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What's Your Beef About Your Work?

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Michigan CG

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............... I remember an AI instructor indicating that qualitative adjustments are the best route for appraisals going to court, and although many clients (and some appraisers) turn their nose to this method..........

I have seen my fair share of other appraiser's work. I have never seen a residential appraisal using qualitative analysis only, but I have written a couple. By far the best appraisal I have ever seen using qualitative analysis was from an MAI firm in the Detroit area. When I got done reading it I had no question to the value even though there was not one quantitative adjustment.

Most of my land and farm appraisals are written quantitatively for private parties and a couple lenders who keep the loans in-house. I can't remember the last time I did a land report for an AMC but I have read plenty of them and the "land form" used by most is terrible and I have never seen support for any adjustment in one of those reports.
 

Terrel L. Shields

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I remember an AI instructor indicating that qualitative adjustments are the best route for appraisals going to court, and although many clients (and some appraisers) turn their nose to this method, it acknowledges scarcity of data in the market as well as the variance in adjustment levels, and although it is not perfect, it is more resilient to this type of examination
Exactly. I would rather have a list of sales from low to high and compare the subject to them and say where in the array the property should fall. The issue with any adjustment is that you can vary the adjustment and the choice of comps makes a huge difference sometimes.
 

leasedfee

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Well, you perhaps haven't gotten the memo, but many federal court judges disagree with you and no longer will accept some boomer appraiser getting on the stand and pulling a number out of his behind as an adjustment.

Finally getting around to responding to this old post. RebelNYC, I am not a boomer. My adjustments are based on economic logic using cost, sales, and income approach principles to find match-pairs or capitalization for differentials, and so forth. These adjustments relate to the language of people in the marketplace, like an investor saying, "Renovated units rent for $150/month more....." Thus, the sales comparison adjustment will reflect this. Yes, there is judgement but no wild guesses or pure feelings. Of course, sometimes the support is weak, and that data suggests a higher adjustment than what makes sense so you reconcile to a lower adjustment amount. If in doubt, I don't adjust and simply explain. If the market data shows adjustments over 20%, I sleep well at night applying it.

Regression analysis is not robotic; it requires extensive judgment. What is the quality of the data (this alone makes garbage of most so-called statistical analysis)?What dependent variable and why (e.g., $ or $/sf)? Which variables do you use? Which you don't? Which you pull out? What is the criteria for dropping a variable? Linear or non-linear? Dummy variables or not? Sampling criteria? As residential appraisal has, maybe, 20 variables to consider, the regression analysis needs n - 20 degress of freedom > 30, equaling n>50 homogeneous data points to be considered statistical. It is worse for commercial. Most of what passes for statistical analysis in appraisal is really just "trend analysis". It is similar to a handful of match-pairs. And if you can't find something worthy of match-pairing, then you won't do better with multiple regression analysis. RebelNYC, start running or requesting Confidence Intervals on such analysis. It will be shocking and disappointing.

When I was on the TAJ peer review panel, there was an academic article that used "cul de sac" as its sole residential location adjustment while using metropolitan-wide SFR sales. I asked them why this one but not others variables? I am guessing that it was available in their downloaded database so they felt "We have it so it must be important". How many variables should they have considered but did not have data on?

The #1 problem with the academic work is that they ignore correlation and decide to chase the p-stat. For the unfamiliar reader, a p-stat of a variable nearer to zero is said to have statistical significance. For example, if we sample adult male height, the p-stat will approach zero as we get a larger and larger and larger sample. But it doesn't mean much to say we are 99% certain that the average adult male, if we are to grab one randomly, is between (for sake of conversation) 5'0" and 7'0". Um okay. Here is how it relates to real estate appraisal. One academic study I begged TAJ to not published had a sample of nearly 10,000s scraper houses, average houses, mansions, and condominiums . They threw it into the same blender and declared victory because the p-stats were low just like in my height example. Yet the correlation not surprisingly (i.e., the quality of the regression line) was like 20%.

"Do not confuse statistical significance with practical significance. With very large sample sizes it is possible to obtain statistically significant results for same values of b1; in such cases one must exercise care in concluding that the relationship has practical significance."Statistics for Business and Economics, 4th Ed., Anderson, et al., p. 507.​

I am glad Federal court judges question and doubt wild *** guessing by appraisers. It is about time. It is probably harder for them to evaluate statistical alchemy. Having said that, Federal court judges are not the marketplace and I wish them luck.
 

Artyman1200

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I can appreciate the institute pushing statistics to make appraisers somewhat informed, but the data set is usually too small to be reliable. While it looks good and sounds nice, the actual statistical measurements usually indicate we've wasted our time. Just because the data plots are near the trend line doesn't mean we have support. Any statistician will tell you to throw out the model with an R-squared value under 70%. I started incorporating regression analysis in the sales approach a while back but I consistently found the R-squared value to be under 30% after my quantitative adjustments. I don't want to deal with that on the witness stand.
 

Michael S

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I can appreciate the institute pushing statistics to make appraisers somewhat informed, but the data set is usually too small to be reliable. While it looks good and sounds nice, the actual statistical measurements usually indicate we've wasted our time. Just because the data plots are near the trend line doesn't mean we have support. Any statistician will tell you to throw out the model with an R-squared value under 70%. I started incorporating regression analysis in the sales approach a while back but I consistently found the R-squared value to be under 30% after my quantitative adjustments. I don't want to deal with that on the witness stand.

On investment properties we'll typically incorporate a regression analysis based on NOI/SF and Sale Price/SF as additional support. Usually the R squared value is 0.8 or higher. Especially on single-tenant NNN investment sales. Those will commonly have a value of 0.9 or 0.95 as the cap rate range is typically very tight and the Price per SF is almost entirely dependent on the NOI/SF.

I've used regression analysis on a few other things, but given the heterogeneous and limited amount of data it's rarely very meaningful. I have found it useful for things like land size or land:building ratio adjustments for industrial properties.
 

kiritf

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Absolutely no intentional bashing at all, but I think it would be nice if the people on here that have actually been to court and have experienced said issues would do a check-in. A REAL one. That means at least say, 5-10 visits to a circuit and/or a federal court (if you're talking Federal, then your experience should be in Federal). I call BS on a lot of this stuff and it's locker room talk of opinions and not real experience. NOT all of it. There's plenty that's been stated that has merit and is progressing in the court room. I've been to court I'm guessing 70-75+ times including Federal Bankruptcy, tons of municipal circuit courts, etc. I've never been bashed over making an adjustment and explaining that typical rents and sales in that area are typically about 10% higher for the same property type (say for a location adjustment). Call me a boomer, but if I have sound economic reasoning and logic, it's completely acceptable. I was in Federal Bankruptcy court in November in Harrisonburg, VA and that was a very accepted response. Perhaps ye old Virginia is just behind the times...... Did things change in the last 3 months????? That's it. Done. I mean, most of the time, the other side has an appraiser who is really really bad and so I don't even need to do much at all. Occasionally, one of my peers who's decent, is on the stand and there's honestly not a lot to go over. The differences have been pretty nominal except maybe 1 or 2 cases and in those cases, I felt and the judge felt that both appraisers had pretty decent rationales for an adjustment and a conclusion.

Michael S,, I would tend have a statement very similar to yours. Certain cases yes, and many, no. There's the "perfect world" which doesn't exist and then there's "reality", which, hold on, does exist. :ohmy:
 
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kiritf

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and nice writing too leasedfee. I tend to agree with most all of your points on this page. Good brain sir.
 

Terrel L. Shields

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Any statistician will tell you to throw out the model with an R-squared value under 70%
Depends, I've seen r=1 and not credible, (bathroom was 29000 & bedrooms at 1100.)
 

leasedfee

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Depends, I've seen r=1 and not credible, (bathroom was 29000 & bedrooms at 1100.)
True, Terrel. And a match-pair has a 100% R^2. (We all have extracted a match-pair which indicates the opposite direction of what theory and our expectations expect.) A polynomial linear regression can always be fitted for R^2=1. Anscombe's quartet, below, all have the same properties: mean of x; variance of x; mean of y, variance of y; correlation; and linear regression line (81.6% in the wikipedia illustration, so it's a "good" fit).

The bottom right also illustrates how much leverage one outlier has on a regression line. Tests for "leverage" tends to be ignored in 101 stats courses. Basically, the regression relationship rotates like a wrench around the axis depending on the placement of the outlier.
1280px-Anscombe's_quartet_3.svg.png

The confounding limitations go beyond linear regression analysis, and also pertains to the simpler statistical tests and elementary logic of the scientific method. Furthermore, our work tends to be with non-normal curve (i.e., bell) distributions like lognormal and bimodal distributions of housing prices, income, property age, demographics age. Our data sets muddle cross-neighborhoods data and commingle property type data (e.g., warehouse lumped together with flex with hard to classify industrial structures). It takes a massive amount of time to cleanup a data set.

As appraisers, not only do we work with bad data, but do we also have to become sufficiently expert in statistical analysis besides experts in our own field? When hanging out with my non-AF appraiser friends, I find that they are so rushed and burdened with their work load and trying to balance it with a social life that they simply can't take on an expertise in another field when some of them struggle with obtaining expertise in their chosen field.
 
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