Cost Management: A Strategic Emphasis 5th Edition by David Stout, Edward Blocher, Gary Cokins

Exercise 1

Using Regression to Estimate the Value of Commercial Real Estate

Estimating Real Estate Values for Apartment Buildings and Office Buildings As expected, real estate appraisers performing regression analysis to appraise the value of an apartment building or an office building use as the dominant independent variable the property’s past, current, and expected future net operating income (NOI). That is, the chief determinant of the value of the property is its ability to produce cash flows and profits. Other variables regarding the property include its size (as measured by the number of units, number of square feet, number of two-bedroom and one-bedroom apartments, etc.), its age, and the relevant vacancy rate in the property and in the submarket area where it is located. Since the regression analysis is usually built from actual sales numbers over a period of time, these appraisers also use a trend variable to tie the sales price of the property to the year it was sold.

Estimating Real Estate Values for Warehouses and Manufacturing Plants Similarly, real estate appraisers have developed regression analyses for warehouses and manufacturing plants using their size, age, and location. The NOI variable is usually not relevant. They also use a trend variable to distinguish sales of properties in different years. For example, an analysis of sales value (per square foot) of industrial properties in the Los Angeles area in the early 1990s showed a significant trend variable (–2.83 per square foot per year); the coefficient on the trend variable was negative because prices were falling during that period. A significant size variable (–2.43 per square foot, per 100,000 square feet of space) indicated that larger buildings had on average lower sales prices per square foot. Age was also a factor, the coefficient being – 0.41 per square foot per year of age. The location variable was also significant, showing that properties in certain counties in the Los Angeles area (Orange County, San Bernardino, etc.) were predicted to have as much as a $2.32 difference in value per square foot.

Sources: Stephen T. Crosson, Charles G. Dannis, and Thomas G. Thibodeau, “Regression Analysis: A Cost-Effective Approach for the Valuation of Commercial Property,” Real Estate Finance, Winter 1996; Maxwell O. Ramsland Jr. and Daniel E. Markham, “Market-Supported Adjustments Using Multiple Regression Analysis,” The Appraisal Journal, April 1998, pp. 181–191; and Stephen C. Kincheloe, “Linear Regression Analysis of Economic Variables in the Sales Comparison and Income Approaches,” The Appraisal Journal, October 1993.