I’m an appraiser, just like you.
This blog is dedicated to asking big questions, inviting opinions, exploring new ways of doing things. I’m daring you and me to step out of our collective comfort zones and think in new ways about our profession. I’m hoping that the dialog that we develop will lead to positive changes. Questions from a number of peers motivated this effort.
These questions include:
• Why do we use valuation solutions developed in 1932 to solve 2016 valuation problems?
• What are the 2016 solutions to our 2016 valuation problems?
• Is our valuation process really scientific?
• Why do we follow a defined “valuation process,” instead of the scientific method?
• Have our appraisal institutions failed us?
• And finally – Is the appraisal profession dying?
To begin, my background and education informs a different viewpoint about the valuation profession. My BA was in accounting. I witnessed the resistance of that profession to technology change – the spreadsheet — and the eventual embracing of its power. My academic love was (and remains) economics. Specifically, econometrics as a blend of economic theory, mathematics and “statistics.” I always saw appraisal as a subset of econometrics, where the subject matter (appraisal theory) is the econometric theory of assets.
Econometrics seemed simpler.
Econometrics equals measurement of economic things – like real estate and other assets. My econometric education focused the tools to measure those economic things. It made sense, even as some of the statistics did not. Anyway, I learned a lot about statistical software packages (instead of spreadsheets). Today the most popular data analysis package is ‘R’ and its environment ‘RStudio.’
Early on, I was impressed at how practical these tools were and how well the practice of appraisal did its job. Even then, though, it bothered me that most of the software, including spreadsheets, assumed I had to take a random sample. I knew appraisers did no such thing. We did something different. We collected muddy data, compared numbers and a few ratios – to show the client a thinking process to arrive at the estimate of value. When I began my career these methods and tools seemed to work well. Pick the comps, make adjustments, Go. However, that world was about to change.
Lurking in a corner, on the floor of my trainer’s office, was something called a computer. He said it might be wonderful, but he did not know how put it together or use it. We had just started typing our form reports, and to him that was a big enough change. But that thing in the corner bothered me. I knew it was quicker, and very obedient, so long as you could put it together and tell it exactly what to do. But learning the computer also involved learning scary programming stuff.
To overcome this fear, I went back to school, three different ones. Math brush up, multivariate calculus, theoretical econometrics at UCSD, applied econometrics and statistics at SDSU, and computer science at NU. Mostly graduate-level courses. 16 years of two classes per semester. Every statistics and econometrics graduate class taught at two San Diego universities. And a certificate program in GIS/GIA (Geographic Information Analysis) at UCR extension. I was torn between getting a PhD (yes I admit I wanted it for its prestige) versus the crazy desire to see the next class in a related field, and see how it may apply to appraisal. My instructors included two Nobel Prize winners (in time-series analysis), and an instructor from Moscow, teaching insurance (risk) in the mathematics department at SDSU. (He described how difficult it was to teach the profit motive in the Soviet Union). And I took classes in computer programming. At the time, I did not know that I was striving to be a data scientist. The field had not yet been invented.
I did know that I wanted things to be simple and useful.
I wanted things to make sense, instead of . . . well not making sense. Unfortunately, my life at three universities created dissonance as I tried to reconcile my appraisal education with my econometric education. When I began my career, traditional appraisal techniques worked well. But things had already started to change and those traditional tools couldn’t keep pace and lacked the analytical muscle of the statistical tools I was learning. I became active in the Appraisal Institute. I naively thought I could bring my new-found truths of statistics, graphs and computer power to the profession. But, there was resistance. Yet, this resistance could not stop the economic principle of change. These changes magnified the differences between traditional tools and statistics. Today, we do see descriptive statistics and graphs in our curricula. This makes me happy. But . . .
Also mudding the waters were colleagues who advocated statistical practices that had become outdated by advanced methods, big data and newly developed software solutions. In other words, some of our leaders failed to keep abreast of state of the art academic practices. But who could blame them? It’s hard to replace accepted thinking every few years.
This blog, website, classes, and soon to be published forum are dedicated to exploring, to asking questions, to helping appraisers live the truth. To provide valuers simple, useful tools which make sense. To truly serve the public good. To reduce, or even prevent the next swing of economic insanity. To bring some excitement, satisfaction, and joy to the individual appraiser.
We can make personal sense. We can feel better about what we do. And we can see ourselves in a positive light of service. It is possible.
That is our dedication.
August 10, 2016 @ 11:33 pm
You have good questions. I would like to see where this goes but you also sound like a professor from Harvard and very intimidating. I dare not ask a stupid question or give a stupid opinion because I will look bad in front of my peers. It sounds like you are the guru of appraising and economics and statistics so if anyone is not doing what you are doing then they they are obviously wrong. I would love to share my experiences, ask question on unusual assignments and feel free to express my opinion without fear of a heavy handed hammer falling on my head and telling me I’m all wrong if I participate. I cheer your blog but will watch on the sidelines for a while while I see where this leads. Thank you. Carlo.
August 23, 2016 @ 6:44 pm
Carlo — Thank you. The questions are mostly what I have heard from other appraisers. We hope to give them voice, as well as a discussion on what may be done. Not a guru, but have spent a considerable amount of time and learning of how data science and new technologies can help the individual appraiser, as well as the profession as a whole. It will not be possible for me to answer all questions, but there is a considerable group of people willing to do so. We are just starting. This is my first blog response! It is ok to be a ‘lurker’ as they say. Jump in when you want. The point is to help others.
August 11, 2016 @ 9:04 am
How do you reconcile the effect of varying degrees of updating ( value increased by 10-40% ), in the use of any regression process?, how is regression data affected by greatly varying condition?
August 23, 2016 @ 6:49 pm
By updating, do you mean time adjustments or price indexing? If so, then time itself is a predictor, (change in market conditions they call it). Simple regression (just time and sale prices), on just the CMS (competitive market segment) really works well. A straight line often is enough.
Different condition in comparables is also a predictor variable. But it has to be ranked.
August 22, 2016 @ 11:19 am
Regressions could be used to quantify the effect of varying degrees of updating When I run several regressions varying the data input by whatever factor(s) ie, include, then exclude, the regression can indicate or at least suggest what effect is indicated by the “varying degrees” data. I almost never run a random sample, or sweep (all the market data regardless of comparability) – the parameters used to compile the comp data set yields a qualified selection. Then the analytics go to work on the qualified sample and give me indications of how the property elements are interacting- within the pre qualified and selected COMPARABLE property data set. If I had widely differing condition or updating, my “what is a comp” parameter routine might decide that bands outside of similar to my subject (in whatever aspect is a concern) are not going to be used to build a predictive model since they don’t correlate to my subject. I find the biggest value of analytics is watching the results and differences when doing “what if’s” in a sense to gain an understanding of how elements are playing off each other.
August 23, 2016 @ 6:55 pm
I like your use of the word “sweep” — like just sweep everything into the dustpan, good or not. Not-relevant data just reduces the reliability (sureness) of the answer you get. Similarly, not using all the competitive sale data means there is loss of valuable information. Your concept of varying “what-ifs” may be an eventual solution to good adjustment estimation. There are so, so many tools available today, but few people working to figure them out how to improve valuation estimates and predictions.
August 27, 2016 @ 10:22 am
Just a couple of thoughts, after looking at the nascent blog online:
I wonder about who the audience is. It seems to me there is one group of appraisers who have embraced the use of statistical tools and a second, much larger number who have not, will not, are afraid to, etc. And I suspect a third group of appraisers with advanced understanding of statistics is very small indeed. Many of the second group may be including graphs in their reports created by their MLS, Dataquick, or their 1004MC plug-in but they may not really know what the graphs mean or how they were created.
Even in the first group I would guess that the majority have only scratched the surface of appraisal statistics (I would include myself in that group, even after several of your classes), and are not motivated to do anywhere near the depth of study that you have done.
I find the first three replies to the blog illuminating, and perhaps they illustrate my question about audience. Please understand I am only using these initial posts as an example, and my illustration may not reflect the actual posters’ reality. The first post sounds representative of the second group–hesitant about or not too comfortable with appraisal statistics; maybe some of the blog writing would be over this group’s heads or not meaningful to them, and might make them reluctant to participate. The second post sounds like me–a member of the first group who has at least scratched the surface, but still has many questions. The third post sounds like that small third group with advanced understanding, and their writing might be beyond the understanding of the first group and even much of the second group.
Maybe the audience thing will work itself out like it does in other online communities, where the discussions might range from explaining how to do a simple regression to discussing how null sets impact graphs. It sounds like a big bite to chew. I guess the interaction among the posters and readers, and the guidance of the moderator will sort a lot of that out.
September 2, 2016 @ 7:25 pm
Thank you Matt,
The questions asked come from a variety of appraisers of varying experience education and attitude! We hope to each voice, as well as a discussion on what may be done with today’s technology.
The first type of appraiser has probably tried various forms of ‘statistics.’ I suspect this person will quickly become a contributor to this community, particularly as we distill down to the real questions and real answers.
The second type, uncomfortable with statistics in our practice. For this person, the lack of sensibility or intuitive understanding may be the real issue. *(I put myself into this class).* The complexity and esoteric nature of much of the “advanced” quantitative methods being taught can be, uh — perplexing. (An upcoming blog post will dive into the issues and even misdirection of some of these teachings).
The third type of appraiser, those with advanced statistics, may well be over the heads of some others. I believe this forum and community will soon recognize two categories within this “advanced” group.
Group A will be steeped with generally an upper division class or two in statistics, or perhaps even a class or two in graduate school. These people can be dangerous, but not of their fault . . . The education they got (except perhaps in economics departments) will have been almost entirely on *inferential statistics.* Inferential statistics is sample statistics. Random samples. The random sample then represents the population you are trying to characterize or describe (via mean, variance, skew, etc.). The education involves hypothesis tests, central limit theorem, confidence intervals, p-values, z-test, F-test, and Chi-squared test — seldom relevant, because “Predictive models are predominant in most valuation settings.” ARE p.736
Given today’s data, in most areas, we do not need to deal with the mysterious statistics. As we focus on data science, and working with real markets, complete data sets, the computer-aided methods become simple, useful, and sensible.
This is the focus of “Group B,” people who know that not only do they need to understand what is being analyzed, but our clients do also. Join with me and become a “Group 3B” person. It is a lot more fun.
3B’rs are cool!
February 21, 2017 @ 6:33 am
An experienced appraiser uses the Cost Method as a means of stablization when there is a wide range in values. There are only 3 adjustment needed: location, condition and size. Time adjustments are also needed at times but If I am using $150 per sf and 25% allocation for the land in that area, then the comps should be similar. If not, why? maybe it’s not a good comp? If all are higher value, then a time adjustment is needed. All appraisals are an opinion even a computer generated value.
January 30, 2021 @ 3:36 am
Appraisers can be agents for change. Good, bad, ugly or corrupt report quality has an effect on financial and institutional stability.
“To truly serve the public good. To reduce, or even prevent the next swing of economic insanity. To bring some excitement, satisfaction, and joy to the individual appraiser. We can make personal sense. We can feel better about what we do. And we can see ourselves in a positive light of service. It is possible.”
The intent of licensing after the S&L crises was to restore public trust by improving the proffesion. It did not stop the Great Recession and egregious corruption in the lending industry as a whole. Enforcement of “appraisal law” has always been lackluster. Psychology 101- it is mainly introjection (belief) in the rule of law that can quell corruption, keep a civil society and our institutions serving the public good.