Blogsberger's Box

I've been interested in statistics for a long time, and there's plenty of opportunities to use (or misuse) it in appraisal work.  So, when I saw a topic come up on an appraisers' social networking site, I was happy to add my "2 cents worth".  I presume he was working with Excel's statistics features, as I frequently use it to do graphs from MLS data myself.

=== The author wrote ===

"What does anyone know about these numbers (R Square, P-values and t-
Stat) in regards to linear regression? When doing a linear regression
are there acceptable values attached to these that would lend greater
significance / credibility in any given analysis? In addition, in
doing a "linear" regression is it incorrect or misleading to use a
trend graph that is polynomial?"

====  I replied ===

'"There are three kinds of lies: lies, damned lies, and statistics." Mark Twain (from Disraeli)

Statistics is an art - given a limited amount of data , we try to figure out what the reality and relationships were between the factors we're analyzing. (Or, if you have other intentions in mind, refer to a nice little book from about 50 years ago, amazingly still in print)

I'd advise not to use or refer to the P-values and t-stats unless you're doing a very formal analysis and can defend them well.

As Gary Grantham noted, the R-squared results tells how correlated the variables are -- i.e. how well they predict the relationship you're graphing.

Based on the reality of our business (lots of unknown variables in every real estate transaction) I'd advise to use the lowest order polynomial curve possible when creating your graphs in Excel.

The order of the curve is based on how many degrees of freedom (number of unspecified parameters, or independent variables) you've controlled for in fitting the points.

If you're graphing the price change in $/SF over time, and your price per SF is also adjusted for lot size, condition, location, buyer and seller motivations etc., etc., then you could use an n-1 order curve for your n-points, so the curve goes through every point, since you really do KNOW that the only variable left is the date of sale...

I think you see my point.

If you're graphing 5 year price trends, and you know from the broader market data that there's likely been a single 'hump' as prices rose through 2006, and have fallen since, then the 'art' of applying the statistics would say you use a 2nd order curve to capture the 1 hump for your neighborhood data, but don't get too fancy and say that the data 'prove' there have been 4 swings in the market because your 5th degree polynomial curve fit through your 100 sales "proves" it. :)

Best Regards,

Matt Boxberger'