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
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.
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:
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 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 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.
This simulation shows the relation ship between the number of houses for sale (Supply), the amount of people searching for home (Demand) and how these factors affect the prices within the real estate market, with the purpose of forecasting when the best time to buy and sell property is. It is assumed in this model that the number of houses are a constant, as we are looking at existing housing infrastructure rather than an emerging market or apartment buildings.
This model spans over 20 years, showing the long-term flow of the real estate market within this time frame. Price is a linear function of the number of houses for sale (positive) and also a liner function of the number of people searching for homes (negative).
Demand Elasticity Of price, Price Elasticity of Demand, Price elasticity of Supply, Supply Elasticity of Price and Interest rates can all be adjusted through the use of the sliders to create different scenarios with supply, demand and interest rates. The sales variable passes information into both the buying and sold variables upon completion of a relevant transaction.
While demand is low, the price is typically low too, however the supply of houses is high. While the demand is high the price is driven up and the supply of houses is lower than the demand.
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 first task is to define what is an optometrist and to outline a career path.
The first issue is how to select and keep optometry students. The main theme is balance out student expectations with reality (see first task) especially when many students use optometry as a proxy for later entry into a medical course.
The second problem is that the current entry demographic does not meet the needs in terms of culture and expected practice location Unfolding version of IM-569 by keith Masnick
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."
Shown here is a diagram of a Real Estate Market where in which variables like price, supply and demand are found to be present and play a role in the sides of the buyers and the sellers.
When prices go up the supply of sellers increase while the demand of buyers decrease. When prices go down the supply of buyers increase in the real estate market while the demand of sellers decreases.
It is the simple economic rule found in plain sight in the real estate market.
1 - Price elasticity of Supply with the sellers is high due to their ability to adapt to sudden changes in prices in the market.
2 - Demand elasticity of price on the other hand was not proven to be as high in the calculations since there was no factual data as to how fast the buyers reacted to an increase in supply or a decrease in price. Although seen is the increase in demand when a the price is lowered.
3 - Increases in Median Price lead to a increased Supply from the Sellers.
4 - Decrease in Median Price lead to a increased demand from the Buyers.
The real estate market is heavily influenced by social and economical factors that effect the price of dwellings. Two of the main factors that contribute to not only the housing market but most consumables are supply and demand.
Supply is the amount of one good or service in that market. A market driven economy could utilize supply to regulate price. Generally, if the supply is higher than the demand for that good the price will be lower. If the supply is lower than the demand, the price will tend to increase.
Demand is the amount of consumers that want/need that good or service. Price is also influenced by demand. Generally, if the demand is higher than the supply the price will increase. If the demand is lower than the supply, the price tends to decrease.
Price can be derived from demand and supply.
The following model depicts how the changes in supply and demand of Households effect pricing. The sliders replicate how each of the variables interact with price.
Secondary factors, such as interest rate, foreign investors and government policies also all weigh in on determining a price for a dwelling.
