I was actually going to hold off on writing any more about this until next week, but since yesterday’s post about falling home prices in the East Bay has generated an insane number of comments, I’ll add a little more today.
So I was trying to do two things in yesterday’s post. One, demonstrate the logical flaw in an argument made elsewhere that a 12% drop in the median sales price of condos is evidence of overbuilding by comparing the drop in median home sales prices using the same metric. Two, highlight the relative affordability of Oakland in comparison to neighboring cities.
I didn’t think I was really saying anything controversial, but I got a ton of comments in response that appeared, at least to me, to be attempting to explain the discrepancy between the drop in condo prices and drop in single family home prices. The reasons people came up with (condos are often in better condition, condos are in nicer neighborhoods, etc.) seemed for the most part logical. But where I’m getting lost is that it sort of seems to me like people are then making a leap from the fact that they can see reasons for the relative price drops to assuming that means the data should be dismissed. But why? The market doesn’t exist in a vacuum. Obviously there are reasons for that different properties have different values. People will pay more to live in a nice neighborhood than a crappy one, people will pay more to live somewhere that’s in better condition, and so on. Just because there’s a rational explanation for something doesn’t mean it isn’t true. Quite the opposite, actually.
Anyway, I’m going to hold off on addressing some of the more specific issues of data collection or getting into neighborhood based data until next week, but for now, look at this as a fresh place to comment on the same subject, with the added benefit of a little bit more citywide data. The charts below illustrate median prices for condos and single family homes in Oakland over the last year. The first chart shows the overall market, then the following four are broken into quartiles (that is, the first chart shows the most expensive 25% of properties in each category, the second chart shows the next most expensive 25% of properties, and so on).