Appraisal Statistics

[Eli]

The use of statistic modeling in real estate appraisal is appropriate when buyers and sellers start behaving with scientific consistency. Statistical sampling last accomplishment was getting the 2016 Presidential Election absolutely wrong, when they only had two choices, their R2 was 0.

I liked you and unliked you. Be careful lol.

 

[Randolph Kinney]

I wonder, can statistical association without cause and effect be useful for any long term prediction? Or is it doomed to a one time use for any given data set?

 

[Artemis Fowl]

The use of statistic modeling in real estate appraisal is appropriate when buyers and sellers start behaving with scientific consistency. Statistical sampling's last accomplishment was getting the 2016 Presidential Election absolutely wrong, when they only had two choices, their R2 was 0.

Watch a couple of Flipping Las Vegas shows on cable and try to figure out why buyers will pay 25% more per square foot for gold glitter.

Predictive modeling is very powerful when built and used appropriately.
The election? Garbage in resulted in garbage out. Many polls you see are not intended to represent reality but rather to influence behaviour. Trump polling so bad that you should not even bother to vote...waste of your time because he's gonna lose anyway. THAT was what they wanted their models to say so no big surprise they were wrong.

 

[Meandering]

Garbage in resulted in garbage out. Many polls you see are not intended to represent reality but rather to influence behaviour.

+

As is the news.
Is Melania showing up at a disaster site in high heels really news?
Or were women supposed to run out and buy steel toed boots, so they could be politically correct?


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[Randolph Kinney]

Many polls you see are not intended to represent reality but rather to influence behaviour.

Yep, people want to be on the winning side, generally speaking. However, that will not be the case when persons are not motivated by the winning side, that is to say, they don't believe they will be rewarded. In fact, just the opposite will occur.

 

[Meandering]

I wonder, can statistical association without cause and effect be useful for any long term prediction? Or is it doomed to a one time use for any given data set?

Cause and effect is the key to the entire issue, outside of physical characteristics.


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[Pittsburgh Pete]

+

As is the news.
Is Melania showing up at a disaster site in high heels really news?
Or were women supposed to run out and buy steel toed boots, so they could be politically correct?


.

Thanks for yet another clueless post.

 

[Randolph Kinney]

The skirt length theory is a superstitious idea that skirt lengths are a predictor of the stock market direction. According to the theory, if short skirts are growing in popularity, it means the markets are going up. If longer skirt lengths are gaining traction in the fashion world, it means the markets are heading down. The skirt length theory is also called the hemline indicator or the "bare knees, bull market" theory.

The idea behind skirt length theory is that shorter skirts tend to appear in times when general consumer confidence and excitement is high, meaning the markets are bullish. In contrast, the theory says long skirts are worn more in times of fear and general gloom, indicating that things are bearish.

Although investors may secretly believe in such a theory, most serious analysts and investors prefer market fundamentals and economic data to hemlines. The case for skirt length theory is really based on two points in history. In the 1920s - AKA the Roaring Twenties - the economic strength of the U.S. led to a period of sustained growth in personal wealth for most of the population. This, in turn, led to new ventures in all areas, including entertainment and fashion. Fashions that would have been socially scandalous a decade before, such as skirts that ended above the knees, were all the rage. Then came the Crash of 1929 and the Great Depression, which saw new fashions dwindle and die in favor of the cheaper and plainer fashions that preceded them.

This pattern seemingly repeated in the 1980s when mini-skirts were popularized along with the millionaire boom that accompanied Reaganomics. The pendulum of fashion swung back to longer skirts in the late 80s, roughly coinciding with the stock market crash of 1987. However, the timing of these incidents, let alone the strength of the potential correlation, is questionable. Although there may be a defendable thesis around periods of sustained economic growth leading to bolder fashion choices, it is not a practical investment thesis to work with. Even benchmarking skirt length in North American would be a challenging undertaking. The time spent auditing clothing outlets to establish the length of top selling skirts would take more time than it is worth considering that it is far from proven as to whether the hemline indicator is leading or lagging.

Skirt length theory is a fun theory to talk about, but it would be impractical and dangerous to invest according to it.

https://www.investopedia.com/terms/s/skirtlengththeory.asp

Here is a classic statistical association backed by a proposed cause and effect.

 

[Elliott]

I'd have a little more faith in statistical analysis associated with residential appraisal if I knew buyers were attending meetings and coordinating their ideas about the value contribution of various components. But I don't see that happening. I see other residential and commercial reports, and I can always spot where the appraiser took an unsupported leap to justify value, that's usually a one-off.

 

[Michael P Jacobs MAI]

The use of statistic modeling in real estate appraisal is appropriate when buyers and sellers start behaving with scientific consistency. Statistical sampling's last accomplishment was getting the 2016 Presidential Election absolutely wrong, when they only had two choices, their R2 was 0.

Some pollsters got it right, though, at least that's the story.

Taking your post and the OP's one step further, assuming every borrower wants to maximize LTV, the mortgage portfolio investor's outcome will be flawed when the portfolio sponsor relies on pure value-modeling. An originator should be able to recognize that artificially (mistakenly) higher LTVs would encourage borrowers so that the vast majority of "over-valued" property loan requests would be funded, and the mortgage pool would therefore skew toward higher average loan-to-"actual value" ratios. At the same time, the "under-valued" property borrowers will most likely end up going elsewhere to maximize LTV. So, portfolios valued purely by modeling will contain an extraordinary number of "at risk" LTVs overall with relatively few ideal LTVs in the mix. At the extreme, it's 2007-2008 all over again with MBS investors unable to discern total risk because of the after-market's lack of transparency.

I realize that this is taking the potential to the extreme - nobody wants extraordinary skew in LTVs to the detriment of the markets - but it's interesting to assume that in an industry dominated by Big Data the likely mark-up in secondary markets would trickle up to the original borrowers, erasing any savings in appraisal fees (the overall loan cost will be more expensive).

Also, what about the argument that appraisers are no better at valuing real estate than Big Data's models? Doesn't the "unnecessary and obsolete appraisal-by-appraiser model" in fact have the same flaws? I do not buy that argument but that's for a different thread.