RDP 2016-12: The Household Cash Flow Channel of Monetary Policy 6. A Closer Look at the Borrower Cash Flow Channel

6.1 Variable-rate and Fixed-rate Mortgage Debt

Next, we take a closer look at the borrower cash flow channel by exploiting variation between variable-rate and fixed-rate borrowers in their response to changes in required mortgage repayments. The key difference between these two groups of borrowers is arguably the cash flow effect of an interest rate change; the other channels, such as the wealth and substitution channels, should operate in a similar fashion for both types of households.

To examine this, a regression model is estimated based on an experimental research design in which the variable-rate borrowers are the treatment' group, the fixed-rate borrowers are the ‘control’ group and we can think of an unexpected change in required mortgage repayments (due to interest rate changes) as the ‘treatment’. If the borrower cash flow channel exists, the cash flows and spending of variable-rate borrowers should increase (decrease) relative to fixed-rate borrowers when interest rates fall (rise).

The graphical evidence indicates that interest rate changes have a larger effect on the cash flows and spending of variable-rate borrowers than on fixed-rate borrowers (Figure 7). Focusing on the sample period of 2006–10, the comparatively large decline in nominal lending rates on variable-rate mortgages in 2009 contributed to a much larger decline in required repayments (left-hand panel of Figure 7) and a larger increase in cash flows for the median variable-rate borrower compared to the median fixed-rate borrower (middle panel of Figure 7). At least some of this appears to have translated into higher spending by the median variable-rate borrower compared to the median fixed-rate borrower (right-hand panel of Figure 7). Taken together, these results are consistent with a borrower cash flow channel.

Figure 7: Cash Flows and Spending of Borrowers
Log level, 2008 = 100
Figure 7: Cash Flows and Spending of Borrowers

Note: (a) Cash flows are equal to gross household income less taxes and required mortgage payments

Sources: Authors' calculations; HILDA Survey Release 14.0

To confirm the graphical evidence, we estimate the following regression model on the sample of households that have owner-occupier debt:

where each indebted household makes required repayments on either variable-rate mortgage debt (MVR) or fixed-rate mortgage debt (MFR). These two variables are constructed by multiplying the required repayments of each indebted household by a dummy variable indicating whether the household holds variable-rate or fixed-rate mortgage debt. If the cash flow channel exists, there should be a negative correlation between the change in required repayments and consumption for variable-rate borrowers (βVR < 0), and this effect should be larger for variable-rate borrowers than for fixed-rate borrowers (βVR < βFR).

To interpret our estimates as the causal effect of changes in required repayments on spending, the households would be ideally ‘ randomly assigned’ to the treatment and control groups. In other words, there should be no systematic differences between fixed-rate and variable-rate mortgage holders that might determine the sensitivity of their spending to changes in cash flows.

According to the HILDA Survey, fixed- and variable-rate mortgage borrowers are very similar in terms of their observed characteristics (Table 6). This suggests that selection effects are unlikely to be significant. While about 75–80 per cent of Australian mortgagors have variable-rate mortgage debt, the only really notable differences are that variable-rate borrowers are required to make slightly larger repayments and have higher total net wealth than fixed-rate borrowers, on average.

Table 6: Characteristics of Borrowers
Annual average, 2002 to 2014
  Variable-rate   Fixed-rate
Mean Median Std dev Mean Median Std dev
Durables expenditure ($'000) 12.1 6.4 17.3   11.2 5.1 15.5
Total expenditure ($'000) 49.2 42.9 29.2   45.3 38.9 26.2
Cash flows ($'000) 92.5 82.0 60.2   86.1 73.1 90.6
Required mortgage repayments ($'000) 18.4 16.0 14.0   24.7 21.4 19.5
Usual mortgage repayments ($'000) 23.0 20.7 14.8   25.3 22.2 27.1
Interest-earning liquid assets ($'000) 25.5 7.5 70.1   23.8 8.2 56.7
Interest-earning debt ($'000) 261.3 186.4 316.7   275.8 208.4 335.0
Net interest-earning liquid assets ($'000) −235.7 −169.5 315.0   −252.0 −194.8 333.6
Net total wealth ($'000) 724.2 502.5 936.4   651.6 393.8 948.1
Age of household head (years) 43.8 43.0 10.5   42.6 41.0 11.2
Age of mortgage (years) 7.2 5.0 6.8   6.2 3.9 7.0
Share that are tertiary educated (%) 32.1       30.4    
Share that are liquidity constrained (%) 17.8       16.4    
Observations   6,097       1,495  

Notes: All variables in dollar amounts are deflated by the consumer price index and in 2014 dollars; all estimates are based on HILDA Survey wealth module years (i.e. 2002, 2006, 2010, 2014)

Sources: ABS; Authors' calculations; HILDA Survey Release 14.0

To further alleviate selection concerns, the regression model includes a wide range of household-level variables to control for some of the observable differences between the two groups. On top of this, there are unobserved characteristics, such as the household's risk aversion or uncertainty about future income, that might also determine consumption and be correlated with the decision to hold a fixed-rate or variable-rate loan, which would confound any causal effect. We do not explicitly model the household choice between fixed- and variable-rate loans. But, if the characteristics that determine this choice do not change over time, they are controlled for because the model includes household fixed effects.

For causal inference, it is also important that an interest rate change does not cause some households to move from one treatment group to the other. For example, a sharp fall in interest rates may encourage some households to refinance from a variable-rate loan to a fixed-rate loan if they expect interest rates to revert back later in the cycle. Conversely, some households may respond by switching from a fixed-rate to variable-rate loan if they expect interest rates to continue falling.

We do not observe the loan type of mortgagor households prior to 2010 so we do not observe the extent to which households switched between the two borrower groups over the sample period. However, the sample window is quite short (at five years), which should limit the number of households switching treatment status.

Furthermore, we would expect that most borrowers will transition from fixed-rate to variable-rate mortgages rather than vice versa. This is due to a structural feature of the Australian mortgage market – that most fixed-rate mortgage contracts are only fixed for three years before they become a variable-rate contract. The research design would wrongly classify these borrowers as being in the ‘control group’ when, in reality, their spending was potentially sensitive to changes in interest rates via the cash flow channel. This would cause us to underestimate the effect of cash flows on spending as we rely on the differential spending response of the variable-rate and fixed-rate borrowers. In other words, it suggests that such selection issues will impart a negative bias to our borrower cash flow channel estimates.

In support of this, we observe the loan type in both the 2010 and 2014 Surveys, which provides some guide as to the shares of borrowers that switch between types of mortgages. Between 2010 and 2014, outstanding mortgage lending rates declined by between 150 and 200 basis points. The HILDA Survey indicates that around half of the households that had a fixed-rate mortgage in 2010 switched to a variable-rate mortgage by 2014. In contrast, about 19 per cent of borrowers switched from having a variable-rate mortgage to a fixed-rate mortgage over the same period. This implies that most borrowers transitioned from a fixed-rate to variable-rate mortgage between 2010 and 2014.

In a similar vein, some mortgage borrowers may choose to switch between a fixed-rate mortgage with a high interest rate to one with a low interest rate. This again would imply that the cash flows of the control group are affected by changes in interest rates and again will cause us to underestimate the borrower cash flow channel.

The regression results support the graphical evidence and suggest that there is a negative correlation between required payments and durables consumption for only variable-rate borrowers (Table 7). The implied MPCs indicate that an additional dollar of cash flows due to lower mortgage payments is associated with around 17 cents being spent on durable goods for the variable-rate borrowers. For fixed-rate borrowers, the estimated coefficient is not statistically different to zero. These results are upheld in the same regressions on the sub-samples of HtM and non-HtM borrowers. The estimated elasticities and implied MPCs are stronger for HtM borrowers than for non-HtM borrowers. Together, the results point to the observed negative relationship between spending and required repayments being due to a cash flow effect rather than a wealth effect or intertemporal substitution effect.

Table 7: Sensitivity of Durables Spending to Required Mortgage Repayments
  All borrowers HtM borrowers Non-HtM borrowers
Fixed-rate repayments (βFR) 0.30
MPC 0.92 −0.02 1.78
Variable-rate repayments (βVR) −0.25***
MPC −0.17 −0.22 −0.15
R2 0.55 0.56 0.56
Within R2 0.01 0.02 0.01
Observations 7,016 2,760 4,256

Notes: ***, ** and * denote statistical significance at the 1, 5 and 10 per cent levels, respectively; t statistics shown in parentheses; standard errors are clustered by household; coefficient estimates on control variables are omitted; samples exclude the top and bottom 1 per cent of the household distribution for growth in durable goods spending and growth in cash flows

Sources: Authors' calculations; HILDA Survey Release 14.0

6.2 Mortgage Prepayments and Household Deleveraging

Next, we examine the extent to which mortgage prepayment behaviour affects the sensitivity of the economy to changes in interest rates via the borrower cash flow channel. A household making excess repayments can smooth their total mortgage repayments through interest rate cycles, reducing the sensitivity of household spending to interest rate changes. Moreover, over time, a household that consistently makes excess repayments will lower the outstanding balance on their mortgage below the scheduled balance and potentially build up a large pool of savings that are available for redraw.

When mortgage lending rates fall, borrowers with variable-rate mortgages have two options: 1) they can choose to reduce their repayments to the lower minimum scheduled repayment (i.e. cash flow increases) or 2) they can maintain their existing repayments and make larger prepayments (i.e. cash flows are unchanged). The extent to which borrowers adjust their repayments depends on a number of factors, including lender processes, the level of mortgage lending rates, and the level of mortgage debt.

Interestingly, institutional factors could also introduce asymmetries in the cash flow channel of monetary policy. In particular, interest rate increases should have a larger effect on cash flows for borrowers than interest rate decreases. This is because, when interest rates fall, both the bank and borrower may choose to not adjust the repayment schedule, so cash flows will be unchanged, and the loan will just be repaid faster. But, when interest rates rise, the bank will not allow the borrower to extend the life of the mortgage beyond the original schedule – the bank will definitely raise the required repayments and reduce the cash flows of the borrower (assuming the borrower does not refinance the mortgage).

A unique feature of the HILDA Survey is the availability of survey information that can be used to construct household-level estimates of both usual and required mortgage repayments. We can therefore estimate excess repayments (the difference between usual and required repayments) and gauge how mortgage prepayment behaviour varies with factors such as interest rates.

According to the HILDA Survey, the median mortgage borrower typically makes excess repayments of close to 3 per cent of disposable income (Figure 8). Moreover, the size of these excess repayments varies within the interest rate cycle. In general, the excess repayments were declining over the early to mid 2000s when lending rates were rising and have been rising gradually since the global financial crisis when lending rates have typically fallen.

Figure 8: Mortgage Repayments
Share of household disposable income
Figure 8: Mortgage Repayments

Notes: Required repayments are estimated based on a credit-foncier model for the median indebted household using information on mortgage interest rates, current loan balance, loan balance at origination and age of the mortgage (adjusted for refinancing); excess repayments are equal to the difference between usual repayments (reported by survey respondents) and required repayments

Sources: Authors' calculations; HILDA Survey Release 14.0

The consumption response to an interest rate change through the borrower cash flow channel can be dampened by an increase in precautionary saving through mortgage prepayment. To see this, suppose lower interest rates reduced the current required repayments of a variable-rate mortgage borrower by a dollar. In Australia, a variable-rate mortgage borrower has the option to pay down the mortgage principal by an extra dollar. By making a dollar of prepayment, the household effectively ‘saves’ the additional money, and there are no additional cash flows to spend on goods and services in the current period.

To understand how this relates to the earlier MPC estimates, consider a simple flow of funds constraint for a household with mortgage debt:

where the total use of funds on the left-hand side of the equation includes expenditure on goods and services (C), usual mortgage payments (MU) and saving in liquid assets, such as bank deposits and mortgage offset accounts, as well as illiquid assets, such as superannuation (S). The total availability of funds on the right-hand side of the equation includes disposable income (YD) and borrowing (B). Usual mortgage repayments (MU) can be decomposed into required repayments (M) and excess repayments (X):

In this setting, holding constant the availability of funds, an additional dollar of cash flows due to lower required mortgage repayments (M) can be either: 1) spent on goods and services (C); 2) saved in excess repayments by paying down mortgage principal (X); or 3) saved in other assets (S).

The marginal propensity to save in excess repayments (or the ‘marginal propensity to prepay’) can be estimated by examining how excess repayments (X) respond to a change in required repayments (M). The estimated propensity to prepay is useful for a few reasons. First, it provides an effective upper bound estimate of the MPC out of interest-sensitive cash flows (more specifically, it provides an estimate of 1 – MPC, assuming that the household does not save in other assets). Second, unlike the consumption data, information on usual, required and excess repayments is available for the full sample period of 2001 to 2014. This allows for an examination of how the propensity to prepay varies over interest rate cycles.

The following regression model is estimated:

This regression specification is essentially the same as the consumption model estimated earlier (Equation (1)), except that the dependent variable is now the level of excess mortgage repayments and the model is specified in levels rather than log levels.[19] The coefficient estimate is expected to be negative if borrowers increase their excess repayments in response to lower required repayments (βX < 0). This coefficient provides a direct estimate of the marginal propensity to prepay.

This framework can be extended to examine whether the sensitivity of prepayment to cash flows varies over time. For instance, households may have become more likely to deleverage (and hence prepay their mortgage) in the period since the global financial crisis. To examine this, we split the sample into two periods before and after 2010. A dummy variable (POST) is included that is equal to one for the period after 2010 (and is equal to zero otherwise). This dummy variable is interacted with the required repayments variable to examine whether the sensitivity of excess repayments to required repayments has changed since the crisis:

The results of estimating Equations (4) and (5) are presented in Table 8.

Table 8: Response of Mortgage Prepayments to Cash Flows
  Equation (4) Equation (5)
Required repayments −0.70***
Required repayments (pre-2010)   −0.60***
Required repayments (post-2010)   −0.14***
Other cash flows 0.02*** 0.02***
  (2.86) (2.98)
R2 0.69 0.70
Within R2 0.32 0.33
Observations 18,627 18,627

Notes: ***, ** and * denote statistical significance at the 1, 5 and 10 per cent levels, respectively; t statistics shown in parentheses; standard errors are clustered by household; coefficient estimates on control variables, household fixed effects and time fixed effects are omitted

Sources: Authors' calculations; HILDA Survey Release 14.0

The results indicate that the marginal propensity to prepay is about 70 cents in the dollar, on average, over the 2001 to 2014 period. Taken at face value, and recalling the earlier MPC estimates, this suggests that, for the average mortgagor household, a given change in required repayments is roughly divided amongst mortgage prepayment, durable goods spending and other saving by about 70 cents, 16 cents and 14 cents respectively. However, these estimates should be treated with some caution as they rely on different model specifications and sample periods.[20]

The results also show that the marginal propensity to prepay has increased from around 60 per cent in the period from 2001 to 2010 to around 74 cents in the period after 2010. This provides some evidence that mortgagor households have become more inclined in recent years to use any additional cash flows from lower interest rates to deleverage rather than spend. This is also consistent with the idea that, in a low interest rate environment, mortgagor households are more likely to save for precautionary reasons in expectation of higher interest rates in the future.

Notably, this deleveraging process provides a mechanism through which a temporary disposable income shock (due to a cyclical change in monetary policy) can reduce the expected remaining life of the mortgage. By promoting the speed of deleveraging, lower interest rates may ‘bring forward’ the time at which households feel comfortable with spending out of interest-sensitive cash flows.


This is due to the fact that excess repayments can be negative and these observations would be excluded by using logarithms. [19]

The results are robust to other sampling changes, including excluding outliers and focusing solely on variable-rate mortgage borrowers. For instance, we find slightly stronger estimates of the propensity to prepay for variable-rate borrowers. This makes sense given that fixed-rate mortgage borrowers typically have to pay an additional cost to prepay their mortgages. However, for this exercise, the sample is restricted to the period from 2010 onwards so it is possible that the stronger propensity to prepay is due to the low interest rate environment. [20]