I was going back and updating some of my lesser-used spreadsheets today when I came upon one that I thought was particularly interesting. The graph below is an updated version of something I presented originally in the post Home Buying Demand vs. Price Changes back in April.
The chart below takes the year-to-year change in the single-family median home price as reported by the NWMLS and compares it to the persons per closed (SFH) sale, using the monthly Civilian Labor Force series from Workforce Explorer. All of the data is for King County as a whole.
The green dots represent the period of increasing home price appreciation from July 2003 through January 2006, while the red dots are the data from January 2006 through November 2008, when home price appreciation has been on the decline. The unfilled white dots represent January 2000 through June 2003, when appreciation was in a state of fluctuation.
There is no time-shifting in this chart, just the rolling averages to smooth out the noisy data sets. Here’s a plot of the raw data (thin dashed lines) and the rolling averages for each set:
To be honest, I’m not entirely sure what to make of this data. I realize that the last time I posted charts similar to these some folks railed into me (rightly so) for some statistical nonsense in a few of the charts. This time I have attempted to keep things simple, and I think the results are quite interesting.
From what I can tell, there seems to be a very close relationship between the number of sales as a percentage of the population and the change in price. The strange thing to me is how different the slopes are on the red and green lines.
I would postulate that the closely clustered white dots represent the relationship between sales volume and price changes in a “normal” or “balanced” market, while the dramatically-sloped green dots are a result of the out-of-whack mentality of the bubble market, and the red dots are the result of a correcting post-bubble market.
So what do you think? I know there are quite a few readers out there that are more statistically-inclined than myself, and I’d love to have some of you download the data for yourself and give me your take on it.
How about a graph of Closed Sales vs. # licensed Realtors(R)?
http://housing-analysis.blogspot.com/2008/11/ubergeek-post-update-price-changes-and.html
The implication is that sales volume has been strongly correlated with price changes for roughly the past two years, which seems quite reasonable.
In California, I imagine a similar analysis would show they did the same thing for a while, but now have moved off of that tend. At this point, sales volume is rising, but prices are not. They are having a flatish bear rally or are bottoming.
We’re still quite clearly in the free-fall stage and will stay here until we see a significant increase in volume.
At first glance, my gut tells me that this means more people are renting and less are buying as the labor force increases and less people are paying top dollar to own a home.
Just proves that things go ’round and ’round. Might be a Lissajou figure….
This actually matches what I would expect, having thought about it a bit. The RE cycle is basically an oval, moving around clockwise (as you have graphed it). The prevailing economic forces affect it somewhat (for example, the government interference in the market is pushing the down-slope out to the right and lengthening the amount of time it will be below the zero line), but eventually it should loop around and rejoin the green line, or something close. Without the market interference, the oval would be more pronounced (sales volume would be be higher and price declines would be steeper, as the market corrected faster).