Rental Growth Top 100
Rental Growth Top 100
Investor Score | Houses & Units
In 2024, the Australian rental market is poised for significant upheaval, characterised by a substantial escalation in rental costs that is set to exert intense pressure on household budgets nationwide. Our comprehensive analysis anticipates an average increase of 11% in house rents across Australian suburbs. Even more striking is the forecasted surge in unit rents, which are expected to climb by an astonishing 27%. This unprecedented rise underscores a challenging landscape for renters, particularly in the unit market where affordability has traditionally been a key attraction.
Delving into the specifics of market dynamics, our report reveals a nuanced picture. While rent reductions are anticipated in a select few suburban areas, a mere 7% of suburbs are likely to witness a decrease in house rents. This contrast highlights a relatively stable yet strained market for houses compared to units. The unit market, in particular, stands out for its acute rent hikes, driven by a complex interplay of demand and affordability.
A pivotal aspect of our analysis is the examination of rental affordability, a metric that gauges the share of household income dedicated to rent payments. Our projections indicate that, on average, Australian renters will allocate 32% of their household income to rent by December 2024, based on median household income figures adjusted to future values. Units, however, emerge as a comparatively more affordable option, consuming an average of 26% of household income nationwide. This relative affordability is identified as a key driver behind the pronounced rent increases projected for the unit market.
Our report meticulously covers 100 suburban areas for both units and houses, selected based on their favourable rental affordability metrics as of January 2024. Suburbs where this metric exceeded 30% currently were deliberately excluded from our top 100 list, underscoring our stance that affordability remains a crucial consideration in the rental housing market, affecting not only renters but investors as well.
The selection criteria for our final top 100 list were grounded in three main metrics: 1) the forecasted rental increase over the next 12 months; 2) current rental affordability, defined as less than 30% of household income; and 3) our proprietary investor score ranking. This multifaceted approach ensures a balanced perspective, recognising the interplay between future rental growth potential, present affordability, and overall investment attractiveness.
In summary, the 2024 outlook for the Australian rental market signals a period of significant adjustment, with steep rent increases set to challenge both renters and investors. Despite the looming affordability crunch, certain suburbs—particularly those in the unit market—present notable opportunities for investment, buoyed by the dual prospects of robust rent growth and maintained affordability. Our analysis, by dissecting these trends and identifying top-performing suburbs, offers critical insights for navigating this dynamic landscape, highlighting areas with the greatest potential for rental yield and investment return amidst the broader context of market volatility.
In our study, we've grouped rental markets by broader suburban areas, aligning with the Australian Bureau of Statistics' Statistical Area 2 (SA2) standard. This approach enhances our modelling by providing a more stable and meaningful analysis base. However, to improve our forecast's precision and address the volatility often seen at the SA2 or suburb level, we shifted our focus to the Statistical Area 3 level (SA3). This strategic decision allowed us to capture broader market trends without the noise of short-term fluctuations.
Our Approach with the Gradient Boost Model:
Data Preparation: Utilising suburbtrends rental market data, our model harnessed detailed information across various SA3 regions. This data spanned different property types (houses and units) and included historical rental prices and market dynamics, offering a comprehensive view of the real estate landscape. This also extended to a simple 20% indexation adjustment applied to household income data from census 2021 data to estimate December 2024 income levels, helping us with affordability measures.
Model Training: We employed a Gradient Boosting model, a sophisticated machine learning algorithm renowned for its accuracy in handling complex datasets. The model operates by creating a series of decision trees, each one refining the predictions made by the previous, thereby improving accuracy incrementally.
Prediction Process: The model was tasked with projecting rental prices 12 months into the future for over 300 SA3 regions and more than 2,200 suburb areas (SA2s) . This involved analysing patterns and trends from historical data to predict future market movements.
Performance and Interpretation: Our model achieved an R-squared value of approximately 0.943. Put simply, this means it could explain about 94.3% of future rental price changes based on the factors it analysed. This high level of accuracy indicates that our model is adept at identifying key market drivers.
Sample Size and Coverage: The model drew on a diverse and extensive range of data covering numerous SA2 and SA3 regions, ensuring a robust and representative forecast. To enter the final report, only SA2 markets with more than 100 private rentals have been selected (one for houses and another for units).
Additional Processing for Accuracy: To refine our results, we averaged the last 12-month rental changes at the SA2 level with the 12-month forecasts at the SA3 level. To counter extremely high forecasts in some SA3 unit markets, we applied a maximum cap of 50% on increases.
Statistical models, including our Gradient Boost model, utilise historical data as a foundation to project future trends, such as rental price movements in the real estate market. These models are sophisticated tools designed to parse through vast datasets, identifying patterns and correlations that might not be immediately apparent. Their primary strength lies in their ability to digest and learn from previous market behaviours, thereby offering predictions about future trends.
However, it's crucial to understand that these forecasts are inherently subject to volatility and potential inaccuracies. This susceptibility stems from several factors. First, the real estate market is influenced by a myriad of variables — economic indicators, interest rates, employment rates, and even unforeseen global events — all of which can shift rapidly and without warning. Since our model bases its predictions on past data, any sudden change in these variables can impact its accuracy.
Second, while statistical models are adept at identifying trends, they operate under the assumption that future patterns will mirror historical ones. This assumption does not always hold, as market dynamics are constantly evolving. New legislation, changes in consumer behaviour, or technological advancements can all introduce new trends that the model, relying on historical data, might not anticipate.
Moreover, every statistical model, no matter how advanced, has limitations. These can arise from the quality of the data fed into them, the model's specific design, or the complexity of the market being analysed. Small inaccuracies in data or oversimplifications in the model's structure can lead to errors in prediction.
Given these considerations, while statistical models like the Gradient Boost are invaluable for offering insights and aiding in decision-making, they should not be solely relied upon. They provide a forecast based on available data, serving as a guide rather than an absolute prediction. This is why investors are strongly advised to consult with a quality valuer before making any decisions. A professional valuer can assess the unique aspects of a property and its surroundings, offering a comprehensive evaluation that complements the model's forecasts. This dual approach ensures that investment decisions are not only data-driven but also grounded in the nuanced realities of the market, thereby mitigating risk and enhancing the potential for positive outcomes.
The Investor Score, a pivotal tool developed by Suburbtrends, provides a nuanced understanding of property investment prospects by aggregating various critical factors. This comprehensive score is derived through a weighted analysis of eight key components, each offering unique insights into the property market:
- Ownership Score: Reflects the proportion of owner-occupiers, indicating market stability and growth potential.
- Rental Tenure Score: Assesses the balance of rental properties, crucial for understanding market demand and landlord risk.
- Vacancy Score: Measures the rate of unoccupied properties, a vital indicator of rental market health.
- Socio-Economic Score: Analyses the area's socio-economic profile, influencing property values and investment appeal.
- House Yield Score: Focuses on rental income return, essential for evaluating investment profitability.
- House Rent Affordability Score: Considers rental affordability in the context of average incomes, impacting tenant demand.
- House Inventory Score: Examines the supply of properties for sale, affecting market dynamics.
- House Stock on Market Score: Looks at the volume of available properties, providing insights into market trends and opportunities.
Each component is meticulously weighted to compile the Investor Score, offering investors a robust, data-driven tool for assessing the attractiveness and potential profitability of real estate investments.