THE DANGERS OF DATA DRIVEN DECISIONS

Danger of Data Driven Advocacy

Many buyers and even buyers advocates now lean heavily on data-driven models to inform their recommendations. While data has an important role to play, over-reliance on these models introduces several risks and limitations, especially in real estate, where context and nuance matter greatly. Here are the key dangers:

1. Blind Spots in the Data

• Data can’t see behind walls: Models may not pick up poor floorplans, quality of renovations, construction issues, or intangible factors like natural light, street noise, or neighbour dynamics.
• Renovation quality: A model may treat all “renovated” properties equally, missing the difference between a cosmetic patch-up and a high-end, well-executed upgrade.

2. Lack of Local Context

• Suburb vs. street-level detail: Data might favour a postcode or suburb, but not differentiate between the premium pockets and inferior locations.
• Nuances of demand: A particular school zone, café precinct, or distance from a train line can make a 10–20% difference in price and capital growth—something a model may overlook.

3. Historical Bias

• Most models are backward-looking, relying on past growth and historical trends, which can mislead in changing markets or areas experiencing gentrification, rezoning, or infrastructure development.
• They can overvalue areas that have performed well but are now overcooked, or undervalue emerging areas.

4. Overemphasis on Quantitative Metrics

• Growth %, yield, vacancy rates, etc., are important—but too much weight on these can push investors toward properties that "look good on paper" but are low quality or have limited long-term demand.
• High-yielding areas are often high-risk and low in capital growth; models may not highlight this adequately.

5. Failure to Consider the Client’s Individual Goals

• A model doesn’t ask why the client is buying: Is it for family lifestyle, future upsize, retirement planning, or risk mitigation? Data doesn't capture personal context or financial strategy.

6. Limited or Outdated Inputs

• Many models rely on public data that lags. In fast-moving markets, this can lead to outdated recommendations.
• Off-market activity, current buyer sentiment, and agent insight are often missing entirely.

7. Risk of Herd Mentality

• When many buyers and advocates use similar data tools (CoreLogic, Suburbtrends, etc.), you get crowded strategies. Everyone chases the same metrics, inflating prices and reducing opportunities.

8. False Sense of Precision

• A model might say Property A has a 5.4% projected growth vs. Property B’s 4.9%—but this falsely implies a level of accuracy that just doesn't exist in real-world property markets.

Conclusion:

Data is a powerful tool, but it should serve as a starting point and to assist with your due diligence, however it is not the whole story. The best outcomes come when data is combined with:

• On-the-ground knowledge,
• Human judgment,
• A tailored understanding of the client,
• And qualitative insights from local experience.