Should You Believe Everything You Read?

There are 3 Kinds of Lies: Lies, Damned Lies, and Statistics  
Mark Twain
Over the past 6 months, the public has been inundated with sensationalist information in the media that has contributed to our current housing slowdown. Just like in the run-up in housing prices through the 2000s, the media has been a large contributor to a market psychology that is decoupled from market fundamentals. The difference being that the story is now negative.
Below are the 3 most commonly MISUSED STATISTICS in the media:

1) Housing Starts Drop 70%!

This shouldn't really matter to buyers or sellers out there. Sure, this is related to the Real Estate market, but really, we're already overbuilt and it only makes sense for developers to stop when prices are no longer escalating.
Remember, these are CONSTRUCTION figures. Not sales or pricing figures. Unless you're a construction worker or materials supplier, this type of information is largely irrelevant to your real estate decision-making process.
This kind of information is important for buyers and sellers to know and also helpful for realtors to use. Gone are the days when a realtor could put up a sign and sell it $20,000 over list price in 12 hours. Back then, product was king and realtors spent most of their time trying to convince sellers to list with them. Now, with more product available and time-on-market figures increasing, the market is more balanced.
That said, a drop in sales has no bearing on price. Remember, these are UNIT SALE figures, not price figures. As an example, in December, the number of home sales dropped off in Kelowna; however, the average home sale price increased.
3) Average House Prices Expected to Drop 11% in 2009!
This is the most damaging type of media reports that come out. Yes, it is technically true that Average Canadian Home Prices in 2009 will likely show an 11% drop from the Average Price in 2008; however, it does not take into account the fact that the market already turned in the middle of 2008, with the average price falling drastically since then. Also, the number of units sold in a given period has a huge effect on how averages are calculated.
A simple example:
2008 Jan - June > 100 Units Sell at $200,000 Average
2008 July - Dec > 50 Units Sell at $170,000 Average
What is the Average for 2008? $190,000
Of course, at the beginning of the year in January 2009, prices are ALREADY at the December 2008 figure of $170,000, or 11% BELOW the 2008 Average of $190,000. In this example, the slowdown began in the middle of 2008.
So even though the average price in 2009 is expected to be 11% below the average price in 2008, the January price already reflects this difference and a further drop in prices is not expected. Using these predictive models, we can see how average prices over the year can really skew the figures.


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