SF Home Prices at 2003 Levels

Sorry, folks, but it’s that bad — or good, depending on your perspective.

I tracked average and median prices going back to 2000 for the ten combined MLS districts that comprise the San Francisco Multiple Listing Service — the big database that realtors use to list properties and record sales information .  (The MLS District Map is here, on my Market Trends page.)  Here are the results (click to make the chart bigger):

san-francisco-price-trends-2000-presentsmall

Pretty scary stuff, especially when you look at the suislide (a new word is born?) that started in June of last year and shows no signs of slowing down.

Before you head for the windows, or call your realtor (me!) to start looking for a house to buy, consider this:  as I’ve said before, there’s nothing so local as real estate.  It really does matter what neighborhood you’re talking about. This chart, while it does illustrate something meaningful about the overall SF market, doesn’t tell you anything about values in any particular neighborhood.  It lumps together data from neighborhoods as diverse as Hunter’s Point and Visitacion Valley in District 10, which has been slammed  for well over a year now, with neighborhoods like Noe Valley  in District 5 and St. Francis Wood  in District 4, both of which seem to be holding up pretty well.  Go to my Market Trends page to see the charts for individual districts.  I’ll be creating charts for individual neighborhoods within districts for future blogs.

And if it’s any consolation, SF real estate is holding up a heck of a lot better than the stock market, don’t-cha-know.  Today the S&P 500 closed at 682.55.  The last time it closed under 683 was on May 17, 1996.

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DOM Roll Please

A couple of posts ago, we dispensed with Absorption Rate as a good barometer of the market since there appeared to be no correlation between how much inventory was available in relation to sales rates and where median prices were going.  I asked whether there might be a different metric that would correlate better, like the oft-quoted Days on Market or “DOM.”

In essence, DOM tracks the average number of days that properties have been on the market from the time they became active on the MLS (Multiple Listing Service used by realtors) to the time they actually sell.

Great minds must think alike because it turns out that my friends over at Inside SF Real Estate have been exploring the same thing.  Head over to their recent post for a look at DOM trends over 14 years.  What they haven’t done, however, is track DOM against median prices.  Ha!  I have, and here are the results for the last three years tracked by month (my numbers are pulled directly from the MLS database  — click to make the chart larger).

dom-chart

Now that’s what I call correlation! Note that the right-hand Y axis tracks DOM inversely, with longer periods at the bottom and shorter periods at the top.  So, this chart is basically showing that during periods, even relatively short periods, when the average DOM falls, prices rise, and when properties stay “on the market” for longer, prices fall.  This is just what you’d expect.

Why?  My guess is that DOM captures many of the factors in play in the real estate market at any given time.  For example, if credit is tight and appraisals are rigorous, you’d expect that transactions would take longer to get approved.  Likewise, if lots of people are bidding on the same house, you’d expect that the winning bidder would promise a quick “no contingency” close and that there would be no haggling on the sale price.  When the market slows, you’d expect more cautious buyers, more haggling on price, longer closing periods — all reflected ultimately in the DOM.

As my friends over at InsideSFRealestate pointed out in their post on DOM, realtors can play games with DOM.  For example, if a property doesn’t sell, they’ll take it off the market, and then put it back on as a “new listing” at a lower price and voila, the DOM resets to zero.  Still, that would just tend to increase the “down” side of the line — the correlation would still hold.

The only other point I’d add is to note the seasonal trend in the chart.  It seems that every December/January, DOM increases and prices dip.  Perhaps that’s the best time to buy.

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Absorption R.I.P.

After talking to people about my last post on Absorption Rates and the lack of a correlation between slower absorption and lower median prices (or faster absorption and higher prices), I got the impression that there was some curiosity — skepticism?  — about the underlying numbers.  So I thought a post mortem of sorts was in order.  Here’s a chart that simply tracks total listings and total sales over a little more than the two years covered by the Absorption Rate chart.
on-market-vs-sold

Total listings is defined as new listings plus anything that’s under contract but still  “contingent” in the parlance of realtors.  Total sales is exactly that.  The chart reflects the raw monthly numbers with no averaging.  This really highlights the seasonal fluctations:  ie. the very evident drop-off in activity at the end/beginning of each year.

Other than the seasonal dips, maybe you can conclude that both listings and sales are trending downward, but I sure don’t see any evidence of a major change of direction in either.

A couple of closing thoughts.  My absorption rate conclusions were based on an analysis of single family homes only.  It’s possible that the conclusion would be different if I’d included condos and TIC’s as well.  ie.  Looking at the broader market might change the results.

On the other hand, it’s possible that correlations between absorption and price would appear if we looked at finer segments of the market.  For example, we might find that absorption rates are longer at the high end of the market and that in fact prices have come down as we’d expect for that portion of the market.

Alas, the MLS database that’s the repository for sales information for brokers/realtors simply doesn’t allow you to do this sort of data-mining easily, so we’ll never know.

I stand by my previous conclusions:  First, San Francisco just isn’t that overbuilt a market. Second, if you take out the seasonal fluctuations, the absorption rate doesn’t seem to have moved that much anyway.  Finally, and perhaps most importantly, absorption rate doesn’t tell you how much activity (offers)  each available listing is generating — in the end, just one property gets sold.

The question is whether there are other metrics that do a better job of tracking whether the market’s “hot.”  Stay tuned.

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