Supply Models

These models and simulations have been tagged “Supply”.

Related tagsDemandHousingEconomics

The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market.     Residents, or the general population of individuals, place significant reliance on financial instituti
The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market. 

Residents, or the general population of individuals, place significant reliance on financial institutions to provide sources of capital i.e mortgages, to fund their purchases of homes. The rate of interest charged by these organisations in turn gives buyers (consumers) purchasing power, creating demand. 

Supply is made up of the number of houses in the market, and consequently, of these, the number of houses which are up for sale. As the prices of houses for sale increases, the demand for purchase of these properties decreases. Conversely, the lower price, the higher the demand. Once the market reaches an equilibrium point, to which buyers and sellers form an agreement, houses are sold accordingly. An underlying factor to consider is the cost of construction, which impacts producers, or suppliers in this instance, and thus the number of homes for sale, and the expected profit sellers hope to achieve. 

The simulated graph highlights the common scenario within the housing market, to which we see that as price increases, the total number for houses for sale decreases, generating an opposite slope to the price. As the price for houses increases, the demand for the houses decreases and vice versa. The equilibrium is evident at time 14 whereby the price of houses and the number of houses for sale overlaps which in turn creates a market to which both buyers and sellers are happy.
 השינויים בביקוש ובהיצע כתלות בעיכובים בהעברת המידע

השינויים בביקוש ובהיצע כתלות בעיכובים בהעברת המידע

 Supply and Demand tend to oscillate back and forth though Price.

Supply and Demand tend to oscillate back and forth though Price.

 Documentation       The Insight shown demonstrates how demand and supply in a real estate market can affect pricing.      Demand, Supply and Price have been represented by stocks. Each has an inflow where it has an increase in stock, and a corresponding outflow where stock is decreased.      Linkin
Documentation

The Insight shown demonstrates how demand and supply in a real estate market can affect pricing. 

Demand, Supply and Price have been represented by stocks. Each has an inflow where it has an increase in stock, and a corresponding outflow where stock is decreased. 

Linking each stock and flow is a variable that changes that which it is linked to. These have been labelled appropriately. Each variable takes a decimal value and multiplies it with that it is linked to, such as the rate of demand affecting the price set as 0.001*Demand. This is to generate the loops required to show the rise and fall in price, demand and supply.

Adjustments can be made to the price, supply and demand stocks to simulate different scenarios. Price can be between 400 (400,000) and 1000 (1,000,000) in accordance to average housing prices. Demand and supply can be between 0 (0%) and 100 (100%), although having these set as realistic figures will demonstrate the simulation best. 

Each simulation can be focused on how either demand and price interact over time or supply and price. These are shown in different tabs. 

When the simulation is carried out, the way in which demand and supply rates affect pricing can be seen. Demand and supply are shown with price following shortly after with a slight delay, since changes in market behavior does not immediately affect prices of housing. 

It should also be noted that the lines that represent each stock do not directly reflect the prices of housing in reality. Prices do not fluctuate so rapidly from 400 to near 0 like they do on the graph, however these are just representations of the interactions between each stock in a marketplace.
Author: Brandon Sultana 43268080  This is a 3
year model that depicts the flows between Price, Supply and Demand in the real-estate
market. 

 Throughout the
model viewers can observe how the figures Price, Supply and Demand alter each
other in an increasing or decreasing way.  

 Price is
decreased
Author: Brandon Sultana 43268080

This is a 3 year model that depicts the flows between Price, Supply and Demand in the real-estate market.

Throughout the model viewers can observe how the figures Price, Supply and Demand alter each other in an increasing or decreasing way.

Price is decreased by the growing supply of HousesForSale and increased by the growing demand of people wanting to buy. As Price decreases, HousesForSale increases and Price decreases as HousesForSale increase.

From the use of the graph it is evident that over 3 years the flow of house prices fluctuate and therefore more houses are sold at different times over 3 years.

The purpose of this insight is to help consumers and Businesses depict the best times to either buy or sell houses to maximize profits.

Additionally the market had to respect the number of possible consumers who are opting to build new houses, based on the rise and fall of house prices the real-estate analyses the new houses and residents in the area grow overtime.

Due to population growth, This cycle remains continuous so long as the real-estate company manages their resources effectively

 Supply and Demand tend to oscillate back and forth though Price.

Supply and Demand tend to oscillate back and forth though Price.

 Mind map example of a medication management project.  See also  IM-692  for CLD

Mind map example of a medication management project.

See also IM-692 for CLD

This is a model that depicts the interactions between buyers and sellers in regards to the position of price to the median price.     This model works on the premise that when house prices drop below median price, buyers will increase and sellers will decrease, and vise versa.       When the values
This is a model that depicts the interactions between buyers and sellers in regards to the position of price to the median price. 

This model works on the premise that when house prices drop below median price, buyers will increase and sellers will decrease, and vise versa.  

When the values for Price, Buyers and Sellers are set to 50, the system will be in Equilibrium. 

Delays have not been added in order to show how components instantly respond to changing parameters. 

A more in-depth description is provided in the story. 
 FONDO: 

   

 El siguiente
modelo de simulación demuestra la relación entre la oferta, la demanda y los
precios dentro del mundo valoración de nuevos productos generados en base al
capital del conocimiento o productos de base tecnológica. He basado el modelo
en una ciudad pequeña con una población

FONDO:

 

El siguiente modelo de simulación demuestra la relación entre la oferta, la demanda y los precios dentro del mundo valoración de nuevos productos generados en base al capital del conocimiento o productos de base tecnológica. He basado el modelo en una ciudad pequeña con una población de 100,000 residentes a partir de 2015.

 

EJE:

 

X-Axis

El X-Axis muestra el tiempo. Comienza en 2015 en el mes de octubre y continúa durante 36 años consecutivos.

 

Eje Y

Hay 2 ejes Y en este modelo. El lado izquierdo se relaciona con el precio, la demanda y el suministro, mientras que el lado derecho solo enumera la población.

 

Como se puede ver, esta ciudad tiene una población de 100,000 residentes hasta la fecha. La parte inferior del modelo muestra un bucle de población que produce una tasa de crecimiento exponencial del 2.5%. Esta ciudad dinámica y en crecimiento puebla aproximadamente a 240,000 residentes después de 36 años.

 

MODELO

 

El modelo consta de 2 carpetas llamadas: Compradores / Consumidores y Proveedores / Productores. Esta primera carpeta representa la 'Demanda'. Incluye una tasa de crecimiento de compradores, aumento y disminución de interés de los compradores, una demanda de precio y el precio de demanda. Las fórmulas forman un aumento exponencial de la demanda debido al rápido y continuo aumento de la población en este mercado. A medida que la población aumenta, también lo hace la demanda de los compradores.

 

La segunda carpeta transporta el suministro de nuevos productos de base tecnológica generados en centros de I+D . Incluye un bucle sofisticado de bienes inmuebles. La población que posee productos de base tecnológica en el mercado deciden vender o consumir el producto de base tecnológica . Esto se convierte en las productos de base tecnológica en venta. Estos nuevos productos de base tecnológica se venden y los nuevos productos vendidos vuelven a entrar al mercado y el ciclo continúa.

 

El suministro tiene una relación inversa con el precio. Cuando los precios bajan, los suministros bajan porque la demanda aumenta. Y cuando el precio sube, también lo hace el suministro. Esto representará el crecimiento de nuevas productos en el mercado.

 

PRECIO Nota: El precio se basa en las tarifas de alquiler mensual. El precio depende de muchas variables. Lo más importante es la oferta y la demanda. También incluye factores tales como las expectativas y el valor económico de la casa. He incluido un valor económico estable, 'bueno' para todos los hogares ya que esta ciudad ficticia se encuentra en un área estable y en crecimiento. El precio fluctúa durante toda la simulación, sin embargo, también sube de precio. Con el paso de los años, las casas siguen subiendo de precio mientras fluctúan regularmente. Por ejemplo, en 2018 (3 años después), el precio máximo para una casa fue: $ 4254.7 y el precio mínimo fue de $ 852.98. Por otro lado, en octubre de 2051 (36 años después), el precio máximo fue: $ 14906 y el precio mínimo fue de $ 7661. (Esto se basa en los siguientes datos: Casas en Venta: 500, productos nuevos de base tecnológica  que han vendido: 100, Casas en el Mercado: 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:
- Houses for Sale.
- Houses that have Sold.
- Houses in the Market.

 Hay 3 controles deslizantes en la parte inferior que se pueden modificar. La simulación reaccionaría en consecuencia. Los 3 controles deslizantes incluyen datos modificables en:- Nuevos productos  en venta.- Productos de base tecnológica que han vendido.- productos de base tecnológica  en el mercado.

 Supply and Demand tend to oscillate back and forth though Price.

Supply and Demand tend to oscillate back and forth though Price.

A model that depicts the interactions between buyers and sellers in regards to the position of price to the median price.     This model works on the premise that when house prices drop below median price, buyers will increase and sellers will decrease, and vise versa.       Delays have not been add
A model that depicts the interactions between buyers and sellers in regards to the position of price to the median price. 

This model works on the premise that when house prices drop below median price, buyers will increase and sellers will decrease, and vise versa.  

Delays have not been added in order to show how components instantly respond to changing parameters. 
 Diamond mined in a war zone and sold to finance an insurgency.     Autors: Jakub Rybka​, Alexej Savčin
Diamond mined in a war zone and sold to finance an insurgency.

Autors: Jakub Rybka​, Alexej Savčin
 ​BACKGROUND:    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.
​BACKGROUND:

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:
- Houses for Sale.
- Houses that have Sold.
- Houses in the Market.


The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market.     Residents, or the general population of individuals, place significant reliance on financial instituti
The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market. 

Residents, or the general population of individuals, place significant reliance on financial institutions to provide sources of capital i.e mortgages, to fund their purchases of homes. The rate of interest charged by these organisations in turn gives buyers (consumers) purchasing power, creating demand. 

Supply is made up of the number of houses in the market, and consequently, of these, the number of houses which are up for sale. As the prices of houses for sale increases, the demand for purchase of these properties decreases. Conversely, the lower price, the higher the demand. Once the market reaches an equilibrium point, to which buyers and sellers form an agreement, houses are sold accordingly. An underlying factor to consider is the cost of construction, which impacts producers, or suppliers in this instance, and thus the number of homes for sale, and the expected profit sellers hope to achieve. 

The simulated graph highlights the common scenario within the housing market, to which we see that as price increases, the total number for houses for sale decreases, generating an opposite slope to the price. As the price for houses increases, the demand for the houses decreases and vice versa. The equilibrium is evident at time 14 whereby the price of houses and the number of houses for sale overlaps which in turn creates a market to which both buyers and sellers are happy.
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 compon
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)

 ​BACKGROUND:    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.
​BACKGROUND:

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:
- Houses for Sale.
- Houses that have Sold.
- Houses in the Market.


The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market.     Residents, or the general population of individuals, place significant reliance on financial instituti
The housing market is heavily dependent on two main factors; supply and demand. Both play a major role in determining an equilibrium price for both sellers and buyers in the real estate market. 

Residents, or the general population of individuals, place significant reliance on financial institutions to provide sources of capital i.e mortgages, to fund their purchases of homes. The rate of interest charged by these organisations in turn gives buyers (consumers) purchasing power, creating demand. 

Supply is made up of the number of houses in the market, and consequently, of these, the number of houses which are up for sale. As the prices of houses for sale increases, the demand for purchase of these properties decreases. Conversely, the lower price, the higher the demand. Once the market reaches an equilibrium point, to which buyers and sellers form an agreement, houses are sold accordingly. An underlying factor to consider is the cost of construction, which impacts producers, or suppliers in this instance, and thus the number of homes for sale, and the expected profit sellers hope to achieve. 

The simulated graph highlights the common scenario within the housing market, to which we see that as price increases, the total number for houses for sale decreases, generating an opposite slope to the price. As the price for houses increases, the demand for the houses decreases and vice versa. The equilibrium is evident at time 14 whereby the price of houses and the number of houses for sale overlaps which in turn creates a market to which both buyers and sellers are happy.
 The dynamics of health care spending, or why it keeps growing. Adapted from Fig 7.6 p154 of Sauwakon Ratanawijitrasin's PhD thesis SUNY Albany 1993 "The dynamics of health care finance: A feedback view of system behavior."

The dynamics of health care spending, or why it keeps growing. Adapted from Fig 7.6 p154 of Sauwakon Ratanawijitrasin's PhD thesis SUNY Albany 1993 "The dynamics of health care finance: A feedback view of system behavior."