Housing | Insight Maker

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)

Ryan Nakhle

34

The following is a complex system depicting the nature of real estate and showing the deciding factors of its activities.

House supply and house prices have a direct relationship (linear - positive). Buyers and house prices have an indirect relationship (linear - negative). Interest rate and desire are directly related (linear - positive).

Complex Systems - 43036317

Dileesha Sigera

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.

Zachary Chapman - 43309399 - Assignment 3 Final

Zachary Chapman

3

The following model shows a real estate market created by a web-based modelling and simulation tool; Insight Maker. This model shows the relationships and links between many different players within the real estate market and how some changes are able to change the marketplace overall.

Information from Model

The information generated from this model includes "Wanting to Buy", "Houses for Sale" and "Price". "Wanting to Buy" refers to the players who demand or wish to buy houses, "Houses for Sale" refers to the total supply of houses within this marketplace whilst "Price" refers to the price an individual bought a house at a certain point in time.

Variables Included

All variables on the model are shown as ovals and are either fixed or can be adjusted. The fixed variables are shown in yellow, where fixed refers to not being able to be changed by non-editors viewing this model. These variables can not be adjusted as these variables are directly related from the information produced within the model. For example, the variable "Price" can not be adjusted as it would produce incorrect information when the model is simulated

All other variables shown in orange can be adjusted by moving the respective sliders. These variables are able to be adjusted according to what the view wishes.

Interesting Parameters

One adjusted that the user can make is to increase the "Demand Elasticity of Price" from 100 to 200. This increase in demand elasticity means a greater amount of individuals would be willing to purchases a house. As with the laws of supply and demand, an increase in demand will lead to a increase in price. Once simulated, this model shows that over time the price for a house increases.

One other interesting parameter that the user could do is to increase the level of "Destroy Houses" from 0.05 to 0.1. This increase would lead to a fall in the total amount of houses as the level of houses being destroyed (0.1) is greater than the level of houses being build (0.05). As shown in the simulation, this would lead to a fall in the total houses available in the marketplace.

Real Estate Market - Raymund Paradeza 43626491

Raymund Paradeza

4

A causal loop diagram illustrating solutions for the homelessness problem

Homelessness Model

Fred Nel

Macquarie University | MGMT220: Fundamentals of Business Analytics | Assignment Task #3: Complex Systems by Ying Chen (42151619)

This simple model uses the following key factors to demostrate the behaviour within the real estate market, bank's interest rates, median sale price, and listed sale price.

Sliders located below can be used to set values to simulate the affects over time.

Factors Affecting the Real Estate Market by 42151619

Ying Chen

4

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.

Complex Systems Assignment #3 MGMT220 - Jeff Sun 42869129

Xu Jeff Sun

4

System Dynamics

Chan Kian Quan

A causal loop diagram illustrating solutions for the homelessness problem

Clone of Homelessness Model

HANNAH OLTMAN

A Complex System SD Model displaying the fluctuations and variables affecting the housing market.

Refer to storyboard for information of the SD Model

MGMT220 - Assignment 3 - 42845971 -Emma HASTINGS

Emma Hastings

Fig 17.15 p700 Causal structure of commercial real estate markets of Case Study from John Sterman's 2000 Business Dynamics Book

Boom and bust in Commercial Real Estate

Geoff McDonnell

3

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.

Clone of Zachary Chapman - 43309399 - Assignment 3 Final

Isaiah Ritzmann

In the real-estate domain, buyers (demand) and sellers (supply) directly influence the price of houses. However, their motivation to buy and sell is directly influenced in respect to how the median price of houses is compared to the sensitivity (elasticity) of the actual prices and demand of houses.

This model will represent how the changes in price elasticity compared to the median price, affect the supply and demand within the real-estate domain.

1) Price and Demand Elasticity can be changed between 0-100 to show variances within the sensitivity of price in the market.

2) Buyers & Sellers are set to 75 however this can be fluctuated between 0-150 to represent variances within the demand & supply in the market.

3) Median Price is set to 75 since the total amount of buyers and sellers within the market is set to a maximum of 150. 75 is therefore the ‘median’.

4) Interest rates are also a factor in the demand of houses and influence the motivation for Buyers. If interest rates are high, Buyers are less motivated to buy due to increase in mortgages, therefore having a decrease in the demand and vice versa for when they are low. The following equation compares the price of houses to the median price, and if this condition is true, will apply Interest Rate(s) to the following logic.

if [Price]<[Median Price] Then

([Interest Rate]+[Demand Growth Ratio]-[Demand Growth Ratio])*[Buyers] end if

5) The price of houses is directly influenced by elasticity of demand and price within the market place. For example; if the elasticity of demand is relatively inelastic, the percentage change in the amount of houses demanded is smaller than that of the price. The following equation applies the logic listed above.

60-[Supply Elasticity of Price]/100*[Total Sellers]+[Demand Elasticity of Price]/100*[Total Buyers]+[Price Elasticity of Supply]/100*[Total Sellers]

Nicholas Clancy -Assignment 3- 43275796

Nicholas Clancy

The law of supply and demand is a basic economic principle that explains the relationship between supply and demand for a good or service and how the interaction affects the price of that good or service. The relationship of supply and demand affects the housing market and the price of a house.

A number of factors including government policy affects the law of demand and supply, which I hope my diagram illustrates

When there is a high demand for properties in a particular city or state and a lack of supply of good quality properties, the prices of houses tend to rise. When there is no demand for housing due to a weak economy and an oversupply of properties is available, the prices of houses tend to fall.

Clone of 42988241 Leon Dhemba

Margarita Stergiou

Assignment 3: Complex Systems

Ricky Su 43671942

Retail Re-state Market

Insight Maker is a program that allows users to create complex interactions among industry or social factors. This program was used to model the relationships among different players in the real estate market.

This model shows the effects of changes in supply and demand, demand price and supply cost, availability of houses and houses prices on the Retail Re-state Market. Each simulation can be focused on how either Demand, Supply, supply cost, demand price, interest rate and availability of houses interacts with one another over time.

Demand, Demand Price, Supply, Supply Cost, Interest Rate, House Market and Houses Prices can be all adjusted by the user using the sliders [Slider Value 0-100 (000 000 value)] except for interest rate where it limited to 0-0.10. Allowing the user to simulate different scenarios and view changes in the market.

For Example: A slight change in House Prices will affect the Availability of Houses, House Price and the Supply and Demand dramatically.

When Supply increases, the availability of houses increases while Demand decreases.

Fixed variables/relationships:

Buyer Growth Rate, the Demand Price, Price Demand, Price Decrease, Supply Rate, Supply Price, Price Supply, Price Decrease.

These variables/relationships are shown on yellow. Fixed refers to no adjustments or changes is allow to those specific variables/relationships by the non -editor viewer. These variables cannot be change or adjusted as these variables/relationships are directly related to the information produced. With any changes to the fixed variables/relationships, it can cause incorrect simulation of the model to viewers.

Supply/Supply Cost has a direct relationship to one another as one variable increases the other one decreases. Demand and Demand Price has an inversely relationship related to Price.

House Price is the main reason why someone would sell or buy a house. Price is made up of various different factors. Demand price, demand, supply cost and supply are the main reasons houses price fluctuates.

Availability of Houses is simply New houses and houses Sold, based on If statements, If Demand=>Supply or If Supply <= Demand, the quantity will adjusted to meet the Demand and Supply levels.

Houses Price is simply House Price Increase and Houses Price decrease, based on if statements, If Demand=>Supply Cost or If Supply Cost <= Demand Price, the price will be adjusted to meet the Demand and Supply price.

Interesting Parameter:

This point is interesting because it shows greatest fluctuations. You will see in Price Impact on Demand/Supply display when house prices increases, supply will increase while demand will decrease, as people are less willing to purchase a house at that price due to high cost vice versa. Market availability and prices, as availability of houses increases there is a decrease in houses prices causing excess of supply, leading to an increase in demand as people are more willing to purchase properties when prices are lower vice versa. Construction shows availability of new houses, over time houses are sold, when sold houses hits zero, new houses will increase as there will be a deficient of supply vice versa.

Setting Demand at 100

Setting Demand Price at 100

Supply at 50

Supply Cost 60

Availability of Houses 100

House Prices 85

Interest Rate 0.05

Notes:

There is a slight delay when a change variable is changed in this model. This represents the real market as a change in price, demand or supply doesn’t translate to an immediate action on the market as shown by this model.

Ricky Su 43671942 (Assessment 3)

Ricky Su

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.

Clone of Zachary Chapman - 43309399 - Assignment 3 Final

Jeb Eddy

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.

Real Estate Simulation Assignment - Mitchell Bassil 43290264

Mitchell Bassil

8

The following model shows a real estate market created by a web-based modelling and simulation tool; Insight Maker. This model shows the relationships and links between many different players within the real estate market and how some changes are able to change the marketplace overall.

Information from Model

The information generated from this model includes "Wanting to Buy", "Houses for Sale" and "Price". "Wanting to Buy" refers to the players who demand or wish to buy houses, "Houses for Sale" refers to the total supply of houses within this marketplace whilst "Price" refers to the price an individual bought a house at a certain point in time.

Variables Included

All variables on the model are shown as ovals and are either fixed or can be adjusted. The fixed variables are shown in yellow, where fixed refers to not being able to be changed by non-editors viewing this model. These variables can not be adjusted as these variables are directly related from the information produced within the model. For example, the variable "Price" can not be adjusted as it would produce incorrect information when the model is simulated

All other variables shown in orange can be adjusted by moving the respective sliders. These variables are able to be adjusted according to what the view wishes.

Interesting Parameters

One adjusted that the user can make is to increase the "Demand Elasticity of Price" from 100 to 200. This increase in demand elasticity means a greater amount of individuals would be willing to purchases a house. As with the laws of supply and demand, an increase in demand will lead to a increase in price. Once simulated, this model shows that over time the price for a house increases.

One other interesting parameter that the user could do is to increase the level of "Destroy Houses" from 0.05 to 0.1. This increase would lead to a fall in the total amount of houses as the level of houses being destroyed (0.1) is greater than the level of houses being build (0.05). As shown in the simulation, this would lead to a fall in the total houses available in the marketplace.

Clone of Real Estate Market - Raymund Paradeza 43626491

sub cribed

Based on Norbari APHA presentation 2016 An agent-based model to examine the impact of unaffordable housing on obesity risk in early childhood, via Kurt Kreuger

Housing and Food in Childhood Obesity

Geoff McDonnell

Modeling water saving potential with urban planning, demand management practice, and alternative technologies

Clone of Neighborhood growth & water use

Heonyeong Lee

The purpose of this model is to provide users with an understanding of the relationships among different players in a real estate marketplace. The main focus of this model is on Demand, Supply & Prices in the period of 3 years(36 months).

Key players of the real estate marketplace includes investors, 1st homebuyers, banks, governments, housing developers and real estate agents. This model is designed in a real estate agent perspective, therefore, it is not included in the model.

In the real estate marketplace, HousingPrices can be affected by InterestRate, HousesForSale(Supply), WantingToBuy(Demand) and ConstructionCost.
The Motivation for Demand can be affect by InterestRate. Demand for housing can be affected by HousingPrice and so is the supply for housing.

Change the variables below using the sliders or input the value you want and see how it affects the simulation.

When ConstructionCost is high, less Profit received and vice versa.
When InterestRate is high, Motivation decrease-->Demand decrease and vice versa.
Having longer or shorter SalesTime can affect SalesRatio which can affect increase/decrease of Sales.
Having high/low initial amount of housing can affect HousesToMarket-->HousesForSale-->
DesiredSales-->SalesRatio.

MGMT220-Assignment3-Chi Ho Lee(Steven)-43441610-Demand&SupplyInRealEstateMarket

Steven Lee

This model aims to analyze how conservation from 2013 to 2017 needs improving in order to meet the needs to repopulate the Florida panther based on Acreage of conservation. Human population and housing development challenge conservation efforts, this model produces scenarios that test policy efforts in repopulate the Florida Panther. Our goal is to conserve 16 million acres for a habitat with 500 panthers.

SOS 212: Final Project

Christopher P Ryan

Broadmoor Gentrification

Ryan Mast

Demographic change is driving need for housing which can be adapted to the needs of those with health conditions, domiciliary care and care home places. Forecasts are required to guide providers of housing and care homes, as well as health and social care services. This model provides a framework for identifying the drivers of demand and developing a consensus forecast.

Housing and care homes

David Plummer

42

Housing Models