Property data is relied upon by many investors and property advisors to make informed decisions about where to invest for maximum benefits. However, there are important property data risks that need to be considered before relying on it alone. Having completed a PhD in the past, I understand the importance of the reliability, accuracy and up-to-date data sets.
In this review, I will summarise some of the key property investment data commonly relied upon and explain why buyers and investors should be cautious when using it as the sole basis for decision-making.
Median Property Price Movements: More Than Meets the Eye
Most property data reports focus on the median property price. The median is not the average; instead, it represents the middle value when all sales prices are ranked from lowest to highest.
While median values can be useful in suburbs where housing stock is relatively uniform, they can be misleading in areas with a wide variety of property types sold over a given period.
What Is Compositional Bias in Property Data?
Compositional bias in property data occurs when changes in the type of properties sold distort median prices, making values appear higher or lower than actual market movements.
For example, in Brisbane, some blue-chip suburbs contain a mix of fully renovated, elevated homes with city views alongside older, unrenovated properties located in flood-affected areas. In any given month, if more premium homes sell, this can artificially inflate the median price. Conversely, if more inferior properties transact, the median may appear to fall.
What appears in the property data is therefore often a reflection of what sold, not necessarily how the overall market performed.
Understanding Price Fluctuations in Median Property Data
When tracking median values in a specific location, price fluctuations are often the result of changes in the composition of dwellings sold, rather than true shifts in capital growth.
Property is not homogeneous. One home is not a direct substitute for another, and the makeup of residential property sales is continuously changing. As a result, large swings in median price data do not always signal volatile market performance. They can simply be statistical anomalies. This is where local knowledge of what is actually selling becomes critical to correctly interpreting the data.
Days on Market Data: The Real Story Behind the Numbers
Days on market data measures the number of days a property is listed for sale before it is sold. In most cases, a property is deemed to be on the market from the moment a listing becomes publicly available.
In theory, lower days on market suggest stronger buyer demand, while longer days indicate a slower market. However, this indicator is not always reliable.
How Reliable Is Days on Market Data?
Days on market data can be unreliable because it depends heavily on how agents record timeframes. These may include days to contract, days to unconditional, or days to settlement.
In fast-moving markets, we regularly observe properties selling within days of being listed. Yet the reported days on market figures often show much longer selling periods. This discrepancy raises questions around how consistently this data is recorded and whether it truly reflects market conditions.
What Factors Can Skew Days on Market Figures?
There are several factors that can distort days on market data:
- Properties sold off-market or prior to public listing.
- Listings that fall through and are re-marketed.
- Homes listed with one agent and later relisted with another.
- Differences in how agents define a sale
As buyers agents actively involved in transactions, we often see significant differences between what is happening on the ground and what the property data reports. This is a concern when investors rely solely on these figures to guide decisions.
Why Local Knowledge Matters
This is another example of why local property knowledge matters. Property data is only as reliable as the way it is collected, recorded, and interpreted. Without context, statistics can easily paint an incomplete or misleading picture of market conditions.
Vacancy Rates and Rental Yields as Property Data Indicators
Vacancy rates and rental yields are two commonly referenced property market indicators, particularly among investors assessing rental performance.
Vacancy Rate Data: A Measure of Rental Availability
Vacancy rate data measures how many rental properties in a location are currently vacant. For example, if there are 200 rental properties in a suburb and two are vacant, the vacancy rate is 1 percent.
However, this data can be inaccurate at a specific point in time. Properties may be listed for rent even though they are still occupied, with availability scheduled for a future date. This can distort vacancy rate figures and misrepresent actual rental demand.
Rental Yield Data: How Reliable Is It?
Gross rental yield is one of the most unreliable property data metrics frequently quoted. This is because both inputs used to calculate yield, being median rents and median property prices, are highly susceptible to statistical anomalies.
How Compositional Bias Affects Rental Yield Data
Rental yield data is particularly vulnerable to compositional bias when there are limited sales, few rental listings, or a low number of leases signed within a given period. Different property types within the same suburb can also generate vastly different rental yields.
For this reason, yields should always be calculated on a property-by-property basis, rather than relying on suburb-wide averages that may not reflect individual investment performance.
Conclusion: Balancing Property Data with Local Market Knowledge
There are many other property data indicators referenced when analysing markets. This article has not aimed to cover them all. Instead, it highlights the importance of understanding what property data actually represents and recognising its limitations.
ata plays an increasingly important role in property research and is invaluable for identifying macro-level trends. However, when decisions are made at a suburb or property-specific level, local knowledge becomes just as critical. This is where experienced buyers agents services can provide insight that property data alone cannot.
How Streamline Property Buyers Help Interpret Property Data Beyond the Numbers
At Streamline Property Buyers, our experienced and award-winning buyers agents help clients make informed decisions by looking beyond raw property data. While data provides useful market indicators, it often fails to capture the nuances of individual properties, including unique characteristics, location-specific risks, or factors that influence long-term value.
By combining data analysis with on-the-ground market knowledge, we assess real-world conditions, local demand, property history, and broader market fundamentals. This ensures investment decisions are based not just on statistics, but on a clear understanding of what drives performance at a property level.
If interpreting property data accurately and avoiding costly assumptions matters to you, working with a professional buyers agent can provide the clarity and confidence needed to make smarter property decisions.
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