Understanding the nuances of property data is crucial for making informed investment decisions. If the data you are analysing is flawed, then the results from your research using flawed data will also be problematic at best, completey inaccuare at worst.

Let's explore the process of where and how the Government data is created and distributed. The commercial, public or other entities that gain access to the data (property data aggregators) and the challenges with the raw data and what and why inaccuarcies occur, and why data normalisation is essential to create accurate metrics and reporting. Most data aggregators do very little to mitigate against errors and inaccuracies in the raw data and/or normalise data entries that arent truely reflective of the selling process. We explain these issues below and give you insights in how we at Plotspot mitigate these inconsistencies to provide more accurate insights, keepung in mind that there is no such thing as a "perfect data set".

The Challenge of Raw Data

Government property databases contain many millions of historical sales records, but records of sales transactions are not always consistent or accurately recorded in its raw form:

• Manual data entry errors
• Inconsistent address formatting
• Duplicate records
• Missing information
• Correct information, but outliers
• Classification inconsistencies

Our Normalisation Process

We've spent years developing algorithms to clean and standardise this data:

Address Standardization

Converting address naming standards such as "St", "Street", "Str", or "Cct", "Circuit", Crt" into consistent types and/or fuzzy logic, ensuring more accurate street-level analysis.

Duplicate Detection

Identifying and merging duplicate property records that could skew statistical analysis.

Property Type Classification

Consistently categorizing properties (house, unit, townhouse, etc.) across different data sources.

Cross-Reference Validation

Verifying data accuracy by cross-referencing multiple government sources:

• Sales data vs. valuation data
• Planning records vs. title records
• True Cadestral boundaries VS a Real Estate agents understaning of an address location

Why This Matters

Accurate data leads to better decisions. A single misclassified property or incorrect sale prices can throw off entire suburb statistics.

With PlotSpot, you can trust that the trends and insights you're seeing are based on thoroughly validated, normalized data - giving you a genuine competitive advantage in the NSW property market.