Demographic and Housing Demand Analysis.Passaic County.New Jersey

DEMOGRAPHICS, POPULATION AND HOUSING DEMANDS
Passaic County, New Jersey
FALL 2016
Methods of
Planning
Analysis II
Himadri
Kundu
M.C.R.P. 2017
EJB School of
Planning and
Public Policy
Rutgers
University
image source: www.google.com

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Introduction
In order to completely understand what should be the visions for future growth for any community, understanding
both its temporal and spatial population distribution is essential. Only then the future development decisions can
be the most effective to serve the needs of the community.
The primary objective of this paper is the
exploration of the various ways to project
future populations, analyze and compare
the effectiveness of those predictions to
explain the current and future conditions.
Passaic County in New Jersey was chosen as
the study area due to its diversity and ability
to represent the northeastern metropolitan
region of United States of America.
Location
Passaic County, located in the northeastern
part of New Jersey contains a diverse
landscape from both geographic and
demographic aspects. Being 8 miles away
from Manhattan at one point, the
southeastern part of the County is
expectedly urbanized. However, a number
of municipalities like Bloomingdale,
Pompton Lakes, Ringwood, Wanaque, and
West Milford located in the northwestern
part of Passaic County are included in the protected region of New Jersey’s highland preservation area which are
relatively underdeveloped and extends to the foothills of the Appalachian Mountains. The diversity in spatial
distribution of urban development in the Passaic County can mostly be attributable to its geographical location
around the Tri-state area, shape, and transportation infrastructure. Close to two-thirds of the County’s total
population is concentrated in its southern part with urban centers like Paterson, and Clifton, and suburban
township of Wayne due to the proximity as well as the transportation access to the regional employment centers,
such as New York City. The spatial distribution of these communities within the Passaic County as well that of the
county within New Jersey can be seen in Figure 1(A).
Figure 1(A): Location of Passaic County, New Jersey

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The presence of transportation infrastructure in the county as shown in Figure 2 also follows these population
centers, with Garden State Parkway and US
Interstate passing through Paterson, and NJ
Transit’s Main Line connecting both Clifton
and Paterson to New York City. The “skinny
part” of Passaic County marks the northern
extent of major transportation
infrastructures with US Interstate 287 near
to Pompton Lakes. However, the two
airports that the county has are located in
the northwestern parts and the ones closer
to the major urban centers are in the
neighboring counties.
Background and Demographics
Rich in both pre and post-independence
history, Patterson City in Passaic County was
the first planned industrial city of the US
(“Paterson: History”, City-Data.com, 2016)
which is shown in the early map of 1872 in
Figure 1(B), and by the start of the 20th
century its population had grown to become the fifteenth largest of the country. Being an industrial hub, different
types of industries, most of them manufacturing clustered around the Great Falls in Paterson and other location
along the Passaic River. The prime industrial location attracted a continuous stream of immigrant workers
throughout the nineteenth and the early part of the twentieth century. However, during the early part of the
twentieth century Paterson saw a few problems with labor strikes, and then the Great Depression was followed
by wide-spread de-industrialization with the silk industry dying away. Even though it tried to bounce back like
before the slow death of its other manufacturing industries moving away over the years to newer and cheaper
towns in other counties, Passaic County suffered a large loss in population and along with that its importance as
an industrial center diminished. This period saw a slowdown in the inward migration with eventual emigration
from the major urban centers like Paterson and (the City of) Passaic.
Figure 2: Transportation access for Passaic County, New Jersey

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Post World War II however the trend reversed after 1950s when the suburban communities of Passaic County led
by Wayne Township saw rapid growth in population as middle class residents flowed in not only from the local
cities but also from regional centers like New York City. There are still a few remnants of the garment industry in
Paterson, but it mostly serves as a historic importance with landmark buildings, or industrial buildings converted
to newer uses. However, currently the city is in transition into a service provider for the surrounding municipalities
in the East Coast; mostly focusing on financial, sales and healthcare industries for its new economic growth.
The increasing trend of Passaic County’s population can be seen from Table 1, when it grew by 151,429 between
the years of 1940 to 1970 (US Census Bureau). Post war Passaic County has experienced a decrease in population
only once, that too slightly which was from 1970 to 1980, as shown by Figure 3. However, the growing trend of
population still continues till today, even though the percentage increase in population for the county has slowed
down from an 8% increase in 2000 to a mere 2% increase in 2010, but an imminent drop in the total population
does not seem to be on the cards in the near future due to transition of its largest city Paterson into a service
provider for the surrounding municipalities and well connected suburban communities. Also, the economic
slowdown of 2008 might have been influential in the slight slowdown of the population growth rate.
Figure 1(B): Passaic County, New Jersey in 1872
Source: “Historical Maps of New Jersey”, url: http://mapmaker.rutgers.edu/1872Atlas/Bergen_Passaic_1872.jpg

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Today the median household income of Passaic County is $59,513 (2010-2014, American Community Survey 5-
Year Estimate, 2014), which is above the national median but still well below its wealthy neighbors of Morris
County, Bergen County or Sussex County. According to the 2010 US Census, Passaic County was the ninth most
populous county in the state with a total work force population of 386,557 in 2010, accounting for 64.6 % of the
total population. As on 2010, the majority of the population is White constituting 62.65% of the total population,
while only 12.8% were reported to be African American and 37% were reported to be Hispanic or Latino of any
race. The percentage of females in 2010 was 51.5% slightly higher than that of the State’s ratio. The following
section analyzes the historic population characteristics and trends in Passaic County, in particular age and sex.
Population by Age and Sex
Age-sex pyramids were prepared in order to examine the historic and current population demographics of Passaic
County with respect to age and sex in a more detailed manner. This section aims at tracing the recent trends in
population cohorts by using the Decennial data collected from US Census
1990 Population
In 1990, as shown in Figure 4, there is a
considerable bulge in the middle of the age-
sex pyramid, mostly for the age cohorts of
25 - 29 and 30 - 34 years, for both genders.
This clearly indicates the effects of the
suburbanization by the middle class
families that begun during the 1950s, which
were mostly young families and the age
cohorts of 30 – 34 and 25 – 29 are most
likely comprise of their children. The “baby
boom” during the post war period was
common across the country, in particular in
the growing suburban communities. The
major bulk of the population appears to be
concentrated in the middle portion
between the age cohorts of 40 – 44 and 15
– 19, before it tapers off on either side with the exception of the youngest age cohort of 0 – 4. These young cohorts
are the pre- “millennials” and to a certain extent the “baby boomers” are also responsible for the high numbers
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Population (persons)
AgeCohort(inyears)
Figure 4: 1990 Population, Passsaic County, New
Jersey
Females
Males
Source: 1990 US Census

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in this age cohort as well, since by this decade few of them also started having families of their own. The interesting
thing to note in this year’s population is that the number of males relative to females in each age cohort starts to
decrease drastically only when we move above the age cohort of 45 – 49, which is reflective of the lower survival
rates for males in general above middle ages.
2000 Population
In 2000, as shown by Figure 5, the
“baby boomers” have grown up to
the age cohorts of 40 – 44 down till
30 – 34, which is where the majority
of the bulge in the age-sex pyramid
appears to be. However, there is
hardly any difference to the previous
decade’s population distribution
across ages other than the heavy
bottom of the pyramid, which clearly
marks the birth of the “millennials” in
the age cohorts of 5 – 9 and 0 – 4.
Since by this time most of the “baby
boomers” have grown up to be adults
and were likely to have their own
children, the age cohorts of the
“millennials” are pretty close in
numbers to that of their parents. The
parents of the “baby boomers”
however shows a decrease in their
numbers as they grew older with decreasing survival rates and also some may have migrated to warmer climates.
Thus the tapering of the pyramid is greater for this year.
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
AgeCohort(inyears)
Jersey
Females in
2000
Males in
2000

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2010 Population
The population in 2010, as shown in Figure 6, with a bulge between the upper middle age cohorts of 40 – 44 and
50 – 54 that represent the “baby boomers”. Although their numbers have decreased slightly from those in 2000,
as few would be likely to move to warmer climates leaving their large suburban housing once their children moved
out with their own families. This trend of a slight decrease is also visible from Figure 7 when we compare the other
age cohorts of 25 – 29 and 30 - 34 in
2000 population with that of their
corresponding older cohorts in 2010
population pyramid. In the 2010
population, one of the “millennials”
age cohort of 15 – 19 has the highest
numbers, closely followed by their
counterparts and the adjacent age
cohorts of the pre – “millennials” to
create a firm younger foundation of
the pyramid. The numbers in the
youngest age cohorts of 0 – 4 and also
that of 5 – 9 saw only a slight decrease
from that of 2000, which evidently
points to the strong middle age
groups starting new families and also
the late “baby boomers” who are still
within the fertility range.
Overall, the recent trend in the
population have been dominated by both the “baby boomers” and the “millennials” that improved, and create a
strong and robust age-sex pyramid of 2010. This suggest a stable and fertile period for population growth of the
county as it shifts from an industrial center into a more residential and service sector population. The increase in
the population of the oldest cohort in 2010 (Figure7) also shows the improvement in health care facilities as well
as the quality of life. Thus improving the survival rates of this cohort.
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
AgeCohort(inyears)
Jersey
Females
in 2010
Males in
2010

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Population Projections
Trend Extrapolation
The total population counts for the years 1940 to 2000 were collected for Passaic County from the US Census.
Population projections for 2010 were created using seven different methods of direct aggregate models for
population extrapolation from this data. Every model has its own strengths and weaknesses, but the model with
the “best” performance was determined statistically by evaluating their R2
values, Mean Absolute Percentage
Errors (MAPE) and comparing the predicted values with the actual data of 2010 population from the US Census.
The value of R2
indicates how much of the variability in the population trend can be explained by the model. For
example, say the R2
value for a particular model is 0.8, that means the model can only explain 80% of the variability
of the population trend data. MAPE value also displays the average accuracy of the model through mean
percentage error. So, higher value of R2
shows better fit, while a lower value of MAPE is better. The summary of
the results from each of the seven models can be seen from Table 1 below. It should be noted that the Moving
Average method uses averaging historic values to predict the future values and since there is no equation for this
method, it does not have a R2
value. If we were to evaluate the models purely based on the values of R2
, then
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
AgeCohort(inyears)
Figure 7: Comparison of 2000 & 2010 Population, Passsaic
County, New Jersey
Females in
2000
Males in
2000
Males in
2010
Females in
2010
Source: 2000 and 2010 US Census

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probably we would end up choosing one that fits the data the best, which in this case would be the red trend-line
in Figure 8 over the black one in the same graph. The red trend-line is of a Polynomial model in the fifth order,
however, it was not included in the comparison and instead the polynomial model of the second order (blue line)
was chosen over it. The reason behind this was the uneven nature of prediction, even in the short range which
results in overshooting the predicted value for 2010 population by almost 80,000 even though the R2
value was
almost perfect at 0.9953 that explains 99.53% of the population trend between 1940 and 2000. But this where
the absolute percentage error for 2010 (2010 APE) comes into play. The 2010 APE shows how accurate is the
prediction for 2010, and this is where most of the models did poorly even if they had high R2
values other than
the Power and the Moving Average (2 period) models. The reason behind choosing the moving average model for
2 periods over a 3 period moving average is almost similar, as the former fared better in explaining the population
trend and also in predicting the 2010 population with a lower 2010 APE which can be seen from both Table 1 and
also the red graph in Figure 9. As for the linear and the logarithmic models both ended up having a similar
prediction with a 2010 APE value of around 5.8% and similar R2
values of 0.86, and in both instances overshooting
the population by similar amounts. As a result, the MAPEs for these models were also in the higher range as can
be seen from the color coded values in Table 1. The exponential model overshot the observed population in its
prediction by the highest margin of around 48,775. When we compare the various R2
values between different
models, even though the polynomial model of the second order comes close to the highest value of 0.941 of the
power model, still its 2010 APE and the MAPE values are much higher than the power model’s.
Table 1: Comparing parameter estimates for 2010 population prediction by trend extrapolation
Model Equation R2
MAPE
Actual
2010
Predicted
2010 2010 APE
Linear y=ax+b 0.860 5.1% 501,226 530,790 5.9%
Exponential y=a*ebx
0.842 6.1% 501,226 550,002 9.7%
Logarithmic y=a+b*ln(x) 0.862 5.0% 501,226 530,062 5.8%
Polynomial y=ax2
+bx+c 0.934 3.7% 501,226 472,552 5.7%
Power y=a*xb 0.941 2.9% 501,226 504,280 0.6%
Moving average (2
periods) yt=avg(yt-1,yt-2) N.A. 4.0% 501,226 495,138 1.2%
Moving average (3
periods) yt=avg(yt-1,yt-2,yt-3) N.A. 9.0% 501,226 472,172 5.8%
Modified exponential y=c-a*(b^x) 0.874 4.3% 501,226 516,535 3.1%

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Hence after the comparison between the Models in Table 1 in order to choose the best model for our objective,
Power and 2 period Moving Average models which have the lowest 2010 APE and MAPE with the Power Model
having a high R2 value as well. The 2010 APE in case of the Power Model is almost half of that of the Moving
Average model, and also has a high R2
value of 0.941 with a MAPE of 2.9%. Further, the moving average model
will always underestimate for a growing population trend as it averages the previous lower population to derive
its estimate of the future. From these facts it is obvious that the prediction from the Power curve comes closest
to the actual 2010 population even though it overshoots by 3053.
The Modified Exponential Model (Figure 10) utilizes the logarithmic transformation of an exponential equation to
fit a linear curve on the data with the consideration for a maximum ceiling population of the county which is the
capacity that can be supported by the resources of the county over the long term. This ‘capacity’ parameter (c)
was assumed on the basis that even though there may be ample amount of space available in Passaic County,
however most of the rural northwest comes under preservation areas and also suburban communities are mostly
built up already thus there is hardly enough room for a rapid growth of the population in the near future from
“greenfield” development. However, there may be room for increased density with vertical construction,
redevelopment or infill, and revitalization of some of the decaying old neighborhoods and those of the built up
southern urban parts of the county, but these types of developmental growth are hardly rapid enough to be
considered for a short term projection. It should be noted that, there is enough room for growth from the future
long term perspective though. On the basis of these consideration about the spatial characteristics of Passaic
County, the capacity for the County was fixed at 800,000 which makes the modified exponential model somewhat
y = -49.25321x2 + 196,957.62500x - 196,425,229.21429
R² = 0.93409
y = -0.0033x5 + 32.7442x4 - 129,728.5171x3 + 256,967,412.7530x2 - 254,484,947,838.0310x +
100,803,961,008,307.0000
R² = 0.9953
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Population(persons)
Years
Figure 8: Population Projection - Polynomial Model, Passaic County, NJ
Observed Population
Poly. (Observed Population)
Poly. (Observed Population)
Power (Observed Population) Source: US Decennial Census

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intuitive of the geographic realities. Still the prediction values for 2010 overshoots by 15,309 with an APE of 3.1%,
MAPE of 4.3% and R2
of 0.874. Thus, even though the modified exponential model can account for long term
growth characteristics in a better way and thus may be better for long term population projection, for the
prediction of 2010 population based on the historic population trend from 1940 to 2000, the Power model with
its geometric function was the “best” model in this scenario.
0
100000
200000
300000
400000
500000
600000
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Population
Years
Figure 10: Population Projection - Modified Exponential Model, Passaic
County, NJ
Estimated Population
Observed Population
Source: US Decennial Census
0
100,000
200,000
300,000
400,000
500,000
600,000
1930 1940 1950 1960 1970 1980 1990 2000 2010
Population
Figure 9: Population Projection - Moving Average Model, Passaic
County, NJ
Observed Population
2 per. Mov. Avg. (Observed
Population)
3 per. Mov. Avg. (Observed
Population)

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Cohort Component Model
There is another way to predict future population other than the historic trend extrapolation by direct aggregate
models. In this approach, which is a part of the direct component models, we would be using one of its bottom-
up approaches for projection of future population using the Cohort Component Model.
The cohort component model takes into account various population growth parameters or sources; such as
number of births, deaths as well as migration. Thus various county-wide data for the years of 1990 and 2000 had
to be collected to project the 2010 population using this model which included the following: number of bkirths
per child-bearing cohorts, number of deaths per cohorts and total population per cohort for both the years
mentioned above. Several rates were required based on these data like fertility rates, survival rates and birth rates
to accurately estimate the 2010 population from the aspect of births and deaths reported for the county. It is
important to note here that fertility rates county-wide were not available for both the years and the 2000 data
had to be used in both cases. Also, State-wide data for birth rates as percentage of males and females instead of
county specific ones due to unavailability. Further, as the model factors in migration for determining the
population growth, residual migration method was used by adding the difference between the projected the
observed population of 2000 to the projected population of 2010. Thus the projection of the 2010 population
from the actual 2000 population without residual migration was 529,022 and that with the residual migration was
500,434 assuming the migrating population stays the same for 2010 with that of 2000. On comparison to the
actual 2010 population the projected population from this method underestimates by a mere count of 792, which
shows the power of this bottom up approach, even when there were lot of assumptions and data not available
for the intermediary steps.
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
AgeCohort(inyears)
Figure 11: Estimated Population in 2010 of Passaic
County, NJ
Estimated Males Estimated Females
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
AgeCohort(inyears)
Figure 12: Observed Population in 2010 of Passaic
County, NJ
Observed Males Observed Female

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The unique ability of this model is also its power to analyze the projected population by age cohorts and again by
sex in each cohort. Thus on the basis of the results from this model age-sex pyramid was constructed for the which
is visible in Figure 11. A comparative pyramid in Figure 13 shows how similar the projected cohorts to the actual
2010 data. Other than a few major differences in the extreme ends, there are only minor differences in the middles
age cohorts, and the shape of both the pyramids are almost similar. The differences in the youngest age cohorts
may steam from the fact that the data for birth rates used to project the intermediary populations of 1995 and
that of 2000 were not that of 1990 due to unavailability and instead that of 2000 had to be used. Further, the
difference between the projected and actual age cohorts above 70 – 79 increases drastically relative to the rest
and also it is more pronounced for the males than the females of 85+ age cohorts. One of the reasons for this
difference can be the use of annual crude mortality rates to derive the survival rates over a period of five years,
and thus increasing the number to a greater extent than in reality, which then increases the residual migration
counts and ultimately reduces the projected population of the age cohorts in the 2010 population. The use of the
2000 fertility rates for estimating the intermediary populations of 1995, 2000 and 2005 also leads to an
overestimation of total number of births, which then leads to higher residual migration numbers and ultimately a
lower projection for the for the 0 – 4 age cohorts of 2010. Another reason that might have contributed would be
from to the measurement error of the
fertility rates itself, since the total
population that was used to calculate
the fertility rates (Natality Rates) and the
number of births for the county in 2003
– 2006 by Center for Disease Control was
almost seventy-five percent that of the
actual population for both 1990 and
2000 respectively. Further, since the
fertility rates for ages under 15 years
and over 44 years were not available
even though few births were recorded
for these groups, so the fertility rates for
the adjacent groups were used as
estimates for them. This could also
increase the number of total births for
the intermediary populations to cause
the differences in the youngest age
cohorts.
The population projections using direct
component methods like the cohort
component model are extremely critical
from the aspect of future community planning. These estimated future cohort populations will help define the
predict the future demand for services in the community. In particular, the importance of housing demand for
planning is crucial.
25,000 20,000 15,000 10,000 5,000 0 5,000 10,000 15,000 20,000 25,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
AgeCohort(inyears)
Figure 13: Comparison between Estimated and
Observed Population in 2010 of Passaic County, NJ
Observed Female Observed Males

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Housing Demands
The demand for unmet housing in Passaic County were calculated based on the data from US Census for 2000 and
2010, which reveals that out of the 175,966 units only 5.2% or 9,181 units were vacant in 2010. However, the
vacancy rate in 2000 was only 3.6%. Even though the total populations and predicted future populations are a
major factor in estimating whether or not the supply of the housing stock will be able to meet the demands in the
future, however, total population is not the only consideration. There are other critical parameters for the
estimation of housing
demands such as; housing
population and size, total
housing units, number of
households, occupied units,
housing units that needs to be
replaced, the building permits
issued, and those that are
completed. Thus using all these
data collected from both the
2000 and 2010 Census, the
unmet housing demand was
estimated for the period of 2010 – 2020.
The projection for 2020 population was done using a simple linear model that estimates the total population of
Passaic County in 2020 to be 513,403, which is a 2% increase from the observed population in 2010. Similarly,
assuming a linear growth rate for the number of households in Passaic County for this period the estimated
households for 2020 was found to be 169,372. Now, if it is assumed that the housing stock remains unchanged in
2020 from what it was observed in 2010, the vacancy rate comes down to 3.5%, which might not look terribly bad
considering the fact that it is the same as that of 2000. However, during the 1990 – 2000 decade, the growth rate
for total population was 8%, which is four times higher than what has been predicted for the coming decade in
this projection. One of the reason for this low vacancy rates and high population growth rate the prices in the
housing market shot up not only in the county but state-wide, which ultimately had some role to play in the
housing bubble of 2008. In order to avoid such economic and also social ramifications for Passaic County there
needs to be informed decision making from the policy makers to improve the supply and quality of housing.
Housing demand that is currently not met as per the projections based on certain assumption and the Census data
was calculated from the difference between the total number of units needed from 2000 – 2020 and the total
Table 2: Housing Demand Estimation for 2020 in Passaic County, New Jersey
Housing Demand
Change in the Number of of Households 2000-2020 5,306
Change in the Number of Vacant Units 2000-2020 5,978
Units Lost to Disaster 2000-2020 3,401
Units Lost to Conversion 2000-2020 850
Units Lost to Demolition 2000-2020 3,300
Units that Must Be Replaced 2000-2020 7,551
Total Number of Units Needed 2000-2020 18,835
Projected Housing Completions 2001-2020 8,613
Unmet Housing Demand 2011-2020 10,222

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14
number of housing completions during that period. The number of housing completions was calculated from the
count-wide permits information, with the consideration of that the rate of completions will stay the same over
the period of 2010 – 2020 to that of the previous decade. While the total number of units required was calculated
from the change in housing units being occupied, number of households, change in vacant units and also the
number of units that need to be replaced. The requirement for replacement could steam from several factors like
the loss to disasters, or conversions and demolitions. The number of units lost to demolition was available from
the state department, but those lost to disasters and conversion was determined at rate on the basis of local
knowledge of the county. It is interesting to note that the during 2010 the number of housing units lost to
demolition was the highest in the last decade while 2009 saw the second fewest building permits for residential
constructions issued. May be one of the reasons for such an abnormality in the trend could be attributable to the
crash of the housing market in 2008, which slowed down the supply considerably. This data does not include the
units lost to the consecutive hurricanes Irene and Sandy in 2011 and 2012, which will severely impact the housing
market in Passaic County due to its considerable effect on many of the suburban and urban communities
throughout the county, in particular along the Passaic River. Thus the rate to calculate the units lost to disaster
was estimated at 2% of the housing stock, however the actual number may be even larger. Loss to conversion
was estimated at a moderate rate of 0.5% of the housing stock. Thus, through the above mentioned process the
unmet housing demand was estimated at 10,222 units for the period of 2000 – 2020. This signifies that the county
is in need of 10,222 units till 2020 to meet the demand expected from the linear growth of the projected
population.
The projected housing demand is expected to exceed the housing supply by 2020 on the basis of the linear
population and household growth model used here. Like it has been mentioned above this could have extreme
consequences on the communities if new housing opportunities both in the urban and the suburban areas are not
developed. One of the outcomes if the unmet demands are not catered for, will be the increase in the prices and
rents for housing, which in turn might force many to look for housing elsewhere and even outside of the county.
The loss of tax revenues for the community even if a fraction of the population in proportion of the unmet housing
demand or some part of it moves out of the county. Further, the rate of growth for average household size has
not been extremely high but still it is growing from 2.92 in 2000 to 2.96 in 2020, which might indicate the demand
for single family housing units will be on the rise. May be this is one of the reason for the lower number of
completion permits for multifamily units, relative to the single family ones. However, based on the trends of the
“millennials” wanting to move back into the cities or urban centers, this factor can outweigh the assumption on
the average household sizes. Further, the types of units lost to different factors needs to be analyzed with context
of where and which type of housing demands are not being met. The spatial context of the demand in relation to

Himadri Kundu
15
the demographic preferences based on lifestyle changes will be a key factor in determining how the unmet
housing demand influence the population and life in this county.
Conclusion
This paper’s effort at the prediction of the population based on the historical trends as well as cohort component
models with its application on the housing market, can be influential in taking decisions for future policy making.
In reality there are a number of factors that can influence the demographics of any place and without analyzing
the specifics, accurate predictions are not possible but only at the cost of huge efforts. However, most of the
models used here came extremely close to the actual population of 2010, with the power model being 0.6% and
the cohort component model being even closer at 0.16% of the actual population of 2010. The slight variations
shown by the other models might be due to the volatile economic conditions during the late 2000s, when families
might have decided against migration, or to hold of starting a family. The slowdown in the housing supply was
evident from the permits issued and the demolition of units we have already seen.
The importance of this type of study when projecting for the future needs of an y community are significant. Since
such analysis, are required as a pre-requisite for informed decision making to improve the planning and public
policy formulation which can impact the community in the longer run. As it has been mentioned earlier, estimating
and predicting the future population is the heart of understanding how any community can prosper in the future.
Passaic County had been able to maintain a steady and almost stable growth rate despite its struggle with the
historical industrial center. Even though the growth rate had been modest, it has lead the population to have a
robust structure with respect different age cohorts and create a more diverse community, rather than being
overwhelmed by the suburban sprawl of the 1950s and 60s. Its major urban centers like that of Paterson and
Passaic City can promote urban redevelopment to support the younger generations of the millennials and post
millennials who generally have been seen to prefer an urban setting but need modest rents. Thus by improving
the housing supply at the urban center with increase in density, a more vibrant core could be developed which
has the high likelihood of increasing the demand and revenue through the transit access to New York City and
other regional employment centers.

Himadri Kundu
16
References
“Paterson: History”, City-Data.com. Retrieved November 2016. url: http://www.city-data.com/#data
“Historical Maps of New Jersey”. Retrieved November 2016. url:
http://mapmaker.rutgers.edu/1872Atlas/Bergen_Passaic_1872.jpg
US Census Bureau. “State & County QuickFacts: Passaic County, New Jersey”. Retrieved November 2016. url:
http://www.census.gov/quickfacts/table/PST045215/34031,00

Passaic County, NJ
1990 Age/Sex
Age Total Females Male Males
0-4 36,037 17759 18,278 -18,278
5-9 30,620 14835 15,785 -15,785
10-14 30,160 14758 15,402 -15,402
15-19 33,477 16560 16,917 -16,917
20-24 38,567 19135 19,432 -19,432
25-29 42,035 20957 21,078 -21,078
30-34 41,559 20846 20,713 -20,713
35-39 36,281 18337 17,944 -17,944
40-44 33,289 17134 16,155 -16,155
45-49 26,923 14036 12,887 -12,887
50-54 22,155 11561 10,594 -10,594
55-59 20,518 10733 9,785 -9,785
60-64 20,596 11009 9,587 -9,587
65-69 18,893 10558 8,335 -8,335
70-74 14,871 8774 6,097 -6,097
75-79 11,674 7305 4,369 -4,369
80-84 7,389 4851 2,538 -2,538
85+ 5,832 4,329 1,503 -1,503
470,876
2000 Age/Sex
Age Total Females in 20Male Males in 2000
0-4 36,129 17764 18365 -18365
5-9 37,178 18386 18792 -18792
10-14 34,776 16997 17779 -17779
15-19 32,341 15716 16625 -16625
20-24 32,540 16208 16332 -16332
25-29 34,818 17435 17383 -17383
30-34 38,678 19343 19335 -19335
35-39 40,932 20537 20395 -20395
40-44 38,591 19637 18954 -18954
45-49 32,827 16709 16118 -16118
50-54 29,765 15533 14232 -14232
55-59 23,207 12306 10901 -10901
60-64 18,234 9672 8562 -8562
65-69 15,520 8594 6926 -6926
70-74 14,573 8431 6142 -6142
75-79 12,626 7650 4976 -4976
80-84 8,617 5595 3022 -3022
85+ 7,697 5528 2169 -2169
489,049
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
AgeCohort(inyears)
Figure 4: 1990 Population, Passsaic County, New Jersey
Females
Males
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Population
AgeCohort(inyears)
Females
in 2000
Males in
2000

2010 Age/Sex
Age Total Females in 20Male Males in 2010
0-4 34247 16669 17578 -17578
5-9 34008 16704 17304 -17304
10-14 34536 16852 17684 -17684
15-19 37604 18555 19049 -19049
20-24 36025 17872 18153 -18153
25-29 33501 16890 16611 -16611
30-34 33173 16919 16254 -16254
35-39 33499 17201 16298 -16298
40-44 35615 18142 17473 -17473
45-49 37411 19114 18297 -18297
50-54 35704 18540 17164 -17164
55-59 30182 15646 14536 -14536
60-64 25397 13527 11870 -11870
65-69 18226 9926 8300 -8300
70-74 13624 7569 6055 -6055
75-79 10679 6339 4340 -4340
80-84 8784 5385 3399 -3399
85+ 9011 6252 2759 -2759
501,226
30,000 20,000 10,000 0 10,000 20,000 30,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Population
AgeCohort(inyears)
Figure 7: Comparison of 2000 & 2010 Population,
Passsaic County, New Jersey
Female
s in
2000
Males
in 2000
Males
in 2010
Female
s in
2010

25,000 20,000 15,000 10,000 5,000 0 5,000 10,000 15,000 20,000 25,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Population
AgeCohort(inyears)
Females
in 2010
Males in
2010

Passaic County, NJ
Historical Population
Year Observed Populatio Predicted Error Abs % Error
1940 309,353 327,793 (18,440.16) 6.0%
1950 337,093 356,793 (19,699.77) 5.8%
1960 406,618 385,792 20,825.62 5.1%
1970 460,782 414,792 45,990.01 10.0%
1980 447,585 443,792 3,793.40 0.8%
1990 453,060 472,791 (19,731.21) 4.4%
2000 489,049 501,791 (12,741.82) 2.6%
2010 501,226 530,790 (29,564.43) 5.9%
Linear y=ax+b
a= 2899.961000
b= -5298131.179000
R2
= 0.86
MAPE= 5.0%
y = 2,899.961x - 5,298,131.179
R² = 0.860
0
100,000
200,000
300,000
400,000
500,000
600,000
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Population
Year
Population Prediction

309,353
337,093
406,618
460,782 447,585 453,060
489,049 501,226
0
100,000
200,000
300,000
400,000
500,000
600,000
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Population(persons)
Years
Observed Population

Passaic County, NJ
Year X Observed Population Predicted Error Abs % Error
1940 1 309,353 328,515 (19,161.75) 6.2%
1950 2 337,093 353,613 (16,519.52) 4.9%
1960 3 406,618 380,628 25,990.30 6.4%
1970 4 460,782 409,707 51,075.22 11.1%
1980 5 447,585 441,007 6,577.57 1.5%
1990 6 453,060 474,699 (21,639.38) 4.8%
2000 7 489,049 510,965 (21,916.32) 4.5%
2010 8 501,226 550,002 (48,775.90) 9.7%
Exponential y=a*ebx
a= 305198
b= 0.0736
R2
= 0.8424
MAPE= 5.6%
y = 305,198.29932e0.07362x
R² = 0.84242
0
100,000
200,000
300,000
400,000
500,000
600,000
0 1 2 3 4 5 6 7 8 9
Population
Years

Passaic County, NJ
Year Observed Population Predicted Error Abs % Error
1940 309,353 327,312 (17,958.64) 5.8%
1950 337,093 356,720 (19,626.75) 5.8%
1960 406,618 385,977 20,640.56 5.1%
1970 460,782 415,086 45,695.77 9.9%
1980 447,585 444,048 3,537.37 0.8%
1990 453,060 472,863 (19,803.13) 4.4%
2000 489,049 501,534 (12,485.19) 2.6%
2010 501,226 530,062 (28,836.25) 5.8%
Logarithmic y=a+b*ln(x)
a= -42974598.8372300
b= 5719864.615540
R
2
= 0.86191
MAPE= 4.9%
y = 5,719,864.61554ln(x) - 42,974,598.83723
R² = 0.86191
0
100,000
200,000
300,000
400,000
500,000
600,000
1930 1940 1950 1960 1970 1980 1990 2000 2010
Population
Years

Passaic County, NJ
Year Observed Population Predicted Error Abs % Error
1940 309,353 303,972 5,380.51 1.7%
1950 337,093 357,607 (20,514.04) 6.1%
1960 406,618 401,391 5,227.01 1.3%
1970 460,782 435,324 25,457.66 5.5%
1980 447,585 459,407 (11,822.09) 2.6%
1990 453,060 473,639 (20,579.24) 4.5%
2000 489,049 478,021 11,028.21 2.3%
2010 501,226 472,552 28,674.26 5.7%
Polynomial y=ax2
+bx+c
a= -49.253000
b= 196957.625000
c= -196425229.214000
R
2
= 0.934000
MAPE= 3.4%
y = -49.25321x2 + 196,957.62500x - 196,425,229.21429
R² = 0.93409
y = -0.0033x5 + 32.7442x4 - 129,728.5171x3 + 256,967,412.7530x2 - 254,484,947,838.0310x +
100,803,961,008,307.0000
R² = 0.9953
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Figure 8: Population Projection - Polynomial Model, Passaic County, NJ
Observed
Population
Poly. (Observed
Population)
Poly. (Observed
Population)

Passaic County, NJ
Year X Observed Population Predicted Error Abs % Error
1940 1 309,353 304,819 4,533.76 1.5%
1950 2 337,093 360,511 (23,418.17) 6.9%
1960 3 406,618 397,694 8,924.34 2.2%
1970 4 460,782 426,378 34,403.71 7.5%
1980 5 447,585 450,045 (2,460.09) 0.5%
1990 6 453,060 470,354 (17,294.19) 3.8%
2000 7 489,049 488,239 810.34 0.2%
2010 8 501,226 504,280 (3,053.64) 0.6%
Geometric y=a*xb
a= 304,819
b= 0.2421
R2
= 0.941
MAPE= 3%
y = 304,393.19512x0.24395
R² = 0.92969
0
100,000
200,000
300,000
400,000
500,000
600,000
0 1 2 3 4 5 6 7 8 9
POPULATION
X
POPULATION PREDICTION

Middlesex County, NJ
Passaic County, NJ
Year Observed Population 2 per. Moving Avg Error Abs % Error 3 per. Moving Avg Error Abs % Error
1940 309,353
1950 337,093 323,223 13,870.00 4.1%
1960 406,618 371,856 34,762.50 8.5% 347,539 59,078.75 14.5%
1970 460,782 433,700 27,082.00 5.9% 376,260 84,522.50 18.3%
1980 447,585 454,184 (6,598.50) 1.5% 419,913 27,672.00 6.2%
1990 453,060 450,323 2,737.50 0.6% 446,069 6,991.33 1.5%
2000 489,049 471,055 17,994.50 3.7% 458,520 30,528.83 6.2%
2010 501,226 495,138 6,088.50 1.2% 472,172 29,054.50 5.8%
Moving average yt=average(yt-1,yt-2,yt-3,…)
(number of periods = 2 and 3)
MAPE (2 period) 4%
MAPE (3 period) 9%
0
100,000
200,000
300,000
400,000
500,000
600,000
1930 1940 1950 1960 1970 1980 1990 2000 2010
Figure 9: Population Projection - Moving Average, Passaic County, NJ
Observed Population
2 per. Mov. Avg. (Observed Population)
3 per. Mov. Avg. (Observed Population)

Passaic County, NJ
Historical Population y=c-a*(b^x)
Year X Observed Population (c-y) log(c-y) Predicted log(c-y) Predicted (c-y) Predicted (y) Error Abs. % Err Estimated Population
1940 1 309,353 490,647 5.69076915 5.6758 474,024 325,976 16,623 5.4% 325976
1950 2 337,093 462,907 5.66549375 5.6439 440,453 359,547 22,454 6.7% 359547
1960 3 406,618 393,382 5.59481448 5.612 409,261 390,739 (15,879) 3.9% 390739
1970 4 460,782 339,218 5.53047889 5.5801 380,277 419,723 (41,059) 8.9% 419723
1980 5 447,585 352,415 5.54705439 5.5482 353,346 446,654 (931) 0.2% 446654
1990 6 453,060 346,940 5.54025437 5.5163 328,322 471,678 18,618 4.1% 471678
2000 7 489,049 310,951 5.49269196 5.4844 305,070 494,930 5,881 1.2% 494930
2010 8 501226 5.4525 283465.3633 516,535 15,309 3.1% 516535
Modified Exponential y=c-a*(b^x)
log(c-y)=log(a) + log(b)*x
c= 800,000
log(a)= 5.7077 a= 510152.477
log(b)= -0.0319 b= 0.92918031
R2
= 0.8742
MAPE= 4.3%
y = -0.0319x + 5.7077
R² = 0.8742
5.4
5.45
5.5
5.55
5.6
5.65
5.7
5.75
0 1 2 3 4 5 6 7 8 9
Log(c-y)
X
log(c-y)
0
100000
200000
300000
400000
500000
600000
1930 1940 1950 1960 1970 1980 1990 2000 2010 2020
Population
Years
Figure 10: Population Projection - Modified Exponential Model, Passaic County, NJ
Estimated Population
Observed Population

Comparing parameter estimates
Passaic County, NJ
Model Equation R2
MAPE Actual 2010 Predicted 2010 2010 APE
Linear y=ax+b 0.860 5.1% 501,226 530,790 5.9%
Exponential y=a*ebx
0.842 6.1% 501,226 550,002 9.7%
Logarithmic y=a+b*ln(x) 0.862 5.0% 501,226 530,062 5.8%
Polynomial y=ax2
+bx+c 0.934 3.7% 501,226 472,552 5.7%
Power y=a*xb 0.941 2.9% 501,226 504,280 0.6%
Moving average (2 periods) yt=avg(yt-1,yt-2) N.A. 4.0% 501,226 495,138 1.2%
Moving average (3 periods) yt=avg(yt-1,yt-2,yt-3) N.A. 9.0% 501,226 472,172 5.8%
Modified exponential y=c-a*(b^x) 0.874 4.3% 501,226 516,535 3.1%

48%
52%
Male Female Male Female Male Female Male Female
0-4 17,759 18,278 0.012743 0.009794 0.9379 0.9520 24,300 22,756.91
5-9 14,835 15,785 0.000248 0.000225 0.9988 0.9989 16,656 17,400
10-14 14,758 15,402 0.000315 0.000211 0.9984 0.9989 0.03771 2,904 14,817 15,767 2,973
15-19 16,560 16,917 0.000883 0.000399 0.9956 0.9980 0.03771 3,190 14,735 15,386 2,901
20-24 19,135 19,432 0.001318 0.000464 0.9934 0.9977 0.09711 9,435 16,487 16,883 8,198
25-29 20,957 21,078 0.002068 0.001035 0.9897 0.9948 0.11935 12,578 19,009 19,387 11,569
30-34 20,846 20,713 0.002068 0.001035 0.9897 0.9948 0.11406 11,813 20,741 20,969 11,959
35-39 18,337 17,944 0.0039130 0.0018470 0.9806 0.9908 0.05881 5,276 20,631 20,606 6,059
40-44 17,134 16,155 0.0039130 0.0018470 0.9806 0.9908 0.01281 1,035 17,981 17,779 1,139
45-49 14,036 12,887 0.0070150 0.0036330 0.9654 0.9820 0.01281 825 16,801 16,006 1,025
50-54 11,561 10,594 0.0070150 0.0036330 0.9654 0.9820 13,551 12,655
55-59 10,733 9,785 0.0157070 0.0083830 0.9239 0.9588 11,161 10,403
60-64 11,009 9,587 0.0157070 0.0083830 0.9239 0.9588 9,916 9,382
65-69 10,558 8,335 0.0358980 0.0206000 0.8329 0.9012 10,171 9,192
70-74 8,774 6,097 0.0358980 0.0206000 0.8329 0.9012 8,794 7,511
75-79 7,305 4,369 0.0805070 0.0531240 0.6573 0.7611 7,308 5,494
80-84 4,851 2,538 0.0805070 0.0531240 0.6573 0.7611 4,801 3,325
85+ 4,329 1,503 0.1932700 0.1486650 0.3417 0.4472 4,668 2,604
Total 47,056 Total 45,823
Note: For the 2000 analysis, refer to comments attached to the 1990 analysis, but with the 2000 survival rates.
Male Female Male Female Male Female Male Female
0-4 18,365 17,764 0.0067190 0.0052320 0.9669 0.9741 22,639 21,202
5-9 18,792 18,386 0.000118 0.000115 0.9994 0.9994 17,756 17,304
10-14 17,779 16,997 0.000158 0.000168 0.9992 0.9992 0.03771 3,205 18,781 18,375 3,465
15-19 16,625 15,716 0.000555 0.000267 0.9972 0.9987 0.03771 2,963 17,765 16,983 3,202
20-24 16,332 16,208 0.001099 0.000406 0.9945 0.9980 0.09711 7,870 16,579 15,695 7,621
25-29 17,383 17,435 0.001271 0.000618 0.9937 0.9969 0.11935 10,404 16,242 16,175 9,653
30-34 19,335 19,343 0.001271 0.000618 0.9937 0.9969 0.11406 11,031 17,273 17,381 9,912
35-39 20,395 20,537 0.002284 0.001322 0.9886 0.9934 0.05881 6,039 19,212 19,283 5,670
40-44 18,954 19,637 0.0022840 0.0013220 0.9886 0.9934 0.01281 1,258 20,163 20,402 1,307
45-49 16,118 16,709 0.0048960 0.0029110 0.9758 0.9855 0.01281 1,070 18,739 19,508 1,249
50-54 14,232 15,533 0.0048960 0.0029110 0.9758 0.9855 15,727 16,467
55-59 10,901 12,306 0.0103180 0.0060610 0.9495 0.9701 13,887 15,308
60-64 8,562 9,672 0.0103180 0.0060610 0.9495 0.9701 10,350 11,938
65-69 6,926 8,594 0.0236950 0.0153370 0.8870 0.9256 8,129 9,382
70-74 6,142 8,431 0.0236950 0.0153370 0.8870 0.9256 6,143 7,955
75-79 4,976 7,650 0.0616950 0.0430780 0.7273 0.8024 5,448 7,804
80-84 3,022 5,595 0.0616950 0.0430780 0.7273 0.8024 3,619 6,138
85+ 2,169 5,528 ######## 0.1377060 0.4227 0.4767 2,198 4,489
Total 43,840 Total 37,365
Births 05-09
Births 95-99
Births 00-04
2005 Projection
% female births
% male births
Age
1990 rude Death Rate (per perso Survival Rate
Fertility Rate Births 90-94
1995 Projection
Age
2000 rude Death Rate (per perso Survival Rate
Fertility Rate

Fertility Passaic County NJ
Source: CDC, Natality, 2003-2006
(per 1,000 women)
Age Births Females Rate
Under 15 yea 11
Male Female Age Male Female 15-19 years 672 17821 37.7
23,662 22,160 0-4 (5,297) (4,396) 20-24 years 1638 16868 97.1
22,790 21,664 5-9 (3,998) (3,278) 25-29 years 2024 16958 119
16,635 17,381 10-14 1,144 (384) 30-34 years 1940 17009 114
14,793 15,751 15-19 1,832 (35) 35-39 years 1074 18263 58.8
14,670 15,355 20-24 1,662 853 40-44 years 250 19512 12.8
16,379 16,844 25-29 1,004 591 45-49 years 15
18,813 19,287 30-34 522 56
20,528 20,861 35-39 (133) (324) Mortality Passaic County NJ
20,231 20,416 40-44 (1,277) (779) Source: CDC, Compressed Mortality
17,632 17,615 45-49 (1,514) (906) 1979-1998 (per 100,000 people)
16,220 15,718 50-54 (1,988) (185) Age Male Rate Female Rate
13,082 12,426 55-59 (2,181) (120) < 1 year 1051.9 828.6
10,312 9,974 60-64 (1,750) (302) 1-4 years 55.6 37.7
9,161 8,995 65-69 (2,235) (401) 5-9 years 24.8 22.5
8,472 8,283 70-74 (2,330) 148 10-14 years 31.5 21.1
7,325 6,769 75-79 (2,349) 881 15-19 years 88.3 39.9
4,803 4,182 80-84 (1,781) 1,413 20-24 years 131.8 46.4
4,751 3,696 85+ (2,582) 1,832 25-34 years 206.8 103.5
260,260 257,377 -23,252 -5,336 35-44 years 391.3 184.7
-28588 45-54 years 701.5 363.3
55-64 years 1570.7 838.3
65-74 years 3589.8 2060
Male Female Age Male Female 75-84 years 8050.7 5312.4
19,295 18,070 0-4 13,997 13,674 85+ years 19327 14866.5
21,888 20,653 5-9 17,890 17,375
17,746 17,294 10-14 18,889 16,910 Mortality Passaic County NJ
18,766 18,360 15-19 20,598 18,325 Source: CDC, Compressed Mortality
17,716 16,960 20-24 19,378 17,813 1998 - 2010 (per 100,000 people)
16,488 15,663 25-29 17,492 16,254 Age Male Rate Female Rate
16,139 16,125 30-34 16,661 16,181 < 1 year 577.9 457.6
17,163 17,328 35-39 17,031 17,004 1-4 years 23.5 16.4
18,994 19,156 40-44 17,717 18,377 5-9 years 11.8 11.5
19,934 20,267 45-49 18,420 19,361 10-14 years 15.8 16.8
18,284 19,225 50-54 16,296 19,041 15-19 years 55.5 26.7
15,346 16,229 55-59 13,165 16,109 20-24 years 109.9 40.6
13,185 14,850 60-64 11,435 14,548 25-34 years 127.1 61.8
9,827 11,580 65-69 7,592 11,179 35-44 years 228.4 132.2
7,211 8,685 70-74 4,881 8,832 45-54 years 489.6 291.1
5,449 7,363 75-79 3,100 8,245 55-64 years 1031.8 606.1
3,962 6,262 80-84 2,181 7,675 65-74 years 2369.5 1533.7
2,632 4,925 85+ 51 6,758 75-84 years 6169.5 4307.8
260,027 268,995 236,775 263,659 85+ years 15819.1 13770.6
###### 500434
2010 Projection Projection with Migration 2010
2000 Projection Migration Residual 1990-2000

Age Male Female Age Male Female
0-4 13,997 13,674 0-4 17,578 16,669
5-9 17,890 17,375 5-9 17,304 16,704
10-14 18,889 16,910 10-14 17,684 16,852
15-19 20,598 18,325 15-19 19,049 18,555
20-24 19,378 17,813 20-24 18,153 17,872
25-29 17,492 16,254 25-29 16,611 16,890
30-34 16,661 16,181 30-34 16,254 16,919
35-39 17,031 17,004 35-39 16,298 17,201
40-44 17,717 18,377 40-44 17,473 18,142
45-49 18,420 19,361 45-49 18,297 19,114
50-54 16,296 19,041 50-54 17,164 18,540
55-59 13,165 16,109 55-59 14,536 15,646
60-64 11,435 14,548 60-64 11,870 13,527
65-69 7,592 11,179 65-69 8,300 9,926
70-74 4,881 8,832 70-74 6,055 7,569
75-79 3,100 8,245 75-79 4,340 6,339
80-84 2,181 7,675 80-84 3,399 5,385
85+ 51 6,758 85+ 2,759 6,252
Total 236,775 263,659 Total 243,124 258,102
ratio 0.485 0.515
2010 Population
Total projected population from cohort component model 500,434
Total actual population from census 501,226
Difference (Under/Over estimated) (792)
Age Male Estimated Femalested Males
0-4 13,997 13,674
5-9 17,890 17,375
10-14 18,889 16,910
15-19 20,598 18,325
20-24 19,378 17,813
25-29 17,492 16,254
30-34 16,661 16,181
35-39 17,031 17,004
40-44 17,717 18,377
45-49 18,420 19,361
50-54 16,296 19,041
55-59 13,165 16,109
60-64 11,435 14,548
65-69 7,592 11,179
70-74 4,881 8,832
75-79 3,100 8,245
80-84 2,181 7,675
85+ 51 6,758
Total 236,775 263,659
Age Male Observed Female Observed Males
0-4 17,578 16,669
5-9 17,304 16,704
10-14 17,684 16,852
15-19 19,049 18,555
20-24 18,153 17,872
Projected 2010 Population with Migration Actual 2010 Population from Decennial Census
Projected 2010 Population with Migration
Actual 2010 Population from Decennial Census
25,000 20,000 15,000 10,000 5,000 0 5,000 10,000 15,000 20,000 25,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Population
AgeCohort(inyears)
Figure 13: Comparison between Estimated and Observed
Population in 2010 of Passaic County, NJ
Observed Female Observed Males Estimated Males Estimated Females
25,000 20,000 15,000 10,000 5,000 0 5,000 10,000 15,000 20,000 25,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Population
AgeCohort(inyears)
Figure 11: Estimated Population in 2010 of Passaic County, NJ
Estimated Males Estimated Females
25,000 20,000 15,000 10,000 5,000 0 5,000 10,000 15,000 20,000 25,000
0-4
5-9
10-14
15-19
20-24
25-29
30-34
35-39
40-44
45-49
50-54
55-59
60-64
65-69
70-74
75-79
80-84
85+
Population
AgeCohort(inyears)
Figure 12: Observed Population in 2010 of Passaic County, NJ
Observed Males Observed Female

Housing Demand in 2020
Passaic County, NJ
Households Projection 2000 Census 2010 Census 2020 Projection
Total Population 489,049 501,226 513,403
Population in Group Quarters 9,976 11,019 12,062
Household Population 479,073 490,207 501,341
Average HH size 2.92 2.94 2.96
Number of Households 164,066 166,737 169,372
Vacancy Projection 2000 Census 2010 Census 2020 Projection
Total Housing Units 170,048 175,966 181,884
Occupied Housing 163,856 166,785 169,714
Vacant Units 6,192 9,181 12,170
Vacancy Rate 3.6% 5.2% 6.7%
Housing Demand
Change in the Number of of Households 2000-2020 5,306
Change in the Number of Vacant Units 2000-2020 5,978
Units Lost to Disaster 2000-2020 3,401 2.0% Assumption
Units Lost to Conversion 2000-2020 850 0.5% Assumption
Units Lost to Demolition 2000-2020 3,300
Units that Must Be Replaced 2000-2020 7,551
Total Number of Units Needed 2000-2020 18,835
Projected Housing Completions 2001-2020 8,613
Unmet Housing Demand 2011-2020 10,222

Year Residential Demolitions
2001 116
2002 73
2003 175
2004 99
2005 86
2006 124
2007 107
2008 152
2009 122
2010 596
2000-2020 3,300

Total Single Family Two Family Three and Four Family Five and More Family
2001 584 379 10 3 192
2002 621 503 20 13 85
2003 829 741 24 3 61
2004 701 401 10 11 279
2005 428 127 30 7 264
2006 696 288 14 0 394
2007 571 181 30 4 356
2008 180 99 60 4 17
2009 134 64 18 0 52
2010 187 52 4 0 131
Total 4931 2835 220 45 1831
Number of Permits Starts to Permits Ratio Completions to Starts Ratio Number of Completions
Single Family 2835 102.50% 96.50% 2804
Multi Family 2096 77.500% 92.50% 1503
Total 4307
Projected Housing
Completions 2001-2020
8613
Year
Number of Building Permits (in reported units)

Demographic and Housing Demand Analysis.Passaic County.New Jersey

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