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Please do not cite or circulate without the authors’ permission.
AbstractGovernment-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.
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
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 transactjust 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 second loans. The conforming loan limit for 2010 was USD 417,000 for single family houses in most regions in the US (higher limits applied to “high cost” areas). The Office of Federal Housing Enterprise Oversight was responsible for setting these limits between 1992 and 2008 and since 2008 this responsibility has been transferred to the Federal Housing Finance Agency.
380 percent loan to value ratios are widely used in the industry as an important threshold for first lien mortgages. Just above 80 percent the pricing and availability of loans changes very significantly.
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
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).
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.
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 uniquely identifies counties and county equivalents in the United States, certain U.S. possessions, and certain freely associated states. The first two digits are the FIPS state code and the last three are the county code within the state or possession.
• 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:
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
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 ofexactly 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.
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
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
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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Table 1: Summary Statistics Whole Sample
Panel A. House Characteristics. N=4,752,214
Mean Std. Dev. 5 pctile Median 95 pctile
Transaction Value (1000 usd) 298.72 122.45 142.50 275.00 536.10
Loan to value 0.81 0.15 0.51 0.80 0.99 House Size (sqft) 1,603 630 840 1,463 2,846 Lot Size (sqft) 8,669 15,299 0 5,998 27,038 Number of rooms 6.54 1.69 4.00 6.00 10.00 Number of bedrooms 2.94 0.86 2.00 3.00 4.00 Number of bathrooms 1.92 1.00 0.00 2.00 3.00
House age (years) 34.56 26.35 1.00 30.00 84.00
Panel B. House Valuation. N=4,752,214
Mean Std. Dev. 5 pctile Median 95 pctile
Value per sqft (USD/Sqft) 203.21 96.42 88.80 180.47 393.74 Value per sqft residual (USD/Sqft 0.00 43.60 -64.52 -1.34 69.81 Transaction value residual (USD) 0 53,690 -79,703 -751 86,768
Note: Panel A shows the descriptive statistics for all transactions in our data from 1998 to 2008. The data was extracted from deeds records by Dataquick. Panel B shows the different valuation measures we use in the regression analysis. Value per sqft is the transaction amount divided by the size of the house measured in square feet. Both the residual measures are obtained from hedonic regressions run by year and by metropolitan area of value per sqft and transaction value on a set of detailed house charac-teristics. We give more information on the construction of the residuals in Section 2, Data and Methodology.
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 0.16 Chicago 405,725 258.31 106.53 174.00 67.92 0.81 0.15 DC 557,312 307.22 118.58 200.36 91.61 0.83 0.14 Denver 416,826 238.42 91.61 156.47 50.38 0.84 0.15 Las Vegas 280,192 238.16 96.03 132.10 46.23 0.82 0.14 Los Angeles 988,823 321.70 127.51 235.86 106.66 0.81 0.13 Miami 610,156 253.30 107.06 153.71 65.03 0.81 0.14 New York 499,782 337.48 121.30 226.22 97.51 0.78 0.17 San Diego 302,206 332.05 123.01 230.93 95.85 0.80 0.14 San 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
N Obs Mean Std. Dev Mean Std. Dev Mean Std. Dev
1998 156,729 236.85 100.53 138.20 53.11 0.81 0.15 1999 418,980 242.42 103.15 143.51 56.14 0.81 0.15 2000 431,831 252.22 107.49 154.34 63.98 0.81 0.16 2001 449,992 258.37 107.17 161.40 65.20 0.82 0.15 2002 495,545 275.47 112.55 177.20 73.63 0.81 0.15 2003 518,138 294.09 116.43 196.85 81.91 0.81 0.15 2004 630,352 320.33 120.84 225.65 95.43 0.79 0.14 2005 567,804 344.59 123.74 253.26 106.45 0.78 0.13 2006 434,905 352.65 124.02 262.69 109.34 0.79 0.13 2007 337,265 347.97 123.63 253.64 106.18 0.82 0.15 2008 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 10 MSAs. Panel A shows the mean and standard deviation by city of (i)house price, (ii) value per sqft and (iii) loan to value. Panel B the mean and standard deviation by year for the same three variables.
Table 3: Summary Statistics Restricted Sample
Panel A. House Characteristics. N=62260
Mean Std. Dev. 5 pctile Median 95 pctile
Transaction Value (1000 usd) 376.71 54.33 285.00 380.00 450.00
Loan to value 0.80 0.00 0.79 0.80 0.80 House Size (sqft) 1,809 657 989 1,676 3,058 Lot Size (sqft) 10,719 15,967 0 7,000 33,638 Number of rooms 7.14 1.59 5.00 7.00 10.00 Number of bedrooms 3.27 0.77 2.00 3.00 5.00 Number of bathrooms 2.06 0.99 0.00 2.00 4.00
House age (years) 38.11 26.45 1.00 38.00 83.00
Panel B. House Valuation. N=62260
Mean Std. Dev. 5 pctile Median 95 pctile
Value per sqft (USD/Sqft) 238.31 98.79 110.27 220.37 420.42 Value per sqft residual (USD/Sqft 6.24 45.79 -63.63 3.65 83.29 Transaction value residual (USD) 3,657 44,493 -69,152 4,348 73,248
Note: Panel A shows the descriptive statistics for the transactions in our data that we use for the regressions from 1998 to 2008. The data for our regressions includes only transactions that occur within USD 5,000 from each year’s conforming loan limit divided by 0.8, as well as transactions in the same band in the subsequent year. Panel B shows the different valuation measures we use in the regression analysis. Value per sqft is the transaction amount divided by the size of the house measured in square feet. Both the residual measures are obtained from hedonic regressions run by year and by metropolitan area of value per sqft and transaction value on a set of detailed house characteristics. We give more information on the construction of the residuals in Section 2, Data and Methodology.
Table 4: Summary Statistics For Restricted 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 4,702 366.55 53.25 217.60 71.43 0.80 0.00 Chicago 2,863 364.63 55.17 200.82 81.71 0.80 0.00 DC 8,296 381.51 53.68 211.39 93.00 0.80 0.00 Denver 2,970 356.61 51.50 163.56 57.54 0.80 0.00 Las Vegas 1,484 396.95 46.46 155.43 43.57 0.80 0.00 Los Angeles 17,647 374.43 53.94 266.46 104.21 0.80 0.00 Miami 3,674 383.19 54.59 170.36 65.12 0.80 0.00 New York 6,591 405.48 48.18 255.57 86.99 0.80 0.00 San Diego 6,269 375.94 52.08 253.43 101.74 0.80 0.00 San 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
N Obs Mean Std. Dev Mean Std. Dev Mean Std. Dev
1998 1,228 283.17 2.93 153.04 50.27 0.80 0.00 1999 5,996 291.53 9.30 160.62 53.05 0.80 0.00 2000 5,942 308.33 8.43 170.94 59.60 0.80 0.00 2001 6,434 329.20 13.43 183.55 61.72 0.80 0.00 2002 7,141 359.58 17.13 208.66 71.23 0.80 0.00 2003 8,287 388.32 12.93 237.21 77.71 0.80 0.00 2004 11,018 409.04 8.26 273.45 88.43 0.80 0.00 2005 11,039 434.18 15.82 303.40 100.89 0.80 0.00 2006 5,175 448.84 2.43 323.04 109.50 0.80 0.00 Total 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 in our regressions, i.e. only transactions within a USD 5,000 distance from each year’s conforming loan limit divided by 0.8, as well as transactions within that band in the subsequent year. Panel A shows the mean and standard deviation by city of (i)house price, (ii) value per sqft and (iii) loan to value. Panel B the mean and standard deviation by year for the same three variables.
Table 5: Summary Statistics for the Regression Sample in the Year in which CLL is in Effect 1998 1999 2000 2001 2002 2003 2004 2005 “Below CLL/0.8” Av. Price 280,537 299,010 313,987 340,511 374,154 400,150 415,010 446,295 Share LTV=0.8 0.92 0.96 0.95 0.94 0.96 0.98 0.98 0.97 Loan Difference 3,458 4,038 4,554 2,473 3,921 3,612 3,960 3,061 “Above CLL/0.8” Av. Price 285,816 303,700 319,055 345,819 379,131 405,605 419,842 450,448 Share LTV=0.8 0.23 0.29 0.34 0.14 0.23 0.31 0.53 0.63 Loan 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 table shows 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 (second row), as well as the difference between the average mortgage taken out by borrowers 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 and CLL/0.8+5,000.
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 -0.931 Year 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 -546.3 Year 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 -0.460 Year 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 year regressions that use three alternative measures of valuation as the dependent variable in each of the three panels. Expensive refers to transactions up to USD 5000 above the conforming loan limit divided by 0.8 and Year CLL is the year in which the conforming loan limit is in effect.
Table 7: Effect of CLL on Alternative Valuation Measures
Panel A: Value Per Square Foot
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 -0.632 Year 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 11,011 R2 0.665 0.650 0.666 0.674 0.657 0.609 0.621 0.615
Panel B: Transaction Value Residual from Hedonic Regressions
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 -119.0 Year 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 10,858 R2 0.466 0.440 0.440 0.411 0.441 0.451 0.463 0.479
Panel C: Value Per Square Foot Residual from Hedonic Regressions
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 -0.257 Year 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 10,862 R2 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 of house value as the dependent variable in each of the three panels. Expensive refers to transactions up to USD 5000 above the conforming loan limit divided by 0.8 and Year CLL is the year in which the conforming loan limit is in effect. All regressions include zip code fixed effects. Standard errors are clustered by MSA and are shown in parenthesis.
Table 8: Effect of CLL on Alternative Valuation Measures (Fama-McBeth) Using Wider Band of USD 10,000
Panel A: Value Per Square Foot
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 0.230 Year CLL (0.725) (0.810) (0.835) No. Obs. 116,076 44,733 71,343
Panel B: Transaction Value Residual from Hedonic Regressions
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 -1,469.7 115.4 Year CLL (527.1) (913.4) (208.0) No. Obs. 113,391 43,181 70,210
Panel C: Value Per Square Foot Residual from Hedonic Regressions
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 0.085 Year 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 year regressions that use three alternative measures of valuation as the dependent variable in each of the three panels. Expensive refers to transactions up to USD 5000 above the conforming loan limit divided by 0.8 and Year CLL is the year in which the conforming loan limit is in effect.
Table 9: Effect of CLL on Alternative Valuation Measures Using Wider Band of USD 10,000
Panel A: Value Per Square Foot
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 0.877 Year 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 20,346 R2 0.629 0.614 0.624 0.636 0.639 0.596 0.580 0.598
Panel B: Transaction Value Residual from Hedonic Regressions
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 -481.8 368.4 Year 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 20,075 R2 0.390 0.389 0.388 0.366 0.379 0.421 0.422 0.448
Panel C: Value Per Square Foot Residual from Hedonic Regressions
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 0.115 Year 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 20,079 R2 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 of house value as the dependent variable in each of the three panels. Expensive refers to transactions up to USD 5000 above the conforming loan limit divided by 0.8 and Year CLL is the year in which the conforming loan limit is in effect. All regressions include zip code fixed effects. Standard errors are clustered by MSA and are shown in parenthesis.
Figure 1: Transaction-Loan Value Surface, Year 2000
Note: This figure shows the frequency of transactions at each house price-loan value combination for the year 2000 and the 10 MSAs covered in our data, where both house prices and loan values were binned at USD 5000 intervals. The mass of transactions on the diagonal have a loan to value of approximately 0.8.
Figure 2: Transaction-Loan Value Surface, Year 2004
Note: This figure shows the frequency of transactions at each house price-loan value combination for the year 2004 and the 10 MSAs covered in our data, where both house prices and loan values were binned at USD 5000 intervals. The mass of transactions on the diagonal have a loan to value of approximately 0.8.
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 the whole sample and the restricted sample used in the regression. Years 2007 and 2008 are excluded from the regression sample becasue there was no change on the conforming loan limits on those years
Figure 4: Groups of Transactions for the year 2000 0 50 100 150 200 250 300 Number of transactions
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 December 2001 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 transaction value is 315,875 (CLL/.8). Therefore, for this year a transaction “above CLL/0.8” is a house with a price between USD 315,875 and USD 320,875. On the other hand, a transaction “below CLL/0.8” is a house with a price between USD 310,875 and USD 320,875. Finally, the groups with LTV<0.8 include transactions with LTV lower than 0.8 but greater than 0.787, which implies at most a USD 4,000 bigger down payment compared to a transaction with an LTV=0.8.
Figure 5: Groups of Transactions for the year 2004 0 50 100 150 200 250 300 Number of transactions
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 December 2005 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 transaction value is 417,125 (CLL/.8). Therefore, for this year a transaction “above CLL/0.8” is a house with a price between USD 412,125 and USD 422,125. On the other hand, a transaction “below CLL/0.8” is a house with a price between USD 412,125 and USD 417,125. Finally, the groups with LTV<0.8 include transactions with LTV lower than 0.8 but greater than 0.790, which implies at most a USD 4,000 bigger down payment compared to a transaction with an LTV=0.8.
Figure 6: Placebo Tests for the Coefficient of Interest Value per Sqft 0 1 2 3 4 5 Frequency −2 −1.5 −1 −.5 0 .5 1
Note: At the CLL the coefficient is −1.7.
Transaction Value Residual
0 1 2 3 4 5 Frequency −1500 −1000 −500 0 500 1000
Note: At the CLL the coefficient is −1458.66.
Value per Sqft Residual
0 1 2 3 4 5 Frequency −1.5 −1 −.5 0 .5 1
Note: At the CLL the coefficient is −1.53.
Note: This figure shows histograms for 20 placebo tests we perform by shifting the conforming loan limit 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 7 and forming Fama-MacBeth coefficients for each set of “false” loan limits. The three histograms correspond to the three dependent variables we use in Tables 6 and 7. In all three tests the true conforming loan limits produce 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.
