I’ve been getting this question quite a bit lately. How much has the market gone up? How do you figure that out exactly? This is something we consider all the time when pulling comps, which is why it’s so important. Let’s talk about it.
NOTE: This post is longer because there’s lots to say. You can still skim the bold headings though if you wish.
Here are a few simple things I do to gauge value changes:
1) Look for comps: The best thing we can do is look to the comps for the answers. More specifically, look for the price difference between sales in the past compared with properties that are currently getting into contract.
To gauge a change in value I recommend comparing similar properties from the past to similar properties now. This is often done with sales from last year vs this year, but here’s an example of several years ago with this year. If you’re in a tract neighborhood you might consider comparing a few different similar-sized models from the past with those same models today. If you’re not in a tract area, just compare competitive sales from the past with competitive sales today.
In this example above I looked up a 2,734 sq ft model in South Sacramento and in 2015 this model was selling for about $330,000 while today the most recent sales have been $400,000 (backs highway) and $412,000. There is a pending at $419,126. Ultimately we could say the market has gone up by $80,000 to $90,000 or about 24% to 27%. It might seem odd that I’m looking up 2015 sales, but in this case the subject property sold in 2015, so I was attempting to figure out how much the market changed since then.
When lining up all data from 2018 through 2019 it’s really clear we’ve seen price increases, right?
2) Sales last year & this year: We can also try to find some sales that sold last year and then re-sold this year. I realize if we’re lucky we might find only one or two examples at best. I’m not talking about flips where remodeling has occurred, but an example like the one below where it sold in the exact same condition.
In this example we see the market has shown about a 2% uptick in value over the past year. Of course we have to understand whether these sales sold at reasonable levels. After all, if either sold too high or too low, then it’s probably meaningless data. Ultimately sales like this can be clues into the market (more on this below too).
3) Stats programs: Some people might use fancy excel spreadsheet programs where competitive neighborhood data can be exported to show price changes over time. I don’t personally use something like this, but it’s not a bad idea if it helps you get a good picture of a neighborhood market. If you use something like this, please comment below.
4) Create visuals: I’m a visual guy and it helps me see the market by creating visuals. I find myself spending a good chunk of time on #1 and #2 above as I’m pulling comps, but I also use this step to compliment those steps. If you want to learn to make graphs, here’s a tutorial. If you’ve been thinking about this, why not go for it? Creating images has been revolutionary for the way I see the market and explain it.
CLOSING TIPS:
1) Be careful about zip code data: If you’re pulling data from a zip code or county, just remember trends shown in a larger area may or may not apply equally to every neighborhood or price range (or property type). This is why I don’t look at my recent big market update, see price metrics are up 3-4% this year in the region, and then use that number for every neighborhood. Nope. I advise knowing the larger trend very well, but don’t impose that trend on a smaller area without doing research.
2) Not just one example: It’s dangerous to base a value conclusion on only one example. This is why I wouldn’t look at my example in number two above and definitely state the market has increased by 2%. One example is best considered a piece of the puzzle, and it’s important to find other pieces before understanding the puzzle.
3) The stock market & competitive data: In the stock market you can have different stocks going up, down, and sideways all at once. The same thing happens in real estate. This is exactly why I strongly recommend you look at competitive data in neighborhoods. For example, if you are valuing a 1,200 sq ft house, make sure you are looking at examples of similar-sized homes to understand that market segment instead of 4,000 sq ft homes in the same neighborhood. Otherwise if I only look at larger homes that might be a little more stagnant, I could miss the fact that the bottom of the market is increasing much more rapidly than the top end.
4) Declining market: It’s been years since we’ve had a declining market, but this same methodology above can help you measure the market when prices are declining too.
5) Seasons: When looking at older sales let’s remember the seasons in which they sold. Thus last year I might give a 2% adjustment in a neighborhood for a property that sold in June, but I could give a 5% adjustment for something that closed in December because it sold when the market was down further during a dull fall season.
6) Being quick & reconciliation: There’s not just one way to do this, and there aren’t quick answers. We have to dig in. Most of all, we might choose a few different ways to study the market and then reconcile our findings. What I mean is I might do all of the steps above, get slightly different answers for each step, and then piece them all together (reconcile them) when I use a comp in a report and make a specific adjustment to that comp. In other words, if I adjust a comp up by 2% or 4% or whatever the number is, I’m coming to the table with some sort of rationale or support for why that adjustment makes sense.
I hope this was helpful.
MARKET UPDATE VIDEO: Here’s a video to walk through the growing and slowing market in Sacramento. Enjoy if you wish.
Questions: What do you think of the steps above? Anything else you’d add? What do you do?
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Hi Ryan, hope all is well. I am one of those that likes to upload both general market area data and immediate market area data and then look at it on a quarter by quarter basis. Taking a good look at volume of sales as that can skew the heck out of the data. I agree with the properties going back in time in the immediate area, but I also look at the slightly larger data base areas as it is important in my mind to understand the immediate area and that around it. As long as it is in the same school district, proximity to freeways, city services and those type of items. Thanks for trying to make me smarter. Much appreciated. Brad
Thanks Brad. Well said. I completely agree about sales volume. If we don’t have enough sales we can really misunderstand a market – especially if there’s just one recent sale. This is exactly why we might look to competitive areas nearby too.
As a side note for any onlookers, my first example showed a comparison of only model match sales, but it would be crazy to only look at models. It’s certainly an okay place to start in a tract neighborhood, but we also have to expand our horizons. So if I’m looking at a 2700 sq ft house, as a starting place I am probably giving strong attention to competitive data between 2400 to 3000 sq ft. That’s a good starting place for me anyway.
Hey Ryan, great information as usual. I would add a couple of points to this. Your example of the same house selling in a year is useful if the motivations are understood and if no real updates occurred. Did the second sale occur because of a job change that forced a quick sale? Did the stress of moving the first time lead to a divorce that drove the price down? Did it sell 2% higher because of a kitchen remodel? (that would be useful to know)
My second point is maybe beyond your intended audience but in most cases, a trend line for directly competitive sales is the best way to judge market trends for a specific home.
Thanks Joe. I totally agree. We really have to understand the sales in my example. I hope if people read the entire post the get the gist of what I’m saying here (that we cannot rely on just one data point, but one or two sales can be clues into the market also).
Agreed on the trend line. I actually tend to run two trend lines in my reports. I do the entire neighborhood and I also do the competitive neighborhood. I feel like this is a system of checks and balances to a certain extent. Or rather it helps me see the market more fully.
To me making time adjustments on the sales comparison grid is much like any other adjustment (site, living space, amenities, etc…) in that it is an iterative process. (aka “multiple linear regression” for you Stat nerds) Methods for establishing a baseline for the adjustment vary widely as clearly demonstrated by the article and the comments. But, like any other adjustment, often this step alone proves insufficient. Tweaks (we call them the “extraction”) can be used to refine the adjustment and narrow the range of adjusted value. For time adjustments I start with Zip Code Trend data from a nationally published source but in the end I use extraction to calculate a days-between-dates adjustment (thank god for spreadsheets). I find this to be a more compelling, reproducible and defensible argument than hand-picking data to perform an analysis on.
Thanks Mike. I always appreciate your perspective. There are many ways to “skin a cat” so to speak, so as long as the results are credible, I cannot argue with what a colleague might do differently. I find zip code or county data doesn’t describe specific neighborhood trends very well. For what it’s worth, some national publications include different property types and private sales in their data also (or the boundaries are different than what I might consider). Ultimately I tend to do all of the above, but I definitely rely heavily on my graphs. However, I’ve learned over time it is SO important to compare and contrast what a trend line might suggest with actual sales in the neighborhood. In some cases a trend line might suggest a higher or lower percentage increase, but then looking at similar sales over time might tell a different story. It’s not always easy to get a trend line to behave perfectly on a graph too. Anyway, thanks for your take.
Sellers estimate high. Buyers offer low.
Sellers have a floor. Buyers have a ceiling.
It’s not rocket science. And, yes, every property deserves accurate comparisons. But time, psychology, and location are depicted in the data.
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Thank you Jay. You are definitely correct. Value is complicated. It’s not just about what we see on paper either. What is motivating buyers to purchase? There is a real emotional / psychological element to real estate. It’s never just about the raw numbers, but understanding the story behind the numbers…
Thanks a ton, totally glad to see you working in this way. I work with a small market in Paulden, just north of Prescott. I actually went back to 2007 when the subject and others nearby were sold as new builds. I wanted to see the market reaction at the time to give me more guidance on what was occurring today. It is actually a small adjustment from 2007…had not really looked closely at how current market and last peak were similar. I appreciate your ideas and approach.
Thanks Mark. Wow, I’m so glad you looked closely. Way to go!! I honestly feel a bit naked when I don’t make visuals to help me see the market and provoke my thinking on how the subject might fit into the context. It can be tricky sometimes though too if the market has changed over time. Value is complex. It sounds so easy for an algorithm to value something in seconds, but there’s so much going on…
Great article Ryan! I also always run two different analysis. One for all sales in the neighborhood and one for competitive sales. In recent years I have used Spark and Trendsheet. I agree with Joe and you that a trend line is the best way to analyze the data. I have used resales at times. Whatever an appraiser uses, they should just have good data and be able to explain how they made their analysis. Thanks for sharing this great info!
For sure. Thanks Jamie. I agree with your words. Without graphs it would be a much different game for us since the visual element is huge. Does Trendsheet / Spark break down actual price increase figures? Just curious. I should look more intently. I do have a copy of Trendsheet on my desktop to explore. I’ve not dug in too much though.
Yes, both break down the rate if increase. Trendsheet will breakdown the percentage of price change per quarter. It is very helpful.
Very cool. I’ll look into that more fully. It’s been on my to-do list (like many things). On a side note, I’ve seen some programs that spew out a bunch of visuals and stats, but it’s just not helpful. One can paste visuals in a report, but that doesn’t make the report credible – especially if the images don’t really assist in telling the story of value. It’s just clutter…
That is so true. In most of my reports I do not include charts. I think it may be more confusing to the reader if they don’t understand what the chart is really reflecting. Call me if you need help with the Trendsheet. I also highly recommend Spark as the charts are even more robust and helpful in different areas.
I actually include lots of scatter graphs in my reports to help show the overall neighborhood trend and the competitive trend. In my mind I think it enhances the report, but then again someone could easily call me cluttered, and that’s fine too. But in my mind it’s a strategic attempt to tell the visual story of the market and give the reader some context to consider.
Thanks for the offer. I honestly thought Spark and Trendsheet were the same thing (or that Brandon built both). I’m clearly a newbie here.
I am happy to help! Trendsheet and Spark were both created by Brandon. However, they are different programs.?
Great information Ryan. This is a question I get a lot from peers and I will be sharing this blog post when I do.
Thank you so much Gary. I appreciate it. I’ve had this question quite a bit lately from real estate agent friends. I hope to maybe point back to it. My sense is it’s not a quick and easy question to answer. There are many layers. I hope I haven’t made it more confusing for everyone. Haha.