In today’s data-driven world, industries across the board are leveraging information as a critical asset to inform decision-making, optimize operations, and gain competitive advantages. In real estate, data holds unique significance, as every transaction hinges on accurate, comprehensive, and timely information about properties, markets, and trends. The Multiple Listing Service (MLS) serves as the primary data repository in this sector, offering essential insights into property listings, transactions, and market dynamics. The MLS is the heartbeat of the marketplace for homeownership.

The value of MLS data lies in the work we perform to curate its quality, which is determined by three key factors: accuracy, comprehensiveness, and timeliness. These elements transform MLS data into a powerful tool for the real estate professionals we serve, and the wider community that is economically engaged in the housing market. 

The Value of MLS Data

Today, MLS data serves as the backbone for many decision-making processes in real estate. Buyer agents rely on it to identify available properties, listing agents use it to set competitive prices, and everyone uses it to market homes effectively. Additionally, appraisers, investors, lenders, and developers increasingly turn to MLS data to inform their valuations, risk assessments, and market analyses. 

However, the intrinsic value of this data depends heavily on the quality of the information it provides. Without accuracy, comprehensiveness, and timeliness, even the most well-intentioned data can mislead users, leading to poor decisions and missed opportunities. 

The Role of Accuracy in MLS Data

Accuracy is the foundation of all high-quality data. In the context of MLS, accuracy refers to the correctness and precision of the information related to a property. This includes the essential details that we collect, especially the less obvious, but equally important, data points such as listing attributes. 

The challenge of maintaining accuracy in MLS data is compounded by the sheer volume of listings and the fact that data is often input manually. Human error, outdated information, and inconsistent reporting standards across regions can all contribute to inaccuracies. As such, real estate professionals and MLS operators must implement stringent data governance practices to ensure that information is regularly reviewed, validated, and updated. 

AI represents a major advancement in data accuracy, helping to solve many problems. But an often overlooked item of data accuracy is data composition. With few exceptions – Stellar, Wardex, and others – very few MLSs have adopted the RESO data dictionary as the data schema for organizing MLS listings. Why not?

Doesn’t it seem odd that MLSs would encourage listing input fields that are different from the fields exported from the MLS? As a best practice, the data in and the data out should be identical. 

If any MLS is doing a system conversion – you are going to map the data anyway – this is the perfect time to update to RESO Data Dictionary 2.0

The Importance of Comprehensiveness

While accuracy ensures that the information in MLS databases is correct, comprehensiveness is the effort to collect data on every transaction. Incomplete property data can be just as damaging as inaccurate data, as it deprives users of the full picture necessary to make informed decisions.

Beyond the basics of location, size, and price, comprehensive MLS data includes information on a property’s history that informs market conditions (average days on the market, price trends, or buyer demand). 

Comprehensive data also allows for better comparative market analysis (CMA), used by real estate agents and appraisers to determine a property’s market value based on comparable sales. Without detailed information on comparable properties, CMAs can be inaccurate, leading to under- or over-valuations.

The Department of Justice is pressuring the industry to remove the rule mandating MLS contribution within one day of marketing a property. Ideally, the Mandatory Submission rule should remain undisturbed. The marketing part of this is inconsequential – but off-MLS listings that are not contributed to the MLS would erode comprehensiveness and lead real estate into the barbaric environment of most of the world.

The Need for Timeliness

The real estate market is dynamic, with conditions and prices changing rapidly based on a variety of factors, from economic shifts to seasonal trends. For MLS data to be truly valuable, it must be timely—reflecting the most current information available.

Timeliness in MLS data refers to how quickly data is updated and made available to users after a change occurs. For instance, when a property goes under contract, is taken off the market, or has its price reduced, the MLS listing must reflect these changes immediately. Outdated listings can lead to frustration for both buyers and sellers, as buyers may pursue homes that are no longer available or miss out on properties with new price reductions.

Moreover, market analysts and investors, banks, insurance, and governments who rely on MLS data to gauge trends or predict future market movements require up-to-date information to make accurate forecasts. In a fast-paced market, even a delay of a few days in updating listings can lead to missed opportunities or misinformed decisions.

Leveraging MLS Data as a Strategic Asset

When MLS data is accurate, comprehensive, and timely, it becomes a powerful asset for all stakeholders in the real estate industry. A key element in transforming MLS data into an asset is effective property copyright management. This begins with the authorization by the broker and agent to assign the copyright by license to the MLS. The MLS must then copyright the compilation. 

For the real estate professionals who subscribe to the MLS, and the brokers who authorize those contributions, the ability to leverage high-quality MLS data is a key competitive advantage. 

Data analytics tools can further enhance the value of MLS data by enabling professionals to conduct predictive analysis, segment markets, and identify patterns that may not be immediately apparent from raw data alone. For example, AI-powered tools can analyze historical MLS data to predict future home values, allowing agents to advise their clients on optimal buying or selling times.

In the real estate industry, MLS data serves as a critical resource, but full value depends on the quality of the information it contains. Accuracy ensures that the data reflects reality, comprehensiveness ensures that all relevant information is captured, and timeliness ensures that the data remains relevant in a fast-moving market. By focusing on these three pillars of data quality, MLS data can be transformed from a simple repository of property listings into a strategic asset that drives better decision-making and enhances outcomes for all parties involved.