When Economic Adjustments Can Mislead You

On today’s show we are taking a look at two different measurements of the exact same thing. Imagine for a moment that you wanted to cut a piece of wood. You take out your trusty tape measure and measure the distance you think you need. But then along comes an economist who says, “Oh no. You’re not going to use that simple tape measure are you? That will only give you a nominal measurement. There are a bunch of adjustments to be made in order to get the real measurement.”

This is a joke of course, but often jokes mirror reality. 

The Case-Shiller real estate market index reports both nominal (non-seasonally adjusted) and seasonally adjusted data. 

So when is it appropriate to use the seasonally adjusted number? 

Seasonally Adjusted data is best for analyzing short-term trends, like month-over-month changes, because it removes the noise of seasonality.

Non Seasonally Adjusted data is better for year-over-year comparisons, since seasonal patterns occur in the same month each year.

For example, I would personally compare June of 2025 against June of 2024. That’s a valid comparison for the same point in the seasonal cycle on a year over year basis. For that measurement I would not use seasonally adjusted data. If I wanted to compare June to January which are at different points in the annual cycle, I might use the seasonally adjusted data. But because the seasonal variations are so large I personally would not even perform that comparison even with the seasonal adjustments. I don’t know what conclusions I would draw from the data. 

I personally don’t like to mess around with adjustments at all. I would prefer to compare this January against January the year before, and then February against the same month the year before and so on. That way there is no seasonal adjustment required for the exact same period one year earlier. It’s a like for like comparison. 

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Episode Transcript

(00:02):
Welcome to the Real Estate Espresso podcast, your morning
shot of what's new in the world of real estate investing.
I'm your host, Victor Minash. On today's show, we're taking a
look at two different measurements of the exact same
thing. Imagine for a moment that you
wanted to cut a piece of wood. You take out your trusty tape
measure and measure the distanceyou think you need.
But then along comes an economist who says, oh, no,

(00:22):
you're not going to use that simple tape measure, are you?
That'll only give you a nominal measurement.
There's a bunch of adjustments to be made in order to get the
real measurement. Well, this is a joke of course,
but often jokes mirror reality. The S&P CoreLogic Case Shiller
Real Estate Market Index reportsboth nominal, that is non
seasonally adjusted and seasonally adjusted data.

(00:43):
You're often going to get both of these reported in the news,
and on today's episode we're going to talk about when to use
one number versus the other. The first adjustment is seasonal
adjustment. The primary adjustment is made
to go from nominal to seasonallyadjusted.
It's to remove predictable seasonal patterns from the data.
Residential real estate markets typically experience seasonal
fluctuations, like an increase in activity in the spring and

(01:06):
summer. Most homes tend to be listed and
sold during these warmer months,leading to higher transaction
volumes and potentially strongerprice growth.
People don't like to move in thewinter.
It was generally slower activityin the fall and in the winter,
and school schedules often lead to a slow down in housing
activity. To create the seasonally
adjusted index, S&P Dow Jones Indices, this is the

(01:27):
organization that publishes the Case Shiller index, uses
statistical methods to identify and filter out these recurring
seasonal variations. Now, they don't actually publish
the specific methodology that they use, but it's generally
known that they use an advanced version of the Census Bureau's
X12 ARIMA program. I'll describe what that is in a
moment. The purpose of the adjustment is

(01:48):
to enable meaningful month over month comparisons, and without
the seasonal adjustment, month over month increase in the
spring might reflect A seasonal bump, but not necessarily a true
acceleration of the market. Seasonal adjusted data allows
for more accurate comparisons ofprice changes from one month to
the next, regardless of the timeof year.
The second adjustment is real versus nominal.

(02:08):
That's inflation adjusted. In some cases, it makes sense to
distinguish between seasonal adjustment and inflation
adjustment. Most of the time, the numbers
that are presented are the nominal index.
This is the raw index value reflecting home prices and
current dollars at the time of sale.
It is not adjusted for season orinflation.
To get the real index, you need to adjust the nominal index for
inflation using a consumer priceindex.

(02:30):
But there's more than one consumer price index.
So which is the right one to useto use the Urban Consumers Index
or to use a specific component like the owner equivalent rent,
which is managed by the Bureau of Labor and Statistics?
So it's entirely possible when you read a story in the media on
real estate, you might be getting nominal data, seasonally
adjusted data, or possibly inflation adjusted data.
Each of these adjustments can beenough to take a number that

(02:53):
shows an increase using one measure and have it show it
decrease using either a seasonaladjustment or an inflation
adjustment. Now, the US Census Bureau's
ARIMA software or its predecessors like X12 is
typically used for seasonal adjustments.
It's the standard tool that's used across US economics.
ARIMA is an acronym and it stands for Autoregressive

(03:13):
Integrated Moving Average. I have no idea what that means,
but it sounds cool. It decomposes time into a trend
cycle, a seasonal and irregular components.
So for example, trend cycles arelong term movements.
The seasonal components are repeating patterns like summer
increases, and then irregular components are isolated events,
like for example the COVID-19 pandemic.

(03:34):
The seasonal components are calculated month over month
across multiple years, and then all of these are in fact applied
to the nominal value. So when we look at the same data
for the same cities, we often get 2 completely different
conclusions depending on which number you look at.
Let's look at the most recent report from Case Shiller.
Let's zero in, for example, on the city of Phoenix.

(03:55):
Phoenix, of course, is not just Phoenix.
This is in the entire Maricopa County, which makes up 27
municipalities. In April 2025, the index was at
330.69. That represents a 1.31% year
over year change. For the same period last year
from February to March, prices increased 0.15% and from March

(04:16):
to April they decreased .04%. So question is when is it
appropriate to use the seasonally adjusted number?
For example, if we look at Phoenix again using the
seasonally adjusted number, if we compare the same time
periods, February to March, the seasonally adjusted number would
be -.59% and the March to April number would be -.91%.

(04:40):
On a nominal basis, we would seea year over year increase, but
in fact, on a seasonally adjusted basis we're showing a
fairly significant decline. Which number is a more accurate
reflection of the true story? When you quote a number, you can
use it to justify almost any narrative you want.
So according to Kate Shiller, seasonally adjusted data is best
for analyzing short term trends like month over month changes

(05:01):
because it removes the noise of seasonality.
The non seasonally adjusted is better for year over year
comparisons since seasonal patterns tend to occur in the
same month each year. For example, I would personally
compare June of 2025 against June of 2024.
That's a valid comparison for the same point in the seasonal
cycle on a year over year basis.For that measurement, I would

(05:21):
not use seasonally adjusted data.
If I want to compare June to January, which are different
points in the cycle, then I might use seasonally adjusted
data. But because the seasonal
variations are so large, I personally wouldn't even perform
that comparison. Even with the seasonal
adjustments, I don't know what conclusions I would draw from
the data. I personally don't like to mess
around with adjustments at all. I would prefer to compare this

(05:45):
January against January the yearbefore, and then February
against the same month last year, and so on.
That way, there's no seasonal adjustment required for the
exact same period one year earlier.
It's a like for like comparison.So when you hear numbers quoted
in the media, pay close attention to what the numbers
are actually telling you. Is it adjusted or is it not
adjusted? Often times that number can be

(06:07):
used to promote a narrative thatmay not necessarily reflect
reality. As you think about that, have an
awesome rest of your day. Go make some great things happen
and we'll talk to you again tomorrow.