Tag Archive for: price

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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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Solve Profit First

Suppliers make products and see what markets will bear for them.  That’s precisely backward.

Instead, we can solve for profit potential first and discover product specifications second.

Suppose a market has products for which there are particular quantities, and prices demanded, as shown by the red dots.  We want to avoid competition, so we choose a Target Price, 1, that exploits a price gap.  Given a Demand Frontier, this sets a quantity limit, 2.

With some work (not shown), we find the market supports Features A & B with a green Value Surface (supportable prices based on those features), and that there’s an area of interest with no competition.  Linked to that region are the costs for 1 and 200 units of our new product.  If we constrain the problem (orange planes), we form an enclosure.

We then run Financial Catscans through this region.  Much like brain scans, they are virtual market section cuts.  At the optimum, we solve for the specs of Features A (3) and B (4), and the per-unit profit (5).  Per unit profit (5) times the demand limit quantity (2) yields max potential profit.

In the process, we’ve solved a 4D problem (Feature A, Feature B, Price, Quantity) from a 1D goal (profit).

#innovation #price #value #markets #profit #sales #manangement

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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

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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

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Interest Rates And Currency

Last time, we examined how the amounts of currency and foreign exchange reserves drove currency prices.  There are more forces at work here.

A country’s prime interest rate is one of them.

Below we examine the linked effect of interest rates and currency value from July 12, 2019.  The rates in the study vary widely and are part of what supports the price of money.

At left, we see how the world reacts to the Volume of money, the foreign exchange reserves, and the prime rate, here set to 2% (Sweden’s at that time).  If we change that loan figure to 63% (which Brazil had then), we get the picture at right.  Note the Value response plane is lower.

While the statistics for this analysis are significant (P-Values of 3.30E-12 for the equation, 4.96% for Prime, 3.06E-12 for Volume, 0.01% for Foreign Exchange Reserves), the Mean Absolute Percentage Error (MAPE) is high, at 117.5%, meaning there is more work needed to decompose this market.

#currency #prices #markets #price #investing