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.
I propose we grow this sim model (or similar) over time to help ourselves better understand the opposing investment and austerity strategies now being advocated for the U.S. government. The hope is to build as simple a model as possible that subsumes the major underlying feedback loops that probably exist in the mental models of proponents of each of these positions. Starting this model was inspired by this Investment vs. Austerity discussion http://www.linkedin.com/groups/Investment-vs-Austerity-How-can-4582801.S.157876413
Unfortunately, this model only produces the illusion of functioning, but I did manage to get it to give me the graph. However, because of the use of flows, if you change the time step to and the simulation length to anything other than the same numbers, you'll find the graph showing something that looks more exponential. This is due to the function referencing itself in regards to time, so inevitably each time consumption grows it changes the outcome on the other side of the equation. Still, this is a convincing mock up. I added a "45 degree" line so that one could conceivably see (and also change) the difference made by altering the level of autonomous consumption.
Regulation of resource allocation to service in response to service quality. A non-price-mediated resource allocation system. From Sterman JD Business Dynamics p172 Fig 5-27
WIP Overview model structures of Khalid Saeed's 2014 WPI paper Jay
Forrester’s Disruptive Models of Economic Behavior See also General SD and Macroeconomics CLDs IM-168865
Wealth can be seen as the factories,
infrastructure, goods and services the population of a nation dispose of. According
to Tim Garrett, a scientist who looks at
the economy from the perspective of physics, it is existing wealth that generates
economic activity and growth. This growth demands the use of energy as no
activity can take place without its use. He also points out that the use of this
energy unavoidably leads to concentrations of CO2 in the
atmosphere. All this, Tim Garrett says, follows from the second law of thermodynamics.
If wealth decreases then so does economic activity and growth. The CLD tries to illustrate how wealth,
ironically, now generates the conditions and feedback loops that may cause it to decline. The consequences are inevitably economic stagnation (or secular recession?).
You can
read about the connection Tim Garrett makes between 'Wealth, Economic Growth,
Energy and CO2 Emissions' simply by
Googling 'Tim Garrett and Economy'.
IM-168155 Summary of Ch 27 of Mitchell Wray and Watts Textbook see IM-164967 for book overview with simplified Mike Radzicki's 2003 Evolutionary Economics history article added
This model is an attempt to simulate what is commonly
referred to as the “pesticide treadmill” in agriculture and how it played out
in the cotton industry in Central America after the Second World War until
around the 1990s.
The cotton industry expanded dramatically in Central America
after WW2, increasing from 20,000 hectares to 463,000 in the late 1970s. This
expansion was accompanied by a huge increase in industrial pesticide
application which would eventually become the downfall of the industry.
The primary pest for cotton production, bol weevil, became increasingly
resistant to chemical pesticides as they were applied each year. The
application of pesticides also caused new pests to appear, such as leafworms,
cotton aphids and whitefly, which in turn further fuelled increased application
of pesticides.
The treadmill resulted in massive increases in pesticide
applications: in the early years they were only applied a few times per season,
but this application rose to up to 40 applications per season by the 1970s;
accounting for over 50% of the costs of production in some regions.
The skyrocketing costs associated with increasing pesticide
use were one of the key factors that led to the dramatic decline of the cotton
industry in Central America: decreasing from its peak in the 1970s to less than
100,000 hectares in the 1990s. “In its wake, economic ruin and environmental
devastation were left” as once thriving towns became ghost towns, and once
fertile soils were wasted, eroded and abandoned (Lappe, 1998).
Sources: Douglas L. Murray (1994), Cultivating Crisis: The Human Cost of Pesticides in Latin America, pp35-41;
Francis Moore Lappe et al (1998), World
Hunger: 12 Myths, 2nd Edition, pp54-55.
ECONOMIC GROWTH feeds on itself, provided the growth engine is fed with materials and
finance. In this highly simplified representation some of the factors that influence economic growth
are show in the incircled green fields. Governments can influence economic growth positively
via investments and payouts. The most
obvious tool which governments can use to slow an overheated economy is
taxation.
Added several versions of the model. Added a flow to make C increase. Added a factor to be able to change the value 0.5. Older version cloned at IM-46280
Simple model of the global economy, the global carbon cycle, and planetary energy balance.
The planetary energy balance model is a two-box model, with shallow and deep ocean heat reservoirs. The carbon cycle model is a 4-box model, with the atmosphere, shallow ocean, deep ocean, and terrestrial carbon.
The economic model is based on the Kaya identity, which decomposes CO2 emissions into population, GDP/capita, energy intensity of GDP, and carbon intensity of energy. It allows for temperature-related climate damages to both GDP and the growth rate of GDP.
This model was originally created by Bob Kopp - https://insightmaker.com/user/16029 (Rutgers University) in support of the SESYNC Climate Learning Project.
Steve Conrad (Simon Fraser University) modified the model to include emission/development/and carbon targets for the use by ENV 221.
Simple model of the global economy, the global carbon cycle, and planetary energy balance.
The planetary energy balance model is a two-box model, with shallow and deep ocean heat reservoirs. The carbon cycle model is a 4-box model, with the atmosphere, shallow ocean, deep ocean, and terrestrial carbon.
The economic model is based on the Kaya identity, which decomposes CO2 emissions into population, GDP/capita, energy intensity of GDP, and carbon intensity of energy. It allows for temperature-related climate damages to both GDP and the growth rate of GDP.
This model was originally created by Bob Kopp (Rutgers University) in support of the SESYNC Climate Learning Project.
A Tragedy of the Commons situation exists whenever two or more activities, each, which in order to produce results, rely on a shared limited resource. Results for these activities continue to develop as long as their use of the limited resource doesn't exceed the resource limit. Once this limit is reached the results produced by each activity are limited to the level at which the resource is replenished. As an example, consider multiple departments with an organization using IT resources, until they've exhausted IT capacity.
