Chapter 2: Delinquency choice between mortgage and nonmortgage loans
2.3 Estimation methodology and variables
2.3.2 Data and variables
As in the previous chapter, SCF data for 1998, 2001 and 2004 are used to estimate the multinomial choice model. The sample is different from that of Chapter 1 since not every mortgage borrower holds nonmortgage debts. I select households in the SCF that hold
nonmortgage debts and have regular payment contracts for those loans as well as payments for mortgages40. The number of households in the sample is 12,507 (out of 66,330). The rates of delinquency among them for each loan are presented in Table 2-1. The dependent variable is defined to take on one of four discrete values corresponding to households’ loan payment performances: continuing scheduled payments, in delinquency on mortgage, on any of
39 See Greene (2008, pp.842-847) for details.
40 I include credit lines (not secured by home equity), car loans, loans for other vehicles, education loans and consumer loans in nonmortgage loans here. Excluded are credit lines secured by home equity, home improvement loans and loans concerning real estate investments.
nonmortgage loans or on both41.
As we developed in the previous sections, the model suggests several variables have significant effects on borrowers’ decisions on delinquency from two perspectives: the financial profit theory and the credit constraint theory. Concerning the financial profit theory, I include the current loan-to-value ratio, that is, the mortgage balance divided by the house value, which has played a key role in most of the literature on mortgage default. Previous studies (e.g. Deng, Quigley and Van Order, 2000) consistently suggest that the effect of loan-to-value is non-linear and rapidly increases around 0.8 or 0.9. Therefore I also include the square term of this variable.
Current balance of unsecured debts that would be discharged in bankruptcy proceedings is included in order to capture the financial benefit from filing for bankruptcy.
Borrowers’ expectations about the future capital gains from their homes also play an important role in the financial profit theory, particularly for mortgage default (see Equation (2.1)).
While there is a rich literature on house price expectations, the seminal work of Case and Shiller (1988) suggest that people seem to form their expectations on the basis of past price movements rather than any knowledge of fundamentals. In addition, Case and Shiller (1989) reveal
predictable and persistent movements of house price index in cities. Poterba (1991) argues that housing market participants’ extrapolative expectations of this type, or “backward-looking”
expectations, could account for the U.S. house price changes in 1980’s. As a recent study of the relationship between capital gains expectations and housing demands, Dusansky and Koç (2007) find a positive effect of current house price on housing demands in Florida housing markets, implying a significant impact of backward-looking expectations on households’ economic behavior. In the literature on mortgage default, there are few studies that explicitly examine the
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effect of backward-looking expectations on the exercise of mortgage default. However, Vandell and Thibodeau (1985) adopt backward-looking expectations and report a negative effect on the probability of default, which is consistent with their prediction as well as that of my model. As long as I know, no studies have been done on the relationship between backward-looking expectations and bankruptcy decisions. Thus, I will test the effect of backward-looking expectations in several specifications in this chapter and the next.
Although Vandell and Thibodeau (1985) use a house price index based on Census tracts in their calculation of backward-looking expectations, we cannot use this method because no geographical data is available in the public version of the SCF. Since in the SCF, however, we can observe the purchase price of households’ homes and the year when they acquired them, as well as the current price, we can estimate the average rate of housing price appreciation that each household has actually experienced. The estimation formula adopted here is as follows:
( ) ( )
Year I V G Vc − o +
= ln ln
0 ,
where G 0 is the average rate of house price appreciation per year, Vc is current house price,
Vo is original house price, I is the cost of home improvement (if any) and Year is the number of years since the households acquired their homes. Then I construct a proxy for households’ capital gain expectation (Et
[ ]
G ⋅ in Equation (2.1)) as follows: VtG0
V CapGain= c⋅ ,
where CapGain is the proxy for households’ capital gain expectation that I use in the estimations to follow.
While this approach has some disadvantages compared to the Census tract method, it arguably has advantages, as well. In particular, there is no worry about unobserved house-specific
characteristics, which local house price indices often suffer from, since this estimation is derived from a comparison of houses with basically the same characteristics. Gabriel and Rosenthal (1991) use this proxy variable to represent local housing market conditions in the study of borrowers’ choice between different types of mortgages and find reasonable results. Therefore, I use this variable, CapGain, as a proxy for backward-looking house price expectation of each household.
On the other hand, the main factors for the credit constraint theory are borrowers’ ability to pay (see Equation (2.4)). Thus households’ income and the amount of each loan payment should also be included in the estimation model. Two kinds of shocks to the ability to pay, unemployment experience and unexpected low income, are also included as the explanatory variables.
In addition to these variables for the ability to pay, the SCF allows us to detect credit constrained households directly to a great extent, since it asks the respondents some questions about their loan applications and denials42. Jappelli (1990) confirms the validity of these variables regarding credit constraint by examining the SCF in 1983. Thus these variables should be helpful to the estimations. I also include variables for age, sex, race, supporting children, which are selected for the estimation for credit constrained households in Jappelli (1990). While the dummy variable of credit constraint does not capture the degree of credit constraint of each household, that is, Ct in my model (Equation (2.4)), I expect these explanatory variables are correlated with the degree as well as the existence of credit constraints. The descriptions and summary statistics
42 The SCF has two variables concerning credit constraint; “In the past five years, has a particular lender or creditor turned down any request you or your (husband/wife/partner) made for credit, or not given you as much credit as you applied for?” and “Was there any time in the past five years that you thought of applying for credit at a particular place, but changed your mind because you thought you might be turned down?” The dummy variable used in this
for all the explanatory variables are presented in Table 2-2.
2.4. Empirical results and some extensions