The pandemic has changed all of our lives in the past year. In local real estate, activity paused in March and April as shelter-in-place orders took effect. The Fed dropped interest rates to zero, pushing mortgage rates to historic lows. Homes became more affordable but inventory (the amount of time to sell the current number of active listings in a market) tightened because people didn’t want strangers walking through their homes during a pandemic. Low rates combined with low inventory has supercharged the local residential market.
Davis and Woodland have very few homes for sale at present with multiple offers received on most listings. The lack of inventory, very low interest rates, and lots of competition are pushing prices up as shown on the graphs below.
Davis has relatively few sales in January and March so take the 26% price increase with a grain of salt-that number is likely influenced by a change in what sold, potentially a compositional effect. I wrote about compositional effects recently.
Woodland prices have increased relatively rapidly during the pandemic on a price per square foot basis but have leveled off on a sale price basis.
Because of seasonality, I look at 12 month change in prices for Davis. Prices have increased the past 5 months compared to the previous year. Davis is a clearly appreciating market at present.
Much of my work is in unincorporated Yolo County appraising small acreage residential properties. The graph below shows a significant upward trend in these types of properties. Once again, part of the increase can be attributed to a compositional effect: the average size of homes and lots have increased, pushing up the sale price trendline somewhat. That said, prices are still increasing in this market.
Inventory was 2.5 months when I compiled this graph at the beginning of the month, shockingly low. I have not seen this market with less than 5 months of inventory in the past 10 years. Buyer preferences have shifted to having more space away from neighbors.
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
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.
“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:
I trend sale price of homes sold over time,
I trend price per square foot of homes sold over time, and
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.
Six months after the Covid-19 stay-at-home order hit Yolo County, what’s happened to residential real estate in local markets?
Sales activity in Davis was low at the start of 2020 before the pandemic hit and continued into the spring with a massive drop in May. As shown below, Davis is way behind in sales compared to last year but we may make up ground in the fall.
In contrast, Woodland started 2020 with strong year-over-year sales activity, putting on the brakes in April and May. Some of the missing activity shifted into the summer but Woodland is still behind last year’s numbers.
While demand (sales) fell over the past six months, supply fell even further in both Davis and Woodland. We have seen an increase this summer in homes listed in Davis, hopefully a sign of the traditional summer market spilling into the fall.
Woodland saw a sharp drop in new listings in April and May and is continuing to track lower.
Net effect on both Davis and Woodland is a supply imbalance leading to rising prices. Davis is showing year-over-year increases in five of the past six months.
Woodland prices are rising too as shown on the scatter graph of all sales below.
Below is a quick summary of both markets:
The standout statistic above is the incredibly low inventory in Woodland.
Winters is a much smaller market than Davis or Woodland. As the graph below shows, sales are increasing at present. Also note the lack of sales in April and May in Winters, similar to other Yolo County markets.
Takeaways for Davis and Woodland
Sales volume is down
Inventory has declined more leading to a supply imbalance
Prices are increasing
Pay Attention To
Interest rates. The historically low rates are jet fuel for the residential market. When rates go up, pay attention
The local economy. We’re still in a recession with massive job losses and a large percentage of mortgage forbearances. So far, impacts to local housing have been minimal but that may change in a hurry