6525.0 - Estimates of Imputed Rent, Australia, 2015-16  
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CHANGES TO THE METHODOLOGY OVER TIME

The ABS developed and implemented new experimental methodologies of gross imputed rent for owner-occupied dwellings and other tenure types for the Survey of Income and Housing (SIH) 2013–14.

Following peer review and coherence investigations with National Accounts imputed rent estimates and Census data, further refinements have been made to the gross imputed rent methodology for owner-occupied dwellings. Methodologies have now been finalised and implemented for the 2015–16 SIH and Household Expenditure Survey (HES).

Gross imputed rent methodology for other tenure types remains unchanged and is consistent with the experimental methodology implemented in SIH 2013–14.


REVIEW FINDINGS

Investigations suggested that an upward bias may be present in the experimental estimates for owner-occupied dwellings, particularly for dwellings with four or more bedrooms. For strata of a similar location and dwelling type (e.g. separate houses in Sydney), experimental rental yields were observed to increase with the number of bedrooms. This observation does not align with the general expectation of higher rental yields for lower value dwellings, and vice versa. Investigations also suggested that imputed rents were slightly underestimated for one and two bedroom dwellings.

For example, the rental yield for a two bedroom house in a given region was 0.00059113 under the experimental method. For houses with additional bedrooms in the same areas, yields increased to 0.00089476 for a five bedroom house. Continuing on with this example, a two bedroom house valued at $500,000 and a five bedroom house valued at $900,000 would receive $296 and $805 in gross imputed rent respectively. Note that the latter amount is likely to exceed market rents for a five bedroom in the same area. Additionally, depending on market conditions, rent for a two bedroom house could easily exceed the $296 calculated.

The inclusion of number of bedrooms as a stratification variable was the main cause for this trend. Within the experimental method strata, the composition of rental properties compared to owner-occupied dwellings appeared very different. There are far more owner-occupied properties that were separate dwellings with three or more bedrooms when compared to rental properties (particularly in higher SEIFA quintile areas). Therefore in calculating final base rental yields, the rent paid for rental dwellings (the numerator) tended to be for a smaller number of bedrooms in each strata, while dwelling value based on the Valuers General (VG) sales data (the denominator) related to all dwellings sold in the relevant SA1. As a result, the experimental estimates are likely to have overestimated the rental yields for owner-occupied dwellings, such as in the example above, particularly those with four or more bedrooms, and underestimate the rental yield for one or two bedroom units.

Removing the number of bedrooms as a stratification variable, reduces cases of very high imputed rents for dwellings with a higher number of bedrooms and increases imputed rents for dwellings with fewer bedrooms. Using the same houses from the example above, the rental yield for both dwellings is 0.0007598 under the revised method, which for our two and five bedroom houses would estimate gross imputed rents of $380 and $684 respectively. While the latter imputed rent value may be higher than current market rents for a similar property for the area (based on type and number of bedrooms), it is still plausible. It is often difficult to find a rental market equivalent which reflects the additional features of owner-occupied dwellings. Aspects such as solar panels, insulation and improved heating/cooling and other appliances more prevalent in owner-occupied dwellings can contribute to higher gross imputed rents for these dwellings compared to market alternatives.

This limitation may be addressed if the VG dataset provided dwelling characteristics information such as number of bedrooms or dwelling type, which are currently not available on the dataset. The review did not identify the absence of dwelling type from the VG dataset as a significant limitation. Investigations into Census data revealed that most strata were relatively homogenous for type of dwelling. Similarities between yields for semi-detached dwellings and flats, units and apartments were noted.


NEW FINAL METHODOLOGY OF IMPUTED RENT FOR OWNER-OCCUPIED DWELLINGS

Stratification changes

The final methodologies remove the number of bedrooms as a stratification variable, allowing type of dwelling and rental yield for each region to determine the gross imputed rent for the dwelling. Strata for semi-detached dwellings were also combined with flats, units and apartments due to very similar rental yields.

The overall impact of these changes served to reduce derived rental yield for dwellings in major urban areas with four or more bedrooms, and increase the yield for one and two bedroom units, apartments and semi-detached dwellings.

The distribution of gross imputed rent has remained mostly unchanged between the experimental and final methodologies. Graph 1 below outlines the change in the distribution for owner-occupied dwellings between the experimental methodology implemented in SIH 2013–14 and the finalised methodology, using data for 2013–14 as a comparison. Graph 1 shows that the new final methodology has removed the upward bias for larger dwellings (higher end of the distribution) and the downward bias for one bedroom units (lower end of the distribution).

Graph 1. Distribution of gross imputed rent for owner-occupied dwellings, new final methodology and experimental methodology, 2013‒14


Intercensal adjustment factors

As outlined in the Final Methodology section of this publication, an adjustment factor is used to improve the estimation of changes in gross imputed rent over time. The relative change in mean market rents and the mean value of owner-occupied dwellings between each SIH cycle are used for the adjustment factor. The experimental method adjusted estimates at a state level only. Further analysis revealed that the survey data can support an adjustment that includes a Capital City and Rest of state split for the six states. The final methodology implements this change to intercensal adjustment factors to better account for different patterns of change in house prices and rents across regions.

The revised adjustment factors were validated by comparing the gross imputed rent estimates for the SIH 2011–12 when they were calculated using 2011 Census data and those calculated using Census 2006 data extrapolated forward. As shown in Graph 2, the two results for the 2011–12 period remain consistent in the current method.

Graph 2 Distribution of SIH 2011–12 gross imputed rent for owner-occupied dwellings, using rental yields from Census 2006 (extrapolated) and Census 2011 data




REFERENCES

Household Energy Consumption Survey, Australia: Summary of Results, 2012 (cat. no. 4670.0)

Household Income and Wealth, Australia, 2015–16 (cat. no. 6523.0)

Census of Population and Housing, 2006 and 2011.