That was actually pretty cool in 4.6 the code execution and statistical work is the same so I gave it 238 similar sales and asked it to derive adjustments for an appraisal I'm working on. It was a little off and needs tweaking I only took a couple minutes to do the entire exercise but with some light work and buying 4.8 this could easily replace Synapse which I think is $50 a month vs 4.8 at $20 a month
View attachment 109707
I asked ChatGPT to analyze your results: "What do you think about this OLS analysis and value indication? For example what do they really mean by 2.5% variance?
ChatGPT Response:
Thought for a couple of seconds
Bert, my first reaction is that this is a
useful exploratory regression summary, but I would be cautious about treating the displayed
$570,215 model indication as a strong final value conclusion without more diagnostics, validation, and appraisal reconciliation.
The model is not useless. It has
238 sales, which is a respectable sample size, and the displayed
RMSE of $50,730 / 9.8% is not terrible for rural/acreage residential property, where heterogeneity is often high. But the reported
R² = 0.581 means the model explains only about
58.1% of the variation in sale prices within the analyzed price band. Roughly
41.9% remains unexplained by the included variables. That is a large residual component, especially if the output is being used to support a specific value opinion.
What “2.5% variance” probably means
In the lower panel, the numbers shown are:
Model indication: $570,215
Contract price: $585,000
Variance: 2.5%
That “variance” almost certainly does
not mean statistical variance in the formal sense.
It appears to mean:
585,000−570,215585,000≈2.53%585,000585,000−570,215≈2.53%
or possibly:
585,000−570,215570,215≈2.59%570,215585,000−570,215≈2.59%
Either way, the displayed
2.5% is simply saying that the contract price is about
2.5% above the model’s point estimate.
So, in plain English:
The model predicts about $570,000, while the contract price is $585,000; the contract price is only about 2.5% higher than the model indication.
That is better described as
percent difference,
percent deviation,
price-to-model variance, or
contract-to-model spread. Calling it “variance” is potentially misleading because statisticians and technically trained reviewers may read “variance” as a squared-dispersion measure.
The important comparison is not the 2.5%; it is the RMSE and prediction interval
The contract price being only 2.5% above the model indication sounds very reassuring, but it needs to be interpreted against the model’s error.
The RMSE is
$50,730, while the difference between contract price and model indication is:
585,000−570,215=14,785585,000−570,215=14,785
That is only:
14,78550,730≈0.2950,73014,785≈0.29
So the contract price is only about
0.29 RMSE units above the model prediction. That is small relative to the model’s ordinary prediction error.
In appraisal language, I would say:
The contract price is well within the model’s typical error range and does not appear inconsistent with the regression-based indication.
That is a much stronger and more defensible interpretation than focusing on “2.5% variance.”
The 95% range is very wide
The displayed
95% range of $471k–$670k is also important. That is roughly:
670,000−471,000=199,000670,000−471,000=199,000
So the 95% range is almost
$200,000 wide. The contract price of
$585,000 falls comfortably inside that interval, but so would a very broad range of possible values.
This means the model supports the idea that $585,000 is
plausible, but it does not narrowly prove that $585,000 is the correct market value.
In practical appraisal terms:
The model gives weak-to-moderate support for the contract price, not precise confirmation.
That distinction matters.
Several individual adjustments look suspicious
Some coefficients look plausible, but some deserve scrutiny.
The
above-grade GLA adjustment of +$79/SF and
basement finished area at +$35/SF are directionally reasonable. The
age adjustment of -$716/year is also plausible, depending on the market and how age interacts with condition, renovation, and effective age.
But several items raise flags:
Half bath: -$1,510.
The note correctly says this is probably a multicollinearity artifact. A negative half-bath coefficient is not impossible in a statistical model, but as an appraisal adjustment it is hard to explain unless half baths are acting as a proxy for some other negative feature or are highly correlated with other variables. I would not use that as a literal market-derived adjustment.
Deck/patio/porch: +$289.
The model is essentially saying this feature has no meaningful contributory value in this dataset. That may be true, or it may mean the variable is too crude. A simple presence/absence variable for “deck/patio/porch” may fail to distinguish a small concrete slab from a large high-quality outdoor living area.
Pool: +$15,715 with correlation +0.11.
This is directionally possible, but the low correlation suggests the effect may be weak or context-dependent. In rural/acreage property, pool value can vary sharply by condition, quality, climate, buyer preference, maintenance burden, and market segment.
HOA: +$21,036.
The note is sensible: this probably does not mean “HOA equals +$21,036.” It may be capturing better-maintained roads, superior subdivisions, gated communities, similar-quality homes, or other unmodeled locational/neighborhood effects.
The correlation column is only marginally helpful
The “Corr w/ price” column shows simple bivariate relationships between each feature and price. That can be useful as a sanity check, but it can also be misleading because OLS coefficients are
partial effects conditional on other variables.
For example, if full baths are highly correlated with GLA, quality, bedroom count, and age, then the simple correlation with price does not tell you the independent value of a full bath. The coefficient depends on the whole design matrix.
So I would not put too much weight on the correlation column except to identify weak or suspicious features.
The price-band restriction is both useful and risky
The model is apparently restricted to the
$350k–$750k band. That may improve relevance by excluding very low and very high outliers. But it also introduces a technical issue: filtering by sale price can create sample-selection effects.
In other words, if the dependent variable is sale price, and the dataset is selected partly based on sale price, the coefficients may be affected by the truncation. That does not make the model invalid, but it should be disclosed and tested.
For appraisal use, I would want to know:
- Was the subject reasonably centered within the model’s data range?
- How many sales were near the subject’s predicted value?
- Were there enough truly comparable rural/acreage sales?
- Were outliers reviewed individually?
- Were the coefficients stable if the price band changed slightly?
- Was there any holdout validation or cross-validation?
The displayed model does not answer those questions.
The R² is acceptable for exploration, but not enough by itself
An R² of
0.581 in residential valuation is not shocking, especially for rural/acreage property. Rural properties are messy. Lot utility, views, outbuildings, road quality, privacy, condition, renovation level, school district, proximity to services, flood risk, topography, and buyer-specific motivations may all matter.
