HomeMy WebLinkAboutGeneral Plan Comprehensive Review Trends & Forecasts Final Report - SMS (2016)
Final Forecasts and Analysis
GENERAL PLAN COMPREHENSIVE REVIEW
TRENDS AND FORECASTS
FINAL REPORT – SEPTEMBER 2016
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page i
CONTENTS
INTRODUCTION TO THE SUMMARY REPORT ....................................................................... 1
DEMOGRAPHIC FORECASTS .............................................................................................. 3
RESIDENT POPULATION .................................................................................................. 3
POPULATION PYRAMIDS ................................................................................................. 6
DE FACTO POPULATION ................................................................................................. 9
URBAN POPULATION ......................................................................................................10
NUMBER OF HOUSEHOLDS ............................................................................................12
PERSONS PER HOUSEHOLD ..........................................................................................14
PERSONS IN HOUSEHOLDS ...........................................................................................15
PERSONS LIVING IN GROUP QUARTERS ......................................................................17
ECONOMIC FORECASTS ....................................................................................................19
LABOR FORCE IN THE COUNTY OF HAWAI‘I.................................................................20
EMPLOYMENT BY SECTOR, INDUSTRY, OCCUPATION ...............................................21
JOBS IN THE COUNTY OF HAWAI‘I .................................................................................38
HOUSEHOLD INCOME DISTRIBUTION ...........................................................................46
MEDIAN HOUSEHOLD INCOME ......................................................................................47
REAL ESTATE MARKET AND HOUSING ............................................................................50
TOTAL HOUSING UNITS ..................................................................................................54
TOTAL HOUSING UNITS: OCCUPIED AND VACANT .....................................................56
OCCUPIED HOUSING UNITS: SINGLE-FAMILY AND MULTI-FAMILY ...........................58
OCCUPIED HOUSING UNITS: OWNED AND RENTED ..................................................60
TOTAL HOUSING UNITS: AFFORDABLE ........................................................................61
AVAILABLE HOUSING UNITS ..........................................................................................64
NEEDED UNITS ................................................................................................................65
VISITOR ARRIVALS AND ACCOMMODATIONS..................................................................68
VISITOR ARRIVALS ..........................................................................................................69
VISITOR ACCOMMODATION UNITS ................................................................................71
VISITOR ACCOMMODATIONS UNITS BY TYPE .............................................................73
COMMERCIAL AND INDUSTRIAL SPACE ...........................................................................74
COMMERCIAL SPACE BY TYPE ......................................................................................74
INDUSTRIAL SPACE BY TYPE .........................................................................................76
REVISED FORECAST ANALYSIS ZONES ..............................................................................77
REFERENCES .........................................................................................................................80
APPENDIX A: DEFINITIONS OF JOBS BY INDUSTRY .........................................................84
APPENDIX B: EMPLOYMENT SECTORS, INDUSTRIES, AND OCCUPATIONS ..................89
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page ii
INDEX OF FIGURES
FIGURE 1: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ..................................................4
FIGURE 2: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 2013 .......................................................................6
FIGURE 3: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 2025 ........................................................................7
FIGURE 4: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 2040 ........................................................................8
FIGURE 5: DE FACTO POPULATION, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ..................................................9
FIGURE 6: URBAN POPULATION, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ................................................... 11
FIGURE 7: NUMBER OF HOUSEHOLDS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ............................................ 12
FIGURE 8: PERSONS PER HOUSEHOLD, COUNTY OF HAWAI‘I, 2000-2040 ......................................................... 14
FIGURE 9: PERSONS IN HOUSEHOLDS, COUNTY OF HAWAI‘I, 2000-2040 .......................................................... 15
FIGURE 10: PERSONS LIVING IN GROUP QUARTERS, COUNTY OF HAWAI‘I, 2000-2040 ........................................ 17
FIGURE 11: LABOR FORCE, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ........................................................... 20
FIGURE 13: EMPLOYMENT BY SECTOR, COUNTY OF HAWAI‘I, 2000 THROUGH 2040 ............................................ 23
FIGURE 14: EMPLOYMENT BY TOP FIVE INDUSTRIES, COUNTY OF HAWAI‘I, 2000 THROUGH 2040 .......................... 26
FIGURE 15: EMPLOYMENT BY ADDITIONAL INDUSTRIES, COUNTY OF HAWAI‘I, 2000 THROUGH 2040 ...................... 27
FIGURE 16: EMPLOYMENT BY OCCUPATION, COUNTY OF HAWAI‘I, 2000 THROUGH 2040 .................................... 28
FIGURE 17: JOBS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ...................................................................... 38
FIGURE 18: NUMBER OF JOBS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ...................................................... 40
FIGURE 19: TOP FIVE JOB GROWTH SECTORS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 .................................. 42
FIGURE 20: NEXT FIVE JOB GROWTH SECTORS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ................................. 43
FIGURE 21: TOTAL JOBS FROM LEHD LODES, COUNTY OF HAWAI‘I, 2002-2040 ............................................... 45
FIGURE 22: MEDIAN HOUSEHOLD INCOME, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ..................................... 47
FIGURE 23: PERCENTAGE OF PERSONS AND FAMILIES IN POVERTY, COUNTY OF HAWAI‘I, 1990 TO 2013.................. 49
FIGURE 24: SHELTER BURDEN ESTIMATES IN POVERTY, COUNTY OF HAWAI‘I, 1990 TO 2013 ................................ 52
FIGURE 25: TOTAL HOUSING UNITS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 .............................................. 54
FIGURE 26: OCCUPIED AND VACANT HOUSING UNITS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ........................ 56
FIGURE 27: SINGLE FAMILY DWELLINGS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ......................................... 58
FIGURE 28: MULTI-FAMILY DWELLINGS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ......................................... 59
FIGURE 29: HOUSING TENURE, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ..................................................... 60
FIGURE 30: AFFORDABLE HOUSING, COUNTY OF HAWAI‘I, 2009 THROUGH 2040 ............................................... 61
FIGURE 31: AVAILABLE HOUSING UNITS, COUNTY OF HAWAI‘I, 2009 THROUGH 2040 ......................................... 64
FIGURE 32: VISITOR ARRIVALS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ..................................................... 69
FIGURE 33: VISITOR ACCOMMODATION UNITS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040 ................................ 71
FIGURE 34: VISITOR ACCOMMODATIONS BY TYPE, COUNTY OF HAWAI‘I, 2005 THROUGH 2040 ............................ 73
FIGURE 35: COMMERCIAL ESTABLISHMENTS, COUNTY OF HAWAI‘I, 1997 THROUGH 2040 .................................... 74
FIGURE 36: COMMERCIAL ESTABLISHMENTS BY TYPE, COUNTY OF HAWAI‘I, 1997 THROUGH 2040 ........................ 75
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INDEX OF TABLES
TABLE 1: URBAN CLUSTERS, COUNTY OF HAWAI‘I, 1990-2015 ...................................................................... 10
TABLE 2: EMPLOYMENT BY INDUSTRY, COUNTY OF HAWAI‘I, 1990-2015 ......................................................... 24
TABLE 3: SUMMARY OF IMPACT: LOCATION QUOTIENTS FOR FAZ RELATIVE TO COUNTY DISTRIBUTION OF JOBS, 2013 TO
2014. ........................................................................................................................................ 30
TABLE4: LOCATION QUOTIENTS: EMPLOYMENT FOR 20 NAICS CLASSES, COUNTY OF HAWAI‘I VS. STATE OF HAWAI‘I,
2001-2014 ................................................................................................................................ 37
TABLE 5: PERCENTAGE OF TOTAL NON-AGRICULTURAL WAGE AND SALARY JOBS BY SECTOR, COUNTY OF HAWAI‘I,
1990-2015 ................................................................................................................................ 41
TABLE 6: HOUSEHOLD INCOME DISTRIBUTION, COUNTY OF HAWAI‘I, 2000-2013 .............................................. 46
TABLE 7: VALUE OF OWNER-OCCUPIED HOUSING UNITS, COUNTY OF HAWAI‘I, 2000-2013 ................................. 50
TABLE 8: PERCENT OF HOUSEHOLD INCOME PAID FOR HOUSING, COUNTY OF HAWAI‘I, 2000-2013 ....................... 51
TABLE 9: OVERCROWDING, COUNTY OF HAWAI‘I, 2000-2013 ....................................................................... 53
TABLE 10: NEEDED HOUSING UNITS, COUNTY OF HAWAI‘I, 2015-2040 ........................................................... 65
TABLE 11: REVISED LIST OF FORECAST ANALYSIS ZONES ................................................................................ 78
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INTRODUCTION TO THE SUMMARY REPORT
This report provides forecasts and discussions of a set of variables relevant to the County of
Hawai‘i’s General Plan Update for 2016. The data contained in this report and in the accompanying Excel workbook provide both trend and forecast data for several key variables
relevant to the County’s planning. The data have been deemed necessary for the geodesign-
based scenario modeling which the County will use as part of its planning process.
This report is designed to accompany the trend and forecast data by providing definitions of key
variables, identification of forecast sources, explanations of forecast methods, and discussion of important issues that the County and its other consultants may wish to take into consideration
when using and updating the data in ongoing planning. Accompanying the discussion of each variable is a graph of county-level data. In the case of graphs that include forecasts, the primary forecast line begins with historical data going back to the year 1990 and ends in the
year 2040. High and low forecast lines are also included as a margin around the primary forecast.
In addition to the county-level data, historical and forecast data were provided for each of the variables at the Census Tract and Forecast Analysis Zone (FAZ) levels. The FAZs were
groupings of census tracts based on the geographic location and characteristics of population
centers in Hawai‘i County (see the FAZ definitions at the end of this report). For the most part, Tract and FAZ data are not presented in this report but are provided in the forecast database
Excel file.
For most of the forecast variables, historical data were allocated to the FAZs using a constant
share approach, independently applied for each variable. This was consistent with the ceteris
paribus (business as usual) approach to the county level forecasts. For example, if the census tracts in a given FAZ accounted for 12 percent of the county’s total in 2015, it was assumed that
the FAZ would account for the same percentage (constant share) of the total in the future. There were two fundamental exceptions to this approach. The first exception was forecast for
total housing units. These were allocated to FAZs using an average of three approaches. One approach was the typical constant share method applied to Census housing unit data. The
second and third approaches used a rate-of-change method in lieu of a constant share method
to account for the significant differences in rates of growth among FAZs. Forecasts were scaled back so that County totals remained consistent. The second and third approaches differed in
their source of data. The second approach used housing unit data from the County of Hawai‘i
Real Property Tax (RPT) records for the years 2004 to 2014 (the period over which reliable data were available). The third approach used population growth data; growth rates from 2000 to
2014 were used to correspond closely with the timeframe covered by the RPT data. This first exception had ripple effects through other FAZ forecasts. Population, number of
households, housing units, and affordable housing units were allocated in tandem, as parts of a
set. Allocating each one independently would yield inconsistent results with increasing divergence over the forecast period. Therefore, after the FAZ forecasts for housing units was
completed, the FAZ forecasts for the other three related metrics were recalibrated. The second exception to the constant share approach was for non-residential square footage.
As with housing units, these FAZ forecasts were an average of two approaches, each with
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different methods and data sources. One approach used the typical constant share approach applied to DBEDT job growth projection data, with jobs converted to non-residential square feet.
The second was the 2000-2015 rate of change in non-residential development from RPT
records. As with housing units, rate-of-change forecasts were scaled back so that County totals remained consistent.
The forecast database Excel file and a procedures manual covering methods by which data can be updated were provided separately.
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Demographic Forecasts
In 2013, there were 194,190 residents living in the County of Hawai‘i. The de facto population or all persons (residents and visitors) present in the County at a given point in time, was
approximately 221,200. The median age for all residents was 41.3 years. Nearly one-quarter of
the County’s population (24.2%) was age 65 or older. As this subset of the population continues to age, it will present a variety of challenges with regard to housing and healthcare.
During the last two decades, there has been an average of 2,218 births and 1,269 deaths per year in the County, resulting in a net increase of almost 950 people annually. Also contributing
to population growth are the approximately 2,300 individuals, on average, who choose to move to Hawai‘i County each year.
Hawai‘i County’s current resident population lives in approximately 70,000 households, with an average household size of 2.75 persons. The number of households has increased by more
than 17 percent over the past decade.
The County of Hawai‘i includes 87 square miles of urban land with an average of 1,300 people
living in every square mile. It also has an average of 18 persons per square mile across its
3,942 square miles of rural land1.
In the following sections, those demographic variables most relevant to the County of Hawai‘i’s
long-range planning efforts are discussed in detail.
RESIDENT POPULATION
Definition: Resident population is the number of persons residing in the County of Hawai‘i in a
given year. Persons were said to be residents if they live in the County for a minimum of five
months of the year. The term includes part-time residents, but excludes visitors (tourists), students, and military personnel stationed in Hawai‘i who maintain a home of record outside the
State of Hawaiʻi. The number of residents of the County may differ from time to time during the
year and is usually presented as an average population for the year centered on July 1.
1 Urban and Rural Areas in the State of Hawai‘i, by County: 2010. Hawai‘i State Data Center. Sept. 2013. http://files.hawaii.gov/dbedt/census/Census_2010/Other/2010urban_rural_report.pdf
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FIGURE 1: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: Data for the years 1990, 2000, and 2010 taken from the U.S. Decennial Census. Non-census years 2005 through 2013 were taken from the American Community Survey. Estimates
for the years 2005 through 2013 were five-year combined files. Estimates for the years 2002
through 2004 were one or three-year combined files as available. Estimates for some intercensal years between 1991 and 2002 were developed from data in the Hawaiʻi Housing
Planning Study, 2011, Housing Model. Forecast: The forecast estimates were based on the Hawaiʻi Department of Business,
Economic Development and Tourism, Research and Economic Analysis Division, Population and Economic Projections for the State of Hawaiʻi to 2040 (2040 Forecast). By 2013, the
empirical data on the population of the County of Hawaiʻi had already shown that the 2040
Forecasts were too high. The temporary solution was to use the trend data for 1990 through 2013, and the forecast data from 2020 through 2040. The interim years were estimated by
fitting a third order polynomial curve to the data. While the resulting forecast is open to change
and to interpretation, the forecast represents a reasonable estimate of future population change assuming that there are no serious changes in demographic, social, or economic factors
underlying any population model. The low and high estimates are based on plus and minus
about 5 percent in the years through 2040.
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Discussion: The data can be interpreted as a three-cycle system with peaks in 1990, 2005, and 2016. The last of those peaks is actually an artifact of the smoothing process applied to
join the empirical trend with the DBEDT forecast. The cycles happen to run a close parallel to
economic growth trend for the County of Hawaiʻi as well. This was not built into the population model used here. Note the average annual growth rate bars show we are accepting a
population growth rate that is declining from about 1.7 percent per annum to about 1.4 percent per annum over about 20 years. That would be continuing a long-range logarithmic decline in growth rates through the last 25 years. The basic population forecast is shown here as roughly
linear, but we can reasonably expect that the economic sine wave around the linear trend will continue into the future.
Considerations: This seems a rational place to start the planning process. It answers the question, what would the population be in each of the next 25 years if existing trends continue
unabated? It incorporates all of the general agreements about forecasts across the state,
including the idea that population growth will be slower than in previous decades, and statewide population growth will be higher on the outer islands than on O‘ahu.
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POPULATION PYRAMIDS
Definition: A population pyramid is a graphic illustration showing the distribution of age groups
in a population. It takes the shape of a pyramid when the population is growing. Departures from the pyramid shape represent meaningful characteristics of the local population. A population
pyramid typically consists of two back-to-back bar graphs, with the population size plotted on
the X-axis and age on the Y-axis. One side of the pyramid presents the number of males and the other side represents females in the population. Ages are presented in five-year age groups
called cohorts. Population pyramids are often viewed as the optimal way to graphically depict
the age and gender distribution of a population.
Trend: Rather than being in the shape of a pyramid, the population of the County of Hawai‘i shown in Figure 2 displays similar numbers of residents for the cohorts from birth to 44 years of age. There is a bulge depicting the larger number of residents between the ages of 45 and
64. The graph then tapers for residents age 65 and older, with a slight increase among the oldest residents (85 years and over).
FIGURE 2: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 2013
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Discussion: The population pyramid for 2025, shown below, shows bulges indicating larger numbers of residents between the ages of 25 to 34 and between 65 and 74 years of age.
FIGURE 3: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 2025
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Figure 4 displays the population pyramid for 2040. The graph is nearly rectangular, indicating similar numbers of residents from birth through age 49. Between the ages of 50 and 84, the
population tapers to increasingly smaller numbers of Hawai‘i County residents, before a notable
bulge in the uppermost age category of 85 years and older.
FIGURE 4: RESIDENT POPULATION, COUNTY OF HAWAI‘I, 2040
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DE FACTO POPULATION
Definition: The de facto population is a count of all persons present in the County at a given
point in time. It was estimated as the resident population, plus non-residents who are present in the County, minus residents who are temporarily absent. In practice in Hawaiʻi, the de facto
population is usually considered to be the sum of the resident population and the average daily
visitor census.
FIGURE 5: DE FACTO POPULATION, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: The de facto population data for the years 1990 through 2013 were taken from the U.S. Census, 1990, 2000, and 2010, as they appear in the DBEDT Hawaiʻi Data Book Time Series,
and also from the Hawaiʻi County Data Book, 2010, Table 1.18. Forecast data were taken from DBEDT’s 2040 Forecast. Periods of population growth in the late nineties and between 2002
and 2007, occurred during periods of economic growth. They are a bit more obvious in the de
facto population trend because growth resulted from an increase in visitors, net in-migration, and increased natural growth.
Forecast: The forecasting method for de facto population was more straightforward than for other series. The estimate was the sum of the forecasts for resident population and the average
daily visitor census for each year. The average annual growth rate was calculated directly from the data.
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URBAN POPULATION
Definition: The Census Bureau defines urban areas as densely developed territories that may
include residential, commercial, and other non-residential urban land uses. Urban areas generally have a densely settled core of census tracts and/or blocks that meet minimum
population density requirements. They must also encompass adjacent territory with non-residential urban land uses and territories with low population density that link outlying, densely settled territory with a densely settled core. In other words, an urban area consists of a dense
urban (commercial and residential) core surrounded by less dense residential areas that are dependent on the commercial, industrial, and administrative activity in the core.
To qualify as an urban area, the territory must encompass at least 2,500 people, at least 1,500 of which reside outside institutional group quarters.2
The Census Bureau identifies two types of urban areas:
Urbanized Areas (UAs) of 50,000 or more people;
Urban Clusters (UCs) of at least 2,500 and less than 50,000 people.
Table 1 includes a list of the urban clusters in the County of Hawai‘i.
TABLE 1: URBAN CLUSTERS, COUNTY OF HAWAI‘I, 1990-2015
Figure 6 shows the resident civilian and urban populations of the County of Hawaiʻi, including both urban areas and urban clusters, for the five decades covered by the forecasts.
"Rural Areas" were defined as all areas of the County that were not classified as urban areas. Both urban and rural areas described the land, population, and housing units within the area.
2 https://www.census.gov/geo/reference/ua/uafaq.html
Population
Housing
Units
Land Area
(sq km)
Land Area
(sq mi)
Population
Density
Hawaii County 114,766 47,680 224.7 86.8 1,323.10
Captain Cook 4,175 1,640 10.8 4.2 998.6
Hawaiian Paradise Park 20,503 8,062 83.6 32.3 635.1
Hilo 43,925 17,091 63.5 24.5 1,791.00
Honokaa 2,667 972 4.1 1.6 1,698.70
Holualoa 28,850 14,269 37.1 14.3 2,012.50
Kapaau 3,597 1,263 10.5 4.1 887.9
Waikoloa Village 4,089 1,838 2.8 1.1 3,799.00
Waimea 6,960 2,545 12.2 4.7 1,475.30
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FIGURE 6: URBAN POPULATION, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: The 1990, 2000, and 2010 trend data was taken from the Decennial Census Table P2
for each census year. The American Community Survey does not report the percentage of the resident population classified as urban or rural so the intercensal years were calculated as a
function of the anchor points for each decade.
By the Census definition, the majority (60 percent) of the County’s population has been
classified as urban throughout the last 25 years. The years 1990 through 2015 were years of substantial population growth for the County of Hawaiʻi. They also witnessed changes in the distribution of non-agricultural workers to new areas in Kona, Waikoloa, etc. Yet the Census
data shows us that relatively little change occurred in the percent of the population classified as urban.
Forecast: The forecast for the percent of the population classified as urban was determined as a percentage of the total population forecast. The high and low estimates were developed in
proportion to the high and low estimates of the total population. The forecast carries through
the general trend of the last thirty years, which predicts growth in the urban and rural populations, but no change at all in the percent of the population classified as urban according
to census definitions. We show the low and high estimates as relatively close to the mid-level
forecast. If we accept the basic zero change theory of forecasting, there is little reason to believe that the level of error in predicting the 2040 urban population of the county will vary
much from the base forecast.
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Discussion: Forecasts of the relative sizes of the urban and rural populations of the County of Hawai‘i are useful in planning for the maintenance of rural character and economic viability, and
for providing opportunity for economic growth in urban areas. There are several definitions of
urban and rural in use by various agencies of the federal government and other agencies that support the interest of urban and rural areas. The census definition used here seems
particularly appropriate for Hawai‘i County. The forecast is for no change. That, too, is appropriate considering past trends.
NUMBER OF HOUSEHOLDS
Definition: A household consists of all people who occupy a housing unit regardless of relationship to each other. A household may consist of a person living alone or multiple
unrelated individuals or families living together. The number of people in a household includes all persons residing there, related or unrelated.
FIGURE 7: NUMBER OF HOUSEHOLDS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
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Trend: The data for the years 1990 through 2013 were taken from the U.S. Census, 1990, 2000, and 2010, as they appear in the DBEDT Hawaiʻi Data Book Time Series, the ACS data for
the years 2005 through 2013, and from the Hawaiʻi County Data Book, 2010. No household
forecast data were located.
The number of households can be calculated as the total persons in households divided by persons per household3. Also by definition, the number of households in any Census geographic location is equal to the number of occupied housing units in the same geographic
area. Forecast: The household forecast reflects the 2040 Forecast only in that it has resident
population as its base. As noted above, the household population forecast was developed in tandem with the forecasts for persons in households and the ratio of persons to households. All
three variables use trend data from the same sources.
Discussion: The series "number of households" follows the same trajectory as the resident
population forecast. They show a steady increase for fifty years, with significant variation
around the trend line. That variation has the same relationship to the average trend line over time, and deviations from that line that are consistent with economic trends. In the case of the
household trend, there is a relationship to housing supply as well, because the definition of a
household requires a housing unit.
3 Estimating Households by Household Size Using the Poisson Distribution. http://paa2013.princeton.edu/papers/130204
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PERSONS PER HOUSEHOLD
Definition: Persons per household was defined here as the number of persons in households
divided by the total number of households. This differs arithmetically from the average household size, although the concept is the same. Persons per household was the most
appropriate variable to use here because it will be used to develop our forecast estimates for
persons per household and thereafter the number of households.
FIGURE 8: PERSONS PER HOUSEHOLD, COUNTY OF HAWAI‘I, 2000-2040
Trend: Prior to 2009, the number of persons per household was reported only in the decennial census years. With the advent of ACS, we began to get annual estimates of this ratio and other
important population and household variables. The trend showed that persons per household
dropped from 2.86 in 1990 to 2.75 in 2000. It fell again to 2.71 in 2010. The ACS data show that persons per household began to climb again in the present decade, reaching 2.75 in 2015.
Forecast: To assure the comparability of the current data, forecasts for persons per household, persons in households, and number of households were carried out in a tandem process. The
ratio, persons per household, therefore defines the relationship between population (households) and housing units for the forecasts..
The forecast for persons per household shows a steady upward climb through 2025 (to 2.79 persons per household) and then continued increase but at a slower rate through 2040 (to 2.85). The rationale was based on the trend and expectations for population growth and
housing unit supply. The trend line showed a rebound in persons per household that could be
-0.4%
-0.2%
0.0%
0.2%
0.4%
0.6%
0.8%
2.00
2.10
2.20
2.30
2.40
2.50
2.60
2.70
2.80
2.90
3.00
Average Annual Growth RatePerson Per HouseholdAnnual growth rate
HI
Persons per Household - Mid
Lo
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an anomaly in the data for the last Census. A decrease of that magnitude had not been witnessed in Hawai‘i County before, and was not reflected in the average family size. The
return to 2.75 will be shown to be on target to about 2.80 or more in 2014. The figure will
continue to rise through 2020 or 2025 because we expect population growth to run ahead of growth in housing stock. Thereafter, the ratio will continue to climb but at a slower rate.
Population growth will proceed at a slower pace, and additions to the housing stock will remain at about the current pace.
PERSONS IN HOUSEHOLDS
Definition: "Persons in households" is the number of individuals in the resident population who
reside in households, that is, the number of persons who live in housing units. Residents who
do not live in housing units were said to live in group quarters (see Figure 10).
FIGURE 9: PERSONS IN HOUSEHOLDS, COUNTY OF HAWAI‘I, 2000-2040
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Trend: The data for the years 1990 through 2013 were taken from the U.S. Census, 1990, 2000, and 2010, as they appear in the DBEDT Hawaiʻi Data Book Time Series, and also from
the Hawaiʻi County Data Book, 2010, Table 1.18. Forecast data were taken from DBEDT’s
2040 Forecasts. The number of persons in households has a profile curve that is very similar to that shown for the resident population.
Forecast: As with the resident population forecast, the forecasting method for persons in households was based on the 2040 Forecast. Once again, census data showed that empirical
data were running below the 2040 Forecast estimate by 2015 and it was necessary to merge the two series using data from the ACS from 2005 through 2013. The number of persons in households was expected to rise as a function of population change. The rate of growth
reflected that set by DBEDT in the 2040 forecasts.
Discussion: The series "persons in households" followed the same trajectory as the resident
population forecast. They showed a steady increase for fifty years, with some variation around the trend line. That variation again appeared to be similar to the rise and fall of local economic
conditions over time. It can also be interpreted as the impact of the economy on household
formation rates. In a slow economy, the number of persons per household may rise as higher prices and lower supply work to increase crowding and decrease new household formation. In
a rising economy, housing opportunities increase, crowded households empty out, and average household size decreases.
Considerations: The focus should probably be on the ratio of persons to households. Using the current definition, the rate has varied from about 2.7 to 2.86 with an average of 2.76 over the last 30 years. Planning decisions that involved ratios outside that range might require some
explanation.
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PERSONS LIVING IN GROUP QUARTERS
Definition: Group Quarters (GQ) are defined by the Census as places where people live or
stay, in a group living arrangement, which is owned or managed by an organization that provides housing and/or services for the residents. These services may include custodial or
medical care, or other types of assistance, and residency is commonly restricted to those
receiving services. People living in group quarters are usually not related to each other. Group quarters include such places as college residence halls, residential treatment centers, skilled
nursing facilities, group homes, military barracks, correctional facilities, and workers’
dormitories.
Group quarters are classified as one of two types - institutional and non-institutional - by the American Community Survey (ACS)4. Institutional Group Quarters are facilities for people who have been placed under formally authorized, supervised care or custody. Group quarters
include correctional facilities, nursing facilities/skilled nursing facilities, in-patient hospice
facilities, mental (psychiatric) hospitals, group homes for juveniles, and residential treatment centers for juveniles. Non-institutional Group Quarters include facilities not classified as
institutional group quarters, such as college/university housing, group homes intended for adults, residential treatment facilities for adults, workers’ group living quarters, Job Corps centers, and religious group quarters.
FIGURE 10: PERSONS LIVING IN GROUP QUARTERS, COUNTY OF HAWAI‘I, 2000-2040
4 ACS began including group quarters population in the sample in 2006. Prior to that time, only the population living in households was included.
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
19901992199419961998200020022004200620082010201220142016201820202022202420262028203020322034203620382040.Average Annual Growth RateGroup Quarters PopulationAnnual Growth Rate
Hi
Pop in Group Quarters - Mid
Lo
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 18
Trend: The historical data were taken from the Hawaiʻi State Data Book 2013 Table 1.53 and
DBEDT Census 1990 Selected Profile for the State by Counties. Figure 10 shows only the
group quarters population. There are two peaks and two troughs in the data between 1995 and 2010. The peaks correspond roughly to the last two housing run-ups in Hawaiʻi and the troughs
to the adjustment or recessionary periods thereafter. In general, the trend line for institutional group quarters residents (about 40 to 50 percent of the group quarters population) is a nearly straight line between 1990 and 2013. The variation is primarily due to changes in the number of
non-institutionalized persons in group quarters. These people include unrelated individuals living in residential units. Because none are related by blood, marriage, or adoption, they cannot be classified as persons in households.
Forecast: Persons in group quarters is equal to resident population minus persons in
households. Therefore the basis for forecasting persons in group quarters depends on the
DBEDT 2040 forecast for resident population and persons in households. ACS data trends for persons in group quarters were extremely volatile between 2005 and 20145, so the trend line
was merged to the forecast line by fitting a third order polynomial curve through 2014. The mid-
level estimate then assumed very slow growth in the percent of the County’s total population housed in group quarters through 2040. The low and high estimates for the group quarters
population were fairly narrow. The rationale for that was that there had been very little variation
in the institutionalized population which remains the larger segment of the group quarters population. We might expect considerable variation in the non-institutionalized segment. That
variation is expected to be driven by future volatility in housing prices, and thus will be expressed as variation around the non-institutionalized group quarters forecast line.
Discussion: The group quarters population is sometimes thought of as a residual segment of the population – people not in housing units are in group quarters. In fact, the Census Bureau calculates these data and they are built up from actual counts. The institutionalized group
quarters population is, in part, dependent on the number of institutionalized housing units in the County. Its growth may be hindered by barriers to supply and its growth rate may underestimate the number of people who need institutionalized housing. In fact, the Hawaiʻi
Housing Planning Survey 2011 included a special study on housing of persons with special needs, which suggests that may be true for all Counties.
Considerations: There are many ways to interpret changes in the number of persons in non-institutionalized group quarters. Even without speculating on the issues now, we can be certain
that there will be increased interest in information on the group quarters population. Their
number, rate of change, characteristics, behavior, and relationship to planning issues like housing, employment, and social services, will increase demand for data and analysis. The
easy availability and growing reliability of ACS data on groups quarters people will boost supply of information to match that demand.
5 Most likely due to early imprecision and low sample sizes, rather than actual variation in the number of persons in
group quarters in the County of Hawai‘i.
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 19
Economic Forecasts
The County of Hawai‘i’s current labor force was comprised of just over 90,000 persons age 16 or older. The labor force had grown at an average annual rate of 1.7 percent over the previous
decade.
An estimated 90.4 percent of the labor was employed (81,575 workers). The unemployment
rate for Hawai‘i County followed the overall economic trend. The unemployment rate was 3.5 percent in 1990, then climbed to over 9 percent by the middle of the decade and dropped back down to 4.7 percent by 2000. The rate continued to decline until it reached an all-time low of
3.4 percent in 2007, just prior to the beginning of the Great Recession. After reaching close to 10 percent during the Recession, the County’s economic recovery is evidenced by the current unemployment rate of just 5.6 percent.
Among employed persons who worked outside their homes, roughly 40 percent lived and
worked in the same place6. The average travel time to work for employees increased from 24.5
minutes in 2000 to 27.1 minutes in 2013 (+10.6%).
There were an estimated 103,000 jobs in Hawai‘i County. The average annual growth rate for
jobs ranged from 1.6 to 1.9 percent since 1990, and was expected to remain at that level for the next several decades. Workers were most often employed in one of five key industries:
educational service, healthcare, and social assistance; arts, entertainment, recreation, accommodation, and food services; retail trade; professional, scientific, management, administrative, and waste management; and construction.
The estimated 2015 median household income for Hawai‘i County workers was $51,795. This represented a 1.9 percent decrease over the last ten years. The median household income
peaked in 2008 at $58,500, then dropped 4.9 percent in 2009 as a result of the Recession. Median household income is expected to increase steadily over the next several years and reach its pre-recession level by 2020.
In keeping with the other economic measures, the poverty status of individuals and families in
the County increased over the past five to seven years. The percentage of persons in poverty
was 15.7 percent in 2000 and was on a downward trend, falling to a low of 13.1 percent in 2007. Since that time, the percentage in poverty has increased to a record high of 18.3 percent for
individuals and 13.5 percent among families.
6 As defined by the U.S. Census Bureau; American Community Survey 2013 5-year Summary File: Technical Documentation, the same place refers to the same census block or, if the place of employment could not be
geocoded to the block, in the same city, town or census designated place.
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 20
LABOR FORCE IN THE COUNTY OF HAWAI‘I
Definition: The number of persons in the labor force in the County of Hawai‘i was defined by
the U.S. Census as the number of county residents 16 years of age or older who were either employed (full-time or part-time) or unemployed and actively looking for work. Persons who
were not employed and not actively looking for work were excluded from the definition. Those
persons may include retired persons, students, homemakers, and others who were not seeking employment at the time the measurement was taken. The number of persons in the labor force
was the denominator used in calculating percent employed and the unemployment rate.
FIGURE 11: LABOR FORCE, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: Trend data for the period 1990 to 2013 were taken from the U.S. Census and from the
American Community Survey, 2005 through 2013. To some extent, the shape of the trend data curve is an artifact of available data. Census data for intercensal years between 1990 and 2010 have been interpolated based on the ratio of labor force members to total persons 16 years of
age or older. Within-decade changes in the size of the labor force were unknown. After 2004, year-on-year changes in the size of the labor force may have been exaggerated by small
sample sizes and evolving data collection protocols for the ACS. Evidence of the latter
proposition can be seen in the fluctuation of the annual growth rate between 2005 and 2011.
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 21
Forecast: We are aware of no official forecast for the size of the labor force in Hawai‘i County. The forecast shown here depends on the DBEDT 2040 forecast for number of persons
employed and an assumption that the percent of the labor force who were employed was
relatively constant over time. The low and high estimates were based on plus and minus about 5 percent in the years before 2040.
Discussion: This analysis suggested that the labor force growth rate between 2005 and 2013 was higher than it was between 1990 and 2004. After 2013, the forecast growth rate returned to
its pre-2005 level. The nature of the available data causes us to have less confidence in the 2005-2013 data, but the return to the long-range growth rate for the county seems reasonable for the future. The confidence levels that produced our low and high estimate were based on
the forecast itself and did not include any consideration of the point at which the two trends met.
Considerations: The forecast is the most reliable that is available at this time and we believe it
will serve the planning needs of the County for the short-run. This forecast will benefit greatly from new data available when the next editions of the DBEDT Forecast, the Intra-County I-O
Model, the Housing Planning Study 2015, and the next editions of ACS data become available.
EMPLOYMENT BY SECTOR, INDUSTRY, OCCUPATION
Definition: Employment -- the number of persons employed -- was defined here as the average number of Hawai‘i County residents 16 years of age or older who were employed either
full-time or part-time during a calendar year. The average number of employed persons was
calculated over the course of the 12 months. A person was considered to be employed regardless of the number of jobs he or she may have had in a given month.
FIGURE 12: EMPLOYMENT, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 22
Trend: Data for the years 1990 through 2013 were taken from the U.S. Census, 1990, 2000, and 2010, and for the non-census years 2005 through 2013 from the American Community
Survey. Estimates for the years 2005 through 2013 were taken from five-year combined files.
Estimates for the years 2002 through 2004 were one- or three-year combined files as available. Data were also matched with information from the Hawai‘i County Data Book.
The empirical data on employment (1990 through 2013) reflect well known trends in the economy and jobs during that period. Employment was relatively high in 1990 and 1991 and
then slowed dramatically during the remainder of the decade. Growth took a jump in 2000, one of the best years in Hawai‘i’s economic history. It fell off in 2001 after the disaster of September 11, 2001 and then grew at an increasing rate for six years. The Great Recession caused major
unemployment in 2008 and 2009 and the subsequent recovery period produced irregular gains.
Forecast: Forecast estimates were based on the DBEDT 2040 forecasts once again. The
empirical data on employment fit reasonably well with the DBEDT 2040 forecast in 2014-15 and no extensive curve fitting was required to merge the two data series. The straight-line forecast
seemed consistent with empirical data in terms of the long-range growth rates and of course we
expect that future growth rates will vary around the forecast trend. The high and low estimates are reasonably close to the most probable forecasts, suggesting that we find no data or other
evidence that the DBEDT 2040 forecast might be inaccurate to this point.
Discussion: Employment trend and forecast data are a reliable and accurate estimate for the
County between 1990 and 2040. There were no problems in forecasting and the employment data are consistent with other related variables gathered for this project.
Employment by Sector
The three primary sectors of the Hawai‘i Island economy are the goods producing sector, the
services producing sector, and agriculture. The next figure shows the trend and forecast data
for employment in those three sectors. The data sources and definitions are the same as noted for the employment data shown above. Note that the service producing sector, the largest part
of the Hawai‘i County economy, is shown on a separate scale to the right.
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 23
FIGURE 13: EMPLOYMENT BY SECTOR, COUNTY OF HAWAI‘I, 2000 THROUGH 2040
Employment in the County’s goods producing sector (construction and manufacturing) grew
rapidly during the early part of the last decade and then fell back a bit. DBEDT’s forecast
suggests that the future will bring a slow but steady increase in sector employment.
The forecast for employment in agriculture (agriculture, forestry, fishing, hunting, and mining) is for slower growth and may reflect a less certain future for the sector. The trend line does not follow general economic growth trend as do the other two sectors. Employment in agriculture
dropped slightly during the 2000-2007 booms, the great recession, and recovery. The forecast, however, suggests slow and steady growth through 2040. The rate is lower than for the other
sectors and seems a reasonable foundation for planning at this time.
The services-producing sector (all industries except those classified as goods-producing or
agriculture) showed a growth pattern very similar to the good-producing sector over the last 15
years, albeit with less volatility.
The DBEDT forecast for all three sectors predicted the same steady growth through 2040. It followed the assumption that future growth will continue as it has been in the past. Of course, the forecast was a straight-line prediction and future changes in employment will vary around
the forecast line.
0
20,000
40,000
60,000
80,000
100,000
120,000
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
200020022004200620082010201220142016201820202022202420262028203020322034203620382040Number of Employed Persons - Services-providing Number of Employed Persons - Agriculture and Goods-producing Goods producing Agriculture Services providing
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 24
Table 2 presents some further disaggregation of the forecast data shown in Figure 13. Data for all industry classifications in the years between 1990 and 2040 are included in the data matrix
submitted along with this report. Table 2 shows that the largest sectors in the County of Hawaii
have been construction, retail, education, health, and government services. Those same industries are forecast to be the County’s primary employment areas in 2040.
TABLE 2: EMPLOYMENT BY INDUSTRY, COUNTY OF HAWAI‘I, 1990-2015
Industry 1990 2000 2010 2020 2030 2040
Agriculture, forestry, fishing and hunting, and
mining 5,437 4,600 3,910 4,120 4,724 5,332
Construction 5,153 5,057 9,450 8,282 9,496 10,719
Manufacturing 2,721 1,685 2,282 2,348 2,693 3,040
Wholesale trade 1,754 1,786 2,289 2,204 2,527 2,853
Retail trade 1,055 7,826 10,922 11,629 13,333 15,051
Transportation and warehousing, and utilities 2,208 3,546 3,789 4,364 5,004 5,648
Information 1,251 1,159 1,071 1,018 1,167 1,318
Finance and insurance, and real estate and
rental and leasing 2,810 3,346 5,002 4,545 5,211 5,883
Professional, scientific, and management,
and admin and waste management 8,461 5,596 7,858 9,426 10,808 12,201
Educational services, and health care, and
social assistance 7,338 12,287 16,162 17,223 19,748 22,292
Arts, entertainment, recreation,
accommodation, and food services 1,004 11,462 14,809 16,148 18,516 20,901
Other services, except public administration 2,992 2,911 3,639 4,244 4,866 5,493
Public administration 3,164 3,718 4,597 5,084 5,829 6,580
Source: ACS 5-year Table DP03
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 25
FIGURE 13A: EMPLOYMENT BY INDUSTRY, COUNTY OF HAWAI‘I, 1990-2040
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 26
Employment by Industry
Employment in the County’s 13 major industries is shown here in two figures. Figure 14 presents trend and forecast data for the top five industries by volume and growth rate. Figure
15 offers up the data on the remaining 8 industries. Data sources and definitions are the same
as noted for the rest of the employment data.
FIGURE 14: EMPLOYMENT BY TOP FIVE INDUSTRIES, COUNTY OF HAWAI‘I, 2000 THROUGH 2040
Nearly 70 percent of the employed persons in Hawai‘i County work in one of these industries:
Educational service, healthcare, and social assistance
Arts, entertainment, recreation, accommodation, and food services
Retail trade
Professional, scientific, management, administrative, and waste management
Construction
The two largest components of the employment picture were the health and education, and the
tourist industries7. The employment trend in both industries rose through 2008 and then leveled
7 Health and education includes health care, social assistance, and education. The tourist industry includes arts, entertainment, recreation, accommodations, and food services.
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 27
off during the recession. Employment figures have drifted downward very slightly since about 2010. The forecast for both industries is strong through 2040 and it is likely that increases in
employment will be led by job opportunities in health and tourism.
Employment in the retail trade and construction industries has also been high in the County economy. Employment in both industries rose during the period from 2000 to 2008. Retail
trade drifted down very slightly between 2008 and 2014, and construction employment dropped significantly during the same period. Both industries are expected to grow steadily through 2040, providing the second largest number of employees in the island economy.
FIGURE 15: EMPLOYMENT BY ADDITIONAL INDUSTRIES, COUNTY OF HAWAI‘I, 2000 THROUGH 2040
Employment in the professions, scientific, management, administrative, and waste management
increased steadily through the last 15 years. The forecast predicts a similar steady growth through the next 25 years.
The remaining 30 percent of employed persons work in one of the eight industries shown in
Figure 15. As was true with the construction industry, employment in the Finance, insurance, real estate, rental and leasing industry tends to rise and fall with the housing market. Similarly,
employment trends in the Agriculture, forestry, fishing, hunting and mining industry are matched by the Transportation, warehousing and utilities industry.
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
200020022004200620082010201220142016201820202022202420262028203020322034203620382040Number of Employed Persons - Add'l 8 Industries Public administration Finance and ins., real estate, rental and leasing
Trans., warehousing, and utilities Other svcs., except public admin.
Ag., forestry, fishing, hunting, and mining Manufacturing
Wholesale trade Information
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 28
Employment by Occupation
The number of persons employed in each of five major occupations in Hawai‘i County was also taken from Census data and the foundation data for forecasts were taken from DBEDT. Again,
we present the average number of persons employed at each five occupations in each year
between 2000 and 2040.
FIGURE 16: EMPLOYMENT BY OCCUPATION, COUNTY OF HAWAI‘I, 2000 THROUGH 2040
Management, professional and related occupations account for the largest percentage of
employed persons in the County of Hawai‘i (30.5 percent). Sales and office occupations
account for approximately one-quarter of all employed persons (25.4 percent), followed closely by service occupations (24.6 percent).
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 29
Location Quotients
Definition: One of the more useful statistics that planners use in their work is the “location quotient”. A location quotient (LQ) is used to quantify how concentrated a particular
characteristic or activity is within a geographic area at some specific time. As an example, we
are concerned here with the relative concentration of employment by industry in Hawai‘i County relative to its concentration in the State as a whole (Table 2).
Trend: Note that location quotients are available on the Bureau of Labor Statistics website8 for Hawai‘i County relative to the State for 2001 through 2014. Because the BLS sectors and those
available in DBEDT’s 2040 forecast for employment by county are not exactly aligned, LQs
were calculated for the County relative to the State and for the FAZs relative to the County of Hawai‘i for each year between 2010 and 2040. These data are reported in the forecast data
worksheets. Discussion: The LQs for Hawai‘i County employment by industry explain how the distribution
of jobs in the County of Hawai‘i compare to their distribution across the State. For instance, the number of persons employed in agriculture and forestry was more than three times higher in
Hawai‘i County than in the state. That suggests a much higher concentration of agricultural jobs
in the County than in the state as a whole. Hawai‘i County also employed about 1.5 times as many people in utilities and in hotel professions as the rest of the State. This is expected to
continue throughout the forecast period for utilities jobs, and only decrease slightly from 1.53 to
1.44 for hotel jobs.
Professional services is the employment classification for which jobs in Hawai‘i County are
underrepresented as compared to the rest of the state. In 2010, the location quotient for professional services employees was 0.54, meaning that the concentration of persons
employed in professional services in the county was just over half of that across the state.
Employment in the finance and insurance industry has also been relatively low in Hawai‘i County compared to the state. In 2010, the concentration of finance and insurance jobs in the
County was approximately two-thirds of the State’s concentration. This is expected to continue to decline throughout the forecast period, reaching 0.60 by 2040.
Table 3 presents the distribution of wage and salary jobs for each of the County’s 13 FAZ in 2013 and 2040. The LQ for 2040 is also presented there. Then distribution of jobs across FAZs
is similar at the beginning and end of the forecast period, reflecting a forecast strategy of
maintaining the “business as usual” approach.
The location quotients reflect the existing concentration of jobs across the FAZ’s given our
forecasts for population and job growth. Recall that jobs were distributed according to industry location (where he jobs are) and employment was distributed according to population growth
(where the people live).
8 http://data.bls.gov/location_quotient/ControllerServlet
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 30
TABLE 3: SUMMARY OF IMPACT: LOCATION QUOTIENTS FOR ALL FAZ RELATIVE TO COUNTY DISTRIBUTION OF JOBS, 2013 TO 2014.
CIVILIAN WAGE &
SALARY JOBS BY SECTOR
Forecast Analysis Zones, County of Hawai‘i Hilo North Hilo - Hāmākua Coast Villages Honoka‘a-Pa‘auilo Waimea North Kohala Kawaihae-Puakō-Waikoloa-Waikoloa Resorts North Kona South Kona Villages Kaʻū Kea‘au-Kurtistown Upper Puna HPP-Orchidland Lower Puna FAZ Number 1 2 3 4 5 6 7 8 9 10 11 12 13
All Jobs
2013 19749 2808 2513 3142 1988 3461 15977 3781 2085 1463 3588 4349 2343
2040 23229 3,734 3,904 5,052 3,098 4,554 23288 5,829 3,145 2,176 4,650 6,744 3,688
Agriculture, mining,
and construction
2013 843 199 316 517 165 283 1394 554 352 158 303 540 387
2040 1,036 309 467 529 198 250 1,644 722 450 223 347 551 319
LQ 0.61 1.14 1.64 1.44 0.88 0.75 0.97 1.70 1.96 1.41 1.02 1.12 1.19
Food processing and other
manufacturing
2013 323 33 60 133 16 48 217 56 85 22 65 107 58
2040 401 25 36 163 28 44 244 58 120 15 84 97 125
LQ 1.29 0.49 0.69 2.41 0.68 0.72 0.78 0.74 2.84 0.50 1.34 1.07 2.53
Transportation and
utilities
2013 938 115 50 124 25 193 741 77 91 61 75 122 181
2040 1,207 140 36 163 28 147 895 123 126 69 53 179 157
LQ 1.41 1.02 0.25 0.88 0.25 0.88 1.05 0.58 1.09 0.86 0.31 0.72 1.16
Information
2013 293 19 27 18 58 34 163 46 21 3 7 62 31
2040 280 33 32 12 22 12 168 71 43 16 17 146 48
LQ 1.08 0.78 0.74 0.22 0.63 0.24 0.64 1.09 1.21 0.67 0.33 1.94 1.17
Wholesale trade
2013 530 47 32 64 25 20 242 52 0 58 112 152 292
2040 551 141 39 21 76 31 331 38 4 53 207 122 297
LQ 1.13 1.81 0.48 0.19 1.16 0.32 0.68 0.31 0.07 1.16 2.13 0.87 3.84
Retail trade
2013 2383 364 201 332 220 432 2718 542 292 151 520 617 275
2040 2,882 336 418 367 328 500 3,105 657 282 181 464 581 480
LQ 1.16 0.84 1.00 0.68 0.99 1.03 1.25 1.05 0.84 0.78 0.93 0.81 1.22
Continued
County of Hawai‘i, General Plan Update, Finalized Forecast and Analysis Page 31
TABLE 3 (CONTINUED): SUMMARY OF IMPACT: LOCATION QUOTIENTS FOR ALL FAZ RELATIVE TO COUNTY DISTRIBUTION OF JOBS, 2013 TO 2014.
CIVILIAN WAGE &
SALARY JOBS BY SECTOR
Forecast Analysis Zones, County of Hawai‘i
Hilo North Hilo - Hāmākua Coast Villages Honoka‘a-Pa‘auilo Waimea North Kohala Kawaihae-Puakō-Waikoloa-Waikoloa Resorts North Kona South Kona Villages Kaʻū Kea‘au-Kurtistown Upper Puna HPP-Orchidland Lower Puna FAZ Number 1 2 3 4 5 6 7 8 9 10 11 12 13
Finance and insurance, Real
estate and rentals
2013 743 73 70 75 111 222 931 210 40 25 82 152 119
2040 878 57 86 138 94 175 1,126 250 43 36 109 211 98
LQ 1.24 0.50 0.72 0.89 0.99 1.26 1.58 1.41 0.45 0.54 0.77 1.02 0.87
Professional
services and Business services
2013 982 136 154 323 162 170 1780 350 128 90 171 314 164
2040 1,219 154 208 636 220 247 1,921 343 172 130 235 296 239
LQ 0.53 0.42 0.54 1.27 0.72 0.55 0.83 0.59 0.55 0.60 0.51 0.44 0.66
Educational services and Health
services
2013 3181 398 354 360 176 181 1346 483 271 217 486 656 364
2040 3,648 535 546 608 183 244 1,430 547 361 300 485 1,091 505
LQ 0.82 0.74 0.73 0.63 0.31 0.28 0.32 0.49 0.60 0.72 0.54 0.84 0.71
Arts/entertainment,
hotels, eating, drinking
2013 2028 520 805 895 769 1276 4087 609 265 253 524 586 180
2040 2,114 469 927 868 1,028 1,410 4,862 938 415 272 817 622 320
LQ 0.50 0.70 1.32 0.95 1.84 1.72 1.16 0.89 0.73 0.69 0.97 0.51 0.48
Other services
2013 742 113 113 122 49 107 655 127 121 27 82 84 55
2040 782 79 130 133 40 64 796 235 85 39 172 112 158
LQ 1.20 0.76 1.19 0.93 0.46 0.50 1.22 1.43 0.96 0.64 1.31 0.59 1.52
Government
2013 6763 789 332 177 211 494 1703 674 419 398 1162 957 236
2040 8,230 772 246 468 273 577 2,405 754 455 434 789 1,474 251
LQ 1.71 1.00 0.30 0.45 0.43 0.61 0.50 0.62 0.70 0.96 0.82 1.05 0.33
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 32
The following figure displays location quotients for each FAZ by job type. Recall that the
location quotient is a ratio of ratios, here describing the relative concentration of job types
across the County. For a particular job type, a FAZ LQ of 1.0 means that FAZ has about the
same percentage of that job type as does the county as a whole. If the LQ is higher than 1.0,
then there are more of those jobs in the FAZ than in the County as a whole. If the LQ is lower,
then the FAZ has fewer of those jobs than the average for the county.
FIGURE 16A: LOCATION QUOTIENTS FOR CIVILIAN WAGE AND SALARY JOBS BY JOB TYPE AND FAZ, COUNTY OF
HAWAI’I, 2000-2040
0
0.5
1
1.5
2
2.5
Agriculture, mining, and construction
Agriculture, mining, and
construction
0
0.5
1
1.5
2
2.5
3
Food processing and other manufacturing
Food processing and other
manufacturing
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 33
0
0.2
0.40.6
0.8
1
1.21.4
1.6
Transportation and Utilities
Transportation and utilities
0
0.5
1
1.5
2
2.5
Information
Information
00.511.522.533.544.5
Wholesale Trade
Wholesale trade
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 34
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Retail Trade
Retail trade
00.20.40.60.811.21.41.61.8
Finance and Insurance, Real Estate and Rentals
Finance and insurance, Real
estate and rentals
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Professional and Business Services
Professional services and
Business services
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 35
00.10.20.30.40.50.60.70.80.9
Educational and Health Services
Educational services and Health
services
00.20.40.60.811.21.41.61.82
Arts and Entertainment
Arts/entertainment, hotels,
eating, drinking
00.2
0.4
0.6
0.81
1.2
1.4
1.6
Other Services
Other services
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 36
Note also that location quotients were not forecasted nor allocated to tracts and FAZS. They
were calculated from empirical or forecast data for each job and FAZ of interest.
Table 4 presents the location quotients for the County of Hawaii viz-a-viz the State of Hawai‘i. Here the LQs measured the difference between the relative concentration of jobs at the County and State levels. It was not surprising then to find much higher concentration of agricultural jobs
in Hawaii County than in the State. Neither was it surprising to find that Hawaii Island had fewer jobs than the State as a whole in the information industry, finance and insurance, or the manufacturing sector.
00.20.40.60.811.21.41.61.8
Government
Government
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 37
TABLE 4: LOCATION QUOTIENTS: EMPLOYMENT FOR 20 NAICS CLASSES, COUNTY OF HAWAI‘I VS. STATE OF HAWAI‘I,
2001-2014
2010 2015 2020 2025 2030 2035 2040
Agriculture 3.39 3.32 3.25 3.19 3.14 3.10 3.06
Utilities 1.52 1.55 1.55 1.55 1.55 1.53 1.52
Hotels 1.53 1.52 1.51 1.49 1.47 1.46 1.44
Arts and entertainment 1.46 1.45 1.43 1.40 1.37 1.34 1.31
Real estate and rentals 1.35 1.32 1.30 1.27 1.25 1.24 1.22
Retail trade 1.26 1.24 1.22 1.20 1.18 1.16 1.13
Food processing 1.14 1.12 1.09 1.07 1.06 1.03 1.02
Health services 1.03 1.08 1.13 1.17 1.21 1.25 1.28
Mining and construction 1.05 1.02 1.00 1.00 0.99 0.98 0.97
Government 0.95 0.94 0.94 0.94 0.94 0.94 0.94
Eating and drinking 0.89 0.91 0.91 0.92 0.92 0.92 0.92
Transportation 0.90 0.89 0.88 0.87 0.86 0.85 0.84
Educational services 0.83 0.86 0.89 0.93 0.95 0.97 0.99
Wholesale trade 0.85 0.85 0.83 0.83 0.82 0.81 0.80
Information 0.74 0.72 0.74 0.73 0.74 0.74 0.73
Other services 0.71 0.70 0.70 0.68 0.67 0.66 0.64
Business services 0.66 0.69 0.72 0.75 0.77 0.80 0.82
Other manufacturing 0.69 0.69 0.67 0.67 0.66 0.65 0.64
Finance and insurance 0.67 0.66 0.65 0.64 0.63 0.61 0.60
Professional services 0.54 0.55 0.56 0.56 0.56 0.56 0.56
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 38
JOBS IN THE COUNTY OF HAWAI‘I
Definition: “Jobs” is usually used to refer to paid positions of regular employment. The number
of jobs is one of the most common measures used by economists to measure economic well- being and economic growth. The number of jobs (that is, available positions in which a person
could be employed) is a measure of demand for labor. The measure of jobs is different from the measure of employment for two reasons. First, not all jobs are filled positions. Second, jobs may be filled by more than one person. Figure 17 presents the historical and forecast data for
total jobs in the County of Hawaiʻi between 1990 and 2040.
FIGURE 17: JOBS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: Because the number of jobs is one of the few topics not covered by the U.S. Census Bureau, jobs data were taken from the Hawaiʻi State Department of Labor and Industrial
Relations (DLIR). DLIR’s historical data series of job counts by North American Industry Classification System (NAICS) classification (not seasonally adjusted)9 was reported by
DBEDT. Total jobs data as prepared by DBEDT also provide detailed descriptions of individual
job types included in each industry classification. These definitions are included in Appendix A. Finally, DBEDT 2040 also provided a comprehensive data set for total jobs, wage and salary
jobs, and self-employment for the period between 1990 and 2040. These data were used as
the basis for trends and forecasts reported here.
9 http://www.hiwi.org/gsipub/index.asp?docid=421
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 39
Job growth was somewhat chaotic during the nineties, but resulted overall in slow growth pattern until 2000. The year 2000 was a high growth year, and 2002 fell dramatically after the
events of September 11, 2001. Jobs grew quickly from 2003 through 2007, and then fell off
during the Great Recession. By 2011, slow and steady job growth began at about the level that DBEDT 2040 had predicted in the earlier forecast.
The number of persons who are self-employed has grown at a relatively steady pace throughout the last three decades. Wage and salary jobs, on the other hand, reflect economic conditions.
Their numbers grow faster during periods of economic growth and slow down during economic downturns.
Forecast: The job forecast estimates were taken directly from the 2040 Forecast Tables A-43 and A-48. The historical data, as we have noted, matched the 2040 forecasts very well, and no
adjustment was required. The number of jobs is expected to increase at a decreasing rate
through the next 25 years from about 100,000 in 2014 to about 150,000 in 2040. The high estimate is about ten percent more than the forecast in 2040. The low estimate is down about
five percent. Discussion: The total job count forecast seems to be a reasonable estimate and we found no
alternative forecasts that suggested significant changes should be considered.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 40
Jobs by Sector
Definition: Sector data for jobs were available only for wage and salary jobs. Job sectors were a set of 15 categories of jobs developed by DBEDT and shared by most State and County
governmental agencies. Definitions of the various industry sectors are shown in Appendix A
and appear in figures presented in this section. Figure 18 shows the number of wage and salary jobs by sector between 1990 and 2040.
FIGURE 18: NUMBER OF JOBS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Graphing 15 job categories over 50 years produces results that are difficult to read. The chart
provides a rough overview of the historical and predicted growth patterns for wage and salary jobs in Hawaiʻi County. Table 5 presents the number of jobs recorded and forecast for the years
1990 through 2015. Figures for all years between 1990 and 2040 have been included in the data matrix submitted along with this report.
0
5,000
10,000
15,000
20,000
25,000
19901992199419961998200020022004200620082010201220142016201820202022202420262028203020322034203620382040Number of JobsNatural resources, mining & construction jobs
Manufacturing jobs
Wholesale trade jobs
Retail trade jobs
Transportation, warehousing & utilities jobs
Information jobs
Financial activities jobs
Professional & business services jobs
Educational services jobs
Health care & social assistance jobs
Arts, entertainment & recreation jobs
Accommodation jobs
Food services & drinking places jobs
Other services jobs
Government jobs
Agriculture wage and salary jobs
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 41
TABLE 5: PERCENTAGE OF TOTAL NON-AGRICULTURAL WAGE AND SALARY JOBS BY SECTOR, COUNTY OF HAWAI‘I,
1990-2015
The top five growth sectors in the DBEDT 2040 forecast are government jobs, food services and
drinking place jobs, health care and social services jobs, education services jobs, and professional and business services jobs. Historical growth has been significant in all five sectors. All but the business sector have made up large portions of the County’s total job count.
The five sectors comprise the services jobs component of the county economy and it will surprise no one that the forecast describes an economy increasingly based on service jobs.
Figures 19 and 20 show details from Figure 18. In Figure 19, we separate out the trends and forecasts for the five sectors with the highest growth rates in DBEDT 2040. Figure 20 presents
similar data for the next five highest growth sectors.
1990 1995 2000 2005 2010 2015
Total wage and salary jobs 49,000 49,550 56,000 64,700 62,950
Total non agriculture wage & salary jobs 45,500 47,100 53,300 62,200 60,300 62,500*
Natural resources, mining & construction 8.0%5.9%5.9%7.8%5.2%4.4%
Manufacturing 5.4%3.6%3.1%2.4%2.1%1.8%
Wholesale trade 2.9%2.7%2.4%2.7%2.6%2.6%
Retail trade 15.1%16.1%14.8%14.3%14.3%14.8%
Transportation, warehousing & utilities 5.3%4.6%4.1%4.7%4.2%4.8%
Information 1.3%1.5%1.3%1.0%1.2%0.8%
Financial activities 5.2%5.0%4.0%4.2%4.6%3.7%
Professional & business services 5.6%6.9%7.7%7.7%7.9%10.6%
Educational services 0.8%1.0%1.0%1.5%2.0%2.2%
Health care & social assistance 5.6%7.4%9.3%9.4%10.9%10.9%
Arts, entertainment & recreation 1.3%1.8%2.2%2.5%2.7%2.2%
Accommodation 13.6%11.6%13.4%11.4%9.2%8.8%
Food services & drinking places 8.7%7.9%7.9%8.5%8.8%9.5%
Other services 3.0%2.8%2.4%3.2%3.3%2.6%
Government 18.5%21.5%20.5%18.6%21.1%20.2%
Agriculture wage and salary 7.7%5.2%5.1%4.0%4.4%0.0%
* 2015 data are first and second quarters only
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 42
FIGURE 19: TOP FIVE JOB GROWTH SECTORS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Three sector forecasts do not match the historical data well and we expect that the next set of
forecasts, due next year, will remedy that fact. Government jobs have not performed at the
forecast level and, in fact, have shown very little growth since 2004. The forecasted growth rate for government jobs, however, seems consistent with the historical behavior of this data series.
The same situation exists with the forecast for health care and social services jobs.
Performance has been about 1,500 jobs below what the forecast predicted. More importantly, the forecast rate of growth is higher than the rate of growth between 2000 and 2013. Finally,
the forecast for professional and business services is somewhat lower than its historical performance.
For the next five most important job sectors (Figure 15) the trend data have been volatile, making forecasting more risky. In general, we do not anticipate serious problems with these forecasts, however.
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
16,000
18,000
20,000
Number of JobsProfessional & business services jobs
Educational services jobs
Health care & social assistance jobs
Food services & drinking places jobs
Government jobs
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 43
For information sector jobs, high growth accompanied the housing bubble economy until 2007 and then fell again during the Great Recession. The forecast rate, however, may be lower than
the pre-2013 performance would support.
Jobs in the accommodations sector and the construction sector varied widely since 1990. The
intersection of the historical data and the DBEDT 2040 forecast were slightly lower than predicted and their forecasted rates of growth are moderate. All three of these sectors may perform better in the next 25 years than is indicated in the forecast shown here.
Forecasts for transportation, warehousing and utilities jobs, and for retail trade jobs, seem reasonable when compared with their historical data series.
FIGURE 20: NEXT FIVE JOB GROWTH SECTORS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Discussion: The jobs by sector trends and forecasts provided by DLIR and DBEDT should
suffice for most planning decisions in the short-run. The next edition of the forecasts is underway and should be available in late 2016. Before that time, planning decisions that
depend heavily on sectors discussed above should be approached carefully. Jobs in
government, health and social services, and the information sector jobs should considered in the category. The disconnect between trends (sector performance in the recent past) and
forecasts (future growth rates) may affect the reliability of those forecasts.
0
2,000
4,000
6,000
8,000
10,000
12,000
19901992199419961998200020022004200620082010201220142016201820202022202420262028203020322034203620382040Number of JobsNatural resources, mining & construction jobs
Retail trade jobs
Transportation, warehousing & utilities jobs
Information jobs
Accommodation jobs
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 44
Longitudinal Employer-Household Dynamics
Definition: Longitudinal Employer-Household Dynamics (LEHD) provides several data products that may be used to research and characterize workforce dynamics for specific groups.
These data products include the Quarterly Workforce Indicators (QWI) and LEHD Origin-
Destination Employment Statistics (LODES) data sets.
LEHD Origin-Destination Employment Statistics (LODES) data are state-based and organized
into three types: Origin-Destination (OD), Residence Area Characteristics (RAC), and Workplace Area Characteristics (WAC). This information is available at the census block
geographic level. For the present study, WAC data were utilized. Trend: From the LEHD LODES data, tract level data regarding the total number of jobs were
assembled for 2002 through 201310 and is displayed in Figure 16 below. The average annual growth rate ranged from – 9.4 percent in 2009 to 5.7 percent in 2005.
The job counts provided by LEHD LODES and that provided by DLIR are somewhat different. This is because the LODES data come from Unemployment Insurance (UI) records. Review of
their fundamental data definitions describes them as monthly data for all persons employed by
employers who are registered in the UI system11. Companies with no employees, or sole proprietorships, are not counted in the LODES data but are included in ACS data. There were
approximately 16,500 sole proprietorships in Hawai‘i County in 2013. Also excluded from the
LODES job counts are the military and other security-related federal agencies, postal workers, some employees at nonprofits and religious institutions, informal workers, and the self-
employed.
It is possible that the LEHD LODES data does not include certain employment types because it
was primarily developed for transportation modeling. Transportation modeling must link where
people live and where they go to work on regular basis. The self-employed, informal sectors, postal workers, etc. all tend to have unusual or nonconforming transportation patterns, making
them outliers for transportation modeling. The LEHD is intended to detail where people are working, not just whether they have a job (which is the primary focus of the DLIR data).
Forecast: To generate the forecast for 2014 through 2040, a standard linear regression equation was applied. The resulting projection bears a striking resemblance to the jobs forecast
based on DLIR and DBEDT data (Figure 12). The high forecast estimate begins with the mid-
level estimate in 2014 and applies a standard five percent increase through 2040. Similarly, the low estimate begins with the mid-level estimate in 2014 and applies a standard five percent
decrease to 2040. The average annual growth rate for the period 2014 through 2040 was 0.8
percent.
10 http://lehd.ces.census.gov/data/ 11 All employers with one or more employees must register with UI and report data monthly
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 45
FIGURE 21: TOTAL JOBS FROM LEHD LODES, COUNTY OF HAWAI‘I, 2002-2040
Discussion: In addition to providing insight into the nature of jobs across the County, these
data will serve as the foundation for analyses of the total commercial and industrial square
footage occupied and available in the County of Hawai‘i.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 46
HOUSEHOLD INCOME DISTRIBUTION
Definition: Household income is the income of the householder and all other household members 15 years of age or older, whether they are related to the householder or not, during a 12-month period. Household income excludes the income of any members who earned money
during the year, but are not currently residing in the home. Income includes money received on a regular basis (exclusive of capital gains) before payments
for personal income taxes, social security, union dues, Medicare deductions, etc. Therefore, money income does not reflect income in the form of noncash benefits, such as food stamps,
and other transfer payments. The Census measure of income is generally considered more
accurate than more comprehensive measures of income because it is easier for respondents to remember wage and salary income.
TABLE 6: HOUSEHOLD INCOME DISTRIBUTION, COUNTY OF HAWAI‘I, 2000-2013
Trend: Household income data were taken from the U.S. Decennial Census for 1990 and
2000, and from the American Community Surveys 2005-2013.12 Estimates were calculated for
the intercensal years from 2001 to 2004. The average annual growth rate was calculated directly from the data. The decennial census data do not provide much detail for the first twenty
years of the series, but the introduction of the ACS data resulted in household incomes rising
from about 2005 through 2008. The data are based on relatively small samples and are less reliable than we might wish. It is consistent with the fact that average household income in
Hawaiʻi County rose during the 2003-2007 economic boom and fell between 2008 and 2010.
The Area Median Income (AMI) for Hawai‘i County in 2015 is $62,400. Between 2000 and
2010, the percentage of households earning $100,000 or more nearly doubled (10.5% in 2000 to 20.6% in 2010). During that same decade, the percentage of households earning less than $25,000 dropped from 30.9 percent to 24.4 percent.
12 2005-2006: single-year estimates; 2007-2008: three-year estimates; 2009-2013: five-year estimates
2000 2005 2010 2011 2012 2013
Total households 52,945 59,470 64,382 64,270 64,556 64,909
Annual Household Income
Less than $10,000 6,135 4,356 5,184 5,211 5,473 6,187
$10,000 to $14,999 3,489 3,770 3,862 3,712 3,717 3,532
$15,000 to $24,999 6,730 5,205 6,638 6,645 7,211 7,541
$25,000 to $34,999 7,043 6,587 5,613 6,264 6,318 6,427
$35,000 to $49,999 8,727 10,739 8,138 8,509 8,402 7,990
$50,000 to $74,999 9,764 12,790 12,641 12,002 11,771 12,217
$75,000 to $99,999 5,493 7,464 9,023 9,114 8,759 8,703
$100,000 to $149,999 3,715 5,834 8,623 8,170 8,357 7,803
$150,000 to $199,999 910 1,374 2,457 2,721 2,684 2,672
$200,000 or more 939 1,351 2,203 1,922 1,864 1,837
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 47
MEDIAN HOUSEHOLD INCOME
Definition: Median household income divides the income distribution into two equal parts, with
one-half of the cases falling below the median income and one-half above the median. For households, the median income is based on the distribution of the total number of households
(including those with no income).
FIGURE 22: MEDIAN HOUSEHOLD INCOME, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: Household income data were taken from the U.S. Decennial Census for 1990 and
2000, and from the American Community Surveys 2005-2013.13 Estimates were calculated for
the intercensal years from 2001 to 2004. The average annual growth rate was calculated directly from the data. The decennial census data do not provide much detail for the first twenty
years of the series, but the introduction of the ACS data resulted in household incomes rising
from about 2005 through 2008. The data are based on relatively small samples and are less reliable than we might wish. It is consistent with the fact that average household income in
Hawaiʻi County rose during the 2003-2007 economic boom and fell between 2008 and 2010.
Forecast: The mid-level median household income forecast was computed as a function of
total personal income (for which forecast data are available from DBEDT Data Book 2014 Table 13.12). The rationale is that total income divided by persons produces per capita income. In the same sense, total income divided by the number of household yields an estimate of average
household income in the County. The Data Book also provided data on the total number of households, the ratio of median income to personal income, and current and constant dollar
household income figures for the State. After 2030, the forecast takes its shape from the total
income growth pattern. Between 2014 and 2030 the data were fit to a set of curves to approximate household incomes based on total income.
13 2005-2006: single-year estimates; 2007-2008: three-year estimates; 2009-2013: five-year estimates
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 48
Discussion: Forecasting household income is an imprecise undertaking. It is rarely attempted. The determinants of household income form a complex array including household formation and
household characteristics, number of employed persons per household, the changing
distribution of incomes over time, and the array of income sources throughout the household. Even when the median household income exhibits a clear and unchanging pattern over time,
the component and independent variables may be changing. In all, the pattern of change in household income since 1990 is of interest and helps to explain
the demographic, economic, and housing issues over those years. The forecast, on the other hand, is an acceptable indication of what might happen to household incomes between 2016 and 2040.
Poverty Status
Trend data regarding the percentage of Hawai‘i County residents and families in poverty were
taken from the U.S. Decennial Census for 1990 and 2000, and from the American Community
Surveys 2005-2013.14 In 1990, 14.2 percent of persons and 10.9 percent of families were reportedly living below the poverty threshold. While the percentage of impoverished persons increased slightly over the next decade to 15.7 percent, the percentage of families in poverty
remained relatively unchanged (11.0%).
Between 2000 and 2010, the percentage of persons in poverty fell to as low as 13.1 percent
(2007) and was back to just over the 1990 level by 2010 (14.4%). Since 2010, this percentage has jumped nearly 4 percentage points to an all-time high of 18.3 percent in 2013. For families,
the percentage in poverty ranged from a low of 9.3 percent in 2008 to a peak of 11.5 percent in
2006. Since 2010, the percentage of families in poverty has increased by 3.2 percentage points to 13.5 percent in 2013.
14 2005-2006: single-year estimates; 2007-2008: three-year estimates; 2009-2013: five-year estimates
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 49
FIGURE 23: PERCENTAGE OF PERSONS AND FAMILIES IN POVERTY, COUNTY OF HAWAI‘I, 1990 TO 2013
An additional indicator of the financial well-being of residents is the self-sufficiency measure.
Unfortunately, the self-sufficiency data currently available addresses a very specific subset of
the population. Because this narrow focus is not especially useful for planning purposes, it was not included in the present report.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 50
Real Estate Market and Housing
The number of housing units in the County of Hawai‘i in 2013 was estimated to be 87,310. Among those, approximately 80 percent were single-family dwellings and the remainder were
multi-family units. Roughly 80 percent of the County’s housing stock was occupied and among
the occupied housing units, 64 percent were owner-occupied
HOUSING VALUES
Definition: The median value of the housing units on Hawaii Island increased by more than
150 percent between 2000 and 2010. Between 2010 and 2013, however, the value of owner-
occupied homes has slightly but steadily declined (-3.4%) to a median value of $373,833.
TABLE 7: VALUE OF OWNER-OCCUPIED HOUSING UNITS, COUNTY OF HAWAI‘I, 2000-2013
Source: Decennial Census 2000, American Community Survey 2005-2013, Hawaii County Data Book
Trend: The data were taken from the standard sources as shown above, and there is no existing forecast of housing value for the County. Notice that the values are given only for
owned units with a mortgage and only for cases in which the ACS has complete data on
housing characteristics and estimated housing value (self-reported). The general pattern of these data is very similar to the trend for home prices across the state in the last 13 years.
Prices or values rose rapidly during the price run-up of the last decade. Unlike other states,
however, housing unit values did not drop precipitously during the Great Recession. Rather, prices held steady until 2013. The drop during the last year shown here is relatively small and is
not expected to lead to major devaluation of real estate during this decade. In fact, local forecasters, including TZ Economics, UHERO, and DBEDT, have all predicted significant growth in housing prices through 2020.
2000 2005 2010 2011 2012 2013
Total Specified Owner-
Occupied Housing Units 29,914 39,949 42,591 42,334 42,042 42,661
Total Value
Less than $50,000 1,444 748 897 963 1,134 1,144
$50,000 to $99,999 5,793 1,892 1,550 1,729 1,651 1,828
$100,000 to $149,999 1,792 4,037 1,790 2,141 2,490 2,925
$150,000 to $199,999 6,588 3,212 3,414 3,689 3,947 4,515
$200,000 to $299,999 5,264 7,791 8,649 9,043 9,425 10,094
$300,000 to $499,999 11,977 13,227 13,441 13,456 13,326
$500,000 to $999,999 8,579 10,202 8,892 7,864 7,074
$1,000,000 or more 1,713 2,862 2,436 2,075 1,755
Median (dollars)$153,700 $329,900 $387,154 $381,065 $376,534 $373,833
9,033
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 51
SHELTER BURDEN
Definition: HUD housing guidelines suggest that households should devote no more than 30
percent of their income to pay monthly housing expenses. By 2005, the ACS was gathering data on this definition of shelter burdened household for both owned and rented housing units.
Table 8 presents a review of those data for the County of Hawaii from 2000 through 2013.
TABLE 8: PERCENT OF HOUSEHOLD INCOME PAID FOR HOUSING, COUNTY OF HAWAI‘I, 2000-2013
The monthly shelter payments were extracted separately for owners and renters and compared with HUD household income guidelines for the County of Hawaii. HUD guidelines are
standardized for household size and household income and are thus comparable for the entire housing stock. Thus we see that, in 2013, 49 percent of all owner households in the County were paying less than 30 percent of their monthly income for shelter. Among renter
households, that percentage was notably lower at only 35.3 percent.
Adjusting those data for incomplete data, we arrived at the summary of shelter burdened
households shown in Figure 24. We offer two observations there, one for the standard 30 percent above AMI, and the other for households with shelter payments above 35 percent of the
County AMI.
2000 2005 2010 2011 2012 2013
Selected housing units with a mortgage:19,167 25,055 26,773 26,586 26,221 26,538
Less than 15 percent 19.8%15.8%15.2%13.8%12.9%13.0%
15.0 to 19.9 percent 15.2%16.6%13.9%12.5%12.2%11.9%
20.0 to 24.9 percent 15.7%14.5%13.3%12.0%12.6%13.4%
25.0 to 29.9 percent 11.6%10.1%9.0%10.8%10.4%10.7%
30.0 to 34.9 percent 9.1%10.6%8.3%8.7%8.9%9.1%
35 percent or more 27.8%32.4%39.7%41.4%42.0%41.2%
Not computed 0.8%0.0%0.6%0.8%1.0%0.8%
Selected rental housing units:*18,382 20,087 21,791 21,936 22,514 22,248
Less than 15 percent 16.8%13.7%11.1%9.2%9.6%9.2%
15.0 to 19.9 percent 11.6%11.5%11.4%10.3%9.5%8.4%
20.0 to 24.9 percent 11.4%11.0%10.6%9.9%10.4%10.9%
25.0 to 29.9 percent 9.1%9.1%9.1%9.1%8.5%6.8%
30.0 to 34.9 percent 6.2%6.8%7.3%7.5%6.4%6.9%
35 percent or more 30.7%34.1%36.9%38.4%39.3%40.3%
Not computed 14.2%13.9%13.6%15.5%16.4%17.5%
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 52
FIGURE 24: SHELTER BURDEN ESTIMATES IN POVERTY, COUNTY OF HAWAI‘I, 1990 TO 2013
Source: ACS Data as shown in Figure 24. Notice the change in scale on the year axis.
In 2000, only 36.9 percent of owner households with a mortgage were paying more than 30 percent of their income for housing. By 2010, this had increased to 48 percent, and by 2013 it had risen to over half of owner households (50.1%). Historically, renter households tend to
dedicate a larger percentage of their income to housing payments. In 2000, 43 percent of all renter households were rent burdened. A decade later, 51.2 percent of renter households were
spending over 30 percent of their income on housing. This percentage had increased to 57.2
percent by 2013.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 53
CROWDING
Definition: We adopted the standard U.S. Census definition for household overcrowding. It
states that a household is overcrowded when the ratio of household member to rooms in the housing unit exceed 1.0. A household is classified as extremely overcrowded then that ratio
exceeds 1.50.
Trend: Crowding may be the result of an increase in house prices or rents, or to a decrease in
household income. It may also be a response to decrease in jobs or employment, or to a
sudden increase in population, due either to natural increase or net in-migration. Regardless crowding is considered to be a negative indicator if the health of a local housing market. It is
associated with pent-up demand, a sign that the market is unable to supply the number and types of housing units needed by newly forming households and newly arriving residents. Table 9 presents the Census and ACS data for overcrowding in the County of Hawaii between 2000
and 2013.
TABLE 9: OVERCROWDING, COUNTY OF HAWAI‘I, 2000-2013
At 5.6 percent of owner-occupied units, the level of overcrowding in Hawai‘i County’s owner-
occupied units is the highest among all of Hawai‘i’s counties. The percentage of overcrowded renter-occupied units, however, is notably higher at 12.6 percent of rented units.
2000 2005 2020 2011 2012 2013
Total Occupied Housing Units 52,985 59,470 64,382 64,270 64,556 64,909
Owner occupied 34,166 39,949 42,591 42,334 42,042 42,661
0.50 if fewer persons per room 19,199 24,011 28,142 27,701 28,224 28,394
0.51 to 1.00 persons per room 11,607 13,426 12,106 12,192 11,486 11,874
1.01 to 1.50 persons per room 1,972 1,684 1,619 1,767 1,606 1,633
1.51 to 2.00 persons per room 969 567 584 554 557 596
2.01 or more persons per room 419 261 140 120 169 164
percent overcrowded 9.8%6.3%5.5%5.8%5.5%5.6%
percent extremely overcrowded 2.8%1.4%1.4%1.3%1.3%1.4%
Renter occupied 18,819 19,521 21,791 21,936 22,514 22,248
0.50 if fewer persons per room 7,860 8,529 10,626 11,068 11,039 11,246
0.51 to 1.00 persons per room 7,531 8,431 8,474 7,949 8,325 8,191
1.01 to 1.50 persons per room 1,817 1,712 1,691 1,678 1,834 1,613
1.51 to 2.00 persons per room 1,208 795 776 1,109 1,163 1,050
2.01 or more persons per room 403 54 224 132 153 148
percent overcrowded 18.2%13.1%12.3%13.3%14.0%12.6%
percent extremely overcrowded 6.4%4.1%3.6%5.1%5.2%4.7%
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 54
TOTAL HOUSING UNITS
Definition: Total housing units is the count of all structures intended for housing residents in
the year indicated. The count included all such units present and enumerated including occupied and vacant units, owned and rented units, and regardless of physical condition. The
count included abandoned units if the unit was structurally sound (had functional roof, walls,
doors, and windows). Commercial residential units, those used to house transient short-term temporary residents for cash payment (hotels, apartment hotels, condominium hotels, hostels,
and others) were excluded from total housing units. Timeshare units were included in the count
as seasonal units. Second homes (those used for housing residents only at certain times during the year) were also included.
Trend: The data for 1990 and 2010 were taken from the U.S. Decennial Census for those years, with interpolation for intercensal years. Data for the years 2005 through 2013 were taken
from ACS data. The forecast data came from DBEDT 2040.
FIGURE 25: TOTAL HOUSING UNITS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 55
There has been little long-range change in this series. The beginning of the series showed relatively high growth as the late-eighties housing boom slowed down. The mid-nineties were
marked by very slow growth in housing starts and growth picked up to between two and three
percent during the most recent housing boom. That boom ended in 2007, but residual permits and federal economic incentive programs held housing production above economic growth until
2010. A steep drop in production between 2013 and 2015 suggests that housing production, and therefore growth in total housing units, has returned to the norm.
Forecast: ACS empirical data for 1990 through 2013 were a reasonable match for the DBEDT 2040 data, suggesting that the empirical change in housing units was similar to what had been predicted by DBEDT. The two series were joined using long-range fourth order polynomial
curve fitting and the DBEDT 2040 forecast was maintained from 2015 onward. The low and high estimates were again formed using rough error estimates around the forecast line. The
high and low estimates are not exactly equal suggesting that there is less likelihood of lower
growth rates than higher growth rates between now and the year 2040. Overall, the growth rate will decline over the next 25 years from about 1.8 percent to 1.2 percent per year.
Discussion: The forecast is fairly straightforward and matched both the empirical and the existing forecast data. The high-growth aberration for the middle of the last decade is consistent
with construction activity during the last housing boom. So is the steep drop through 2013. The pattern is carried though to produce a slight increase in housing stock by the end of the present decade. Thereafter the forecast will continue to increase at a decreasing rate though the year
2040. The housing unit forecast line is a straight trend line. We can safely assume that Hawai‘i’s volatile housing market will continue to produce significant growth rate variation around that line.
Considerations: We believe that the general forecast for housing units in the County of Hawai‘i is reasonable and reliable. Variation in the content and configuration of the housing stock may
be less regular over the next 25 years.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 56
TOTAL HOUSING UNITS: OCCUPIED AND VACANT
Definition: Hawai‘i County housing units were classified as either occupied or vacant.
Occupied housing units were housing units currently occupied by a household and by definition, their number is equal to the number of households in the County.
Vacant units were categorized as either vacant-and-available or vacant-and-unavailable. The first category includes units that were vacant for rent, rented but not yet occupied, vacant for
sale, and sold but not yet occupied. Available vacant units were the slack in the housing
market, units that are vacant between occupants. Vacant unavailable units included units held for occasional or seasonal use, units for migrant workers, and “other vacant” a catch-all
category that includes a variety of unit types. Seasonal units are especially important in Hawaiʻi because the category includes second homes and timeshare units, along with the traditional summer homes and hunting lodges. Migrant worker units are rare. "Other vacant units" is the
fastest growing class in the nation for reasons not yet fully understood. The category includes units that are off the market because they are part of a trust, they are in foreclosure, the owner has passed away without a will or the will is contested, units being refurbished, and other similar
financial and construction issues. It may be that unregistered vacation rentals fall into this category as well. Regardless of the type, vacant units reduce the number of housing units
being used by local residents.
FIGURE 26: OCCUPIED AND VACANT HOUSING UNITS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 57
Trend: The historical data on occupied and vacant units were gathered from the Decennial Census for 1990 and 2000; ACS 1-year, 3-year, and 5-year estimates for the years 2005
through 2013 (Table B25024); and from the Hawaiʻi County Data Book. The crude vacancy rate
shown above was simply the percent of total housing stock classified as vacant each year. The rate started off very low as the late eighties housing boom ended, and rose during the recession
years in the mid-nineties. Vacancy rates decreased again during the run-up between 2003 and 2008. Recently, the crude vacancy rate reached a record high level.
Forecast: The forecast data were taken again from DBEDT 2040 for occupied housing units. Vacant unit estimates were calculated by subtracting occupied units from the total housing stock forecasted. The results suggested that, for the years between now and 2040, the number
vacant units will increase at an increasing rate each year. Individual forecasts for each of the components of the vacant unit group are not available. It is clear, however, that the chief
components of growth in the recent trend data are growth in seasonal and “other” vacant unit
types. It is not unreasonable to assume that these two types of vacant units will drive the increased growth rate of vacant units between now and 2040.
Discussion: The mix of occupied and vacant units in an area’s housing stock has always been of interest to planners and developers. Vacancy rates have always been used as a measure of
the health of the area housing market. The forecast present here will be useful and reliable for planners at the County of Hawaiʻi.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 58
OCCUPIED HOUSING UNITS: SINGLE-FAMILY AND MULTI-FAMILY
Definition: Occupied housing units are further classified according to several different
characteristics. One that is nearly always of interest is the number of units in a structure. This variable is often reduced to a dwelling unit’s status as single-family or multi-family.
The Census divides the total housing inventory into ten categories according to the number of units in each housing structure. Here we consider only single-family and multi-family housing
units. Single-family units (attached or detached) are those residences intended for occupancy
by only one household. Multi-family housing units refer to individual dwelling units located in structures with more than one housing unit (duplexes, multiplexes, apartments, townhomes,
etc.).
FIGURE 27: SINGLE FAMILY DWELLINGS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 59
FIGURE 28: MULTI-FAMILY DWELLINGS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: Historical data on the number of units in housing structures was taken from the
Decennial Census for 1990 and 2000; from ACS 1-year, 3-year, and 5-year estimates for 2005 through 2013 (Table B25024); and from the Hawaiʻi County Data Book (Table 16.18). Trend
data show that the relative numbers of single-family and multi-family occupied housing units tend to change very little over time. Within the growth rate for total occupied units, the homeownership rate changes very little. There was a slight increase in the percent multi-family
units in the last decade. That change may be, at least in part, an artifact of the introduction of ACS data. The increase never exceeded one percentage point and the rate returned to normal
by the end of the decade. Overall, about 20 percent of Hawaiʻi County’s occupied housing units
are and have been located in multi-family structures. In Hawai‘i’s other three counties, multi-family dwellings account for a significantly larger percentage of the total housing stock (26%-
44%). Forecast: The mid-level projections for single-family and multi-family dwellings were based on
the DBEDT 2014 Population Series and County Social, Business, and Economic Trends in Hawaiʻi 1990-2013 (Table A-5). The high estimate applies the present proportion of single-family dwelling units in the total housing stock to the high estimate for total housing units.
Discussion: The forecast predicts that there will be no change in the percent of occupied units that will be in multi-family structures. The forecast is a reasonable prediction based on past
trends. Recall that we are developing ceteris paribus forecasts, predicting what will happen if conditions and policies do not change. The very steady ratio of multi-family units to total occupied units is ample indication that the ratio will not change significantly in the next 25 years.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 60
OCCUPIED HOUSING UNITS: OWNED AND RENTED
Definition: Occupied housing units were also classified according to tenure. Housing unit
tenure was defined as the means by which households occupy housing units. All occupied housing units were classified first as either owned or rented. A housing unit was owned if the
name of one or more of the resident household is on the deed or mortgage for the unit.
Otherwise, the unit is classified as rented. Rented units are further classified as either paying rent or occupied without payment of cash rent.
FIGURE 29: HOUSING TENURE, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
Trend: Historical data on the number of units that are owned, rented, or occupied without
payment of cash rent were taken from the Decennial Census for 1990 and 2000; from ACS 1-
year, 3-year, and 5-year estimates for 2005 through 2013 (Table B25024); and from the Hawaiʻi County Data Book. Trend data show that patterns for these three types of tenure have different
patterns of growth and different growth rates. The homeownership rate, however, changes at a
slow pace.
Forecast: The long-range growth of homeownership from 1990 through 2006 was steady,
reaching a high of over 66 percent in 2006. The years between 2006 and 2013 showed a slow but steady decline in homeownership, dropping back to 65 percent. The forecast shows a
further decline to about 64 percent in 2040. These changes are small and do little to alter the
long-range homeownership rate in the County of Hawaiʻi. The county has long been a place where about two-thirds of all occupied housing units are owned. It is likely to continue in that
mode. While the homeownership rate has declined slightly, it remains three to ten percent higher than for the other counties in the state.
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
0
10000
20000
30000
40000
50000
60000
70000
199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013201420152016201720182019202020212022202320242025202620272028202920302031203220332034203520362037203820392040Annual Growth RateNumber of Occupied Housing UnitsHomeownership Rate
Total Owner Occupied-Mid
Paying Rent
Occupy without Payment
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 61
TOTAL HOUSING UNITS: AFFORDABLE
Definition: We defined affordable housing units as units that are affordable to households with
annual household incomes less than 80 percent of the HUD Area Median Income (AMI). ACS Public Use Microdata Sample (PUMS) data provide monthly rent, monthly mortgage payment,
and annual household income for all occupied housing units. PUMS data also provide asking
rents and asking sales prices for units that are vacant for rent or for sale. These data were used to calculate the number of units with prices (contract rents or monthly mortgage payments) that
were less than 80 percent of the household income15 guidelines established by HUD each year.
All other housing units were classified as being affordable to households with annual household incomes greater than 80 percent of AMI.
The analyses were conducted separately for occupied and vacant units, and owned and rented units, for the years 2009 through 2013. Results were then accumulated to produce the best
estimate of the number of housing units within Hawai‘i County’s available housing stock16 that
were affordable to low and moderate-income households each year between 2009 and 2013.
The forecast for affordable occupied housing units from 2014 through 2040 was a compilation of separate forecasts for occupied, vacant for rent, and vacant for sale housing units. For each, we used a third order polynomial regression based on components of the affordable housing
estimate and available unit forecasts. Results are shown in Figure 30 and discussed below.
FIGURE 30: AFFORDABLE HOUSING, COUNTY OF HAWAI‘I, 2009 THROUGH 2040
15 The 80 percent of AMI guideline is used by HUD to define households that are extremely low income (less than 30% AMI), low income (30 to 50% AMI) and moderate income (50 to 80% AMI). 16 Available units include occupied housing units and vacant units that are available for rent or sale, and those that are rented buy not yet occupied and sold but not yet occupied.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 62
Note that PUMS data and numbers of affordable units were also available for each County of Hawai‘i census tract. PUMS data for occupied and vacant housing units were evaluated, using
the same method detailed above, to determine the number of level of affordable housing units
per tract. Those figures were aggregated to the FAZs used in this study and reported in the data matrix.
Note also that, while the forecasting and FAZ allocation were calculated using housing units numbers, Table 30 reports data as a percent of affordable units in the County.
Trend: Between 2009 and 2013, roughly 56 percent of all housing units available to residents of Hawai‘i County were classified as affordable by our definition. That period covers that last 18
months of the Great Recession and the first three years of the recovery. As expected, the estimate of affordable units in the local housing stock changed during that period. The percent
of housing units that were affordable fell from over 59 percent to about 54 percent in four years.
Affordability was being altered by the number of units in the stock (growing slowly), the shelter price (stable, then rising) and both the average household income (falling) and the household
incomes of low- and moderate-income households (falling). Within this complex system of
variables, the percent of affordable housing units stayed relatively stable in 2012 and 2013.
The measure of affordability we have selected for this project is difficult to compare over time and space. We can, however, gain some perspective by considering another, frequently used measure of housing affordability, the shelter-to-income ratio (STI). The STI ratio is calculated
as the current monthly shelter cost expressed as a percentage of current monthly household income17. To further simplify the measure we report the percentage of households with STI greater than 30 percent. Some version of the STI ratio is the basis of most affordability
measures in use today18.
In 2014, about 39.1 percent of Hawai‘i County households paid more than 30 percent of their
income for shelter payments. Nationally, 35.3 percent of households paid more than 30 percent. For the State of Hawai‘i, the comparable figure is 42.8 percent, third highest among the 50 States, Washington D.C., and Puerto Rico. The two higher states were California
(45.6%) and New Jersey (43.6%). The lowest score in the nation was North Dakota’s at 21.8 percent. Within Hawai‘i, Hawai‘i County’s STI ratio was lowest in the state, behind Kauai
County (41.9%), Maui County (43.5%), and the City and County of Honolulu (43.6%). As noted
earlier (Table 8), the County’s rating on this measure of affordability has been steadily increasing since 2000.
Forecast: The forecast method for affordable occupied housing units based on both the growth in total housing units and the annual change in housing prices. The forecast suggests the
annual rate of change in the percent of affordable housing units will reach almost 2.5 percent in 2014 and then fall to about 1.0 percent per year by 2040.
Comparing this measure of affordable housing to other times or places is somewhat
problematic. The data needed to make the estimates has only been available since 2009 and
17 The ratio is calculated separately for owner and renter occupied units, and is based on all households with non-zero shelter payments and non-zero household incomes. 18 STI-based measures have been criticized for their exclusion of relatively large numbers of households for lack of data. Economists also disagree with the rather arbitrary cutoff at 30 percent of household
income. They argue that households with STI above 0.30 may have freely chosen a lifestyle in which they spend greater amounts of their income on housing than on some other household expenditure.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 63
the analysis is time consuming. More important, the five-year trend data series is less than we would like as a base for a 2040 forecast. The solution was to rely on the percent of the housing
stock that is affordable, as described above. The forecast is valid under the assumption that the
relationship between housing supply and price remains stable over the next 25 years.
The forecast suggests that a relatively steady percentage of the County’s housing stock (56%) will be affordable in the near future. That stable prediction seems reasonable because it is similar to other data we have reported here. The shelter burden or shelter-to-income ratio data
have remained stable over the last five years, as have the data on crowding. In the future, the affordable housing stock will hover around 56 percent through 2030 and then decrease to 55% by 2040.
The future performance of the measure depends on many factors. To properly model the
effects of local (and external) supply and demand on local housing prices would require a level
of effort that is currently outside the scope of this project. Further steps will be taken in the Hawai‘i Housing Planning Study, 2016.
For now, this new measure of affordability is clearly superior to anything we have had in the past. Previous measures (monthly payments, shelter burden, rent-to-income ratios, crowding
and doubling up, etc.) were all measures based on the residents’ condition. The method applied here, being based on the price of housing units, seems better suited to planning and policy-making.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 64
AVAILABLE HOUSING UNITS
Definition: Available Housing Units (AHU) were defined as those housing units available to be
used by county residents. Available units, also referred to as the housing stock, are equal to occupied housing units plus vacant-and-available units. All currently occupied units are
“available” to the local market by definition. Vacant units classified as vacant for rent; vacant,
rented but not occupied; vacant for sale; or vacant, sold but not occupied are also “available”. They are available for use by the residents or intended residents of the County. Unavailable
housing units are units intended for and suitable for use by county residents, but currently not
available to county residents. These include vacant and held for seasonal or occasional use, vacant units held for migrant workers, and other vacant units.
FIGURE 31: AVAILABLE HOUSING UNITS, COUNTY OF HAWAI‘I, 2009 THROUGH 2040
Trend: The trend data on occupied units and vacant and available units were taken from the
Decennial Census for 1990 and 2000; from ACS 1-year, 3-year, and 5-year estimates for 2005 through 2013 (Table B25024). Available units are shown here with total units to emphasize the
pattern of change in available units. In particular, we note the decrease in the rate of change
between 2007 and 2012. The difference is largely the result of an increase in the number of “other vacant” units, units held off the market due to indecision on the best course of action in a
housing market characterized by high prices, very low inventory, and little or no new supply.
Forecast: The forecast is based on the assumption that the ratio of available to total units will
return to its pre-recession trend. That is, we expect that the rising price of housing (market
prices and rents), and pressure on regulating agencies will generate new housing development and push the resale market to bring “other vacant” units back onto the market. We expect the
return to pre-recession availability rates will occur slowly over the next two or three years.
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
1991199319951997199920012003200520072009201120132015201720192021202320252027202920312033203520372039Housing Units Available to Residents
Total Housing Units- Mid
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 65
NEEDED UNITS
There are many definitions of this term. In fact, “needed units” is not formally a part of the
lexicon of housing research. But, after all the analysis and forecasting are finished, there is a practical question about the number of units that might be needed to plan appropriately for the
citizenry. In the present planning context, we considered the question, “How many housing
units will be needed to accommodate the County population forecast?” But we might just as well have addressed other questions, including “How many units must be produced using
government resources?” “How many units will be needed to fill demand for housing for very
low-income families or for workforce families?” “How many units are needed to address homelessness?”
Historically, that would involve gathering data on the number of new households and the number of new housing units added to the County of Hawai‘i housing stock each year since
1990. The forecast method is straightforward, being a function of new household formation and new units provided in the past.
Definition: In this study, Needed Housing Units (NHU) equals the total number of persons added to the resident population each year (new resident population) minus the estimated
number of persons in group quarters, divided by the average household size (persons in
households divided by number of households). The resulting number of households is then multiplied by one plus the ratio of vacant-and-available housing units to total available housing
units to satisfy the demand for vacant-and-available units.
NHU is the number of housing units that must be added to the County’s housing stock each
year. Current estimates are shown in Table 10.
TABLE 10: NEEDED HOUSING UNITS, COUNTY OF HAWAI‘I, 2015-2040
2015 2020 2025 2030 2035 2040
Total Needed Units 1,406 1,647 1,401 1,407 1,388 1,383
Needed Single-family Units 1,126 1,319 1,122 1,127 1,112 1,108
Needed Multi-family Units 280 328 279 280 276 275 Forecast: The figures shown in this table represent the number of housing units needed to
accommodate the new households formed in each of the years shown. They are not cumulative estimates for five-year periods. Estimates for individual years have been delivered in the master data file prepared for this project. The table also presents estimates for single-family
housing units and multi-family housing units. Those are based on the relative percentages of each type of unit produced in the past. By this definition, the County of Hawai‘i will need to
produce about 1,400 housing units per year between 2016 and 2040.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 66
NHU is the number of housing units to be added to the available housing stock each year, under the following assumptions:
1. the average household size is similar to and rationally related to recent trends; 2. no unusual growth or decline in the percent of the population living in group quarters;
3. no significant change in the housing demand and supply trends; 4. no change in the ratio of vacant to occupied housing units in Hawai‘i County; 5. no change in pent-up demand, doubling up, or homelessness.
Discussion: These assumptions represent a rigid approach to forecasting housing need that could produce very conservative estimates of needed units. Examining each in turn may be
useful here.
1. Average Household Size: Our own approach to the forecasting problem postulates a
slightly increasing household size. Unless the limitations on supply that have characterized housing production in Hawai‘i for the last 20 years are lifted, average
household size must rise. If the average household size were to decrease from the
current 2.75 persons per household in Hawai‘i, even more new units would be needed.
2. Group Quarters: Then number of persons in group quarters has been rising for the last several decades. If that number were to fall, more new units would be needed. Efforts to increase housing in group quarters (more dormitories, rooming houses, etc.) would
reduce the number of new units needed. 3. Supply and Demand: In a sense, all of the forecasts produced here are based on
supply and demand in the recent past. Both the data series collected and the forecasts incorporated have an underlying dependence on the number of housing units purchased and the number of units supplied in the past. But the relationship between supply and
demand and the number of housing units needed has not been specifically applied in a rigorously designed housing market model. We are not aware of any such model in
Hawai‘i. If housing supply were to increase in the next 25 years, the number of needed
units would diminish each year. If demand, especially external demand were to increase, the number of units needed to house the County’s population would increase.
4. Vacant Units: There has been relatively little change in rental vacancy rates (11% to 12%) or for sale vacancy rates (3%) since 2000. The composition of the vacant unit
classification, however, has changed dramatically. In 1990, 50 percent of all vacant units were either for sale or for rent. By 2010, that figure had dropped to 28 percent. The percentage of seasonal-held-for-occasional-use units rose from 44 percent to 55
percent between 1990 and 2000, and then dropped to 51 percent in 2010. The percentage of “other vacant” units rose from 4 percent in 1990 to 21 percent in 2010. The forecasting assumption was that all of these fluctuating trends would remain
relatively stable between 2016 and 2040. Obviously, changes in the number and types of vacant housing units will affect the housing stock (available units) and the number of units needed to house the residents of the County.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 67
5. Market Inefficiencies: A perfectly efficient housing market will provide sufficient and appropriate housing units for all interested and qualified households with occasional
interruptions to accommodate equilibrium adjustments. That is, it will balance supply
and demand. Inefficient housing markets with inelastic supply will produce pent-up demand, crowding, doubling-up, and homelessness. The forecasting assumption was
that there would be no change in these effects, suggesting that any inefficiency in the past two or three decades will be preserved in forecast estimates. Thus, the number of needed units is the number required to preserve the status quo in the County’s housing
situation. These assumptions are, in their turn, related to the types of questions in the first paragraph of
this section. The needed unit forecasts, like others presented in this report, are ceteris paribus forecasts. They predict what will happen if there were no major changes in population, policy,
or procedures between 2014 and 2040. Planning is precisely about producing changes. These
forecasts are the baseline from which planners work.
Two other sets of housing forecasts deserve mention here. The first is the number of needed
units presented in each of the last four iterations of the Hawai‘i Housing Planning Study19. HHPS estimates address the question, “How many units are needed to provide housing for
County residents in addition to those produced by the housing market?” In other words, it
addresses assumption 5, above, and focuses attention on ameliorating the effects of the County’s limitations on supply. As such, it will be notably lower than the forecast produced
here. The second forecast was produced this year by DBEDT and addresses a slightly different
question about total housing demand between 2015 and 202520. It produces a demand
estimate of about 1,900 units per year for the County of Hawai‘i, about 500 units higher than our forecast. The difference between the questions addressed is reflected in slightly different
forecasting methods and assumptions. Our forecast used a slightly higher and rising estimate of household size. We focused on available units rather than total housing units. We used empirical and slightly rising vacancy rates rather than fixed estimates. Finally, the forecast here
is for “needed units” and the DBEDT appropriately labels theirs a “demand estimate”.
19 HHPS published needed units estimates in 1997, 2003, 2006, and 2011. 20 Measuring Housing Demand in Hawaii, 2015-2025. DBEDT. April, 2015.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 68
Visitor Arrivals and Accommodations
Hawai‘i County had a peak number of visitors in 2007 (1.62 million) just prior to the beginning of the Great Recession. As was the case for all of the Counties, Hawai‘i County experienced a
sharp decline in visitor arrivals between 2008 and 2010. The recovery that began with a
substantial 8.7 percent increase in visitor arrivals from 2010 to 2011 has continued over the past several years.
Arrivals by air to Hawai‘i Island rose one percent from 2013 to 1,449,070 visitors in 2014. The average daily census increased 2.6 percent during the same period to 30,008 visitors present
on the island on any given day. A longer length of stay resulted in a 2.6 percent growth in visitor days, which contributed to an increase in visitor expenditures to $1.9 billion (+2.3% over 2013). Nearly half (46.2%) of the visitors who visited Hawai‘i Island spent their entire trip there as
opposed to dividing their vacation between Hawai‘i’s islands.
With the increasing number of visitors traveling to Hawai‘i County comes the challenge of
planning for their impact on the local economy, especially with regard to accommodations. Hotel rooms account for the majority of the visitor accommodation units in the County of Hawai‘i
(59.5%). A distant second and third were timeshare properties and Visitor Rental Units (VRUs),
with 13 and 12 percent of the total visitor units, respectively. With the upward trend in visitor arrivals expected to increase through 2040, increasing demand for visitor units is likely.
One of the significant difficulties in keeping track of visitor accommodations units on Hawai‘i Island is that the number of housing units being let to visitors as short term rentals is unknown.
Residential units used for this purpose were referred to as Transient Vacation Rentals (TVRs). Their number is known because their owners pay General Excise Tax (GET) and Transient Accommodation Tax (TAT) and their number is recorded in the Hawai‘i Tourism Authority’s
annual Visitor Plant Inventory. However, it is strongly suspected that significant numbers of homeowners rent all or parts of their units to visitors as short term rentals without registering their rental activity and without paying the required taxes. There is no estimate for the number
for those properties. If their number is substantial, then the estimate for visitor accommodations units in the County would be low, and the estimate of residential rental units would be high.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 69
VISITOR ARRIVALS
Definition: The foundation statistic for monitoring the number of visitors in Hawaiʻi is the
number of visitors arriving in the State during a given year. The visitor arrival count is the fundamental measurement in the system for visitor data, the basis for many planning decisions,
and the basis for most forecasting. Data collection for the visitor industry is currently the
responsibility of the Hawaiʻi Tourism Authority (HTA). They carry out that responsibility using a complex system of surveys and maintain the data as part of the Basic Data Series. The
particular statistic used here is total visitor arrivals by air. A small number of visitors arriving by
sea have been excluded from our analysis. Visitors arriving by sea were treated as a constant in the 2040 Forecasts and we have taken the same approach here.
FIGURE 32: VISITOR ARRIVALS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 70
Trend: Data for the years 1990 through 2013 were taken from HTA Annual Research Reports. Other relevant data were taken from DBEDT 2040 Forecasts, Table A-64, Table A-69 and from
DBEDT’s Annual Visitor Report 2012-2013 Table 60, and finally from DBEDT Data Warehouse
for 2014 data.
Forecast: The official forecast was taken from the DBEDT 2040 Forecasts. The empirical data from HTA were running a little higher than the forecast expected. Hawaiʻi has enjoyed three consecutive record years in tourism arrival growth. We used a logarithmic curve to fit a merged
line between the visitor arrivals data series and the 2040 Forecast data. The result is shown in Figure 32.
The forecast for visitor arrivals was carried out in tandem with visitor accommodations units, visitor days, and average daily visitor census. Note also that the forecast is a smooth line
representing the trend for visitor arrivals over the next 25 years. The visitor industry has been
quite volatile in the past and the annual counts can be expected to vary widely around the forecast trend line.
Discussion: The forecast for visitor arrivals reflects the DBEDT 2040 forecast in which the number of arrivals increases steadily at an increasing rate, from about 1.4 million in 2013 to
about 1.9 million in 2040. It reflects both the known trends for visitor arrivals from all ten of Hawaiʻi’s Major Market Areas (MMAs). It also reflects the expected impact of Hawaiʻi’s visitor industry development strategy, which includes shifting visitor arrivals from O`ahu to other
islands. Considerations: If the General Plan update process were to adopt strategies aimed at either
increasing or decreasing the expected number of visitor arrivals from the path suggested by the 2040 Forecasts, the visitor arrivals forecast must be changed. Since it is the foundation for all other visitor-related statistics covered in this project, the changes will have far-reaching effects.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 71
VISITOR ACCOMMODATION UNITS
Definition: The number of visitor accommodations units is the housing stock reserved for the
visitor industry. It includes individual commercial housing units classified as hotel rooms, condominium hotel rooms, apartment hotel rooms, bed & breakfast units, timeshare units,
vacation rental units, hostel units (beds), and other commercial housing units intended for the
use of short-term visitors to the State.
Note also the additional lines presented for the unit demand estimate, and units being used.
The unit demand estimate is an estimate of the number of accommodations units that would be needed to house the total number of visitors in the County of Hawaiʻi on an average day in a
given year. The value is calculated by dividing the average daily visitor census by the party size (to get the average daily party census) and then multiplying by the percent of all visitor parties requiring commercial units21. The measure of units being used was calculated by multiplying the
total number of visitor accommodations by the average occupancy rate for a given year.
FIGURE 33: VISITOR ACCOMMODATION UNITS, COUNTY OF HAWAI‘I, 1990 THROUGH 2040
21 Not all visitor parties are housed in commercial housing units. Some are housed in the homes of
friends and relatives, in their own second homes, at campgrounds or camping vehicles, or other, non-commercial housing arrangements.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 72
Trend: Data for the years 1990 through 2014 were taken from HTA’s annual Visitor Plant Inventory (VPI).
Forecast: The official forecast was taken from the DBEDT 2040 Forecasts. The empirical data from HTA were running considerably lower than the forecast for 2014. A substantial part of the
difference may have been the result of measurement issues documented in the VPI. No adjustment has been made for the difference in number of visitor accommodations units in 2015.
The forecast for visitor accommodations units was developed in tandem with visitor arrivals, visitor days, and average daily visitor census. Two tracking estimates, the unit demand
estimate and units being used, were also used in analyzing the trend and forecast data.
The forecast appears as a smooth line representing the trend for visitor accommodations units
over the next 25 years. We expect that actual performance will vary significantly around the trend line.
Discussion: The years between 2010 and 2014 were marked by a rapid growth of vacation rental units in Hawaiʻi. A substantial part of that growth was the result of transforming other unit
types (mostly condominium hotel units, but also hotel and timeshare units) to vacation rental status. The VPI update method (surveys of known properties) was not able to account for the increased number of vacation rentals. A separate study commissioned by HTA in 2014
(Individually Advertised Units in Hawaiʻi, 2014) reported that there were 9,986 vacation rental units in the County of Hawaiʻi. That would bring the total of visitor accommodations units up from 10,666 to 13,969. The DBEDT 2040 forecast for the County of Hawaiʻi was 11,600 units.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 73
VISITOR ACCOMMODATIONS UNITS BY TYPE
Definition: In the Visitor Plant Inventory, visitor accommodation units are assigned to one of
five categories according to the nature of the unit: hotel, condo-hotel, timeshare, visitor rental unit (individual vacation unit, visitor rental-condo, visitor rental-house), and other units.
FIGURE 34: VISITOR ACCOMMODATIONS BY TYPE, COUNTY OF HAWAI‘I, 2005 THROUGH 2040
Note. Visitor Rental Units includes IVU, VR-condo, and VR-house Trend: The historical data were compiled from the Hawaiʻi Tourism Authority’s Visitor Plant
Inventory annual reports.
Forecast: The forecast referenced the mid-level projections for total visitor units. The current
relative contribution of each of the various unit types to the total visitor unit inventory was maintained through 2040.
Discussion: Hotel rooms account for the majority of the visitor accommodation units in the County of Hawai‘i (59.5 percent). Timeshare properties and Visitor Rental Units (VRUs) are a distant second and third, with 13 and 12 percent of the total visitor units, respectively Considerations: There are numerous challenges with regard to the VPI data and identifying
visitor accommodation units.
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
0
200
400
600
800
1,000
1,200
1,400
1,600
1,800
2,000
200520072009201120132015201720192021202320252027202920312033203520372039Number of Hotel Units Number of Condo hotel, timeshare, VRU, and Other Units Condo hotel
Timeshare
Visitor Rental Units
Other
Hotel
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 74
Commercial and Industrial Space
COMMERCIAL SPACE BY TYPE Definition: Commercial real estate is defined as buildings or land intended to generate profit.
Commercial real estate classifications include retail or wholesale trade, hotels, restaurant, offices, clinics, warehouses, light manufacturing, and other uses. Residential buildings are not
classified as commercial real estate.
FIGURE 35: COMMERCIAL ESTABLISHMENTS, COUNTY OF HAWAI‘I, 1997 THROUGH 2040
Trend: The historical data for the period from 1997 through 2012 were taken from the DBEDT
Data Book 2014 Table 15.16. Those data were supplemented using North American Industry Classification Systems (NAICS) data.
Forecast: The forecast is a logarithmic function using the trend data from 1997 through 2002 as the basis.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 75
Discussion: Based on data obtained from the County of Hawaiʻi Planning Department, the total square footage for commercial establishments is 6.26 million, with a median of 3,432 per
establishment. The current median value of commercial buildings is $126,700. This represents
a 1.04 percent increase over the 2014 median value. The median value of commercial land has increased 3.04 percent over the past year, from $170,800 in 2014 to $176,000 in 2015.
Considering the large number of tourists that visit the County of Hawai‘i each year, it is not surprising that thirty percent of all commercial establishments in the County are retail trade locations. Tourism also supports the additional twenty percent of all commercial establishments
that provide accommodations and food services.
FIGURE 36: COMMERCIAL ESTABLISHMENTS BY TYPE, COUNTY OF HAWAI‘I, 1997 THROUGH 2040
0
100
200
300
400
500
600
700
800
1997199920012003200520072009201120132015201720192021202320252027202920312033203520372039Number of Commercial Establishments Retail trade
Accom. and food svcs.
Prof., sci., and technical
Other services
Admin., support, waste mgt., and remed.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 76
INDUSTRIAL SPACE BY TYPE
Definition: Industrial real estate is considered a sub-category of commercial real estate. An
industrial property is defined as a property used for the actual manufacturing of something, and can be considered either a factory or plant. This is usually zoned for light, medium or heavy
industry. This includes things such as warehouses, garages, and distribution centers.
Trend: The data for industrial spaces were obtained from the County of Hawaiʻi’s Planning
Department for 2015 only. Discussion: In 2015, the total square footage of industrial space in the County of Hawaiʻi is
5.77 million. This translates into a median square footage of 7,200 per industrial establishment. The median value among industrial buildings in the County is $360,500. This represents a 1.01 percent increase over 2014 ($356,900). The value of industrial land also increased from 2014
to 2015. The median land value for industrial spaces was $304,900 in 2014 and $333,800 in 2015, a 9.48 percent increase.
Considerations: The lack of available data for industrial space in the County of Hawai‘i, as well as the intended use of the information for planning purposes, require this information to be used with considerable caution.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 77
REVISED FORECAST ANALYSIS ZONES
In the Phase I Interim Report, SMS recommended a set of forecast analysis zones (FAZ) that
would create the geographic boundaries for all lower-than-county level forecasts.
SMS developed FAZs in order to establish a geographic level of forecasting that accounts for
both:
1. Hawaiʻi County Population centers – geographic boundaries of towns, residential areas,
etc. that are colloquially used by residents to differentiate segments of the county
2. Census tracts – the lowest level of geography for which data are consistently available across the range of variables included in this forecast.
Following the delivery of recommendations for FAZs in the Phase I Interim Report, SMS met with Hawai‘i County and Placeways to review the FAZs for accuracy from the County’s
standpoint and usability from Placeways’ standpoint. Subsequently, Hawai‘i County made
recommendations for the modifications of the FAZs that would reassign some census tracts and census-designated places (CDPs) in a manner that was more reflective of the way that Hawai‘i
County’s residents see the island as being divided.
The final revised list of FAZs is included in Table 1 below. Each FAZ is named according to the
recommendation of Hawai‘i County. The table includes the population centers, census
statistical geography (which include both tracts and CDPs), and the census tracts associated with each FAZ.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 78
TABLE 11: REVISED LIST OF FORECAST ANALYSIS ZONES
Forecast Analysis Zone Hawai`i County Population Centers Census Statistical Geography Census Tracts
Hilo Hilo
Hilo CDP (multiple tracts), micro statistical area, & urban cluster Keaukaha & Pana‘ewa Hawaiian
Homelands
202.02
203 204 205
206
207.01
207.02 208.01 208.02
209 Wainaku Wainaku CDP
North Hilo-Hāmākua Coast Villages
Hāmākua Coast Villages
Pauka‘a CDP
Pāpa‘ikou CDP Pepe‘ekeo CDP Honomū CDP
Hawaiian Homelands mauka of Honomū
201 221.02
Pa‘auilo-Laupāhoehoe Laupāhoehoe CDP & Tract 221.02
Honoka‘a-Pa‘auilo Honoka‘a
Pa‘auilo CDP & Tract 220 Honoka‘a CDP & urban cluster
Kukuihaele CDP Waipi‘o Hawaiian Homelands Tract 219.02
219.02
220
Waimea Waimea Waimea CDP & urban cluster Hawaiian Homelands 217.02
North Kohala
Hāwī Hāwī CDP
218 Kapa‘au Kapa‘au CDP & urban cluster Hala‘ula CDP
Rural North Kohala Tract 218
Kawaihae-Puakō-Waikoloa-Waikoloa
Resorts
Kawaihae Portion of Tract 217.04
Hawaiian Homelands
217.04 Puakō Portion of Tract 217.04
Waikoloa Waikoloa Village CDP & urban
cluster
Waikoloa Resort Puakō CDP
North Kona Kailua-Kona-Kūki‘o
Kalaoa CDP
Kailua CDP Hōlualoa CDP Kailua/Hōlualoa urban cluster
Kahalu‘u-Keahou CDP Honalo CDP Portion of Tract 215.07
215.02
215.04 215.07 215.09 216.01 216.04
South Kona Villages South Kona Villages
Kealakekua CDP Captain Cook CDP & urban cluster Hōnaunau-Nāpō‘opo‘o CDP Portions of Tract 213
Miloli‘i (portion of Tract 213)
213 214.02
Kaʻū
Ocean View Hawaiian Ocean View CDP
212.02
South Point Portion of Tract 212.02
Hawaiian Homeland
Discovery Harbour Discovery Harbour CDP
Wai‘ohinu Wai‘ohinu CDP
Nā‘ālehu Nā‘ālehu CDP
Pāhala Pāhala CDP
Kea‘au-Kurtistown
Highway 19 Puna Subdivisions & Villages –
North
Kurtistown CDP
210.13
Kea‘au Kea‘au CDP
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 79
TABLE 11 (CONTINUED): REVISED LIST OF FORECAST ANALYSIS ZONES
Forecast Analysis Zone Hawai`i County Population Centers Census Statistical Geography Census Tracts
Upper Puna
Volcano Volcano CDP
210.10 210.11
Highway 19 Puna Subdivisions &
Villages – North
Mountain View CDP
Highway 19 Puna Subdivisions & Villages – South
Hawaiian Acres CDP Fern Acres CDP Eden Roc CDP
Fern Forest CDP
HPP-Orchidland
Highway 130 Puna Subdivisions – Mauka Orchidland Estates CDP Ainaloa CDP 210.03 210.05 Highway 130 Puna Subdivisions – Makai Hawaiian Paradise Park CDP & urban cluster
Lower Puna
Pāhoa Pāhoa CDP
211.01 211.06
Highway 130 Puna Subdivisions – Makai Hawaiian Beaches CDP Maku‘u Hawaiian Homeland
Lower Puna Subdivisions Nānāwale Estates CDP
Lower Puna Subdivisions Leilani Estates CDP Tract 211.01
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 80
REFERENCES
County of Hawai‘i (2015). Current Data Book Table 1.18: Households, Population in
Households, and Population per Household, Hawai‘i County: 1980, 1990, 2000, and 2010.
County of Hawai‘i (2015). Current Data Book Table 16.2: Number and Value of Building
Permits, by Type, Hawai‘i County: 2012.
County of Hawai‘i (2015). Current Data Book Table 16.2a: Number and Value of Building
Permits, by Type, Hawai‘i County: 2011.
County of Hawai‘i (2015). Current Data Book Table 16.2b: Number and Value of Building Permits, by Type, Hawai‘i County: 2010.
County of Hawai‘i (2015). Current Data Book Table 16.18: Selected Housing Characteristics,
Hawai‘i County: 1990, 2000, and 2010.
County of Hawai‘i (2015). Current Data Book Table 16.26: Multiple Listing Service Sales, by
Type of Property, By Zone, Hawai‘i County: 2012.
County of Hawai‘i (2015). Current Data Book Table 16.26a: Multiple Listing Service Sales, by
Type of Property, By Zone, Hawai‘i County: 2011.
County of Hawai‘i (2015). Current Data Book Table 16.26b: Multiple Listing Service Sales, by Type of Property, By Zone, Hawai‘i County: 2010.
County of Hawai‘i (2015). Current Data Book Table 16.27: Multiple Listing Service Sales, by
Type of Property, By Zone, Hawai‘i County: 2009.
County of Hawai‘i (2015). Current Data Book Table 16.28: Multiple Listing Service Sales, by
Type of Property, By Zone, Hawai‘i County: 2008.
County of Hawai‘i (2015). Current Data Book Table 16.22: Value of Owner-Occupied Housing Units of County Divisions and Census Designated Places, Hawai‘i County: 2000.
Hawai‘i Tourism Authority (1991-2013). Annual Visitor Research Report Table 2: Summary of
Visitor Characteristics.
Hawai‘i Tourism Authority (1991-2013). Annual Visitor Research Report Table 60: Hawai‘i Island
Visitor Characteristics (Arrivals by Air).
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). Visitor Plan Inventory Figure 7: Hawai‘i Island – Inventory by Unit Type.
State of Hawai‘i Department of Business, Economic Development & Tourism (2013). Visitor
Plan Inventory Figure 6: Hawai‘i Island – Inventory by Unit Type.
State of Hawai‘i Department of Business, Economic Development & Tourism (2011). Visitor
Plan Inventory Figure 6: Hawai‘i Island – Inventory by Unit Type.
Hawai‘i State Data Center (1990). General Profile for the State of Hawai‘i by Counties: 1990 U.
S. Bureau of the Census Table STF1A.
SMS Research (2011). Hawai‘i Housing Planning Study, Prepared for and Published by the State of Hawai‘i Housing Finance Development Corporation Table 13: Needed Units.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 81
State of Hawai‘i Department of Business, Economic Development & Tourism – Research and Analysis Department (2015). Data Warehouse: Construction and Housing, Private
Building Permits.
State of Hawai‘i Department of Business, Economic Development & Tourism – Research and Analysis Department (2015). Data Warehouse: Construction and Housing, Home Sales
and Prices (Board of Realtors Data).
State of Hawai‘i Department of Business, Economic Development & Tourism – Research and Analysis Department (2015). Data Warehouse: Population and Vital Statistics – Vital
Statistics – Resident Births.
State of Hawai‘i Department of Business, Economic Development & Tourism – Research and Analysis Department (2015). Data Warehouse: Population and Vital Statistics – Vital
Statistics – Resident Deaths.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). 2014 State
of Hawai‘i Data Book Table 21.34: Multiple Listing Service, Number of Single Family and
Condominium Sales, by County: 1999 to 2014.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). 2014 State
of Hawai‘i Data Book Table 21.35: Multiple Listing Service, Median Sales Price of Single
Family and Condominium Sales, by County: 1999 to 2014.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). County
Social, Business, and Economic Trends in Hawai‘i 1990-2013 Table A-3: Resident
Births.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). County
Social, Business, and Economic Trends in Hawai‘i 1990-2013 Table A-4: Resident
Deaths.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). County
Social, Business, and Economic Trends in Hawai‘i 1990-2013 Table A-5: Housing Units.
State of Hawai‘i Department of Business, Economic Development & Tourism (2015). Quarterly Statistical & Economic Report 2nd Quarter 2015 Tables A-1 thru A-27.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). Quarterly
Statistical & Economic Report 4th Quarter 2014 Table G-2: Civilian Labor Force - Hawai‘i.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). Quarterly
Statistical & Economic Report 4th Quarter 2014 Table G-6: Civilian Employed - Hawai‘i.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). Quarterly
Statistical & Economic Report 4th Quarter 2014 Table G-10: Civilian Unemployed -
Hawai‘i.
State of Hawai‘i Department of Business, Economic Development & Tourism (2014). Quarterly
Statistical & Economic Report 4th Quarter 2014 Table G-10: Civilian Unemployment Rate
- Hawai‘i.
State of Hawai‘i Department of Business, Economic Development & Tourism (2011). Quarterly
Statistical & Economic Report 4th Quarter 2011 Tables A-1 thru A-27.
State of Hawai‘i Department of Business, Economic Development & Tourism (2013). The State of Hawai‘i Data Book 2013 Table 1.53: Population in Group Quarters, by Type of Group
Quarter, by County, 2010.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 82
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
1-2: De Facto Population by County 1980-2040.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
1-7: Actual and Projected Civilian Jobs.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
1-8: Actual and Projected Civilian Employed.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
A-10: Hawai‘i County Total Resident Population by 5-year Age Group 1980-2040.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population
and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
A-38: Total Civilian Jobs, Labor Force, and Employment for Hawai‘i County 1980-2040.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population
and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
A-43: Hawai‘i County Total Civilian Jobs by Sector, 2010-2040.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population
and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
A-48: Hawai‘i County Civilian Wage and Salary Jobs by Sector, 2010-2040.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population
and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
A-64: Historical Visitor Statistics for Hawai‘i County, 1990-2010.
State of Hawai‘i Department of Business, Economic Development & Tourism (2012). Population
and Economic Projections for the State of Hawai‘i to 2040: DBEDT 2040 Series, Table
A-69: Hawai‘i Visitor Projection, 2010-2040.
State of Hawai‘i Department of Business, Economic Development & Tourism (2010). The State
of Hawai‘i Data Book 2010 Table 1.06: Resident Population, by County, 2007-2009.
State of Hawai‘i Department of Business, Economic Development & Tourism (2010). The State of Hawai‘i Data Book 2010 Table 1.52: Subfamilies, by County, 2007-2009.
State of Hawai‘i Department of Labor and Industrial Relations – Research and Statistics Office (2015). Labor Force, Employment and Jobs Statistics for the County of Hawai‘i.
State of Hawai‘i Office of Planning (2015). GIS Program, GIS data.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates, Table DP-1: Profile of General Population and Housing Characteristics (2000 SF1 100%
data).
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates, Table DP-03: Selected Economic Characteristics (2000 SF3).
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates,
Table 3QT-H12: Average Household Size of Occupied Housing Units by Tenure (2000 SF3).
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 83
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates, Table HCT-046: Contract Rent (2000 SF4).
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates,
Table HCT-051: Gross Rent (2000 SF4).
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates,
Table DP03: Selected Economic Characteristics.
United States Census Bureau. (2015). American Community Survey (ACS) 3-Year Estimates, Table S2301: Employment Status.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates
(2009, 2011-2013), Table B25010: Average Household Size of Occupied Housing Units by Tenure.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates (2009-2013) Table S2501: Occupancy Characteristics.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates,
Table B25024: Units in Structure.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates,
Table B25075: Value.
United States Census Bureau. (2015). American Community Survey (ACS) 3-Year Estimates, Table B25056: Contract Rent.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates,
Table B25063: Gross Rent.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates
(2006), Table B25070: Gross Rent as a Percentage of Household Income in the Past 12
Months.
United States Census Bureau. (2015). American Community Survey (ACS) 1-Year Estimates
(2007-2008), Table B25070: Gross Rent as a Percentage of Household Income in the
Past 12 Months.
United States Census Bureau. (2015). American Community Survey (ACS) 5-Year Estimates,
Table S2502: Demographic Characteristics for Occupied Housing Units.
United States Census Bureau (2015). Small Area Income and Poverty Estimates 2003 – 2008.
United States Census Bureau (2015). Population Estimates Historical Data 2000s, 2010s,
Intercensal Estimates.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 84
APPENDIX A: DEFINITIONS OF JOBS BY INDUSTRY
Accommodation Jobs This industry provides lodging or short-term accommodations for travelers, vacationers, and others.
Some provide lodging only; while others provide meals, laundry, and recreational facilities, as well as lodging. There are three industry groups: (1) traveler accommodation includes establishments that
primarily provide traditional types of lodging services. This group includes hotels, motels, and bed and breakfast inns. (2) recreational accommodation includes establishments that operate lodging facilities
primarily designed to accommodate outdoor enthusiasts. Included are travel trailer campsites, recreation vehicle parks, and outdoor adventure retreats; and (3) rooming and boarding houses includes
establishments providing temporary or longer-term accommodations that for the period of occupancy may serve as a principal residence. Board (i.e., meals) may be provided but is not essential.
Agriculture Wage and Salary Jobs
Full- and part-time jobs for which payment was made by the employer in the survey week containing the 12th of the month. Includes farm jobs and machinery operators. Closely related management and
services jobs are included for sugar and pineapple but are excluded for other types of agriculture.
Arts, Entertainment & Recreation Jobs This industry includes a wide range of establishments that operate facilities or provide services to meet
varied cultural, entertainment, and recreational interests of their patrons. This sector comprises (1) establishments that are involved in producing, promoting, or participating in live performances, events, or
exhibits intended for public viewing; (2) establishments that preserve and exhibit objects and sites of historical, cultural, or educational interest; and (3) establishments that operate facilities or provide
services that enable patrons to participate in recreational activities or pursue amusement, hobby, and leisure-time interests. Excluded from this sector are: (1) establishments that provide both
accommodations and recreational facilities, such as hunting and fishing camps and resort and casino hotels; (2) restaurants and night clubs that provide live entertainment in addition to the sale of food and
beverages, (3) motion picture theaters, libraries and archives, and publishers of newspapers, magazines, books, periodicals, and computer software; and (4) establishments using transportation equipment to
provide recreational and entertainment services, such as those operating sightseeing buses, dinner cruises, or helicopter rides.
Civilian Labor Force
Total of employed and unemployed persons, excluding those in the armed forces and 16 years of age or under. U.S. Census Bureau does surveys for the U.S. Bureau of Labor Statistics.
Educational Services Jobs
This industry comprises establishments that provide instruction and training by specialized establishments, such as schools, colleges, universities, and training centers. These establishments may
be privately owned and operated for profit or not for profit, or they may be publicly owned and operated. They may also offer food and accommodation services to their students. Educational services are usually
delivered by teachers or instructors that explain, tell, demonstrate, supervise, and direct learning, in diverse settings, such as educational institutions, the workplace, or the home through correspondence, television, or other means.
Federal Government Jobs Includes those in the civilian agencies, i.e. Department of Defense, Agriculture, United States Postal
Service and the various armed services exchange systems.
County of Hawai‘i, General Plan Update, Final Forecast and Analysis Page 85
Financial Activities Jobs Establishments primarily engaged in financial transactions, i.e. those transactions involving the creation,
liquidation, or change in ownership of financial assets, and/or in facilitating financial transactions. Three principal types of activities are identified: (1) raising funds by taking deposits and/or issuing securities
and, in the process, incurring liabilities; (2) pooling of risk by underwriting insurance and annuities, (3) providing specialized services facilitating or supporting financial intermediation, insurance, and employee
benefit programs. In addition, monetary authorities charged with monetary control are included in this sector. This also includes real estate and the rental and leasing sector which is comprised of
establishments primarily engaged in renting, leasing, or otherwise allowing the use of tangible or intangible assets, and establishments providing related services. The major portion of this sector
comprises establishments that rent, lease, or otherwise allow the use of their own assets by others. The assets may be tangible, as is the case of real estate and equipment, or intangible, as is the case with
patents and trademarks. Excluded from this sector are real estate investment trusts (REITS) and establishments primarily engaged in renting or leasing equipment with operators. In many cases, such as
the rental of heavy construction equipment, the operator is essential to operate the equipment.
Food Services & Drinking Places Jobs These industries prepare meals, snacks, and beverages to customer order for immediate on-premises
and off-premises consumption. Some provide food and drink only; while others provide various combinations of seating space, waiter/waitress services and incidental amenities, such as limited
entertainment. The industries are grouped based on the type and level of services provided, i.e. full-service restaurants; limited-service eating places; and special food services including caterers and mobile
food services. Food services and drinking activities at hotels and motels; amusement parks, theaters, casinos, country clubs, and similar recreational facilities; and civic and social organizations are included
here only if these services are provided by a separate establishment primarily engaged in providing food and beverage services. Excluded are establishments operating dinner cruises which are classified as
scenic and sightseeing transportation because those establishments utilize transportation equipment to provide scenic recreational entertainment.
Health Care & Social Assistance Jobs
This industry includes establishments that provide both health care and social assistance for individuals. Both are included as it is sometimes difficult to distinguish between the boundaries of these two activities
starting with those establishments providing medical care exclusively, continuing with those providing health care and social assistance, and finally finishing with those providing only social assistance. These
services are delivered by trained professionals and the industries are defined based on the educational degree held by its practitioners. Excluded are aerobic classes and nonmedical diet and weight reducing
centers. Although these can be viewed as health services, these services are not typically delivered by health practitioners.
Information Jobs
Are in establishments engaged in the following processes: (a) producing and distributing information and cultural products, (b) providing the means to transmit or distribute these products as well as data or
communications, and (c) processing data. Included are publishing industries, including software publishing, and both traditional publishing and publishing exclusively on the Internet; the motion picture
and sound recording industries; the broadcasting industries, including traditional broadcasting and those broadcasting exclusively over the Internet; the telecommunications industries; the industries known as
Internet service providers and web search portals, data processing industries, and the information services industries. For the purpose of developing NAICS, it is the transformation of information into a
commodity that is produced and distributed by a number of growing industries that is at issue. The Information sector groups three types of establishments: (1) those engaged in producing and distributing
information and cultural products; (2) those that provide the means to transmit or distribute these products as well as data or communications; and (3) those that process data. Many of the industries in the NAICS
Information sector are engaged in producing products protected by copyright law, or in distributing them (other than distribution by traditional wholesale and retail methods). Examples are traditional publishing
industries, software and directory and mailing list publishing industries, and film and sound industries. Broadcasting and telecommunications industries and information providers and processors are also
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included in the Information sector, because their technologies are so closely linked to other industries in the Information sector.
Local Government Jobs
Includes those in the county governments.
Manufacturing Jobs Are in establishments engaged in the mechanical, physical, or chemical transformation of materials,
substances, or components into new products. The assembling of component parts of manufactured products is considered manufacturing, except in cases where the activity is appropriately classified as
construction. Included in manufacturing are plants, factories, or mills and characteristically use power-driven machines and materials-handling equipment. However, establishments that transform materials or
substances into new products by hand or in the worker’s home and those engaged in selling to the general public products made on the same premises from which they are sold, such as bakeries, candy
stores, and custom tailors, may also be included in this sector. Manufacturing establishments may process materials or may contract with other establishments to process their materials for them. Both
types of establishments are included in manufacturing. The following activities are considered manufacturing in NAICS: milk or water bottling and pasteurizing; fresh fish packaging; printing and
related activities; ready-mixed concrete production; ship repair and renovation; machine shops; and tire retreading. Conversely, there are activities that NAICS classifies as non-manufacturing, i.e. agriculture,
forestry, fishing and hunting; the construction of structures and fabricating operations performed at the site of construction by contractors, establishments engaged in breaking of bulk and redistribution in
smaller lots, including packaging, repackaging, or bottling products, customized assembly of computers, etc.
Natural Resources, Mining & Construction Jobs
In Hawaiʻi, 99 percent of the jobs in this category are in Construction and the remaining 1 percent in Mining. Included are those establishments that extract naturally occurring mineral solids, such as coal
and ores; liquid minerals, such as crude petroleum; and gases, such as natural gas. There are two activities: mine operation and mining support activities. The construction sector comprises
establishments primarily engaged in the construction of buildings or engineering projects (e.g., highways and utility systems), additions, alterations, or maintenance and repairs. Establishments primarily
engaged in the preparation of sites for new construction and establishments primarily engaged in subdividing land for sale as building sites also are included in this sector.
Non-Agriculture Wage and Salary Jobs
Derived monthly from a sample survey of employers reporting the number of persons who received pay for any part of the pay period containing the 12th of the month. Includes all full- and part-time non-
agricultural workers, permanent and temporary workers, and workers on paid sick leave and paid vacation.
Other Services (except public administration) Jobs
Are in establishments engaged in providing services not specifically provided for elsewhere in the classification system, such as equipment and machinery repairing, promoting or administering religious
activities, grant making, advocacy, and providing dry cleaning and laundry services, personal care services, death care services, pet care services, photofinishing services, temporary parking services, and
dating services. Private households that engage in employing workers on or about the premises in activities primarily concerned with the operation of the household are included in this sector. Excluded
from this sector are establishments primarily engaged in retailing new equipment and also performing repairs and general maintenance on equipment.
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Professional & Business Services Jobs Are in establishments that specialize in performing professional, scientific, and technical activities for
others and require a high degree of expertise and training. These activities include: legal advice and representation; accounting, bookkeeping, and payroll services; architectural, engineering, and specialized
design services; computer services; consulting services; research services; advertising services; photographic services; translation and interpretation services; veterinary services; and other professional,
scientific, and technical services to business and sometimes to households, but excludes establishments primarily engaged in providing a range of day-to-day office administrative services, such as financial
planning, billing and recordkeeping, personnel, and physical distribution and logistics. The management sector comprises establishments that hold the securities of companies and enterprises for the purpose of
owning a controlling interest or influencing management decisions or establishments that administer, oversee, and manage establishments of the company or enterprise and that normally undertake the
strategic or organizational planning and decision making role of the company or enterprise. Establishments in this sector perform essential activities that are often undertaken, in-house, by
establishments in many sectors of the economy but by consolidating the performance of these activities, economies of scale are achieved. Also included are establishments that perform routine support
activities, such as office administration, hiring and placing of personnel, document preparation and similar clerical services, solicitation, collection, security and surveillance services, cleaning, and waste disposal
services on a contract or fee basis.
Retail Trade Jobs Are in establishments engaged in retailing merchandise, generally without transformation, and rendering
services incidental to the sale of merchandise. The retailing process is the final step in the distribution of merchandise; retailers are, therefore, organized to sell merchandise in small quantities to the general
public. This sector comprises two main types of retailers: store and non-store retailers. Store retailers operate fixed point-of-sale locations, located and designed to attract a high volume of walk-in customers
where they have extensive displays of merchandise and use mass-media advertising to attract customers from the general public for personal or household consumption, but some also serve business and
institutional clients. These include establishments such as office supply stores, computer and software stores, building materials dealers, plumbing supply stores, and electrical supply stores. Catalog
showrooms, gasoline services stations, automotive dealers, and mobile home dealers are treated as store retailers. Generally, establishments engaged in retailing merchandise and providing after-sales
services are classified in this sector. Non-store retailers are also organized to serve the general public, but they reach customers and market merchandise with methods, such as the broadcasting of
“infomercials,” the broadcasting and publishing of direct-response advertising, the publishing of paper and electronic catalogs, door-to-door solicitation, in-home demonstration, selling from portable stalls (street
vendors, except food), and distribution through vending machines.
State Government Jobs Education includes all employees under the Department of Education (DOE) and the University of Hawaiʻi
System. This includes public library personnel, substitute teachers who received pay during the pay period including the 12th of the month, and support personnel such as cafeteria workers, classroom
cleaners, and school security attendants. Also included are part-time employees in special programs such as the A+ After School.
Non-Education – Includes all other executive branch departments (i.e. Agriculture, Transportation, etc.), the legislative and judicial branches, and the Research Corporation of the University of Hawaiʻi (RCUH).
Total Wage and Salary Jobs
Includes nonagricultural wage and salary workers and agriculture wage and salary workers. Persons on the payroll of more than one establishment during the pay period are counted in each establishment that
reports them.
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Transportation, Warehousing, & Utilities Jobs Are in industries providing transportation of passengers and cargo, warehousing and storage for goods,
scenic and sightseeing transportation, and support activities related to modes of transportation – air, rail, water, road, and pipeline. The sector distinguishes subsectors for each mode of transportation, a
subsector for warehousing and storage, and a subsector for establishments providing support activities for transportation. In addition, there are subsectors for establishments that provide passenger
transportation for scenic and sightseeing purposes, postal services, and courier services. Warehousing establishments in this sector are distinguished from merchant wholesaling in that the warehouse
establishments do not sell the goods. Excluded are establishments primarily engaged in providing travel agent services that support transportation and other establishments, such as hotels, businesses, and
government agencies. The Utilities sector comprises establishments engaged in the provision of the following utility services: electric power, natural gas, steam supply, water supply, and sewage removal.
Within this sector, the specific activities associated with the utility services provided vary by utility: electric power includes generation, transmission, and distribution; natural gas includes distribution; steam supply
includes provision and/or distribution; water supply includes treatment and distribution; and sewage removal includes collection, treatment, and disposal of waste through sewer systems and sewage
treatment facilities. Excluded from this sector are establishments primarily engaged in waste management services that do not use sewer systems or sewage treatment facilities.
Wholesale Trade Jobs
Are in establishments engaged in wholesaling merchandise, generally without transformation, and rendering services incidental to the sale of merchandise and includes the outputs of agriculture, mining,
manufacturing, and certain information industries, such as publishing. It is an intermediate step in the distribution of merchandise. Wholesalers are organized to sell or arrange the purchase or sale of goods
for resale, capital or durable non-consumer goods, and raw and intermediate materials and supplies used in production. Wholesalers sell merchandise to other businesses and normally operate from a warehouse
or office that have little or no display of merchandise and do not solicit walk-in traffic. Transactions are often conducted between wholesalers and clients that have long-standing business relationships. There
are two main types: merchant wholesalers that sell goods on their own account and business to business electronic markets, agents, and brokers that arrange sales and purchases for others generally for a
commission or fee.
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APPENDIX B: EMPLOYMENT SECTORS, INDUSTRIES, AND
OCCUPATIONS
SECTOR Goods producing
Services providing Agriculture
INDUSTRY Agriculture, forestry, fishing and hunting, and mining
Construction Manufacturing Wholesale trade
Retail trade Transportation and warehousing, and utilities Information
Finance and insurance, and real estate and rental and leasing Professional, scientific, and management, and admin and waste mgmt. Educational services, and health care, and social assistance
Arts, entertainment, and recreation, and accommodation, and food services Other services, except public administration Public administration
OCCUPATION
Management, professional, and related occupations
Service occupations Sales and office occupations
Farming, fishing, and forestry occupations Construction, extraction, maintenance and repair occupations Production, transportation, and material moving occupations