Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
by
David William Roache, PE
BS. Civil and Environmental Engineering, 2003
Northeastern University
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate
Development in Partial Fulfillment of the Requirements for the Degree of Master of Science in Real
Estate Development
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at the
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
Massachusetts Institute of Technology
September, 2015
@2015 David W. Roache
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AUG 202015
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The author herby grants to MIT permission to reproduce and to distribute publicly paper and electronic
copies of this thesis document in whole or in part in any medium now known or hereafter created.
Signature of Author
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Center for Real Estate
July 30, 2015
Certified by
Signature redacted
William C. Wheaton
Professor Emeritus, MIT Department of Economics
Thesis Supervisor
Accepted by
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bEf(Saiz
Daniel Rose Associate Professor of Urban Economics and Real Estate,
Department of Urban Studies and Center for Real Estate
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
by
David William Roache, PE
Submitted to the Program in Real Estate Development in Conjunction with the Center for Real Estate
on July 30, 2015 in Partial Fulfillment of the Requirements for the Degree of Master of Science in Real
Estate Development
ABSTRACT
The current generation of young adults dubbed the "Millennials" are far different from past
generations in many ways. They prefer renting to owning, shun the suburbs for cities, are likely
to live at home with their parents, are putting off marriage and they are well educated. This
thesis seeks to study how the living arrangements of the Millennial generation compare to
those of the past generations to find out how true this conventional wisdom is. It studies U.S.
Census Data from past decades, focusing on the population segment between ages 22 and 31
at each decennial census from 1980-2010.
The demographic characteristics of age, marriage and education are studied to determine their
influence on the living arrangements of this young adult cohort. Using linear regression models,
the propensity to live in different forms of tenure or within a center city of and MSA are parsed
out to find what portion of this propensity is due to the delay of marriage, increase in education
or changes in the young adult population. The study is then further broken down to determine
to what extent changes in living arrangements are due to changes in the preferences of the
population versus changes in the demographic composition of the population.
From 1980-2010 there has been a decline in the marriage rate and homeownership rate of the
population, markedly so amongst young adults. Conversely, there has been an increase in those
completing four years of college and the rate of the population living in a home where their parent is the
head of household. This study shows that the decline in marriage has reduced the homeownership rate,
but there is an increased preference for homeownership amongst those never married especially so
amongst young adults. In general there has been a large increase in the preference of young adults to
live at home and a decline in the preference to own or rent indicating that those not buying are opting
to move in with their parents rather than rent. There has not been an increase amongst Millennials in
preference or total propensity to live in center cities.
Thesis Supervisor: William C. Wheaton
Title: Professor Emeritus, MIT Department of Economics
2
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Acknowledgments
I would like to thank the following people for making this thesis possible:
"
Lyndsey Rolheiser for being so willing to help me out with even the most basic question
as dipped into the amazing world of statistics
"
My family, including most importantly my amazing wife Amanda for her love, support
*
and patience through this journey as I pursued my dream.
And lastly my thesis advisor, Bill Wheaton. Your enthusiasm for the topic helped make
this study an enjoyable experience.
3
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Table of Contents
Chapter 1: Introduction ......................................................................................................................
1.1.
1.2.
1.3.
1.4.
1.5.
1.6.
1.7.
7
7
M illennials and Generational Definitions - Selecting an Age Cohort ...............................................
8
M illennials and M arriage ......................................................................................................................
9
M illennials and Education .....................................................................................................................
10
M illennials and Housing Tenure......................................................................................................
11
M illennials and the Suburbs: Returning "Hom e"..................... .......................................................
12
M illennials and Urban Dwelling .....................................................................................................
Thesis Intent and Hypothesis..............................................................................................................13
Chapter 2: Census Data ....................................................................................................................
14
2.1. Data Source .........................................................................................................................................
2.2. Data Overview .....................................................................................................................................
2.3. Center City Definition and Selection of M SAs.................................................................................
14
14
17
Chapter 3: Dem ographic and Housing Trends...............................................................................
20
3.1.
3.2.
3.3.
3.4.
3.5.
3.6.
3.7.
3.8.
Linear Regression Methodology - Simple single linear regression of data ....................................
Delay of M arriage................................................................................................................................21
Education Level...................................................................................................................................21
Personal Incom e..................................................................................................................................22
Hom e Ow nership ................................................................................................................................
.........................................................................................................................................
Renting
Living
w ith Parents............................................................................................................24
Increase in
City Living .........................................................................................................................................
Chapter 4: Pooled M ultiple Linear Regression Analysis...................................................................
4.1.
4.2.
4.3.
4.4.
4.5.
4.6.
4.7.
4.8.
20
23
24
25
27
M ethodology.......................................................................................................................................27
Regression Equation - Impact of changes in demographics on housing of Total Population........27
Regression Equation - Impact of changes in demographics on housing of young adults..............28
28
H om e Ow nership ................................................................................................................................
29
.........................................................................................................................................
Renting
Living w ith Parents..............................................................................................................................30
31
Living in Center City Of M SAs ..............................................................................................................
Regression Equation Fit.......................................................................................................................32
Chapter 5: Individual Multiple Linear Regression Analysis of Decennial Data .................................
5.1. M ethodology...............................................................................................
.....................
..............
33
33
5.2. Regression Equation - Separating impact of changes in demographics vs changes in effects .......... 33
5.3. Equation Expansion.............................................................................................................................34
34
5.4. Sources of Change - Total Population ............................................................................................
35
5.5. Sources of Change - Young Adults.................................................................................................
.-...35
............... . ---..............
5.6. Hom e Ow nership .................................................................................
4
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
5.7. Renting
.........................................................................................................................................
36
5.8. Living with Parents..............................................................................................................................38
5.9. Living in Center City of M SAs ..............................................................................................................
39
Chapter 6: Results Sum m ary.............................................................................................................41
6.1. Change in Hom e Ownership ...............................................................................................................
41
6.2. Change in Rental Tenure .....................................................................................................................
41
42
6.3. Change in Living at
6.4. Central City Concentration..................................................................................................................42
43
6.5. The Im pact of Changing M arriage Rate .........................................................................................
44
6.6. The Im pact of Increased Educational Attainment ..........................................................................
Chapter 7: Conclusions and Further Study.....................................................................................
47
Bibliography ....................................................................................................................................
48
49
Appendix .......................................................................................................................................
Exhibit A. Single Variable Regression Output (STATA Form at)............................................................. 49
Exhibit B. Multiple Linear Regression Output -All Data Sets Pooled (STATA Format).........................61
Exhibit C. Multiple Linear Regression Output - Individual Decennial Data (STATA Format)................65
5
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Table of Figures
Figure 1-1 - M edian Age at First M arriage ................................................................................
9
Figure 1-2 - Educational Attainment Ages 25 and Over ..........................................................
10
Figure 1-3 - 'Total Tenure' Ages 22-31 .....................................................................................
12
Figure 2-1 - IPUM S 'M ETRO' Variable Tabulations....................................................................
17
Figure 2-2 - Re-coded 'METRO' Variable Tabulations-Select MSAs
19
Figure 3-1 - Percentage of Population Never Married ............................................................
.-
-.---
21
Figure 3-2 - Percentage of Population That Has Completed Four Years of College..................22
Figure 3-3 - CPI Adjusted Mean Personal Income ...................................................................
23
Figure 3-4 - Home Ownership Rate of Total Population Age>22 vs Age22-31 Cohort..............23
Figure 3-5 - Rental Tenure Rate of Total Population Age>22 vs Age22-31 Cohort...................24
Figure 3-6 - Rate of Living with Parents Total Population Age>22 vs Age22-31 Cohort ........... 25
Figure 3-7 - Rate of Living in Center City of MSA Total Population Age>22 vs Age22-31 Cohort.25
6
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Chapter 1:
Introduction
In real estate economics, trends in the young adult segment of the population are particularly
important to study. As individuals transition from dependent members of households to the formation
of their own households, their collective decisions set the landscape for the long-term trends in the
housing market. When holding the natural vacancy and scrappage rate of the residential housing stock
constant, the need for new residential construction is largely fueled by household formation. Therefore,
the trends and preferences in the living arrangements of young adults have a significant impact on
demand in the housing market.
Recently many articles and studies have focused their attention on the current generation of
young adults, commonly referred to as the "Millennial" generation. Specifically, much emphasis has
been placed upon how remarkably different they are from previous generations. There are many ways
in which the characteristics of this generation differ from their predecessors. They are immersed in
technology in ways that past generations could never have imagined. Perhaps as a result of this new
world they live in, their social behaviors and attitudes are also very different from those of past
generations. This paper will quantify some of these differences through time and determine to what
extent changes in the demographic characteristics of young adults influence their living arrangements.
1.1. Millennials and Generational Definitions - Selecting an Age Cohort
One particular challenge in studying and comparing generations is the varied definitions of
generations. While generational definitions may solidify over time such as has happened with the "Baby
Boomer" generation, this has not yet occurred for the Millennial generation. Definitions of this cohort
by birth year may range from 17 years (1981-1997) (Fry, This year, Millennials will overtake Baby
Boomers, 2015) to 23 years (1980-2004) (Hoover, 2009). This paper will focus less on the generational
definition and instead will adopt a definition of "young adults" that is normalized through time to
compare the Millennial generation to the past.
7
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
To establish a cohort to analyze, it is important to consider the goals of the study. This paper will
use data from the United States Decennial Census and the American Community Survey (collectively
referred to as "Census Data") to compare a specific age bracket. The lower bound of the age bracket is
set at 22 to ensure that the cohort will include those that have typically moved on from living with their
parents as well as those that have typically completed a bachelor's degree if they have pursued one. The
upper bound is set at 31 so that for each decennial census there is no overlap as the cohort includes a
decade-long span of birth years. The data to be compared will include the 1980-2000 U.S. Census and
the 2010 American Community Survey. The birth years will for each cohort sample will include 19491958 (Older Baby Boomers), 1959-1968 (Younger Baby Boomers/older Generation X), 1969-1978
(Generation X), and 1979-1988 (Younger Millennials).
1.2. Millennials and Marriage
In the primary housing market, demand increases by the net formation of new households. The
formation of new households is driven by young adults leaving their parent's home and establishing
their own. While, the term household may elicit the image of a married couple starting a family, there
are many forms of households and increasingly fewer of them include married couples. Although one
may think the reduction in marriage of young adults would have a great effect on net housing demand,
its effect is relatively minimal as long as the individual leaves their parent's home. In the absence of
marriage young adults typically form a different type of household by "doubling up" with a roommate.
There is, however, an expected effect of changes to the marriage rate on housing tenure. The
majority of households that are headed by a married couple have children, and vice-versa. While there
may not be a presumed correlation between the rate of marriage in young adults and the overall
demand for housing, it is understood that there is a correlation between having children (and thus
marriage) and housing tenure (DiPasquale & Wheaton, 1996). It can be expected that a delay in
marriage would bring about a delay in child bearing and a resultant delay in homeownership.
8
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
There has been a significant increase over time in the median age at which Americans first
marry. As shown in Figure 1-1, this increase began in the mid-1970s and has increased by 3.5 years for
men and 4.5 years for women during the 1980-2010 study period (U.S. Census Bureau, 2011). While
there is no single reason for this trend, polls suggest a few key factors in not marrying. In recent years
amongst those never married, the top three reasons for not marrying were selectivity, age, and financial
considerations. (Gallup, 2013) Although there has been a resultant increase in the median age of first
marriage, most young adults still desire marriage. In 2013, amongst Americans ages 18 to 34, only 9%
indicate that they do not ever want to be married. (Gallup, 2013) If this is in fact true, it is expected that
any decline in the homeownership rate due to the increase in delay of marriage may create an increase
in the demand for homeownership in the future.
Median Age at First Marriage 1950-2010
Source: U.S. Census Bureau
30.0
29.0
28.0
27.0
26.0
25.0
24.0
23.0
22.0
21.0
20.0
1950
1955
1960
1965
1970
1975
-Men
1980
1985
1990
1995
2000
2005
2010
-Women
Figure 1-1 - Median Age at First Marriage
1.3. Millennials and Education
Two major trends in education impact the current generation of young adults in a way that their
predecessors did not face. In 1980, 17% of adults 25 and older had completed four or more years of
college. In 2010, this rate increased to 29.9%. (U.S. Census Bureau, 2015) Additionally, the cost of
education has increased significantly. Adjusted for 2014 dollars, the average annual cost of tuition and
9
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
fees for a four year nonprofit private college has almost tripled from 1980 to 2010. (College Board,
2015) The combination of these two trends results in an even greater level of income that must be
devoted to paying for education. Increasingly this cost is paid with some sort of loan as the percent of
bachelor's degree recipients graduating with education-related debt has increased from less than 50% in
1993 to almost 70% in 2010. (Kantrowitz, 2014) Adjusted for 2012 dollars, this has amounted to an
increase in the total amount borrowed for higher education from $24 billion in 1990-91 to $110 billion in
2012-2013. (Baum, 2013) While this additional educational attainment will result in an increase in
lifetime earning power for degree recipients, the portion of the income of young adults that must be
devoted to paying down college loans has also increased. This again suggests that young adults may
initially be hampered by college expenses, but in the future when their debt has retired they will be
financially ready for homeownership if they desire.
Educational Attainment - Pecent of Population Ages
25 and Over with Bachelor's Degrees
Source: U.S. Census Bureau
35.0%
30.0%
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
Figure 1-2 - Educational Attainment Ages 25 and Over
1.4. Millennials and Housing Tenure
It has been noted that the current generation of young adults are less likely to own a home than
their predecessors. While there are many reasons for a shift away from home ownership amongst young
adults, polls have shown that most young adults still expect to eventually own a home. (Lachman, 2015,
10
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
p. 23) The reduced propensity of young adults to own a home as well as their expectation of eventually
owning a home are similar to the trends and preferences of this cohort towards marriage. When
considering future housing, the attribute young adults place the most emphasis on is cost. (Lachman,
2015, p. 26) While young adults exhibit a desire to own a home, they may simply lack the financial ability
to do so.
The majority of young adults rent their home. Amongst young adults who rent, the most
appealing characteristics of renting are lack of maintenance responsibilities and flexibility/commitment
considerations. This would indicate that an increase in the rate of young adults who rent may represent
and increased desire for flexibility and a reduced desire to spend time maintaining their home.
1.5. Millennials and the Suburbs: Returning "Home"
Perhaps one of the more widely discussed characteristics of the Millennial generation is the
increased likelihood that they will either continue to live in their parent's home as young adults or they
will return to and remain at their parent's home after attending college. Approximately 21% of
Millennials as defined by ULI live with their parents, mostly for financial reasons. (Lachman, 2015) The
steep decline in the marriage rate of Millennials has also been credited as a contributor to the trend of
living at home. (Fry, A Rising Share of Young Adults Live in Their Parents' Home, 2013)
When considering housing tenure, statistics are typically broken down into ownership versus
renting. For the majority of the population, these are the only two choices considered. However, with
the increased rate of young adults living with their parents it truly represents a third choice for this age
group. For the purposes of this paper, these three living arrangement choices shown in Figure 1-3 are
called "Total Tenure" because only studying the tenure choices of renting versus owning would leave
out a significant portion of this group. Given that the portion of the population living at home is largely
doing so for financial reasons, one may expect that this would have the greatest impact on the
propensity to rent as this segment of the population traditionally would not have the means to purchase
11
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
a home. However, the percentage of young adults renting has held steady and the increase of young
adults living at home with their parents has coincided with a decrease in home ownership amongst the
cohort. What is unclear is if this is a result of "trickling down" of tenure (those who in the past would
own now rent and those who in the past would rent live at home) or if many folks are living at home as a
means of saving up for a home purchase.
'Total Tenure' Ages 22-31
Source: U.S. Census Data Tabulations, IPUMS-USA, University of Minnesota,
www.ipums.org
120%
-
100%
80%
60%
40%
20%
0%
2000
1990
1980
0 Rent
N Own
A Live w/Parents
2010
Undefined
Figure 1-3 - 'Total Tenure'Ages 22-31
1.6. Millennials and Urban Dwelling
Millennials are known for shunning the suburban life of past generations for dense urban
neighborhoods. While a recent study indicates that young adults have no greater propensity to live in
the downtown "core" of a center city, it does show that there is a greater desire to live within the outer
areas of a center city. (Lachman, 2015, p. 4) Conversely, a different survey however shows that 66% of
Millennials want to live in the suburbs. (Hudson, 2015) These conflicting results suggest that preference
polls may not be the best way to study the locational propensity of the cohort.
12
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
1.7. Thesis Intent and Hypothesis
While most studies and preference polls focus on the short term trends of the current market,
this thesis intends to compare the characteristics of the current population of young adults to past
generations of the same age. Rather than taking a snapshot of preferences, it will look at long-range
shifts in propensities. It will first look at the housing characteristics of young adults over time including
tenure and location using survey data from the U.S. Census. Then it will study two key demographic
features in education and marriage rate. It is expected that over time, changes in education level and
marriage rate will have an influence on housing tenure and location. Additionally, it is expected that
over time that simply being a young adult will have a similar influence.
Using these two key demographic qualities along with studying the cohort of young adults, this
thesis will identify to what extent changes in housing tenure and locational preference are derived from
these characteristics. It will then parse out the changes to determine to what extent they are a result of
preferences of the population or the demographic complexion of the population. It is expected that
changes in the preferences will have a more dynamic impact on the outcome than will changes in the
population mix.
13
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Chapter 2:
Census Data
2.1. Data Source
This thesis will study microdata samples from each of the decennial U.S. Censuses from 19802000 as well as the 2010 American Community Survey (ACS) to search for trends and correlations
amongst select characteristics of the entire U.S. population. (Ruggles, et al., 2010) This data is available
from the Integrated Public Use Microdata Series (IPUMS-USA) in the form of microdata, which are not a
collection of statistics, but rather are samples of individual records from Census Data. By using samples
of individuals rather than tabulations, one can use statistical software to analyze millions of individual
records focusing on selected characteristics of their choosing.
2.2. Data Overview
A challenge in using IPUMS data is the inconsistent availability of certain variables and changes
in the construction of the samples over time. For example, the acreage of a property is only available
from 1960-2000 so if one is conducting a study focused on property size, they are limited to focusing on
that specific timeframe. From 1850-2000, the samples are taken from the decennial U.S. Census. While
most of these samples represent 1% of the total population, from 1980-2000 a 5% sample was taken.
Unfortunately, a 5% sample has not yet been made available for the 2010 Census. From 2000-2013, the
available data is only from the ACS. The ACS data is not robust from 2000-2004 as it typically only
includes a 0.4% sample. However from 2005-2013, the sample size increased to 1%. Although it would
be preferable to consistently work with a 5% sample for all 4 periods from 1980-2010, the variables to
be studied apply to a large portion of the population so using a 1% sample will not produce a significant
distortion in results when compared with the 5% samples.
This paper will limit its focus to the three "total tenure" choices described previously, the
propensity to live in center cities, the delay of marriage, and the education level of young adults as it
14
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
compares to the remainder of the adult population as well as past generations. Most of these choices
are derived from the IPUMS data detailed in Table 2-1.
Table 2-1 Selected Census Variables from IPUMS
IPUMS Codes
Variable
Code Code Label
OWNERSHP Property Tenure Status
0
N/A
1
Owned or Being Bought
2
Rented
Relationship to Household Head
RELATE
METRO
IPUMS Codes
Code Label
ARIABLE Code
AETAREA1 MSA of Residence
3-digit OMB code specific to
000MSA of residence if applicable
936
City of Residence
ITY
0000- 4-digit code specific to city of
residence if applicable
7650
VOARST
Marital Status
1
Head/Householder
1
Married, Spouse Present
2
3
4
Spouse
Child
Child-in-law
2
Married, Spouse Absent
3
Separated
4
Divorced
5
Parent
5
Widowed
6
Parent-in-Law
6
Never Married
7
8
Sibling
Sibling-in-Law
9
Grandchild
10
Other Relatives
11
Partner, Friend, Visitor
12
13
Other Non-Relatives
Institutional Inmates
MSA Status
0
Not Identifiable
1
Not in Metro Area
2
In Metro Area, Central city
3
In Metro Area, Outside
4
City status unknown
EDUC
Educational Attainment
N/A or No Schooling
0
Nursery School to Grade 4
1
Grade 5, 6, 7 or 8
2
Grade 9
3
Grade 10
4
5
Grade 11
6
7
8
9
10
11
Grade 12
1 Year of College
2 Years of College
3 Years of College
4 Years of College
5+ Years of College
From the variables identified in Table 2-1, five dummy variables were created as detailed in
Table 2-2. These dummy variables are binary indicators in that they either have the value of 1 indicating
that an individual includes the defined characteristic or 0 meaning they do not include that
characteristic. Additionally, the dummy variable "MILLENNIAL" was created using individuals of the ages
15
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
of 22-31 for simplification of analysis. The dummy variable "CITYDUM" applies only to the subsample
described in section 2.3.
Table 2-2 Dummy Variables
Variable
Qualifications
ATHOME
RELATE =3, 4, or 9
OWNERSHP = 1 and ATHOME =0
OWNERSHP =2 and ATHOME =0
OWN
RENTER
NEVMARRY
BACHELORS
MILLENNIAL
CITYDUM
MARST= 6
EDUC >= 10
AGE >=22 and AGE <=31
Located in Center City of MSA
Studying the propensity of young adults to live in urban environments is particularly challenging
or not an individual
when using IPUMS data. To do so, one must find a variable that represents whether
the best
lives in an urban area that is consistent for all of the data samples to be studied. Perhaps
in which an individual
metric for identifying a location as urban is the population density of the location
Public Use
lives. While density is not an available variable, for the year 2000, the area of an individual's
samples,
Microdata Area (PUMA) is available. The PUMA is the smallest geographical identifier of later
area, one
but the 2000 PUMA definitions are only consistent for the years 2000-2011. Using the PUMA
area but
could construct a density variable for the data by dividing the PUMA population by the PUMA
this variable would not be applicable to samples outside of the years from 2000-2011.
as it
The variable "METRO" within the IPUMS data would be particularly useful for this purpose
if so does that
identifies whether or not an individual lives in a Metropolitan Statistical Area (MSA) and
requirements
individual live in the center city of that MSA. Unfortunately due to both the confidentiality
(See Figure 2of the sample as well as the geographic identifiers of the data, this variable is incomplete.
MSA, it cannot be
1) For some individuals, this variable is either not identifiable or if they are within an
in the 1% Sample for
determined if they are in the Center City. Additionally, this variable is only available
1990.
16
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
IPUMS 'METRO' Variable Tabulations
Source: U.S. Census Data Tabulations, IPUMS-USA, University of Minnesota
100%
80%
60%
20%
20%
19901%
19805%
a Outside City o Metro Undefined
E Central City
Figure 2-1 -
20005%
Non-Metro
2010 ACS
0 Not Identifiable
IPUMS 'METRO' Variable Tabulations
2.3. Center City Definition and Selection of MSAs
For several cities, the "CITY" variable is not identifiable for many or all sample years. This is also
true for the "METAREA" variable for many small MSAs. This inconsistency and incompleteness is a result
of PUMA definitions. In many cases, a PUMA boundary straddles a municipal boundary so it cannot be
determined which side of the boundary the individuals within a PUMA reside. These individuals are
either tagged as "Not identifiable" or "Metro Area Center City Status Undefined" Where this occurs at a
city boundary, a 10% threshold for error of inclusion or exclusion is allowed meaning that the city
variable may slightly overestimate or underestimate the population of specific cities. If this error
exceeds the 10% threshold, the city in question is deemed unidentifiable for that sample. In the case of
MSA boundaries, any PUMA that straddles a boundary is excluded and determined to be not identifiable
resulting in an underestimation of the population of some MSAs. In some instances this underestimation
is very significant.
For select MSAs, the Center City is identifiable for all years from 1980-2010. Ideally a sample
identifying the Center City status would include the entire U.S. population, but this is not possible.
However, if a sufficient number of major MSAs are included, a meaningful study of the propensity to be
located in the Center City of an MSA can be conducted. Within this group of MSAs with identifiable
17
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Center Cities, a number have a relatively low portion of the MSA that is excluded as it is not identifiable.
In some cases of the 1980 or 1990 sample for certain cities, the 1% sample must be substituted for
either the city population, the outer MSA population or both. For this paper a set of 47 MSAs have been
established for study in which the Center City is identifiable and no more than 9% of the MSA is
excluded for all study years.
Table 2-3 - Selected MSAs for Study
Metropolitan area
MSA
New York-Northeastern
Los Angeles-Long Beach, CA
Chicago, IL
Houston-Brazoria, TX
Rank
1
2
3
5
Metropolitan area
Nashville, TN
Milwaukee, WI
Richmond-Petersburg,
Buffalo-Niagara Falls
Philadelphia, PA/NJ
6
Rochester, NY
Washington, DC/MD/VA
Miami-Hialeah, FL
Boston, MA-NH
7
Fresno, CA
8
Albany-Schenectady-Troy, NY
Bakersfield, CA
Cleveland, OH
10
11
12
13
14
15
16
19
20
21
23
24
25
27
31
Indianapolis, IN
33
Austin,TX
35
San Francisco-Oakland
Phoenix, AZ
Riverside-San Bernardino, CA
Detroit, MI
Seattle-Everett, WA
Minneapolis-St. Paul,
St. Louis, MO-IL
Baltimore, MD
Denver-Boulder, CO
Pittsburgh, PA
Portland, OR-WA
San Antonio, TX
Sacramento, CA
Baton Rouge, LA
Greensboro-Winston Salem-High Point, NC
Stockton, CA
Akron, OH
MSA
Rank
36
39
44
50
51
56
61
62
70
74
77
78
Syracuse, NY
82
Wichita, KS
84
Toledo, OH/MI
Jackson, MS
Chattanooga, TN/GA
Spokane, WA
Modesto, CA
91
93
99
Fort Wayne, IN
Mobile, AL
Beaumont-Port Arthur-Orange, TX
101
103
123
127
130
Peoria, IL
138
As shown in Table 2-3, this results in a robust selection of 47 MSAs for study including 8 of the
10 largest, 20 of the 25 largest and 41 of the 100 largest MSAs. For the study years, these MSAs
represent on average 41% of the total U.S. Population. With this constructed data set, the members of
the data set that live in a Center City versus the outer MSA can now be identified. These MSAs are
18
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
extracted into a separated data set, substituting 1% samples for specific cities during specific years only
when necessary. The variable METRO is recoded within this set so that the "Central City" indicator
applied to all members living only within the most major city of the MSA. All other individuals in the
MSA are coded as "Outside of Central City". (See Figure 2-2)
Re-coded 'METRO' Variable Tabulations-Select MSAs
100%
80%
60%
40%
20%
0%
1990
1980
n Central City
2000
2010
U Outside City
Figure 2-2 - Re-coded 'METRO' Variable Tabulations-Select MSAs
19
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Chapter 3:
Demographic and Housing Trends
3.1. Linear Regression Methodology - Simple single linear regression of data
To begin to understand the most basic trends in the demographics and housing arrangements of
the adult population of the samples, one must start by understanding the propensity of the 22-31 study
cohort to include the characteristics of concern. One way to understand the probability of this cohort to
include a characteristic is through a very simple linear regression of the following equation form:
y =oc +flx, where:
y = Probabilitymember of population is "y"
x = Member of population is ages 22 - 31
oc= Intercept (Probabilitypopulation > age 31 is "y")
fl = Coefficient if age is 22 - 31
With the exception of income, all of the following variables are binary values. For example, one
has either been married before or they have never been married. The value of y cannot be less than zero
or greater than one. Typically, this calls for the use of a logistic function and therefore a logistic
regression with odds ratios for an outcome. However, it has been shown that a linear regression might
not only be useful but also preferable in some cases where the dependent variable is a binary. (Hellevik,
2007) This preference for a linear regression will apply to this study as the independent variables used
will also be binary and the sample size is very large.
The data will be analyzed using the Stata 14 software. (StataCorp, 2015) For the full sample
data, the svyset command is been used to account for stratification and clustering using the STRATA and
CLUSTER variables. The simple regressions are performed to compare the 22-31 cohort to the total adult
population over age 22.
20
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
3.2. Delay of Marriage
The first regression compares the NEVMARRY variable as a dependent with the MILLENNIAL
variable as the sole predictor variable. This regression is performed for each year and the results are
shown in Figure 3-1. Note that "Total Population" refers to all individuals over age 22, inclusive of the
age 22-31 cohort.
Never Married
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
1980
1990
a Total Population
2000
2010
N Population Ages 22-31
Figure 3-1 - Percentage of Population Never Married
The results of this first analysis are quite remarkable and clearly reflect the trend in delaying
marriage in the population. Since 1980, this statistic for young adults has nearly inverted where once
only 32% of young adults had never been married. In 2010 62% of young adults had never been married
meaning only 38% of the cohort has ever been married. The approximate 30% change in young adults
having never been married took its two greatest steps in 1980-1990 (10%) and 2000-2010 (14%).
3.3. Education Level
The next regression compares the BACHELORS variable as a dependent with the MILLENNIAL
variable as the sole predictor variable. The results are shown in Figure 3-2.
21
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
4 Years of College Education
30.00%
20.00%
10.00%j
0.00%
1980
1990
0 Total Population
2000
2010
* Population Ages 22-31
Figure 3-2 - Percentage of Population That Has Completed Four Years of College
While in 1980, there is a relatively large gap in the education level of young adults compared
with the total population, by 1990, the statistic has levelled off to the point that both young adults and
the total adult population have essentially the same probability of having completed 4 years of college.
3.4. Personal Income
This regression compares the only continuous variable studied INCTOT, which is an individuals
total personal income. With INCTOT as a dependent with the MILLENNIAL variable as the sole predictor
variable, we can see how the income level of young adults has changed through time relative to the
total adult population when adjusted for the CPI of 2000 using the CP199 variable. The results are shown
in Figure 3-3.
The income gap between young adults and the total adult population is relatively small in 1980,
and it grew steadily until 2000. Presumably, the drop in income levels exhibited in 2010 are resultant
from the great recession of 2008-2009. This change has a greater impact on young adults as the total
population income levels are still greater than those of 1980 and 1990, while the income of young adults
is reduced significantly from historical levels.
22
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Mean Personal Income (CPI Adjusted to Year 2000)
$35,000
$30,000
$25,000
$20,000
3
7
$10,000
$5,000
1980
2000
1990
n Total Population
a Population
2010
Ages 22-31
Figure 3-3 - CPI Adjusted Mean Personal Income
3.5. Home Ownership
The following regression compares the OWN variable as a dependent with the MILLENNIAL
variable as the sole predictor variable. The results are shown in Figure 3-4.
Home Ownership
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
1980
1990
mTotal Population
2000
2010
a Population Ages 22-31
Figure 3-4 - Home Ownership Rate of Total Population Age>22 vs Age22-31 Cohort
While home ownership levels for the total population have held relatively steady for the adult
population, the rate has fallen by almost 14% for young adults from 1980-2010. Similar to the marriage
statistic, the greatest changes are seen in 1980-1990 (7%) and 2000-2010 (6.6%)
23
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
3.6. Renting
To study the trends in the propensity to rent for young adults, a regression of the RENTER
variable as a dependent with the MILLENNIAL variable as the sole predictor variable is performed. The
results are shown in Figure 3-5.
Rental Tenure
50.00%
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
1980
1990
MTotal Population
2000
2010
* Population Ages 22-31
Figure 3-5 - Rental Tenure Rate of Total Population Age>22 vs Age22-31 Cohort
Interestingly, in spite of all of the changes within the population and specifically the young adult
cohort, the propensity to rent for both young adults and the total population has held steady.
3.7. Increase in Living with Parents
Next, a regression of the ATHOME variable as a dependent with the MILLENNIAL variable as the
sole predictor variable is performed. The results are shown in Figure 3-6.
Similar to both the delay in marriage and the decrease in home ownership amongst young
adults, there has been a large increase of 11% in the number of young adults living with their parents
with the greatest increases occurring from 1980-1990 (4.4%) and 2000-2010 (7.8%). There is almost a
1% drop in this rate from 1990-2000.
24
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Living at Home
30.00%
25.00%
20.00%
15.00%
15.00%
10.00%
10.00%
1980
1990
0 Total Population
2000
2010
w Population Ages 22-31
Figure 3-6 - Rate of Living with Parents Total Population Age>22 vs Age22-31 Cohort
3.8. City Living
Finally, a regression of the CITYDUM variable as a dependent with the MILLENNIAL variable as
the sole predictor variable is performed. This is performed using a standard linear regression as the
complete survey set of data is not included. The results are shown in Figure 3-7.
Center City of MSA (Select Areas Only)
40.00%
35.00%
30.00%
25.00%
0
20.00%
15.00%
10.00%
5.00%
0.00%
1980
1990
a Total Population
2000
2010
N Population Ages 22-31
Figure 3-7 - Rate of Living in Center City of MSA Total Population Age>22 vs Age22-31 Cohort
Overall, the concentration of the total population in the center city relative to the total MSA has
declined over time. This is to be expected as MSA definitions typically expand over time to include outer
25
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
areas while the city boundaries remain fixed. In spite of MSA expansion, the concentration of young
adults in the center city has held fairly steady.
26
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Pooled Multiple Linear Regression Analysis
Chapter 4:
With the basic relationship of the defining characteristics to the young adult cohort established,
the relationship of the demographic variables to the housing outcomes will now be explored. First we
will pool the data for all four census samples to see what impact the changes to the population mix by
marriage rate, educational attainment level and being a young adult have on housing outcomes.
4.1. Methodology
A multiple linear regression model will be created again using only the binary independent
variables MILLENNIAL, NEVMARRY and BACHELORS to predict the dependent variables: OWN, RENTER,
ATHOME, and CITYDUM. The results will determine the average preference towards a specific housing
outcome resultant from the combination of these different independent characteristics.
4.2. Regression Equation - Impact of changes in demographics on housing of Total Population
The multiple linear regression equation for the total adult population will take the form:
y
1x 1 + l2x 2 + f3x 3
fO +
=
y = Probabilitymember of population is "y"
x=
Member of populationis ages 22 - 31
X2=
Member of populationhas never been married
Member of populationhas attended 4 or more years of college
f 0 = Intercept (Probabilityfor pop > age 31, married,and not coll. educated "Pure Cohort")
X3=
f 1 = Preference Coefficient if age is 22 - 31
#
2 =
=
fl
PreferenceCoefficient if person has never been married
PreferenceCoefficient if person has attended 4 or more years of college
#3xa
fx = BX
h, + ?1x1 +
Preference = [fl 0 f1 N 2 f3] = [Other Chort Never Married 4 Years of College]
Total Population
1-
Vector form: y =
B =
T
=Cohort
Population
y = BXT
Ay
= BXT
-BXT-1
Ay = B[Xr
- Xr-
1
]
27
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
4.3. Regression Equation - Impact of changes in demographics on housing of young adults
The multiple linear regression equation for the young adult subpopulation will take the form:
y
+ /1x
=fo
1
+ fl2 x 2
y = Probabilitymember of population is "y"
Member of population has never been married
x=
x2= Member of population has attended 4 or more years of college
/l
#
Intercept (Probabilityfor pop > age 31, married,and not coll. educated)
Coefficient if person has never been married
=
=
1
Coefficient if person has attended 4 or more years of college
Vector form: v = B + B., x, + 8,x, = BX
fl 2 =
B
=
Preference =
[fl 0 fl1
I
X
=
Mix
=
1=
xT
=X
2] = [Other
Never Married
4 Years of College]
Cohort Population
PopulationNever Married
Populationwith 4 Years of College]l
y = BXT
Ay = BXT - BXT-1
Ay = B[XT
- XT-1]
4.4. Home Ownership
To identify how the changes in the demographic mix impact the home ownership rate over time,
we start with the Ay = B[XT - XT-1] vector equation from above. From this, we can decompose the
changes in home ownership rate from decade to decade based on the changes to the combination of
these characteristics while using the average preference B over this time period. Tables 4-la & 4-1b
detail these results.
Table 4-1a
Components
AOwn from Change in Mix (Adults 22 and Over)
Population Change
Rate Change
2000-10
1990-00
1980-90
2000-10
1990-00
1980-90
Other Effects
0.00%
0.00%
0.00%
17,719,182
17,490,307
17,174,772
Young Adult
Never Married
Bachelor's Degree
0.73%
-1.23%
0.41%
1.13%
-0.44%
0.18%
-1.79%
(467,851)
(3,261,668)
679,818
(2,313,311)
(833,853)
(5,426,632)
0.40%
0.39%
1,103,845
1,275,268
1,435,187
Predicted Change
-0.08%
1.10%
-1.22%
15,093,508
17,132,081
12,349,474
Actual Change
-2.05%
1.40%
-2.83%
12,212,469
17,693,597
8,923,115
Prediction Error
1.97%
-0.30%
1.60%
2,881,039
(561,516)
3,426,359
28
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Table 4-1b
Components
AOwn from Change in Mix (Adults 22-31)
Population Change
Rate Change
2000-10
1990-00
1980-90
2000-10
1990-00
1980-90
0.00%
0.00%
0.00%
-3.58%
-1.95%
-4.81%
Bachelor's Degree
0.05%
0.31%
0.24%
Predicted Change
-3.52%
-1.63%
-4.56%
Other Effects
Never Married
Actual Change
Prediction Error
-6.91%
-0.78%
-6.61%
3.39%
-0.86%
2.05%
842,347
(1,223,985)
1,501,320
(1,669,718)
(365,180)
(2,537,917)
49,250
77,421
163,224
(778,121)
(1,511,745)
(873,373)
(2,123,643)
(1,163,088)
(1,722,549)
1,345,522
(348,657)
849,176
Using the average preference coefficients while ignoring changes in these preference over time
causes an incorrect estimation of the actual rate changes, in some cases this error is severe. This will be
the case for most of the following analyses. For both young adults and the total population, having
never married produces a strong negative impact on the home ownership rate for all periods. From
1980-2000, being a young adult increases the likelihood of home ownership, but this diminishes in 20002010. Having completed 4 years of college has a steady impact of a 0.4% rate increase from decade to
decade for the total population. The impact of a college degree on the home ownership rate is positive
for young adults, but it only begins to trend upward in a significant manner in 1990.
4.5. Renting
Continuing with the same format Tables 4-2a & 4-2b show the effects of the independent
variables on Rental Tenure. For the total population, the preferences from the regression analysis
produce a relatively good fit, reflecting the relatively minimal and steady change in the rental rate. Again
college education has a steady impact on rental rate. This is likely more reflective of the steady increase
in the education rate of the population than any other reason. Being a young adult has a negative
impact on the rental rate while never marrying had a positive impact. These effects cancel out from
1980-1990, while from 1990-2000 the impact of being a young adult is greater. This then shifts
considerably towards the rate of never marrying, reflective of the change in in this rate.
29
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Table 4-2a
Components
Other Effects
Young Adult
Never Married
Bachelor's Degree
ARenter from Change in Mix (Adults 22 and Over)
Population Change
Rate Change
1990-00
1980-90
2000-10
1990-00
1980-90
5,429,755
5,500,808
0.00%
0.00%
0.00%
(447,908)
308,250
-0.12%
-0.75%
-0.48%
749,923
1,057,359
0.58%
0.14%
0.40%
(673,223)
(582,727)
-0.21%
-0.21%
-0.22%
2000-10
5,331,800
549,396
1,759,191
(757,645)
Predicted Change
-0.30%
-0.82%
0.26%
6,283,690
5,058,548
6,882,741
Actual Change
0.51%
-1.00%
1.03%
7,468,215
4,719,034
8,516,868
Prediction Error
-0.81%
0.18%
-0.77%
(1,184,525)
339,514
(1,634,127)
For the young adult regression, the prediction is far off from the actual changes suggesting that
factors not considered in this model are likely responsible for the changes.
Table 4-2b
Components
Other Effects
Never Married
Bachelor's Degree
ARenter from Change in Mix (Adults 22-31)
Population Change
Rate Change
2000-10
1990-00
1980-90
2000-10
1990-00
1980-90
(1,105,552) 1,356,052
0.00% 760,841
0.00%
0.00%
169,777
24,429
111,698
0.32%
0.13%
0.24%
60,283
28,593
18,189
0.09%
0.11%
0.02%
Predicted Change
0.26%
0.24%
0.41%
890,728
(1,052,529)
1,586,111
Actual Change
1.91%
1.42%
-0.90%
1,541,994
(604,681)
1,080,542
Prediction Error
-1.65%
-1.17%
1.31%
(651,266)
(447,848)
505,569
4.6. Living with Parents
Tables 4-3a & 4-3b show the effects of the independent variables on Living at Home. As one
would expect in both cases there is a strong positive correlation between never marrying and living at
home. Having a Bachelor's degree and being a young adult has a slight negative impact on the rate
change. Among young adults, the negative impact of a Bachelors degree is only present to a significant
degree starting in 1990 and the trend mirrors the impact of education on homeownership.
30
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Table 4-3a
Components
AAthome from Change in Mix (Adults 22 and Over)
Population Change
Rate Change
1990-00
2000-10
1980-90
1990-00
2000-10
1980-90
Other Effects
Young Adult
Never Married
Bachelor's Degree
0.00%
-0.27%
0.71%
-0.12%
0.00%
-0.41%
0.25%
-0.12%
0.00%
-0.06%
1.03%
-0.11%
406,392
170,803
1,879,650
(320,885)
401,143
(248,188)
1,333,127
(370,717)
393,906
304,424
3,127,287
(417,206)
Predicted Change
0.32%
-0.28%
0.85%
2,135,961
1,115,364
3,408,411
Actual Change
1.27%
-0.63%
2.18%
3,512,024
429,204
6,186,658
-0.95%
0.35%
-1.32%
(1,376,063)
686,160
(2,778,247)
Prediction Error
Table 4-3b
AAthome from Change in Mix (Adults 22-31)
Population Change
Rate Change
2000-10
1990-00
1980-90
2000-10
1990-00
1980-90
270,762
(220,745)
151,917
0.00%
0.00%
0.00%
2,161,525
311,021
1,422,087
4.09%
1.66%
3.05%
(169,836)
(80,557)
-0.25% (51,245)
-0.32%
-0.05%
Components
Other Effects
Never Married
Bachelor's Degree
Predicted Change
2.99%
1.33%
3.84%
1,522,758
9,720
2,262,452
Actual Change
4.35%
-0.91%
7.75%
2,064,918
(865,113)
3,836,370
-1.36%
2.24%
-3.91%
(542,160)
874,833
(1,573,918)
Prediction Error
4.7. Living in Center City Of MSAs
Tables 4-4a & 4-4b indicate the effects of marriage and education on the propensity to live in
the center city of an MSA.
Table 4-4a
Components
Other Effects
Young Adult
Never Married
Bachelor's Degree
ACitydum from Change in Mix (Adults 22 and Over)
Population Change
Rate Change
2000-10
1990-00
1980-90
2000-10
1990-00
1980-90
2,617,514
2,954,400
3,075,638
0.00%
0.00%
0.00%
8,840
(7,772)
10,057
0.00%
-0.03%
-0.01%
879,555
382,050
643,675
0.69%
0.12%
0.59%
(265,286)
(239,074)
(227,179)
-0.18%
-0.18%
-0.21%
Predicted Change
0.36%
-0.08%
0.51%
3,502,191
3,089,604
3,240,624
Actual Change
-2.96%
-2.25%
-1.66%
1,650,484
1,479,328
1,228,842
Prediction Error
3.32%
2.16%
2.17%
1,851,707
1,610,276
2,011,782
31
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Both for the total adult population and the subset of young adults, the model is a relatively poor
fit. It does however show that on average having never married increases the likelihood of living in a
center city.
Table 4-4b
Components
Other Effects
Never Married
Bachelor's Degree
Predicted Change
Actual Change
Prediction Error
ACitydum from Change in Mix (Adults 22-31)
Population Change
Rate Change
2000-10
1990-00
1980-90
2000-10
1990-00
1980-90
395,809
(347,973)
450,283
0.00%
0.00%
0.00%
289,177
20,905
1.25% 233,164
0.44%
1.01%
9,261
4,034
5,771
0.03%
0.04%
0.02%
1.03%
0.48%
1.28%
689,219
(323,035)
694,247
-2.58%
1.26%
-0.67%
88,303
(183,703)
344,412
1.55%
-1.74%
-0.62%
(777,522)
506,738
(1,038,659)
4.8. Regression Equation Fit
The fit of the regression equations are fairly poor when compared to the actual rate of change
from year to year. In most cases, it reflects the cyclical nature of the actual results, but the amplitude of
the change is muted. This is likely because although the rate of change in marriage delay is somewhat
cyclical from 1980-2010, the equation is only accounting for that rate of change and not the rate of
change in preferences relative to marriage delay the other independent variables. Additionally, when
applied to the rate change, this model only accounts for the independent variables studied and neglects
to include a term for other unknown effects. This is because although there is a "Pure Cohort" term in
the equation, it is cancelled out as
flo is constant and x0 = 1. To resolve this and develop a better fit, we
will run the regressions independently for each decennial sample set accounting for the changes in the B
vector allowing both for better accounting for changes in effects over time and the inclusion of rate of
change in other effects not studied.
32
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Individual Multiple Linear Regression Analysis of Decennial Data
Chapter 5:
As demonstrated in the previous chapter, a regression of the data for all of the decennial data
samples provides some insight into the components of the rate of change in living arrangements.
Holding a constant coefficient for effects (B), and applying it to the change in composition of the
population( X, - XT_-), yields a somewhat inaccurate prediction of the various rate changes. To obtain
a more accurate prediction both the change in composition of the population (XT - XT-1) and the
change in the effects of various characteristics (BT - BT-1), must be assessed.
5.1. Methodology
The same multiple linear regression model from Chapter 4 will be created again, only this time it
will be run individually on each sample to obtain separate BT vectors. The results demonstrate the
effect that the change in the independent characteristics combined with the change in the effects of
those characteristics over time have on housing outcomes.
5.2. Regression Equation - Separating impact of changes in demographics vs changes in effects
The same equations used in Chapter 4 will remain only this time the B vector will have the
subscript T, denoting the change in B for each year.
=
[Wb
I1 f 2
f3 ]r = [Pure Cohort
1 xT
=Cohort
Total Population
Population
Population Never Married
X2
XX
Chort Never Married 4 Years of College]T
-
Br
T
.Populationwith 4 Years of College-
y = BrXT
Now the equation for the change in y, will include the change in B.
Ay = BTXT
-
Br-Xr-1
33
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
5.3. Equation Expansion
Starting with the equation above, it can be expanded to include separate terms for the change
in X and the change B.
Ay
Ay= BRX- Ay
= BT[XT
= BTXT -
. + BrX_ I- BTIXyl
R__
-
BT-1XT-1
X-
1]
+ [B
- BT-1XT-1
5.4. Sources of Change - Total Population
The following term represents the portion of the change in rate of y that is a result of a change
in X from year to year (change in population mix):
BT[XT - XT-1]
For the total population, this includes the change in the percent of the total population that is a young
adult, never married and/or has completed four or more years of college. In vector form, it is expanded
as:
r'1-
XT-Xr=x,
1
Total Population
_
Cohort Population
xl
X2
X2
-X3- T
-X3- T-1
PopulationNever Married
[Populationwith 4 Yrs College. T
Total Population
1
Cohort Population
PopulationNever Married
Populationwith 4 Yrs College.
T-1
The following term represents the portion of the change in rate of y that is a result of a change
in X from year to year (change in population preferences):
[Br - Br-1]XT1
For the total population, this includes the change in the effect of being a young adult, never married
and/or completing four or more years of college. In vector form, it is expanded as:
I1
Br - BT-1 = [WO
fl2
P3]T -
[WO
I1
P2
fl3]r-1
-
[Pure Cohort Chort Never Married 4 Years of College]
-
[Pure Cohort Chort Never Married 4 Years of College]-1
34
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
5.5. Sources of Change - Young Adults
For the sources of change in young adults, the equations and terms are the same, less the f and
x values associated with being a young adult.
1
XT-XT1 =
-
_x,:)
BT
Cohort Population
Cohort Population
1
PopulationNever Married
PopulationNever Married
=
x1
X-3 xz Population with 4 Yrs Collegel Population with 4 Yrs College T-1
- BT- 1 =
[A3
f-
fl2IT -
[#o
u1
P2]T-1
=
[Chort Never Married 4 Years of College]T
-
[Chort Never Married 4 Years of College]T-1
5.6. Home Ownership
When comparing the decade-to-decade changes in the homeownership rate, a much better
picture of what is driving the changes can be seen using separate regression analyses for each sample
set. In Table 5-1a and all subsequent tables, the portion of the change resultant from the known
differences in the population composition is in the "Mix" column while the portion of the change
resultant from the difference in the characteristic effects (preference factor) is in the "Pref." column.
The intercept value for other effects is omitted from the prediction. From this we can see the total
predicted changes resultant from the studied effects. When considering the intercept value, there is
little actual error, so the bottom line number in the following tables should be considered the total
change due to other unknown effects.
Table 5-1a
AOwn from Change in Mix and Preferences (Adults 22 and Over)
2000-10
1990-00
1980-90
Components
Mix
Pref.
Mix
Pref.
Mix
Pref.
0.19%
-0.25%
1.17%
-0.11%
0.74%
-0.73%
Young Adult
-1.62%
0.28%
-0.42%
0.55%
-1.27%
0.51%
Never Married
0.52%
0.77%
0.41%
0.37%
0.34%
0.21%
Bachelor's Degree
Predicted Rate Change
-0.01%
-0.19%
0.81%
1.16%
0.79%
-0.91%
Total Prediction
Actual Rate Change
-0.20%
-2.05%
1.97%
1.40%
-0.12%
-2.83%
Error/Other Effects
-1.85%
-0.58%
-2.71%
35
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
The portion of the differences resultant from change in the population mix are substantially
similar to the rate change components from the single regression from all samples shown in Table 4-la.
What is clear is that the difference between the actual rate change and the predicted rate change in
Table 4-1a is a result of the change in preference effects. The ownership preference of those with
college education has steadily grown. "Other Effects" make up the greatest portion of the change in the
declining periods of 1980-1990 and 2000-2010.
Table 5-1b
AOwn from Change in Mix and Preferences (Adults 22-31)
2000-10
1990-00
1980-90
Components
Mix
Pref.
Mix
Pref.
Mix
Pref.
-3.85%
1.84%
-1.77%
1.60%
-3.65%
2.16%
Never Married
0.35%
0.88%
0.30%
0.16%
0.04%
0.03%
Bachelor's Degree
Predicted Rate Change
2.19%
-3.60%
1.76%
-1.47%
2.72%
-3.50%
Total Prediction
-1.41%
0.29%
-0.78%
Actual Rate Change
-6.91%
-0.78%
-6.61%
Error/Other Effects
-5.51%
-1.07%
-5.83%
For young adults, the results are similar. The relative preference of those being never married
has a surprisingly positive effect on homeownership as shown in Table 5-1b, but the dramatic shift away
from marriage results in a net reduction in the homeownership rate. While the change in population
that has never married has a large effect on the change in the homeownership rate, there are other
effects that have an even greater impact.
5.7. Renting
As shown in Table 5-2a, the effects of being never married, having a bachelor's degree and
being a young adult has a combined 1.6% negative effect on the change in renter rate from 2000-2010.
This is cancelled out however by the 2.5% positive impact of other effects. Recall Table 4-2b
demonstrates that changes in the characteristics of the population of young adults does not explain the
36
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
change in rental rate of young adults. In reality, there are increases in the rate both from 1980-1990 and
from 2000-2010 and a decrease from 1990-2000, but the model suggested little to no change in the rate
for all 30 years.
Table 5-2a
ARenter from Change in Mix and Preferences (Adults 22 and Over)
2000-10
1990-00
1980-90
Components
Mix
Pref.
Mix
Pref.
Mix
Pref.
-0.10%
-0.60%
-0.78%
-0.16%
-0.52%
0.12%
Young Adult
0.60%
-0.44%
0.18%
0.97%
0.30%
-0.12%
Never Married
-0.31%
-0.64%
-0.21%
-0.36%
-0.14%
-0.10%
Bachelor's Degree
0.19%
-1.69%
-0.81%
0.46%
-0.37%
-0.09%
Predicted Rate Change
Total Prediction
-0.46%
-0.35%
-1.49%
Actual Rate Change
0.51%
-1.00%
1.03%
Error/Other Effects
0.96%
-0.64%
2.52%
When the change in the preferences are accounted for, the model is a much better fit as shown
in Table 5-2b. Interestingly, changes in the composition of the population generally play an insignificant
role in the rate change. The change in preference creates essentially all change in the rental rate of
young adults. Much of this is resultant from other effects, but the effect of marriage plays a differing but
significant role.
Table 5-2b
ARenter from Change in Mix and Preferences (Adults 22-31)
2000-10
1990-00
1980-90
Components
Mix
Pref.
Mix
Pref.
Mix
Pref.
-0.01%
-3.05%
0.35%
3.18%
-0.12%
-1.51%
Never Married
0.03%
-0.62%
0.14%
-0.13%
0.03%
0.26%
Bachelor's Degree
Predicted Rate Change
-1.25%
-0.10%
3.05%
0.49%
-3.67%
0.02%
Total Prediction
-1.34%
3.55%
-3.65%
Actual Rate Change
1.91%
1.42%
-0.90%
Error/Other Effects
3.25%
-2.13%
2.75%
37
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
5.8. Living with Parents
While delay in marriage has the largest impact on the change in rate of adults living with their
parents, from 2000-2010, the preferences of young adults plays the largest role. This means that it
became more likely for a young adult to live at home than in previous years as is shown in Table 5-3a.
Table 5-3a
AAthome from Change in Mix and Preferences (Adults 22 and Over)
2000-10
1990-00
1980-90
Components
Mix
Pref.
Mix
Pref.
Mix
Pref.
-0.10%
1.07%
-0.38%
0.02%
-0.24%
0.49%
Young Adult
0.85%
-0.22%
0.22%
-1.01%
0.82%
-0.21%
Never Married
-0.13%
-0.11%
-0.12%
0.05%
-0.13%
-0.01%
Bachelor's Degree
Predicted Rate Change
0.45%
0.27%
-0.93%
-0.27%
0.62%
0.73%
Total Prediction
0.72%
-1.21%
1.35%
Actual Rate Change
1.27%
-0.63%
2.18%
Error/Other Effects
0.54%
0.58%
0.82%
Amongst young adults, the change in the marriage rate generally increases the rate of living at
home as shown in Table 5-3b. However from 1990-2000, the preference of those never married to live
at home decreases so significantly that on whole, never marrying results in a 2% decrease in the rate.
This occurs in spite of a steady increase in the rate of marriage delay. From 2000-2010, this preference
disappears while other effects results in a substantial increase of 4.6% in this rate.
Table 5-3b
AAthome from Change in Mix and Preferences (Adults 22-31)
2000-10
1990-00
1980-90
Components
Mix
Pref.
Mix
Pref.
Mix
Pref.
3.49%
0.06%
1.40%
-3.28%
3.38%
-0.39%
Never Married
Bachelor's Degree
-0.09%
-0.06%
0.06%
-0.32%
-0.17%
-0.28%
Predicted Rate Change
-0.48%
3.32%
-3.22%
1.08%
-0.11%
3.21%
Total Prediction
2.84%
-2.14%
3.10%
Actual Rate Change
4.35%
-0.91%
7.75%
Error/Other Effects
1.51%
1.23%
4.65%
38
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
5.9. Living in Center City of MSAs
In Table 4-4a, there is a disconnect between the predicted population-based rate change and
the actual rate change in the concentration of populations in the center cities of an MSA. By just using
education and marriage as a predictor, a flat to very slightly positive change in the rate is expected.
However, as Table 5-4a shows, there is a consistent decline in the rate of approximately 3% per decade
resultant from other effects. The there is no change in the effect of never marrying, so only the change
in the rate of marriage itself has an impact. The effect of college education has a greater impact from
2000-2010. A change in the preferences of young adults only occurs from 1990-2000 as there was a 1%
increase in the rate from that preference during that period.
Table 5-4a
ACitydum from Change in Mix and Preferences (Adults 22 and Over)
2000-10
1990-00
1980-90
Components
Pref.
-0.22%
Mix
0.04%
Never Married
Bachelor's Degree
-0.08%
0.05%
0.62%
-0.23%
Predicted Rate Change
-0.24%
0.43%
Young Adult
Mix
Pref.
Mix
Pref.
-0.01%
0.23%
-0.08%
0.13%
-0.15%
-0.08%
0.04%
0.53%
-0.01%
0.74%
-0.08%
1.21%
-0.10%
0.49%
0.65%
0.99%
Total Prediction
0.19%
1.11%
1.14%
Actual Rate Change
-2.96%
-2.25%
-1.66%
Error/Other Effects
-3.14%
-3.35%
-2.80%
The change in the preferences of young adults from 1990-2000 can be seen in Table 5-4b, where
the rate of young adults living in center cities increases. Outside of that period, the 3% negative impact
of other effects can be seen as is present in the total population.
39
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Table 5-4b
ACitydum from Change in Mix and Preferences (Adults 22-31)
Components
Never Married
Bachelor's Degree
Predicted Rate Change
Total Prediction
1980-90
Pref.
-0.44%
-0.30%
1990-00
2000-10
Mix
1.11%
Pref.
-0.31%
Mix
0.45%
Pref.
-0.21%
Mix
1.23%
-0.03%
0.59%
0.03%
1.19%
0.19%
1.08%
-0.75%
0.33%
0.48%
0.28%
0.76%
1.42%
0.98%
2.40%
Actual Rate Change
-2.58%
1.26%
-0.67%
Error/Other Effects
-2.91%
0.51%
-3.07%
As was noted by Jed Kolko, Chief Economist at Trulia, although over time there is relatively little
change in the rate of young adults in center cities, the composition of the population of young adults in
center cities is increasingly educated. (Kolko, 2015) This can be seen in the 1.4% rate increase resultant
from both the population change and the preference effect change of those with a bachelor's degree.
40
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Chapter 6:
Results Summary
6.1. Change in Home Ownership
For the total adult population in the U.S., the change in homeownership rate exhibits a decline
from 1980-1990 (-2%), an increase from 1990-2000 (+1.4%) and it again a decline from 2000-2010 (2.8%). This oscillating pattern is exhibited in many of the studied characteristics. In the down years, it
can be explained partially by the change in the rate of never marrying (-0.8%/+0.1%/-1.3%) and in 19902000 the increased population of young adults has a positive impact not seen in the other periods
(0%/+1%/0%). The impact of having a bachelor's degree rose slightly (+0.6%/+0.8%/+1.3%).
Similarly, for young adults the change in ownership rate oscillates from -7% to -0.8% to -6.6%. As
is the case for the total adult population, never marrying accounts for a portion of the rate of decline (1.5%/-0.2%/-2%) but it is influenced to a greater extent by other effects.
6.2. Change in Rental Tenure
For the total adult population, the rental rate changes are opposite of the ownership rate
changes. The changes are smaller (+0.5%/-1%/+1%) than that of the ownership rate. Having never
married always increases the propensity to rent, however this rate oscillates (+0.2%/+1.2%/+0.2%) like
many of the other trends do in this study. The impacts of other effects oscillate and are significant from
2000-2010 (+1%/-0.7%/+2.5%).
For young adults, the change in rental rate increases steadily from 1980-2000 and then declines
from 2000-2010. The overall changes in rental rate of the cohort are relatively modest (+1.9%/+1.4%/0.9%), but this hides significant changes in the preferences of those who have not married to rent their
home. This preference oscillates significantly (-1.6%/+3.5%/-3%) however it is largely offset by other
unknown effects (+3.3%/-2.1%/+2.8%) resulting in a deceivingly steady rental rate for the cohort.
41
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
6.3. Change in Living at Home
The change in rate of adults living at home oscillates similarly to the change in rate of renting,
but with greater amplitude (+1.3%/-0.2%/+2.2%). While the preference of those never married has
steadily trended away from living at home with their parents, this has been offset to some extent by the
increase in the volume of people that are never married. On the whole, never marrying has a net
positive effect from 1980-1990 (+0.6%) and 2000-2010 (+0.6%). The net effect is negative from 19902000 (-0.8%). The preference of young adults to live at home increases from 1980-1990 (+0.5%) and
2000-2010 (+1.1%) and holds steady from 1990-2000. Other effects are steadily increasing
(+0.6%/+0.6%/+0.8%)
Within the young adult cohort, the results are fairly similar except that unknown other effects
increases dramatically by +4.7%. The preference of those never married oscillates similarly but with
greater amplitude and the volume of individuals never marrying increases the rate greatly for all periods
(+3.4%/+1.4%/+3.5%). Generally, the education level of the young adult cohort played an insignificant
role in the rate change for all periods.
6.4. Central City Concentration
The concentration of adults in center cities has steadily decreased relative to the total
population of an MSA. Although there is a consistently negative trend (3%/-2.3%/-1.7%), other effects
make up the majority of this trend (-3.2%/-3.4%/-2.8%). This is likely a result in the expansion in the
definition of MSAs relative to their center cities. Ignoring these other effects, having never married
consistently increases the likelihood of living in the center city (+0.5%/+0.1%/+0.8%), while having a
bachelor's degree has steadily grown in its positive impact (-0.2%/+0.1%/+0.5%).
Most notable in this study is the change in the preference of young adults to live in center cities.
This preference only increases from 1990-2000, while it was generally flat or decreasing in other
periods. (-0.3%/+1.0%/-0.1%) This shows that the preference of Millennials to live in center cities is
42
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
slightly less than that of Generation X and that it was Generation X that generally had a greater
preference for living in center cities than their predecessors.
This preference amplifies when studying the cohorts alone. Amongst this age group, the rate
change oscillates (-2.6%/+1.3%/-0.7%) rather than staying steadily negative as it is for the total adult
population. While this trend is mostly driven by other effects (-2.9%/+0.5%/-3.1%), this is counteracted a
bit by those never marrying (+0.7%/+0.1%/+1%). As was the case for the total population, the impact of
education grows steadily and to a greater degree (-0.3%/+0.6%/+1.4%)
6.5. The Impact of Changing Marriage Rate
As expected, the change in the marriage rate of the population generally has a negative effect
on the home ownership rate as shown in the "Mix" column of Table 6-1a. However, this shift in the
population is as likely or more likely to increase the rate of the population living at home than the
portion of the population renting.
Table 6-1a
Tenure Type
Ownership
Renting
Total Tenure Shift from Never Married Rate (Adults 22 and Over)
2000-10
1990-00
1980-90
Net
Mix
Pref.
Net
Mix
Pref.
Net
Mix
Pref.
-1.35%
-1.62%
0.28%
0.13%
0.55% -0.42%
0.51% -1.27% -0.77%
0.16%
0.60%
1.15% -0.44%
0.18%
0.97%
0.18%
0.30%
-0.12%
0.62%
0.85%
0.22% -0.78% -0.22%
0.61% -1.01%
0.82%
Living at Home -0.21%
When combining the change in the population with the change in effects of never marrying, this
still largely holds true in 1980-1990 and 2000-2010.
Table 6-1b
Tenure Type
Ownership
Renting
Total Tenure Shift from Never Married Rate
1990-00
1980-90
Mix
Pref.
Net
Mix
Pref.
1.60% -1.77%
2.16% -3.65% -1.48%
0.35%
3.18%
-1.51% -0.12% -1.63%
(Adults 22-31)
2000-10
Net
-0.17%
3.53%
Pref.
1.84%
-3.05%
Mix
-3.85%
-0.01%
Net
-2.01%
-3.06%
3.55%
3.49%
0.06%
1.40% -1.88%
2.99% -3.28%
3.38%
Living at Home -0.39%
delay
the
2000-2010,
and
in
1980-1990
that
except
similar,
where
results
the
For young adults
of marriage in young adults reduces both the rental and ownership rates, while it increases the rate of
43
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
living at home as shown in Table 6-1b. This is mainly due to changes in the population as there is an
unintuitive increase in the preferences towards home ownership from young adults never marrying. This
positive effect is much smaller than the impact of the large increase in the number of individuals not
marrying, which has a large negative effect on ownership. When looking at just the most recent cohort,
the preference of those never marrying is significantly negative on the rental rate, but this looks is if it
may be more of a mean reversion for the preference as it is strongly positive for the previous period.
In general, the change in marriage rate increases the rate of the population living at home while
reducing the ownership rate. It is likely that this will have a lasting impact on the rental market if the
delay in marriage has made it more culturally acceptable to live at home. Even when the proportion of
the population that is never married stabilizes, if the propensity to rent amongst those never married
does not increase, there will be a lasting shift in the rental rate of young adults.
6.6. The Impact of Increased Educational Attainment
The proportion of the population completing four years of college education has steadily
increased over the last three decades. This trend has had a continual positive impact on the home
ownership rate in the U.S. and a negative impact on the rental rate. As shown in Table 6-2a, this
increase is this increase is a combination of both the increase in the educated portion of the population
and an increase in the positive impact of education on home ownership.
Table 6-2a
Total Tenure Shift from Bachelor's Degree Achievement Rate (Adults 22 and Over)
2000-10
1990-00
1980-90
Tenure Type
Net
Mix
Pref.
Net
Mix
Pref.
Net
Mix
Pref.
1.29%
0.52%
0.77%
0.78%
0.41%
0.37%
0.55%
0.34%
0.21%
Ownership
-0.10% -0.14% -0.24% -0.36% -0.21% -0.57% -0.64% -0.31% -0.95%
Renting
Living at Home
-0.01%
-0.13%
-0.14%
0.05%
-0.12%
-0.07%
-0.11%
-0.13%
-0.24%
44
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
While the proportion of the population with a college degree to those without is likely to level
off at some point, there has been a modest but significant impact of college education on the
homeownership rate in the U.S.
Table 6-2b
Total Tenure Shift from Bachelor's Degree Achievement Rate (Adults 22-31)
2000-10
1990-00
1980-90
Tenure Type
Net
Mix
Pref.
Net
Mix
Pref.
Net
Mix
Pref.
1.23%
0.35%
0.88%
0.46%
0.30%
0.16%
0.08%
0.04%
0.03%
Ownership
0.03% -0.59%
0.95% -0.62%
1.08%
0.29% -0.13%
0.03%
0.26%
Renting
0.06% -0.32% -0.26% -0.17% -0.28% -0.45%
Living at Home -0.09% -0.06% -0.14%
Amongst young adults, this has had a more recent impact on the home ownership rate. As
shown in Table 6-2b, there effect in the young adult cohort was somewhat minimal until 2000-2010.
Another difference in the cohort when compared to the total adult population is the role college
education plays in the propensity to live at home. Perhaps this is because the cohort is simply more
likely to live at home, but it does show that the role student debt plays in living at home may not be
significant.
Table 6-3a
Tenure Type
Ownership
Renting
Living at Home
Complete Total Tenure Shift (Adults 22 and Over)
2000-10
1990-00
1980-90
Net
Mix
Pref.
Net
Mix
Pref.
Net
Mix
Pref.
-2.83%
-0.91%
-1.92%
1.39%
1.16%
0.23%
-1.86% -0.19% -2.05%
1.04%
0.19%
0.84%
0.51% -0.19% -0.81% -1.00%
0.88% -0.37%
2.17%
0.62%
1.56%
1.27% -0.36% -0.27% -0.63%
0.45%
0.82%
As shown in Table 6-3a, proportionally amongst the adult population, the declining preference
to own appears to be resultant from an increase in the preference to live at home. When looking at the
preference shift amongst young adults from 2000-2010, it is clear that the home ownership rate is
declining significantly from changing preferences (-3.1%) and the preference to rent is also declining
slightly (-0.9%). The preference to live at home is increasing significantly (+4.5%). It is clear that the
decline in homeownership is resultant from a preference to live at home.
45
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Table 6-3b
Tenure Type
Ownership
Renting
Living at Home
Complete Total Tenure Shift (Adults 22
1990-00
1980-90
Mix
Pref.
Net
Mix
Pref.
0.70% -1.47%
-3.32% -3.60% -6.92%
0.49%
0.92%
1.91%
2.00% -0.10%
1.03%
3.32%
4.35%
-1.99%
1.08%
and Over)
2000-10
Net
-0.77%
Pref.
-3.12%
Mix
-3.50%
Net
-6.62%
1.42%
-0.92%
0.02%
-0.90%
-0.91%
4.54%
3.21%
7.75%
46
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Chapter 7:
Conclusions and Further Study
It is clear that changes in the demographic makeup of the young adult population are impacting
their housing decisions. While the homeownership rate amongst young adults has continually declined
from 1980-2010, the decline was most significant in the 1980s and the 2000s. Within these two periods,
the delay of marriage accounted for a portion of this change (-1% and -2% respectively to the total rate).
There are other factors influencing this change in both periods and in both cases these other factors
resulted in a greater than 5% decline in the homeownership rates amongst young adults. It would be
useful to examine whether the factors include access to credit, debt load or other changes.
What is also clear is that the decline in homeownership has had a relatively minimal recent
impact on the rental rate amongst young adults. This is entirely a result of the strongly decreased
preference for renting amongst young adults who have never married. This would give credence to the
theory that some portion of young adults are delaying marriage as a result of a greater preference for
homeownership than renting. These young adults are moving back in with their parents in hopes of
eventual home ownership.
Finally, there is not a large push for young adults to move into center cities. There is an uptick in
the composition of young adults in center cities with bachelor's degrees. This is perhaps indicative of the
rising cost of living in these areas as a college degree indicates greater earning potential. It would be
helpful to examine the trends in the impact of student debt on these educated urban dwellers to see if
there is a lower debt level amongst this group when compared to the average educated young adult.
47
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Bibliography
Baum, S. (2013). The Evolution of Student Debt in the U.S.: An Overview. Ann Arbor, MI: W.E. Upjohn
Institute for Employment Research.
College Board. (2015). Trends in Higher Education: Trends in College Pricing. Retrieved from College
Board: http://trends.collegeboard.org/college-pricing/figures-tables/tuition-fees-room-boardtime-1974-75-2014-15-selected-years
DiPasquale, D., & Wheaton, W. C. (1996). Urban Economics and Real Estate Markets. Upper Saddle
River, New Jersey: Prentice-Hall, Inc.
Fry, R. (2013, AUgust 1). A Rising Share of Young Adults Live in Their Parents' Home. Retrieved from Pew
Research Center: http://www.pewsocialtrends.org/2013/08/01/a-rising-share-of-young-adultslive-in-their-parents-home/
Fry, R. (2015, January 16). This year, Millennials will overtake Baby Boomers. Retrieved from Pew
Research Center: http://www.pewresearch.org/fact-tank/2015/01/16/this-year-millennials-willovertake-baby-boomers/
Gallup. (2013, August 2). Most in U.S. Want Marriage, but Its Importance Has Dropped. Retrieved from
Gallup: http://www.gallup.com/poll/163802/marriage-importance-dropped.aspx
Hellevik, 0. (2007). Linear versus logistic regression when the dependent variable is a dichotomy. Oslo:
Springer Science+Business Media B.V.
Hoover, E. (2009). The Millennial Muddle. The Chronicle of Higher Education.
Hudson, K. (2015, January 21). Generation Y Prefers Suburban Home Over City Condo. Wall Street
Journal.
Kantrowitz, M. (2014). Debt at Graduation. Las Vegas: Edvisors Network Inc.
Kolko, J. (2015, April 7). Why Millennials Are Less Urban Than You Think. Retrieved from FiveThirtyEight
Economics: http://fivethirtyeight.com/features/why-millennials-are-less-urban-than-you-think/
Lachman, M. L. (2015). Gen Y and Housing: What They Want and Where They Want it. Washington D.C.:
Urban Land Institute.
Ruggles, S., Alexander, J. T., Genadek, K., Goeken, R., Schroeder, M. B., & Sobek., M. (2010). Integrated
Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University
of Minnesota.
StataCorp. (2015). Stata Statistical Software: Release 14. College Station, TX, USA.
U.S. Census Bureau. (2011, November). Table MS-2. Estimated Median Age at First Marriage, by Sex:
1890 to Present. Retrieved from http:/www.census.gov/apsd/techdoc/cps/cps-main.html
U.S. Census Bureau. (2015). Educational Attainment. Retrieved from United States Census Bureau:
http://www.census.gov/hhes/socdemo/education/data/cps/historical/
48
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Appendix
Exhibit A.
Single Variable Regression Output (STATA Format)
Marriage - 1980
Survey: Linear regression
Number of obs
Number of strata =
48
Population size
Number of PSUs = 4,310,259
Subpop. no. obs = 7,263,459
Subpop. size
= 145,269,180
Design df
= 4,310,211
F( 1,4310211) = 419168.75
Prob>F
=
0.0000
R-squared
=
0.1116
Linearized
nevmarry
millennial
_cons
=
=
11,343,120
226,862,400
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.2554282
.0639515
.0003945
.0001127
647.43
567.52
0.000
0.000
.2546549
.0637306
.2562015
.0641724
Marriage - 1990
Survey: Linear regression
Number of obs
116
Number of strata =
Population size
Number of PSUs = 4,854,397
Subpop. no. obs = 8,500,732
= 169,459,190
Subpop. size
Design df
= 4,854,281
F( 1,4854281) = 512945.15
Prob > F
=
0.0000
0.1507
=
R-squared
=
=
12,501,046
248,107,628
Linearized
nevmarry
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
cons
.3373551
.0845867
.000471
.0001303
716.20
649.08
0.000
0.000
.3364319
.0843313
.3382783
.0848422
Marriage - 2000
Survey: Linear regression
Number of strata =
128
Number of obs
= 14,081,466
49
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Population size = 281,421,906
Number of PSUs = 5,663,214
Subpop. no. obs = 9,655,900
= 193,336,742
Subpop. size
Design df
= 5,663,086
F( 1,5663086) = 557802.55
Prob > F
=
0.0000
=
0.1520
R-squared
Linearized
nevmarry
millennial
_cons
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.3737541
.1039839
.0005004
.0001281
746.86
811.48
0.000
0.000
.3727733
.1037328
.3747349
.1042351
Marriage - 2010
Survey: Linear regression
Number of obs
Number of strata = 2,069
Population size
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
Subpop. size = 216,783,530
= 1,281,607
Design df
F( 1,1281607) = 153417.48
0.0000
=
Prob > F
0.2047
=
R-squared
Linearized
nevmarry
millennial
_cons
=
=
3,061,692
309,349,689
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.4809914
.1346704
.001228
.000353
391.69
381.52
0.000
0.000
.4785846
.1339785
.4833983
.1353622
College Attendance - 1980
Survey: Linear regression
Number of obs
48
Number of strata =
Population size
Number of PSUs = 4,310,259
Subpop. no. obs = 7,263,459
= 145,269,180
Subpop. size
= 4,310,211
Design df
F( 1,4310211) = 23964.24
0.0000
=
Prob > F
0.0045
=
R-squared
=
=
11,343,120
226,862,400
50
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Linearized
bachelors
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
.0555003
_cons
.1456938
.0003585
.0001752
154.80
831.71
0.000
0.000
.0547976
.1453505
.056203
.1460371
College Attendance - 1990
Survey: Linear regression
48
Number of obs
Number of strata =
Population size
Number of PSUs = 4,310,259
Subpop. no. obs = 7,263,459
= 145,269,180
Subpop. size
= 4,310,211
Design df
F( 1,4310211) = 23964.24
0.0000
=
Prob> F
0.0045
=
R-squared
=
=
11,343,120
226,862,400
Linearized
Bachelors
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
.0555003
_cons
.1456938
.0003585
.0001752
154.80
831.71
0.000
0.000
.0547976
.1453505
.056203
.1460371
College Attendance - 2000
Survey: Linear regression
Number of obs
128
Number of strata =
Population size
Number of PSUs = 5,663,214
=
9,655,900
no.
obs
Subpop.
= 193,336,742
Subpop. size
= 5,663,086
Design df
488.55
F( 1,5663086) =
0.0000
=
Prob> F
0.0001
=
R-squared
Linearized
bachelors
millennial
_cons
=
=
14,081,466
281,421,906
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.0098493
.2380356
.0004456
.0001947
22.10
1222.62
0.000
0.000
.0089759
.2376541
.0107226
.2384172
College Attendance - 2010
51
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Survey: Linear regression
Number of obs
Number of strata = 2,069
Population size
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
Subpop. size
= 216,783,530
Design df
= 1,281,607
F( 1,1281607) =
1.76
0.1849
=
Prob>F
R-squared
=
0.0000
=
=
3,061,692
309,349,689
Linearized
bachelors
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
.0014433
_cons
.2779128
.0010886
.0004359
1.33
637.56
0.185
0.000
-.0006904
.2770584
.003577
.2787671
Marriage - 1980
In 1980
Survey: Linear regression
Number of obs
48
Number of strata =
Population size
Number of PSUs = 4,310,259
Subpop. no. obs = 7,263,459
= 145,269,180
Subpop. size
= 4,310,211
Design df
F( 1,4310211) = 88745.89
=
0.0000
Prob > F
0.0071
=
R-squared
=
=
11,343,120
226,862,400
Linearized
incadj
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
-4912.882
_cons
25581.43
16.49158
10.76855
-297.90
2375.57
0.000
0.000
-4945.205 -4880.559
25560.33 25602.54
Marriage - 1990
Survey: Linear regression
Number of obs
116
Number of strata =
Population size
Number of PSUs = 4,854,397
Subpop. no. obs = 8,500,732
= 169,459,190
Subpop. size
= 4,854,281
Design df
F( 1,4854281) = 149410.39
=
=
12,501,046
248,107,628
52
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Prob > F
R-squared
=
=
0.0000
0.0122
Linearized
incadj
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
_cons
-8200.473
28853.06
21.21527
13.92865
-386.54
2071.49
0.000
0.000
-8242.055 -8158.892
28825.76 28880.36
Marriage - 2000
Survey: Linear regression
Number of obs
128
Number of strata =
Population size
Number of PSUs = 5,663,214
Subpop. no. obs = 9,655,900
Subpop. size
= 193,336,742
Design df
= 5,663,086
F( 1,5663086) = 178308.56
Prob > F
=
0.0000
R-squared
=
0.0120
Linearized
incadj
millennial
_cons
=
=
14,081,466
281,421,906
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-11420.43
33015.86
27.04558
17.44482
-422.27
1892.59
0.000
0.000
-11473.44 -11367.42
32981.67 33050.06
Marriage - 2010
Survey: Linear regression
Number of obs
Number of strata = 2,069
Population size
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
= 216,783,530
Subpop. size
= 1,281,607
Design df
F( 1,1281607) = 55062.47
Prob > F
=
0.0000
R-squared
=
0.0175
Linearized
incadj
millennial
_cons
=
=
3,061,692
309,349,689
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-12269.28
30166.26
52.2867
32.50357
-234.65
928.09
0.000
0.000
-12371.76
30102.56
-12166.8
30229.97
53
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Home Ownership - 1980
Survey: Linear regression
48
Number of obs
Number of strata =
Population size
Number of PSUs = 4,310,259
Subpop. no. obs = 7,263,459
= 145,269,180
Subpop. size
Design df
= 4,310,211
F( 1,4310211) = 490150.13
Prob > F
=
0.0000
0.1005
=
R-squared
Linearized
own
millennial
_cons
=
=
11,343,120
226,862,400
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-.3406972
.7407581
.0004866
.0001503
-700.11
4929.34
0.000
0.000
-.341651 -.3397434
.7404636 .7410527
Home Ownership - 1990
Survey: Linear regression
Number of obs
116
Number of strata =
Population size
Number of PSUs = 4,854,397
Subpop. no. obs = 8,500,732
= 169,459,190
Subpop. size
= 4,854,281
Design df
F( 1,4854281) = 636461.11
0.0000
=
Prob>F
0.1210
=
R-squared
Linearized
own
millennial
_cons
=
=
12,501,046
248,107,628
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-.3922708
.7232046
.0004917
.000162
-797.79
4465.58
0.000
0.000
-.3932345 -.3913071
.7228872 .7235221
Home Ownership - 2000
Survey: Linear regression
Number of obs
128
Number of strata =
Population size
Number of PSUs = 5,663,214
9,655,900
=
obs
no.
Subpop.
= 193,336,742
Subpop. size
= 5,663,086
Design df
F( 1,5663086) = 608958.44
=
=
14,081,466
281,421,906
54
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Prob > F
R-squared
=
=
0.0000
0.1100
Linearized
own
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
cons
-.3981308
.7212783
.0005102
.00015
-780.36
4809.50
0.000
0.000
-.3991308 -.3971309
.7209843 .7215722
Home Ownership - 2010
Survey: Linear regression
Number of obs
Number of strata = 2,069
Population size
Number of PSU s = 1,283,676
Subpop. no. obs = 2,212,918
= 216,783,530
Subpop. size
= 1,281,607
Design df
F( 1,1281607) = 142203.84
0.0000
=
Prob > F
0.1278
=
R-squared
Linearized
own
millennial
_cons
=
=
3,061,692
309,349,689
Coef.
Std. Err.
t
P>t
-.4416249
.6986712
.0011711
.000529
-377.10
1320.73
0.000
0.000
[95% Conf. Interval]
-.4439202 -.4393295
.6976343 .699708
Renting - 1980
Survey: Linear regression
Number of obs
Number of strata =
48
Population size
Number of PSUs = 4,310,259
Subpop. no. ob s = 7,263,459
= 145,269,180
Subpop. size
4,310,211
Design df
F( 1,4310211) = 185088.01
0.0000
=
Prob>F
=
0.0414
R-squared
Linearized
renter
millennial
_cons
=
=
11,343,120
226,862,400
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.2041205
.2180152
.0004745
.0001408
430.22
1547.97
0.000
0.000
.2031906
.2177391
.2050505
.2182912
55
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Renting - 1990
Survey: Linear regression
Number of strata =
116
Number of obs
Population size
Number of PSUs = 4,854,397
Subpop. no. obs = 8,500,732
Subpop. size = 169,459,190
Design df
= 4,854,281
F( 1,4854281) = 183731.65
Prob > F
=
0.0000
R-squared
=
0.0423
=
=
12,501,046
248,107,628
Linearized
renter
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
cons
.2150616
.226148
.0005017
.0001493
428.64
1514.38
0.000
0.000
.2140782
.2258553
.216045
.2264407
Renting - 2000
Survey: Linear regression
Number of obs
128
Number of strata =
Population size
Number of PSUs = 5,663,214
Subpop. no. obs = 9,655,900
Subpop. size
= 193,336,742
Design df
=
5,663,086
196451.52
F( 1,5663086)
Prob>F
=
0.0000
0.0443
R-squared
=
Linearized
renter
millennial
_cons
=
=
14,081,466
281,421,906
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.233552
.221846
.0005269
.0001374
443.23
1614.03
0.000
0.000
.2325193
.2215766
.2345848
.2221154
Renting - 2010
Survey: Linear regression
Number of obs
Number of strata = 2,069
Population size
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
= 216,783,530
Subpop. size
1,281,607
=
Design df
F( 1,1281607) = 23729.89
=
=
3,061,692
309,349,689
56
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Prob > F
R-squared
=
=
0.0000
0.0333
Linearized
renter
Coef.
Std. Err.
t
P>t
millennial
cons
2077025
.2386645
.0013483
.0005115
154.05
466.57
0.000
0.000
[95% Conf. Interval]
.2050598
.2376619
.2103451
.239667
Living at Home - 1980
Survey: Linear regression
Number of obs
Number of strata =
48
Population size
Number of PSUs = 4,310,259
Subpop. no. ob s = 7,263,459
Subpop. size
= 145,269,180
= 4,310,211
Design df
F( 1,4310211) = 193387.38
Prob>F
=
0.0000
R-squared
=
0.0665
Linearized
athome
millennial
_cons
=
=
11,343,120
226,862,400
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.1338512
.0201605
.0003044
.0000674
439.76
299.03
0.000
0.000
.1332546
.0200284
.1344477
.0202926
Living at Home - 1990
Survey: Linear regression
Number of obs
116
Number of strata =
Population size
Number of PSUs = 4,854,397
Subpop. no. obs = 8,500,732
= 169,459,190
Subpop. size
= 4,854,281
Design df
F( 1,4854281) = 212198.03
0.0000
=
Prob > F
0.0822
=
R-squared
=
=
12,501,046
248,107,628
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
cons
.1695966
.0279176
.0003682
.0000773
460.65
360.98
0.000
0.000
.168875 .1703182
.0277661 .0280692
57
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Living at Home - 1990
Survey: Linear regression
Number of strata =
116
Number of obs
Number of PSUs = 4,854,397
Population size
Subpop. no. ob s = 8,500,732
= 169,459,190
Subpop. size
4,854,281
Design df
F( 1,4854281) = 212198.03
0.0000
Prob>F
=
0.0822
=
R-squared
Linearized
athome
millennial
_cons
=
=
12,501,046
248,107,628
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.1695966
.0279176
.0003682
.0000773
460.65
360.98
0.000
0.000
.168875 .1703182
.0277661 .0280692
Living at Home - 2000
Survey: Linear regression
Number of obs
128
Number of strata =
Population size
Number of PSUs = 5,663,214
Subpop. no. ob s = 9,655,900
= 193,336,742
Subpop. size
5,663,086
Design df
F( 1,5663086) = 172640.87
=
0.0000
Prob>F
=
0.0668
R-squared
Linearized
athome
millennial
_cons
=
=
14,081,466
281,421,906
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.156926
.0315014
.0003777
.000077
415.50
409.02
0.000
0.000
.1561858
.0313504
.1576662
.0316523
Living at Home - 2010
Survey: Linear regression
Number of obs
Number of strata = 2,069
Population size
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
Subpop. size = 216,783,530
= 1,281,607
Design df
F( 1,1281607) = 43044.83
=
=
3,061,692
309,349,689
58
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Prob > F
R-squared
=
=
0.0000
0.1014
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
_cons
.2246824
.0412824
.001083
.0002172
207.47
190.04
0.000
0.000
.2225598
.0408566
.2268049
.0417081
Center City - 1980
Linear regression
Number of obs
1322.32
F(1, 2550416) =
= 0.0000
Prob > F
0.0007
R-squared
=
.47891
Root MSE
=
2,550,418
Robust
citydum
Coef.
millennial
_cons
.0284583
.3490661
Std. Err.
.0007826
.0004023
t
36.36
867.60
P>t
0.000
0.000
[95% Conf. Interval]
.0269244 .0299922
.3482775.3498547
Center City - 1990
Linear regression
Number of obs
1796.05
F(1, 2899207) =
= 0.0000
Prob > F
0.0009
R-squared
=
.46901
Root MSE
=
2,899,209
Robust
citydum
Coef.
millennial
_cons
.0326896
.3190034
Std. Err.
.0007713
.0003738
t
42.38
853.30
P>t
0.000
0.000
[95% Conf. Interval]
.0311778 .0342014
.3182707 .3197361
Center City - 2000
Linear regression
Number of obs
11816.61
=
3642285)
F(1,
= 0.0000
Prob > F
=
0.0044
R-squared
=
.45933
Root MSE
3,642,287
Robust
citydum
Coef.
millennial
_cons
.0751277
.2892
Std. Err.
.0006911
.0002938
t
P>t
108.70
984.47
0.000
0.000
[95% Conf. Interval]
.0737731 .0764823
.2886242 .2897758
Center City - 2010
59
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Linear regression
F(1, 831512)
Prob > F
R-squared
Root MSE
Number of obs
2825.28
0.0000
=
0.0060
=
.45154
=
831,514
=
=
Robust
citydum
Coef.
millennial
cons
.0870704
.2705638
Std. Err.
.0016381
.0006371
t
53.15
424.71
P>t
0.000
0.000
[95% Conf. Interval]
.0838598 .090281
.2693152 .2718124
60
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Multiple Linear Regression Output - All Data Sets Pooled (STATA Format)
Exhibit B.
Ownership Total Adult Population
Survey: Linear regression
Number of strata = 2,197
Number of PSUs = 16111546
Subpop. no. obs = 27,633,009
= 724,848,642
Subpop. size
16,109,349
df
=
Design
718490.88
F( 3,16109347) =
=
0.0000
Prob > F
=
0.1930
R-squared
Linearized
own
millennial
nevmarry
bachelors
_cons
Number of obs = 40,987,324
Population size = 1,065,741,623
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-.2606444
-.3676791
.103233
.7325711
.0004119
.0004594
.0003374
.000198
-632.83
-800.30
305.97
3700.13
0.000
0.000
-.2614516 -.2598371
-.3685796 -.3667787
.1025717 .1038942
.732183 .7329591
0.000
0.000
Rental Total Adult Population
Survey: Linear regression
Number of strata = 2,197
Number of PSUs = 16111546
Subpop. no. obs = 27,633,009
= 724,848,642
Subpop. size
=
16,109,349
Design df
108771.90
F( 3,16109347) =
=
0.0000
Prob > F
=
0.0509
R-squared
Linearized
renter
millennial
nevmarry
bachelors
_cons
Number of obs = 40,987,324
Population size = 1,065,741,623
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.1717154
.1192463
-.0544759
.2274203
0004434
.0005143
.0003493
.0001961
387.29
231.87
-155.98
1159.90
0.000
.1708464 .1725844
.1182383 .1202542
-.0551604 -.0537913
.227036 .2278046
0.000
0.000
0.000
Living at Home Total Adult Population
Survey: Linear regression
61
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Number of strata = 2,197
Number of PSUs = 16111546
Subpop. no. obs = 27,633,009
Subpop. size
= 724,848,642
Design df
= 16,109,349
158506.89
F( 3,16109347) =
0.0000
Prob>F
0.1668
R-squared
=
.
_cons
.
millennial
nevmarry
bachelors
C oef.
-
Linearized
athome
)951359
2118524
.0300028
0167964
Number of obs = 40,987,324
Population size = 1,065,741,623
Std. Err.
t
P>t
[95% Conf. Interval]
.0002945
.0003833
.0001847
.0000815
323.06
552.65
-162.44
206.00
0.000
0.000
.0945587 .0957131
.211101 .2126037
-.0303648 -.0296408
.0166366 .0169562
0.000
0.000
Living in Center City Total Adult Population
Number of obs = 298601712
MS
df
SS
Source
F(3, 298601708) > 99999.00
= 0.0000
3 363357.201 Prob>F
Model 1090071.6
= 0.0169
Residual 63397100.8 298601708 .212313256 R-squared
Adj R-squared = 0.0169
= .46077
Total 64487172.4 298601711 .215963841 Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
_cons
.006935
.1390739
-.0414398
.2950322
.0000693
.000071
.0000604
.0000352
100.14
1958.47
-685.58
8379.07
0.000
0.000
.0067993 .0070708
.1389347 .1392131
-.0415583 -.0413214
.2949632 .2951012
0.000
0.000
Ownership Young Adults
Survey: Linear regression
Number of strata
Number of PSUs
Subpop. no. obs
Subpop. size
=
=
Design df
F( 2,16109348)
Prob>F
=
R-squared
= 2,197
= 16111546
6,093,212
=
160,630,105
16,109,349
160861.20
=
Number of obs = 40,987,324
Population size = 1,065,741,623
0.0000
0.1386
62
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Linearized
own
nevmarry
bachelors
_cons
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-.3485743
.077519
.4691558
.0006188
.0007301
.0004743
-563.34
106.18
989.12
0.000
-. 3497871 -.3473616
.0760881 .0789499
.4682261 .4700854
0.000
0.000
Rental Young Adults
Rental Young Adults
Survey: Linear regression
Number of obs = 40,987,324
Population size = 1,065,741,623
Number of strata = 2,197
Number of PSUs = 16111546
6,093,212
Subpop. no. obs =
160,630,105
=
Subpop. size
=
16,109,349
Design df
160861.20
F( 2,16109348) =
0.0000
=
Prob > F
0.1386
=
R-squared
Linearized
own
nevmarry
bachelors
_cons
[95% Conf. Interval]
Coef.
Std. Err.
t
P>t
-.3485743
.077519
.4691558
.0006188
.0007301
.0004743
-563.34
106.18
989.12
0.000 -. 3497871 -.3473616
0.000 .0760881 .0789499
0.000 .4682261 .4700854
Living at Home Young Adults
Survey: Linear regression
Number of strata = 2,197
Number of PSUs = 16111546
6,093,212
Subpop. no. obs =
= 160,630,105
Subpop. size
16,109,349
=
Design df
121483.12
F( 2,16109348) =
0.0000
=
Prob>F
0.1386
=
R-squared
Number of obs = 40,987,324
Population size = 1,065,741,623
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
.2969339
-.0806588
.0006113
.0006272
485.76
-128.60
0.000
0.000
.2957358 .2981319
-.0818881 -.0794295
63
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
-cons
.0846248
.0003085
274.31
0.000
.0840201
.0852295
Living in Center City Young Adults
Source
SS
df
MS
Number of obs = 68687057
F(2, 68687054) > 99999.00
Model 159779.741
2 79889.8704 Prob>F
= 0.0000
Residual 15712373.3 68687054 .228753053 R-squared
= 0.0101
Adj R-squared = 0.0101
= .47828
Total 15872153 68687056 .231079245 Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
.0956968
.0084941
.310486
.0001158
.0001289
.000089
826.59
65.89
3486.99
0.000
0.000
0.000
.0954699
.0082414
.3103114
_cons
.0959237
.0087467
.3106605
64
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Multiple Linear Regression Output - Individual Decennial Data (STATA Format)
Exhibit C.
Ownership Total Adult Population - 1980
Survey: Linear regression
Number of strata =
48
Number of PSUs = 6,350,343
Subpop. no. obs = 7,263,459
= 145,269,180
Subpop. size
= 6,350,295
Design df
F( 3,6350293) = 556923.35
=
0.0000
Prob > F
0.1806
=
R-sauared
Linearized
own
millennial
nevmarry
bachelors
_cons
Number of obs = 17,499,994
Population size = 348,750,344
[95% Conf. Interval]
Coef.
Std. Err.
t
P>t
-.2375392
-.4196569
.0726874
.7570057
.0005039
.0005379
.000468
.0001745
-471.37
-780.19
155.31
4336.95
0.000 -.2385268 -.2365515
0.000 -.4207112 -.4186027
0.000
0.000
.07177 .0736047
.7566636 .7573478
Ownership Total Adult Population - 1990
Survey: Linear regression
116
Number of strata =
Number of PSUs = 14532128
Subpop. no. obs = 8,500,732
= 16 9,459,190
Subpop. size
Design df
= 14, 532,012
F( 3,14532010) = 545651.03
0..0000
=
Prob > F
0.1972
=
R-squared
Number of obs = 37,153,983
Population size = 739,080,416
Linearized
own
Coef.
Std. Err.
t
P>t
millennial
nevmarry
bachelors
cons
-.2642796
-.3817338
.0857002
.7384691
.0005463
.0005556
.0004486
.0002325
-483.79
-687.08
191.02
3176.52
0.000 -.2653502 -.2632089
0.000 -. 3828227 -.3806449
0.000 .0848209 .0865796
0.000 .7380134 .7389247
[95% Conf. Interval]
Ownership Total Adult Population - 2000
Survey: Linear regression
65
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Number of strata =
128
Number of PSUs = 14827870
Subpop. no. obs = 9,655,900
= 193,336,742
Subpop. size
Design df
= 14,827,742
F( 3,14827740) = 491753.41
Prob > F
=
0.0000
=
0.1828
R-squared
Number of obs = 37,925,632
Population size = 756,391,934
Linearized
own
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
cons
-.2689222
-.3484507
.1041933
.7327098
.0005571
.0005279
.0003943
.0002062
-482.71
-660.03
264.24
3553.59
0.000
-.2700141 -.2678303
-.3494854 -.347416
.1034204 .1049661
.7323057 .7331139
0.000
0.000
0.000
Ownership Total Adult Population - 2010
Survey: Linear regression
Number of strata = 2,069
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
= 216,783,530
Subpop. size
= 1,281,607
Design df
F( 3,1281605) = 104529.76
0.0000
=
Prob > F
0.2097
=
R-squared
Linearized
own
millennial
nevmarry
bachelors
_cons
Number of obs =
Population size
=
3,061,692
309,349,689
[95% Conf. Interval]
Coef.
Std. Err.
t
P>t
-.2816961
-.3329072
.1363238
.7056178
.0013422
.001235
.0008735
.0006258
-209.87
-269.56
156.07
1127.47
0.000 -. 2843268 -.2790654
0.000 -. 3353277 -.3304866
0.000
0.000
.1346118
.7043911
.1380358
.7068444
Renter Total Adult Population - 1980
Survey: Linear regression
Number
Number
Subpop.
Subpop.
of strata
of PSUs
no. obs
=
size
48
=
= 6,350,343
= 7,263,459
145,269,180
Number of obs = 17,499,994
Population size = 348,750,344
66
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Design df
=
6,350,295
F( 3,6350293) = 74661.17
0.0000
Prob>F
0.0467
R-squared
=
Linearized
renter
Cc )ef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
cons
.1 807198
.0 980459
)29603
.2 16058
.0005052
.0006041
.0004859
.0001711
357.70
162.29
-60.93
1262.77
0.000
0.000
0.000
.1797296 .1817101
.09923
.0968618
-.0305553 -.0286507
.2157226 .2163933
0.000
Renter Total Adult Population - 1990
Survey: Linear regression
116
Number of strata =
Number of PSUs = 14532128
Subpop. no. obs = 8,500,732
= 169,459,190
Subpop. size
= 14,532,012
Design df
F( 3,14532010) = 67262.13
0.0000
=
Prob > F
0.0477
=
R-squared
Linearized
renter
millennial
nevmarry
bachelors
_cons
Number of obs = 37,153,983
Population size = 739,080,416
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.1852828
.0892467
-.0357579
.2257025
.0005693
.0006352
.0004666
.0002278
325.46
140.51
-76.64
990.67
0.000
.184167 .1863987
.0880018 .0904916
-.0366723 -.0348434
.225256 .2261491
0.000
0.000
0.000
Renter Total Adult Population - 2000
Survey: Linear regression
128
Number of strata =
Number of PSUs = 14827870
Subpop. no. obs = 9,655,900
= 193,336,742
Subpop. size
= 14,827,742
Design df
F( 3,14827740) = 98119.28
0.0000
=
Prob > F
0.0604
=
R-squared
Number of obs = 37,925,632
Population size = 756,391,934
67
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Linearized
renter
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
cons
.1789277
.1475634
-.0536186
.2192649
.0005812
.0005542
.0003987
.0001978
307.85
266.25
-134.48
1108.29
0.000
0.000
.1777886 .1800669
.1464771 .1486496
-.0544 -.0528371
.2188772 .2196527
0.000
0.000
Renter Total Adult Population - 2010
Survey: Linear regression
Number of obs =
Population size
=
3,061,692
309,349,689
-
Number of strat = 2,069
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
= 216,783,530
Subpop. size
1,281,607
Design df
F( 3,1281605) = 13862.32
0.0000
Prob>F
0.0505
R-squared
Linearized
renter
millennial
nevmarry
bachelors
_cons
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.1486886
.1229335
-.0804459
.2444659
.0015064
.0013421
.0009104
.0006243
98.71
91.60
-88.36
391.59
0.000
0.000
.1457362 .151641
.1203031 .1255639
-.0822304 -.0786615
.2432423 .2456895
0.000
0.000
Living at Home Total Adult Population - 1980
Survey: Linear regression
48
Number of strata =
Number of PSUs = 6,350,343
Subpop. no. obs = 7,263,459
= 145,269,180
Subpop. size
= 6,350,295
Design df
F( 3,6350293) = 123549.50
0.0000
=
Prob > F
0.1987
=
R-squared
Number of obs = 17,499,994
Population size = 348,750,344
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
.0686558
.0002515
272.97
0.000
.0681628
.0691487
68
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
nevmarry
bachelors
cons
.2621409
-.0317613
.0080236
.0005008
.0002294
.0000707
523.41
-138.46
113.43
0.000
0.000
0.000
.2611593 .2631225
-.0322109 -.0313117
.007885 .0081623
Living at Home Total Adult Population - 1990
Survey: Linear regression
116
Number of strata =
Number of PSUs = 14532128
Subpop. no. obs = 8,500,732
= 169,459,190
Subpop. size
= 14,532,012
Design df
F( 3,14532010) = 124003.59
=
0.0000
Prob > F
0.1940
=
R-squared
Number of obs = 37,153,983
Population size = 739,080,416
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
cons
.0867381
.2464913
-.
0322251
.0134696
.0003216
.0004856
.0002309
.0000863
269.68
507.62
-139.58
156.06
0.000
0.000
.0861077 .0873685
.2455396 .247443
-.
0326776 -.0317726
.0133004 .0136387
0.000
0.000
Living at Home Total Adult Population - 2000
Survey: Linear regression
128
Number of strata =
Number of PSUs = 14827870
Subpop. no. obs = 9,655,900
= 193,336,742
Subpop. size
= 14,827,742
Design df
F( 3,14827740) = 109614.59
0.0000
=
Prob > F
0.1417
=
R-squared
Number of obs = 37,925,632
Population size = 756,391,934
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
cons
.0877164
.1859583
-.029761
.0192489
.0003569
.0004061
.0001946
.0000884
245.75
457.94
-152.94
217.72
0.000
0.000
.0870168 .088416
.1851624 .1867542
0293796
-.0301423 -.
.0190756 .0194221
0.000
0.000
Living at Home Total Adult Population - 2010
69
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Survey: Linear regression
Number of obs =
Population size
Number of strata = 2,069
Number of PSUs = 1,283,676
Subpop. no. obs = 2,212,918
= 216,783,530
Subpop. size
= 1,281,607
Design df
F( 3,1281605) = 23576.07
Prob > F
=
0.0000
0.1590
=
R-squared
Linearized
athome
millennial
nevmarry
bachelors
_cons
=
3,061,692
309,349,689
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.1413433
.1733682
-.0342994
.0274671
.0010778
.0009459
.0004924
.0002612
131.14
183.29
-69.66
105.15
0.000
.1392309 .1434556
.1715143 .175222
-.0352644 -.0333343
.0269551 .0279791
0.000
0.000
0.000
Living in Center City Total Adult Population - 1980
Source
SS
df
MS
Model
Residual
182366.147
13497719.9
3
59605316
60788.7158
.226451613
Total
13680086.1
59605319
.229511163
Number of obs = 59605320
F(3, 59605316) > 99999.00
= 0.0000
Prob > F
= 0.0133
R-squared
Adj R-squared = 0.0133
=
.47587
Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
_cons
-.
0141528
.1511098
-.
0490021
.3455828
.0001486
.0001805
.0001585
.0000782
-95.25
837.16
-309.18
4417.65
0.000
0.000
0.000
0.000
-.014444
.1507561
-.0493127
.3454295
-.0138616
.1514636
-.0486915
.3457361
Living in Center City Total Adult Population - 1990
Source
SS
df
MS
Model
Residual
235070.836
15184054.6
3
70031208
78356.9453
.216818401
Total
15419125.4
70031211
22017505
Number of obs = 70031212
F(3, 70031208) > 99999.00
= 0.0000
Prob > F
= 0.0152
R-squared
Adj R-squared = 0.0152
= .46564
Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
70
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
millennial
nevmarry
bachelors
cons
-.
0220378
.1464362
-.
0462176
.3141321
.0001402
.0001518
.0001308
.0000728
-157.23
964.63
-353.28
4317.37
0.000 -.0223126 -.0217631
0.000 .1461387 .1467337
046474 -.0459612
0.000 -.
0.000 .3139895 .3142747
Living in Center City Total Adult Population - 2000
Source
SS
df
MS
Model
Residual
331111.824
16631415.5
3
80046122
110370.608
.207772907
Total
16962527.3
80046125
.211909412
Number of obs = 80046126
F(3, 80046122) > 99999.00
Prob > F
= 0.0000
= 0.0195
R-squared
Adj R-squared = 0.0195
.45582
=
Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
cons
.0169247
.1459266
-.0365618
.2806015
.000137
.0001361
.0001135
.0000673
123.54
1072.50
-322.27
4166.73
0.000
0.000
.0166562
.1456599
-.
0367842
.2804695
0.000
0.000
.0171932
.1461933
-.
0363395
.2807335
Living in Center City Total Adult Population - 2010
Source
SS
df
MS
Model
Residual
412725.498
17825223.7
3
88919050
137575.166
.200465746
Total
18237949.2
88919053
.205107326
Number of obs = 88919054
F(3, 88919050) > 99999.00
= 0.0000
Prob > F
= 0.0226
R-squared
Adj R-squared = 0.0226
=
.44773
Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
millennial
nevmarry
bachelors
_cons
.0132722
.1475995
-.0178409
.2525411
.0001331
.0001214
.0001014
.0000656
99.70
1215.32
-175.89
3852.09
0.000
0.000
.0130113 .0135331
.1473615 .1478376
-.0180397 -.0176421
.2524126 .2526696
0.000
0.000
Ownership Young Adults - 1980
Survey: Linear regression
48
Number of strata =
Number of PSUs = 6,350,343
Subpop. no. obs = 1,965,773
= 39,315,460
Subpop. size
= 6,350,295
Design df
F( 2,6350294) = 206384.01
=
0.0000
Prob > F
Number of obs = 17,499,994
Population size = 348,750,344
71
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
R-squared
=
0.1607
Linearized
own
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
_cons
-.4230736
.0655644
.5219909
.0006586
.0008901
.0004994
-642.40
73.66
1045.18
0.000
0.000
0.000
-.4243644 -.4217828
.0638198 .0673091
.521012 .5229698
Ownership Young Adults - 1990
Survey: Linear regression
103
Number of strata =
Number of PSUs = 13079788
Subpop. no. obs = 1,955,105
= 41,110,743
Subpop. size
= 13,079,685
Design df
F( 2,13079684) = 109386.86
0.0000
=
Prob > F
0.1389
=
R-squared
Linearized
own
nevmarry
bachelors
_cons
Number of obs = 33,885,176
Population size = 673,570,445
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-.3554883
.0671195
.4669777
.0007619
.0009656
.0006317
-466.60
69.51
739.27
0.000
0.000
-. 3569816 -.3539951
.0652269 .0690121
.4657396 .4682157
0.000
Ownership Young Adults - 2000
Survey: Linear regression
104
Number of strata =
Number of PSUs = 11856876
Subpop. no. obs = 1,816,584
= 38,502,079
Subpop. size
11,856,772
=
df
Design
F( 2,11856771) = 72802.58
0.0000
=
Prob > F
0.1174
=
R-squared
Number of obs = 30,485,856
Population size = 606,922,294
Linearized
own
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
-.3175377
.0008422
-377.03
0.000
-.3191884
-.315887
72
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
bachelors
_cons
.0749636
.456265
.0009686
.0007108
77.40
641.95
0.000
0.000
.0730652
.4548719
.076862
.457658
Ownership Young Adults - 2010
Survey: Linear regression
Number of obs = 3,061,692
Population size = 309,349,689
Number of strata = 2,069
Number of PSUs = 1,283,676
355,750
Subpop. no. obs =
= 41,701,823
Subpop. size
= 1,281,607
Design df
9862.88
F( 2,1281606) =
0.0000
=
Prob > F
0.1099
=
R-squared
Linearized
own
nevmarry
bachelors
_cons
Coef.
Std. Err.
t
P>t
-.2789796
.1105972
.3979074
.0021856
.0021071
.0020226
-127.64
52.49
196.73
0.000 -. 2832632 -.2746959
0.000
0.000
[95% Conf. Interval]
.1064674
.3939431
.114727
.4018716
Rental Young Adults - 1980
Survey: Linear regression
Number of obs = 17,499,994
Population size = 348,750,344
48
Number of strata =
Number of PSUs = 6,350,343
Subpop. no. obs = 1,965,773
= 39,315,460
Subpop. size
= 6,350,295
Design df
1427.28
F( 2,6350294) =
0.0000
=
Prob > F
0.0018
=
R-squared
Linearized
renter
nevmarry
bachelors
_cons
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.0352863
.0281001
.4052124
.0008375
.0009978
.0004929
42.13
28.16
822.09
0.000
.0336447
.0261444
.4042463
0.000
0.000
.0369278
.0300558
.4061785
Rental Young Adults - 1990
Survey: Linear regression
Number of strata =
103
Number of obs
= 33,885,176
73
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Population size = 673,570,445
Number of PSUs = 13079788
Subpop. no. obs = 1,955,105
= 41,110,743
Subpop. size
Design df
= 13,079,685
714.95
F( 2,13079684) =
0.0000
=
Prob > F
0.0012
R-squared
Linearized
renter
Coef.
Std. Err.
t
P>t
nevmarry
bachelors
cons
-.0119707
.0411991
.4376968
.0009351
.0011297
.0006454
-12.80
36.47
678.16
0.000 -.0138035 -.0101379
0.000
0.000
[95% Conf. Interval]
.038985 .0434132
.4364318 .4389618
Rental Young Adults - 2000
Survey: Linear regression
Number of obs = 30,485,856
Population size = 606,922,294
104
Number of strata =
Number of PSUs = 11856876
Subpop. no. obs = 1,816,584
= 38,502,079
Subpop. size
= 11,856,772
Design df
2825.45
F( 2,11856771) =
0.0000
=
Prob > F
0.0052
=
R-squared
Linearized
renter
nevmarry
bachelors
_cons
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.0633301
.035127
.4164354
.0009565
.0010988
.0007209
66.21
31.97
577.64
0.000
0.000
0.000
.0614553
.0329734
.4150224
.0652049
.0372806
.4178484
Rental Young Adults - 2010
Survey: Linear regression
Number of strata = 2,069
Number of PSUs = 1,283,676
355,750
Subpop. no. obs =
= 41,701,823
Subpop. size
= 1,281,607
Design df
7.63
F( 2,1281606) =
0.0005
=
Prob > F
0.0001
=
R-squared
Number of obs =
Population size
=
3,061,692
309,349,689
74
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Linearized
renter
nevmarry
bachelors
_cons
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
-.0005489
.0100015
.4439109
.0025196
.0025735
.0021797
-0.22
3.89
203.66
0.828
0.000
-.0054871
.0049574
.4396387
0.000
.0043894
.0150455
.448183
Living at Home Young Adults - 1980
Survey: Linear regression
48
Number of strata =
Number of PSUs = 6,350,343
Subpop. no. obs = 1,965,773
= 39,315,460
Subpop. size
= 6,350,295
Design df
F( 2,6350294) = 124266.56
0.0000
=
Prob > F
0.1945
=
R-squared
Number of obs = 17,499,994
Population size = 348,750,344
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
cons
.3414039
-.0795403
.0609772
.0006926
.0005992
.0002476
492.90
-132.75
246.25
0.000
.3400464
-. 0807147
.0604919
0.000
0.000
.3427615
-.078366
.0614626
Living at Home Young Adults - 1990
Survey: Linear regression
103
Number of strata =
Number of PSUs = 13079788
Subpop. no. obs = 1,955,105
= 41,110,743
Subpop. size
= 13,079,685
Design df
F( 2,13079684) = 106337.69
0.0000
=
Prob > F
0.1682
=
R-squared
Number of obs = 33,885,176
Population size = 673,570,445
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
.329238
-.0839244
.0007223
.0007502
455.85
-111.87
0.000
0.000
.3278224
-.0853947
.3306536
-.082454
75
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
.0760397
_cons
.0003283
231.62
0.000
.0753962
.0766831
Living at Home Young Adults - 2000
Survey: Linear regression
Number of obs = 30,485,856
Population size = 606,922,294
104
Number of strata =
Number of PSUs = 11856876
Subpop. no. obs = 1,816,584
= 38,502,079
Subpop. size
11,856,772
=
df
Design
F( 2,11856771) = 69957.84
0.0000
=
Prob > F
0.1082
=
R-squared
Linearized
athome
nevmarry
bachelors
_cons
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
.2515184
-.0808944
.08832
.0006936
.0007257
.0004054
362.61
-111.47
217.87
0.000
.2501589
.2528778
0.000 -.0823166 -.0794721
0.000 .0875255 .0891145
Living at Home Young Adults - 2010
Survey: Linear regression
Number of obs = 3,061,692
Population size = 309,349,689
Number of strata = 2,069
Number of PSUs = 1,283,676
355,750
Subpop. no. obs =
41,701,823
=
Subpop. size
= 1,281,607
Design df
F( 2,1281606) = 10642.77
=
0.0000
Prob > F
0.0858
=
R-squared
Linearized
athome
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
cons
.2528345
-.0877668
.1348224
.0018546
.0019389
.0014487
136.33
-45.27
93.07
0.000
.2491996 .2564694
-.0915669 -.0839666
.131983 .1376618
Number of obs = 16325780
F(2, 16325777) > 99999.00
= 0.0000
Prob > F
= 0.0135
R-squared
0.000
0.000
Living in Center City Young Adults - 1980
Source
SS
df
MS
Model
Residual
51740.686
3784813.09
2
16325777
25870.343
.231830503
76
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
Total
3836553.78
16325779
.234999737
Adj R-squared = 0.0135
= .48149
Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
_cons
.1162312
-.0035515
.3341639
.0002466
.0002838
.0001616
471.27
-12.51
2067.54
0.000
0.000
0.000
.1157478 .1167146
-.0041078 -.0029952
.3338472 .3344807
Living in Center City Young Adults - 1990
Source
SS
df
MS
Model
Residual
48405.3297
4004604.7
2
17775965
24202.6648
.225281986
Total
4053010.03
17775967
.228005038
Number of obs = 17775968
F(2, 17775965) > 99999.00
= 0.0000
Prob > F
= 0.0119
R-squared
Adj R-squared = 0.0119
= .47464
Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
-cons
.1044676
-.0166474
.3050797
.0002258
.0002604
.0001672
462.56
-63.94
1825.00
0.000
.104025 .1049103
-.0171577 -.0161371
.304752 .3054073
0.000
0.000
Living in Center City Young Adults - 2000
Source
SS
df
MS
Model
Residual
40343.3161
3816903.57
2
16655278
20171.658
.229170811
Total
3857246.88
16655280
.231593037
Number of obs = 16655281
F(2, 16655278) = 88020.19
= 0.0000
Prob > F
= 0.0105
R-squared
Adj R-squared = 0.0105
= .47872
Root MSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
_cons
.0981043
.0067716
.3101291
.0002354
.0002573
.000185
416.70
26.32
1676.47
0.000
.0976429
.0062673
.3097665
Number of obs = 17930028
97175.73
F(2, 17930025)
= 0.0000
Prob > F
= 0.0107
R-squared
Adj R-squared = 0.0107
0.000
0.000
.0985657
.0072758
.3104917
Living in Center City Young Adults - 2010
Source
SS
df
MS
Model
Residual
44169.9766
4074931.06
2
17930025
22084.9883
.227268566
77
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults
=
.47673
Total
4119101.04
17930027
.229732004
Root MVSE
citydum
Coef.
Std. Err.
t
P>t
[95% Conf. Interval]
nevmarry
bachelors
.0942171
.0468134
.2794647
.0002381
.0002384
.00021
395.65
196.40
1330.48
0.000
0.000
0.000
.0937504 .0946839
.0463462 .0472805
.279053 .2798764
_cons
78
Housing the Millennial Generation - Trends in the Living Arrangements of Young Adults