In my first article in this series, I covered why building proprietary real estate datasets gives you a competitive edge over rivals using only generic third-party data. But simply having data is just the first step. The true unlock is architecting that data into an AI-powered model tailored for your specific investment strategies.

How do you take ownership and construct that model from the ground up? What processes and best practices make the difference between fragmented data chaos and a high-functioning AI engine? Read on for the critical preparations to make your data AI and machine-learning ready

Segregating and structuring quantitative vs. qualitative data

At the foundational level, creating an AI data model requires segregating the types of data and creating distinct tracking processes for each.

Quantitative data

Quantitative data includes financial metrics, operational stats, and inputs tied to a property’s income and profitability:

These numerical inputs are straightforward. Feed them into analytical models for valuation, forecasting, and more.

Qualitative data

You also want to track subjective, descriptive details that don’t neatly fit into strict numbers. This qualitative data includes factors like:

  • Condition of on-site amenities and property features.
  • Profiles of surrounding businesses, communities, and competition.
  • Neighborhood characteristics and localized factors.

Systematic tracking

From here, set up distinct yet connected tracking processes for each data type within your centralized data model.

For the qualitative side, leverage techniques like:

  • Checklists for property features (condition of HVAC, need for parking lot repairs, etc.).
  • Open text fields for descriptive notes on surrounding businesses and communities.
  • Tagging systems to categorize factors like amenities, transit accessibility, or zoning details.

Separating number data from descriptive details and connecting them lets you and AI models find meaningful patterns across your full real estate dataset.

Best practices for centralizing and managing the data model

After creating the foundational processes for your data, the next step is creating a centralized place to store and manage that data.

Best practices differ based on the size and makeup of your team, but some general guidelines apply to data model management:

For individuals or small teams

For individual investors or very small teams, spreadsheets may work for managing your data model initially.

But as your portfolio and data needs grow, using spreadsheets quickly becomes unmanageable because:

  • Data lives in disparate silos, making it difficult to get a unified view
  • Spreadsheets aren’t built for collaborative, multi-user environments
  • Lack of access controls and versioning leads to data integrity issues
  • Relating and combining data across multiple spreadsheets is cumbersome
  • Manual data entry and transformation is error-prone and inefficient
  • Spreadsheets lack the scalability to handle increasingly large data volumes

Centralized cloud databases

A better approach is to implement a centralized, cloud-based database platform that is accessible to everyone on your team. This allows:

  • Real-time updating and syncing as new data is added or changed
  • Avoiding versioning issues with a “single source of truth”
  • More effective management of related data entities

Evaluating options

When evaluating platforms, look for:

  • Solutions designed for cross-functional team collaboration
  • Easy-to-use interfaces requiring little technical expertise
  • Future scalability that allows for Integrating with other software and data sources

Validating data integrity and control processes

With your data flowing into a centralized repository, the next key factor is validating that information’s accuracy and putting control processes in place.

Data accuracy

Making sure your data is accurate and complete should be the top priority from day one. Decide upfront exactly what property details and performance metrics you want to track, and commit to collecting that information consistently across all properties.

As new data comes in, have a process in place to validate it manually or with automated checks. Look for any data points that just don’t seem right based on your experience and knowledge of typical real estate norms.

Tagging system

Instead of broad condition categories, use a flexible tagging approach to capture specific details you observe.

For example, when evaluating landscaping, you may notice overgrown bushes, cracked sidewalks, and dried-out grass. Rather than just marking “Fair” condition, apply multiple tags like “Overgrown Shrubbery,” “Hardscaping Issues,” “Brown Lawn.”

The bundle of tags creates a structured data profile for that property’s actual attributes and conditioning. You are free from the constraints of preset categories.

Apply as many relevant tags as needed to track the on-the-ground qualitative details in an organized way. The tags provide flexibility with structure.

Review workflows

Set up a process where someone double-checks the details and tags entered into your property records. After someone catalogs qualitative observations with tags, have a second person review that information. They can add notes, confirm the tagging is accurate, and flag any potential mistakes or inconsistencies they spot.

It’s also important to maintain a “master” record of the finalized, reviewed property data. Don’t let multiple versions of records circulate, as that leads to conflicting information. Update the centralized master record after it has gone through the review process and verification.

Data governance

As you collect more data, implement additional practices:

  • Document where each data point originated from.
  • Test new sources of data before adding them to your records.
  • Set quality standards for acceptable data.
  • Assign people to oversee and manage data quality.

Wrapping up

With the practices outlined in this guide, you’ve established a solid framework for structuring your proprietary real estate data in an AI-ready manner. When you centralize datasets and implement validation processes and governance controls, you lay the groundwork for data-driven decision-making.

The next article will cover real-world examples of leveraging machine learning capabilities on top of this data foundation to drive better investment decisions and returns.