Market Distress Threshold: Technical Whitepaper

1. Abstract

This paper presents a univariate threshold that measures market distress in Australian suburbs. The threshold uses a single variable: the percentage of house sales in a suburb that settle below their previous purchase price. Suburbs with fewer than 15% of sales at a loss are classified as low-distress. Suburbs with more than 30% of sales at a loss are classified as high-distress.

Low-distress suburbs outperformed the national median by +0.58 percentage points per year over rolling 2-year windows. High-distress suburbs underperformed by -2.09 percentage points. The spread between top and bottom tiers is 2.68 percentage points per year. This result is based on 491,844 property sales from 2008 to 2023.

The signal was tested across 63 quarterly periods, 27 individual sample dates, and 11 GCCSA regions. Low-distress suburbs outperformed high-distress suburbs in 92% of quarters. The result was statistically significant at 22 of 27 sample dates. The spread was positive in 10 of 11 regions. The t-statistic is 113.96, with an effectively zero p-value. These results indicate a persistent, geographically broad relationship between distress levels and subsequent property growth.

2. Methodology

2.1 Variable Definition

This threshold uses a single variable: the percentage of house sales in a suburb that settle below their previous purchase price. We measure this from property transaction records across all Australian suburbs. Each sale is compared to the most recent prior sale of the same property. If the sale price is lower than the prior price, it counts as a sale at a loss.

We aggregate this across all house sales in each suburb to compute the distress rate. A suburb where 8% of sales are at a loss has a distress rate of 0.08. A suburb where 40% of sales are at a loss has a distress rate of 0.40.

2.2 Threshold Definition

We define three tiers based on the distress rate:

• Top Tier (Low Distress): Below 15% of sales at a loss
• Middle Tier: 15% to 30% of sales at a loss
• Bottom Tier (High Distress): Above 30% of sales at a loss

Note that this threshold is inverted compared to most indices. Low values are good (few distressed sales) and high values are bad (many distressed sales).

2.3 Growth Measurement

Growth is measured over rolling 2-year windows. For each property sale, we compute the annualised growth rate relative to the national median growth rate over the same period. This controls for broad market movements. A suburb that grows 5% per year when the national median grows 4% per year has a relative outperformance of +1%.

2.4 Performance Metric

The primary metric is the difference in median annualised 2-year growth between each tier and the national median. Statistical significance is assessed using a two-sided t-test against the null hypothesis that the tier's mean growth equals the national mean.

3. Tier Performance

The threshold sorts suburbs into three tiers based on their distress rate. Each tier has a distinct growth profile. The table below shows the full results.

4. Temporal Analysis

A signal that works at one point in time could be a fluke. We tested the Market Distress threshold across every quarter from 2008-Q1 to 2023-Q3. The chart below tracks the 2-year annualised growth rate for low-distress and high-distress suburbs over time.

4.1 Date-by-Date Consistency

We tested the top tier's outperformance at 27 individual sample dates between 2008 and 2023. At each date, the spread was computed independently. The result was statistically significant at 22 of 27 dates.

5. Regional Robustness

A signal that works only in one city is less useful than one that works nationally. We tested the Market Distress threshold across 11 GCCSA (Capital City Statistical Area) regions in Australia.

5.1 Full Regional Table

All growth rates are annualised over 2 years. The spread column shows the difference between low-distress and high-distress growth rates.

6. Suburb-Level Evidence

Suburb-level comparisons for selected cities are available on the summary page.

7. Defence of Method

7.1 Why a Single Variable Works

The distress rate is not just a number. It is a summary statistic that captures several underlying market dynamics. A suburb with many loss-making sales typically has oversupply, weak demand, poor lending conditions, or structural economic decline. A suburb with few loss-making sales has tight supply, strong demand, and owners who are not forced to sell.

Unlike composite indices, this threshold has complete transparency. Any investor can compute the distress rate for a suburb from public transaction records. There is no black box.

7.2 Statistical Significance

The overall t-statistic is 113.96. The probability of observing a 2.68% spread across 491,844 sales by random chance is effectively zero. For context, a p-value below 0.05 is the standard threshold for statistical significance. This result exceeds that standard by a wide margin.

7.3 Consistency Over Time

The signal was statistically significant at 22 of 27 sample dates spanning 15 years. The five non-significant dates all have clear explanations: three early dates (2008 to 2009) had small sample sizes, and two late dates (2022) had very few high-distress suburbs remaining after the property boom. A signal that works 81% of the time across a full market cycle is reliable.

7.4 Geographic Breadth

The spread is positive in 10 of 11 regions. It works in rising markets (Rest of Queensland, Brisbane) and falling markets (Perth, Rest of Western Australia). Even Melbourne, which saw broad declines, showed a positive 0.54% spread. The only exception is Sydney, where supply constraints support prices even in higher-distress suburbs.

7.5 Practical Use

Investors do not need a model to predict exact prices. They need a signal to tilt the odds in their favour across a portfolio of purchases. A 2.68 percentage point advantage per year, compounded over time, is meaningful. This threshold is one input among several in the Microburbs research programme.

8. Limitations

8.1 Distress Rate Is Backward-Looking

The distress rate measures what has already happened, not what will happen next. A suburb that had 35% of sales at a loss last year may be recovering. A suburb with 5% at a loss may be about to enter a downturn. The threshold does not predict turning points.

8.2 Small Sample in Some Periods

During strong market conditions (2020 to 2022), very few suburbs had distress rates above 30%. At 2022-03, only 71 bottom-tier sales were available. Results from small-sample periods carry wider confidence intervals.

8.3 Sydney Exception

The signal does not work in Sydney. Sydney's severe supply constraints, strong population growth, and foreign investment flows may insulate even distressed suburbs from underperformance. Investors in Sydney should not rely on this threshold alone.

8.4 Individual Suburb Variation

Even within the top tier, individual suburb outcomes vary widely. The threshold provides a statistical edge across large numbers of purchases, not a guarantee for any single suburb.

8.5 No Causal Claim

This paper documents a correlation between low distress rates and higher subsequent growth. We hypothesise that low distress signals tight supply, strong demand, and favourable lending conditions. But the data does not prove causation. Other unmeasured variables may explain part or all of the observed relationship.