- The document analyzes factors that influence housing prices in the Chicago market using hedonic regression analysis on data from 2000 homes.
- Key factors found to significantly impact price based on the regression analysis include number of rooms, living area, effective age of the home, lot size, air conditioning, property taxes, median income, distance from downtown Chicago, and whether the home was located in Cook County or DuPage County.
- Three factors - spending per student, number of bathrooms, and distance to the nearest expressway - were found to not significantly impact price based on additional regression runs and subset F tests.
SSRN Paper: Expected Returns and the Expected Growth in Rents of Commercial R...Bob Lowery
Investigations of whether the cap rate, that is, the rent-price ratio in commercial
real estate incorporates information about future expected real estate returns
and future growth in rents.
El documento trata sobre Imágenes DICOM en la Escuela Superior de Medicina del Instituto Politécnico Nacional. Se refiere a un estudiante de nombre Hernández Vargas Ivón Lucely en el grupo 4CM5.
This paper examines the relationship between a country's per student public education expenditures and economic growth. It tests whether human capital from education can be represented as a production input in neoclassical growth models. The paper analyzes national growth trends from 1985 to 2010 across development levels. It finds that for most countries, higher per student education spending is negatively associated with growth, except for high-income countries where it may be beneficial. The paper reviews literature on the relationship between education quality/quantity and growth, finding mixed results on the impact of primary versus secondary versus tertiary education on growth.
The document describes a series of eco-friendly products for a hotel called Aegon. It includes Waterella, an umbrella stand that waters plants using water from umbrellas. Sprilla includes a shower mat and showerhead that filter and purify water from showers to water plants. Drola is a spiral ceramic sculpture that collects and filters rainwater and recycled water to flow through its path. The hotel focuses on collecting and recycling water through these products and its rainwater collection system.
This paper examines the relationship between a country's per student public education expenditures and economic growth. It tests whether human capital from education can be represented as a production input in neoclassical growth models. The paper analyzes national growth trends from 1985 to 2010 across development classes. It finds that education spending is negatively associated with growth for all countries except high-income countries, suggesting inefficient spending. The introduction provides context on human capital and economic growth theories being tested. A literature review summarizes several studies examining the relationship between education quality/quantity, institutions, and economic growth.
This document analyzes housing data from Cook and DuPage counties in the Chicago area using hedonic regression. It finds that 54.6% of variation in home prices can be explained by attributes like number of rooms, living area, age, lot size, amenities, taxes, income, distance to downtown and an airport. The effective age of a house has a significant negative impact on price, indicating that older homes are less desirable and valuable. Variables for number of bathrooms, school spending and distance to the nearest expressway were removed from the model as insignificant predictors of home value.
NPOWER Social Media Presentation FINALRachel Duran
This document provides social media best practices for job seekers, including how to build an effective LinkedIn profile, develop a personal brand on social media, and connect with potential employers and contacts. It outlines key aspects of popular social media platforms and recommends CA Technologies employees for job seekers to follow on sites like LinkedIn and Twitter to learn more about the company.
SSRN Paper: Expected Returns and the Expected Growth in Rents of Commercial R...Bob Lowery
Investigations of whether the cap rate, that is, the rent-price ratio in commercial
real estate incorporates information about future expected real estate returns
and future growth in rents.
El documento trata sobre Imágenes DICOM en la Escuela Superior de Medicina del Instituto Politécnico Nacional. Se refiere a un estudiante de nombre Hernández Vargas Ivón Lucely en el grupo 4CM5.
This paper examines the relationship between a country's per student public education expenditures and economic growth. It tests whether human capital from education can be represented as a production input in neoclassical growth models. The paper analyzes national growth trends from 1985 to 2010 across development levels. It finds that for most countries, higher per student education spending is negatively associated with growth, except for high-income countries where it may be beneficial. The paper reviews literature on the relationship between education quality/quantity and growth, finding mixed results on the impact of primary versus secondary versus tertiary education on growth.
The document describes a series of eco-friendly products for a hotel called Aegon. It includes Waterella, an umbrella stand that waters plants using water from umbrellas. Sprilla includes a shower mat and showerhead that filter and purify water from showers to water plants. Drola is a spiral ceramic sculpture that collects and filters rainwater and recycled water to flow through its path. The hotel focuses on collecting and recycling water through these products and its rainwater collection system.
This paper examines the relationship between a country's per student public education expenditures and economic growth. It tests whether human capital from education can be represented as a production input in neoclassical growth models. The paper analyzes national growth trends from 1985 to 2010 across development classes. It finds that education spending is negatively associated with growth for all countries except high-income countries, suggesting inefficient spending. The introduction provides context on human capital and economic growth theories being tested. A literature review summarizes several studies examining the relationship between education quality/quantity, institutions, and economic growth.
This document analyzes housing data from Cook and DuPage counties in the Chicago area using hedonic regression. It finds that 54.6% of variation in home prices can be explained by attributes like number of rooms, living area, age, lot size, amenities, taxes, income, distance to downtown and an airport. The effective age of a house has a significant negative impact on price, indicating that older homes are less desirable and valuable. Variables for number of bathrooms, school spending and distance to the nearest expressway were removed from the model as insignificant predictors of home value.
NPOWER Social Media Presentation FINALRachel Duran
This document provides social media best practices for job seekers, including how to build an effective LinkedIn profile, develop a personal brand on social media, and connect with potential employers and contacts. It outlines key aspects of popular social media platforms and recommends CA Technologies employees for job seekers to follow on sites like LinkedIn and Twitter to learn more about the company.
Analisis of housing bubble in USA, addresing from data panel model. Faculty of Economics and Centre for International Macroeconomics and Finance (CIMF).
University of Cambridge, Cambridge, CB3 9DD, UK
1) The document analyzes how economic geography, jobs, and regulations impact urban land prices in the San Francisco Bay Area using a unique dataset of over 7,000 land transactions.
2) It finds that factors like proximity to jobs, topography, demographics, and restrictive land use regulations significantly influence land values and their variation within metropolitan areas.
3) Restrictive regulations, in particular, are shown to substantially increase land prices, and both economic geography and regulation directly impact single-family home prices through their effect on the cost of land.
Housing Affordability in Metro Atlanta: It's ComplicatedARCResearch
The document discusses various ways to measure housing affordability in metro Atlanta. It analyzes data on home prices, sales prices, the housing opportunity index, and the percentage of income spent on housing and transportation costs. While metro Atlanta has relatively affordable home prices, affordability depends on factors like income, transportation costs, and whether households are renters or owners. Maps show that areas with lower incomes and higher poverty rates also tend to have less affordable housing costs as a percentage of income.
A modelling approach to establish whether or not there is a north-south divide in the UK in terms of home ownership. Data used included UK Census and UK Quarterly Labour Force Survey
Grade: 78%
Regional Snapshot: ARC Employment Centers: Core Locations for Jobs, not for A...ARCResearch
This month’s Regional Snapshot picks up where the July Regional Snapshot on Affordable Housing left off. In the October Regional Snapshot we take a deeper dive into affordable housing data, mapping it onto our region’s employment centers in an effort to visualize the relationship between housing affordability and concentrations of regional employment.
Project #4 Urban Population Dynamics This project will acquaint y.pdfanandinternational01
Project #4: Urban Population Dynamics This project will acquaint you with population
modeling and how linear algebra tools may be used to study it. Background Kolman, pages
305-307. Population modeling is useful from many different perspectives: planners at the city,
state, and national level who look at human populations and need forecasts of populations in
order to do planning for future needs. These future needs include housing, schools, care for the
elderly, jobs, and utilities such as electricity,water and transportation. businesses do population
planning so as to predict how the portions of the population that use their product will be
changing. Ecologists use population models to study ecological systems, especially those where
endangered species are involved so as to try to find measures that will restore the population.
medical researchers treat microorganisms and viruses as populations and seek to understand the
dynamics of their populations; especially why some thrive in certain environments but don\'t in
others. In human situations, it is normal to take intervals of 10 years as the census is taken every
10 years. Thus the age groups would be 0-9,10-19,11-20 etc , so 8 or 9 age categories would
probably be appropriate. The survival fractions would then show the fraction of \"newborns\" (0-
9) who survive to age 10, the fraction of 10 to 19 year olds who survive to 20 etc. This type of
data is compiled, for example, by actuaries working for insurance companies for life and medical
insurance purposes. The basic equations we begin with are (1) x(k+1) = Ax(k) k=0,1,2,. . . and
x(0) given with solution found iteratively to be (2) x(k) = Akx(0) (see Kolman for details of the
structure of A, which is 7 x 7 in this case). Your Project Suppose we are studying the
population dynamics of Los Angeles for the purpose of making a planning proposal to the city
which will form the basis for predicting school, transportation, housing, water, and electrical
needs for the years from 2000 on. As above, we take the unit of time to be 10 years, and take 7
age groups: 0-9,10-19,...,50-59,60+. Suppose further that the population distribution as of 1990
(the last census) is (3.1, 2.8, 2.0, 2.5, 2.0, 1.8, 2.9) (x105 ) and that the Leslie matrix,A, for this
model appears as Part One: Interpret carefully each of the nonzero terms in the matrix. In
addition, indicate what factors you think might change those numbers (they might be social,
economical, political or environmental). Part Two: Predict: what the population distribution
will look like in 2000, 2010, 2020 and 2030 what the total population will be in each of those
years by what fraction the total population changed each year Additionally, what does your
software tell you the largest, positive eigenvalue of A is? Part Three: Decide if you believe the
population is going to zero, becoming stable, or is unstable in the long run. Be sure and describe
in your write up how you arrived at your conclusion. If.
This study projects the impact of population aging on future housing stock and prices in both provincial and national markets.
Mario Fortin,
Professor,
Université de Sherbrooke
This document summarizes previous research on factors that impact recycling rates in Canada from 2002-2012. Several studies found that policies like pay-as-you-throw programs and access to curbside pickup positively influence recycling rates. Demographic factors like income, age, education, and household size were also found to correlate with recycling rates in some analyses. However, one study of Ontario municipalities found no significant relationship between spending on recycling promotion/education and recycling rates.
TOWARDS A HETERODOX THEORY OF THE SPATIAL ECONOMYpkconference
The document discusses developing a heterodox theory of spatial economics by extending the heterodox social surplus approach to urban economic issues and incorporating spatial factors. It presents models to describe the structure of production within a city, social accounting, financial flows, output and employment. The models show how the local and external ruling classes influence economic activity and connectivity between cities. Spatial elements are discussed, including how space is socially produced and regulated. Factors influencing school closures like enrollment and demographics are examined.
This document summarizes a model that analyzes the effects of environmental policy reforms on welfare in a context of reciprocal dumping between two similar countries. The model considers pollution quotas set by each country that restrict local production and reduce harmful pollution. However, these quotas also act as a trade barrier, inhibiting employment and consumer surplus benefits. The paper analyzes how uniform reductions in pollution quotas or harmonization of quotas across countries may impact global and domestic welfare under different market conditions.
This document discusses income distribution and inequality. It begins by explaining how inequality is measured using Lorenz curves and the Gini coefficient. It then analyzes how income distribution changes with economic development, initially following an inverted U pattern as described by Kuznets. Several models are discussed that attempt to explain this pattern. The document also examines the impact of income distribution on economic growth, finding that high inequality can negatively impact growth by increasing political instability. Finally, it provides case studies of the income distribution and economic policies of Taiwan and Brazil over time.
Shadowlake neighborhood report-10-13-21Ben N Huynh
This document provides a neighborhood report for Shadowlake in Houston, TX that was prepared by Ben Huynh, a real estate agent. The report includes statistics on the housing market, demographics, economy and quality of life for Shadowlake and compares it to the city of Houston, Harris County and state of Texas. Charts are included that show trends in areas like median home sales price, population density, household income and more.
environment eco-1 Coase theorem and Hedonic p.pdfJanmejayaAcharya
The document discusses the Coase Theorem and hedonic pricing. The Coase Theorem states that when property rights are well-defined and transaction costs are low, private negotiations can lead to an efficient outcome regardless of the initial allocation of property rights. It is based on assumptions of few parties, low negotiation costs, no transaction costs or wealth/income effects, and no government interference. Hedonic pricing uses surrogate goods like housing prices to value environmental attributes by analyzing how characteristics like proximity to parks or mines affect prices. Regression analysis estimates how asset prices vary with characteristics to derive implicit prices for non-market goods.
ECONOMICS 201 Introduction to MicroeconomicsNUMBER ONE {60 poin.docxjack60216
This document contains 5 practice problems for determining the convergence of infinite series using various tests. The tests covered are direct comparison, alternating series, ratio, limit comparison, and root tests. Students are asked to use these convergence tests to analyze the convergence of 5 given infinite series.
Linear Algebra Project Urban Population Dynamics This project is.pdfairflyluggage
Linear Algebra Project : Urban Population Dynamics
This project is about population modeling and how linear algebra tools may be used to study it.
Background
Population modeling is useful from dierent perpectives :
1. planners at the city, state, and national level who look at human populations and need
forecasts of
populations in order to do planning for future needs. These future needs include housing,
schools, care
for elderly, jobs, and utilities such as electricity, water and transportation.
2. businesses do population planning so as to predict how the portions of the population that use
their
product will be changing.
3. ecologists use population models to study ecological systems, especially those where
endangered species
are involved so as to try to nd measures that will restore the population.
4. medical researchers treat microorganisms and viruses as populations and seek to understand
the dy-
namics of their populations ; especially why some thrive in certain environments but don\'t in
others.
In human situations, it is normal to take the intervals of 10 years as the census is taken every 10
years.
Thus the age groups would be 0-9, 10-19, 20-29 etc, so 8 or 9 age categories would probably be
appropriate.
The survival fractions would then show the fraction of \"newborns\" (0-9) who survive to age
10, the fraction
of 10 to 19 years old who survive to 20 etc. This type of data is compiled, for example, by
actuaries working
for insurance companies for life and medical insurance purposes.
The basic equations we begin with are
x(k + 1) = Ax(k) k = 0; 1; 2; ::: and x(0) given (0.1)
with solution found iteratively to be
x(k) = Akx(0) (0.2)
Your project
Suppose we are studying the population dynamics of Los Angeles for the purpose of making
planning proposal.
As above, we take the unit of time to be 10 years, and take 7 age groups : 0-9, 10-19,..., 50-59,
60+. Suppose
further that the population distribution as of 1990 is
(3:1; 2:8; 2:0; 2:5; 2:0; 1:8; 2:9)(105)
1
and that the Leslie matrix, A, for this model appears as
A :=
2
666666664
:2 1:2 1:1 :9 :1 0 0
:7 0 0 0 0 0 0
0 :82 0 0 0 0 0
0 0 :97 0 0 0 0
0 0 0 :97 0 0 0
0 0 0 0 :90 0 0
0 0 0 0 0 :87 0
3
777777775
Part One :
Interpret carefully each of the nonzero terms in the matrix. In addition, indicate what factors you
think
might change those numbers (they might be social, economical, political or environmental).
Part Two :
Predict :
what the population distribution will look like in 2000, 2010, 2020, and 2030 ?
what the total population will be in each of these years ?
by what fraction the total population changed each year ?
Additionally, what does your software tell you the largest positive eigenvalue of A is ?
Part Three :
Decide if you believe the population is going to zero, becoming stable, or is unstable in the long
run. Be
sure and describe in your write up how you arrived at your conclusion. If you have decided it is
unstable,
simulate it long enough that the column matrices for.
This document presents a study analyzing the relationship between home prices in Ottawa and several key economic indicators from 1990 to 2012. It examines home prices as the dependent variable and how they may be influenced by independent variables like the consumer price index, mortgage rates, overnight lending rates, and hourly income rates. Statistical tools like descriptive statistics, hypothesis testing, regression analysis, and chi-squared tests are used to analyze the relationship between these variables and identify the factors that have most significantly impacted home price changes over the period under review. Limitations of the study and opportunities for future research are also discussed.
This month's regional snapshot provides an assessment of regional housing affordability in the Atlanta region. Starting with a review of historic trends in housing construction and costs, the snapshot then steps through the definition of regional "subareas" based on inventory, price, and affordability characteristics.
The document provides an overview of housing affordability in the Atlanta region based on a study by the Atlanta Regional Commission. It finds that the number of cost-burdened renter households has increased steadily over the last decade while the number of cost-burdened owner households has declined. Recently, the greatest increase in cost-burdened households has been among those with annual incomes of $35,000 to $50,000. The document also analyzes housing affordability trends and statistics in 10 subareas that make up the Atlanta region.
- Philadelphia lost over 300,000 residents between 1970-1980, a 13% reduction, with continued population declines each decade until a slight 1% increase in 2010.
- This 2010 population growth was attributed solely to an increase in the city's foreign-born residents, as the native-born population continued declining.
- Between 1970-2010, the proportion of Philadelphia's native-born residents who were born in-state declined steadily, while those born out-of-state grew slowly.
Analisis of housing bubble in USA, addresing from data panel model. Faculty of Economics and Centre for International Macroeconomics and Finance (CIMF).
University of Cambridge, Cambridge, CB3 9DD, UK
1) The document analyzes how economic geography, jobs, and regulations impact urban land prices in the San Francisco Bay Area using a unique dataset of over 7,000 land transactions.
2) It finds that factors like proximity to jobs, topography, demographics, and restrictive land use regulations significantly influence land values and their variation within metropolitan areas.
3) Restrictive regulations, in particular, are shown to substantially increase land prices, and both economic geography and regulation directly impact single-family home prices through their effect on the cost of land.
Housing Affordability in Metro Atlanta: It's ComplicatedARCResearch
The document discusses various ways to measure housing affordability in metro Atlanta. It analyzes data on home prices, sales prices, the housing opportunity index, and the percentage of income spent on housing and transportation costs. While metro Atlanta has relatively affordable home prices, affordability depends on factors like income, transportation costs, and whether households are renters or owners. Maps show that areas with lower incomes and higher poverty rates also tend to have less affordable housing costs as a percentage of income.
A modelling approach to establish whether or not there is a north-south divide in the UK in terms of home ownership. Data used included UK Census and UK Quarterly Labour Force Survey
Grade: 78%
Regional Snapshot: ARC Employment Centers: Core Locations for Jobs, not for A...ARCResearch
This month’s Regional Snapshot picks up where the July Regional Snapshot on Affordable Housing left off. In the October Regional Snapshot we take a deeper dive into affordable housing data, mapping it onto our region’s employment centers in an effort to visualize the relationship between housing affordability and concentrations of regional employment.
Project #4 Urban Population Dynamics This project will acquaint y.pdfanandinternational01
Project #4: Urban Population Dynamics This project will acquaint you with population
modeling and how linear algebra tools may be used to study it. Background Kolman, pages
305-307. Population modeling is useful from many different perspectives: planners at the city,
state, and national level who look at human populations and need forecasts of populations in
order to do planning for future needs. These future needs include housing, schools, care for the
elderly, jobs, and utilities such as electricity,water and transportation. businesses do population
planning so as to predict how the portions of the population that use their product will be
changing. Ecologists use population models to study ecological systems, especially those where
endangered species are involved so as to try to find measures that will restore the population.
medical researchers treat microorganisms and viruses as populations and seek to understand the
dynamics of their populations; especially why some thrive in certain environments but don\'t in
others. In human situations, it is normal to take intervals of 10 years as the census is taken every
10 years. Thus the age groups would be 0-9,10-19,11-20 etc , so 8 or 9 age categories would
probably be appropriate. The survival fractions would then show the fraction of \"newborns\" (0-
9) who survive to age 10, the fraction of 10 to 19 year olds who survive to 20 etc. This type of
data is compiled, for example, by actuaries working for insurance companies for life and medical
insurance purposes. The basic equations we begin with are (1) x(k+1) = Ax(k) k=0,1,2,. . . and
x(0) given with solution found iteratively to be (2) x(k) = Akx(0) (see Kolman for details of the
structure of A, which is 7 x 7 in this case). Your Project Suppose we are studying the
population dynamics of Los Angeles for the purpose of making a planning proposal to the city
which will form the basis for predicting school, transportation, housing, water, and electrical
needs for the years from 2000 on. As above, we take the unit of time to be 10 years, and take 7
age groups: 0-9,10-19,...,50-59,60+. Suppose further that the population distribution as of 1990
(the last census) is (3.1, 2.8, 2.0, 2.5, 2.0, 1.8, 2.9) (x105 ) and that the Leslie matrix,A, for this
model appears as Part One: Interpret carefully each of the nonzero terms in the matrix. In
addition, indicate what factors you think might change those numbers (they might be social,
economical, political or environmental). Part Two: Predict: what the population distribution
will look like in 2000, 2010, 2020 and 2030 what the total population will be in each of those
years by what fraction the total population changed each year Additionally, what does your
software tell you the largest, positive eigenvalue of A is? Part Three: Decide if you believe the
population is going to zero, becoming stable, or is unstable in the long run. Be sure and describe
in your write up how you arrived at your conclusion. If.
This study projects the impact of population aging on future housing stock and prices in both provincial and national markets.
Mario Fortin,
Professor,
Université de Sherbrooke
This document summarizes previous research on factors that impact recycling rates in Canada from 2002-2012. Several studies found that policies like pay-as-you-throw programs and access to curbside pickup positively influence recycling rates. Demographic factors like income, age, education, and household size were also found to correlate with recycling rates in some analyses. However, one study of Ontario municipalities found no significant relationship between spending on recycling promotion/education and recycling rates.
TOWARDS A HETERODOX THEORY OF THE SPATIAL ECONOMYpkconference
The document discusses developing a heterodox theory of spatial economics by extending the heterodox social surplus approach to urban economic issues and incorporating spatial factors. It presents models to describe the structure of production within a city, social accounting, financial flows, output and employment. The models show how the local and external ruling classes influence economic activity and connectivity between cities. Spatial elements are discussed, including how space is socially produced and regulated. Factors influencing school closures like enrollment and demographics are examined.
This document summarizes a model that analyzes the effects of environmental policy reforms on welfare in a context of reciprocal dumping between two similar countries. The model considers pollution quotas set by each country that restrict local production and reduce harmful pollution. However, these quotas also act as a trade barrier, inhibiting employment and consumer surplus benefits. The paper analyzes how uniform reductions in pollution quotas or harmonization of quotas across countries may impact global and domestic welfare under different market conditions.
This document discusses income distribution and inequality. It begins by explaining how inequality is measured using Lorenz curves and the Gini coefficient. It then analyzes how income distribution changes with economic development, initially following an inverted U pattern as described by Kuznets. Several models are discussed that attempt to explain this pattern. The document also examines the impact of income distribution on economic growth, finding that high inequality can negatively impact growth by increasing political instability. Finally, it provides case studies of the income distribution and economic policies of Taiwan and Brazil over time.
Shadowlake neighborhood report-10-13-21Ben N Huynh
This document provides a neighborhood report for Shadowlake in Houston, TX that was prepared by Ben Huynh, a real estate agent. The report includes statistics on the housing market, demographics, economy and quality of life for Shadowlake and compares it to the city of Houston, Harris County and state of Texas. Charts are included that show trends in areas like median home sales price, population density, household income and more.
environment eco-1 Coase theorem and Hedonic p.pdfJanmejayaAcharya
The document discusses the Coase Theorem and hedonic pricing. The Coase Theorem states that when property rights are well-defined and transaction costs are low, private negotiations can lead to an efficient outcome regardless of the initial allocation of property rights. It is based on assumptions of few parties, low negotiation costs, no transaction costs or wealth/income effects, and no government interference. Hedonic pricing uses surrogate goods like housing prices to value environmental attributes by analyzing how characteristics like proximity to parks or mines affect prices. Regression analysis estimates how asset prices vary with characteristics to derive implicit prices for non-market goods.
ECONOMICS 201 Introduction to MicroeconomicsNUMBER ONE {60 poin.docxjack60216
This document contains 5 practice problems for determining the convergence of infinite series using various tests. The tests covered are direct comparison, alternating series, ratio, limit comparison, and root tests. Students are asked to use these convergence tests to analyze the convergence of 5 given infinite series.
Linear Algebra Project Urban Population Dynamics This project is.pdfairflyluggage
Linear Algebra Project : Urban Population Dynamics
This project is about population modeling and how linear algebra tools may be used to study it.
Background
Population modeling is useful from dierent perpectives :
1. planners at the city, state, and national level who look at human populations and need
forecasts of
populations in order to do planning for future needs. These future needs include housing,
schools, care
for elderly, jobs, and utilities such as electricity, water and transportation.
2. businesses do population planning so as to predict how the portions of the population that use
their
product will be changing.
3. ecologists use population models to study ecological systems, especially those where
endangered species
are involved so as to try to nd measures that will restore the population.
4. medical researchers treat microorganisms and viruses as populations and seek to understand
the dy-
namics of their populations ; especially why some thrive in certain environments but don\'t in
others.
In human situations, it is normal to take the intervals of 10 years as the census is taken every 10
years.
Thus the age groups would be 0-9, 10-19, 20-29 etc, so 8 or 9 age categories would probably be
appropriate.
The survival fractions would then show the fraction of \"newborns\" (0-9) who survive to age
10, the fraction
of 10 to 19 years old who survive to 20 etc. This type of data is compiled, for example, by
actuaries working
for insurance companies for life and medical insurance purposes.
The basic equations we begin with are
x(k + 1) = Ax(k) k = 0; 1; 2; ::: and x(0) given (0.1)
with solution found iteratively to be
x(k) = Akx(0) (0.2)
Your project
Suppose we are studying the population dynamics of Los Angeles for the purpose of making
planning proposal.
As above, we take the unit of time to be 10 years, and take 7 age groups : 0-9, 10-19,..., 50-59,
60+. Suppose
further that the population distribution as of 1990 is
(3:1; 2:8; 2:0; 2:5; 2:0; 1:8; 2:9)(105)
1
and that the Leslie matrix, A, for this model appears as
A :=
2
666666664
:2 1:2 1:1 :9 :1 0 0
:7 0 0 0 0 0 0
0 :82 0 0 0 0 0
0 0 :97 0 0 0 0
0 0 0 :97 0 0 0
0 0 0 0 :90 0 0
0 0 0 0 0 :87 0
3
777777775
Part One :
Interpret carefully each of the nonzero terms in the matrix. In addition, indicate what factors you
think
might change those numbers (they might be social, economical, political or environmental).
Part Two :
Predict :
what the population distribution will look like in 2000, 2010, 2020, and 2030 ?
what the total population will be in each of these years ?
by what fraction the total population changed each year ?
Additionally, what does your software tell you the largest positive eigenvalue of A is ?
Part Three :
Decide if you believe the population is going to zero, becoming stable, or is unstable in the long
run. Be
sure and describe in your write up how you arrived at your conclusion. If you have decided it is
unstable,
simulate it long enough that the column matrices for.
This document presents a study analyzing the relationship between home prices in Ottawa and several key economic indicators from 1990 to 2012. It examines home prices as the dependent variable and how they may be influenced by independent variables like the consumer price index, mortgage rates, overnight lending rates, and hourly income rates. Statistical tools like descriptive statistics, hypothesis testing, regression analysis, and chi-squared tests are used to analyze the relationship between these variables and identify the factors that have most significantly impacted home price changes over the period under review. Limitations of the study and opportunities for future research are also discussed.
This month's regional snapshot provides an assessment of regional housing affordability in the Atlanta region. Starting with a review of historic trends in housing construction and costs, the snapshot then steps through the definition of regional "subareas" based on inventory, price, and affordability characteristics.
The document provides an overview of housing affordability in the Atlanta region based on a study by the Atlanta Regional Commission. It finds that the number of cost-burdened renter households has increased steadily over the last decade while the number of cost-burdened owner households has declined. Recently, the greatest increase in cost-burdened households has been among those with annual incomes of $35,000 to $50,000. The document also analyzes housing affordability trends and statistics in 10 subareas that make up the Atlanta region.
- Philadelphia lost over 300,000 residents between 1970-1980, a 13% reduction, with continued population declines each decade until a slight 1% increase in 2010.
- This 2010 population growth was attributed solely to an increase in the city's foreign-born residents, as the native-born population continued declining.
- Between 1970-2010, the proportion of Philadelphia's native-born residents who were born in-state declined steadily, while those born out-of-state grew slowly.
1. SAN FRANCISCO STATE UNIVERSITY
Hedonic Regression Analysis In
The Chicago Housing Market
Econ 312: Modeling Project, Fall 2014
Dion Rosete, ID#911507147
12/15/2014
2. 1
I. Introduction
According to economist Kelvin Lancaster’ attributes theory of the consumer, it is not that
“goods are the direct objects of utility,” but “it is the properties or characteristics of a good from
which utility is derived” (Bluestone 2008) The housing market is marked by extreme
heterogeneity – no two homes are exactly alike. They vary widely along many useful dimensions
related to size, quality, and location. With hedonic pricing, the price of the good depends on this
bundle of attributes – if this bundle differs widely, then so will the price. All else equal, should
size, quality, or location increase, so will total price. Through hedonic regression analysis, one
can analyze how the individual attributes of a house contribute towards its price (Potepan 2014).
The housing market generates huge ripple effects. Historically, housing accounts for
17%-18% of GDP, approximately 5% in residential investment and 12%-13% in housing
services. In 2013, the housing market contributed to 15.6% of total GDP - 3.1% in residential
investment, and 12.5% in housing services (NAHB 2014). Additionally, in a 2012 Census
Bureau report, property taxes provide 65.27% of the revenue from local sources and 29.05%
from total sources to public school systems (Dixon 2014). There is an interplay - quality of local
schools drives home value, and the property tax on home values fund local schools. If the
housing supply within a metropolitan area does not expand to match demand, prices and rents
can rise high enough to dissuade economic expansion, particularly through slower employment
and population growth (Bluestone 2008). The attributes and price given to a house affect not
only the homeowners, but whole communities.
The sample data consists of 17 variables - 1 dependent, and 16 covariates - across 2000
observations of the housing market taken from Cook and DuPage Counties, two of the four
counties of the Chicago Primary Metropolitan Statistical Area (PMSA). The data measures
3. 2
attributes in the dwelling/housing itself, its neighborhood, local public goods, and city center
proximity, in their contribution to house contract sales price.
Variable Definition, Units, and Sources Natural
Logarithm
Expected
Signs
Dependent
SPRICE Contract sales price of the house in dollars. Does not include
any closing cost normally chargeable to the purchaser.
(Federal Housing Association 1989-1990)
LSPRICE
Covariates
NROOMS Total number of habitable rooms within the house. (FHA) LNROOMS positive
LVAREA Total living area in square feet. (FHA) LLVAREA positive
HAGEEFF Effective age of the house in years. (FHA) LHAGEFF negative
LSIZE Total area of lot in square feet. (FHA) LLSIZE positive
AIRCON Type of air-conditioning; If house possesses central air-
conditioning = 2, if window or wall air-conditioning = 1, and
if none = 0. (FHA)
(unlogged) positive
NBATH Total number of bathrooms within the dwelling unit; any room
containing “lavoratory or sink, water closet or toilet, and/ or a
tub or shower or both.” (FHA)
(unlogged) positive
GARAGE Type of parking garage; If house contains built-in garage = 4,
if carport (a simple roof or unwalled shed) = 3, if detached
garage = 2, if on-site parking = 1, and, if none = 0. (FHA)
(unlogged) positive
PTAXES Property tax rate in the township district in the year of
purchase, expressed as percentage. (Illinois Department of
Revenue)
LPTAXES negative
PCTWHT Percentage of white people within the local population,
according to the 1990 census tract. (US Census Bureau, 1990)
(unlogged) positive
MEDINC Local median income in dollars. (US Census Bureau, 1990) LMEDINC positive
DFCL Distance in tenths of a mile from the Loop area in downtown
Chicago. (Contemporary city maps)
LDFCL negative
DFNI Distance in tenths of a mile to the nearest expressway
entrance. (Contemporary city maps)
(unlogged) negative
SSPEND Operating expenses in dollars per student in the local school
district. (Illinois State Board of Education’s Annual Statistical
Reports, 1989 and 1990)
LSSPEND positive
4. 3
MSPEND Municipal government expenditures per capita: primarily
measured to the nearest dollar, but a few instances to the
nearest cent. (Census of Government, 1987)
LMSPEND positive
COOK Dummy variable. If located in Cook County = 1; if located in
DuPage County = 0.
(unlogged) positive
OHARE Dummy variable. If located within 5-mile radius of O’Hare
business district = 1; if located outside = 0
(unlogged) positive
One could predict the coefficients attached to number of rooms, number of bathrooms,
living area, and lot size to be positive – one pays for larger quantities of property, and there is
obvious utility in spaciousness. With the natural deterioration of age, the house loses general
quality and function, costs of maintenance increase, and the house becomes less desirable,
resulting in a negative relationship with price. Air-conditioning’s and garage’s effect should be
positive; as the variable increases, the type used is upgraded.
The property tax rate is another extra cost to potential homeowners, so it should be
negative. Per capita municipal government spending is a proxy variable for quality and care of
city infrastructure, while per student school district spending is a proxy variable for the quality
and care of education offered nearby; both should be positive in relation to price. Median income
is a proxy variable for standard of living, so one would expect it to have a positive relationship as
well. With the controversial issues of upward filtering and gentrification, when upper income
households move into a dilapidated inner city neighborhood, they us their economic and political
privilege to help to improve the community, but in doing so, the neighborhood’s property values
rise (Bluestone 2014). Measuring percentage of white population plays into the racial prejudices
of society, which provide them with more opportunity; a product of gentrification is that
neighborhoods become less diverse. The socioeconomic issues of race and class spawn the
perception/issue of crime. They would have a positive relationship with home price.
5. 4
If the Alonso Bid-Rent Model’s tendencies hold true, then one should expect DFCL’s
coefficient to be negative, and OHARE’s to be positive; “willingness to bid” on parcels of land
decreases with distance from the central business district or city center, as potential land users
save in transportation costs by being closer (Bluestone 2014). By the same token, with increasing
distance from the expressway, there are increasing commute costs, so it should have a negative
relationship. Furthermore, though Chicago is located in between Cook and DuPage Counties, the
majority of it and its municipal government is in Cook County; the coefficient for COOK should
therefore be positive.
Proposed Population Model
log(𝑠𝑝𝑟𝑖𝑐𝑒) = 𝛽1 + 𝛽2 log(𝑛𝑟𝑜𝑜𝑚𝑠) + 𝛽3log(𝑙𝑣𝑎𝑟𝑒𝑎) + 𝛽4log(ℎ𝑎𝑔𝑒𝑒𝑓𝑓) + 𝛽5log(𝑙𝑠𝑖𝑧𝑒) +
𝛽6 𝑎𝑖𝑟𝑐𝑜𝑛 + 𝛽7 𝑛𝑏𝑎𝑡ℎ + 𝛽8 𝑔𝑎𝑟𝑎𝑔𝑒 + 𝛽9 log(𝑝𝑡𝑎𝑥𝑒𝑠) + 𝛽10 𝑝𝑐𝑡𝑤ℎ𝑡 + 𝛽11log(𝑚𝑒𝑑𝑖𝑛𝑐) +
𝛽12log(𝑑𝑓𝑐𝑙) + 𝛽13dfni + 𝛽14log(𝑠𝑠𝑝𝑒𝑛𝑑) + 𝛽15log(𝑚𝑠𝑝𝑒𝑛𝑑) + 𝛽16 𝑐𝑜𝑜𝑘 + 𝛽17 𝑜ℎ𝑎𝑟𝑒 + 𝑢𝑖
II. Regressions and Hypotheses Testing
Assuming the model is rendered linear in parameter, there does not exist an exact linear
relationship between regressors [for example, 𝑁𝐵𝐴𝑇𝐻 + 𝐿𝑁𝑅𝑂𝑂𝑀𝑆 ≠ 𝐿𝑉𝐴𝑅𝐸𝐴], expected
value of the error term is zero [as in, E(𝑢𝑖) = 0], variance of the error term is homoscedastic
[var(𝑢𝑖) = 𝜎 𝑢
2
], covariance between error terms is zero [cov(𝑢𝑖, 𝑢𝑗) = 0], and the error term
follows normal distribution [𝑢𝑖~𝑁(0, 𝜎 𝑢
2
)], OLS estimators are best least unbiased estimators.
First Run: Original Estimated Regression Equation
R2
= 0.5460; F(16,1983) = 149.02
6. 5
log(𝑠𝑝𝑟𝑖𝑐𝑒) = 4.9945 + . 1051⏟
(.0404)
log(𝑛𝑟𝑜𝑜𝑚𝑠) + . 3259⏟
(.0374)
log(𝑙𝑣𝑎𝑟𝑒𝑎) − . 0853⏟
(.0096)
log(ℎ𝑎𝑔𝑒𝑒𝑓𝑓)
+ . 0893⏟
(.0110)
log(𝑙𝑠𝑖𝑧𝑒) + . 0288⏟
(.0066)
𝑎𝑖𝑟𝑐𝑜𝑛 + . 0168⏟
(.0146)
𝑛𝑏𝑎𝑡ℎ + . 0131⏟
(.0042)
𝑔𝑎𝑟𝑎𝑔𝑒
− . 5917⏟
(.0575)
log(𝑝𝑡𝑎𝑥𝑒𝑠) + . 0029⏟
(.0003)
𝑝𝑐𝑡𝑤ℎ𝑡 + . 4753⏟
(.0279)
log(𝑚𝑒𝑑𝑖𝑛𝑐) − . 2148⏟
(.0160)
log(𝑑𝑓𝑐𝑙)
− . 0047⏟
(.0039)
dfni + . 0109⏟
(.0511)
log(𝑠𝑠𝑝𝑒𝑛𝑑) − . 0361⏟
(.0208)
log(𝑚𝑠𝑝𝑒𝑛𝑑) + . 0830⏟
(.0276)
𝑐𝑜𝑜𝑘
+ . 0652⏟
(.0311)
𝑜ℎ𝑎𝑟𝑒 + 𝑒 𝑖
R-squared signifies 54.60% of the variation is explained in the regression of the natural
logarithm of house contract sales price on all 16 covariates as measures of house attributes. At
the 5% significance level, there is enough sample information to conclude at least one of the
explanatory variables has a significant influence on home contract sales price (logged).
H0: 𝛽2 = 𝛽3 = 𝛽4 = 𝛽5 … = 𝛽17 = 0
Ha: 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 ≠ 0
Decision Rule: Reject 𝐻0 if 𝐹 > 𝐹(𝛼,𝑘−1,𝑛−𝑘); Reject 𝐻0 if 𝑃 𝑣𝑎𝑙𝑢𝑒 < 𝛼
𝐹(16,1983) =
𝑅2
(𝑘 − 1)⁄
(1 − 𝑅2)/(𝑛 − 𝑘)
=
. 5460 (17 − 1)⁄
(1 − .5460)/(2000 − 17)
= 149.0526 ≈ 149.02
𝐹(𝛼,𝑘−1,𝑛−𝑘) = 𝐹(.05,16,1983) = 𝐹. 𝐼𝑁𝑉. 𝑅𝑇(0.05,16,1983) = 1.6486
P value = F. DIST. RT(149.02,16,1983) = 0.0000
149.02 > 1.6486 → 𝑅𝑒𝑗𝑒𝑐𝑡; 0 < .05 → Reject
Through the t-test, at the 5% significance level, there is enough sample information to
conclude that the effective age of a house has a significant influence on contract home sales price
(logged).
𝐻0: 𝛽4 = 0
𝐻 𝑎: 𝛽4 < 0
Decision Rule: Reject 𝐻0 if 𝑡 < 𝑡(𝑛−2,𝛼) ; Reject 𝐻0 if 𝑃 𝑣𝑎𝑙𝑢𝑒 < 𝛼
𝑡 =
𝑏4 − 𝛽4
𝑆 𝑏4
=
𝑏4
𝑆 𝑏4
=
−.0852755
. 0095621
= −8.92
𝑡(𝑛−2,𝛼) = 𝑡(1998, .05) = 𝑇. 𝐼𝑁𝑉(0.05,1998) = −1.6456
𝑃 𝑣𝑎𝑙𝑢𝑒 = T. DIST(−8.92,1998, 𝑇𝑅𝑈𝐸) = 5.14292 ∗ 10−19
−8.92 < −1.65 → 𝑅𝑒𝑗𝑒𝑐𝑡; 5.14292 ∗ 10−19
< .05 → 𝑅𝑒𝑗𝑒𝑐𝑡
7. 6
In subsequent runs, LSSPEND (p-value = .831), NBATH (p-value = .251), and DFNI (p-
value = .222) were dropped one at a time, deemed insignificant at the 5% level. Through the
subset F test, at the 5% significance level, there is enough sample information to conclude that
the dropping of the three variables was justified.
𝐻0: 𝛽7 = 𝛽13 = 𝛽14 = 0
𝐻𝐴 : 𝑎𝑡 𝑙𝑒𝑎𝑠𝑡 𝑜𝑛𝑒 ≠ 0
Decision Rule: Reject H0 if 𝐹 > 𝐹(𝛼,𝑞,𝑛−𝑘); Reject 𝐻0 if 𝑃 𝑣𝑎𝑙𝑢𝑒 < 𝛼
𝐹 =
(𝑅 𝑈𝑅
2
− 𝑅 𝑅
2
) 𝑞⁄
(1 − 𝑅 𝑈𝑅
2
)/(𝑛 − 𝑘)
=
(.5460 − .5453) 3⁄
(1 − .5460)/(2000 − 17)
=
(0.0007) 3⁄
(0.454)/(1983)
= 1.0192
𝐹(𝛼,𝑞,𝑛−𝑘) = 𝐹(.05,3,1983) = F. INV. RT(0.05,3,1983) = 2.6094
𝑃 𝑣𝑎𝑙𝑢𝑒 = F. DIST. RT(1.0192,3,1983) =0.3831
1.0192 < 2.6094 → Fail To Reject; 0.3831 > .05 → Fail to Reject
Fifth Run: Revised Regression Equation
𝑅 𝑅
2
= 0.5453; F(13,1986) = 183.21
log(𝑠𝑝𝑟𝑖𝑐𝑒) = 5.0151 + . 1163⏟
(.0388)
log(𝑛𝑟𝑜𝑜𝑚𝑠) + . 3410⏟
(.0350)
log(𝑙𝑣𝑎𝑟𝑒𝑎) − . 0849⏟
(.0094)
log(ℎ𝑎𝑔𝑒𝑒𝑓𝑓)
+ . 0883⏟
(.0108)
log(𝑙𝑠𝑖𝑧𝑒) + . 0290⏟
(.0066)
𝑎𝑖𝑟𝑐𝑜𝑛 + . 0125⏟
(.0042)
𝑔𝑎𝑟𝑎𝑔𝑒
− . 6059⏟
(.0495)
log(𝑝𝑡𝑎𝑥𝑒𝑠) + . 0028⏟
(.0003)
𝑝𝑐𝑡𝑤ℎ𝑡 + . 4762⏟
(.0279)
log(𝑚𝑒𝑑𝑖𝑛𝑐) − . 2156⏟
(.0158)
log(𝑑𝑓𝑐𝑙)
− . 0386⏟
(.0164)
log(𝑚𝑠𝑝𝑒𝑛𝑑) + . 0943⏟
(.0229)
𝑐𝑜𝑜𝑘 + . 0701⏟
(.0306)
𝑜ℎ𝑎𝑟𝑒 + 𝑒𝑖
III. Preliminary Interpretations
LPTAX, LMEDINC, and LDFCL were the most significant. All else equal, for every 1%
increase in property tax (t = -12.24), sales price decreases by .6059%; for every 1% increase in
median income (t = 17.10), sales price increases by .4762%; and for every 1% increase in
distance from the Loop area (t = -13.65), sales price decreases by .2156%, on average.
8. 7
With 95% confidence, the interval [.00224, .00336] contains the true population
parameter 𝛽10, the coefficient for PCTWHT, and the interval [.06709, .10953] contains the true
population parameter 𝛽5, the coefficient for LLSIZE. All of the remaining variables have
confidence intervals that do not contain zero, while the ones eliminated did.
Confidence Interval: 𝑏𝑗 ± 𝑀𝑂𝐸, where 𝑀𝑂𝐸 = 𝑡 𝑛−2,𝛼/2 ∗ 𝑆 𝑏 𝑗
𝑏𝑗 − 𝑡 𝑛−2,𝛼/2 ∗ 𝑆 𝑏 𝑗
< 𝛽𝑗 < 𝑏𝑗 + 𝑡 𝑛−2,𝛼/2 ∗ 𝑆 𝑏 𝑗
± 𝑡 𝑛−2,𝛼/2 = ± 𝑡1998 ,.05/2 = 𝑇. 𝐼𝑁𝑉. 2𝑇(0.05,1998) = ± 1.961152015
𝑏10 = .0028045; 𝑆 𝑏10
= .0002856
𝑡1998,.025 ∗ 𝑆 𝑏10
= 1.961152015 ∗ .0002856 = 0.000560105
𝑏10 − 𝑡 𝑛−2,𝛼/2 ∗ 𝑆 𝑏10
= .0028045 − 0.000560105 =0.002244395
𝑏10 + 𝑡 𝑛−2,𝛼/2 ∗ 𝑆 𝑏10
= .0028045 + 0.000560105 =0.003364605
.002244 < 𝛽10 < .00336
𝑏5 = .0883127; 𝑆 𝑏5
= .0108195
𝑡1998,.025 ∗ 𝑆 𝑏5
= 1.961152015 ∗ .0108195 =0.021218684
𝑏10 − 𝑡 𝑛−2,𝛼/2 ∗ 𝑆 𝑏10
= .0883127 − 0.021218684 =0.06709
𝑏10 + 𝑡 𝑛−2,𝛼/2 ∗ 𝑆 𝑏10
= .0883127 + 0.021218684 =0.109531
. 06709 < 𝛽5 < .10953
IV. Conclusions
According to the regression of house sales price, distance from nearest expressway, local
school spending per student, and number of bathrooms have no significant influence on home
price; but number of rooms, living area, lot size, effective age, property tax, local median
income, type of garage, air-conditioning, government spending, and proximity to places of
activity definitely do. All the variables’ coefficients had their expected signs, except for
LMSPEND, which turned out negative. Municipal spending may not be a measure of quality and
9. 8
care, but instead, constant deterioration, inefficient spending, and high crime. The insignificance
of distance from nearest expressway intuitively makes sense – by being closer, the home owner
may cut commute costs, but also experience constant car noise and exhaust; the benefits are
negated by the costs. The insignificance of school district spending per student is a bit surprising
– it would seem that more spending attractively implies better education. But, it is only a proxy,
only implicit in quality of the school. There is the issue of how school spending is used
constructively. It was surprising number of bathrooms was deemed insignificant, let alone zero in
some observations. Perhaps bathrooms are taken for granted as a necessity, or it is superfluous to
have many for less house members. NBATH also was highly correlated with LLVAREA (r =
.7977) and LNROOMS (.7805).
On that note, both before and after the variables were dropped, none of the variables
exhibited high variable inflation factors – in the end, multi-collinearity did not seem severe, so
the data became sufficient. But, there are also high correlations between student spending and
municipal government spending (r = 0.6879), number of rooms and living area (0.8987),
percentage white population and median income (0.6827), property tax rate and being in Cook
County (0.6579), median income and distance from Loop area (0.7070). This may require further
study on causality, but these relationships are somewhat intuitive – education spending draws
from local government, white populations tend to be more privileged, bigger living area allows
for more rooms, property tax is levied on more valuable property in Cook County, and higher
median income often is in the suburbs, away from the Loop area.
In order to efficiently accommodate potential homeowners and “bid up” price, perhaps
one must not focus on constructing/selling closer to the expressway or with more bathrooms, but
instead on lowering property tax rate, or constructing/selling within higher-income
10. 9
neighborhoods, closer to The Loop, among other significant attributes. Through manipulating
these significant attributes, one can increase price in an effort to increase school revenue through
property tax, upward-filter/gentrify a neighborhood, or generate general economic growth. One
must find a happy medium in property tax rate in juggling homeowners’ desires, home sales
price, and tax revenue; to raise tax revenue, one can increase the property tax rate, but
compensate by providing more of these positive attributes. One can also cool down the housing
market for the sake of population and employment growth by providing less of these positive
significant attributes and more of the negative ones, like increasing tax rates or distance to
central business districts. The insignificant school spending proxy variable reminds us that data
can only take us so far – sometimes quality is immeasurable, and should be directly observed.
V: Appendix
References
Bluestone, Barry, Mary Huff Stevenson, and Russell Williams. 2008. The Urban Experience:
Economics, Society, and Public Policy. New York: Oxford University Press.
Chattopadhyay, Sudip. 1999. "Estimating the Demand for Air Quality: New Evidence Based on
the Chicago Housing Market." Land Economics, Vol. 75, no. No. 1: Pp. 22-38.
Accessed November 12, 2014. http://www.jstor.org/stable/3146991.
Dixon, Mark. 2014. "Public Education Finances: 2012." United States Census Bureau.
Accessed December 13, 2014. http://www2.census.gov/govs/school/12f33pub.pdf.
NAHB. “Housing's Contribution to Gross Domestic Product (GDP)." 2014. National Association
of Home Builders (NAHB). Accessed December 13, 2014.
http://www.nahb.org/generic.aspx?genericContentID=66226.
11. 10
Potepan, Michael. Fall 2014. “Chapter 12: Urban Housing Markets & Residential Location.”
Class lecture for Urban Economics at San Francisco State University, San Francisco, CA.