Transcript Suggestions for measuring the effect of distressed sales

SUGGESTIONS FOR
MEASURING THE EFFECT OF
DISTRESSED SALES ON
ASSESSMENTS
Chuck Hicks, RES
Review: How did we get here?
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Collapse of the Dot.com bubble, 1999-2000
The Fed began lowering interest rates to stimulate
the economy and prevent a recession, 2001
Lending practices eased, GSE’s backed risky
mortgages
Between 2001-2006, an asset bubble in real
estate formed: “malinvestment”
Anatomy of a catastrophe
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Consumer spending rose, fueled by low credit card
and equity line rates
Developers/builders discovered a lack of capital and
loanable funds. Projects were abandoned.
Mortgage defaults rose. The real estate bubble
collapsed (2007)
“Nobody saw this coming.” Really?
“The day of reckoning for all this mischief is now at hand… Federal Reserve
credit has not stimulated economic growth, but a lot of it ended up in the
expanding real estate bubble… This, too, will burst as all bubbles do.”
~ Texas Congressman Ron Paul, September 6, 2001
About those “green shoots”
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2 million more homes to go into foreclosure
between 2012-2013
General economic uncertainty = weak seller
confidence
Low interest rates reduce sense of urgency among
sellers and buyers
Source: Jonathan Miller, Miller Samuel, Inc., (February 2012)
…but what about those “upticks”?
“No market trends straight up or straight down.”
~ Doug Casey, Casey Research (2012)
The Assessor’s Dilemma
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“When there is a glut of distress sales in the
marketplace, and those properties are truly
comparable to the subject, it would be misleading not
to use them as part (or in some cases all) of the basis
for a value conclusion.”
~ ASB USPAP opinion, June 10, 2011
G.S. 105-283: the standard definition of market
value, “neither (buyer & seller) under any compulsion”
H.R. 1755
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Sec. 3(a)(2)(b) “…use comparable sales involving
arms length transactions to make such an assessment
or review.”
Sec. 4(2)(B) “…not include any transaction involving a
short sale or foreclosed property or any other
distressed real property.”
To include or exclude…
Excluding distressed sales maintains the letter of G.S.
105-283 and keeps dispersion levels low.
But, where distressed activity rises vs. arm’s length
sales, “the exceptions become the rule.”
…that is the question.
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Including distressed sales in the analysis greatly
increases dispersion.
But distressed activity should be thought of as
economic obsolescence. Its impact on the market can
be measured.
Let us count the ways…
Different strokes…
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Ex post facto – market factored adjustments at the
end of the review cycle.
e.g. the ASR is 105% at the end of the review.
Solution: factor the neighborhood by -5 to -10%.
Downside: property owners perceive this as arbitrary
and unfair.
…for statistical folks
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The weighted average of distressed vs. arm’s length
can be computed. But this requires either constant,
real time sales review, or amazing clairvoyance.
Don’t go MIA…try MRA
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Multiple regression modeling (MRA) is an handy tool
for measuring the impact of distressed sales – without
the appearance of arbitrariness
Proxy variables (0, 1) can be assigned to each type
of “distress” sale: short, bank, REO, foreclosure
Without delineating the type of distress, the
dispersion level in the model will rise
Geospatially speaking…
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The best way to improve appraisal results is to link
the regression to location, location, location…
“Kriging” – named for South African mining
engineer Daniel Gerhardus Krige
Interpolates value of a random field (i.e. price) at
an unobserved location from observations at
nearby locations
This is so kriging awesome!
Before…
After…
Advantages
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Geospatial modeling reduces the amount of dispersion
in the assessment statistics
The distress factor is location-driven, but lacks the
appearance of being arbitrary
Requires only periodic uploads of sales data and can
be maintained for all future revaluations. As market
conditions change, the model seamlessly adjusts. Small
jurisdictions can network to create models.
Conclusions
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Distressed sales aren’t going away, and the market
will “bounce along the bottom” for at least another
three years
The problem – economic obsolescence – can’t be
ignored, but it can be interpolated into assessments
Geospatial modeling appears to be a sound way to
render credible appraisals in the presence of
distressed sales
No free lunch…but free software?
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“R” is a free, open source statistical program that
has a capacity for geospatial modeling. Grab it
here:
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www.r-project.org
Cran.r-project.org
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Pass it to your in-house statistical geek
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