Regression analysis

[Donna Quixote]

Glad to know that this does not appear to be a common demand that is being complained on in the forum. I thought that unless you have what appears to be an unreasonable adjustment that stood out from similar reports on similar properties using the same comps, then we are fine. I can see if generally reports are coming in on similar homes in an area for $2,000 per garage stall and a report that appears to be pushing values comes in at $6,000 per garage stall, then that would send up a red flag which would warrant an explanation. Coming up with some statistical proof for every picking thing would be the final nail in the coffin for me.

 

[NachoPerito]

Generations of appraisers are dividing like the Grand Canyon

 

[normando]

I knew regression analysis before it was popular. The problem is the fee does not justify my additional time in doing regressions. Since my peers were not doing regression, there was no need. So everyone, stop doing regression analysis especially if you don't know what you are doing. It's another tool I can use to manipulate values.

 

[NachoPerito]

Regression analysis is a great tool, but I don't see it as feasible on every report when charging competitive fees. I already don't understand how you guys/gals get enough work done to make money in this business at $350 a pop (I do commercial work). Regression analysis should be common when fees jump up to $600 per assignment.

 

[Su Long]

Ordinary least squares regression, conceptually, is tailor made to arriving at measuring the marginal contribution of a variable in home prices, and therefore calculating an adjustment. Simply regressing price on GLA or any other individual variable is not appropriate, given what we know about the contributing elements of other factors such as lot size, bath count, pool, etc (for residential properties). That's why a multivariate regression is appropriate (and just as easy to perform). Some others alluded to it, but I think the major complicating factor for measuring adjustments in the real estate market is just how inefficient the market is for real estate...unknown unknowns, unquantifiable and non-universal preferences, asymmetry of information between parties. In addition, there are the normal complications: non-linearity, biased intercepts, endogenous variables, and heteroskedasticity. Regarding non-linearity, remember that a logarithmic function is still linear and is, for example, the assumed relationship between the falling marginal contribution of things like square feet and price...so not reason alone to ditch regression analysis. All said, though, it is a powerful tool. They don't pay us enough to do it? Well, that's another question.

 

[Meandering]

Welcome to the forum Su Long,

That was a mouthful and well worth the read.

Moderator - Make that post a sticky!

.

 

[jay trotta]

Ordinary least squares regression, conceptually, is tailor made to arriving at measuring the marginal contribution of a variable in home prices, and therefore calculating an adjustment. Simply regressing price on GLA or any other individual variable is not appropriate, given what we know about the contributing elements of other factors such as lot size, bath count, pool, etc (for residential properties). That's why a multivariate regression is appropriate (and just as easy to perform). Some others alluded to it, but I think the major complicating factor for measuring adjustments in the real estate market is just how inefficient the market is for real estate...unknown unknowns, unquantifiable and non-universal preferences, asymmetry of information between parties. In addition, there are the normal complications: non-linearity, biased intercepts, endogenous variables, and heteroskedasticity. Regarding non-linearity, remember that a logarithmic function is still linear and is, for example, the assumed relationship between the falling marginal contribution of things like square feet and price...so not reason alone to ditch regression analysis. All said, though, it is a powerful tool. They don't pay us enough to do it? Well, that's another question.

Welcome, and in my rural/suburban market, how many units (sales) would be required in the 90 day / 6 month time line ?
"the major complicating factor for measuring adjustments in the real estate market is just how inefficient the market is for real estate...unknown unknowns, unquantifiable and non-universal preferences" You may have just described the market in which I work, may I interpret that as "Complex" ?

 

[Terrel L. Shields]

how many units (sales) would be required in the 90 day / 6 month time line ?

How many independent variables are you going to apply? For the 4 or 5 major items, it rarely takes an entire population of sales. Glean out the best and that "20" CU seems fixated on is more than enough.

 

[J Grant]

Does applying regression analysis lead to a better result than pairing sales/ extraction , or does it lead to "better " numerical support to show a reader, but the result may be questionable? (perhaps it varies )

I can see the advantage in showing support for an adjustment that is harder to challenge, whether the result is more meaningful or credible is another matter. If appraisers are being forced into a defensive position on even the simplest of reports and asked to explain, in detail, and demonstrate how they derived, even the most straightforward adjustment, then perhaps we have to adapt RA and charge for it . The clients are not willing to pay a dime for anything that helps a report, beyond spending $ they don't even comprehend how allowing an extra day over 48 hours helps develop a report, the future looks dim if applying RA properly means enough exrta time to add $

Then again software companies are introducing RA for dummies where one feeds in data and something comes out.
If one feeds in non verified sales from MLS with half of the data incorrect or missing (which is typical of MLS), how reliable are the results?

 

[Peter LeQuire]

Ordinary least squares regression, conceptually, is tailor made to arriving at measuring the marginal contribution of a variable in home prices, and therefore calculating an adjustment. Simply regressing price on GLA or any other individual variable is not appropriate, given what we know about the contributing elements of other factors such as lot size, bath count, pool, etc (for residential properties). That's why a multivariate regression is appropriate (and just as easy to perform). Some others alluded to it, but I think the major complicating factor for measuring adjustments in the real estate market is just how inefficient the market is for real estate...unknown unknowns, unquantifiable and non-universal preferences, asymmetry of information between parties. In addition, there are the normal complications: non-linearity, biased intercepts, endogenous variables, and heteroskedasticity. Regarding non-linearity, remember that a logarithmic function is still linear and is, for example, the assumed relationship between the falling marginal contribution of things like square feet and price...so not reason alone to ditch regression analysis. All said, though, it is a powerful tool. They don't pay us enough to do it? Well, that's another question.

Pricing such analysis is a separate issue. But, I like the way this post how the nature of the decision-making process of people buying single family houses intended for owner occupancy is more complex than is simplistically "explained" by analyzing a very limited number of variables in a market that is multi-variant. I may omit three sentences of this post - the first and the ending two - and include the rest of it in my discussion of why I did or did not do certain things in my exponentially-expanding reports of appraisals, wit attribution.

It also is a claxon warning to appraisers about how users of residential appraisals - up to and including the GSEs - may misapply "regression analysis" in their review and demands for additional explanation of analyses. (Which leads to the additional thought that the pressure for additional summaries of analyses will provoke more and more appraisers to learn just enough about regression to produce a graph to avoid being chopped for not adequately summarizing their analyses.)