Quantitative Adjustments of Real Estate Sales Comparisons

Instructor: Kyle Aken

Kyle is a journalist and marketer that has taught writing to a number of different children and adults after graduating from college with a degree in Journalism. He has a passion for not just the written word, but for finding the universal truths of the world.

Appraisers use quantitative adjustments to find adjustment rates for differences in similar properties. These quantitative adjustments utilize several statistical methods to valuate these differences.

The Comparison Approach

When using the Sales Comparison Approach for valuation of real estate property, it is necessary to make adjustments as no two properties are exactly the same. The appraiser should do their very best to mimic the market with these adjustments. Factors that can account for these adjustments include, but are not limited to, location, size, and topography. The factors that should be looked at for adjustment will be the factors that an average buyer would consider when making a purchase in real estate. Appraisers use, both, qualitative and quantitative adjustments. Quantitative Adjustments involve adjustments in percentage and/or dollar amounts in order to account for the differences between the subject property and its comparables. This method of using quantitative adjustments does have its strengths and weaknesses.

Advantages and Disadvantages

Quantitative adjustments are desirable as they are able to provide measurable and quantifiable adjustments. For this reason, it is considered much more objective than qualitative adjustments. This kind of analysis/adjustment is almost always viewed as more precise and reliable due to the scientific nature of quantitative analysis. At the same time, it can be very difficult and sometimes even impossible to find the necessary data to verify the quantitative adjustments. Let's examine some of the methods used in quantitative adjusting.

Paired Data Analysis

Paired Data Analysis is the most common method of performing quantitative adjustments. It involves comparing two properties to one another to examine one specific difference. These properties should be as similar to one another as possible aside from the major difference that is being analyzed. For example, this analysis could be examining two lots that are almost identical aside from one being an interior lot while the other lot is a corner.

Example

If an interior lot is worth seven thousand dollars ($7,000) and the corner lot is worth ten thousand dollars ($10,000), then the appraiser can presume that the corner lot has an adjustment rate of three thousand dollars ($3,000) or thirty percent (30%).

10,000 - 7,000 = 3,000 (this is the adjustment rate in dollars or the valuation of the corner)

In order to find the adjustment rate as a percentage, you divide the adjustment rate in dollars by the dollar value of the property with the specific difference (the more expensive property - in this case, the corner lot):

3,000 / 10,000 = .3 (to find the percent, simply move the decimal two places to the right)

.3 = 30%

The rate of adjustment is thirty percent (30%).

Linear Regression

Linear Regression is a quantitative adjustment method using statistical analysis. When using paired data analysis, you only have a sample size of two. In quantitative analysis, this is not considered a suitable sample size. Linear regression allows us to use a much larger sample size to present more reliable and verifiable data for appraisal. With today's technology, it is possible to analyze hundreds or even thousands of real estate sales for this use.

When information is presented using linear regression, graphical analysis can be used to determine varying factors in appraisal/valuation. These graphs can depict a wide variety of information such as price per square foot or the age of a property and its negative correlation with predicted prices. Graphical analysis basically displays the linear regression of various aspects of a property to assist with making quantitative adjustments.

Measures of central tendency are also used with this method. Simply put, a measure of central tendency is an average of a data set that is found when using statistical or graphical analysis for quantitative adjustments. The three measures of central tendency are mean, median, and mode. The mean is literally the average of the data set. To find the mean, add all numbers (or data points) in the set then divide by the total number of data points. Here is an easy example:

Data set: 1, 3, 5, 7, 9

1 + 3 + 5 + 7 + 9 = 25

25 / 5 = 5

The mean for this data set is five (5).

The median is literally the middle number when all numbers are placed in order from lowest to highest (or highest to lowest). Here is an example using the same data set (but rearranged):

Data set: 3, 7, 9, 1, 5

Arrange in numerical order: 1, 3, 5, 7, 9

Find the middle number. For this data set, the median is five (5).

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