Category Archives: Statistical

The Uniform Appraisal Dataset

This article first appeared in Appraisal Today. Thank you Ann O’Rourke for publishing!


The Uniform Appraisal Dataset (UAD) was designed to help bring some uniformity into the appraisal process related to mortgage work. After all, appraisers were not very uniform in the way they described condition and quality.  This applied not just to condition and quality ratings, but to a myriad of other factors such as view and location, down to how basement finish was identified. The Collateral Underwriter (CU) was designed in part to synthesize all of this information and also to identify areas where appraisers differed markedly from each other, as well as even within their own work product.

We had a lot of angst at the outset of CU related to reusing quality and condition ratings, and possibly changing them from one report to another. This was a valid concern because it is one of the factors that does get measured within the CU.  Since the UAD has been required since 2011 for mortgage related work intended for Fannie Mae or Freddie Mac, we would expect, five years after the fact, that appraisers would be well versed with what the specific ratings entailed. As both a reviewer and appraiser in the field, I find this is unfortunately far from the truth. The truth seems to be, that confusion related to the requirements is still rampant, and my contention is, that it is not the appraisers who are at fault, but the definitions themselves. There are simply too many grey areas. Either that, or education has not been sufficient in helping appraisers understand what is expected. It is a good possibility however, based on the continued widespread variances, that the definitions themselves are too vague.

Let us take a look at quality for a Q4 and a Q5 house. The ratings in the 1004 form show:

Q4 Dwellings with this quality rating meet or exceed the requirements of applicable building codes. Standard or modified standard building plans are utilized and the design includes adequate fenestration and some exterior ornamentation and interior refinements. Materials, workmanship, finish, and equipment are of stock or builder grade and may feature some upgrades.

Q5 Dwellings with this quality rating feature economy of construction and basic functionality as main considerations. Such dwellings feature a plain design using readily available or basic floor plans featuring minimal fenestration and basic finishes with minimal exterior ornamentation and limited interior detail. These dwellings meet minimum building codes and are constructed with inexpensive, stock materials with limited refinements and upgrades.

Q4 indicates stock builder grade and may feature some upgrades. Q5 indicates basic finishes and inexpensive, stock materials with limited refinements and upgrades. Both say stock, but Q4 says it may feature some upgrades. What are these upgrades? Are they upgrades to cabinetry, or are they upgrades to the building itself? Does taking a standard high-volume production build quality for the starter market, but adding higher-end cabinetry bump the quality level up?

What about condition? Probably the two ratings that are utilized the most are C3 and C4, which are as follows:

C3 The improvements are well maintained and feature limited physical depreciation due to normal wear and tear. Some components, but not every major building component, may be updated or recently rehabilitated. The structure has been well maintained.

*Note: The improvement is in its first-cycle of replacing short-lived building components (appliances, floor coverings, HVAC, etc.) and is being well maintained. Its estimated effective age is less than its actual age. It also may reflect a property in which the majority of short-lived building components have been replaced but not to the level of a complete renovation.

C4 The improvements feature some minor deferred maintenance and physical deterioration due to normal wear and tear. The dwelling has been adequately maintained and requires only minimal repairs to building components/mechanical systems and cosmetic repairs. All major building components have been adequately maintained and are functionally adequate.

 *Note: The estimated effective age may be close to or equal to its actual age. It reflects a property in which some of the short-lived building components have been replaced, and some short-lived building components are at or near the end of their physical life expectancy; however, they still function adequately. Most minor repairs have been addressed on an ongoing basis resulting in an adequately maintained property

Given the scenario that follows, the C3 and C4 ratings are most likely in play, and the difference between the two are that on C3, that some components may be updated or recently rehabilitated and well maintained, and on C4 they are adequately maintained and functionally adequate. What about quality ratings for this same property?

Because this is a question that seems to engender different answers, I asked this question through SurveyMonkey:

If the subject property is a solid Q5 or Q4 production house, but the owners have installed a new high quality kitchen and bathrooms, does the quality rating change?

Figure about a 10-year old house and all sales were built the same initially.


I purposely limited the responses to three choices because I did not want to get too many options, or otherwise there would be little consensus from the respondents. The choices included 1) that recently installed new high-quality kitchen and bathroom changed the quality, 2) that it did not change the quality but changed condition, and 3) that it did not change quality but was addressed as a line item adjustment. At this writing, there were 442 responses, which is more than sufficient to have a good sense of appraiser’s opinions.


20.36% of the respondents considered it an upgrade of quality

54.52% considered it a condition rating change

25.11% considered it a line item adjustment

In essence, 79% of the respondents considered it did not change quality but was either a condition change, or warranted a separate line-item adjustment.

So, what is the answer?

Strictly speaking, the UAD language indicates it is a change of quality, but in the minds of appraisers and/or users, is it? If “once a manufactured home, always a manufactured home” is true, how does swapping out a higher quality kitchen and/or bathroom from stock raise the quality of the whole house? How does changing a kitchen and bathrooms affect the structure of the house? Does it do anything other than change condition? If it is condition, how is this different than installing a new kitchen and bathrooms of stock quality in the stock quality house? Would it be best addressed as a condition item, but also as a line item because one aspect of the house is now atypical for the quality of the typical house by this production builder?

I do not have the answers, throw this out for discussion, because as proposed at the beginning of this article, the UAD ratings are too vague, and open to interpretation. Perhaps now is the time to drill down to what is truly expected within the various ratings and start a discussion on how upgrades to some non-structural elements to a house could, or could not, affect the overall quality rating of the building. Hopefully this brief article spurs on discussion of these ratings, and helps ferment more consistency between appraisers who do mortgage related work, since we are all judged by the actions of our peers in our market and we want to be judged fairly.

As in all things appraisal, when in doubt, disclose. Write more, explain more. It certainly helps mitigate those grey areas to inform the intended users why one condition or quality rating was chosen over the other options.

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

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

Thrilling? You Bet! Part 3-Final Part

TA article 3


OK, so what does the Cost approach tell appraisers (at least, those who are willing to listen) about the market? The listening is in the analytics.

Consider, first of all, the site value as if vacant. This is a separate appraisal within the appraisal. If the appraiser does not know to a professional certainty the value of the subject site as if vacant, how can that appraiser adjust the comparable sites (as if vacant) to it? It is ironic that, to adjust the comparable site’s value to that of the subject, the appraiser must know, to the same certainty, the value of the comparable site as if vacant, too. Assuming three comps and a listing, then the appraiser goes thru this site-as-if-vacant analysis five (5) times. If the appraiser is listening, an analysis five (5) sites deep will tell the appraiser an abundance about a market.

Now, consider the entrepreneurial incentive/profit aspect of the analytics of the Cost approach.

Too many appraisers have fallen into the trap of saying there is not such a profit or incentive in the cost approach since the sale of the property is from one retail buyer to another. It is not a sale from a builder/developer to a retail buyer. This is true. It is true, yet it is also thunderously irrelevant. An appraiser builds the house new on paper. Don’t new houses sell (hopefully!) at a profit from the developer/builder to a retail user? Since it is physically impossible to construct a used house, the analytics of the Cost approach assume a new house first. Therefore to estimate the reproduction or replacement cost new of a house and not include an entrepreneurial incentive or profit is to fail to take a step the market commonly takes. How reflective of the market is that?

For grins and giggles, assume a 15% entrepreneurial incentive to the cost new before any depreciation. This is a trail balloon the appraiser floats to see if there is such a reward in the market. If the market will not pay such a reward there is, by definition, an external obsolescence factor in the market. This is a market condition which the appraiser has an obligation to describe within the report.   Yet, if the appraiser is not willing to measure the market to see if such a reward is present, how can s/he report it? The analytics of the Cost approach show if such a reward is present. The analytics of the Sales Comparison approach do not and cannot.

A blog such as this one is not the time or place to present a long article on calculating depreciation. The point is that while FannieMae, et al, no longer require a Cost approach to be part of an appraisal report going to them, whoever said the analytics of the Cost approach should not be part of an appraisal? FannieMae surely did not. How can an appraiser listen to, and then interpret the market, if s/he ignores two-thirds (Cost and Income approaches) of what it says?

Thrilling? You Bet! Part 2


Given there have not been all that many flat residential real estate markets in the past 10-years, how market-accurate, then, are the published tables? SR2-3 requires appraisers to certify to the fact that the statements of facts in an appraisal report are both true and correct. If there have been essentially no flat markets in the last 10-years, how can we certify our depreciation is both true and correct if the published depreciation tables are based on a flat market? If markets are dynamic, but the published tables assume a flat market, how accurate are they?

Another issue with the published tables is their self-recognized inability to speak to the appraiser about functional obsolescence and external or locational obsolescence. Appraisers know there are three components to accrued depreciation. Yet they depend on the published tables to conclude as to all three of depreciation’s components. These tables do not and cannot estimate the latter two forms of depreciation. In addition, it is a logical fallacy to assume a property has only one form of depreciation (even, sometimes, in a new one).

The Comment to SR1-3(a) is very clear about unsupported assumptions. If the appraiser does not engage in the analytics of the Cost approach, how is the appraiser sure there is no functional obsolescence? If the appraiser does not engage in the analytics of the Cost approach, how is the appraiser sure there is no external or locational obsolescence? If the appraiser does not engage in the analytics of the Cost approach, how can the appraiser certify that everything in the Cost Approach is both true and correct? Falling rents and/or falling multipliers may indicate the presence of these other two components of accrued depreciation. However, how many appraisers, via the residential income approach, go to the effort to read the market’s tea-leaves?

To professional appraisers, then, the issue is to extract accrued deprecation from market data. Published tables may be a help with depreciation’s age-life component, true. But they cannot aid the appraiser with conclusions as to functional or locational/external obsolescence. These tables simply cannot calculate them; the appraiser must extract them from the market evidence. Yet, unfortunately, many do not. And, equally unfortunately, many appraisers do not understand when, where, and how to account for an entrepreneurial profit/incentive. Because of this lack of competency, therefore, many appraisers do not understand the market since they are unable to listen to it.

Thrilling? You Bet! Part 1

TA article 1

A lot of appraisers were thrilled when FannieMae, et al made their great announcement. Which great announcement? FannieMae publishes announcements all the time. It was when they announced they would no longer require the Cost approach to be part of an appraisal.

Hooray, a real time saver! Thank you FannieMae et al! The Cost approach is a waste of time anyway! Nobody understands accrued depreciation. Even builders cannot decide on what’s a hard cost, a soft cost, and overhead. And that entrepreneurial profit/incentive thing! What difference do they make anyhow?! The house is up and built! People don’t expect an entrepreneurial profit/incentive when they sell their house, so why have to worry about it in an appraisal!? Give me three recent sales in the same neighborhood, and I’m in and out of that appraisal in no more than four hours! Cha-ching! That’s how I can put some money in the bank! Besides, I always back into the Cost approach from the Sales Comparison approach!

Perhaps, there is another facet of the issue to consider?

It’s true “the market” does not typically does not use the Cost approach to buy and sell houses. It’s also true we appraisers enshrine the market and its trends. That’s what we do. So, if we enshrine the market, why are we willing to ignore what it tells us? But, how do we ignore it if that’s not how the market trades houses? Simple. True, the Cost approach may not talk to us in a transcendent baritone. It more likely advises us sotto voce. We listen when the market “talks” to us. Why are we less disposed to accept its advice when it merely whispers? Lovers whisper; antagonists SHOUT.

So, how does the Cost approach whisper the market’s trends to us? There are at least two ways it communicates market trends to us, if we will but listen.

The first is thru accrued depreciation. Many appraisers take accrued depreciation from published depreciation tables. There is no question these are mathematically correct. However, these tables assume residential housing wears out at a more-or-less curvilinear rate. That rate indicates little depreciation per year when a property is new, then more per year as it ages. This, too, is true. These tables, however, do not and cannot account for incredibly hot markets, or dead ones. What do these mean?

An incredibly hot market may see the man-made improvements to a site actually appreciate in value. Yes, this is a function of speculation. A speculative market is not one that meets the definition of market value. Yet they exist and we have to interpret them. The published tables do not reflect this market condition, so we have to.

Next, to consider is that dead market. Prices have fallen faster than a Senator’s job-efficiency ratings. Our broker friends tell us the properties are selling for little more than land value. Again, the published tables do not and cannot reflect this situation.

In other words, the published tables are at their most accurate in a flat market. How many of those have you seen in the last 10-years?

Charlottesville, VA School District Values

By Woody Fincham, SRA

When looking at residential values in a given market region, school districts will generally play a large role in what consumers are willing to pay for a home. The Wall Street Journal published an article in 2008 about this, and stated, “Even for buyers and owners who don’t have school-age children, good schools can ensure consistent demand for properties — and strong prices. Taxes are also a big factor when talking about schools and home prices because in many states property taxes fund education (Peck, 2008).”

Brendon DeSimone, a well-recognized realtor recently wrote in a blog on Zillow blog that:

1. You’ll pay more to live in a good school district

2. A good school district might protect you from the real estate market’s ups and downs

3. Though it may cost more to buy near a good school, it will be good for resale (DeSimone, 2013)

So how does that apply to the market here in Charlottesville? We are fortunate to have six school districts here that are all accredited and test well per state metrics. I ran some basic metrics on an array o f sold properties from 2013. All sales occurred in 2013, all are detached homes.

You will notice that Venable, Burnley-Moran and Jackson-Via lead the city with most sales per school with 72, 79 and 72, respectively. The chart is arrayed from highest price per square foot to least (Above grade finished). Venable leads the pack in price per square foot at $225.48/SqFt.

So how does this data speak to the local real estate agents? Does this reflect your understanding of the school district relationship to sales price? Are there any inherent biases that may influence this method of data metrics?

I would love to hear any comments related to this topic. I would also be open to looking at other unique ways to analyze residential data within the city markets, if you have a thought of two of more data points that are worth looking at, please suggest them. I will happily see how that data reacts when I run it through some charts.

Remember if you need any residential valuation services please give me an email:

Works Cited

DeSimone, B. (2013, October 4). 3 Reasons to Consider School Districts When Buying a Home. Retrieved from Zillow Blog:

Peck, E. (2008, February 20). Wall Street Journal. Retrieved from Buying a New Home: How Important Is the School District?:

Appraising The Right Way Part 1: Requiem for a Dream



We are two appraisers separated by a three- hour flight or a nine-hour car ride.  We have never met in person, but have come to know one another through social media.  We are designated and recognized experts in residential valuation in our respective regions; both have had successful careers working in various positions within the profession; we are separated by enough distance that we experience completely different market stimuli.  We subscribe to doing valuation work the right way.  The way it should be done: defensible and well supported. Yet, we (and many others in the profession) are watching it being dismantled by the lenders, appraisal management companies (AMCs) and even from within the profession itself.  This is not the way that it should be, yet we still stick to our guns and we dream about how it should be regardless of the present reality.

We share a dream:

Like any great dream, it is lofty, challenging and worthwhile. We dream that we can make a living as fee appraisers, doing our jobs the proper way. The dream is to take the time to analyze the problem to be solved; research the market thoroughly including market trends; interview the market participants; analyze the sales and extract market adjustments; and then report  the opinion of value  in a way that the client can understand the  thought processes. Within this, there will be good support for conclusions and the appraisal will make complete sense to the reader. It will not leave gaping holes or questions. The opinion of value will be well supported by sales that are both inferior to the subject as well as those that are superior (and ideally equal). The appraisal will address the current market conditions and the active competition as well as the closed and pending sales.

Analysis is what we do, refined by the appraisal process, tempered by ethics and integrity all rounded out by participation in a profession that is carried out by like-mined and well-intentioned practitioners.

The dream continues:

Our clients  will truly care about the analysis and it will be meaningful to them. They need something of substance, and not simply paper for a loan closing package, or simply a report for a divorce or bankruptcy proceeding. The client understands that the valuation is based on fact, but in the end is an educated and well-supported opinion. The client understands that each report is a unique and extensive research project that is custom designed. The client is comfortable with the opinion of value because they reached out to a well-qualified and experienced appraiser; one that is rewarded the report because they are respected professionals, not just another step in a loan closing process or the cheapest one they could find.


We realize this is getting into the lofty and idealist side of things, hence the title of the blog.  What this series is going to focus on is some of the challenges appraisers face, and how we should handle them.  There is constant pressure on appraisers to adhere to scope of work enhancements from clients.  While we may mention customary and reasonable fees and the dynamic that the cost of business plays in the appraisal process in the course of this series, this is about what appraisers should be doing after they accept an assignment.

Rachel has years of experience reviewing appraisal reports working within the lending world as a staff reviewer and manager, and in the fee world through her private practice. Rachel has recently earned the new residential review designation with the Appraisal Institute.  Woody has been doing private fee review work for years and also has to review reports for tax assessment appeal as part of his position within the assessor’s office in Albemarle County, VA.  Between our combined experiences, we will focus on some issues that we see pop up repeatedly throughout various reports that have made their way across our respective desks over the years. 

Valuation from Both the Fee and Assessment Side of Things, Part 1

By Woody Fincham, SRA

This was originally posted over at Appraisal Buzz


This is the first part of a series that will briefly compare and contrast real estate assessment and standard fee practice. There are lots of similarities as well as differences between the two disciplines. There are both superior and inferior aspects to both sides, with both sides producing appraisers and analysts that are unique to their respective sides. Having worked both sides at staff and management levels, I can see how a combination of both disciplines could very well produce valuation professionals that are, to borrow one of my favorite band’s lyrics, “Some Kind of Monster”. Of course, I mean monster in a good sense. In my opinion, there are key items that both sides could benefit from learning from the other.

Often, I will be with a group of fee appraisers and hear some negative comments about assessment values or about the staff appraisers that work for a city or county. Having cut my teeth in fee work, many of my colleagues will confide stories of this or that about how “wrong” assessment is as a rule. Sometimes I think they are just trying to get a rise from me. I also get similar comments from assessment appraisers and head assessors saying things about fee appraisers such as “I can’t believe that appraiser…” I always listen, sometimes I try to explain where one side or the other may be coming from, and sometimes I just smile and say, “How about that”.

Of course, both sides of the fence have good points and both offer tremendous merit to valuation as a whole. I think because both sides work within their own respective universes without understanding how similar they really are and have minimal to no understanding why there are differences. More specifically, there are good reasons why things are different. Appraisers and assessors would be better suited to have a conceptual understanding of the differences between the two sides. After all, both groups are working towards the same end: market value.

What is the difference between assessment valuation and fee appraisal valuation? When I start doing research, I often start with very basic steps. Most often, I pull out my dictionary or a related textbook so let us try that here using the Appraisal Institute’s The Dictionary of Real Estate Appraisal, 5th edition:

1. The act or process of developing an opinion of value.
2. An opinion of value. (USPAP, 2010-2011 ed.) (Appraisal Institute )

Fee appraiser:
An appraiser who is paid a fee for the appraisal assignments he or she performs
(Appraisal Institute ).

1. The official valuation of property for ad valorem taxation (Appraisal Institute ).

1. The head of an assessment agency; sometimes used collectively to refer to all administrators of the assessment function. (IAAO)
2. One who discovers, lists, and values real property for ad valorem taxation (Appraisal Institute ).

Just looking at the definitions, one can infer that both fee appraisers and assessors are essentially doing the same thing: developing value. The stated difference really is the purpose of the value and the implied is for whom the valuation is performed. With fee reports, the appraiser is valuing for whoever hires him or her. With assessment, the purpose is to value for ad valorem taxation, and generally this is done for a local governmental entity, but can also be for state governments and in some parts of the world the national level of government. Since local code or state law usually requires assessment, laws and precedent can limit the methodology or manner used for valuation.

The most obvious difference one will note is that with fee appraisal, a single property is valued at a time using standardized practices and technique. With assessment, a group of properties is valued at a time, using standardized practices and techniques. In both cases, the professionals performing the valuation follow technique and practices as established by the valuation profession. Most reading this blog already have a well-informed understanding of single-property appraisal; fewer will have a professional understanding of exactly how assessment of groups of properties or, mass appraisal works.

Mass appraisal:
the process of valuing a universe of properties as of a given date using standard methodology, employing common data, and allowing for statistical testing. (USPAP, 2010-2011 ed.) Often associated with real estate tax assessment valuation (Appraisal Institute ).

Well that wraps up this installment, but I will be following this up soon with the next part. I encourage everyone to take the time to comment and ask any assessment related questions that you may have. Whatever side of the valuation fence you may be familiar with, I welcome your input and inquiry.

Works Cited
Appraisal Institute . The Dictionary of Real Estate Appraisal, 5th ed. Chicago : Appraisal Institute, 2010.