Case-Shiller: Spring Continues for Chicago Home Prices

It’s time for our monthly check-in of the S&P/Case-Shiller Home Price Indices (HPI). The Case-Shiller data is generally considered to be the most reliable measure of overall home price changes for a region, since they only consider repeat sales of homes when calculating their index, instead of looking at all the homes that sold in a given month.

For the full source data behind this post, hit the S&P/Case-Shiller website. For a more detailed explanation of how the Case-Shiller Home Price Index is calculated, check out their methodology pdf. Also remember that the data released on the last Tuesday of a given month is for the period two months prior (i.e. – May data is released in July).

Here are the basic Case-Shiller stats for the Chicago area* as of May:

May 2011
Month to Month: Up 1.7%
Year to Year: Down 8.1%
Prices at this level in: April 2001
Peak month: September 2006
Change from Peak: Down 33.6% in 56 months
Low Tier: Under $157,390
Mid Tier: $157,390 to $265,869
Hi Tier: Over $265,869

Only three of the twenty metro areas tracked by Case-Shiller saw a decrease in their HPI between April and May (down from 7 in April and 18 in March). Boston ousted DC for the biggest increase, gaining 2.7% on the month. Only Tampa, Las Vegas, and Detroit continued to fall.

Here’s a look at the latest local tiered data, back through 2000:


And here’s a closer look at the recent changes, with the vertical and horizontal axes zoomed in to show just the last year:


Chicago’s high tier appears to be benefiting the most from this spring’s bounce, while the low tier barely inched up. Month to month, the low tier was up 0.4%, the middle tier rose 1.1%, and the high tier increased 1.7%.

Here’s a chart of Case-Shiller HPIs for all the markets that Redfin serves:


Here’s our peak decline chart, in which we line up the peak Case-Shiller HPI value for each of Redfin’s markets, so we can see how long each market has been declining, and how much it has dropped from the peak.


Just three of the twenty cities tracked by Case-Shiller hit another new post-peak low as of May as the 20-city composite ticked up again, hitting its highest point since January.

Methodology: The Case-Shiller index tracks price changes in sets of homes of similar size and style to better determine changes in what people are willing to pay for the same home over time. If data is available from an earlier transaction for the same home, the two sales are paired and treated as a “repeat sale.” Repeat sales that are too far apart, sales between family members, lot splits, remodels, and property type changes (e.g. from single-family to condos) are excluded from the calculations. All remaining repeat sales are totaled together and weighted based on the time between each sale, then the data for the most recent three months is averaged together to create a given month’s index value (i.e. – September’s index represents the average of the data from July through September).

The three price tiers plotted in the charts below simply represent the top, middle, and bottom third of all sales, based on the initial sale price. In other words, if there were 3,000 sales in the three-month period, 1,000 of them would be in the low tier, 1,000 in the middle tier, and 1,000 in the high tier, by definition.

*[Case-Shiller defines Chicago as the entire Chicago-Naperville-Joliet, IL Metropolitan Division, which includes all of the following counties: Cook IL, DeKalb IL, Du Page IL, Grundy IL, Kane IL, Kendal IL, McHenry IL, and Will IL.]

  • Mark M.

    HUGE thanks, Tim!  Your posting of the local monthly Case-Shiller data and graphs makes this site and blog SO professional.  It really shows you all are really on top of what is going on in each major market, and thier sub-markets.  PLS keep it up!

  • madopal

    While the Redfin coverage is better than most, the Case-Schiller index is becoming more and more out of touch.  By not counting anything other than single family homes (how many condominiums are there in Cook County as opposed to homes?), by only looking at repeat-sales (gee, there wasn't a lot of home building going on during this bubble, was there?), by discounting homes that were renovated (no, there wasn't any second mortgage money being poured into home additions or anything), and by ignoring foreclosures, they're looking at home sales figures month to month that are the textbook definition of small sample size.  The data is too noisy to be meaningful.

    In addition, using averages this way is doubly dangerous.  One flipped home (say,… can easily skew an average.  So what if the other 90% of homes in less hot areas lost 15%+, that one home gained 176%!   It only takes a few big homes like that to skew an average, especially month-to-month.  If rich areas are doing well and poorer areas are getting hammered, the averages would look good, even if fewer rich homes sell.  It's the flaw in averages.