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.
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:
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.
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.
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 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."
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 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)
