By Francis X. (Rich) Finigan
Copyrighted © 2019 Calypso Continuing Education™
As real estate appraisers, we are interested in determining how a set of characteristics, such as the number of baths, bedrooms, or GLA etc. relate to the value of a property.
Our appraisals contain bold claims that the adjustments we make on the sales comparison grid are supported by the marketplace. But, are they?
With the evolution of AMCs, greater oversight by regulatory bodies, and the need to expand our market presence, appraisers need to employ tools that can save them time, reduce the liability, and open new markets.
Regression analysis is one of those tools. It’s a statistical tool that can be used to estimate the value of a whole property or components that make up the property. In the field of economics, regression analysis has been a mainstay for a very long time. With the development of inexpensive software, like Excel, regression analysis is making headway as a powerful tool in the appraiser’s toolbox.
Regression analysis by appraisers is nothing more than comparable sales, whether residential or commercial, in its most accurate form.
Specific property characteristics affect value, i.e. decks, granite countertops, views, etc. In regression analysis these characteristics are called independent or predictor variables. Their contributory value assists in predicting the value of a subject property. The value opinion or estimated value represents the dependent variable because it depends on the predictor variables in any given market.
When building a regression model for an appraisal, the dependent variable will be the sale price, which is regressed by specific property characteristics and their relationship to the marketplace i.e. independent/predictor variables. The result is an accurate opinion of value with adjustments on the sales grid that are statistically proven. Heck, I remember when I used to use the SWAG theory. My how times have changed!
To fuel this sort of statistical analysis an appraiser needs to have lots of data. Well, most appraisers do have lots of data. It’s just sitting in your files doing nothing other than complying with the RECORD-KEEPING RULES of USPAP. Let’s put that data to work!
In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables.
Regression analysis sounds like a lot of work, but with an Excel spreadsheet and a little bit of effort, you can save a lot of time answering questions from AMCs or defending your work if is called into question by a local appraisal board.
Create a new income stream with local builders. Builders spend tens of thousands of dollars on super adequacies instead of and maximizing their profits. You could provide local builders with statistical data identifying what the market will pay for certain features in a home.
Stay tuned for our next Food For Thought: Appraisal Modeling Regression Methods
And our upcoming 4-hour course on using Excel to develop appraisal regression analysis models. Regression analysis so easy even a caveman can do it!
Interesting factoid: The term “regression” was used by Francis Galton in his 1886 paper “Regression towards mediocrity in hereditary stature”. It has since become a mainstay in the fields of economics and political science.
Good luck and do good work.
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