For example, let’s say that 14,372 active listings and 3,049 pending sales were reported for July for a city in North Carolina. Then, when August numbers are released, those same July figures come up as 14,571 active listings and 3,018 pending sales. What happened? In some cases sales fell through, people were late with their reporting, or home statuses changed. It’s inevitable.
The real estate market is constantly changing, as that real example emphasizes. If we were to freeze data and report static numbers from month to month, we would be reporting on how the market was, and not on how it is. Things evolve over time; our market intelligence reflects the current state of the market.
Historical revisions are common practice in the data world. As new information becomes available, revisions are frequently issued for GDP, unemployment rates, job growth, inflation, earnings and so on.
In real estate, discrete counts like closed sales, inventory and new listings are more apt to change from month to month, while derived metrics such as days on market, median sales price and months’ supply of inventory tend to be more stable. Important characteristics of markets (e.g., contract fall-through rates) are often discovered because of this methodology.
A live data method prioritizes today’s accuracy. While a frozen approach can ensure consistency, it wouldn’t account for the human element of data. Our objective at ShowingTime is always transparency in data methodology. If you want to discuss data methodology and reporting with us further, please email firstname.lastname@example.org for more information.
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