![]() ![]() This is why the appraisers can get away with the price per square foot calculations when they limit comparing homes that are within plus or minus 10% of the same size of the subject property. But it is close enough for an appraiser to do their job. While in this example, there is a measurable difference in the price per square feet of $158 and $147. Take for example the two data points in our example of 1829 6th St (which sold for $183,000 at 1157 TSF) and 1680 Heather Dr (which sold at $180,000 at 1222 TSF). Can this cause inaccurate calculations? Absolutely! However, in most cases where the data points are very close, the difference can be negligible and throwing it all together can work when you are comparing data points that are close in size. By ignoring this, I mean that in most price per square foot calculations the y-intercept value isn’t used or considered, and the y-intercept value is basically just thrown into the slope (price per square foot) calculation. In most cases, the y-intercept value is ignored when real estate professionals use the price per square foot calculation. The y-intercept value would be comparable to what the “sunk” costs would be in building a home. It is where the line would cross the y-axis. The y-intercept is the value (or price in this case) of y when the x (square footage) is 0. M (the slope) is the price per square foot, Y is the price of the home (as you can see in the graph), So for our example, when we are plotting the Scattergram graph: Let me provide you another explanation which requires a little more math (hopefully you’re brushed up on your high school algebra).Īs you may recall, the equation of a straight line is: y = Mx + B. For larger sized homes, the sunk cost (which is the same for both homes) is less for each square foot, thus artificially increasing the price per square foot to a much lesser degree. For smaller sized home, the sunk cost is greater for each square foot, thus artificially increasing the price per square foot much more. Because of this, those sunk costs are spread across the size of the home. While this is a somewhat simplified way of saying it, but there are a lot of “overhead” or “sunk” costs when you purchase a property, and those costs are somewhat consistent/independent on the size of the home built. There is a basic “sunk” cost required just to build a home. So why is that? What is the key difference? Why do scattergram trend lines help determine the value of a home (as you can see in this example) and the price per square foot doesn’t? Let me ask you, if the price per square foot of a home was $120 per square foot could I purchase a one square foot home for $120? Of course not! When a home is built there is a lot of “overhead’ such as price of land, the cost of bringing in the utilities, permits, fees, etc. This quick example very easily shows you that the trend line on a scattergram is not the same as pricing based on price per square foot. Or stated another way, there is an inverse relationship between the size and the price per square foot. If you take a quick look at all that data points in this example, you can see that as the size increases, the price per square foot decreases. You can see there is a big difference (almost a 2 to 1 difference) between the price per square feet even in the example where you have two data points right at the same point in relation to the trend line. Whereas the top data point, the price is $229,000 and the total square feet is 2710, and the price per square feet is $85. In the first (or bottom) data point, the price is $175,000 and the total square feet is 1036, so we can see the price per square foot is $169. In this example, let’s use the top and bottom data points, both of which are just below the trend line. ![]()
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