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Bottom-Calling: Inventory-Based Forecast

Posted on February 16, 2009July 30, 2009 by The Tim

Long-time readers may recall Deejayoh’s inaugural Seattle Bubble article from June 2007: Why Inventory Matters. In it, he postulated that the Seattle-area Case-Shiller Home Price Index could be relatively accurately predicted fourteen months in advance by looking at year-over-year house inventory on the MLS.

Let’s extend Deejayoh’s analysis fourteen months into the future to see if it will predict a bottom.

Here are our basic assumptions for the Inventory-Based forecast (see Why Inventory Matters for more detail).

  • Inventory changes forecast home price changes by 14 months.
  • Equation: HomePricesYOY = (-0.386 * InventoryYOY) + 0.092
  • I’ll be using King County SFH inventory only, as that provides the best fit through 2007 and 2008.

Given these assumptions, here’s a rough picture of what Seattle’s Case-Shiller Home Price Index would look like through late 2009:

Bottom-Calling Method 1: Inventory-Based Forecast

Using the Inventory-Based forecast model, Seattle-area home prices (as measured by the Case-Shiller HPI) will hit a bottom early this year, and proceed with a sharp “V-shaped recovery,” ending the year well above their current levels.

As Deejayoh demonstrated in 2007, the Inventory-Based forecast model has a pretty good record from 2001 through 2007. However, the forecast for the coming year does not pass the “sniff test.” No other signs currently point to such a sharp recovery this year, despite what this model is predicting.

Unfortunately, it would appear that the Inventory-Based forecast model has reached the end of its useful life.

Method 1: Inventory-Based Forecast (Summary)
Bottom Month: April 2009
Bottom Value: 20.1% off peak
Likelihood*: 10%

* Likelihood is a totally subjective value assigned according to The Tim’s gut feeling. Treat it accordingly.

Bottom-Calling Week on Seattle Bubble

  • Introduction: Bottom-Calling Week on Seattle Bubble
  • Method 0: Blind Optimism
  • Method 1: Inventory-Based Forecast
  • Method 2: Dollars per Square Foot Linear Forecast
  • Method 3: Simple Mirror Forecast
  • Method 4: Affordability Index Forecast
  • Method 5: San Diego Lag Forecast
  • Conclusion: So Where’s the Bottom?

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