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Structure Turns Up

Things don’t turn up in this world until somebody turns them up.
James A. Garfield

In the mid-1900s, there was a race to find the mechanism that passed on genetic instructions.  The double helix structure of DNA that James Watson and Francis Crick discovered was a simpler solution than many biologists thought possible and caught many of them by surprise (A).

Imagine the surprise of many, then, when no one else thought to look, the law of supply and demand is supplanted by the Law of Value and Demand from Hypernomics, which, like DNA, is a long-standing structure which only turns up with lots of hard work and a little imagination.  As shown in B, two market dimensions, Dividends and Earnings Per Share, describe a surface (P-Value 1.55E-06) that drove the Value of the Dow 30 stocks yesterday (stocks above it may be overpriced, those below it, undervalued).  That Value determines Price, a third market dimension, which, in turn, limits the Quantity sold, a fourth market dimension.

The secret, such as it is, is that such structures occur in mathematical, not physical space.  But they turn up in all markets since they began, just as our DNA has always been with us.

#marketDNA #technology #markets #innovation #hypernomics #4D

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The Utility Case Vs. Value Analysis

Every time you spend money, you’re casting a vote for the kind of world you want – Anne Lappé

Recently, long-dead Jeremy Bentham took a cross-campus trip. Shunning a wooden cupboard he long occupied in the Wilkins Building at University College London, he moved, greatly aided, to a shiny new booth in UCL’s Student Centre (A). Famously, his penultimate journey was to a UCL council meeting, where they recorded him as “present, but not voting.”

Bentham’s ideas about utility theory still hold sway. Many firms used it to figure cell phone prices (B). Such studies queried participants’ willingness to pay for features, to which researchers assigned utils, a utility measure. Then analysts converted utils to dollars.

Hypernomics notes respondents gave hypothetical answers; they weren’t buying phones, that util value varies widely, and that this method ignored available relevant communication data at their peril.

We display our willingness to pay to connect when we put up cash to make towers (C), walkie-talkies (D), or car phones (E). Value Analysis considers past and present market states to predict the future.

Jeremy may not vote, but we do.

The best utility case holds its inventor.

#value #utility #hypernomics #innovation #marketanalysis

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A Sufficient Condition

For a successful technology, reality must take precedence over public relations, for Nature cannot be fooled
Richard P. Feynman

Feynman’s observation about truth is one that can move past physics, where he won the Nobel prize.

It happens that in all markets, we collectively form relationships about how we respond to product features and prices. It is our Nature.

As we build, say, a helicopter for the President of the United States (A), we might be inclined to keep adding to it: unprecedented speed, a larger cabin, extra defensive systems. After all, we might tell ourselves; there won’t be a more important thing in the sky. It is a necessary condition to protect it and everyone inside.

The bonus features drive an increase in Value – and Price.
But at a group-induced limit we call the Demand Frontier, sales stop. We do not have any more monies to buy more of the product in question past the points on that line. Trying to exceed the Demand Frontier is a sufficient condition to stop any program.

In the case of the VH-71, USG requirements creep let it become untenable. All of that could have been avoided by analyzing the market as in B.

Doing a market analysis is hard.

Losing $4.2 billion is harder.

#innovation #demand #marketanalysis #business

 

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DeLorean CSI

In October 1982, the US government charged John DeLorean with cocaine trafficking in a deal he thought would stave off bankruptcy for his self-named company.

How did it all go wrong?

His DeLorean Motor Company sports car (A) came with several sales features. It had a rear-mounted engine, brushed stainless steel body panels, and its iconic gull-wing doors. Its original designation was the DMC-12, the “12,” reflecting its price, in thousands. But, when it came time to start taking orders, DeLorean dropped the name and raised the price.

The renamed DeLorean entered the market with 130 horsepower, priced at $25,000. As we see in B, no car with that amount of power came close to its price. The 1981 Audi 5000 Turbo, with the same horsepower, sold for $7,000 less.
Statistics reveal the sustainable prices for 1981 cars were a function of their horsepower and units sold (both P-values < 0.01).

As shown in C, the DeLorean’s predicted sustainable price was $15,500; its posted price was nearly three standard deviations too high.

To sell all 7,500 units it produced for $25K, D shows us its installed horsepower should have doubled to 262. As 1981 ended, it only sold 3,000.

Moral of the story: do market math.

#innovation #marketanalysis #valueanalysis #pricing #cars

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Demand By Proxy

You can’t always get what you want…

Finding data can be hard. Say you needed to model demand for Japanese bullet train travel, or the London to Paris run. Ideally, both firms would post these figures, and you could reduce that to insight. While you can locate that information for NYC cab data (see one of my previous posts), you won’t for The Shinkansen or the Chunnel. What to do?

Let’s suppose train operators match the number of seats offered by class to their demand. Why wouldn’t they? If they had too many high-priced seats open, they’d either drop the price or change the seating arrangement. Eventually, they would come to a configuration that works most of the time.

Below, we see the number of seats by price for Japanese (A) and European (B) high-speed rail services. By themselves, neither has enough data to form a viable study. Together, in (C), they reveal collective thinking from different sides of the planet – and it’s much the same. Yes, that’s only two routes. More would be better. But this shows us we can gain an understanding of a market using the files we have instead of the ones we want.

…If you try sometimes, you just might find…you get what you need (Mick Jagger/Keith Richards).

#innovation #demand #markets #marketanalysis #strategy

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Visualizing Value

In Multidimensional Economics, Value is a sustainable product price based on its features.  Producers set Prices.  Customers determine Value.  When they don’t match, problems arise.  Buyers pay no mind to cost when considering Value.  If you paid $1000 for a laptop, you don’t care if its cost was $1900, $900, or $90.  You just know it satisfied your Value proposition.  How do markets establish Value?

Value is whatever the market says it is.  For business jets, Fig. A shows us there is a positive correlation between speed and price.  The faster the planes go, the more buyers who can are willing to pay.  Note, though; there is high variation in A near 560 MPH, reflected in the Mean Absolute Percentage Error in D.  Fliers like to be able to take people along with them; thus, it makes sense in B that buyers pay for added capacity.  No one wants to be cramped, either, so observe in C that taller cabins fetch more money than shorter ones.  As we add features B & C, we lower errors in D.

Aircraft speed, capacity, and comfort value terms are analogous to those for computers. Laptop buyers want processor speed, short- and long-term memory, and easy to read screens.

Analysts should consider all features markets find useful.

#business #value #marketanalysis #price #innovation

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Cannabis Laffer Curve Expanded:
The Netherlands Sparked North American Interest

In an earlier post, we examined the recreational pot tax structure.  Using US-only data, we discovered that at its frontier, a Laffer Curve formed that described the maximum amount of tax revenues possible given specific tax rates.

Here we entertain other authorities taxing legal recreational pot.  Added to the blue points forming a limit is another describing the tax rate and revenue per user for The Netherlands (NL) in 2008 (adjusted for inflation).  Through these blue points, the Laffer Curve explains 90% of their variation and is highly negative (power exponent -1.61).

Also considered now but not part of the Laffer Curve is the recent experience of British Columbia (BL 2019).  Observe it registered minuscule tax revenues.

At least 3 factors influence cannabis tax receipts: 1) Ease of legal access: BC, OR, and CA lag far behind their better-organized counterparts in making legal recreational marijuana sufficiently available.  2) Tax rate: From 15.3% (NV 2019) to 108% (WA 2014), revenues go up as tax percentages go down.  3) The proximity of lower-cost options: some would-be CA or CA tourist receipts or go to NV or black markets.

#laffercurve #market #marketanalysis #price #taxpolicy #demand #tax

Laffer Curve Quantified: Pot Taxes Get Too High

The Laffer Curve is the relationship between tax rates and revenues.  For income, taxes of 0% or 100% produce no tax revenue.  Maximum tax receipts lie in-between.

The study of this phenomenon has mainly been theoretical.

The recent rush of states legalizing recreational marijuana gives us a real-world example.

In 2014, Colorado and Washington legalized recreational pot.  Other states followed suit, all with different tax rates.  If we exclude the results for Oregon and California in 2019 (in red), the remaining six blue points form most of the Laffer curve for cannabis.  This blue power curve is highly negative (exponent -1.55) and significant (P-value 1.96E-03).  It explains why Nevada, in 2019, made over 30 times as much per cannabis user as did Washington State in 2014.

In 2019, California, with nearly 13 times the population of its neighbor Nevada, made barely half of the receipts of The Silver State.  California struggles mightily with the cannabis black market because of its tax policy.  There’s a lesson here: Never turn a market analysis problem into a legal one.  If someone blows smoke your way arguing for high marijuana taxes, don’t inhale.

#Laffercurve #markets #marketanalysis #cannabisnews #cannabistax #taxpolicy

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Walk This Way – It Could Be More Lucrative

How do businesses’ pay to give workers a short stroll to amenities?  Using open-source data, we find companies buy easy access to nearby banks, stores, and cafes as they pay for office space and zip codes.

In A, we see LA commercial real estate prices rise with square footage and nearby household income (P-values 3.82E-16 and 0.01%, respectively), as shown by the surface.  Included in the calculation of that log-linear plane is “Walk Score,” which “measures the walkability of any address (www.walkscore.com, no affiliation with me).”

B shows the Walk Scores of 60 properties versus their prices.  Walk Score is a statistically significant (P-value 0.69%) contributor to Value (as sustainable prices).  The overall equation uses square footage, household income, and Walk Score.  It has an adjusted R^2 of 71.8%, implying there’s more work to do.

Figures C and D reveal that in Feb 2020 LA, doubling the Walk Score more than proportionally lifted the sustainable price.  Firms wishing to put up a new facility need to know this.  If the added Value of a new building exceeds its added cost, it may be worthwhile to set it up in high walkability areas.

Is NYC like LA? Look at the next post.

#price #marketanalysis #marketintelligence #realestate #target

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Physical and Market Targets

Aiming at and missing is much the same for battles and marketplaces.  We account for inaccuracies in the same way.

In A, a pilot sits in a Caudron G.3, an Allied surveillance plane in WWI. Note the fuselage aft of his seat.  Scenario B recounts a different G.3 pilot on an observation sortie, climbing out during a massive artillery battle.  An unseen friendly battery aims at a bridge, 1.  Their shell comes up short (2), wide (3), and high, as it strikes the plane (4).  As we move from 1 to 4, those lines trace out a Ballistic Error Tetrahedron, a miss across three dimensions (latitude, longitude, and altitude).  Figure C reveals the mission’s surviving American Expeditionary Force officer, my grandfather.  The round shot off part of the plane behind him.

We see the same construct in D. Here, a manufacturer sets a price for a business jet, 1.  However, if the airframer loses passenger capacity, the plane’s value falls to 2.  If it drops more features, as maximum speed and range, its sustainable price shifts to 3, then 4, respectively; this, too, is a 3-dimensional error, one we call a Value Error Tetrahedron.  Combined with the last post’s analysis, it reveals a 4D miscue.

#price #target #marketanalysis #marketintelligence