(20f2) How to Price a Home: Similar Homes, Different Prices

(20f2) How to Price a Home: Similar Homes, Different Prices

  • Gene Keyser
  • 06/1/21

The Probability of Top Dollar for Your Home

(BTW - If you haven’t read Part 1 of my discussion on why homes will probably sell for different prices, you should start there.)

Property Value Distribution

This is what a realistic property valuation looks like for an individual home – with possible prices as the domain (x-axis) and the frequency (or probability) of getting those prices the range (y-axis).

This is not a normal distribution – which means most likely price paid for this home is not in the middle of the range. This is called a right skewed probability density function. This pattern implies the lowest prices a home will sell for are much closer to most likely price than the highest prices a home will sell for.

In finance, the concept of skewness is utilized in the analysis of the distribution of the returns of investments. Although many finance theories and models assume that the returns from securities follow a normal distribution, in reality, the returns are usually skewed. Real estate prices track this concept.

The positive skewness of a distribution indicates that an investor (or homeowner) is protected from large losses (though the probability of small losses exists), but does have some potential to make large gains from the investment. Thus positively skewed distributions of investment returns are generally more desirable.

Reading the Chart

Looking at the first chart, note the value of median price is lower than the mean (or simple average), and the mode is lower than the median. As home sales price decrease, the probabilities increase.

As explained in part one, average values, median values, and price distributions are something one never encounters in real life - no matter how good we are at estimating. This chart is just one example of distributions that could be associated with a particular property. Hard to value, very unique, and ultra-luxury properties can have very complicated distributions. But for most homes, this is pretty accurate.

Statistics is Not Intuitive: A Powerful Concept

A comparative market analysis (CMA) for a property worth about $1Million would produce a range of values similar to the blue box above (pretend you don’t know what the probability distribution looks for a minute). $950K on the low end, $1.15 on the high end.

It’s tempting to take the simple average (mean) of the range = (950k + 1.15M)/2 = $1.05M, guided by the common assertion that average is usually good starting point…maybe even achievable. To get a seller's business, an agent might say "Heck, let’s go for a little above average" without any idea of how improbable that might be, and simply not worth risking the excessive time on market.

We base all of our real estate decisions on historic data, which could be off due to human manipulation, and is derived from comparable properties that are quite different. The decisions to buy those properties were made by people with motivations and perspectives that are not the same as your potential buyers. There is no way to replicate the unobservable, random conditions that brought those parties together.

Look for upcoming discussions about how time on market can impact expected returns (typically negatively), and for more information on the secret sauce for an optimal home selling strategy.

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For 15 years, we’ve helped clients navigate the fast-paced real estate landscape of New York City. We’ve gained a reputation for our hands-on, disciplined approach. From finance to architecture, our deep understanding of New York City’s unique market ensures our clients come out on top when selling their property—and when buying the next one.