Monthly Archives: February 2021

The Freddie Mac House Price Index Is Your Retrospective Friend

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Are you familiar with the Freddie Mac House Price Index? Go take a peek.

Freddie Mac House Price Index

The FMHPI is your friend. The index reports monthly change in house prices for national, state, and metropolitan statistical areas (MSAs). The data series starts with January, 1975 and usually runs two to three months behind the current date. The data is available in Excel or text files with seasonal adjustments or non-seasonally adjusted.

As an appraiser, I use the FMHPI in several ways for current work:

  • Regional market trends.
    I use this data set if I want to show how house prices are trending in my home regions of Sacramento or Fairfield/Vallejo.
  • Market areas with little activity.
    I cover Stonyford, a small community on the eastern edge of the coastal mountains with very few sales. I trend surrounding MSAs to show regional activity and offer this as the best evidence available for market trends in Stonyford.
  • Market areas with significant non-conformity.
    I cover the Sacramento Delta area, a place with a mix of small towns and small acreage residential properties plus some high quality mansions. Market trends are often influenced by the mix of sales and frequently are not reliable so I use the Sacramento MSA instead.

Where the FMHPI really shines is with retrospective work. My local MLS has data going back to 1998 for my county. That’s great for the past 23 years but what if a client wants a date of value before then? In the past two years I have had requests for appraisals dated in the 1980s and early 1990s on multiple occasions. Each time I used the FMHPI to determine market trends and calculate time adjustments.

Normally I download the data into Excel and generate the chart I want, discussed below. However, you can use the pre-built chart and table tool. This is the default chart:

FMHPI default chart

You can add multiple states or MSAs for comparison by clicking the blue button:

FMHPI with Sacramento MSA

You can also adjust the time period on the chart by sliding the time frame below:

FMHPI Date Range Adjusted

Once you have the chart you want, you can print or download it as a PNG file to stick in your report or work file:

Print or download a PNG graphics file

This tool is versatile and includes a table view showing percentage change.

However, I normally download the data into my Excel work file and create my own graphs. The FMHPI MSAs Excel workbooks are two sheets with MSAs A-L on Sheet 1 and M-Z on Sheet 2. Column A is the Month with MSAs in columns in alphabetical order like this:

Month on the far left column, MSAs in alphabetical order

Here’s my simple process to deal with the way dates are formatted.

Click the link

Download the Seasonally Adjusted MSAs Index:

  • Click the link 
  • Open the file and copy the Month Column into your Excel work file plus the columns of data for any MSAs you want to analyze.
  • Leave a column to the immediate left of the MSAs you want to work with to fill in Excel-readable dates. I usually label it “Date Sold.”
  • Then fill in the Date Sold column matching the dates in the FMHPI Month Column. 1975M01 is 1/1/75, 1975M02 is 2/1/75, etc. Fortunately, once you have the pattern, you can use Excel to autofill to the bottom:

Copy the columns, leaving space for Date Sold, autofill date sold

You can now use this data in Excel to make line graphs or scatter graphs. If you don’t need all 46 years of data in your chart, graph only what you need. Here’s an example of how I would prepare the Sacramento MSA data for the past two years and past 10 years plus the graphs:

Data copied from original columns for ease of creating graphs

And here are the resulting graphs:

Line Graph Example

Scatter Graph

I hope you can see the value of this data. Let me know in the comments if you have questions or are already using this data.

If you have questions about this data, there is an FAQ from Freddie Mac.

Thanks to Penny Woods for encouraging me to share this with her Retrospective Appraisal class.

And thanks to Len Kiefer for sharing the charting tool built into the Freddie Mac website.

 

Composition Effects for Appraisers

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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.

Simple linear regression trendline showing an increasing submarket

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:

Change in Home Size during the Covid-19 Pandemic in the Sacramento Region from Ryan Lundquist

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).

Sale Price trendline showing an increasing market

Prices are clearly increasing in the top graph.

PSF Trendline is stable

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:

GLA trended 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.