How to Build a Regression Model in 8 Simple Steps

Hand pointing at spreadsheet on laptop screen

Regression is a mathematical tool used by real estate appraisers to determine the likely value, or adjustment rates, of various property characteristics and ultimately predict sale prices. Rather than relying on human opinion, listings, or previous appraisals, regression analyzes actual sales data to determine adjustment rates, assigning statistical value to characteristics such as GLA square footage, number of garages, acreage, age, and so on.

Regression modeling requires computer software but relies on human logic and commonsense. Here’s how to build a regression model in eight steps.

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Step 1: Acquire regression-modeling software

Microsoft Excel is a useful processing tool. If you choose to use Excel, perform an internet search for “Load the analysis tool pack in Excel.” Install the software.

Step 2: Acquire your sales data

You may export sales data from your local MLS or from other sources. Unlike paired-sales analysis, regression modeling works best with a large quantity of data. Focus on obtaining a large pool of sales, ignoring estimates or active listings.

Step 3: Cleanse your data

Review and cleanse your data to ensure it is reliable and usable.

Step 4: Select usable data

Select the data you’re going to use.

Step 5: Filter your data

Filter your data, beginning with basic variables. Select for straightforward traits such as square footage and site size.

Step 6: Narrow down your data even more

Continue narrowing down your data, filtering it by increasingly complex variables. Select for more complicated data, such as recent renovations or the number of bathrooms, to improve your model’s specificity and accuracy.

Step 7: Select variables to account for market forces

To do this, add variables like Days Past Sales or whether a property was sold HUD or REO. Experiment with selecting other variables, such as a certain builder or subdivision.

Step 8: Conduct a back test

Use your regression model to predict the sales price of a home that has already been sold. Then compare your predicted sales price with the actual sales price. If the actual sales price matches the sales price your regression model arrived at, you know you can use your regression model to predict the prices of other homes with a good degree of accuracy.

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