How to Identify Listings with Missing or Bad Data?

How to Identify Listings with Missing or Bad Data?

“Bad data” typically refers to incomplete or ambiguous listing information, such as missing city or county names, vague geographic labels (like “Other” or “Out of Area”), or completely null fields. These issues usually originate in the MLS and can affect the accuracy and usability of the statistics.


How to find which listings have missing or incorrect data

You can easily identify these listings using the Market Insights dashboard. Here’s a step-by-step guide:

  1. Open the Market Insights dashboard.

  2. The Time Series chart is selected by default—this is ideal for spotting bad data trends over time.

  3. From the Metric filter, choose New Listings.

  4. Open the filter panel on top using the 'Show Filters" toggle switch to narrow down to the segment you're interested in, like selecting “blank” or “null” cities.

  5. The chart will then display all new listings that fall into that segment.

  6. Double-click on any data point along the line in the Time Series to activate the drill-through feature.

  7. A dialogue box will appear, displaying the individual listings that meet your criteria.  These are the listings with bad data!


Can I fix the listing data myself?

Possibly!  Often times, the agent who entered the listing must correct it, though rules vary by MLS.  You can search for the listing in the MLS using the Listing ID that you identified above, and correct it if you have access to do so.  You can also notify the MLS administrator/team of the error for them to take action and make the correction.

Once the listing is updated in the MLS, our system will automatically pick up the corrected data in our nightly update cycle.


Why not just filter out the bad data by default?

Sometimes we do!  For example, we know that '99999' is not a valid zip code.  Nor is '88888', '77777', etc.  We null those out, which then displays a single null field in the filter dropdown list rather than all permutations of what we know is invalid data.  Ultimately, we believe in providing transparent and comprehensive data, even when it includes some imperfections. Automatically removing or hiding all listings with bad data could skew your analysis, or worse, hide underlying data quality issues that need to be addressed at the source.

Instead, we give you the tools to:

  • Identify and isolate problematic listings

  • Take corrective action through the MLS

  • Maintain trust in the accuracy and completeness of your analysis

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