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The 3 Best Metrics to Determine Where to Buy an Investment Property

“Where should I buy an investment property?”

This is the most popular question asked on the social media by property investors that are just starting out. To the investor’s surprise the first response is quite often in a form of another question:

  • What’s your budget?
  • Are you pursuing capital growth or higher yields?
  • How risk tolerant are you?
  • Do you plan to invest in property again and expand your portfolio?
  • In other words, what is your strategy?

Fortunately, these questions are easily answered, as the investor’s personal circumstances and financial outlook will help establish their property investment strategy.

Once the strategy is defined, the investor will likely get a more definitive answer to their original question. However, the answers are frequently opinionated, carry a market bias and vary depending on the locality of the respondent.

With over 15,000 suburbs and localities in Australia, no one can have the full scope of knowledge to provide a country-wide list of suburbs that match any given strategy.

And whilst the investor’s budget and indicative gross yield can produce a shortlist of markets, the list will quite often have a few hundred suburbs and localities that need to be assessed and compared.

How does the investor match these areas to their strategy and pick the best possible market? Will one market perform better than another? Is there a way to know?

Market Fundamentals & Supply/Demand Balance

As every real estate professional would tell you, the best markets are those that have a healthy supply/demand balance and strong fundamentals.

HtAG Analytics provides over 80 metrics that help establish which markets are better positioned in terms of fundamentals, supply and demand criteria.

However, having to assess this many metrics can result in information overload. Some metrics carry higher weighting than others and are more impactful on market performance. Whilst a combination of other less important metrics can in some scenarios surpass higher weighted indicators.

Having to assess over 80 real estate market indicators can result in information overload. Some metrics carry higher weighting than others & have a higher impact on market performance.

Furthermore, it’s not just the current metric values but also their trends that need to be considered. If we were to combine both metrics and their trends, we will be left with a list of over 80 data points against which each market needs to be assessed.

It is not humanly possible to take all the provided information into consideration and adequately rank markets.

It’s a task for a computer algorithm.

Meet Relative Composite Score™

RCS (Relative Composite Score)™ is designed to rank markets on 3 key parameters – capital growth, cashflow and risk. The scores are updated on a monthly basis, so investors can keep up-to-date with changes in the market.

The data for more than 80 real estate market metrics serves as an input into an algorithm that generates a composite score for all suburbs and localities in our dataset. Input metrics are weighted differently for each of the 3 scores: capital growth, cashflow and lower risk RCS™.

  • Capital growth weightings are assigned based on 5 year backtest that establishes causal impact core metrics have on property price changes. In addition, supply/demand metric weights are assigned based on industry norms and domain knowledge.
  • Lower risk weightings are assigned based on a combination of environmental, market and data quality factors described in detail below.
  • Cashflow weightings are assigned based on indicative rental yield, rental projections as well as metrics such as vacancy rates that help establish the rental market outlook.

Suburb Ranking Table with RCS™ Scores

Let’s explore each RCS™ metric in detail.

Capital Growth RCS™

Capital growth score assigns higher weighting to metrics that indicate better long-term growth outlook. Whilst long-term price trend analysis is at the core of the Capital Growth RCS™, there are several other important metrics that determine the end result.

The capital growth RCS™ is calculated using the following metrics, with different assigned weightings. We only list a subset of the most impactful metrics below:

  1. Long-term price trend projection
  2. GRC Price Index
  3. Unit to houses ratio
  4. Buy SI
  5. Vacancy Rates
  6. Renter to Owner ratio
  7. Days on market
  8. Stock on Market
  9. Average monthly sales volume
  10. IRSAD
  11. Inventory
  12. Building Approvals
  13. Clearance Rates
  14. Infrastructure Investment
  15. Plus over 60 other metrics…

Where historical data for a metric exists, both the current month value and historical trend for the metric are inputted into the algorithm. If you’re unsure what a metric means, feel free to learn more by visiting our Data Dictionary page.

The Capital Growth RCS™ focuses on pinpointing locations with substantial long-term capital growth prospects, extending over a period of 5 years or more.

This is why the metric with the highest weighting emphasizes long-term price trend projections.

While long-term price projections are indeed vital, it’s essential that they are supported by robust market fundamentals, including factors like IRSAD (Index of Relative Socio-economic Advantage and Disadvantage), Renter to Owner Ratio, Unit to Houses Ratio, GRC, and average sales volume. These elements collectively contribute to strengthening the accuracy and reliability of the analysis.

Lastly, supply and demand indicators, like Stock on Market, Days on Market, Vacancy Rates, Search Index, and more, also play a critical role in the evaluation process.

Proof that Capital Growth RCS™ Works

This section delves into the intricate process of backtesting, a crucial step used to optimise metrics for the Capital Growth RCS. When RCS was initially launched in late 2022, this method proved instrumental in determining the weightings for this ranking system. Thanks to the precision of this method, we can confidently rank markets based on a variety of parameters.

Backtesting operates on a five-year period, requiring the retrieval of data from five years prior and using it to compute the RCS score. Therein lies the challenge: given our current dataset includes over 80 metrics while the data inventory from five years past was relatively rudimentary, making backtesting quite the balancing act.

The workaround was to develop a lean version of the original – a prototype utilising a distilled dataset focused on 15 core metrics. This Alpha version of RCS served as a valuable preliminary input into the general approach to rank markets based on a variety of parameters and assignment of weightings.

The graph above visually captures the typical price growth on a monthly basis over the backtest period. The red line serves as a reference, denoting the average suburb price changes across the country.

This chart also presents three distinct RCS bands portrayed on the same graph:

  • Capital Growth (CG) RCS ranging from 0 to 33 is depicted as a blue line
  • The second band, encapsulates CG RCS from 34 to 66 via an orange line
  • The third band, reflecting CG RCS from 66 to 100, is represented by a green line

As the graph clarifies, the green line, symbolising the highest CG RCS range, consistently outperforms the nation’s average predominantly throughout the backtest timeframe.

Conversely, the blue line, marking the low CG RCS band of 33 and below, generally underperforms relative to the national average. The orange line, which represents the middle band of CG RCS scores, typically aligns with the general house price trend.

It’s important to note tat the dataset from 2018 omits several key indicators, including demand and supply stats as well as more specialised metrics like building approvals, school rank scores and affordability index. Consequently, the results aren’t a perfect reflection of what the 2023 RCS system produces with comprehensive data.

The table below shows top 10 suburbs with the highest Capital Growth RCS (2018 Alpha) and 5 year typical price change for houses over the backtest period (2018-2023).

SuburbCG RCS (2018)Price Change 2018-2023
Gagebrook, TAS 703098120.88%
Aintree, VIC 33369781.09%
Waverley, TAS 725096126.20%
Mayfield, TAS 724895117.65%
Coonamble, NSW 282994144.56%
Ravenswood, TAS 725093101.16%
South kempsey, NSW 244092131.18%
Montville, QLD 45609172.82%
Curra, QLD 45709096.30%
Elizabeth park, SA 51138987.43%

The backtest period, spanning back five years, possessed distinctive characteristics and growth idiosyncrasies that may not necessarily replicate in the next five years. As such, relying solely on backtested data is insufficient to generate optimal results.

Therefore, we complement these insights with the domain-specific knowledge of industry advisors and real estate professionals who use HtAG Analytics regularly to research property markets. Collaborating with our expert users via a blind validation panel has allowed us to further discern which metrics carry more weight, instructing us on how to tailor application of these metrics in the RCS algorithm.

The amalgamation of information from backtest results and user validation panel helped us craft a metric that works best in all conditions, irrespective of where the broader market is positioned in its growth cycle.

It’s imperative to remember that relying solely on the Capital Growth RCS might not suffice for investors pursuing risk-averse strategies. If you have a low-risk tolerance, it’s critical to integrate both Capital Growth and Lower Risk scores during your research. While Capital Growth RCS focuses on the expected growth rates in an ideal market scenario, it doesn’t take into account the potential risks associated with it.

Lower Risk RCS™

We highly recommend to cross-validate your shortlisted locations via the Lower Risk RCS when researching the markets for your investment strategy. This approach will ensure that you have a comprehensive understanding of the market’s potential, accounting for both growth/cashflow and risk factors.

Lower risk score assigns higher values to markets with less risk. The risk is mainly comprised of environmental metrics such as likelihood of floods/bushfires as well as market fundamentals such as IRSAD, Renter to Owner Ratio, economic diversity etc. Data quality is also taken into consideration, with lower volumes of transactional data as well as forecast error rates serving as key data risk metrics.

  1. Flood Risk
  2. Bushfire risk
  3. Coastal and/or river erosion risk
  4. IRSAD (Index of Relative Socio-economic Advantage and Disadvantage)
  5. Renter to owner ratio
  6. GRC (Growth Rate Cycle)
  7. Economic diversity
  8. Average age of properties
  9. Average monthly sales volume
  10. Average monthly rentals volume
  11. Error rate
  12. Plus over 60 other metrics…

Environmental risk can have a significant impact on the value of a property, and on the ability to sell it in the future. That’s why it is assigned the highest weightings when computing the Lower Risk score.

Lower Risk RCS assigns higher values to markets with less risk. The risk is mainly comprised of environmental metrics, market fundamentals and data quality indicators.

Higher risk areas are often subject to insurance premiums that are much higher than average. This is because insurers view properties in these areas as being at a higher risk of flooding, bushfires, market instability etc., and thus charge higher premiums to cover this risk.

Fundamental metrics, including IRSAD (Index of Relative Socio-economic Advantage and Disadvantage ), Renter to Owner Ratio, average age of properties, economic diversity play a crucial role in determining market risk. These metrics serve as vital indicators of the prevailing socio-economic conditions and overall market health.

Lastly, the average sales and rental volumes, as well as the error rate, help determine the data risk associated with a specific market. Lower data volumes can lead to reduced model accuracy, which is why they are also factored into the risk score calculation. The inclusion of the error rate serves to indicate the possible price volatility within the market.

Cashflow RCS™

Cashflow score prioritises metrics that offer better yields and point to healthy rental market outlook. Here is a subset of the most impactful cashflow metrics listed in the order of assigned weightings:

  1. Indicative Gross Yield
  2. Vacancy Rates
  3. Long-term rental price projection
  4. Rent SI
  5. GRC Rent Index
  6. Average monthly rentals volume
  7. Units to houses ratio
  8. Renter to owner ratio
  9. Plus over 70 other metrics…

Both metric current values and historical trends are inputted into the algorithm, where applicable.

Although Indicative Gross Yield is the main metric that drives the cashflow score calculation, it can be outweighed by other factors such as higher vacancy rates, lower long-term rent projections or below average search interest from potential tenants.

Cashflow Relative Composite Score prioritises metrics that offer better yields and point to healthy rental market outlook.

GRC (Growth Rate Cycle) Rent Index is another important factor that measures the number of periods rent prices increased vs periods with rent decreases throughout the market lifecycle. It too can have a high influence on the Cashflow score.

Lastly, market fundamentals are incorporated into the cashflow score calculation, albeit with lower weightings, to ensure the overall robustness and reliability of the cashflow score.

Overall RCS™

The Overall RCS™ provides a comprehensive assessment by averaging the previously discussed Capital Growth, Cashflow, and Lower Risk scores.

Though the combined score is a valuable tool for a quick identification of outstanding investment opportunities or potential red flags, we advise referring to each of the three individual scores when making sound investment decisions. This approach enables a more thorough examination of the various factors affecting the property market.

Overall RCS™ provides a comprehensive assessment by averaging the Capital Growth, Cashflow, and Lower Risk Relative Composite Scores.

By utilising the Overall RCS™ score, investors can efficiently discern remarkable prospects or areas requiring caution, as indicated by the consistently high or low scores across all three categories – Capital Growth, Cashflow, and Lower Risk. This strategy allows investors to make well-informed decisions based on a holistic understanding of the market conditions.


Relative Composite Score is derived from a combination of the backtest process and automation of valuable insights gathered from the advanced usage of our platform. It embodies not only the application of vast and varied data but also the insights acquired from user interaction, promoting optimal market research outcomes.

RCS™ allows us to quickly and easily understand how a property market is positioned in terms of potential investment returns. It’s a simplified way to look at data that can be difficult to parse otherwise. A single score is more accurate than relying on multiple metrics, because it accounts for many factors via a comparison algorithm designed to handle data complexity.

We recommend relying on Capital Growth, Cashflow and Lower Risk scores independently. Although the Overall score provides a birds eye view of market outlook, it can’t be used to adequately match a market to an investment strategy.

It’s important to remember a few points about RCS™ before beginning to apply it in market research:

  • It’s a relative score, so a score of 99 means that the market is better positioned for performance than other markets in the dataset. However, it’s not absolute. If the broader market weakens, even the highest ranked markets can follow.
  • The higher the Lower Risk score, the less risk there is in the market. However, there are always properties unimpacted by the broader area risk, even in the higher risk markets. For example, properties positioned on a hill have lower risk of flooding in suburbs classified as high flood risk.
  • RCS™ is independently calculated for houses and units. The score is designed to compare markets within the segment for which it is generated. For example, a score of 70 for houses, doesn’t mean that the area is a less attractive investment than a “units” market with a score of 90. The 2 scores are incomparable.
  • The Capital Growth RCS™ focuses on pinpointing locations with substantial long-term capital growth prospects, and has a horizon of 5 years or more i.e. it will take at least 5 years for capital gains to be realised.
  • As the market conditions change so will the score. With data releases occurring every month, the scores will change as new data is inputted into the ranking model.
  • HtAG Analytics continuously integrates data from internal and external data sources. As new data sources are introduced and integrated into the ranking algorithm, the scores will undergo minor adjustments due to additional data.

Relative Composite Score™ is a feature that was created via a collaborative approach and we are open to receiving further user feedback. Do you have an observation, a comment or a question? Feel free to post it in the comments section below.

10 thoughts on “The 3 Best Metrics to Determine Where to Buy an Investment Property”

  1. We’ve addressed several edge cases by introducing 2 more RCS parameters and redistributing weightings:

    1. Newer markets are now better detected and treated as higher risk in contrast to established markets
    2. Historical vacancy rates are assigned slightly higher weightings for capital growth & cashflow RCS
    3. Long-term trend projections are given higher weighting for capital growth RCS

      • Hi!

        I’ll message you on how to get started.

        In a nutshell, you’ll receive an email with a description of a suburb with statistics attached. We will ask you to rate the suburb on 3 parameters: capital growth, cashflow and risk. The name of the suburb and other identifying details will be withheld to eliminate any bias you may have towards that particular market.

        Looking forward to receiving your input!

  2. Recently, there was a request for me to illustrate the efficacy of the RCS model using case study data from the past 12 to 24 months. Aware that others may share similar interests, I’ve decided to provide my insights via a comment here for everyone’s benefit.

    It’s important to note that our approach prioritises long-term property investment strategies. Given this focus, we’ve trained our model on an extensive data time frame spanning 5 years and above. Hence, its effectiveness is best evaluated over a long-term horizon.

    If you wish to gauge the success of our model based solely on a 12 to 24-month growth period, it would be beneficial to clarify that our model is inherently structured around a more extended timeline.

    We plan to publish individual client case studies as soon as adequate data has been collated. Meanwhile, I encourage you to consider the information above as a broad case study, providing a thorough analysis of our RCS model’s effectiveness across the nation.

    See section titled “Proof that Capital Growth RCS™ Works”. This information includes a chart comparing the RCS model’s effectiveness to the national average. It highlights how markets with the highest scores have outperformed others during our backtest period.

    Furthermore, it’s crucial to understand that our Relative Composite Score extends beyond mere data-based predictions. It incorporates the best practices utilised consistently by experienced real estate professionals on and off our platform.

    During the development process of the model, we didn’t solely rely on data science methodologies. In fact, we combined these approaches with the valuable insights of a user validation panel, thus refining the algorithm’s output to correspond closely with industry best practices.

    This method ensures that the RCS is not just a predictive model; rather, it offers an automated research solution through its use of multiple parameters traditionally employed by industry professionals. This seamless blend of prediction and research automation positions RCS as a unique offering in the Australian real estate industry.

    To conclude, I would like to emphasise that the Capital Growth RCS metric should not be used in isolation. To effectively strategise your investments, our specially designed metric ‘Lower Risk RCS’ should be concurrently considered with the Capital Growth RCS.

    In the event that you identify a market with high Capital Growth RCS, but it ranks low on the Lower Risk RCS (indicating a higher risk), we strongly advise our users to conduct an additional layer of due diligence on that specific market selection.

    In essence, the Capital Growth RCS metric operates under the assumption that no unexpected complications will arise in the long-term projection timeline. On the other hand, the Lower Risk RCS metric highlights the possibility of disruptions occurring due to various factors such as environmental changes, market risks, and other qualitative elements.

    Hence, both these metrics play an integral role in guiding your investment strategy, complementing each other’s strengths to ensure solid, risk-adjusted returns.

  3. Do machine learning algorithms have the capability to mimic human thought processes? 🤔

    This question often surfaced during the development of our predictive model, the Relative Composite Score.

    Over a year, we engaged extensively with our community of real estate professionals to refine this model, ensuring it encapsulated all the nuanced intelligence and industry knowledge.

    Our approach wasn’t merely to inundate the model with data devoid of any real-world context.

    Instead, we married rich domain knowledge with the robust capabilities of machine learning.

    This fusion moves us closer to our objective: streamlining the complex task of property market research that many of our customers face.

    This process wasn’t overnight.

    We were refining, adding over 80 metrics, thanks to feedback and a year of tweaks and turns.

    It was incredibly rewarding to see professionals from various states relating to our model’s insights, especially when it aligned with markets they’ve deeply understood through years of experience.

    That’s when we knew the gears are fitting just right and the model was ready to progress beyond its beta phase.

    However, our journey did not end there.

    To date, we’ve incorporated over 80 metrics into our model, with at least 20 more on the horizon, ensuring the Relative Composite Score’s precision and relevancy continually improve.

    But to circle back to the start: does our model truly “think”?

    The answer is nuanced.

    In our scenario, the algorithm doesn’t need to mimic human thought processes.

    Instead, it learns from human expertise and then applies that accumulated knowledge to analyse vast amounts of data, something far beyond our human capacity.

    This becomes achievable only when two crucial elements converge:

    1️⃣ A vast dataset, in our case, anchored by approximately 1 billion data points that coalesce into 80 real estate metrics and,

    2️⃣ The strategic application of domain expertise atop this data foundation.

    Although our algorithm demonstrates applied intelligence, it does not “think”.

    It’s a tool — sophisticated and refined — that augments our understanding of complex property markets.

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