THE BROKEN LINK BETWEEN SUPPLY AND DEMAND CREATES TURBULENT CHAOTIC DESTRUCTION
The existing global capitalistic growth paradigm is totally flawed
Growth in supply and productivity is a summation of variables as is demand ... when the link between them is broken by catastrophic failure in a component the creation of unpredictable chaotic turbulence puts the controls ito a situation that will never return the system to its initial conditions as it is STIC system (Lorenz)
The chaotic turbulence is the result of the concept of infinite bigness this has been the destructive influence on all empires and now shown up by Feigenbaum numbers and Dunbar numbers for neural netwoirks
See Guy Lakeman Bubble Theory for more details on keeping systems within finite working containers (villages communities)
This simple high level model shows the basic feedback balancing loop for health services. There are many other loops and component interactions at multiple scales that add to the complexity See areas of expenditure IM for some component splits and hospital value IM for some service linkages
Clone of Clone 4of IM 27348, of JPS IM-27150 of original JPS IM-14117 See reference in diagram notes. WIP for Environment part of primary care regional model
See reference in diagram notes. WIP for Environment part of primary care regional model. GP centric calibration by JPS at IM-14117 See also IM-3126 for Regional Health Service Use Context
Clone of Clone 4of IM 27348, of JPS IM-27150 of original JPS IM-14117 See reference in diagram notes. WIP for Environment part of primary care regional model
This diagram implies the simple supply and
demand concept to show the relationship of different roles in the real estate
market and how they affect the price of houses, buyers and sellers.
The motivations are based on the concept, when
the houses’ price goes down (demand goes down) there will be more people
interested in buy a new house (supply goes up).
The simulation will show the range within 36
months as units. It demonstrates the comparison of price, houses buyers and
Houses for sale.
The following simulation model demonstrates the relationship between supply, demand and pricing within the real estate and housing world. I have based the model on a small city with a population of 100,000 residents as of 2015.
AXIS:
X-Axis
The X-Axis shows the time. It begins in 2015 in the month of October and continues for 36 consecutive years.
Y-Axis
There are 2 Y-Axis on this model. The left hand side relates to the price, demand, and supply, while the right hand side solely lists the population.
As you could see, this town has a population of 100,000 residents to-date. The bottom of the model shows a population loop that produces an exponential growth rate of 2.5%. This dynamic and growing city populates approximately 240,000 residents after 36 years.
MODEL
The model consists of 2 folders named: Buyers/Consumers & Suppliers/Producers. This first folder represents the 'Demand'. It includes a buyers growth rate, buyers interest increase and decrease, a price demand and the demand price. The formulas form an exponential rise in demand due to the rapid and continuous increase in population in this new city. As population increases, so does the demand from buyers.
The second folder conveys the supply of houses. It includes a sophisticated loop of real estate. Residents who own houses in the market decide to sell the home. This becomes the Houses for sale, also known as the 'supply'. Those houses are sold and the sold houses re-enter the market and the loop continues.
The supply has an inverse relationship with the price. When prices drop, supplies drop because the demand goes up. And when the price goes up, so does the supply. This will represent the growth of new houses in the market.
PRICE
Note: The price is based on monthly rent rates.
The price is dependant on many variables. Most importantly, the supply and demand. It also includes factors such as expectations & the economic value of the house. I have included a stable, 'good' economic value for all homes as this fictional town is in a stable and growing area.
Price fluctuates throughout the entire simulation, however it also goes up in price. Over the years houses continue to rise in price while they regularly fluctuate. For example, in 2018 (3 years later), the max price for a home was: $4254.7 and min price was: $852.98. On the other hand, in October 2051 (36 years later), the max price was: $14906 and the min price was: $7661. (This is based on the following data: Houses for Sale: 500, Houses that have sold: 100, Houses in the Market: 730).
SLIDERS
There are 3 sliders on the bottom that could be altered. The simulation would react accordingly. The 3 sliders include changeable data on:
Clone of Clone 4of IM 27348, of JPS IM-27150 of original JPS IM-14117 See reference in diagram notes. WIP for Environment part of primary care regional model
The model shown provides a visual representation of the processes that occur when Buyers (Demand), the Sale of Homes (Supply) as well as Price interact when it comes to the Real Estate Marketplace.
Price is the main factor that ultimately influences the movement of both supply and demand within the real estate marketplace. Those considering purchasing a new home will be influenced to buy when prices are lower than that of the median price whereas sellers prefer to sell their homes higher than the median price in order to make a higher return.
Clone of Clone 4of IM 27348, of JPS IM-27150 of original JPS IM-14117 See reference in diagram notes. WIP for Environment part of primary care regional model
