Pub. 6 2016-2017 Issue 1

16 O V E R A C E N T U R Y : B U I L D I N G B E T T E R B A N K S - H E L P I N G C O L O R A D A N S R E A L I Z E D R E A M S FEATURE ARTICLE If you’ve ever had a time when housing numbers didn’t seem to make sense, or if you have colleagues who are not used to digesting this type of information, our hope is that this document helps clear up some confusion. EDWARD CARL SENIOR BUSINESS CONSULTANT ASCENSUS Six Biggest Housing Data Mistakes Have you ever listened to a newscast where it became painfully obvious that the reporter had absolutely no real knowledge of the subject matter? Unfortunately, this is particularly common with news involving data. In our fast-paced world where only the most recent data is reported in a rushed headline, we, as data consumers, can often draw skewed, if not completely wrong, conclusions from the announcement. The purpose of this consolidated white paper is to briefly delve into six of the most common er - rors when it comes to data reporting or data consuming. If you’ve ever had a time when housing numbers didn’t seem to make sense, or if you have colleagues who are not used to digesting this type of information, our hope is that this document helps clear up some confusion. While far from an exhaustive list, we see these particular issues come up very frequently. Summary and Takeaways We know that you are busy so we have taken the liberty of scaling down this full white paper into the following shared takeaways. 1. Takeaway — Seasonally Adjusted Data: can compare month to month, represents an annual number, but is a statistical creation. Non-seasonally adjusted data: data is more raw, but can’t really compare month to month, needs to compare to the same month a year (or more) ago. 2. Takeaway —Margins of Error and Confidence Intervals: just because a change is reported, it doesn’t mean it really happened. Check to see if the change is statistically significant before relying on that data for business decisions.