Within the Case Shiller home price indexes, there are now data sets for three different home price tiers for 17 major MSAs. Different home price tiers within the same MSA often behave completely differently as if those markets belonged to different cities thousands of miles away. This presentation explores this strange phenomenon.

1. Real Estate Price Tiers Analysis Guy Lion July 31, 2009

4. The Price Tiers have performed very differently The Low Tier has bubbled a lot more than the other two. The Low Tier has corrected a lot more than the other two. The Low Tier has experienced home prices reverting much further back into the past.

5. The correction is getting brutal Those are % drop in home price since the peak (focusing on the top 20). The Low Tier occupies the first five spots and seven of the top 10. Besides tiers, the ranking is dominated by MSAs in California, Nevada, and Arizona on the West Coast and Florida on the East Coast. Those are brutal corrections. 19 of the 20 markets have experienced corrections deeper than – 40% since their respective peak. Those are likely crossing an overcorrection territory.

6. A Blast from the Past The top ranking, Phoenix 1 (Low price tier) has seen its current home prices revert back to their April 1995 level. That’s 14 years ago! Any market that has experienced a reverting back in price to much earlier than 2002 is likely to be in an overcorrection mode because the housing bubble had not picked much hot air yet. This does not mean such markets can’t correct much more as they may be affected by large excess supply and other negative macroeconomic factors.

7. Bubble Peak (January 2000 = 100) For Miami 1 (low tier) home prices had increased by 241% (calculation: (341 – 100)/100 = 241%) between January 2000 and the peak of the Bubble. Again, the rankings are dominated by the low price tier and by the same geographic regions.

8. Difference between price tier See narrative on the next page.

9. Difference between price tier narrative We are looking at the distance between the high price tier and the low price tier for the 17 MSAs. The first row shows that for San Francisco the low price tier prices have reverted 3.7 years further back than for the high price tier. Similarly, the low price tier had bubbled 84.8 percentage points more than the high price tier, and it corrected 34.4 percentage points more than the high price tier. The columns to the right convert all these different dimensions in # of standard deviations. We then summed the standard deviations for all three variables. And, we derived that (as defined) the distance between the low price tier and high price tier for San Francisco was 7.85 standard deviations. For Seattle it was only 0.9 standard deviations. All other MSAs fall in between those two values. The above entails that from a real estate performance standpoint some MSA tiers are more closely affiliated to completely different regions than their other price tiers (within same MSA). This is certainly the case for San Francisco. However, for some other MSAs the three price tiers are a lot more cohesive. Such is the case for Seattle.

10. Cluster Analysis Seven Regions or Clusters We ran our 51 markets (17 MSAs x 3 tiers) through a cluster algorithm (XLStat k-mean clustering) using the three variables (Bubble peak price, correction in % since peak, price reverting to previous date). It grouped the 51 markets in seven different clusters as shown below. As you can see only cluster 7 is homogeneous geographically with 6 markets in Portland and Seattle all in the Northwest. All other clusters are a mixed bag of different geographic locations.

11. Cluster Analysis (continued) See how Portland, Seattle, New York, and Tampa are homogenous as all three price tiers are within a same cluster for those MSAs. On the other hand, Phoenix, San Francisco, and Los Angeles are heterogeneous as each of their price tiers are in different clusters. Those findings correlate somewhat (not perfectly) to our findings of statistical distance between tiers within the same MSA (slide 8).

12. Bubble Peak vs Correction since Peak There is obviously a fairly strong negative correlation between how much a market bubbled and how much it corrected.

13. Bubble Peak vs Correction since Peak LA 1 & Miami 1 are closer to each other than their other price tiers. SF 3 is closer to Chic 1 & 2 than SF 1 & SF 2 As shown, there are numerous examples where a specific MSA price tier is a lot closer to other MSAs than its own MSA other two price tiers.

14. Price reverting backward vs Correction There is also a negative correlation between the two variables here.

15. Price reverting backward vs Correction LA 1 & Miami 1 are closer to each other than their other price tiers. SF 1 is closer to Phoenix 2, Miami 1, LV 2 than to its other price tiers. Price tiers often trumps geography.