More graphs: in data science, why are they so important? Graphs provide the way for the human brain and the computer to connect. And why is that important? More Computers are really good at certain things. They can handle lots of data and can quickly carry out complex instructions (algorithms) with no mistakes, with perfect […]
Judgment vs. Data. Computer quants vs field experts. Each thinks they have a better hammer. “Experts” think the quants are hammering screws. Quants think the experts have their eyes closed. Both have sore thumbs. Judgment Here, in part 2 we will look at the field-related expert, excelling in experience and judgment. In part 3, we […]
No computer can replace my expert judgment and experience! We’ve heard words like this. And there is a contingent of “quants” who rely on a high level of competence in quantitative methods, which may include statistics, machine learning, and artificial intelligence. Each expert type has a hammer. And each is right in their own way. […]
Consumer home loan valuations serve those who pay the bill – the lender. This cost is passed on to the buyer, and the buyer usually has a right to get a ‘copy’ of the report. However, the valuation is not there to help the buyer decide whether the price is fair or a good investment. […]
Science is “systematic study through observation and experiment.” A method. But wait, there’s more! Science is both a process and an organized way of thinking. It follows some basic principles. Darn! It seems science does not care if you believe in it or not! There is process in science: There […]
Appraisals, AVMs, evaluations, Broker opinions, and “homeowner’s opinion” – valuation of any sort all have some things in common. The “new valuation modeling paradigm” is a term coined by Marv Wolverton, PhD, MAI (who authored the AI book An Introduction to Statistics for Appraisers). He created the term in a response to my peer-reviewed Appraisal […]
I first heard the word unlearning at an AI national conference. The speaker said that unlearning was as important as learning. Perhaps more so. We human beings tend to want to maintain the status-quo, unless somehow compelled to change. Change is not easy they say. Psychological inertia involves inhibiting any action, just because it causes […]
Why is the class called “Stats, Graphs, and Data Science?” It is easy to see statistics in data science, but why are graphs given importance? To answer this question, we must hear how data science sounds. Yes, it is a science, yet science includes judgment and subject-matter experience. And visualization. Data science comprises competence, computation, […]
AVMs (Automated Valuation Models) have been around for over 20 years now. Appraisals have been around for 100 years now. Why no winner? Let’s look. This is the first part in a series of coming blogs that will compare and contrast “automated” versus “opinion” valuations. Stay tuned to hear the unexpected surprise climax of this […]
Everyone talks about the “market,” but no one tells us exactly what it is. The Dictionary of Real Estate, 5th ed. Appraisal Institute, defines “Real Estate Market” as “Buyers and sellers of particular real estate and the transactions that occur among them.” Close, but no kewpie doll prize. I have had at least three graduate […]
Why should I care about statistical stuff? Appraisal says: Pick some comps, compare, reconcile, and give a number. No statistics there. Data science says let the market pick the data, predict, and provide a reliability score. Traditional appraisal and modern data science have the same goal: help make decisions by analyzing data. Statistical thinking is […]
Judgment versus science. Judgment wins! (Part I of “Data Collection”) I have heard such things as “No science formula will ever replace my experience and training.” And “no AVM (Automated Valuation Model) will ever inspect a house.” Judgment wins. Except for one problem. The market share of valuations continues down for appraisers. AVMs, ‘evaluations’, broker […]
We clean, scrub, transform, wrangle, munge, map, shape, enrich, enhance, prepare, preprocess, improve, and … verify. Data scientists spend 80% of their time on the data. Those of us who continue to move into these more objective methods – find this to be true. In fact, once you have the data prepared and enhanced, the […]
“Ya gotta make this stuff simple” I am told . . . Data Science: Get the right data, clean the data, apply predictives, get results. Property analytics: Get the right data, clean it, apply predictives, get results. So let’s expand this: Step 1. Identify the problem, download all the data you will ever need. Step […]
Hairy scary hybrid parry. Let’s look deeper, now that we seem to be away from the original hype. When this thing first came up from the GSEs (FannieMae and FreddieMac), a hybrid appraisal was narrowly defined. Basically, it separated the desk job from the field job. I was even in a room once where the […]
