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APPRAISAL BUZZ FALL 2019 | 57
Sophisticated “advanced” quantitative
methods make credible appraisals
A high p-value proves one thing: First, the problem
you are trying to solve is to describe a population.
Second, there are some assumptions:
1. You are unable to get all (or most all) of
2. That a random sample represents the
population very well
3. That you have a clear and random
Third, you need to know that the result tells you
nothing about how good your model is.
Thinking that the average says anything
about the spread
This is a key problem with the ‘point-value opinion.’
What clients want and need is one of three things:
• Risk of loss: for collateral decisions, portfolio
management, and stress testing
• Potential for gain: for investments and
value in use
• Fairness: for litigation, administrative law,
or eminent domain
Each of these is dependent on the sureness of the
analytic result. The level of accuracy cannot be
estimated by a point value alone, especially one
which is an opinion rather than a reproducible result.
This may be a ‘groupthink’ problem with the
appraisal community, our standards and regulatory
price and speed.
A high R-squared proves a good
It does not. There are two problems here. First r2 is
2. The little r is for simple regression,
where there is only one predictor variable. The big R
is for multiple regression, with several predictors like
cap rate, GRM, living area, rooms, site area, building
area, and location. In the SGDS1 class we present the
four assumptions of simple regression. These are
seldom, if ever true in real estate markets. And we
also present the ten additional aspects of the data to
be considered alongside the r2 or R2.
In reality, for some regressions, like for price indexing
a market, r-squared is irrelevant on its face due to the
purpose of the regression. R2 has been oversold. It
looks clever. It feels sophisticated. Unfortunately, it
exposes the purveyor as not knowing what they are
doing. It is bogus in most all valuation procedures.
But it is easy to calculate!
This has been one of the most egregious misuses of
‘statistics’ I know. An adjustment, as appraisers apply
it, is based on prior experience. Although these can
be easily abused , even “adjustment cheat sheets”
simplify and align internally as reference points, and
chronicle prior experience.
In theory, the adjustment is a marginal rate of
change, holding all else ceteris paribus – holding
other things constant. The problem here is twofold:
cannot ‘experiment’—we study markets as they are,
not as we may wish we could in order to manipulate
Finally, predictor variables act on each other in
collinearity—correlation between variables. That can
be positive like moving together, similar room counts,
which always coincides with an open area. And
more: some variables interact. For example, large