Standard Property Market Metrics
This table summarises the main HtAG property market metrics presented on the dashboard and LGA pages. Reported for the current calendar quarter unless otherwise stated. Updated at the end of the month.
|Typical Price||Numerical||Live||Typical Price for a particular council area or suburb fit via HtAG Machine Learning model.||$50K-$4M|
|Median Rent||Numerical||Live||Median Rent for a particular council area or suburb produced via HtAG statistical model.||$50-$5K|
|Sales||Numerical||Live||Number of property sales recorded during the calendar quarter. Resets every quarter.||0-5,000|
|Rentals||Numerical||Planned||Number of properties listed for rent during the calendar quarter. Resets every quarter.||0-5,000|
|YoY Change||Percentage||Live||Year on Year Change is the percent difference of a value from any given year to the value in the previous year. Applies to all metrics listed above.||-50% to +50%|
|Cashflow Yield||Percentage||Live||Gross Rental Yield is the value you generate from an investment property represented as a percentage. Derived from Typical Price and Median Rent metrics. Calculated for the current quarter.||0%-15%|
|Capital Growth||Percentage||Live||Capital Growth is the percent difference between the projected future typical value of a property and its current purchase price. Estimated per annum based on 2 year forecast produced by HtAG ML model.||0%-50%|
|Total RoI||Percentage||Live||Return on Investment is a performance measure used to evaluate the efficiency of an investment. Calculated as a sum of per annum Rental Yield + Capital Growth metrics above.||0%-50%|
|Rent Increase||Percentage||Live||Projected per annum growth for Rent prices. Calculated using projected per annum increase of rent values.||0%-50%|
|Error Rate||Percentage||Live||Error represented as an average difference between actual values and the forecasted values. Uses the mean absolute percentage error (MAPE) formula to measure prediction accuracy of HtAG model.||0%-25%|
|Confidence||Categorical||Live||Metric that indicates reliability of the data. Derived from the error rate produced when fitting the data via a statistical model. The lower the confidence, the higher the forecast uncertainty. High Confidence – error rate under 5%, Medium Confidence – error rate under 10%, Low Confidence – error rate under 15%, Very Low Confidence – error rate indeterminate.||High, Medium, Low|
Professional Property Market Metrics
This table summarises current and planned HtAG professional metrics available via downloadable reports from the HtAG Store. Reported for LGAs and suburbs. Unless otherwise specified the property market metrics are calculated for the 3-month rolling period and updated every month.
The Low Supply / Balanced / High Supply ranges are dynamic and are subject to change. Defined for the current market conditions as of Q2 2021.
|Metric||Data Type||Status||Description||Low Supply||Balanced||High Supply|
|SoM||Numerical||Live||Stock on Market. Total number of active ‘for sale’ listings on market.||0-5||5-20||20-50|
|SoM%||Percentage||Live||Stock On Market Percentage is the ratio of SoM to the total number of dwellings in the area. Number of dwellings is calculated based on Australian address database.||0-0.5%||0.5-1.5%||1.5-5%|
|Inventory||Numerical||Live||Average number of properties sold over the past 4 quarters divided by SoM. Used to interpret how absorbent the property market is of the new listings. Measured in Quarters.||0-0.7||0.7-1.5||1.5-5|
|Hold Periods||Numerical||Planned ETA Jun-21||Average interval between dates when a particular owner first buys and then sells their property. “Tightly held” markets have higher Hold Periods. Measured in Years.||TBC||TBC||TBC|
The Low Demand / Balanced / High Demand ranges are dynamic and are subject to change. Defined for the current market conditions as of Q2 2021.
|Metric||Data Type||Status||Description||Low Demand||Balanced||High Demand|
|DoM||Numerical||Live||Days on Market. Average number of days ‘for sale’ listings remain active on the web. Measured in Days.||80-200||35-80||0-35|
|Discounting||Percentage||Live||Typical discount given by property vendors when selling a property.||3-8%||0-3%||-5-0%|
|Vacancy Rate||Percentage||Live||See this interactive story for detailed explanation. There are instances where Vacancy Rate cannot be calculated and is presented as -1.00%.||3-15%||1-3%||0-1%|
|DoRM||Numerical||Live||Days on Rental Market. Average number of days ‘for rent’ listings remain active on the web. Measured in Days. |
Only use this metric when Vacancy Rate is -1.00%.
|Vacancies||Numerical||Live||Number of rental listings that remain online for more than 21 days.||25-150||5-25||5|
|Buy SI||Numerical||Live||Buy Search Index. Ratio of online Buy searches to the state average. Value of 5 signifies that Buy searches are at state average. |
The higher the value, the higher the demand in the property market.
|Rent SI||Numerical||Live||Rent Search Index. Ratio of online Rent searches to the state average. Value of 5 signifies that Rent searches are at state average. |
The higher the value, the higher the demand in the rental market.
|Auction Clearance Rates||Percentage||Planned ETA Jun-21||Percentage of auctions resulting in a sale.||0-50%||50-70%||70-100%|
Other Property Market Metrics
The Unfavorable / Neutral / Opportune ranges are static.
|GRC||Categorical||Live||Growth Rate Cycle. Metric that indicates projected growth or decline in YoY price change. Values are listed in order of significance.||(-)Decreasing|
|R|O Ratio||Scale||Live||Renter to Owner Ratio is the proportion of Renter households to the Owner Occupier or Mortgagee Households. The higher the number, the more renters there are relative to owner occupiers. |
Ranges are defined for Renters i.e. first number before the pipe “|” symbol.
|U|H Ratio||Scale||Live||Unit to House Ratio is the proportion of total unit dwellings to that of houses in the area. Calculated by apportioning the number of units sold in the last 2 years to that of houses. The higher the number, the more units there are relative to houses. |
Ranges are defined for Units i.e. first number before the pipe “|” symbol.
|U|H Value Ratio||Percentage||Live||Unit to House Value Ratio is the proportion of Typical Price of units to that of houses. The higher the percentage, the closer unit prices are to house prices. |
Only use this metric if intending to purchase a unit.
This section details the time-series data, which is made available by HtAG for LGAs and suburbs. Data range is year 2008 to present quarter + 8 future quarters.
Time-series data is not yet available for purchase from the HtAG store. Please contact us for a custom quote.
|Attribute||Data Type||Period||Update Frequency||Status||Description||Example|
|Price||Numerical||Quarterly||Monthly||Live||Historical + forecasted price time-series fit via ML model||$530,000|
|Rent||Numerical||Quarterly||Monthly||Live||Historical + forecasted rent time-series fit via statistical model||$350|
|Sales||Numerical||Quarterly||Monthly||Live||Historical + forecasted number of sales time-series fit via statistical model||23|
|Rentals||Numerical||Quarterly||Monthly||Live||Historical + forecasted number of rentals time-series fit via statistical model||23|
|Yield||Percentage||Quarterly||Monthly||Live||Historical + forecasted time-series Gross Yield derived from Price and Rent metrics above||4.60%|
|Price YoY Change||Percentage||Yearly||Quarterly||Live||Yearly time-series data derived from Price Metric above||3.50%|
|Rent YoY Change||Percentage||Yearly||Quarterly||Live||Yearly time-series data derived from Rent Metric above||3.50%|
|Sales YoY Change||Percentage||Yearly||Quarterly||Planned||Yearly time-series data derived from Sales Metric above||3.50%|
|Yield YoY Change||Percentage||Yearly||Quarterly||Live||Yearly time-series data derived from Yield Metric above||3.50%|
The section below presents reference values that are used to filter data in HtAG reports and dashboards.
|Column / Table Filter||Data Type||Description||Values||Abbreviations|
|Property Type||String||Property type for which the data is reported for.||House, Unit, Residential Land||Type|
|Bedrooms||String||Number of bedrooms for which the data is reported for.||All, 1,2,3,4,5||Beds, Bdrms|
|Suburb||String||Name of suburb for which the data is reported for.||Approximately 15,000 suburbs||Suburbs|
|Council Area (LGA)||String||Name of LGA for which the data is reported for.||Approximately 450 LGAs||LGAs, Councils|
Frequently Asked Questions
1. Download a report from the HtAG Store.
2. Unzip the html file and open it.
3. Apply filters and/or toggle additional columns as required.
4. Export to CSV file using the export button in the middle.
5. Open the exported file using Excel or another spreadsheet software.
Export buttons can be found at the bottom of the downloaded html file and look like this:
1. Format the columns in the spreadsheet based on their data type i.e. currency, percentage or number.
2. Apply “Text to Columns” feature to scale metrics i.e. R|O and U|H Ratios using the pipe symbol (|) as a separator.
3. You will then need to apply number filters to metric columns based on the ranges defined on this page.
Note that percent column filters should be typed in as decimal points i.e. 3% will be “less than” or ” more than” 0.03.
The starting point should be to shortlist suburbs by filtering out Unfavourable, Low Demand and High Supply markets. The resulting list is then up to you to work through by applying additional filters based on property market metrics of your choice, whilst also gauging the potential Capital Growth and ROI based on provided data.
The more metrics fall into the Favourable, High Demand, Low Supply bands for a particular market, the more likely it is that Capital Growth will be towards the high range of the forecast. Be mindful that Neutral/Balanced markets frequently provide great outlooks and should not be discounted from research.
The ranges are determined by data distribution for every metric with 25th and 75th quantiles serving as cut offs between the 3 bands. Note that the data distribution is reflective of the current market conditions. Whereas generic metrics such as R|O Ratio have a constant distribution and ranges, Demand and Supply ranges are subject to change. Given that the ranges are established based on data for the past 3 months, they can migrate up or down as new data is collected between data releases.
When comparing the data from two or more data providers it is always important to ensure that the providers use the same data curation method. If the method is different, then the property market metric values will most likely also be different.
For example, differences in the HtAG Vacancy Rate and that reported by SQM can be explained by different reporting period. HtAG reports it for the rolling quarter (3 months), whereas SQM reports it for the fixed month period.
We are working on a change that will report vacancy rates for the relevant market segment i.e. houses or units. Currently Vacancy Rates are reported for these segments jointly by all data providers.
Most metrics are known to fluctuate at the suburb level quite notably between data releases. For example, a change in Vacancy Rate from 2.4% to 2.0% is not significant. This fluctuation is not because the rates are changing, but because of the sparsity in the underlying data, which results in data noise between updates. That’s why the rates should always be ball-parked against a wide enough range.
As HtAG projections are based on a forecast algorithm, which caries a degree of uncertainty, the CG (Capital Growth) values should be treated as an estimate of the general trend in the rate of growth and not as an exact percentage value. Note that changes of up to plus or minus 3% in CG and all other property market metrics are normal between data releases. Changes in low and very low confidence suburbs can be higher than 3%. This is due to the underlying data sparsity at the suburb level.
Although changes are common between data releases, we find that fluctuations in the reported data even out over time. This is another reason why the values should be treated as ballpark ranges and not exact figures.