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 effect of Supply and Demand on the Housing Market Assignment 3 (43323871)
WIP of several books of Karl Polanyi's thoughts and papers around social science economic history and capitalism. . See also Summary of the Great Transformation IM-10640
This page provides a structural analysis of POTUS Candidate Marco Rubio's
economic policy based on the information at: https://marcorubio.com/issues/debt/ The method used is Integrative Propositional Analysis (IPA)
available:
http://scipolicy.org/uploads/3/4/6/9/3469675/wallis_white_paper_-_the_ipa_answer_2014.12.11.pdf
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
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
Circularity in Economic models including Exports and Imports
In a recent report, the World Economic Forum
considered that the use of robots in economic activity will cause far more job
losses in the near future than there will be new ones created. Every economic
sector will be affected. The CLD tries to illustrate the dynamic effects of
replacing human workers with robots. This dynamic indicates that if there is no replacement of
the income forgone by the laid off
workers, then the economy will soon grind to a halt. To avoid disaster, there
must be enough money in circulation, not parked in off-shore investments, to
permit the purchase of all the goods and services produced by robots. The
challenge for the government is to make sure that this is case.
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.
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.
REM 221 Simple Climate-Carbon-Economic Model with Targets
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 cannot go on forever, although politicians and most economist
seem to think so. The activity involved in economic growth necessarily generates entropy (disorder and environmental degradation). Entorpy in turn generates powerful negative feedback loops which will, as
a response from nature, ensure that economic activity will eventually grind to
a complete halt. In these circumstances organised
society cannot persist and will collapse. The negative
feedback loops shown in this graph have already started to operate. The longer economic growth continues unabated, the more powerful these negative feedback loops will become. How long
can economic growth continue before it is overwhelmed? It may not be very far in the future.
Entropy and Negative Feedback may stop Growth soon
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.
