By ANDREW JEFFERY

This post first appeared on Minyanville.

Americans finally get it: Home prices are falling.

This may seem like a preposterous statement, what with the entire global financial system in disarray after the collapse of the US housing market, but we Americans are stubbornly optimistic people, content to ignore calamity as long as we possibly can.

A study released this week by Zillow, a real estate information website best known for its wildly inaccurate estimates of property valies, shows Americans have finally succumbed to the notion that home prices aren’t going up anymore. 57% of homeowners polled believe their own home lost value during 2008, up from 38% who felt that way just 6 months earlier.

Interestingly, when asked about the future, respondents were upbeat: Only 30% estimate the value of their house will decrease in the next 6 months. Of course, their neighbors aren’t so lucky: Forty-seven percent believe home values in their local markets will fall during the same time period.

Zillow has become something of a cult phenomenon in the past few years, as it  allows homeowners to go online and see how much their house is “worth.” By its own admission, Zillow’s values are merely estimates based on amalgamating sales data from nearby homes, comparing bedroom counts, living area, lot size and other salient characteristics.

What few people realize, however, is that Zillow’s valuation algorithm isn’t just used by John Q. Homeowner: Every big lender in the country uses a similarly opaque formula to price real estate.

Wells Fargo (WFC) – now the biggest US home lender in the country after its acquisition of Wachovia – holds tens of thousands of mortgages on its books, each backed by a unique house. It’s impractical to regularly review each home for a fresh value, so Wells and other big banks like Citigroup (C), JP Morgan (JPM) and Bank of America (BAC) rely on analytics firms to provide property values churned out by what are called Automated Valuation Models, or AVMs.

AVMs rely heavily on recent sales data to drive their valuation estimates. This works reasonably well in a vanilla market, one where home prices move uniformly in a single direction – namely up. Even rapidly rising prices are well accounted for, since liquid markets provide reliable, normal data sets upon which calculations can be made.

AVMs are a bit behind the curve in an appreciating market, offering a conservative estimation of a home’s value. But in a declining, choppy, illiquid market like the one we’re in now, AVMs fall apart.

As sales volume dries up and prices gap down, transactions that are even 3 months old become woefully out of date. Even in distressed markets that are now seeing frenetic buying activity, active listings — and therefore true market prices — are well below all but the most recent sales.

By using AVMs to value housing assets, banks are constantly underestimating losses in a declining market. Unfortunately, there isn’t much of an alternative.

Small, independent valuation firms offer the most reliable estimations of value, but they specialize in local markets by definition, which limits the scale with which huge lenders can effectively use their results to evaluate nationwide portfolios of loans.

Next time you laugh at Zillow’s estimation that a home that just sold for $250,000 is really “worth” between $315,000 and $375,000, remember that your bank is looking at the same data. No wonder they keep asking Uncle Sam for so much money.

By AUSTIN NELSON

In today’s fast paced, data-driven world, it’s easy to get lost in the morass of statistics flashing across our TVs and computer screens at a sometimes maddening pace.

Government officials, bankers, retailers and snake oil salesmen alike throw out statistical arguments at the drop of a hat, telling you why their pitch is the only one worth listening to because they have the data to back it up. But before accepting what you hear or read at face value just because some nameless research institute did a study, stop for a minute to ponder the complexities of even the most seemingly innocuous of statistics: The average.

Let’s first assume some particular data being quoted were reliably gathered and analyzed (This is almost never a safe assumption, but that’s a topic for another day), then examine how the average and another so-called “descriptive statistic” –- the median — are used in the data reports we see every day.

While on the surface it may seem that these two statistical measures could be interchangeable (indeed they are often used interchangeably with no explanation), they tell us very different things about the data they describe.

The median of a given group of data is its middle value. For instance, if your dataset has five data points and you lined them all up from smallest to largest, the third value would be your median. On the other hand, the average, or mean, of a dataset is determined by summing all values and dividing by the number of data points.

For example, suppose you are looking at real estate sales in a certain area within a certain time frame and you had the following 5 values: $300,000, $320,000, $320,000, $450,000, and $1,200,000. The median of this set is $320,000 (the middle value). The average is $518,000 (2,590,000 / 5). As you can see, even in this simple example, the two descriptive statistics are significantly different.

Real estate sales are often represented by the median value. The reasons for this are varied, but center around the fact that a few sales at extremely high levels (like that $2 million house on the top of the hill) can easily skew the average of a dataset towards those properties, even though most homes in the area are selling at lower prices.

For example, in Temecula, CA where most homes sell at modest levels (by California standards) but some homes sell for significantly more, the average sale price in 2008 was about $435,000. The median price, on the other hand, was around $359,000. That’s is a difference of over 20%.

Contrast that with areas where home prices are more homogenous, like Daly City, CA, where the average and median values are more closely in line. In 2008, the average sale price for Daly City was around $562,000 while the median was about $558,000 – a much smaller spread (<1%).

So which is better? Average or median? As can be seen from the examples above, neither.

Both display different aspects of the same set of data points. In Temecula, where median and average wildly diverge, using the average skews the data towards a much higher level. An individual from out of state looking to buy there might incorrectly assume they couldn’t afford to do so. On the other hand, solely looking at the median leaves out the fact that there are million dollar plus estates in Temecula available to buyers looking for that sort of thing.

When the National Association of Realtors releases their monthly sales statistics — which is the real estate pricing data carried by most major news outlets — they present sales price data as both median and average values. These values are used to track sales prices over time to identify trends in sales activity nationwide and regionally. While both median and average values are freely available to anyone with internet access, the median values are often the ones quoted in the popular press.

By focusing exclusively on median values, however, one can miss interesting trends.

For example, on a nationwide level and in three of the four regions identified, median and average home sale prices have been tracking at around the same relative spread since 2005. In the West region, however, the median sales price has been falling faster than the average price.

This widening variance helps tell the story of what’s been happening in Western real estate markets in the past few years. In most markets, high-priced homes have retained their value better than homes that are closer to, or below the median. Since so many lower end homes are being sold, many after foreclosure, the sheer volume of these transactions is dragging down the median figures. The average, on the other hand, is propped up by the few expensive homes still being sold.

This analysis then begs the question, why does the trend only exist in the West? As other regions decline, can we expect the same pattern to play out? Why are higher priced homes holding up better? If expensive homes begin to lose their value, what would that do to the median and average sales prices? What does the data look like on a city or zip code level?

It’s easy to see that just by comparing the median and average sales price trends, much insight — or at the very least another list of questions — can be gained.

I could go on all day about the wealth of information that such a seemingly simple statistic as the average can provide those with the patience and curiosity to “drill down” past the headlines. But my point is simply this: Pay attention! Don’t let the evening news or your favorite web news source gloss over the statistics to prove whatever skewed point they want to make that day. Spend the time to think critically about the information or you run the risk being fleeced regularly for the rest of your life.

At the very least, pay close attention to the source of any information you are receiving, particularly when that information comes in the form of a statistic. If you are being presented with a descriptive statistic like an average or a median, notice which one you are being given and pause for a second to think about why they used one and not the other.

Furthermore, if you notice that a single set of data is being described interchangeably by median and average, this should throw up a huge red flag as to the reliability of the information and its source.