Standardizing Commercial Property Data for Geospatial Applications
In the dynamic landscape of commercial real estate (CRE), centralizing and standardizing property databases is a common challenge faced by many companies worldwide. Fundamentally, this challenge occurs because CRE is a heterogenous asset class. At RealAssetData, we understand the complexities involved in aligning disparate datasets and recognize the crucial role of common standards in establishing a robust foundation for property databases and market analytics. In our product journey to address these challenges, we assembled datasets akin to LEGO pieces. By offering our curated datasets as building blocks, we seek to empower companies to seamlessly construct their proprietary property datasets (i.e. their LEGOs) to meet the unique needs of their respective property sectors.
Critical Components of Standardization Property Data
At the core of our solution lies a meticulous approach focusing on three key components:
- Property Types and Subtypes: We understand that clear categorization is essential for effective data management. By establishing common standards for property types and subtypes, we provide clarity and consistency, enabling seamless integration and analysis across diverse datasets.
- Submarket Assignment: Recognizing the significance of geographical context, we ensure that each property is assigned to its respective submarket. This geographical segmentation enhances the granularity of our datasets, facilitating targeted market analysis. Technology developers like us rely on this for recommendation engines, comp analysis and market forecasts.
- Unique Identifier: To streamline data management and facilitate seamless cross-referencing, we assign a persistent unique identifier to each property. This identifier serves as a reliable reference point within the data model, allowing users to track property attributes and changes over time with precision and ease. We'll write a separate blog post on the importance of the setting up a flexible property centric data model. Parcel or APN or Tax Assessor centric unique identifiers and data models are sub-optimal for CRE data management.
Our Methodology: Sector-Specific Standardization
We understand that different sectors within the real estate industry have unique nuances and requirements. To address this, we adopt a sector-specific approach to standardization, tailoring our solutions to meet the specific needs of each industry segment. This nuance comes into while assigning Property Subtypes where we rely on common standards established within a given property sector.
Real-World Applications: Navigating Complex Property Portfolios
Imagine a scenario where a property is identified as a healthcare facility, specifically a hospital, with a government-backed loan, and currently owned by a Real Estate Investment Trust (REIT). Through our standardized datasets and unique identifiers, users can effortlessly navigate and analyze complex property portfolios, gaining valuable insights for investment decisions and portfolio optimization.
Conclusion
At RealAssetData, we aspire to empower data driven decisions and we firmly believe that standardization is not just a solution to a problem but a catalyst for innovation and growth. By embracing common standards for property databases, we empower companies to unlock the full potential of their data, driving informed decision-making and strategic success in the ever-evolving real estate landscape.
For more information on how RealAssetData can help streamline your property data management and analytics, visit our website or contact our team today. Let us help you build a stronger foundation for your real estate endeavors.
