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.

Clone of The effect of Supply and Demand on the Housing Market Assignment 3 (43323871)

Nickolai Birjoo

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

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 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)]. 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.

Supply/Supply Cost has a direct relationship to one another as one variable increases the other one decreases.

Demand and Demand Price has a inversely relationship related to Price /

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.

Clone of Ricky Su 43671942 (Assessment 3)

Duc Nguyen

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.

Delays have not been added in order to show how components instantly respond to changing parameters.

Clone of Complex Systems Assignment #3 MGMT220

Duc Nguyen

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

PT

Author: Brandon Sultana 43268080

This is a 3 year model that depicts the flows between Price, Supply and Demand in the real-estate market.

Throughout the model viewers can observe how the figures Price, Supply and Demand alter each other in an increasing or decreasing way.

Price is decreased by the growing supply of HousesForSale and increased by the growing demand of people wanting to buy. As Price decreases, HousesForSale increases and Price decreases as HousesForSale increase.

From the use of the graph it is evident that over 3 years the flow of house prices fluctuate and therefore more houses are sold at different times over 3 years.

The purpose of this insight is to help consumers and Businesses depict the best times to either buy or sell houses to maximize profits.

Additionally the market had to respect the number of possible consumers who are opting to build new houses, based on the rise and fall of house prices the real-estate analyses the new houses and residents in the area grow overtime.

Due to population growth, This cycle remains continuous so long as the real-estate company manages their resources effectively

Clone of Real-Estate Cycle Brandon Sultana 43268080

William Pale

Real Estate Marketplace

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.

MGMT220_ASSIGNMENT_3: Noelle Parra - 4327 1790

Noelle Parra

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.

William Pale 42877083 (Assignment 3)

William Pale

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

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.

42988241 Leon Dhemba

Leon Dhemba

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.

Clone of Factors Affecting the Real Estate Market by 42151619

shriddhi Gopali

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.

Clone of Factors Affecting the Real Estate Market by 42151619

Dhruv Khandelwal

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.

Clone of Complex Systems Assignment #3 MGMT220 - Jeff Sun 42869129

Ana Cristina Beja

Fig 2 to 14 Land Use added to 4 Quadrant Model SD Model from Eskanasi 2014 thesis

Housing system dynamics 2 Netherlands

Geoff McDonnell

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.

Clone of Real Estate Simulation Assignment - Mitchell Bassil 43290264

Bryan Angkawidjaya

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.

Clone of Housing and care homes

Holger Huette

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

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.

Clone of Real Estate Simulation Assignment - Mitchell Bassil 43290264

Zaiceva Ekaterina

A stock-flow model of a Real Estate market.

A simple three-year, monthly, systems model.

Price is a linear function (straight line) of the proportion of houses for sale (positive slope), and also a linear function of the proportion of buyers (negative slope).

Coefficients for the Supply Elasticity of Price and the Demand Elasticity of Price can be adjusted by the user using sliders.

Sales in each month are simply the lesser of the number of houses for sale and the number of buyers.

The number of houses for sale is a linear function of Price (positive slope) in the Flow "ToMarket".

Coefficient for Price Elasticity of Supply can be adjusted using a slider.

The number of buyers is a linear function of Price (negative slope) in the Flow "Motivation". Coefficient for Price Elasticity of Demand can be adjusted using a slider.

Simple Real Estate Market

Hume Winzar

3

Haaglanden Social housing Fig 18 SD Model feedback structure from Eskanasi 2014 thesis Other models in the thesis include middle income households and mortgage debt

Housing system dynamics 5 Netherlands

Geoff McDonnell

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.

Delays have not been added in order to show how components instantly respond to changing parameters.

Clone of Clone of Complex Systems Assignment #3 MGMT220

Noelle Parra

Author: Brandon Sultana 43268080

This is a 3 year model that depicts the flows between Price, Supply and Demand in the real-estate market.

Throughout the model viewers can observe how the figures Price, Supply and Demand alter each other in an increasing or decreasing way.

Price is decreased by the growing supply of HousesForSale and increased by the growing demand of people wanting to buy. As Price decreases, HousesForSale increases and Price decreases as HousesForSale increase.

From the use of the graph it is evident that over 3 years the flow of house prices fluctuate and therefore more houses are sold at different times over 3 years.

The purpose of this insight is to help consumers and Businesses depict the best times to either buy or sell houses to maximize profits.

Additionally the market had to respect the number of possible consumers who are opting to build new houses, based on the rise and fall of house prices the real-estate analyses the new houses and residents in the area grow overtime.

Due to population growth, This cycle remains continuous so long as the real-estate company manages their resources effectively

Real-Estate Cycle Brandon Sultana 43268080

Brandon Luke Sultana

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.

Clone of Real Estate Simulation Assignment - Mitchell Bassil 43290264

Joo Hee Lee

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

YTHagakure

Housing Models