

Above CLL/0.8 x -4.947 -4.835 -0.902 0.365 0.160 0.172 -1.914 -0.257Year CLL (2.567) (1.710) (1.820) (1.957) (2.424) (2.209) (2.671) (2.825)

No. Obs. 4,260 5,772 5,929 6,442 7,822 9,730 10,018 10,862R2 0.327 0.295 0.311 0.256 0.255 0.237 0.225 0.202

Note: Table shows OLS regressions using three alternative measures ofhouse value as the dependent variable in each of the three panels. Expensiverefers to transactions up to USD 5000 above the conforming loan limitdivided by 0.8 and Year CLL is the year in which the conforming loan limitis in effect. All regressions include zip code fixed effects. Standard errorsare clustered by MSA and are shown in parenthesis.

21

Table 8: Effect of CLL on Alternative Valuation Measures (Fama-McBeth) Using WiderBand of USD 10,000


All years 1998-2001 2002-2005

Above CLL/0.8 0.709 1.305 0.114(0.577) (0.823) (0.801)

Year CLL -26.509 -16.290 -36.729(5.051) (2.714) (6.484)

Above CLL/0.8 x -1.052 -2.334 0.230Year CLL (0.725) (0.810) (0.835)

No. Obs. 116,076 44,733 71,343


All years 1998-2001 2002-2005

Above CLL/0.8 3,648.2 3,665.1 3,631.3(308.8) (543.1) (387.3)

Year CLL 11,864.5 9,182.4 14,546.6(1,734.0) (1,266.4) (2,762.4)

Above CLL/0.8 x -677.1 -1,469.7 115.4Year CLL (527.1) (913.4) (208.0)

No. Obs. 113,391 43,181 70,210


All years 1998-2001 2002-2005

Above CLL/0.8 1.294 1.726 0.861(0.440) (0.685) (0.555)

Year CLL 2.929 3.471 2.387(0.537) (0.398) (0.997)

Above CLL/0.8 x -0.927 -1.939 0.085Year CLL (0.579) (0.741) (0.576)

No. Obs. 113,457 43,215 70,242

Note: Table shows Fama McBeth coefficients computed from year by yearregressions that use three alternative measures of valuation as the dependentvariable in each of the three panels. Expensive refers to transactions up toUSD 5000 above the conforming loan limit divided by 0.8 and Year CLL isthe year in which the conforming loan limit is in effect.

22

Table 9: Effect of CLL on Alternative Valuation Measures Using Wider Band of USD 10,000


1998 1999 2000 2001 2002 2003 2004 2005

Above CLL/0.8 0.991 2.889 2.204 -0.866 -0.275 0.375 2.111 -1.754(1.091) (1.440) (0.691) (2.287) (1.188) (2.659) (1.502) (2.360)

Year CLL -9.110 -17.673 -16.176 -22.199 -31.043 -51.879 -41.946 -22.049(2.024) (2.755) (1.796) (3.764) (5.613) (7.889) (6.219) (10.700)

Above CLL/0.8 x -2.393 -3.345 -3.572 -0.027 -0.164 2.075 -1.867 0.877Year CLL (1.350) (2.423) (0.839) (2.576) (1.711) (3.800) (1.808) (2.923)

No. Obs. 8,124 9,383 12,733 14,493 15,604 13,490 21,903 20,346R2 0.629 0.614 0.624 0.636 0.639 0.596 0.580 0.598


1998 1999 2000 2001 2002 2003 2004 2005

Above CLL/0.8 3,368.6 4,802.5 4,194.5 2,294.9 3,038.9 3,278.5 4,766.4 3,441.4(1,026.4) (1,025.1) (833.6) (1,346.6) (906.3) (2,169.7) (885.9) (1,380.5)

Year CLL 5,883.0 11,681.9 8,635.9 10,528.7 11,783.3 19,170.1 19,131.6 8,101.5(1,692.2) (3,220.2) (1,588.9) (1,579.2) (2,396.1) (4,443.0) (3,828.7) (3,415.2)

Above CLL/0.8 x -1,571.5 -2,895.0 -2,550.5 1,138.4 146.9 428.0 -481.8 368.4Year CLL (1,668.6) (1,589.2) (1,594.5) (1,561.8) (1,422.7) (2,299.5) (1,574.6) (1,166.2)

No. Obs. 7,907 9,026 12,278 13,970 15,232 13,284 21,619 20,075R2 0.390 0.389 0.388 0.366 0.379 0.421 0.422 0.448


1998 1999 2000 2001 2002 2003 2004 2005

Above CLL/0.8 1.314 3.669 1.470 0.451 -0.036 1.376 2.181 -0.075(0.652) (1.089) (0.712) (1.489) (0.967) (2.464) (1.118) (1.709)

Year CLL 2.966 4.637 2.954 3.328 1.347 2.472 5.143 0.588(0.600) (2.167) (0.788) (0.848) (1.237) (2.318) (2.930) (1.244)

Above CLL/0.8 x -2.252 -3.832 -1.321 -0.351 0.299 1.359 -1.434 0.115Year CLL (0.809) (1.459) (1.096) (1.529) (1.519) (2.838) (1.842) (2.221)

No. Obs. 7,911 9,036 12,290 13,978 15,240 13,296 21,627 20,079R2 0.247 0.234 0.234 0.190 0.184 0.196 0.158 0.153

Note: Table shows OLS regressions using three alternative measures ofhouse value as the dependent variable in each of the three panels. Expensiverefers to transactions up to USD 5000 above the conforming loan limitdivided by 0.8 and Year CLL is the year in which the conforming loan limitis in effect. All regressions include zip code fixed effects. Standard errorsare clustered by MSA and are shown in parenthesis.

23

Figure 1: Transaction-Loan Value Surface, Year 2000

Note: This figure shows the frequency of transactions at each house price-loan value combination forthe year 2000 and the 10 MSAs covered in our data, where both house prices and loan values were binnedat USD 5000 intervals. The mass of transactions on the diagonal have a loan to value of approximately 0.8.

24

Figure 2: Transaction-Loan Value Surface, Year 2004

Note: This figure shows the frequency of transactions at each house price-loan value combination forthe year 2004 and the 10 MSAs covered in our data, where both house prices and loan values were binnedat USD 5000 intervals. The mass of transactions on the diagonal have a loan to value of approximately 0.8.

25

Figure 3: Fraction of Transactions with a Second Lien Loan by Year

1

Note: This figure shows the average fraction of transactions with a second lien loan by year for thewhole sample and the restricted sample used in the regression. Years 2007 and 2008 are excluded from theregression sample becasue there was no change on the conforming loan limits on those years

26

Figure 4: Groups of Transactions for the year 2000

050

100

150

200

250

300

Num

ber

of tr

ansa

ctio

ns

Jan 1999 Jul 1999 Jan 2000 Jul 2000 Jan 2001 Jul 2001 Jan 2002

below CLL/0.8, ltv=0.8 below CLL/0.8, ltv<0.8

above CLL/0.8, ltv=0.8 above CLL/0.8, ltv<0.8

Note: This figure shows details for the number of transaction between January 1999 and December2001 for the two categories used in the empirical analysis, each broken down by the LTV of the transactions(“above CLL/0.8”, LTV<0.8; “above CLL/0.8”, LTV=0.8; “below CLL/0.8”, LTV¡0.8; “below CLL/0.8”,LTV=0.8). The conforming loan limit (CLL) in 2000 is USD 252,700 and the correspondent transactionvalue is 315,875 (CLL/.8). Therefore, for this year a transaction “above CLL/0.8” is a house with a pricebetween USD 315,875 and USD 320,875. On the other hand, a transaction “below CLL/0.8” is a house witha price between USD 310,875 and USD 320,875. Finally, the groups with LTV<0.8 include transactionswith LTV lower than 0.8 but greater than 0.787, which implies at most a USD 4,000 bigger down paymentcompared to a transaction with an LTV=0.8.

27

Figure 5: Groups of Transactions for the year 2004

050

100

150

200

250

300

Num

ber

of tr

ansa

ctio

ns

Jan 2003 Jul 2003 Jan 2004 Jul 2004 Jan 2005 Jul 2005 Jan 2006

below CLL/0.8, ltv=0.8 below CLL/0.8, ltv<0.8

above CLL/0.8, ltv=0.8 above CLL/0.8, ltv<0.8

Note: This figure shows details for the number of transaction between January 2003 and December2005 for the two categories used in the empirical analysis, each broken down by the LTV of the transactions(“above CLL/0.8”, LTV<0.8; “above CLL/0.8”, LTV=0.8; “below CLL/0.8”, LTV¡0.8; “below CLL/0.8”,LTV=0.8). The conforming loan limit (CLL) in 2004 is USD 333,700 and the correspondent transactionvalue is 417,125 (CLL/.8). Therefore, for this year a transaction “above CLL/0.8” is a house with a pricebetween USD 412,125 and USD 422,125. On the other hand, a transaction “below CLL/0.8” is a house witha price between USD 412,125 and USD 417,125. Finally, the groups with LTV<0.8 include transactionswith LTV lower than 0.8 but greater than 0.790, which implies at most a USD 4,000 bigger down paymentcompared to a transaction with an LTV=0.8.

28

Figure 6: Placebo Tests for the Coefficient of InterestValue per Sqft

01

23

45

Fre

quen

cy

−2 −1.5 −1 −.5 0 .5 1

Note: At the CLL the coefficient is −1.7.

Transaction Value Residual

01

23

45

Fre

quen

cy

−1500 −1000 −500 0 500 1000


Value per Sqft Residual

01

23

45

Fre

quen

cy

−1.5 −1 −.5 0 .5 1


Note: This figure shows histograms for 20 placebo tests we perform by shifting the conforming loanlimit in USD 5000 intervals from 0 until USD -100,000 (i.e. the limits of all years are first changed by -5,000,then by -10,000, etc.). We use these placebo loan limits to run year-by-year regressions like those in Table 7and forming Fama-MacBeth coefficients for each set of “false” loan limits. The three histograms correspondto the three dependent variables we use in Tables 6 and 7. In all three tests the true conforming loan limitsproduce the smallest estimate for the coefficient on our variable of interest, i.e. the interaction between our“Expensive” variable and the year in which the conforming loan limit is in effect.

29

Documents

Evidence from Mortgage Market Segmentation