Making sense of market indicators, part 4: bid.

In this corner of paradise called Silicon Valley, “bid” is a common synonym for “offer”, as in “I’d like to put in a bid on that house”. This hints at how competitive the Silicon Valley real estate market has been—and still is, in premium-priced neighborhoods. Over the past ten years the offer process here could more accurately be called the “bidding process”, since it has so frequently involved more than one buyer (and sometimes five, ten, twenty, even thirty or more buyers) competing to please a demanding seller sitting on that scarce and valuable resource called a Silicon Valley home. These multitudes of buyers have had a way of pushing the sales price significantly—and sometimes shockingly—over the list price.

But I’d be remiss if I didn’t point out that over the past ten years Silicon Valley sellers have often slightly under-priced their homes to make them appear more of a value. In other articles I’ve talked about whether this practice is fair to buyers or not, but the important thing is that it exists and, in fact, has been the only reliable way to sell a Silicon Valley home, at least in high-demand neighborhoods where a seller can usually count on buyer demand to bid up the sales price of his or her home to market value.

The under-pricing or “auction” strategy is relevant here because it makes it inevitable that the winning “bid” for a home priced a percentage point or two below market value will always be an over-bid, just to move from an under-valued list price to a full-value sales price. So the question is not whether a home will get over-bids—the seller’s strategy demands them—but how high the winning over-bid will be.

I should add that over-bids aren’t inevitable, even here in la-la land, where all of us are venture capitalists, drive $100,000 cars and light our cigars with $50 bills. Some Silicon Valley neighborhoods, those at each extreme of the price range, don’t have a pool of buyers large enough to make this strategy work—or at least that’s the perception; I suspect that in fact it’s the agents in these neighborhoods, and not the buyers, who resist this strategy.

But it’s also at least partly true that the viability of deliberate under-pricing depends on how desirable a neighborhood is—how “emotional” demand is for that neighborhood. A humdrum neighborhood will generate big over-bids only in the most overheated of markets. It’s also true that buyer mentality—the typical buyer profile for that neighborhood—plays a huge role. Cautious, conservative buyers make cautious, conservative offers, and cautious, conservative buyers travel in packs and live in the same neighborhoods. Sought-after neighborhoods, on the other hand, attract highly-driven, risk-tolerant buyers and, in fact, are priced so high that only highly-driven, risk-tolerant people can afford them.

And it’s also true that whatever drives a market at any point, whether it’s dot-com wealth in 2000 or subprime lending in 2005, determines which segment of the market gets a disproportionate share of the money, and therefore the highest over-bids. Dot-com and subprime never drove the same neighborhoods, either at the same or different times, something that seems to have escaped many an amateur market watcher, but the difference between the two explains why the real estate crashes of 2001 and 2007 hit different neighborhoods hardest and some not at all.

And finally, in some neighborhoods or cities, what might appear to be an over-bid is actually either partially or entirely a seller credit to the buyer to help the buyer pay closing costs. But hold your horses, bubbleheads—this isn’t the easy explanation for “pumped-up prices” you live for. Seller credits to buyers were common only in a small number of the most affordable Silicon Valley neighborhoods, just as 100 percent financing was common only in a small number of neighborhoods.

In this article, the fourth and penultimate in a series on market indicators, I’ll see if changes in the size of the average market “bid” have any relationship to other changes in the market, and whether bid correlates negatively or positively with other market indicators. In other words, we’ll see whether the information that buyers are consistently offering more than list price—”over-bidding”—or less than list price—”under-bidding”—and by how much, can help us predict short-term trends in other market indicators.

In particular, we’ll see if overbids or underbids can tell us short-term movements in price far more quickly than if we track sales prices as escrows close, since bid is known immediately, at least to the buyers, sellers and agents involved in the offer, while sales price isn’t revealed to the real estate community and other interested spectators until close of escrow, often a month or so after the offer is accepted. And in a fast-moving real estate market, even the latest closed sales prices can be stale information.

The chart below illustrates the variation in over-bid between five Silicon Valley cities at the height of bidding frenzy, the peak of the dot-com real estate boom, that halcyon month of March 2000:


No names, please—I never reveal my sources, especially after I’ve called them humdrum—but it’s apparent that back in the day some cities excited buyers more than others. It’s also apparent that over-bids were price-sensitive: the higher the price range, the higher the percentage over-bid, at least until you reached the top end of the market and the pool of buyers began to shrink (although in fact it was the top end that felt the full wallop of Nasdaq wealth). Overbids are still price-sensitive, although during the recent subprime boom even the low end saw unusually large overbids.

Let’s wrap things up with a look at the history of buyer over- and under-bidding for a select group of Silicon Valley homes during one of the most dramatic of real estate markets. No, I’m not talking about the 2005 boom that still looms large in the popular imagination. I’m talking about the real deal, boom-wise, the Oklahoma Land Rush that inundated the cities of Palo Alto, Los Altos, Menlo Park and Mountain View during dot-com’s 2000 peak, and the equally dramatic unraveling of real estate during the dot-com bust of 2001.

Now there was a market to tell your grandchildren about:


Yes, that’s right, the average over-bid for a single-family home sold in those four Silicon Valley cities in March 2000 was a remarkable 32.2 percent. Yes, that’s right, the average single-family home sold for a generous 132.2 percent of list price that memorable month. You can only imagine what kinds of offers the hot properties were getting. And January, February and April of 2000 were nearly as rambunctious.

Next, let’s look at the trend in average sales price over that period:


By comparing the two charts you can see a strong positive correlation between average bid and average sales price during the period. You might think that this correlation would be obvious except that, first of all, very little is obvious in real estate. Second, as over-bids have seemingly pushed prices beyond reason, it’s been an article of faith among market skeptics that the over-bid is an artificial creation, cunningly engineered by real estate agents to make a so-so market look and act red hot. Well, folks, for most of past ten years the Silicon Valley real estate market hasn’t needed any “window dressing”. Because for most of the past ten years, people have been willing to step forward and vote for Silicon Valley real estate with well-funded checkbooks. And when they haven’t, hefty under-bids have made that obvious too.

Next, let’s put the bid and sales prices lines next to each other to better see how they track each other. But to ensure consistency, let’s adjust the sales prices over this period to the same square feet, so that, in effect, we’re tracking one single-family home, of about 1832 sq.ft., in this market as it increases or decreases both in market value (sales price) and whatever bid measures (perceived desirability?):


Okay, that’s kinda exciting. When the market we’re tracking spiked in March 2000, bid exceeded the rise in sales price, but as the market matured and began to cool, bid and sales price maintained a surprisingly consistent and predictable relationship. This suggests not only that bid is organic, and not some artificial appendage grafted onto the market by the real estate industry, but also that sales price does follow bid, up and down. So if you know that a home sold today for five percent above list, you have a strong indication that the market suddenly jumped about five percent. That’s information not everyone has, and it’s good information you can use to write tomorrow’s offer or price tomorrow’s listing. That’s big.

Finally, let’s see if there was a consistent and predictable relationship between bid and something else, the percentage change in sales price, from month to month during this period:


I’ve been staring at this for days and still can’t figure it out. I’m not even sure it’s meaningful, although it sure is interesting. The relationship between bid and percentage price change is more tenuous than that between bid and sales price, especially as dot-com fades to dot-bust in 2001. I could riff about how the percentage of price change may be more volatile than changes in sales price itself because of fluctuations in the desirability of the homes in inventory each month, which never exceeds 231 homes and twice dips as low as 55 sales, or how sales prices bounce around even in a normal year—and neither 2000 nor 2001 were normal—as buyers and sellers migrate seasonally en masse across the landscape of the marketplace. But both explanations sound a little too much like an economist grasping at straws, so I’ll just say what I wish the dismal science would occasionally say: beats the heck out of me.

I will draw your attention to the odd echo-effect bid had early in the boom, as it mimicked changes in sales price but with a one-month lag. Also note that while average sales price jumped wildly from month to month—and as someone who was there, this is not entirely a statistical illusion—bid soon settled into a lethargic decline. Is bid an indicator of consumer confidence? It’s almost as if buyers were a step behind the market early on (although how they could be, I don’t know, since buyers are the market), then gradually ran out of gas as Silicon Valley did.

Not a profound analysis, but it’s free, it’s late, and I’ve done my part for Mother Science this week while still keeping my day job.

In two weeks, the exciting dénouement, Making sense of market indicators, part 5: the absorption rate.

copyright © John Fyten 2008

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