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One simple extraction

I just fielded a call from a potential client who was curious about how an appraiser would go about extracting an adjustment from the market, in this case specifically basement finish. In the discussion I explained that there is no factor that appraisers use, but that we turn to the market to try and show us what buyers are paying. Because different markets can act quite differently, I thought putting up a couple of examples of this type of extraction might be useful, both to my potential client, as well as my audience in general. The following show two different examples of an extraction for basement finish, one in Ann Arbor related to a generally newer house in the $400,000 or so price range, and the other in Lincoln school district in the under $200,000 price range. Both use the same methodology and both show substantial differences in final results, which is why an appraiser cannot just provide a number. Instead the appraiser has to look at the market. The first sample I went back two years and narrowed my market data to houses between 2000 and 3000 sqft, built between 1990-2010 on the west side of Ann Arbor (used areas 82, 83, and 84) and then downloaded all these sales to Excel and segmented the sales between houses with finished basements and without. The results were 37 sales without finished basements and 62 identified with finished basements. I looked at median and average sales price differences and median and average amount of basement finish, and came up with between $21,647 and $24,500 difference in price favoring those with the basement finish, and between $24.24 per sqft and $27.75 per sqft of basement finish. This provided me with some support for my adjustment. I don’t recall what my adjustment was, but I think anywhere between $20,000 and $25,000 is supported based on this data. That and in my experience, basements in this area cost about $40 per sqft to actually finish. Here is what it looks like on a spreadsheet: basement finish a2 400k The next example is using sales in the Lincoln school district, and in this one my isolated properties were between 1,200 – 1,700 sqft in size and built between 1985-2010, also going back two years. I had 48 sales without basement finish and 36 with basement finish, and the median difference in price was $8,953 and the average price difference was $14,420. The median size of finish was 625 sqft and the average size of finish was 703 sqft, supporting adjustments per sqft of $14.32 to $20.51. lincoln As you can see, there are differences in price between the areas and the sizes, as would be expected. Cost remains about the same to complete. Each appraisal may be different, and the numbers found here in these two samples could change depending on how far back the appraiser goes on their data research and what they set as the perimeters for the data search. I offer this to you, my readers, as a simple study showing how I often go about trying to extract an adjustment from the market. A final word of caution; I would not expect to see an appraiser put this analysis into their appraisal. They will likely do it, and say something in the report about the adjustment being analyzed through market data. This is what they likely mean, but won’t put the actual results into the report, instead they will have it in their files, be it in the office in general, or specific to an appraisal they were working on. Hope you all enjoyed this simple explanation, and if you have questions about appraisals and appraisal processes, please feel free to contact me. Easiest way to reach me is via email at rach mass at comcast dot net.

Depreciated Cost, a Test of Reasonableness http://goo.gl/dysa1o


All Three of us worked on this piece.  I won’t post it in the entirety yet as it’s brand new today and should be given full look at through the publishing site.  But if you happen across it here, please click through to read it.

Unraveling CU: DYI

By Timothy C. Anderson, MAI, Msc., CDEI, MAA

In my on-going attempt to unravel some of the mysteries of real estate appraisal, as well as to give appraisers an idea of what it is that CU is and does, I have studied some actual sales in a mid-western state and then summarized those sales data, in graphic form, in the Figures below.

The exhibits that appear below are from the statistical functions in Excel®.  There is some rather scary looking algebra on them.  But do not worry: most of it is for comparison purposes.  You do NOT have to understand how the computer arrived at those formulae (i.e., the algebra and calculus behind them) to understand the topics in this article.  The math behind what those formulae tell us is not really all that difficult, but it is for advanced classes.  This is an article, not an advanced class.

To understand this article, you do not even need to understand statistics.  Just follow the narrative and the thrust of the charts will become clear to you.

First up is an explanation of the data the chart’s use.  These data are from 2013 and 2014, so are recent.  The appraiser who amassed them knows what s/he is doing, so there are no reasons to question his/her professional integrity or ability.  These are actual sales data, culled from the MLS.  All have closed escrow and transferred title from the seller to the buyer.  The sales prices are all cash equivalent (i.e., adjusted for non-realty concessions as necessary). All of these sales data are from the same subdivision, but that subdivision has houses of varying ages, sizes, qualities of construction & maintenance, and so forth.  In other words, the houses here are all subject to the same market forces, but clearly differ one from another.

Since the data were not property-specific (i.e., not all of them would be applicable to a hypothetical subject), what we look at in this article are the subdivision’s trends.   Specifically, we analyze if there is any correlation between (a) the sales price per square foot and the year built; (b) between sales prices per square foot and total size; (c) between sales prices per square foot and the date of sale; and, finally (d) the correlation (if any) between the absolute sales price and the days on market.

Just to jog your memory about statistics, in any comparison there has to be a basis for that comparison.  This basis is called the independent variable.  It is always shown on the graph’s x-axis (e.g., the horizontal line or the base line).  The dependent variable is always shown on the y-axis or the vertical line.

This article’s topic is the correlation between the dependent and independent variables. On the Figures that follow, you will see lots of blue dots and then lines of various colors.   What you are looking for is how well the lines (specifically the red line) track with the blue dots.  When the (red) line and the blue dots are close to each other, there is what is called high correlation (as well as low variance).  All other things being equal, we look for high correlation, typically above 50% (and really, a correlation close to 90% is more-or-less ideal).

When there is a high correlation it means the data explain will the relationship between the independent variable and the dependent variable. When that correlation is low, however, it means the two variables really do not explain each other.  We will see examples of these relationships as the article progresses.

Another purpose of this article is to illustrate (but not explain – too short for that) what it is CU does with all that data with which we have provided it in the past.  When CU flags an appraiser’s entry in a field, it is because it has gone thru an analysis such as one of these (although far more in depth, breadth, and width), and then determined that the appraiser’s response did not correlate properly with the other data it has in its database.  This lack of correlation does not mean the appraiser is “wrong”.  It merely means the appraiser needs to explain how/where s/he derived that particular response.  While there are many ways to respond to such a request, a graph such as one of those below, goes a long way toward that explanation.

Take a look at Figure 1.

figur 1

It looks at the relationship between the sales price per square foot of the properties (y-axis) and the year in which a particular house was built (x-axis).

First, look at the red line.  Notice its trend is slightly uphill from left to right.  This means that newer properties tend to sell for more per square foot than to older properties.  All other things being equal, you would expect this relationship.  However, as you will also notice, relatively few of the blue dots (the sales price per square foot of the component sales) touch the red line.  This means there is a lack of correlation (i.e., a high variance) between the two variables.  In fact, the formula at the figure’s upper left-hand corner shows a correlation of only 1.85%, which is essentially no correlation at all.

What this statistical analysis tell us is that, assuming a particular property were to have been constructed between 1999 to 2007 (and all 77 in the sample were), its age at the date of sale really has nothing to do with its sales price per square foot, since they do not vary in all that much.

Therefore an age & condition adjustment for a property built within these years is likely not necessary.  True, this contradicts the traditional thinking of many appraisers.  But are appraisers incapable of change when the need for that change stares them in the face?

Now look at the purple line (ignore the green one, since it is a variation on the red one).  While the math behind the purple line is more demanding than the math behind the red line, it is more explanatory, too. What this says is that the market current as of the date of appraisal was willing to pay more for houses built in 2002 that for houses built much before or after that date.  However, they do not explain why this is so.

However, despite the fact the purple line touches more of the blue dots than the red line, it shows a correlation of only 13% between year built and sales price per square foot.  While this latter line explains the market better than the red line, it does not explain it all that much better.

This Figure, therefore, indicates that, given solely these data, there really is no compelling reason to make an adjustment based solely on a house’s date of construction.  Given different data, or using less than 77 sales, the graph might have indicated a different result.

Figure 2, however, tells a different story. Looking solely at the blue lines, it is easy to deduce that as size increases (the x-axis), sales price per square foot (the y-axis) decreases.  From looking at the dots, however, that there is an overall decrease is clear, but the rate of decrease is not.  Now look at the red line (ignore the other two since they are essentially the same as the red line).  You’ll notice that, not only does the red line touch a lot of the blue dots but that, of the blue dots that don’t touch it, a whole bunch of them are really close to it.  This indicates that, given this sample of data, there is a high correlation between a house’s square footage and its sales price per square foot.  In fact, the math behind the red line (not shown here, but included by reference) shows there is an 82% correlation between the two.

figure 2

In fact, the formula in the far upper right-hand corner of the Figure quantifies that change in value.  It says that there is a $0.0302 change in sale price per square foot for every 1 square foot of variance in size from the average square footage of this sample (in this case, the average size is 1,998 square feet).  In fact, these data indicate that for an average size house (i.e., 1,998 square feet in this sample), the market recognizes an adjustment of $91 per square foot [(-0.0302[1]x * 1,998) + 151.25 = $90.90].

Therefore, were an appraiser to make an adjustment of $15 per square foot for size differences in this market, based on these sample sales transactions, then CU would (rightly) flag it.  Why so?  Because the market data clearly indicate this market does not support an adjustment at $15 per square foot for this difference.  This analysis is based on these sales, not on traditional rules-of-thumb.  Obviously, using different sales, or using less than the 77 sales here would provide different results.

Now let us consider changes in sales prices per square foot as they relate to changes in sales dates.  In other words, as time progressed over the time period these sales covered, how (if at all) did sales prices per square foot change?  Since the sales date is fixed, it is the independent variable (the x-axis), whereas the sales price per square foot is the y-axis.  See Figure 3.  For purposes of this discussion, we ignore the really funky formulae and concentrate on the “simple” one (the one that calculates the red line).

figure 3

Note in this Figure there are lots of the blue dots that are relatively far away from the red regression line.   Again, this indicates the data were all over the place, thus show a great deal of variance[2] or error. Therefore, in Figure 3, there is a lot of error.  It also means the data are not really reliable at predicting anything other than a trend (i.e., as time passes, value per square foot increases).  The red regression line also shows the correlation of these data in predicting anything is really low at 4.2%, which is no correlation at all.  So these graphs, and the data behind it, are something you would toss into the workfile and forget.

Now move on to Figure 4.  It shows the relationship between total sales prices and confirmed days on market.  Look at the red regression line.  Not a lot of the blue dots touch it, so there is a lot of error there.  Its correlation of <1% indicates there is no more linear correlation between these variables than the operation of mere random chance would explain.

figure 4

However, look at the green regression curve.  This is a lot more complex to calculate, but as you can see it touches a lot more of the blue dots (approximately 26% of them, as a matter of fact).  What this graph demonstrates is that relative inexpensive properties (<$150,000) spent a lot of time on the market before going under contract, whereas more expensive properties ($160,000 to $200,000) spent relatively fewer days on the market before they sold.  Then, at about $200,000+, their higher prices meant they appealed to a smaller submarket of buyers, thus their days on the market increased back to between 140 and 160 days.  So what does this relationship mean to an appraiser?

On page 1 of the 1004 form, it means the “typical” range of values in the neighborhood is from about $160,000 to $200,000, with the sales outside of this range as outliers.  It also means that, were the appraiser to conclude a value outside of the $160,000 to $200,000 range, the appraiser would also be concluding a longer-than-average sales period (here the average was ±61days). However, given the low correlation coefficient of 26%, it also means that there are reasons other than days on the market, that explain difference in sales prices. Thus, whatever conclusions the appraiser were to draw from this graph merit the use of a liberal seasoning of salt.

So what are the take-aways here?  The only graph that really tells us anything is that of Figure 2, given that it shows an 82% correlation.  Therefore, the appraiser can confidently conclude that mere square footage alone accounts for 82% of prices differences.  Further, given this high degree of correlation, the appraiser could use the regression formula (-0.0302x * 151.25)[3] as one fairly accurate tool to use in forming a value conclusion.  Note, however, it is no more than a tool.

What does all of this have to do with CU?  CU’s built-in algorithms[4] do all of the above, plus a whole lot more, and have millions of data points to draw on, not 77, which is what we had here. It can compare all of these data points with each other one variable at a time, or it can look at the “big picture” and compare them all at once via a multi-variable regression analysis.  While a multi-variable regression analysis is far from infallible, and will not work under some circumstances, if FannieMae can use tools such as this one, why should appraisers not use a similar tool, too?  (If you have Excel®, then activate the statistics pack, and you will have all of the statistical computing power and potential you will ever need).

Although some of the regression tools that are popping up all over the web are appealing, Excel® offers everything you need, right at your fingertips. All you will need is a few hours study time to get up to snuff on it, and then it is virtually free. There are courses that are available with different education providers that can walk you through learning how to use it if that is the way you learn best, and even some online tools not related to appraisal that are very inexpensive and accessible (think udemy as well as Microsoft itself).

On a closing note, note the technology to take the appraiser out of the mortgage-lending picture has been in FannieMae’s hands for at least the last five years (and the math has been available since the late 1700s).  The data and technology to do so exist now, and will only become keener in the future.  This article was written with the residential appraiser in mind, to offer a simplified version of how Excel works and a sample from the real world where it is applied.

If appraisers do not start to adapt and change, and keep to the status quo of three or four sales on a grid, without providing some support for their analysis, why should FannieMae and local lenders continue to pay appraisers millions of dollars per year to do what FannieMae can already do essentially for free with literally a few keystrokes of CU?  Algorithms already “grade” our appraisals.  Right now they have the capacity to do everything we do now (for the most part), but CU can do all of this much faster, cheaper and more compliantly.  FannieMae is well ahead of is in this race.  We appraisers can catch-up with technology and thereby show our clients we are the ones to be doing their appraisals.  We should be doing them, not brokers, not AVMs, not unlicensed desk-monkeys, and most certainly not FannieMae whose lenders have a vested interest in getting the numbers it needs to make the loans. 

[1] The fact that this coefficient is negative means the line slopes downward from upper left to lower right.  If this coefficient were positive, the opposite would be true.

[2] In stats-speak “variance” is also called “error”.  This does not mean there is something amiss or the math is wrong somewhere.  It means, instead, that when a point falls well above or below the regression line, it is in error by that distance from the regression line.

[3] In this formula, the “x” is the square footage you want to insert.

[4] Without going into a lot of calculus or philosophy, an algorithm is a “set of rules that precisely defines a sequence of operations”. A computer program is an algorithm.  CU uses algorithms.  Fortunately, appraisers do not have to write these algorithms since they are built into Excel®.  See  http://en.wikipedia.org/wiki/Algorithm.

Highest and Best Use is More Than Just a Check Box

This originally appeared over the Appraisal Buzz on Wednesday, December 3, 2014


As review appraisers, one of the issues that we see all the time is the failure to analyze highest and best use for a market value opinion related to mortgage lending appraisals. This makes sense to a large degree, because many appraisers believe that providing the “yes” answer relieves them of further analysis and communication. We wanted to address this topic and offer some insight as to why one may want to rethink their approach to this common issue. In that light, we thought that we would look at a key part of the valuation process, but one that often gets overlooked in residential reporting: Highest and Best Use. With the majority of reports being written on pre-formatted reports from Fannie Mae, many appraisers skip over this section as nothing but a box to check.

A required characteristic of any valuation professional is the ability to learn, and not just occasionally, but to continuously do so through one’s career. Look at any successful appraiser that you know; chances are that he or she makes time for classes. Many of the leaders in the profession are even known to write course work or review it for publication. So do not look at this article as us telling you that the sky is falling, but rather as a perspective that many of us have adopted in our evolution as valuation professionals. I know that we both will periodically look back at past work and reevaluate how we approached a specific problem. After all, as we learn and experience more, we learn new ways to do things or ways to improve upon what we already do. The goal is continual improvement.

As appraisers, we are by nature opinionated. We have a tendency to believe our way is the only way, or the best way, and although we may expect perfection, none of us come into the world knowing how to appraise. Appraisal learning is life-long, and perfection is not possible, although we strive for it by continuing to have an open mind to gaining new insights. The Uniform Standards of Professional Appraisal Practice (USPAP) even addresses that perfection is impossible to attain, and competence does not require perfection.1 The Standard Rule 1-1 (a) comment also addresses how the principle-of-change it continues to affect the way that appraisers perform their work.2 These items are under the development standard with which we all abide, and are the set up the point we are making – which is that none of us are perfect, and hopefully we all simply try and improve our skillset, each and every day.

The Valuation Process is an eight-step procedure that starts with the identification of the problem to solve; flows on to the determination of the appraiser’s scope of work; data collection and property description; followed by data analysis (see figure 1). Data analysis includes the market analysis as well as the Highest and Best Use Analysis – considering the land as vacant; what the ideal improvement would be, and the property as currently improved. Next, is the land value opinion; application of the approaches to value; reconciliation of the valuation approaches as well as a final opinion of value followed by the reporting of that defined value.

Clearly, the data analysis section requires a highest and best use analysis related to a market value opinion. This is also succinctly addressed in The Appraisal of Real Estate, 14th Edition on pages 42-43 for further reading.3

Figure 1: Courtesy of the Appraisal Institute (used with permission)

The 1004 form, which is the most common report form for residential mortgage assignments, specifically asks the question “is the highest and best use of the subject property as improved (or as proposed per plans and specifications) the present use?” followed by a check box for yes or no, and if no to describe (see figure 2).

Figure 2


As Standard 1-3 (b) in USPAP exhorts us to develop an opinion of highest and best use of the real estate when a market value opinion is developed (page U-19 2014-2015 USPAP), and Standard 2-2(a)(x) states specifically “when an opinion of highest and best use was developed by the appraiser, summarize the support and rationale for that opinion” (page U-24 2014-15 USPAP), checking the box without any further discussion is not adequate. Perhaps it is the lack of description in the box next to “yes” that throws appraisers off, but USPAP is clear that when it is developed, a summary for the opinion is required.5

To even start to address Highest and Best Use, the appraiser needs to have at least visited the zoning ordinance to not only understand what is an allowable use, but also what the minimum site size requirements are; what width is required; what the setbacks are, etc. Often we see zoning mislabeled, and more often than not, no information about what even the minimum site size is for the use. Without this basic information, it is not possible to start analyzing the highest and best use.

Discussing this issue with some appraisers online it was apparent that many do not believe any additional summation is required in the form other than checking the yes box, with the argument that as zoning is reported as either legal or not, meets the legally permissible criteria. That a house is built (or proposed) tests the physically possible criteria, and that reporting of functional depreciation in the cost approach or sales comparison approach addresses overall conformity and therefore financial feasibility, and that finally the remaining economic life provides for highest and best use as currently improved. While this may seem like a reasonable argument, we do not believe it is sufficient for a number of reasons, including it being nothing but an executive summary of real work and does not rise to the level of summation.

In addition, when doing work for a lender client, one must ask, “What is the purpose of this report?” The obvious answer is to determine market value, but the lender uses it as a risk assessment tool. They are trying to ascertain if the property is atypical to the market in any way and if so, how does that affect the value, and ultimately the ability to free them of the collateral in the event the loan goes sour. While an appraisal cannot answer that question in the entirety, it does help them assess their full risk by lending on a specific property.

Since the majority of appraisal work related to mortgage lending completed on form reports is for an improved property, much of the time the conclusion is that the highest and best use of the real property is that which is already in place. How difficult is it to flesh out a short paragraph related to this analysis? Given what we are seeing on a routine basis, it is apparently a monumentally difficult task given that it is rare for us to see anything beyond the “yes” check box.

What we are suggesting is that appraisers take a few extra minutes to summarize the highest and best use analysis. It can be done in as little as a sentence, but usually no more than a paragraph. One of the biggest reasons that we suggest it is that it will force you to slow down and look at your data. There have been instances where one of the authors has found out that some appraisals under review were in an illegal or a legal non-conforming use. During the review, it was discovered that the appraiser did not stop and do the analysis or did not really understand that they should look at it or report it. This puts a lender in a sticky position as they may have to shelf the loan and will not be able to sell it on the secondary or worse, have to buy it back.

In such instances, it may require several pages to support the highest and best use. Once it becomes something more complex, due diligence is paramount. The biggest reason appraisers should care about this is that it puts the appraiser in a more defensible position if something awry happens down the road with the loan. By attempting to address this directly up front you are less likely to be discredited for skipping or going too quickly through a section of the report.

One of the authors has done litigation review work where this specific issue was used by the attorneys as part of their strategy to discredit the appraisal report. In litigation, attorneys will often go to the fundamentals to challenge the appraiser’s work. To a judge or a jury it easy to make the connection that if the report is short on a fundamental concept then it is easy to assume it is also short on the section most scrutinize the heaviest, the sales comparison approach. We have both seen reports that have great sales comparison approaches, but little else in the way of a well-written report. Those are the reports that can hurt you in situations where you must defend your work.

So there you have it folks. A seemingly simple thing that really is not so simple. If anything, we hope this offers you something to think about when you are writing your reports and developing the analysis. We are sure this will create some interesting comments as well. Please feel free to share your thoughts as discourse helps us all learn more.

– See more at: http://appraisalbuzz.com/buzz/features/highest-and-best-use-is-more-than-just-a-check-box#sthash.kXUgU1Qb.dpuf

Non-Lender Valuation: Consumers Should Tread Carefully

By Woody Fincham, SRA

This post was originally posted to the Appraisal Buzz

Competition, in a free market, is a fierce catalyst: one that can effectively sort out the bad apples from the bunch. Capitalism works, it is simple when left unfettered and when all parties are ethical in their approach to business. It works until politicians, however well meaning they try to be, step in with a”solution”. Through the Dodd-Frank reform and the Andrew Cuomo created Home Valuation Code of Conduct that predates Dodd-Frank, congress effectively went anti-small business again. I liken this profession’s recent undermining by congress to how they saw to sort out the small-family farmers by paving the way for companies like Monsanto and ConAgra.

Competition is fierce in the valuation profession these days. For competition to work, it does require a level playing field. Presently, in residential valuation, there is no such thing as a level playing field. There are still lots of mortgage-use reports to do, but these reports are being filtered through appraisal management companies (AMCs). The AMC model chooses the cheapest appraisers competency is a distant second to cost, and like most things, you get what you pay for.

The quality of appraisal reports ordered thorough AMCs is getting bad enough that members of the Appraisal Foundation (TAF) have been quoted recently in the media with some interesting points. In a recent Chicago Tribune, John Brenan, director of appraisal issues, is quoted as stating:

“First, there is no additional revenue to fund AMCs, so the fee that an appraiser would earn is now divided between the AMC and the appraiser. Appraisers are making less money, and they have a new middleman they wind up working through. They’re looking to engage the cheapest and fastest appraisers. So, we’re seeing appraisals done across the country where the appraiser does not have what is, in fact, required under standards we write for geographic competency” (Glink & Tamkin, 2013).

By Mr. Brenan’s comments, it is obvious that enough emphasis was not placed on the things that matter. Instead of requiring the banks to pay for the alteration, a market was enhanced for non-appraisal entities to make money. Instead of enhancing the appraisal process, they provided a market that actually counters retaining well-qualified appraisers. It is a pretty big deal when an organization like TAF is drawing attention to the deficiencies found in the appraisal profession. One should give pause when history has proven repeatedly what happens when the collateral of mortgages is not properly vetted. The recent mortgage bust was partially created by issues with appraisals.

I would also supplement that most of the problems fell squarely on the big banks and how they retained and utilized appraisal services. Instead of requiring lenders to do the correct thing with retaining qualified appraisers, AMCs were given preference as a means to outsource the responsibility or at least the appearance of responsibility. The lenders got the advantage of AMCs seeking out minimally qualified appraisers that follow narrow scope of works (SOWs). Rather than hiring appraisers that are both competent and confident, they hire those that are prone to following without question. They effectively dictate to a large section of these appraisers how to do their job.

I know what you are thinking: Fincham your title says non-lender valuation, so why are you writing about Dodd-Frank and AMCs? Good question…

Non-lender valuation is the last bastion of market share that exists where appraisers can actually bill at a commensurate rate. These types of assignments will include appraisal reports performed for many situations such as wealth-management, divorce, and other litigation related needs. Oftentimes, intended users need to find the most qualified and experienced appraisers. Well-vetted experts are most applicable when testimony is needed. As litigation and divorce proceedings have evolved over the years appraisers are not needed as much for testimony; a report will satisfy the streamlined processes. In these situations, attorneys are not as involved with selecting appraisers as they were in the past.

Attorneys understood the need of retaining the best appraiser he or she could find. They needed someone that could write reports well enough to be seamless and defensible but also handle cross-examination in a trial or handle the craziness that can be a pre-trial deposition. It takes a good professional to write the report, but an even greater one to be effective on the stand or to help with pre-trial preparation. In the case of wealth management: to talk to an accountant and walk them through a report or analysis on the phone.

With less emphasis placed on the interview skills of the appraiser, many attorneys have relegated the retainer of an appraiser back to the client. Most consumers do not really understand what they need. The consumer makes a call, or does an internet search, to find an appraiser based on the only criteria that the do understand: cost. They also negate the importance of selecting the right professional in case they may need testimony later in time.

They can contact a well-qualified appraiser that understands the work involved with their situational needs, or they can contact an appraiser that does mostly government sponsored enterprise (GSE) work. Appraisers that do mostly lender-use work within a very confined box, and unless they have a background in non-lender work, will likely not have the problem solving skills needed for thinking outside of that box. AMCs often provide such detailed instructions to their roster appraisers, that the appraiser is boxed into a very narrow scope of work (SOW). These appraisers are experts at meeting the SOW established with the AMC. However, what happens when these narrow SOWs are removed? You introduce someone that specializes in filling out a form to a world full of variables and possibilities.

An appraiser is only as valuable as their experiences allow them to be. Part of this value is knowing and recognizing the strengths and weaknesses of the approaches to value. An even bigger part is thinking in the abstract and knowing that in trials and depositions, an attorney will exploit a weakness in a report. They will discredit an otherwise good appraiser if that appraiser is incapable of dealing with questioning effectively. Appraisers that concentrate solely on mortgage-use reports have no background to be effective in these types of situations.

So How Does Dodd-Frank Tie In?

Therein lies my problem with the AMC bred and conditioned appraisers. The fees have been beaten down so low for mortgage work that the appraisers that only do AMC related work are now trying to compete in the more lucrative non-lender market. Here we have members of TAF acknowledging that the lender market is using less-than-optimal appraisers. That alone is enough to make a normal person pause and pay attention. This was Washington’s answer to a problem they did not understand, and by stepping in, they created waves that extend beyond their intended design. They destabilized the market for established and trusted professionals.

These same mortgage-use appraisers have discovered that non-lender work pays better: in some cases, much better. They capitalize on the naivety of the consumer base. In a sense, they are capitalizing on a competitive advantage, but only an artificial one that was created by the meddling of politicians. In a very real way, Dodd-Frank is now affecting the valuation profession outside of the mortgage business.

In that same Chicago Tribune article, David Bunton the president of TAF stated “Most appraisers not going to turn around a top quality appraisal in 24 hours, for half of the normal fee. So you get people who are less experienced, who have less business clientele, and they may end up driving 4 to 6 hours for $150. We’re concerned about quality” (Glink & Tamkin, 2013).

Mr. Bunton, we are all concerned over quality. Those of us that have refined our toolsets and experience are being passed over for appraisers that have been subsidized by a flawed mortgage market that is propped up by the AMC model. The weak links of that subset of appraisers are now matriculating into non-lender work. In a way, the biggest user of appraisal services, the mortgage companies have once again undermined the appraisal process. By law, appraisers are required to abide by USPAP to preserve the public trust. Until lenders and AMCs are required to follow it, it will remain nothing more than a very effective tool to make those that claim to be ethical blend in with those of us that actually are beholden to our professional integrity.

The bottom line for appraisers, attempt to educate your attorney clients and colleagues on the differences between what a true professional appraiser is and what a primary mortgage-use appraiser is. Reach out and network with your bar associations and other professional organizations. Distinguish yourselves from the group through education and networking opportunities.

The bottom line for consumers: be careful whom you attempt to retain. Be willing to ask an appraiser why they are a better pick than market of other appraisers. Be willing to check references and ask for a resume. Take your time and make sure this appraiser is well qualified and not just minimally qualified. The inverse to you using a well qualified can actually cost you more money. If you end up in a trial, the cost to have your attorney reorder a better report, or pay a well qualified appraiser to assist in pre-trial analysis. Even worse, you may find that across the courtroom, your opponent hired the appraiser you should have, and now your mortgage-use appraiser will be in contrast to a superior professional.

Works Cited

Glink, I., & Tamkin, S. (2013, December 26). How do you get a great appraisal? Try eliminating the AMC. Retrieved from Chicago Tribune real estate: http://www.chicagotribune.com/classified/realestate/sns-201312221330–tms–realestmctnig-a20131226-20131226,0,4405990.column