RDP 2005-03: Property Owners in Australia: A Snapshot 3. Estimating Property Ownership for Australia

In this section we present the results of an econometric model for property ownership and gearing decisions, taking the factors identified in Section 2 into account. We estimate the model using a new household-level data set for Australia, the Household, Income and Labour Dynamics in Australia (HILDA) Survey. In 2002, this survey included a wealth module that provides data from 7,245 households on home ownership, other residential property ownership, the values invested and property debt associated with these assets.

Before presenting the results of our estimation in Section 3.2 we will discuss our modelling framework.

3.1 Modelling Framework

Property ownership decisions of households involve two dimensions. First, the household has to decide whether to buy an asset or not, which is a binary decision. Second, if the household decides to invest in an asset, it must decide how much to invest in it. Similar decisions are involved for the gearing of a property asset.

For home owners, the ownership decisions amounts to the choice of tenure. How much is invested in their home is partly related to the minimum amount of housing services that the household demands, and partly to the availability of finance and desired investment portfolio decisions. We therefore model home ownership as:

Equation (1) describes the probability of being a home owner as a function of the age, income and wealth of the household and a number of other demographic variables, such as the number of children, whether a household is based on a married couple, or the employment status of the household reference person. In order to account for possible non-linearities in the relationship, we also include squared terms of age, income and wealth. Equation (2) describes the value of the home as a function of similar variables, for those that own a home (obviously, all others have a value of zero).

The gearing of the home – the ratio of home debt to home value – is also modelled in two stages:

where Equation (3) describes the probability of a home owner owing debt on their home, and Equation (4) describes the gearing ratio for home owners with debt.^[5]

The model for home gearing is estimated for households that own their home, but do not own other residential property. The reason is that owners of investment property may make decisions about total property gearing rather than gearing of the home alone. This is discussed in more detail below.

Similarly, for investment property, households decide whether to invest in the asset at all, and, if so, how much to invest:

Equations (5) and (6) are functions of the variables discussed in the home ownership model and a variable that indicates whether a household is already a home owner.

For the gearing decisions of investment property owners we consider total property gearing of the household. The reason for this is that home owners may use a mortgage secured against their own home to finance the purchase of an investment property.^[6] In Australia, the interest costs associated with having a debt-financed rental investment property is tax-deductible, irrespective of whether the debt has been secured against the investment property or their home.

where Equation (7) describes the probability of an investor owing debt on his or her properties, and Equation (8) models the total property gearing ratio for investment property owners with property debt.

In the literature, the Heckman selection model is widely used to estimate models of the type outlined here. However, despite its popularity, some deficiencies have been identified in its application. As noted by Leung and Yu (1996) and Puhani (2000) (and discussed in more detail in Appendix B), the Heckman selection model is susceptible to collinearity problems between the explanatory variables in the value equation and the inverse Mills ratio. A test based on the condition number suggests that our models face such collinearity problems, leading to unstable estimates from the Heckman selection model. In this case, the two-part model is preferred.

Consequently, we model the two-stage decisions in the four models using a two-part model (originally proposed by Cragg 1971). The first stage involves modelling the binary choice of whether a household owns a particular type of property or holds debt using a probit model. The value of the property (or gearing ratio on the property) is then modelled over those households owning property (or holding debt on that property) using sub-sample OLS estimation. Our preferred models were obtained by using a general-to-specific modelling approach, whereby insignificant variables are excluded sequentially. However, we decided to keep insignificant variables if they were of specific theoretical interest. For a more extensive discussion of the issues surrounding the choice of the econometric framework, see Appendix B.

3.2 Estimation Results

3.2.1 Home ownership and home values

Our first model analyses the tenure choice of households and the value of owner-occupied houses. The results from this two-part model are shown in Table 1; more detailed results are available in Appendix C.

Columns 2 to 4 show the results of the probit equation. The coefficients in the second column allow us to gauge whether a variable has a significant effect on predicting whether a household is a home owner. Since the coefficients in a probit model do not have an intuitive economic interpretation, we also report the marginal effect on the implied probabilities of a change of the variable, evaluated at sample means, in the fourth column. The last column represents the coefficients of the regression for the value of the own home. As this is an OLS regression, the coefficients allow a straightforward interpretation as the marginal effect of the independent variable on the value of the home.

As Table 1 illustrates, the life-cycle stage of the household, as measured by the age, influences its propensity to own its primary residence. The first panel in Figure 4 shows the change in the implied probabilities across the different age groups. The propensity to own the home increases for working-age cohorts up until retirement age, but then flattens out, possibly with a slight decline.^[7] Moreover, the last column of Table 1 shows that age does not significantly influence the value of the home once we account for other factors such as a lower income in retirement. This finding contrasts with the life-cycle model, which suggests that older households draw down their assets towards the end of their life cycle. This difference may in part be explained by bequest motives or a general reluctance of older households to sell their own home. It is supported by other studies, such as Kennickel, Starr-McCluer and Surette (2000) and Venti and Wise (2000), who find that older households do not draw down on their housing equity to pay for non-housing consumption, as would be expected by the life-cycle theory. They also find that these households are unlikely to move, except when there is a change in the household structure such as the death of a spouse.

As would be expected, affordability characteristics, such as income and wealth, are important for home ownership. The second and third panels in Figure 4 show that the propensity to own a home increases with household income and net wealth, though the marginal influence of higher income and wealth on ownership is smaller at higher levels of these variables. This is consistent with the downpayment and debt servicing constraints being more binding at the lowest income levels. Furthermore, those who have previously owned another home tend to have higher-valued current homes, possibly reflecting that they have actively ‘traded-up’ to better properties in a more valued location. The importance of affordability for home ownership is consistent with the findings of Gyourko and Linneman (1996) who argue that tenure choice is influenced more by affordability characteristics than the demographic characteristics of households.

Our results suggest that household structure is another important factor that influences home ownership and the value of the primary residence. Households with more adult members are more likely to own their own home, while more adults and children in a household are associated with a higher value for the primary residence. This finding is likely to reflect the greater demand for housing services by larger households.^[8] Married and widowed households are also more likely to own their own home than other households, reflecting the greater stability of current (and past) financial arrangements in the household. This is in accordance with Bourassa (1995b), who finds that widowed households tend to have higher propensities for home ownership than other ‘not married’ households.

Employment status, employment history and educational attainment are other important determinants of home ownership. The propensity for households to own their home rises with the length of employment of the household reference person, while households where the reference person has been unemployed or is a casual worker are less likely to own their home. This could reflect the fact that households looking to own their own home have to credibly demonstrate that they can meet the debt servicing constraints placed on them. Those households where the reference person has completed post-secondary education are also more likely to own their primary residence and have higher-valued property – a reflection of their past (or future) capacity to generate income and build wealth.

3.2.2 Owner-occupier gearing decisions

Buying a home is a large investment, which usually requires debt financing. Due to its size, mortgage debt stays with households for many years. In this section we discuss what determines the gearing choices of owner-occupiers, which partly reflects the financing required at the time of purchase and partly the speed with which home owners decide to pay off that debt.

Table 2 summarises the model estimated for gearing choice – whether to gear and which level, as measured by the current loan-to-valuation ratio – of households which own their own home but have no other residential property assets.

As with home ownership, the decision to hold debt against the own home is strongly influenced by the age, income and wealth of the household. Figure 5 shows, as would be expected, that the sign of the relationship with gearing is different to that with home ownership for age and wealth. Like home ownership, households with higher income are more likely to hold debt, possibly since they are in a better position to service the debt (and therefore to obtain the mortgage in the first instance). In contrast, the likelihood of holding debt falls with wealth, a reflection of past accumulation of savings (and thus of past possibilities to pay off debt). Similarly, gearing ratios among households with debt tends to rise with income and fall with higher wealth, consistent with the findings by Curcuru (2003) for the United States.

Consistent with the life-cycle hypothesis, we find that the likelihood of owner-occupier households holding debt and the loan-to-valuation ratio of those with debt falls steeply as households get older. Similar results have been shown in recent work for Australia by Ellis et al (2003) as well as Curcuru (2003) for the United States.

Household structure is also relevant to the gearing choices of households. Households with more children are more likely to hold debt on their home. This could simply reflect the demand for larger houses associated with larger families and, therefore, the increased need for purchasing a home with debt. However, households with more adults are less likely to gear their homes, in turn reflecting a greater (current or past) ability to finance the increased demand for housing.

Labour force status is also a significant determinant of gearing choices, with households less likely to hold debt on their home if the reference person is not in the labour force, retired or a casual worker. This could perhaps reflect a more uncertain income stream, and potentially greater difficulty in meeting regular repayments (making it harder to qualify for a mortgage).

3.2.3 Investment property ownership and gearing

In our sample 17 per cent of households own other residential property. Table 3 presents the preferred specifications of the models for the ownership and value held in other residential property.

Both investment property and home ownership choices reflect broader motives to invest in property, and therefore it is not surprising that our results for the investment property model are broadly similar to those of the home ownership model. However, there are also some differences, reflecting the fact that the investment motive plays a larger role relative to consumption motives for this type of property.

The life-cycle hypothesis is relatively pronounced when looking at the impact of age on investment property ownership. While the coefficients on the age variables are the same sign as those for home ownership, Figure 6 shows a truly hump-shaped profile across age groups, with a peak at 64 years. This suggests that households, as they enter retirement, sell their holdings in investment property. It is not surprising that this effect shows up in the propensity to hold this asset, rather than the value, as property is a lumpy, illiquid asset.

As would be anticipated for assets driven by investment motives, affordability characteristics, such as income, appear to be relatively more important for the ownership of investment property than for owner-occupied property. Like the case of owner-occupied property, the propensity to own other residential property and the value held increases monotonically with income and wealth.^[9] However, the marginal impact of income on the probability to own property is higher for investment property, consistent with investment property being more concentrated within those groups that have higher income.

Business owners are also more likely to own other residential property, and those with higher business wealth tend to have more invested in it. This might reflect more sophisticated asset management or higher awareness of tax incentives in that group. Also notable is that home owners that are not first-home buyers are more likely to own other property. This is in line with the expectation that households will generally purchase their own home before looking to invest in other property. Not surprisingly, the value of the household's own home (which is not in our measure of wealth) and their investment property assets appear to be closely related, as shown in Table 3.

One would expect that the decision to invest in other residential property is closely related to the decision to gear the investment. Gearing provides a financing option that may seem particularly attractive during a time of rising property prices, as has been witnessed in Australia over the past decade. Moreover, it may be an attractive option for middle- or high-income earners since in Australia the mortgage-related costs of owning investment properties can be tax-deducted. Due to the fungibility of mortgage debt to finance property purchases of either type, we have modelled total property gearing for investor households rather than just the gearing of the investment property.

The results of the gearing model in Table 4 suggests some differences between the factors influencing the gearing decisions of households which only own their own home and those who own investment property. As previously, the propensity to hold property debt increases with income and decreases with wealth. However, comparing Figure 7 with Figure 5 suggests that the probability of property gearing is higher at all levels of income and flattens out at higher incomes. This might suggest that the different ability to service debt repayments is less closely tied to income for investors. It may also suggest that tax incentives of interest deductibility play a uniform role across a large spectrum of middle- and high-income earners.

A different way to look at this result is to ask whether investment property owners are more likely to hold property debt.^[10] We addressed this question by re-estimating the total property gearing model over all property owners, whether they own a home or an investment property or both, and adding a variable indicating whether they are investment property owners.^[11] The results suggest that investment property owners are not only more likely to hold property debt, but they are also more likely to hold more of it relative to their property asset value. This finding is consistent with an earlier result by Ellis et al (2003) who looked at home gearing only and used rental income as an indicator of whether a household owned other residential property.

Unlike the propensity to hold property debt, the gearing ratio for investor households with property debt does not appear to be significantly influenced by income. However, among the set of property investors, the sub-sample which also have debt have less variation in income. This will make it unlikely that income can explain differences in gearing ratios within that sub-sample.

The role of age, on the other hand, is very similar in both the model of gearing for investor households and for home owners. In both models, the propensity to hold debt on property and the gearing ratio declines with the age of the household reference person and are also lower for retired households.

Table 4 shows that there is some limited role for household structure and labour force status in explaining household gearing choices. However, age, income and wealth clearly dominate in the model determining household gearing decisions for investor households.

Footnotes

The decision sequence underlying our gearing model follows Ellis et al (2003). They argue that the home ownership decision should precede the mortgage decision rather than being a joint decision, since only home owners can choose to have a mortgage on the home. [5]

In our data set, 47 per cent of investor households owed debt on their investment property, while 21 per cent owed no debt on their investment property but did have debt secured against their home. [6]

The exact turning point of 70 years and the steepness of the decline could be partly a result of our choice of functional form. Since most of our observations lie in the age groups below 70 years, there is a higher uncertainty around the exact coefficient for these older age groups. [7]

Despite the possible relationship between the household formation and tenure choice decisions discussed by Asberg (1999) and Haurin et al (1994), independent estimation of tenure choice models is possible. As noted by Yates (2000), if the household formation decision is made prior to the tenure choice decision, then estimating the tenure choice relationship using a single equation will yield unbiased and consistent estimates. [8]

The squared terms for these variables mainly account for non-linearities in these monotonic relationships with the turning points lying outside the sample range. [9]

Strictly speaking, Figures 7 and 5 cannot be directly compared because the underlying samples have different characteristics. [10]

Detailed results are available on request from the authors. [11]