How much is my diamond worth?
(And how many times have your clients asked this question?)
Now you can get a head start on the initial appraisal.
The Diamond Price Estimator uses data from over 50,000
graded and priced diamonds to build a value prediction model based upon
the ‘Four Cs’.
How does it work?
Based on options you select for Color, Cut, and Clarity, a
model specific to those parameters is built ‘on-the-fly’ to predict
value from Carat weight.
- GIA color grades D through J
- GIA clarity grades IF through I2
- Cut quality from Fair to Ideal
- Weights ranging from 0.15 to 5.00 carats
Sufficient to handle the majority of diamonds encountered in the
retail trade!
We know you value your reputation: if your input parameters are
outside the confidence of the model, we’ll tell you - so you keep the
confidence of your clients.
The model in action:
Let’s posit that we have a diamond to appraise:
- It’s almost perfectly colorless: the GIA color is ‘E’
- It’s slightly included, and we feel it’s a GIA clarity grade of
SI2
- The cutter did a fine job, and we can call it a premium-grade
cut.
We provide those parameters to the Diamond Price Predictor’s user
interface, which sets up the model.
And then provide the weight of the diamond we have in hand, which
happens to be 1.33 carats, to see an estimated value (rounded to the
nearest $10):
carat_in <- 1.33
est_price <- predict(value_model, newdata = data.frame(carat = carat_in))
print.default(paste0('Estimated value: $', round(est_price, -1)))
## [1] "Estimated value: $7580"
In the application, the estimate is presented both
numerically and graphically in relation to the data used to build the
model.
The estimate we got in the previous slide would be presented as:
