I’m in Portland this Thursday to give a two hour talk on tips for appraising complex properties at the ASA-IFA Portland/Rose City Chapter Monthly Luncheon.
Hope to see you there.
I’m in Portland this Thursday to give a two hour talk on tips for appraising complex properties at the ASA-IFA Portland/Rose City Chapter Monthly Luncheon.
Hope to see you there.
Two recent posts from my friend Jamie Owen at the Cleveland Appraisal Blog plus a planned realtor office visit inspired me to write this. Jamie did a great job blowing up the myth that comparable sales need to be within one mile of the subject in this post. He also tackled geographical competency, or the need to have boots on the ground knowledge about a market in order to credibly value properties in a second post.
Both posts touch on the subject of what is a comparable sale and why should anyone in real estate, or even the general public, care? The quick answer is that “comps” are the basis for how we, both those in the real estate industry and the man on the street, value residential real estate.
Per the Dictionary of Real Estate Appraisal, 3rd Edition, comparables are:
“…similar property sales, rentals, or operating expenses used for comparison in the valuation process; also called comps.“
Comps are used in the Sales Comparison Approach to Value, especially in residential real estate appraisal. All of us, appraisers, real estate agents, and folks considering buying a home, use the theory of substitution to determine the value of a home. What would the typical buyer shopping in that neighborhood buy instead of the subject?
A comparable sale is a sale of a home that the typical buyer of the subject would buy instead of the subject.
Subconsciously, everyone who owns a home compares it to homes in their neighborhood. We learn about a recent sale on our block and place a price on ours based on whether we think it’s better than ours, relatively similar, or inferior. The formal version of this is the sales comparison approach used by appraisers.
We appraisers find the most similar sales, adjust the comparables for differences from the subject, leaving each adjusted comparable sale an indicator of value for the subject. The vast majority of single family residential appraisals in the US rely upon this methodology.
In the context of the sales comparison approach to value, the key is to identify the comps for the subject.
The easiest way to get the value of a single family residence wrong is to get the comps wrong!
Residential real estate, such as a house, a condominium, a home on a small acre lot outside of town, etc., have characteristics (“dimensions”) that serve as descriptions of a specific sale for a specific property. The more similarities between a sale and the subject under consideration, the better a comp. We can go into a deep dive, like George does in his classes; instead, I want to talk about what I do specifically for simple single family residential work in conforming neighborhoods.
Some examples of dimensions and characteristics important to valuing homes include transaction terms (financing, credits, etc.), motivations, location, views, quality, design, condition/age, floor area, and amenities.
Some dimensions/characteristics are more important than others and can vary dramatically in importance depending upon the location. For example, pools are valuable in the Sacramento region but have less value in the Pacific Northwest where the weather is cooler. Basements are common in the Midwest and East Coast but not so here. In the Whisper Creek Subdivision in Arbuckle, CA, a tract of large homes on half acre lots, RV parking is a significant factor unlike other nearby markets. This is why the geographical competency that Jamie discusses is so important. Appraisers with geographical competency understand what characteristics define a true comparable and get the subject’s value right.
Time usually matters except when it doesn’t. If a market is rapidly changing, using the most recent sales can reduce the impact of market change. When a market is relatively stable, time is less important and so using older comparables is reasonable. I downplay time frequently because time is usually the easiest and most reliable adjustment to make.
For a typical tract home in my area, the most important factors are motivations for the purchase or sale, time, location/proximity, and size/floor area. I start with a map search using my neighborhood boundaries and go back 12 months prior to the date of value for closed sales. I exclude from consideration REO sales, short sales, and other transactions where motivations likely had an impact on sale price.
I search for homes a little smaller than the subject because most buyers can make do with a slightly smaller home. Because the typical buyer can accept a larger home than the subject, I set the upper boundary on my floor area range wider than the lower bound. For example, if the subject has 2300 sf of living space, I will search for comparables with 2000 sf to 2800 sf of living space (300 sf smaller to 500 sf larger).
After I set my criteria in the MLS search, I run the search and review the results.
I mentally draw a box around the subject’s important characteristics so I can place it in the competitive market. This is known as bracketing. Reasonably, would the typical buyer consider the sales found suitable substitutes for the subject? Are the sales similar in quality and design? Are there differences in lot size or age? Do I have larger and smaller homes? Do I have homes in similar condition, or inferior and superior? I try to account for every significant characteristic of the subject so I can show, by comparison, the value of the subject by using these comparables.
If I’m comfortable with the sales found, I can start my adjustments analysis. If not, I revise my search criteria and run the search again until I am happy that the sales found reasonably describe the subject.
Once I have my initial candidate comparable sales identified, I dig in and look for most representative comparables of the subject and decide on which sales to research further (view the exterior, contact agents involved in the transaction, etc.). I review outliers, sales outside the normal range, and try to determine why the sales deviate from the norm. I either adjust for the issue or remove the outlier from consideration. The remaining comps, after adjustment, are my indicators of value for the subject.
Comps are usually easy to find in conforming neighborhoods as long as the subject is similar to the rest of the neighborhood. When the subject is unusual, or when there are few sales available and they are all different (“non-conforming”), comparable selection is difficult. The appraisal becomes complex and beyond the scope of this article. I do have tips in my article about appraising complex residential properties.
How do you search for comparables? What are some tips for a real estate agent or new appraiser you can share?
Recently Joseph James Angelo was arrested outside of Sacramento and was accused of being the East Area Rapist. The East Area Rapist terrorized California in the 1970s and committed more than 50 rapes and 12 murders before disappearing more than 30 years ago. My friend Ryan Lundquist started a poll and conversation on his blog: What discount would you expect if the East Area Rapist’s house came on the market?
The results are interesting. Most respondents were in the 0-10% and 10-20% brackets. I was in the 0-10% bracket based on the one time I’ve worked on a similar problem. Several years ago I was completing an appraisal on a house for a purchase in one of my markets and I noticed a weird note in the listing. “Blessed by a deacon.” What the heck did that mean?
I called the listing agent, a friend of mine, and asked her what she meant by that. Turns out there was a murder on the site within the past six months. Would have been nice if she’d let me know when I scheduled the appointment that, oh, by the way, there was a murder at the subject….
I frantically called the lender to warn them that a murder had occurred at the subject in the past six months, that I would need time to analyze this new evidence, and that I needed more money for the report because of the extra due diligence. I called my mentor to get advice on how to deal with this and to see if he had any data (nope). I then searched MLS over the past 10 years but for some reason, listing agents don’t normally advertise “recent murder here” when trying to sell homes so struck out again. No one at the local Realtor meeting could remember any sales of homes after a murder or similar circumstance either. One of my comparables, however, had a death by natural causes within six months of date of sale.
So after a bunch of due diligence, I had jack squat for data. I took a step back. This was an entry tier home at a time where inventory was low in a relatively safe neighborhood where the murder was unlikely to occur again. Three full-price offers were received for the subject and all three potential buyers were aware of the home’s history. Was there a discount because of the murder? My best evidence, the three full-price offers, showed little to no market reaction from the murder. I discussed my research in my report and concluded no market reaction and sent it in. The purchase closed less than a month later.
This is not the exactly same situation as if the East Area Rapist’s house was on the market. First, no reports to date suggest that crimes were committed at the accused’s house while the house I appraised was the site of a murder. Second, the murder at my subject’s property was one off with little news coverage outside of the community where it occurred. The East Area Rapist is notoriously known throughout California, if not the US, especially for those of age at the time of his crimes. A better but not perfect model might be Dorothea Puente, the landlord in Sacramento who murdered at least seven people and buried them in the backyard. Ryan plots the sales of her duplex on his poll results post.
Tony Bizjak, the real estate writer for the Sacramento Bee, liked Ryan’s post enough to turn it into an article and quoted me for the story.
p.s. Randall Bell, PhD, MAI is the national expert on diminution in value and determining crime scene discounts. His book Real Estate Damages is highly recommended. He thinks the discount will be closer to 25% if the home of the East Area Rapist hits the market.
This is the second post in a series describing my journey to move my residential appraisal business workflow from Microsoft Excel to R. Last time out, I made the case for why I’m making the change. This post will be a catalog of the ways I use Excel today to serve as a guide for where I need to go.
I use Excel a lot. Each appraisal I work on, I start a separate Excel file. Us appraisers are required to retain for each report a work file that supports our conclusions and allows for someone else auditing us to understand what we did.
Here’s what I do with Excel today:
This is the bulk of what I do with Excel today. As I start to shift this workflow over to R, I plan to go into more detail about the special or not so special challenges I encounter. I also have high expectations that R will inspire me to come up with new ways of analyzing and presenting my data.
Reminder for appraiser readers in particular: R is a tool. Excel is a tool. Most of what I plan to discuss in this series is about changing tools. Occasionally, I’ll talk about modeling decisions (like covariance above). However, all of what I’m doing is rooted in the Stats, Graphs, and Data Sciences classes I’ve taken. You need to understand the theory so you can make informed decisions about your modeling choices.
Take classes from George, he’s very willing to help. https://georgedell.com/
I’ve come to rely heavily on Microsoft Excel over the years to do my work as a residential appraiser. So much so that I teach classes to other appraisers on how to use Excel in their work. However, after taking George Dell’s first R for Appraisers class recently, I’ve decided to completely revise my workflow and replace what I do in Excel as much as possible with R.
What’s R? might be your first question. R is a free data analytics software package used widely for data analysis. Most university economics programs teach with R these days. Here’s the official description (link). You can download a free copy here.
However, if you’re going to use R, you need to use RStudio, the free integrated development environment for R. It provides a way of seeing more and doing more with the basic R programming language and really extends what you can do with R. Free copy and more information here.
Why would someone who has invested heavily in developing skills in Excel move to a brand new software package? Here are my reasons:
I plan to document my migration from Excel to R here. I’ll share resources I find useful and will discuss issues I run into. I also plan to describe the benefits and drawbacks. This is mainly for me, and maybe Abdur Abdul-Malik and Bruce Hahn, the other appraisers I know making the same journey. And maybe I can help George Dell come up with ideas on how to spread the word to the rest of the industry. Thanks again George for the inspiration.
I just completed an appraisal of a home for a refinance where the subject had sold eight months prior to date of value. I included the prior sale of the subject as Comp#4 and had two very recent, reasonably similar comparable sales that I included in the sales comparison grid. The subject is located in a suburban neighborhood built out in the 1960s-1970s on a ridge with some homes featuring city light views. Many recent sales feature remodeling or recent updating while others are relatively original.
In most suburban neighborhoods, using sale price per square foot (PSF) is the best way to track market trends because using PSF reduces the impact of changes in floor area in your sales comparable data. Generally speaking, the sale price model is subject to variations in floor area in this case and is not the best model. So, following my normal practice, I used the PSF linear regression model to determine time adjustments to comparables.
The PSF linear regression formula is:
Slope of the Trendline * Subject’s GLA = Daily Time Adjustment
So, in this case:
0.0992 * 1794 sf = $178/day
Once I applied these time adjustments, my range of value was from $447,000 to $466,000 with an indicated value of $460,000. However, the most recent sale indicated $450,000 at most as did homes in contract competitive with the subject. Something wasn’t right.
So I tried a sale price linear regression model instead.
Sale Price Linear Regression Model:
Slope of the Trendline = Daily Time Adjustment
In this case, indicated daily time adjustment is $117/day or
Indicated range of value for the subject ranged from $443,000 to $451,000, in line with the most recent sale and competitive homes in contract.
In this case, the Sale Price Linear Regression Model explained my data better than the PSF model.
I’ll have posts explaining more about time and real estate in the near future and will be teaching a class this July in Sacramento. Or you can learn straight from the national expert, George Dell. Sign up for his website and classes here. Thanks George for sharing with the rest of us.
Why do you think the linear model worked better in this case?
Time is your friend when valuing homes, especially when analyzing unusual properties. Sometimes when appraising a property, you find six model match sales within three months of date of value in a very narrow range, the value of the subject is obvious, and you’re done. Next please.
Other times, the subject is located on a busy street, is larger than any sale in town in the past 12 months, and has a moat. How do you deal with this nightmare? This is when time is your friend.
Expand your search parameters back in time until you find a competitive sale that brackets or equals the subject’s unusual feature. That sale from three years ago that was bigger than the subject is a comparable you can use to value it today. Or better yet, use the prior sale of the subject from five years ago so you have a sense of the value impact of that moat. Sometimes the best comp is the prior sale of the subject.
If you have time adjustments in your tool box, you’ll have access to a wider range of comparables than someone who doesn’t.
I’m developing a time adjustments class for REAA Sacramento that I plan to teach in July. I’ll talk about time adjustments more on this blog but will leave you with a couple of ways to determine time adjustments, especially when you want to bring forward in time a dated sale to use as a comparable.
Gary Christensen has a video here that discusses how to do a linear time adjustment in Excel. This works well when you have a set of comparable sales that you can analyze and the market has been relatively consistent. I’ll discuss this in detail in the near future.
Another simple way that works well for comparables from 2+ years ago is to look at the percent change in average (median/mean) sale price or price per square foot for the subject’s market area compared to the date of value. That percent difference-that’s your time adjustment. Apply the percentage to the comparable’s sale price and your old sale is ready to use as a comp. Most MLS systems have market analysis tools that will let you look up an average price in the past. In the worst case, the Freddie Mac House Price Index on the MSA level can help. Or you can use pivot tables to track changes in market areas (more to come soon about pivot tables).
I recently appraised a home in a small project where the most recent sale occurred nine years prior to date of value. I used the Freddie Mac House Price Index to determine a 29% negative time adjustment for this sale, subtracted the dollar amount, and the dated sale was ready for comparison.
Freddie Mac Home Price Index Calculation
|Date||Freddie Mac Index Value|
What is the most time between a date of value and a date of sale for a comparable in one of your valuations?