I'm a residential real estate agent. The most common adjustment I see on appraisals is on the interior square footage (GLA) of a home. How do appraisers arrive at a figure for the adjustment per square foot? I would think the adjustment per square foot would be different for 2 houses that have 2000 and 3000 square feet, respectively, versus 2 that have 9000 and 10000. Is that so?
Condition seems to be another common adjustment. I'm aware of the C1-C6 codes, though it's a challenge trying to determine which code applies to a given house. Where could I find the best definition of these codes? Is it common practice to adjust on a percentage basis between codes? So, for example, a C3 could be worth 10% more than a C4, other factors being equal.
Thanks for any help.
My protocol for determining adjustments is the most complex, but generally, by far, the most accurate:
This is a heavily redacted report that was created using the Subjective Value Containment Approach
valuationengineer.com
I use MARS regression to determine adjustments and it often comes out with different adjustments for different ranges of GLA. In this particular sample, it turns out there is just one adjustment for the entire range ... but most often that is not the case. Generally, there are 2-3 ranges of GLA with different adjustments and we find the adjustment greatest in the lower range, e.g. 500-1200sf, then 1200-3000sf is less per sf, and >3000 sf the least. That is not always the case of course and it really depends on the neighborhood. If you have houses that span the break in adjustments, then your calculations for the GLA adjustment will be more complex. I invariably leave this to the computer to figure out, using Excel or C#.
I am working on a program that will automate the process. Rather a complex program, I'd have to say, and nowadays with the advancing technology, development for one engineer can be a slow and time-consuming process:
Name: MaxTask (TM), (c) 2020, Pacific Vista Net
Software: Desktop WPF, .RabbitMQ, Net Core 3.1, SQL Server, Sqlite, PostGRES SQL, DevExpress Ultimate, Autofac IOC, Entity Framework Core, C#, Blazor, Serilog, Microsoft Office, RabbitMQ, ...
Statistics: MnitTab SPM (Salford Predictive Modeler) MARS
Expected release: Late 2021. WIth earlier partial releases, YouTube and models.