Thursday, August 16, 2018

AVM is a Market Solution, Comparable Sales Analysis isn't (2 of 2)

- Intended for Start-up Analysts and Researchers -

If you provide a subject and a sales population to a group of concerned parties - 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 - you will be unpleasantly surprised by the outcome. They will pick different sets of comps based on their professional requirements and objectives, leading to different, often very conflicting, valuations. For instance, your Assessor may not have your best interest at heart as s/he has to meet a budgetary requirement, 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 which 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 (as I explained before - post 1 of 2) is a fairly 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 identical values - but not so in the world of the comparable sales analysis ("comp sales") as it is very party-specific.
  
Once the pool of sales, that closely represents the subject are properly scored and quantitatively adjusted, becomes comps. Generally, five best comps are then selected to value a subject. Valuers tend to use one of the three common methods - distance, least adjustments and sales recency - to narrow their choices down to the five contributing comps. Please read my prior postings (links below) for more details.   

In this analysis, the attributes of the subject home are: Bldg SF=3,250, Lot SF=17,400 and Bldg Age=26. 

From a large sales population, an optimal pool of 10 comps was algorithmically produced to demonstrate how subjectivity plays a key role in this valuation process. In each approach, the lowest ($308,770) and the highest value ($422,175) comps were removed. 




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, thus making the process sub-optimal.  




The above table (middle one) represents the least adjustment method, meaning the comps that required the least adjustments were 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, lesser time adjustments are proxying for older homes, etc. For example, the second least adjusted comp (# 6) with 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 (bottom one) 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.

When analytics are robotized, this is how the game would be played out (no negotiations with robots until AI 5.0):


1. Assessor and Listing Agent (traditional) will be given the "distance" value.

2. Bank Appraiser and Listing Agent (buyback) will be given the "least adjustment" value.
3. Appeals Consultant and Buyer's Agent will be given the "sales recency" value.

Way to go, Mr. Robot.     


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 each line-up
3. Verify all comps spatially, ensuring they all come from same/compatible neighborhoods
4. Apply time adjustments in line with the local market (do not use national figures)
5. Pay attention to valuation dates (01-01-18 vs 08-16-18 require different adjustments)
6. While using sales recency, contract dates are preferred to closing dates (despite norm)
7. If you are not allowed to use AVM values, show them below the grid with value analysis
8. If the sales population is large, extract sample from the most recent arms-length sales
9. If the subject population is large, automate the process with batching technology.

Good Luck!

Sid Som, MBA, MIM

President, Homequant, Inc.
homequant@gmail.com

LINKS...
AVM is a Market Solution, Comparable Sales Analysis isn't (1 of 2)
Differentiate between Sales and Comparable Sales
Time Adjustment and Flexible Valuation Dates
Least Adjustment Method alongside Distance and Sales Recency
See the Contributing Comps Spatially

No comments:

Post a Comment