Is Your Property Data AI-Ready?
by Gerry Stanley, Product Management Director of Precisely and Michael Ogilvie, Director of Data Army.
AI Starts with Data: Why PropTech’s Biggest Opportunity Depends on Getting the Foundations Right
Artificial intelligence is rapidly reshaping the property industry. From automated valuations and planning assessments to predictive analytics and customer service, AI promises to help organisations work smarter, make faster decisions and uncover new opportunities.
But despite the excitement, many AI initiatives struggle to deliver meaningful results.
The reason is rarely the AI itself.
It’s the data.
AI is only as good as the intelligence layer behind it
Most property organisations do not have a lack-of-data problem.
They have a connected-trust problem and an expansion problem.
The data they hold is fragmented, inconsistent and incomplete across systems.
And beyond their existing portfolio, there are entire markets, regions and asset classes they simply don’t have visibility into.
“Working with data is difficult. It’s rare that everything lines up perfectly across systems,” says Michael Ogilvie, Director of Data Army.
“For many organisations, data has traditionally been viewed as a cost rather than a strategic asset and therefore lacked strategic investment. That’s becoming a significant risk because AI depends on trusted, high-quality data to produce reliable outcomes.”
Gerry Stanley, Product Management Director of Precisely agrees, “AI reasons from the data it’s given. If that data is incomplete or inconsistent, the outputs will reflect those flaws – often with more confidence than they deserve.”
As AI adoption accelerates, organisations with poor data foundations risk producing inaccurate insights, automating flawed processes and ultimately losing trust in AI altogether.
In property, the consequences can be significant.
A valuation model linked to the wrong address, a lending assessment based on outdated parcel boundaries, or an AI agent making decisions from incomplete location data can all lead to costly mistakes while appearing entirely credible.
Property data is more complex than you think
What does a ‘property’ actually mean?
One of the most overlooked challenges in PropTech is that there isn’t a universal definition of a property.
Unlike many industries, property data isn’t governed by a single standard.
Every state, council and government agency maintains information differently, while organisations often have their own internal definitions of what constitutes a property.
“For one organisation, a property may be a parcel of land. For another, it’s a building, a tenancy or a customer address,” says Michael.
“Understanding what a property means to your business is the first step towards building an AI-ready data foundation.”
Once that definition is established, the next challenge is ensuring complete coverage across the entire market, not just the properties already in their own systems.
Understanding the total addressable market requires comprehensive national property, parcel and addressing datasets that provide a consistent foundation for analysis, customer acquisition and AI.
Rather than attempting to standardise hundreds of disparate datasets internally, many organisations benefit from leveraging trusted national datasets that have already undergone extensive validation and standardisation.
Giving AI the context it needs
A property doesn’t exist in isolation.
Its value and characteristics are influenced by a wide range of surrounding factors including:
- Planning controls
- Environmental risks
- Demographics
- Infrastructure
- Market conditions
- Location intelligence
- Regulatory information
Bringing these datasets together creates richer context. Michael describes this as context management.
“AI adds the most value when the context is complex. Data enrichment provides AI with the additional context it needs to produce reliable outputs.”
Gerry refers to this connected ecosystem as an Intelligence Layer.
The more high-quality context you can provide, the better decisions AI can make, and the more meaningful insights AI can deliver.
This is where data enrichment becomes critical.
Rather than simply recognising an address, AI understands the complete environment surrounding that location.
Building the right data foundation
Building the Intelligence Layer starts with trusted reference data.
Authoritative property, parcel and address datasets provide the common foundation that allows organisations to connect operational data, customer information and third-party datasets with confidence.
Location intelligence also plays a critical role.
Accurate geocoding leads the data pipeline – cleansing an address, pinning a verified location and assigning persistent identifiers that create stable, durable relationships between datasets.
“When data has already been connected through trusted identifiers, AI spends less time trying to work out relationships and more time delivering valuable insights,” Gerry explains.
This also improves performance.
Rather than requiring AI to perform complex spatial calculations every time a question is asked, much of the heavy lifting has already been done through pre-connected, enriched datasets.
The result is faster responses, lower processing costs and more reliable outcomes.
Choosing the right property data
According to Gerry, organisations should evaluate third-party data against what he calls “the five C’s”:
- Coverage – Does it represent the market or geography you need?
- Currency – How frequently is the data updated?
- Consistency – Does quality remain consistent across different locations and scenarios?
- Correctness – Is the information accurate enough for your intended use?
- Credibility – Is the supplier trusted and dependable?
Michael adds that the business use case should always come first.
“We work with clients to understand what they’re trying to achieve before recommending data. Once the business need is clear, we can identify gaps, source trusted datasets and prepare them for implementation.”
From data integration to AI implementation
Integration shouldn’t be the hard part
Historically, onboarding third-party data has been complex and time-consuming.
Today, organisations have far more options.
APIs, cloud data marketplaces and modern integration platforms make accessing trusted datasets significantly easier than traditional file-based approaches.
Many authoritative datasets are now available through cloud data marketplaces and native applications, significantly reducing the effort required to discover, test and provision new data.
At the same time, organisations still need expertise to integrate, standardise and operationalise multiple datasets.
This is where partnerships become valuable.
Turning better data into better outcomes
The benefits of high-quality, enriched data are already being demonstrated across the Australian property industry.
Data Army has worked with Archistar to integrate and enrich complex planning and property datasets, helping power AI-driven planning and development solutions.
Rather than spending days manually assessing planning controls, risk factors and development opportunities, users can access comprehensive insights through a single platform, enabling faster and more informed decisions.
Globally, Keller Williams, one of the world’s largest real estate franchises, has used Precisely’s property and location intelligence alongside its own data to provide agents and customers with richer, hyper-local property insights.
By strengthening its underlying data foundation, the organisation has enhanced both customer experiences and decision-making.
The foundation for AI success
For organisations beginning their AI journey, both Michael and Gerry recommend focusing on the fundamentals.
- Define the business problem AI is intended to solve.
- Foundations: Ensure your data platform is fit for purpose and capable of processing the vast amounts of real estate data available.
- Prepare data: Then assess your existing data landscape. Ensure data is complete, accurate, timely, standardised and integrated.
- Model data: Ensure the AI has a clear ontology to work from. Poorly modelled data can results in wastage and inaccuracies in AI.
- Provide context: AI & ML needs context. More is often better. Be aware of the environment in which real estate operates in and is affected by.
- Test & Evaluate: Data & AI is not set and forget. Results must be constantly evaluated and the AI tuned as data changes.
Is your data ready for AI?
Every organisation is at a different stage of its AI journey, but the same question applies to all of them:
Can your current data support the AI outcomes you’re expecting?
For many organisations, the answer isn’t simply about having more data.
It’s about understanding where the gaps are, which datasets will create the greatest value, and how to build a trusted foundation that AI can rely on.
Data Army works with organisations across property and real estate to assess data quality, identify opportunities for enrichment, and develop practical roadmaps for AI readiness.
AI Readiness Assessment
To help organisations take the next step, Data Army and Precisely* are offering an AI Readiness Assessment.
The assessment includes:
- Review of your current data landscape
- Assessment of data quality, completeness and AI readiness
- Identification of opportunities for data enrichment using Precisely’s property and location intelligence datasets*
- Recommendations to improve data governance and integration
- A practical roadmap to accelerate AI adoption
*Preferential pricing is available on applicable Precisely data products where appropriate.