If a subject and a sales population are provided to a group of concerned parties – from an Assessor to a Bank Appraiser to a Listing Agent offering buyback guarantee to a traditional Listing Agent to a Buyer's Agent to an Appeals Consultant – one would be unpleasantly surprised by the outcome.
They will pick different comps based on their professional requirements and objectives, leading to different, often very conflicting valuations. For instance, Assessors may not have the taxpayers' best interest at heart as they have to meet budgetary requirements, paving the way for counterparties like Appeals consultants. A Listing Agent looking to get an "exclusive" may not do well with a set of middle-of-the-road comps that a Buyer's Agent might be interested in. In other words, the selection of comps is a function of the hat the party wears, making the entire process highly subjective. AVM, on the other hand, is a reasonably scientific exercise. All variables interact with one another in an econometric equation and produce the resulting values. Therefore, all other factors remaining constant, two identical homes will have equal values – but not so in the world of the comparable sales analysis (aka, comp sales) as it is very party-specific.
Once the sales pool that closely represents the subject is scored correctly and quantitatively adjusted, it becomes comps. Generally, the five best comps are then selected to value a subject. Valuers tend to use one of the three standard methods – distance, least adjustments, and sales recency – to narrow their choices down to the five contributing comps.
In this analysis, the subject home attributes are Bldg SF=3,250, Lot SF=17,400, and Bldg Age=26. An optimal pool of 10 comps was algorithmically produced from an extensive sales population to demonstrate how subjectivity plays a vital role in this valuation process, removing the lowest ($308,770) and the highest value ($422,175) comps in each approach.
The above table represents the distance method, meaning the five closest (to the subject) comps were considered to be the best comps, producing a value range of $344,820 to $414,940, with a probable subject value of $388,775. Since least adjustments and recency of sales were ignored here, obviously, several comps needing large adjustments or of older originations managed to creep in, making the process sub-optimal.
The above table represents the least adjustment method, meaning the comps that required the least adjustments were considered the best comps. The least adjustment is nothing but a balancing act. In other words, larger lots are compensated in value by smaller building sizes, and lesser time adjustments are proxying for older homes, etc. For example, the second least adjusted comp (# 6) with a much smaller lot was corrected by the larger and older building. It also sacrificed one of the closest (# 8) comps. This method produced a lower subject value of $371,150.
The above table represents the sales recency method, meaning the most recent five comps (in terms of sale dates) are the best ones. This is where the lowest and the highest value comps showed up on the initial line-up, hence substituted with the ones waiting in line. Though this method produced the most compact value range (upper bound was compacted down), it produced the lowest subject value of $360,340.
Therefore, if this comp sales analysis were to be used to cater to the target as mentioned earlier audience, this is how the game would be played out:
1. Assessors and Listing Agents (traditional) will be given the "distance" value (highest value).
2. Bank Appraisers and Listing Agents (buyback) will be given the "least adjustment" value (middle-of-the-road value).
3. Appeals Consultants and Buyer's Agents will be given the "sales recency" value (lowest value).
How to Reduce Subjectivity in Comp Sales
1. Apply meaningful selection, scoring/ranking, and adjustments to the sales population;
2. Build an AVM and insist on two AVM values (4th and 5th) on comps line-up;
3. Verify all comps spatially, ensuring they all come from the same or, at least, compatible neighborhoods;
4. Apply time adjustments in line with the local market (using national figures or adjustments could distort results);
5. Pay attention to valuation dates as 01-01-19 and 08-16-19 are different, often requiring additional adjustments;
6. While using sales recency, contract dates are preferred to closing dates (despite the industry norm);
7. If one is not allowed to use AVM values, one must show the AVM values below the comps grid with detailed value analysis;
8. If the sales population is large, a representative sample might be extracted from the most recent arms-length sales; and
9. If the subject population is large, automate the process with batching technology (batch comps).
-Sid Som
homequant@gmail.com
No comments:
Post a Comment