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Regression for GLA Adjustment
- Thread starter NJAPRAZE
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But does it output results as good as an appraiser.azbassman21
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Nobody reads the narrative portion of an appraisal. They look at the value, run it through CU or send it to the underwriter for a brief checklist review. This is the problem with the appraisal industry, we're appraising to guidelines and processes which applied 20-30 years ago when lenders cared about loans. Now Fannie has turned us into data collectors and value confirmers.
George Hatch
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I review narrative reports and I USUALLY skip a lot of pages as not really adding anything to the analysis. Generally speaking, most boilerplate paragraphs serve as housekeeping functions or to check a box, not to actually help the appraiser or the reader with their own analyses, opinions of conclusions. When you're talking about the subject attributes, the HBU analyses and the comps then that's where you're using your thinking cap, so that's where I want to focus my attention. Particularly in the accuracy of the factual info of your data.
I virtually never even look at the charts/graphs that have become so popular for adjustments. I do straight to the grids and look for consistency in the application, overall reasonableness for the price range and the cumulative effects of the entire combination of adjustment factors. I might test different adjustment factors on my side of the review to see if they could have found a more effective combination, but I'm not going to even comment on any alternatives unless they left a lot of adjustability on the table, enough to affect their conclusions.
We started out with an unadjusted range of sale prices - which the average buyer or seller would use by ranking the subject therein and interpolating for their value conclusion. That unadjusted range had a high-low spread. The function of the adjustments is to refine that spread, usually to result in narrowing it. Those adjustments serve no other function.
Independent of any other choices an appraiser can make with their analyses, data qualification and refinement comes first. Poorly qualified data is always going to cause problems. That's where the action is at in an appraisal. By comparison, the mechanics used for the adjustments add much less value to the process.
Carnivore
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That's because it is all that is expected by the largest user of our services. If we all were educated Like Bert our Lending Clients would not be willing to pay for that expertise and dependability. Just my ever so humble opinion
Here is an example of a trend that we report in our Standard FNMA series
A trend line represents a trend, the long-term movement in time series data after other components have been accounted for. It tells whether a particular data set (say GDP, oil prices or stock AND HOME prices) have increased or decreased over the period of time. A trend line could simply be drawn by eye through a set of data points, but more properly their position and slope is calculated using statistical techniques like linear regression. Trend lines typically are straight lines, although some variations use higher degree polynomials depending on the degree of curvature desired in the line.
Trend lines are sometimes used in business analytics to show changes in data over time. This has the advantage of being simple. Trend lines are often used to argue that a particular action or event (such as training, or an advertising campaign) caused observed changes at a point in time. This is a simple technique, and does not require a control group, experimental design, or a sophisticated analysis technique. However, it suffers from a lack of scientific validity in cases where other potential changes can affect the data.
Fernando
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You're more of an academic and seeing only in numbers.
I see MARS or any computer model as a way to make money if one thinks creatively.
As an experienced appraiser, I noticed one community which RCA should have noticed that seems to lag in prices compared to adjacent cities.
As you know, I think outside the box.
From my intuition, a model can be used to make money from this lag affect before prices increase as view from an investor.
RCA
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Oh there are people who read them - if like me you write an appraisal report that is outside the mold. I am lucky when business is so hot, they are just like you say - just looking for that rubber stamp. But every once in a while someone who can't understand MARS will send the report to someone like Dan Wiley (who has a degree in mathematics and works for Freddie Mac) - who then may let me know. But, more directly, a chief appraiser will call me up and ask me to explain where those adjustments come from - and then I send them over a breakdown - and they are ok with that (so far).
George Hatch
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What percentage of your clients would you say are actually asking for what you're doing? I ask because I've never had one client ask for these types of analyses.
Carnivore
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The design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.
Fernando
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When a chief appraiser has to ask, the report is unclear. Be proactive and spend more time and effort in justifying your models.
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