In my last blog (The Lens of Common Sense) I discussed the Lens Model of judgment in more depth and the idea that one way to implement the lens model is by using multiple linear regression. In today's blog I want to follow up on that idea and show how useful a lens model can be for the purposes of real estate appraisal, and by implication, many other domains that involve judgment under uncertainty.

The data that I want to show you came from an old stats textbook (p. 727) and was provided by a real estate appraisal company who were asked to help an apartment building owner fight a property tax bill. The owner felt that the tax bill was too high and the appraisal company was brought in to formalize the owner's intuition and help argue the owner's case.

The appraisal company randomly selected 25 apartment buildings that were sold in 1990. The data was organized according to 5 indicators of worth along with the apartment building sales price:

The procedure the appraiser used to determine whether the owner was paying too much was to generate a linear model from this data using multiple linear regression. The linear model looked something like this:

Sales Price = X + (Weight1 * Num Apt. Units) + (Weight2 * Age of Structure) + (Weight3 * Lot Size) + (Weight4 * Num Parking Spaces) + (Weight5 * Gross Building Area)

The appraiser then applied the linear model to data from the owner's apartment to arrive at an estimate of it's probable sales price. Any significant discrepancy between the predicted sales price and property tax valuation could be argued to be unfair.

So what we have here is a situation where the owner believed the value of the apartment building was assessed too highly. Why did the owner believe this? Were they able to verbalize all the cues they were using to arrive at that judgment? The real estate appraiser arguably used a formal statistical tool, namely, multiple linear regression, to make the apartment owner's common sense model explicit and probably also improved upon it.

The purpose of today's blog is get a bit more down to earth with the lens model than my last blog and to perhaps convince you that the lens model is a useful "mind tool" for understanding and improving judgment under conditions of uncertainty. One way to interpret and apply the lens model is by using the statistical technique of multiple linear regression which allows you to estimate the weights that should be applied to each indicator in your lens model. There is alot of evidence that if you do this for something you have to make frequent probabilistic judgments about, your lens model will outperform you! Humans lack consistency of judgment but a formalized lens model always outputs the same numbers given the same inputs. Lack of consistency in judgment is one explanation for why a formalized lens model (for judgments under uncertainty) exhibits superior performance to a person's common sense lens model.