Do you have a question about the HtAG Analytics website or our dataset? We do our best to list all common questions & answers raised by our customers on this page. We encourage you to read through the FAQ. It’s very likely that your question has already been answered.
Section that answers general questions about our company and services. Click on the question titles below to expand the tiles and see the answers.
HtAG (Higher than Average Growth) is a Real Estate Analytics portal that assists its customers in making accurate and timely property-related decisions. HtAG leverages the benefits of machine learning algorithms to rank the ROI potential of different Local Government Areas and suburbs, Australia wide. Underpinned by advantages of Data Science and Big Data, HtAG processes large volumes of property statistics to classify the long-term capital value of a particular geographical location.
You can purchase a subscription for as low as 19.69 AUD a month. Our service provides more value for money which is reflected in the competitive pricing of HtAG downloadable reports and subscriptions. See this link for more info.
We have near real-time view of the market thanks to our Web Crawler solution (see DATA section below for details). Data lag in reports produced by other companies is at least 1 month.
We provide detailed property market data. For example, we do not only report general typical price values but further segregate the data based on dwelling type and number of bedrooms. This assists our clients when determining the appropriate ROI of an individual listing.
We provide forecasted median rent values which assists our customers with making more informed decisions as to the cash flow potential of a particular suburb or locality. Other companies provide only historical rent data.
We provide forecasted typical price values which assists our customers in better ascertaining the capital growth potential of a particular location. With our ‘ranking’ option, customers are also well placed to compare the investment potential of different areas based on their year-on-year value change as well as on their forecasted growth. Other companies provide only historical price data.
We provide growth rate cycle analysis data which combined with the ‘forecasted typical price’ option gives you a more accurate understanding of the market position in the growth cycle. This can assist our clients with the timing of their purchase to take advantage of the cyclical movements of their relevant market to maximise their returns.
This question was answered in detail on our forum. Click here for an expanded answer.
Section that addresses common questions related to HtAG Analytics data. Click on the question titles below to expand the tiles and see the answers.
We collect Australian property market data via a Web Crawler which systematically browses real estate portals and agent sites indexing past sales, current ‘for sale’ and ‘for rent’ listings. The collected data is combined with additional offline datasets, cleansed and analysed for key market trends to assist real estate professionals, investors and home buyers with market research. A portion of our dataset is in the public domain. You are welcome to explore it via this link.
HtAG crawler collects property sales, listing and rental data continuously and as a result, we are able to report the key real estate market metrics in near real-time. Our reports are updated on a monthly basis.
HtAG reports are produced for the house and unit segments. Some LGAs only have reports for houses due to no data in the unit segment and vice versa. There are ~410 area reports for houses and ~260 for units. ~150 areas are duplicated across the unit and house segments. Prices for residential land sales are coming soon.
Our overall typical price for any given suburb or council area can be calculated in 2 different ways depending on the composition of the underlying data:
— If there is significant ‘number of sales’ recorded per ‘number of bedrooms’ (i.e. 2,3,4 and 5 bedroom houses) within the area, our model first calculates typical values for the relevant bedroom time-series via a data fit procedure. The overall value is then produced by taking a mean (average) of all of the bedroom time-series.
— If there is no significant number of sales to produce the ‘number of bedrooms’ time-series, the model calculates the overall price by fitting all recorded sales to produce a typical property value for the suburb.
The resulting value is NOT the traditional Median Price reported by other providers. It is, however, a much more astute representation of typical property value within any given suburb or council area.
Our customers find that the reported price values are of most relevance when they can be attributed to the ‘number of bedrooms’ of properties within the geographical boundaries of their research scope. Therefore, we put emphasis on producing typical values per ‘number of bedrooms’ first.
Moreover, the overall value of a suburb is represented more equitably by averaging the ‘number of bedroom’ values, because this approach guarantees that each bedroom series is given an equal weighting. This eliminates statistical bias in markets dominated by a particular property type i.e. a 3 bedroom house.
Other data providers commonly report prices as median values unbalanced through bedroom series averaging and without applying the data fit procedure, which HtAG uses to calculate Typical Price .
Where there is not enough historical sales data to produce typical values, the last known typical value is used to impute the data in the preceding quarters. For example if the sales activity within an area accumulates a significant number of sales in Q1 2018 with no prior sales data recorded, the typical values between Q1 2008 – Q4 2017 are imputed using the Q1 2018 data.
Imputed historical values are common in new/developing markets with new land releases or recent surges in developer activity. Some rural locations with low sales volumes can also exhibit this pattern.
Have a question specific to a property market metric we report on? Visit the Data Dictionary page to learn more.
Section that answers common questions related to HtAG Analytics forecasts. Click on the question titles below to expand the tiles and see the answers.
Our forecast model works off a machine learning algorithm that predicts typical prices with an accuracy of 95%, 2 years into the future. This level of accuracy is observed in areas with high volume of sales data.
Our accuracy is represented as an error rate. This question is best answered with an example. A suburb’s current typical price is $500K with a 2 year forecast at $520K. 2 years into the future the actual typical price is $530K. Use this formula to calculate the error: (actual-forecast)/actual *100 or (530K-520K)/530K * 100 equal to 1.9% rounded error.
To be transparent about the accuracy of the data in our reports, we have devised the confidence metric. High confidence strongly correlates with the number of sales recorded in any given locality. To simplify, the more there are sales – the higher is the accuracy. However, there are factors other than sales that influence this metric. Past model performance is one of them.
High and medium confidence indicate that the forecast error will be below 5% and 10% respectively. Low confidence data has an error rate below 15%.
As a rule of thumb at least 20 sales average per quarter at LGA (Council) level or 5 sales average per quarter at suburb level are needed for the model to produce high and medium confidence forecasts. We simplify all of the above by allocating the confidence metric (High, Medium, Low) to council areas and suburbs in our reports.
At HtAG, we divorce the analysis of causality between fundamental market variables (population, unemployment, other socioeconomic metrics) and property price changes. By recording real-time movements in the property market, the plethora of causes that impact on fluctuations in property prices have already been accounted for within the collected Big Data that serves as an input into HtAG’s algorithms. At HtAG, any market changes become an instantaneous input into the HtAG prediction model ensuring the continuous agility of our predictions and alignment with existing market conditions.
Our philosophy is to apply methodology that produces the best results. We conducted numerous tests and found that external variables (used as regressors) have no material impact on the accuracy of our model. Here are some of the variables that were tested: Population, Unemployment, Building Approvals, Stock on Market, Migration etc.
Instead of relying on external variables, our model learns off a combination of historical and recent — days and weeks — sales data to produce historical values and forecasts via a data fit procedure. Recent data is given higher weighting than the historical data for forecasting. In addition, certain data from parent LGAs is inputted as a regressor into the suburb model, which introduces the market cycle component into the data fit and forecast procedures. This approach was tested rigorously and was proven to produce the best results. Click here for an expanded answer.
There is no “crystal ball”, but because of the cyclical nature of property markets, it is possible to model future trends with a degree of accuracy. We are able to model and forecast property market trends accurately thanks to a large dataset, which is continuously updated by our crawler solution. This question is answered in detail with examples on our forum. Click here to view it.
Projections can have a variance of several percent between data releases and should be interpreted as a range value. For example a high confidence Capital Growth value of 15% should be interpreted as a 13-17% range. The lower the confidence, the wider the range.
Projected metrics represent a general trend established via a 2 year forecast produced by HtAG models. Slight short-term variations occur as new data is recorded and inputted into the model, resulting in value fluidity between data releases.
Our growth rate cycle (GRC) graphs plot the YoY rate of change and not the actual typical price. The decreasing values on the graph that are above the 0 X-Axis (thick red line) indicate that the rate of growth will remain positive but significantly lower than the growth in the preceding years. In contrast to that, an increasing trend that is below the 0 line, indicates that the prices are declining but at a slower rate.
First scenario highlights that the market has reached its’ peak and the growth rate is beginning to slow down. Second scenario is representative of a declining market that has the potential to reach its’ bottom in the near future.
Section that answers questions related to the website search feature. Click on the question titles below to expand the tiles and see the answers.
Jump to our advanced search page and give it another try. Note that our reports are produced at LGA (Council Area) level with suburb level data embedded within, so the search result returned for “Bondi” will be titled “Waverley Council, NSW”.
We did our best to recognise typos and phonetic searches. In a rare case when you can’t see your suburb in search results, try searching for a shortened version of the name i.e. “Druitt” instead of “Mount Druitt”.
There is property market data for ~6,200 suburbs in Australia. If you can’t find your suburb, it means that there was no statistically significant number of sales recorded for that locality. At a minimum we need at least 2-3 sales per quarter to be able to calculate the typical price for a suburb. If a suburb does not have the minimum required number of sales, it is absent from our ranking tables. Nonetheless, your search will take you to the Council Area page, allowing you to lookup similar data for neighbouring localities. We are working on a feature that will allow you to examine data for suburbs with low sales activity in the future.
This section answers miscellaneous questions by our subscribers and prospect customers. Click on the questions below to expand the tiles and see the answers.
You can cancel your subscription any time from the Account page. Cancelled subscriptions remain active for the duration of the current billing cycle. For example if you signed up on the monthly plan on the 1st of January and cancelled your subscription on the 15th of January, it will remain active until the 1st of February.
Local Government Area is an administrative division of Australia that a local government is responsible for. LGAs commonly have the same boundaries as council areas. On average there are 10-15 suburbs within an LGA. However some can have as little as 1 or as many as 70 suburbs.
Rental yield is the value you generate from an investment property represented as a percentage. High yield equates to a
greater cash flow. Our customers use the rental yield metric to evaluate the expected income from investing in a particular location and to compare locations in terms of cash flow.
Our reports show the gross rental yield, which is calculated by dividing the relevant typical price by yearly rental income, multiplied by 100. The higher the percentage, the better. As a rule of thumb, most investors seek rental yields of 3% and above. High rental yields (7% and above) indicate that the property market is under-valued, whereas low rental yields highlight potential for an over-valued market.
Not at this stage. However, if you are assessing an actual property, feel free to examine the metrics reported for the relevant suburb and number of bedrooms.
We are working on a feature that will enable you to generate PDFs of all pages on our site. Contact us if you need a council area or suburb report generated in the meantime. Cost is 475AUD, discount applies if you have an existing Professional subscription with us. You are welcome to co-brand the report with your logo.
Yes, it is. Contact us for details.
Yes. Contact us for details.
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