Tag Archives: defensible

Depreciated Cost, a Test of Reasonableness

By

Rachel Massey, SRA, AI-RRS

Woody Fincham, SRA

and

Tim Andersen,MAI, Msc., CDEI, MAA

Originally published at http://www.appraisalbuzz.com/depreciated-cost-test-reasonableness/

With all of the clamor and excitement that Fannie Mae’s Collateral Underwriter (CU) is creating, we started working on a new article that addresses some possible solutions. In this one, we are expanding a bit on using the cost approach as a means to develop and support some adjustments. Each of the three traditional approaches to value can be used to develop a basis of analysis in any of the approaches. As such, the cost approach can be a reliable means to develop a gross living area adjustment, or lend additional support for it. While it does not work each time, has proven successful for us many times, and as such, we do urge studying it and putting it into your toolbox of solutions for supporting adjustments.

Quantitative adjustments require some type of support. CU is not changing anything regarding this premise. Appraisers are supposed to have support within the workfile for adjustments made, and then support the adjustments with commentary within the report. This is in harmony with USPAP. Many appraisers do not address specifics on the adjustments made, let alone explain how they were developed and applied. So here is one method that can be relied on as a means to support a gross living area (GLA) adjustment. Sometimes it can be used for other items.

One aspect of Collateral Underwriter (CU) that many have been discussing concerns price/SF. In the example from the CU webinar, it is stated that if an appraiser is using $15/SF for adjustments regarding gross living area (GLA) adjustments and the comparables sales indicate $200-$300/SF, then it will be probably be flagged as a higher-risk item. So part of the advantage of using this technique will help you address this with analysis. Let us look at some improved sales now that we have an idea of what site values are for the market

Comp 1 Comp 2 Comp 3 Comp 4
Price $             308,300 $           300,000 $           295,000 $           283,000
GLA                  2,414                2,308                2,468                2,310
$/SF $               127.71 $             129.98 $             119.53 $             122.51

In this data set, we have four sales. The range of price/SF is $119.53 to $129.98. The problem with price/SF is that it deals with all attributes of the property. This can be problematic because it is inclusive of the land, which can skew the usefulness of using it as a unit of comparison. Once we get part way through this article, we will start discussing residual improvement value (RIV). RIV can be an effective defense against overall price per square foot concerns. 

Simple Depreciated Cost
We are going to walk through a case study of a file that Rachel worked on recently. Obviously, some things have been changed. Some of you will notice that the data set is anything but like what we all normally see in classroom case studies. Hardly ever do we see perfect sets of data like what often seen in most case studies in an educational offering. With that said, this may not be something to use if you are starting newly in the profession. This article is written with an experienced residential appraiser in mind.

Depreciated cost can be a test of reasonableness for some adjustments, and here it is used as a basis for the gross living area adjustment, tied to sensitivity analysis. It is not meant as a means of arriving at an adjustment, but instead as either a place to start, or a second or third approach. Because each of us have used it extensively we felt it would be a great place to help some of you establish a benchmark or test of reason to use for a gross living area adjustment, in particular as the example is from the real world.

Site Value
Site value – you really need to get a handle on site values for using this approach (while you can use the depreciation factors to get to land values, having a grasp on site values is easier with land sales). Most communities have land sales, even if they are not in the immediate area. For example, this grouping of data presented here was for a property in Michigan and there have not been a great number of land sales in the immediate area over the past few years. There have been no land sales in the subject neighborhood. There were however, enough land sales from competing areas to provide some basis from an opinion of the value of the subject site as if vacant.

The following chart shows seven sites that sold and three acreage parcels:

Sale Sold date Sold price $ To Acquire DOM Size Frontage $/SF $/FF
Comp 1 (Demo) 9/17/2014 $42,050 $51,050 673 13,068 100 $3.91 $510.50
Comp 2 2/8/2013 $67,500 $67,500 52 13,580 97 $4.97 $695.88
Comp 3 9/30/2014 $70,000 $70,000 428 14,442 166 $4.85 $421.69
Comp 4 9/30/2014 $70,000 $70,000 428 16,236 164 $4.31 $426.83
Comp 5 5/31/2013 $80,000 $80,000 2688 17,424 128 $4.59 $625.00
Comp 6 6/27/2013 $71,000 $71,000 51 19,166 127 $3.70 $559.06
Comp 7 9/30/2014 $60,000 $60,000 428 20,000 100 $3.00 $600.00
Acreage Lots
Comp 8-A 2/20/2014 $75,000 $75,000 2237 43,560 148 $1.72 $506.76
Comp 9-A 4/30/2013 $65,000 $65,000 614 43,560 202 $1.49 $321.78
Comp 10-A 10/11/2013 $67,500 $67,500 131 43,560 125 $1.55 $540.00

*Note we included a couple of acreage properties because one of the improved comparable sales was an acre property and support was needed support for a site adjustment.

In the example, we see that the smaller the lot, of course, typically the higher price per square foot (SF). This is known as increasing and decreasing returns; see definition below. While there are exceptions, this is a general rule. Comparable sale-1 is a tear down property. Because the data is actual real world data, it is not perfect as we typically see in many academic examples, but it does allow a supportable conclusion to be derived.

increasing and decreasing returns
The concept that successive increments of one or more agents of production added to fixed amounts of the other agents will enhance income (in dollars, benefits, or amenities) at an increasing rate until a maximum return is reached. Then, income will decrease until the increment to value becomes increasingly less than the value of the added agent or agents; also called law of increasing returns or law of decreasing returns.[1]

With the data shown above, we can see that price/SF averages $4.19 and the range is $3.00 to $4.97/SF in this market. Front footage (FF) averages $548.42/FF and the range is $421.69 to $695.88/FF. By establishing an estimate of land value for the comparables used in the sales analysis, it helps to develop cost-derived adjustment.

Using comparable sale-1 as an example, the estimated cost looks like this:

Element SF $/SF Extension
Dwelling          2,414 $       87.85 $ 212,069.90
Basement          1,142 $       22.17 $     25,318.14
Basement Finished          1,000 $       15.00 $     15,000.00
Extras $     10,000.00
Garage            504 $       27.57 $     13,895.28
Cost New Estimate $     114.45 $ 276,283.32
Sales Price $ 308,300.00
Site Value $ (55,000.00)
Depreciated Value of Improvements (or RIV) $ 253,300.00
Minus Cost New $     22,983.32
Depreciation % 8.32%

Below, we have estimated the site value and subtracted it from each of the comparable sales. The resulting unit of comparison is much better than overall price/SF. The price /SF-RIV can be used as an indicator of the highest possible reasonable adjustment for GLA. We like this as a test of reasonableness for any adjustment made for differences in gross living area. The resulting $/SF-RIV is going to be the upper limit of how much you can adjust.

Sales Sale Price Land Value RIV GLA $/SF-RIV
Comp 1 $308,300 ($55,000) $253,300    2,414 $104.93
Comp 2 $300,000 ($60,000) $240,000    2,308 $103.99
Comp 3 $295,000 ($70,000) $225,000    2,468 $91.17
Comp 4 $283,000 ($50,000) $233,000    2,310 $100.87
What about Fireplaces and Decks, etc. is this approach right for that? Decks and other items like decks and outbuildings typically depreciate at a faster rate than the house. One would try to steer away from using this methodology with such items. We still believe that this approach can be used in measuring the top end of the adjustment range, or as a test of reasonableness, but with the caveat that the rates of depreciation may vary.Depreciated cost may offer one of the only adjustments that you need at all, if your comparable sales are all very similar. It can be difficult to support adjustments for additional features like decks and fireplaces. Sometimes those types of amenities are sometimes best dealt with using qualitative reasoning.   If you are looking at sales that all have similar external features, are of the same quality/condition as the subject it may not be required to adjust for them. These items are difficult to extract and may be summed up with qualitative reasoning. It will depend on what information you have learned about from the market.     This is an excellent area to discuss with real estate agents and ask if such features are strong considerations by buyers. It is also important to understand how the sellers are looking at such items as well.     We find that talking to both agents on a transaction can be beneficial to glean such information.     In the end, if no adjustments are supportable for such amenities, the appraiser can discuss the additional amenities present for a sale and use that in the final weighting during the reconciliation of the sales comparison approach.

We can apply these figures to the improved sales that we are using in the sales approach to get a residual improvement value (RIV). As mentioned earlier, RIV is a better indication of comparability as it allows us to compare apples to apples. It removes the land component, and other improvements not related specifically to the house itself. Just getting this far into the process with each of the comparables, and looking at the RIV/SF as a metric will assist with the concerns many are having about the CU overall price/square foot metric.

The next process is to take each sale and develop a cost approach using Marshall & Swift Residential Cost Handbook (disclaimer, huge fans here) for the appropriate quality. It is important to make adjustments for energy and foundation (bottom of the page related to the type of housing) if they apply, refinements for floor covering, heating and cooling, etc. as well as applying the quarterly multipliers to region and location. From there you should compare total cost to the depreciated remainder for an account of depreciation.

You would then do one for each of the sales in the study.

Sales Cost New RIV Total Depreciation % Depreciated Age Depreciation/yr
Comp 1 $276,283 $253,300 $22,983 8.32% 14 0.59%
Comp 2 $254,908 $240,000 $14,908 5.85% 16 0.37%
Comp 3 $264,925 $225,000 $39,925 15.07% 29 0.52%
Comp 4 $271,585 $233,000 $38,585 14.21% 20 0.71%

*Note: This type of approach utilizes all forms of depreciation. If there were cases of functional or external depreciation present for any of the comparable sales, this would need to be adjusted for as well. In this case, study, there were neither additional forms of depreciation.

This information can be valuable in terms of understanding depreciation, as well as supporting either an age or a condition adjustment (look at how sales 3 and 4, which are older houses, have much more depreciation than the newer houses overall). Since each house is depreciated between ~6 and ~15 percent, you also have supportable adjustments to make for age or condition.

You can also utilize this type of adjustment for amenities such as basements. For example, say comparable sale-1 has a finished basement that is older and not high quality. The finish costs an additional $15 per square foot rounded over and above the cost of the basement. This finish is a recreation room only and the cost new is around $15,000. The overall rate of depreciation for this property is 8.32% or $1,250(rounded). This means that logically the basement finish would now contribute about $13,750 to the property value. That may not be sufficient to stand alone, but does offer a method of support.

Additional support can be from running simple statistics such as isolating a group of sales based on some commonalities. For the following sample, we took houses in this particular market separated between houses built between 1995 and present, but excluding proposed construction. They were further narrowed to include 1,800 to 2,800 SF and no walkout basement. By doing a simple version of grouped paired analysis, we see the result was a difference between $14,843 and $15,377 between the two types, with many having bathrooms in addition to finished rooms. With an indication of $13,750 from comparable sale-1 and the paired group analysis showing a range of $14,800- $15,400, it is easy to deduce a reasonable adjustment amount.

No Walk Out # Sales Avg Price Median Price Avg GLA Med GLA Avg $/SF Median $/SF
1800-2800 SF Unfinished Basement 42 $340,559 $329,623 2392 2402 $142.37 $137.23
1800-2800 SF Finished Basement 89 $355,402 $345,000 2399 2408 $148.15 $143.27
Difference $14,843 $15,377 7 6 $5.77 $6.04

Completing a cost approach on each sale is a good exercise in terms of seeing cost in action, as well as testing depreciation. The greater the depreciation exhibited in the individual sales, the greater the difference in either condition or age, or a combination of both. So this methodology can also create support for other types of adjustments as well, such as the basement finish adjustment shown above. Many will say this takes a lot of time, and our answer is, “Yes, but it’s something that uses some commonsense and appeals to reasonableness”. We would also add that explaining this is much easier than trying to use regression analysis or find that elusive matched paired sales. Most appraisers can reasonably explain cost-based extractions to a jury or licensing board. It does not require much in the way of additional tools. Excel©, cost estimation software and appraisal software is all that is really needed.

Depreciated cost does work in many markets, so give it a try to see if it is something that will work for you. Use it in addition to some other methods of supporting adjustments. We consider it an excellent test of the reasonableness of both the value conclusion, and the elements of comparison within the value conclusion. We have each successfully used it in lending and non-lending work assignments.

Fannie Mae and CU are specifically going to target our size adjustments. In the past, many appraisers used “rules of thumb” as the basis for a size adjustment. As we are all now aware, rules of thumb do not work anymore because CU has the ability to calculate size adjustments from market sales data. The model above, while not based on CU’s sophisticated algorithm, also functions quite well in isolating the sales price of the improvements. Using this model, appraisers are able to isolate such differences within a reasonable range of values. Even more importantly, this range of values is market-derived, thus in full compliance with CU’s requirements. Be sure, too, to save all of these calculations in the workfile for future reference.   Gone are the days when we can justify out adjustments by invoking “my 30-years of appraisal experience”. Now, we must prove our adjustments. This model is one of those proofs. Finally, what we have presented here is nothing new. This well-known method has been published in numerous books and in courses. We thought presenting a “real-world” example might be helpful in showing how even without perfect results; the results can be, nonetheless, meaningful.

[1] Appraisal Institute, The Dictionary of Real Estate Appraisal, 5th ed. (Chicago: Appraisal Institute, 2010)

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

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

http://appraisalbuzz.com/buzz/features/highest-and-best-use-is-more-than-just-a-check-box

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

4

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