Economist Timothy Taylor posted a discussion about hourly wages recently that had a section that sounded very familiar. Here’s the quote that caught my eye:

“You will sometimes hear statistics people talk about a ‘composition effect,’ which just means that if you are comparing a group over time, you need to beware of the possibility that  the composition of the group is changing.” Why would a residential real estate appraiser care about composition effects?

Much of my time as a residential appraiser is spent determining trends in real estate markets. Every day I create charts like the one below to describe the markets in my reports.

These trendlines assume that the group of homes sold do not differ significantly over time. In most cases, this assumption is reasonable. Sometimes this assumption is false.

For example, the global pandemic has affected buyer tastes. My friend Ryan Lundquist wrote about this recently. After six months of being cooped up from the Covid-19 pandemic, buyers want a larger home. Here’s his chart showing the trends in the Sacramento region:

The average size of a home sold in the Sacramento region has increased 100 sf over the past six months. House size is the primary driver for value, so if all other factors are the same, the average price for that market will increase.

BUT IT’S AN ARTIFICIAL GAIN BECAUSE ANY GIVEN HOME OF THE SAME SIZE WOULD SELL FOR THE SAME PRICE!

Similarly, if homes decrease in size over time with no other changes, that would cause the average price to decline with no impact on individual house prices.

Here’s what I do to have a better understanding of market trends:

1. I trend sale price of homes sold over time,
2. I trend price per square foot of homes sold over time, and
3. I trend home gross living area over time.

Why trend price per square foot? This trick takes into account some variation in size and in conforming areas, can increase the reliability of the trend analysis. However, significant changes in size will influence the PSF trendline.

I see this frequently in Winters, California. Winters is a relatively small city of about 10,000 people located on the western edge of the Sacramento Valley not far from Davis. Below are the three graphs for Winters sales from 1/1/18 to 10/1/19 (all data from Metrolist MLS).

Prices are clearly increasing in the top graph.

Prices are essentially stable when trending price per square foot for the same sales. Why?

Here’s the third chart showing size of homes over time:

The size of a home sold in Winters during this time period increased almost 1 sf per day.

Because of the math,

an increase in the average size of homes sold will push down the price per square foot trendline but will push up the sale price trendline, and

a decrease in the average size of homes sold will push up the price per square foot trendline but will push down the sale price trendline.

Here are some other examples that may cause a market to appear to change over time without a real change in market conditions:

• Average lot size changes, especially for small acreage residential properties
• Average age of homes changes, especially when new construction ramps up or ramps down
• Better quality homes come to market
• An outlier, such as a tear down or the biggest home in the county, pulls the trendline out of true
• A small, heterogeneous market susceptible to change from the latest sale

A good habit is to take a look at your data. Do you see changes in your data or is it relatively similar over time?

When I do run into composition effects, such as sale price going up and price per square foot going down, I graph both together on the same graph and explain why there is a difference. I then reconcile.

“The sale price trendline is increasing while the sale price per square foot trendline is decreasing. The average size of homes have increased during this time period, skewing the sale price trendline up and the price per square foot trendline down.  I conclude that this market has been relatively stable during this time.”

Hope this adds to your understanding of residential real estate markets.

p.s. This website has instructions on how to show two time series on the same graph like the example above.