RDP 2021-10: The Rise in Household Liquidity Appendix C: Household Liquidity and Local Area Housing Prices

Gianni La Cava and Lydia Wang

November 2021

Consistent with the cross-country evidence, we also find that the increase in household liquid assets has closely matched the increase in housing prices across local housing markets. To see this, we divide the HILDA Survey sample into local housing markets based on the Statistical Area Level 4 (SA4). We estimate housing prices in each SA4 based on the average reported (subjective) estimate of housing prices by home owners in the survey.[22] There are about 107 SA4s in Australia and the population size of SA4s varies from around 40,000 to 900,000.

We explore the correlations between household liquidity buffers and both housing prices and mortgage debt (relative to income) across various regressions. The first regression estimates aggregate all liquid assets, housing wealth, mortgage debt and income by local housing market and year. We refer to these as the ‘weighted estimates’ because the ratios of liquidity, housing prices and mortgage debt to income are all aggregated over households, which implicitly puts more weight on richer households (Table C1). We can also estimate these regressions at the household level, but look at the correlation with aggregate local area housing prices, which we refer to as the ‘unweighted estimates’ (Table C2).

Table C1: Aggregate Household Liquidity Buffers
Statistical Area Level 4
  Weighted estimates Unweighted estimates
Housing price-to-income ratio 0.17***
(0.02)		 0.16***
(0.03)
Mortgage debt-to-income ratio   −0.32**
(0.14)
Log of disposable income   −0.14
(0.16)
Constant 0.43***
(0.09)		 2.55 (2.49)
Local area fixed effects No Yes
Year fixed effects Yes Yes
R squared 0.16 0.58
Observations 435 435

Notes: *, ** and *** denote statistical significance at the 10, 5 and 1 per cent levels, respectively; t-statistics in parentheses; estimates for local area and year fixed effects are omitted

Sources: Authors' calculations; HILDA Survey Release 19.0
Table C2: Household-level Liquidity Buffers
  Indebted owners   All households
OLS Fixed effects OLS Fixed effects
No controls Controls No controls Controls
Housing price-to-income ratio 0.18***
(0.04)		 0.21***
(0.05)		 0.08***
(0.03)		   0.05***
(0.02)		 0.05***
(0.01)		 0.02*
(0.01)
Age   −0.21*
(0.12)		 0.29**
(0.13)		     −0.35***
(0.07)		 0.73***
(0.09)
Age squared   0.00***
(0.00)		 −0.00
(0.00)		     0.01***
(0.00)		 −0.00
(0.00)
Log of household disposable income   3.06***
(0.44)		 −1.51***
(0.45)		     2.73***
(0.40)		 −8.12***
(0.53)
Household size   −0.52***
(0.10)		 0.23
(0.16)		     −1.57***
(0.12)		 1.06***
(0.16)
Got pregnant in past year   0.38
(0.40)		 0.68
(0.57)		     0.48
(0.34)		 1.58***
(0.38)
Got sick in past year   −0.27
(0.54)		 0.71
(0.51)		     −1.04*
(0.58)		 0.00
(0.52)
Illness of family member in past year   −0.25
(0.35)		 0.14
(0.39)		     1.26***
(0.48)		 0.20
(0.43)
Retired in past year   1.83
(1.67)		 −0.17
(1.63)		     −0.12
(1.30)		 3.55***
(1.11)
Birth of new child in past year   0.31
(0.47)		 −1.01*
(0.52)		     1.31***
(0.39)		 −1.09**
(0.46)
Got married in past year   −1.97***
(0.32)		 −0.67
(0.51)		     −1.96***
(0.64)		 1.38**
(0.68)
Got separated in past year   0.47
(0.92)		 −0.06
(0.85)		     −1.34***
(0.52)		 −0.91*
(0.52)
Constant 4.02***
(0.31)		 −26.08***
(4.27)		 10.75**
(4.59)		   12.32***
(0.26)		 −17.30***
(3.79)		 66.46***
(4.41)
Household fixed effects No No Yes   No No Yes
Year fixed effects No Yes Yes   No Yes Yes
R squared 0.03 0.06 0.02   0.01 0.14 0.04
Observations 12,000 12,000 12,000   40,294 40,294 40,294
Households     5,508       14,265

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

Sources: Authors' calculations; HILDA Survey Release 19.0

The regression results for the weighted estimates indicate that liquidity buffers are positive associated with local area housing price-to-income ratios. We also find that buffers are negatively associated with mortgage debt-to-income ratios after controlling for housing prices. This is consistent with the process of debt amortisation increasing liquidity for households, even at an aggregate (local area) level.

The regression results for the unweighted estimates also indicate that the liquidity buffers of indebted home owners are positively associated with local area housing price-to-income ratios. Moreover, this effect is stronger than for other households, including outright home owners and renters. This is shown by a comparison of the estimates in the first row across indebted home owners (columns 1 to 3) and all households (columns 4 to 6). The correlation exists even when controlling for a wide range of household-level characteristics.

Taken together, the results are consistent with households looking to rebalance their wealth portfolios towards liquid assets in response to changes in housing prices, and that the process of debt amortisation helps to build liquidity buffers, even at an aggregate level.

Footnote

The HILDA Survey asks home owners to report the market value of their current home. The housing prices of renters are the average of the reported housing price of home owners in the area. [22]