HomeMy Public PortalAboutPKT-CC-2018-06-19DocuSign Envelope ID: F5696983-DAE2-4C23-9FF8-2519C700CC2D
CITY OF
M 0A
UTAH
217 East Center Street
Moab, Utah 84532-2534
Main Number (435) 259-5121
Fax Number (435) 259-4135
Memorandum
To: Councilmembers and Media
From: Mayor Emily S. Niehaus
Date: 6/18/2018
Re: Special Joint City/County Council Meeting
B B
Mayor: Emily S. Niehaus
Council: Tawny Knuteson-Boyd
Rani Derasary
Mike Duncan
Karen Guzman -Newton
Kalen Jones
The City of Moab will hold a Special Moab City Council Meeting on Tuesday, June 19, 2018 at 5:30 PM. The
purpose of this meeting will be:
Discussion Items
A. Update on prevention and clean-up plan of recent fire (Fire Chief Mosher, Sheriff White, Police
Chief Winder, and/or Emergency Management Director Bailey)
B. Presentation on Phase Two of the Assured Housing Study - Nexus Analysis (Matt Kowta, BAE Urban
Economics and Zacharia Levine, Community and Economic Development Director)
(allow 20 minutes)
C. Need for multi -agency funding of the part-time watershed coordinator position for the community
(Zacharia Levine, Community and Economic Development Director)
D. Future joint meeting dates (Mayor Niehaus)
Adjourn
The meeting will begin in the Council Chambers at the Grand County Courthouse, 125 East Center Street, Moab,
Utah.
p--- DocuSigned by:
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Mayor Emily S. Niehaus
6/18/2018 10:49:19 AM PDT
In compliance with the Americans with Disabilities Act, individuals needing special accommodations during this meeting should notify the
Recorder's Office at 217 East Center Street, Moab, Utah 84532; or phone (435) 259-5121 at least three (3) working days prior to the
meeting.
Page 1 of 46
AGENDA SUMMARY
GRAND COUNTY COUNCIL MEETING
June 19, 2018
Agenda Item: B
TITLE:
Presentation on Phase 2 of the Assured Housing Study — Nexus Analysis
FISCAL IMPACT:
N/A
PRESENTERS.)•
•
Matt Kowta, BAE Urban Economics; Zacharia Levine, Community and Economic
Development Director
Prepared By:
ZACHARIA LEVINE
GRAND COUNTY
COMMUNITY &
ECONOMIC
DEVELOPMENT
DIRECTOR
FOR OFFICE USE ONLY:
Attorney Review:
N/A
STATED MOTION :
N/A
PLANNING COMMISSION RECOMMENDATION:
N/A
STAFF RECOMMENDATION:
Proceed to creation, adoption, and implementation of an Assured Housing
policy based on the results of the associated nexus study.
BACKGROUND:
See report.
ATTACHMENT(5):
1. Phase 2 report: Assured Housing Feasibility Study, BAE Urban Economics
Page 2 of 46
bae urban economics
Phase II: Assured Housing Nexus Fee Analysis
for the City of Moab and Grand County, Utah
May 2018
,
e es V
P!!
May 25, 2018
Zacharia Levine
Community Development Director
Grand County
125 East Center Street
Moab, UT 84532
Dear Mr. Levine:
We are pleased to submit this Moab Area Assured Housing Feasibility Analysis Phase II Nexus
Study. We enjoyed completing this work, and it has been a pleasure working with you. I look
forward to the joint City/County meeting on June 190 and recapping the Phase I work and
presenting the results of this Phase II work. In the meantime, please let me know if you have
any questions on the attached.
Sincerely,
/46',A(CUiceN
Matt Kowta, MCP
Managing Principal
San Francisco
2600 10t° St., Suite 300
Berkeley, CA 94710
510.547.9380
Sacram,?nto
803 2nd St., Suite A
Davis, CA 95616
530.750.2195
�€442
Raymond Kennedy, MA
Director of Research
Los Angeles
448 South Hill St., Suite 701
Los Angeles, CA 90013
213.471.2666
Page 4 of 46
,vashins rn, DC
1400 I St. NW, Suite 350
Washington, DC 20005
202.588.8945
Pdero ork City
215 Park Ave. S, 6th Floor
New York, NY 10003
212.683.4486
TABLE OF CONTENTS
EXECUTIVE SUMMARY i
Impact Fee Analysis i
Considerations for Implementation
Summary v
INTRODUCTION 1
COMMERCIAL LINKAGE FEE ANALYSIS 3
Overview of Methodology 3
Step 1: Define Land Uses 3
Step 2: Test Financial Feasibility of Linkage Fee for Defined Land Uses 3
Step 3: Determine Employment Density 4
Step 4: Estimate Worker Households by Income Level 5
Step 5: Calculate Financing Gap per Affordable Unit 7
Step 6: Calculate the Maximum Justifiable Fee per the Nexus Analysis 9
Step 7: Comparison of Nexus and Financially Feasible Fees 10
RESIDENTIAL LINKAGE FEE ANALYSIS 11
Overview of Methodology 11
Step 1: Define Housing Types and Identify Housing Prices and Development Costs 11
Step 2: Test Financial Feasibility of Linkage Fee for Defined Land Uses 11
Step 3: Estimate the Incomes of Households in New Market Rate Housing 12
Step 4: Analyze Projected Spending Patterns for Households in New Market -Rate Units13
Step 5: Estimate New Worker Households by Household Income 15
Step 6: Calculate Financing Gap per Affordable Unit 17
Step 7: Calculate the Maximum Justifiable Fee per the Nexus Analysis 18
Step 8: Comparison of Nexus and Financially Feasible Fees 19
CONSIDERATIONS FOR IMPLEMENTATION 20
Maximum Justifiable Fees vs. Maximum Financially Feasible Fees 20
Market Conditions 20
Phase -In of Requirements 22
Fees per Unit versus Fees per Square Foot 23
Policy Flexibility 23
Revenue Estimate 24
Summary 25
APPENDICES 26
Page 5 of 46
Appendix A: Overview of IMPLAN 26
Appendix B: Detailed Calculation of Employment Supported by New Market -Rate
Housing 29
LIST OF TABLES
Table 1: Summary of Proforma Analysis for Commercial Land Uses 4
Table 2: Employment Density Estimate 5
Table 3: New Hotel Worker Households by HUD Income Category 6
Table 4: Affordability of Market -Rate Rental Housing in Moab 8
Table 5: Financing Gap Analysis 9
Table 6: Maximum Justifiable Hotel Impact Fee 10
Table 7: Summary of Commercial Impact Fee Analysis 10
Table 8: Summary of Proforma Analysis for Residential Land Uses 12
Table 9: Income Requirements for Housing Prototypes 13
Table 10: Income Level by Industry, Working Persons by 2016 Household Income Limits 16
Table 11: Summary of Worker Households Supported by New Market -Rate Units 17
Table 12: Financing Gap Analysis 18
Table 13: Maximum Justifiable Fee per Residential Unit 19
Table 14: Summary of Residential Impact Fee Analysis 19
Table 15: Financing Gap Analysis - Comparative Interest Rates 21
Table 16: Impact Fee Analysis - Comparative Interest Rates 22
Table 17: Annual Estimated Fee Revenue Based on Historic Permit Activity 24
Page 6 of 46
EXECUTIVE SUMMARY
This report represents the second phase of the Moab Area Assured Housing Feasibility
Analysis. The Phase I Study included the following:
• Description of general demographic and economic conditions
• Review of residential and commercial real estate market conditions
• Assessment of workforce housing needs
• Analysis of financial feasibility of applying affordable housing impact fees to various
commercial and residential real estate products under varying market conditions
• Estimate of revenue potentially generated by an assured housing fee
Building on the Phase I analysis, this phase of the study examines in more detail the level of
development "in -lieu" fees in support of affordable housing in Moab and Grand County that
would be financially feasible and justifiable as being linked to, or having a nexus with, the
impacts of a particular type of development. The maximum justifiable fees are based on the
nexus analysis conducted as part of this Phase II Study, and represent the maximum fee
based on the demand for additional affordable housing driven by new commercial and
residential development. However, as indicated in the analysis here and in Phase I, applying a
fee to some commercial and residential development might result in projects which would no
longer generate high enough returns to be financially feasible, and for projects where a fee
might be feasible, it is possible that the maximum feasible fee would be lower than the
maximum justifiable fee as determined by the nexus analysis.
The Phase I analysis indicated that in -lieu fees for affordable housing were financially feasible
for condominiums under a strong market scenario, and for townhomes and single-family
homes under both moderate and strong market scenarios. Fees were also considered for
hotels, retail, and office, with fees determined to be financially feasible for hotels only.
Using the same benchmarks for developer return, this Phase II analysis calculates a maximum
financially feasible fee for each of the six land uses and scenarios where a fee was deemed
feasible. This Phase II report also includes a nexus analysis, to assess the maximum
justifiable fee based on the impacts for each of the scenarios for which a fee was financially
feasible. Finally, the maximum financially feasible fee is compared with the maximum
justifiable fee per the nexus analysis, since the lower of the two fees represents the upper limit
of what can be reasonably charged, representing a fee level that is both within the range
justified by the nexus findings and also not so high as to render projects financially infeasible.
Impact Fee Analysis
A comparison of the maximum justifiable fee per the nexus analysis to the maximum
financially feasible fees for the two hotel scenarios shows that the maximum fee justifiable via
the nexus analysis is considerably lower than the maximum financially feasible fee for either
i
Page 7 of 46
market scenario. This is an indicator that a fee in the range of the maximum justifiable fee
could be considered for implementation by the City and County.
Table ES-1: Summary of Commercial Impact Fee
Analysis
Maximum Financially Feasible Fee
Maximum Justifiable Fee
Fee per Square Foot
Hotel Hotel
Moderate Strong
$30.86 $54.14
$15.57 $15.57
Source: BAE, 2018.
A comparison of the maximum justifiable fee per the nexus analysis to the maximum
financially feasible fees for the various residential scenarios shows that the nexus fee is higher
than the financially feasible fee for any of the market scenarios, especially for the single-family
homes. As a result, if the City and the County choose to implement a residential in -lieu fee,
the level of appropriate fees might be constrained by market conditions. When this is the
case, the revenue generated by a fee that is set at a level that is less than the justifiable
amount means that the funds collected would need to be leveraged with other sources of
subsidy to achieve the necessary level of housing mitigation.
Table ES-2: Summary of Residential Impact Fee Analysis
Maximum Financially
Feasible Fee
Maximum Justifiable Fee
Fee per Square Foot
Condominium Townhome Townhome Single -Family Single -Family
Strong Moderate Strong Moderate Strong
$5.18 $4.64 $8.77 $1.13 $1.62
$10.19 $7.58 $9.29 $7.43 $5.31
Source: BAE, 2018.
Potential In -Lieu Fee Generation
For projects where a linkage fee was feasible, the maximum potentially feasible fee levels
were applied to historic building permit data to estimate revenue that could potentially be
generated from an in -lieu fee program. To partially take into account the variation in feasibility
due to fluctuations in economic conditions over time, the assumed fees were rounded down to
the nearest dollar, and were based on the moderate market scenario, with the exception of
condominiums, where the fee for the strong market was used since a fee was deemed not
feasible under the moderate market scenario.
ii
Page 8 of 46
This assumed fee structure could generate an estimated average annual revenue of
approximately $1.3 million if applied in both the City of Moab and Grand County, assuming the
same rate of development as between 2010 and 2017.1 The City could be expected to
generate substantially more revenue from hotel development than from residential
development, while slightly more than half of Grand County's revenue would come from
residential projects. The City's annual projected share is slightly less than $800,000, and the
County's share is estimated at about $523,000. These average annual revenue estimates
may under- or overstate actual revenue in any given year, depending on the overall economic
cycle.
Table ES-3: Annual Estimated Fee Revenue Based on Historic Permit Activity
Proposed Est. Annual
Fee City of Moab Grand County Revenue
Residential Projects
Single -Family Detached $ 1.00 $ 31,898 $ 44,796 76,694
Townhomes / SFR Nightly Rentals $ 4.00 $ 64,763 $ 82,891 147,653
Condominiums $ 5.00 $ 5,159 $ 150,105 155,2641
Apartments $ - $ $ $ _ ,
Annual Revenue, Residential Projects (a) $ 101,819 $ 277,791 $ 379,611
Commercial Prolects
Retail $ - $ $ $
Office (b) $ - $ - $ - $
Hotel $ 15.00 $ 694,714 $ 245,010 : $ 939,724
Annual Revenue, Commercial Projects (a) $ 694,714 $ 245,010 $ ' 939,724
Annual Revenue by Place $ 796,533 $ 522,801 $ 1,319,334
Notes:
(a) The annual revenue is based the average annual square feet permitted between 2010 and 2017 in the City of Moab and
Grand County. Revenue will vary year to year based on actual development activity.
(b) The building permit data did not contain square footage data for newly constructed office projects. Each office project
was estimated at 8,000 square feet based on the recently built office buildings profiled in the Phase I study.
Sources: City of Moab, 2017; Grand County, 2017; BAE, 2018.
Considerations for Implementation
Market Conditions
Changes in the economy, locally or nationally, could impact both the financial feasibility and
the justifiable nexus fees for the different development types. Changes in economic
conditions that could influence feasibility of different fee levels would include interest rates for
1 These calculations assume that all assured housing obligations are met by payment of fees, rather than
construction of inclusionary housing units.
ill
Page 9 of 46
development and for mortgages, changes in rents, home sale prices, land costs, operating
expenses, acceptable rates of return for developers, and other factors.
Fees per Unit versus Fees per Square Foot
When inclusionary requirements or in -lieu fees are fixed on a "per unit" basis, rather than
varying by the size of the market rate units, this creates an incentive for builders to maximize
the size of their market rate units, so that they can spread the cost of compliance over a
greater quantity of saleable square footage, making market rate housing units less attainable
to middle -income households. This report recommends tying the fee to square feet instead of
per unit.
Phase-ln of Requirements
When first adopting a policy like this, some jurisdictions set a future date for its
implementation, and define how to treat current "pipeline" projects that would have been
started without knowledge of this fee. A phase -in allows developers to adjust their bidding for
development sites with the knowledge of how the applicable requirements affect the residual
land value that they can afford to pay for a site and achieve financial feasibility. For these
reasons, one possibility is to consider a phase -in schedule for initial implementation. In the
case of Moab, we understand that discussion of possible assured housing requirements has
occurred at least over the last two years, in which case a phase -in may not be necessary.
Policy Flexibility
During economic downturns, some jurisdictions have either created special deferral programs
or lowered fees across the board. Some places have built-in mechanisms that require the fees
or inclusionary policy to be re -analyzed at defined time intervals or when there are substantial
changes in economic indicators such as interest rates or development costs. These
approaches demonstrate that the requirements can be customized to adapt to changes in
economic conditions. Because there are many constantly changing variables that influence
affordable housing needs, costs of providing affordable housing units, and feasibility for
market rate development, best practices dictate that analysis underpinning affordable housing
requirements should be updated on a periodic basis, to determine if changes to policy or
program parameters are appropriate.
Another important component of policy flexibility is to offer builders a range of options to
comply with assured housing requirements. Every development project has a unique set of
financial circumstances, and while a given project may not be able to afford to pay adopted in -
lieu fees, there may be other options that would be feasible, such as providing on -site
affordable units, dedicating land that could be utilized by others to construct affordable
housing, or potentially complying with a reduced affordable housing requirement if a reduction
could be justified, based on a finding of reduced affordable housing need or a lack of financial
feasibility to meet the full requirements. An important caveat is to avoid making any of the
options inherently more economically attractive than others, to avoid encouraging builders to
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Page 10 of 46
all select the same compliance option. For example, when in -lieu fees are set at levels that
are substantially below the actual cost to subsidize affordable housing units, developers will
rarely build affordable units onsite. As previously noted, when this is the case, perhaps due to
market -based limitations on in -lieu fees, the City and County might have to leverage other
sources of financing to support affordable housing.
Finally, the program should acknowledge that there may be unforeseen circumstances that
could prevent a unique project from being able to feasibly comply with standard program
requirements. In such cases, the program should provide for a process to modify the
requirements to enable compliance.
Summary
The analysis here and in the Phase I report of this Assured Housing Feasibility Study indicates
that although charging an affordable housing nexus fee increases the cost to build, the fee can
be set at a reasonable level for some land uses by estimating a maximum financially feasible
fee as well as a maximum justifiable fee, and ensuring that the fee is does not exceed the
lower of these two thresholds. In establishing a policy for such a fee, the City and County could
build in some flexibility by monitoring market conditions on an ongoing basis and providing for
updates to the fee calculations if conditions change significantly. Assuming current
development trends continue, a nexus fee could bring in over $1 million annually to assist in
the production of affordable housing units.
v
Page 11 of 46
INTRODUCTION
This report represents the second phase of the Moab Area Assured Housing Feasibility
Analysis. The Phase I Study included the following:
• Description of general demographic and economic conditions
• Review of residential and commercial real estate market conditions
• Assessment of workforce housing needs
• Analysis of financial feasibility of applying affordable housing impact fees to various
commercial and residential real estate products under varying market conditions
• Estimate of revenue potentially generated by an assured housing fee
Building off the Phase I analysis, this phase of the study examines in more detail the level of
development "in -lieu" fees in support of affordable housing in Moab and Grand County that
would be financially feasible and justifiable as being linked to, or having a nexus with, the
impacts of a particular type of development. The maximum justifiable fees are based on the
nexus analysis below, and thus represent the maximum fee based on the demand for
additional affordable housing driven by new commercial and residential development.
However, as indicated in the analysis here and in Phase I, applying a fee to some commercial
and residential development might result in projects which would no longer generate high
enough returns to be financially feasible, and for projects where a fee might be feasible, it is
possible the maximum financially feasible fee would be lower than the maximum justifiable fee
as determined by the nexus analysis.
These are referred to as "in -lieu" fees because they are meant to compensate for, or
substitute for, construction of below market rate housing units that builders/developers
otherwise would have been required to construct as part of their projects, to comply with
assured housing policies.
The Phase I study explored in detail the financial feasibility of fees for different types of
development to determine whether a fee would lower developer financial returns below certain
benchmark levels. BAE ran a sensitivity analysis under moderate and strong market scenarios
for each development type, allowing the City and County to understand how feasibility may
change when market conditions fluctuate. For the moderate market, data for land,
construction costs, rents, and sales prices were taken from 2014, which represented a mid-
point in the recovery after the recession. Inputs for the strong market were taken from 2017.
The residential uses evaluated included apartments, condominiums, townhomes, and single-
family homes. The analysis indicated that in -lieu fees for affordable housing were financially
feasible for condominiums under the strong market scenario, and for townhomes and single-
family homes under both the moderate and strong market scenarios. Fees were also
1
Page 12 of 46
considered for hotels, retail, and office, with fees determined to be financially feasible for
hotels only.
Using the same benchmarks for developer return, this Phase II analysis calculates a maximum
financially feasible fee for each of the six land uses and scenarios where a fee was deemed
feasible. This Phase II report also includes a nexus analysis, to assess the maximum
justifiable fee based on the impacts for each of the scenarios for which a fee was financially
feasible. Finally, the maximum financially feasible fee is compared with the maximum
justifiable fee per the nexus analysis, since the lower of the two fees represents the upper limit
of what can be reasonably charged, representing a fee level that is both within the range
justified by the nexus findings and also not so high as to render projects financially infeasible.
Finally, this Phase II study discusses considerations for implementation, building on the same
discussion presented in the Phase I study.
2
Page 13 of 46
COMMERCIAL LINKAGE FEE ANALYSIS
This report chapter focuses on the potential jobs -housing linkage fees that could be
considered for non-residential development that the Phase I analysis deemed capable of
supporting some level of assured housing requirements; specifically, hotel uses.
Overview of Methodology
The commercial fee analysis conducted for this report is based on the premise that new
commercial land uses generate new employment for workers that will increase demand for
local housing, and have a range of household incomes that influences their ability to pay for
housing. Due to higher housing costs in Moab and Grand County, new workers with extremely
low, very low, low, or moderate household incomes will be unable to afford most market -rate
housing without incurring excessive cost burdens. This situation - the increment of growth in
new worker households facing the lack of affordable housing options - is considered the
impact of new commercial development. The commercial fee would mitigate these impacts by
generating revenue to support the construction of housing affordable to the new lower -income
worker households. The analysis completed as part of Phase I indicates that fees would not
be feasible for retail or office development, so this Phase II analysis focuses on hotels, where
pro forma analysis indicated that some level of a fee was financially feasible.
This section provides an overview of the steps taken to determine the maximum justifiable
hotel fee, based on the relationship ("nexus") between new hotel space and the number of
households of workers supported by that new development that would face affordable housing
challenges.
Step 1: Define Land Uses
The Phase I Analysis assessed the financial feasibility of linkage fees for three commercial
land use categories: office, retail, and hotels. Feasibility was tested for both moderate and
strong market scenarios as defined above in the Introduction.
Step 2: Test Financial Feasibility of Linkage Fee for Defined Land Uses
This step was completed in Phase I; commercial linkage fees were found to be feasible only for
hotels under either the moderate or strong market scenario. However, the maximum fee that
would still allow a project to meet the financial feasibility criteria regarding return on cost and
yield on cost for the two hotel scenarios was not calculated. The table below shows the
maximum feasible fee given those benchmarks.
3
Page 14 of 46
Table 1: Summary of Proforma Analysis for Commercial Land Uses
Assumptions for Baseline (a)
Location, Zoning
Prototypical Building Size
Site Size (sf)
Total Number of Stories (Bldg)
Parking Type
FAR
Total Dev Cost/SF (inc. land)
Rent (psf or per hotel REVPAR)
Return On Cost - Baseline
Yield on Cost - Baseline
Baseline Feasible? (b) - _
New Fee/Sq. Ft. (a) $
New Fee for Prototype Project
Office
Moderate
Strong'
City of Moab, C-3
10,000
15,500
2
Surface
0.65
$ 213 $
$ 18.00 $
-8.4%
5.5%
10,000
15,500
2
Surface
0.65
253
24.00
i
12.0%'
6.2%I
No No;
Retail
Return On Cost with Fees
Yield on Cost with Fees
Feasible with Fee? (b)
New Commercial Fee, as % of Total Dev Costs
Notes:
a) See Phase I Report Appendix for detailed assumptions and proformas for each land use type.
b) Financial feasibility evaluated on 2 metrics:
ROC = 15.0%
YOC: Retail: Office: Hotel:
7.0% 7.0% 8.0%
Moderate Strong
City of Moab, C-3
10,000 10,000
20,500 20,500
1 1
Surface Surface
0.49 0.49
$ 233 $ 286
$ 24.00 $ 30.00
11.7%
6.7%
No
24.0%
6.8%
No
$ $
Moderate Strong
City of Moab, C-3
60,000
48,000
3
Surface
1.25
246 $
105.00 $
60,000
48,000
3
Surface
1.25
263
122.50
39.9% 63.4%
9.1 % 9.8%
Yes Yes
$ 30.86 $ 54.14
$ 1,851,856 $ 3,248,415
Source: BAE, 2018.
23.5%
8.0%
Yes
11.1%
34.1 %
8.0%
Yes
16.9%
Step 3: Determine Employment Density
For the purposes of the following analysis leading to the maximum fee calculations, a hotel
totaling 60,000 square feet is assumed, matching the prototypical size used in the Phase I
analysis.
Hotel employment density can vary widely, depending on the type of hotel and the services
offered. As noted in Phase I, the lodging market in Moab has trended toward a higher
proportion of midscale and upscale hotels, which typically offer a higher level of amenities and
thus require higher staffing levels. The market analysis focused on the midscale to upper
midscale hotel inventory, and the assumed prototype reflects revenues associated with this
type of hotel; the nexus analysis thus assumes an employment density associated with these
classes of hotels. BAE reviewed several studies to estimate average hotel employment
density, which is usually presented as employees per room; most recently, BAE completed a
study in Napa County, California, which has an economy with a strong tourism basis like Moab,
and used employment density factors by hotel type as provided to BAE by Cushman &
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Page 15 of 46
Wakefield? Assuming the prototype hotel would be a full -service hotel and using the median
density for the range provided, the following table shows the calculations of employment
density for the Moab prototype based on those factors. While various studies show a wide
variation in the assumed employment density, the estimate here of 1,425 square feet per
employee is in the general middle range of other sources.
Table 2: Employment Density Estimate
Hotel Type
B&Bs/Small Inns
Limited/Select Service
Full Service
Luxury Hotels & Resorts
Prototype Estimated Sq. Ft.
Workers per Room Number Number of Hotel per
Low High Avg. of Rooms Workers Sq. Ft. Worker
0.20 0.50 0.35
0.23 0.30 0.27
0.30 0.75 0.53 80 42 60,000 1,425
0.50 1.00 0.75
Note:
Square feet per employee rounded to the nearest 25 square feet.
Sources: Cushman & Wakefield, 2018; BAE, 2018.
Step 4: Estimate Worker Households by Income Level
Many households in Moab and Grand County include more than one worker, so this study
groups the employees generated by the prototype hotel into households, to estimate the total
number of worker households generated.
Economists sometimes estimate household income for workers by simply multiplying worker
earnings by industry by the average number of workers per worker household. This
methodology relies on the unsatisfactory assumption that on average workers make the same
amount of money as other workers in their household. Given the diversity of household
composition, this assumption is not appropriate. For example, a household may have a
teacher and a doctor, with significantly different individual earnings.
To address this issue, this analysis makes use of a detailed and rich data set published by the
U.S. Census known as the Public Use Microdata Sample (PUMS). Derived from a five percent
sample of all households per the American Community Survey, and available for certain
defined areas of 100,000 or more of population (known as "PUMAs" or Public Use Microdata
Areas), this data source allows one to cross -tabulate variables such as industry of employment
2 Cushman & Wakefield provided the data directly to BAE. The figures provided are based on Cushman &
Wakefield's specialized practice area analyzing the lodging market nationwide. Furthermore, applying their density
estimate to the prototype hotel considered here resulted in an assumption of 1,425 square feet per employee
based on converting the median of their assumed range of employees per room (0.3 to 0.75) for a full service hotel
to square feet per employee. This number is generally consistent with sources BAE has used for other reports. In
other employment density studies we have reviewed, the numbers range from 800 square feet per employee up to
around 1,700 square feet per employee and from 0.3 workers per room to 1.2 workers per room.
5
Page 16 of 46
and household income. The analysis here uses the most recent available data, from the 2012
through 2016 five-year period. Since Grand County does not meet the 100,000 minimum
population threshold required for a PUMA, it is grouped with several other nearby Utah
counties to create a PUMA. The counties included are Carbon, Daggett, Duchesne, Emery,
Grand, San Juan, Uintah, and Wasatch. By relying on data from this grouping of counties, it is
assumed that employment and household patterns in Grand County are similar to those in the
group of counties as a whole.
The hotel land use is tied to one particular industry, the hotel industry, which is classified
under the North American Industry Classification System (NAICS) as sector 721,
Accommodation, which is part of the larger Accommodation and Food Services sector (NAICS
72). In 2016, there were a total of 1,718 private sector jobs in NAICS 72 in Grand County, as
discussed the Phase I report, of which 768 were in Accommodation.
BAE queried the PUMS data set for this PUMA to identify the number of hotel worker
households by HUD income category, using average household size for each income category
to construct a distribution of households by income category. Table 3 below presents the
distribution of households by HUD income level for the hotel industry for the PUMA, as applied
to the 60,000 square -foot hotel prototype in Grand County. As shown below, there are an
estimated approximately 24.4 worker households for the prototype hotel size of 60,000
square feet. The estimated number of extremely low-, very low-, low-, and moderate -income
worker households for a prototype 60,000 square foot hotel is 12.8 households.
Table 3: New Hotel Worker Households by HUD Income Category
Estimated Jobs by Percent of AMI (a)
Total Extremely Very Above
NAICS Code Industry Jobs (b) Low Low Low Moderate Moderato
Private Sector
721 Hotel/Motel 42.00 1.33 2.25 8.40 7.30 22.72
Total Jobs •
Workers per Households
Number of Households (c)
42.00 1.33 2.25 8.40 7.30 22.72
1.72 1.22 1.43 1.44 1.69 1.95
24.42 1.09 1.57 5.82 4.32 11.63
Notes:
(a) Based on 2016 HUD Income Limits. Percent distribution shown in Table 10 below.
(b) Job estimates are the output of the IMPLAN model, and shows employment generated by new workers in a 60,000-
square foot prototype hotel. Columns to right may not sum to Total Jobs due to independent rounding.
(c) Average number of workers per worker household calculated based on American Community Survey PUMS Analysis,
2012-2016.
Sources: American Community Survey, 2012-2016, including the Public User Microdata Sample; HUD; IMPLAN; BAE,
2018.
Page 17 of 46
Step 5: Calculate Financing Gap per Affordable Unit
The cost to house a lower -income household is the difference between the cost to develop an
affordable unit and the amount of the permanent loan that the developer can borrow to
finance the unit. Using data on recent housing developments gathered in Phase I, this
analysis determines the average cost to build an apartment rental unit in the City. The
supportable permanent loan amounts (by AMI income band) as identified in Step 4 are
deducted from the average per -unit development cost to determine the financing gap for units
serving households at each income level up to 120 percent of AMI.
The next step in the nexus analysis is to calculate the cost to house the extremely low-, very
low-, low-, and moderate -income households calculated in the previous step by determining
the per unit "financing gap" that housing developers encounter when securing a permanent
loan for their projects. In other words, the cost to house a lower -income household is the
difference between the cost to develop the unit and the amount of the permanent loan that
the developer can borrow to finance the unit. The nexus analysis here derives cost and
market rent information from the pro -forma analysis completed in Phase I for apartments
under the moderate market scenario, as the lower rents and costs provide a more
conservative estimate of impacts.
Affordable housing developers secure a permanent loan based on their net operating income
(N01) per unit. N01 is equal to rental income less operating expenses and vacancy.
Households can afford monthly rents ranging from $418 for extremely low-income households
to $1,737 for moderate -income households (see Table 4). These rents are based on
household income limits for three -person households in two -bedroom units and assuming
households can affordably spend 30 percent of their income on rent and utilities.
BAE used conventional financing assumptions to determine the supportable loan amount per
unit for each income level. Standard deductions are taken for operating expenses and
vacancies to determine N01. As shown in Table 5, the supportable loan amount ranges from
$0 per unit for extremely low-income units (i.e., operating expenses exceed N01, leaving no N01
to support debt payments) to $167,924 for units serving moderate -income households.
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Page 18 of 46
Table 4: Affordability of Market -Rate Rental Housing in Moab
3 Person Household
(2 Bedrooms)
Average Market -Rate Rent (a) $1,350
Utility Costs (b) $93
Maximum Affordable Monthly Rent
Extremely Low Income (up to 30% AMI)
Household Income (c) $20,420
Max. Affordable Monthly Rent (d) $418
Amount Above (Below) Market Rate Rent (8933)
Very Low Income (31-50% AMI)
Household Income (c) $30,500
Max. Affordable Monthly Rent (d) $669.50
Amount Above (Below) Market Rate Rent ($681)
Low Income (51-80% AMI)
Household Income (c) $48,750
Max. Affordable Monthly Rent (d) $1,125.75
Amount Above (Below) Market Rate Rent ($224)
Moderate Income (81-120% AMI)
Household Income (c) $73,200
Max. Affordable Monthly Rent (d) $1,737
Amount Above (Below) Market Rate Rent $387
Notes:
(a) From Phase I analysis.
(b) Based on the Southeastem Utah Housing Authority utility allowance schedule for gas
heating, cooking, and water heating and electricity for general lighting and air conditioning.
Analysis assumes water, sewer, and trash collection are included in the monthly rent.
(c) 2017 household Income limits published by HUD for Grand County.
(d) Assumes 30 percent of income spent on rent and utilities.
Sources: HUD; Southeastem Utah Housing Authority; BAE, 2018.
The financing gap per affordable unit is equal to the total development cost less the
supportable loan amount per unit. Based on the supportable loan amount as calculated
above, the financing gap per affordable unit ranges from $172,000 for extremely low-income
units to only $4,076 for moderate -income units (also shown in Table 5).3
It should be noted that no other affordable housing subsidy was assumed in this analysis,
because this calculation is intended to show the actual impact of the new employment -
generating commercial land uses; it is not necessarily the way funds generated by a
commercial fee would be spent on new affordable housing. Instead, in many affordable
housing projects, multiple funding sources would be utilized in combination, enabling limited
public resources from federal, state, and local sources to be combined most effectively. For
some affordable housing projects serving low income households, non -cash subsidies such as
Low Income Housing Tax Credits (LIHTCs) would also be used.
3 These gap estimates are conservative in that they are based on the upper income limit of each income range.
8
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Table 5: Financing Gap Analysis
Household Income Limit (a)
Maximum Affordable Monthly Rent per Unit (b)
Monthly Operating Expenses (c)
Vacancy (d)
Net Operating Income per Unit (e)
Operating Subsidy from Other Sources (f)
Monthly Supportable Debt Service per Unit (g)
Loan Amount (h)
Financing Gap per Affordable Unit (I)
Assumptions
Income Group
Extremely Low Very Low Low Moderate
$20,420 $30,500 $48,750 $73,200
$418 $670 $1,126 $1,737
$458 $458 $458 $458
5% 5% 5% 5%
-$62 $178 $611 $1,192
$62 $0 $0 $0
$0 $142 $489 $953
$0 $25,036 $86,107 $167,924
$172,000 $146,964 $85,893 $4,076
Total Affordable Unit Development Costs (j)
Financing Terms
Debt Coverage Ratio
Interest Rate
Term of Loan (years)
$172,000
1.25
5.50%
30
Notes:
(a) Based on a 3-person household, HUD, 2017.
(b) 30% of income to rent and utilities.
(c) Based on proforma analysis from Phase I.
(d) Standard required assumption for financing applications.
(e) Affordable Monthly Rent less Operating Expenses & Vacancy.
(f) Operating subsidy is necessary for units with negative N01.
(g) Net Operating Income plus Operating Subsidy, divided by Debt Coverage Ratio.
(h) Based on financing terms assumptions.
(1) Total Development Costs less Loan Amount.
(j) Based on proforma analysis from Phase I.
Sources: HUD, 2017; Southeastem Utah Housing Authority; BAE, 2018.
Step 6: Calculate the Maximum Justifiable Fee per the Nexus Analysis
The final step in calculating the maximum justifiable impact fee is to apply the financing gap
per affordable unit for each income level (from Step 5) to the total housing need by income
level (from Step 4) for the hotel land use. This is expressed as the "maximum justifiable fee"
because it is directly derived from the nexus analysis described above (i.e., new commercial
development generating new jobs combined into new worker households distributed by
income band, and the cost to provide new affordable rental housing units to these same
households). This fee is estimated at $15.57 per square foot, as shown in Table 6.
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Table 6: Maximum Justifiable Hotel Impact Fee
Affordable Housing Need per Prototype Hotel
Extremely Low Income (up to 30% AMI)
Very Low Income (31-50% AMI)
Low Income (51-80% AMI)
Moderate Income (81-120% AM])
Total Affordable Housing Need
Financing Gap (a)
1.09
1.57
5.82
4.32
12.79
Extremely Low Income Units $186,632
Very Low Income Units $230,316
Low Income Units $499,490
Moderate Income Units $17.610.
Total Financing Gap $934,054
Maximum Impact Fee per Sq. Ft. $15.57
Assumptions
Building Size 60,000
Financing Gap
Extremely Low Income Units $172,000
Very Low Income Units $146,964
Low Income Units $85,893
Moderate Income Units $4,076
Note:
(a) The financing gap is calculated by multiplying the number of worker households at each income level by the financing
gap per unit at each affordability level.
Source: BAE, 2018.
Step 7: Comparison of Nexus and Financially Feasible Fees
A comparison of the maximum justifiable fee per the nexus analysis to the maximum
financially feasible fees for the two hotel scenarios shows that the maximum fee justifiable via
the nexus analysis is considerably lower than the maximum financially feasible fee for either
market scenario. This is an indicator that a fee in the range of the maximum justifiable fee
could be considered for implementation by the City and County.
Table 7: Summary of Commercial Impact Fee Analysis
Fee per Square Foot
Hotel Hotel
Moderate Strong
Maximum Financially Feasible Fee $30.86 $54.14
Maximum Justifiable Fee
$15.57 $15.57
Source: BAE, 2018, based on sources as described in previous tables.
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RESIDENTIAL.LINKAGE FEE ANALYSIS
This section of report calculates the maximum potential affordable housing linkage or "in -lieu"
fee for residential developments. Analysis included in this section can also be used to identify
the maximum justifiable inclusionary or "assured housing" set -aside that could justifiably be
required of new residential developments instead of paying an in -lieu fee.
Overview of Methodology
This section provides an overview of the steps used to determine the maximum fee for market -
rate residential units. Each step is discussed in more detail in the following sections. The
maximum residential fee calculation is based on the premise that new households in Moab
and Grand County will spend at least some of their disposable income locally, thereby
supporting employment for new workers, a portion of which will be in need of affordable
housing. The intent of the market -rate residential fee is to generate revenue that will support
the construction of affordable housing for these new lower -income worker households.
While these housing unit types have the potential to be used for short-term rentals by visitors
or as second homes where permitted by zoning, this nexus analysis is based on modelling
these housing units assuming occupancy by full-time residents, since the actual uses for these
hypothetical units are unknown.
Step 1: Define Housing Types and Identify Housing Prices and
Development Costs
The Phase I study identified four residential land uses to determine the maximum legal fee for
each residential product type. The residential product types analyzed were rental apartments,
condominiums, townhomes, and single-family detached houses. This analysis included
determining market rate rentals and sale prices as well as development costs for these
residential development types.
Step 2: Test Financial Feasibility of Linkage Fee for Defined Land Uses
Using the information on rents, prices, and development costs developed in Phase I,
residential linkage fees were found to be financially feasible for condominiums, townhomes,
and single-family detached houses. For condominiums, fees were only feasible under strong
market conditions, while they were feasible for townhomes and single-family detached houses
under both moderate and strong market conditions. However, the maximum fee that would
still allow a project to meet the financial feasibility criteria regarding return on cost and yield
on cost was not calculated for land use types where a fee was feasible. The following table
below shows the maximum feasible fee given those benchmarks.
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Table 8: Summary of Proforma Analysis for Residential Land Uses
Assumptions for Baseline
Location. Zoning
Site Size (sf)
Total Number of Units
Average Unit Size
Number of Residential Floors
FAR
Parking Type
Land Costs per Acre
Total Dev Cost/Unit (inc. land)
Total Dev Cost/SF (inc. land)
Sale Price/Sq. Ft.
Sale Price or Rent Per Unit
Return On Cost - Baseline
Yield on Cost - Baseline
Baseline Feasible? (a)
New Fee1Sq. Ft. (a)
New Fee per Unit
Return On Cost with Fees
Yield on Cast with Fees
Feasible with Fee? (a)
New Res Fee, as °
of Total Dev Costs
Notes:
Condominiums
Overnight Rentals
Moderate Strong
Grand County, HC
43,560 43,560
25 25
1,350 1,350
3 3
0.9 0.9
$ 82,500 $ 119,790
S 231,757 $ 253,308
$ 149 $ 163
S 185 S 245
$ 249,750 $ 330,750
2.4°0
NA
No
24.0%
NA
Yes
$ - $ 5.18
$ - $ 6,996
Townhomes
Overnight Rentals
Single -Family
Detached
Moderate Strong Moderate Strong
Grand County. HC Grand County, RR
240,000 240,000
48
1,650
2
0.3
48
1,650
2
0.3
$ 82.764 $ 130.680
$ 253.129 $ 311,202
$ 153 $ 189
S 200 $ 250
$ 330,000 $ 412,500
23.9 %
NA
Yes
NA
Yes
$ 4,64 $ 8.77
$ 7,654 $ 14,474
43.560
1
2,250
1
0.1
43.560
1
3,000
1
0.1
S 80,000 $ 120,000
$ 388,761 $ 690,780
$ 173 $ 230
$ 200 $ 267
$ 450,000 $ 800,000
15.B%
NA
Yes
15.8 io
NA
Yes
$ 1.13 $ 1.62
$ 2,541 S 4,853
20.0% 20.0% 20.0% 15.0% 15.0%
NIA N/A NIA NIA NIA
Yes Yes Yes Yes Yes
3,1% 2.9% 4.4% 0.7% 0.7%
Apartments were shosvn not to support a fee in the Phase I study and are not shown here.
a) Feasibility is measured as follows:
Project must achieve at least. 20.0% Return an Cost far Condominiums and Tovmhomes
15.0% Return on Cost for Apartments and Single -Family Homes
Source: BAE. 2018.
Step 3: Estimate the incomes of Households in New Market Rate
Housing
Based on the sale prices identified in Step 2, this report estimated the household incomes of
occupants in new residential units in Moab where a linkage fee was financially feasible. Using
the threshold of 30 percent of income to housing costs, the table shows the annual household
income levels required to support a mortgage for each of the projects where a linkage fee is
feasible. As shown, the annual incomes range from approximately $100,000 for
condominiums under the strong market scenario and townhomes under the moderate market
scenario upwards to more than $240,000 for a single-family home in the strong market
scenario.
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Table 9: Income Requirements for Housing Prototypes
Housing Profile
Condo -Strong
Townhouse: Moderate
Townhouse: Strong
Single -Family: Moderate
Single -Family. Strong
Annual House -
Hold Income
$99,957
$99,730
$124,663
$135,996
$241,771
Amount Avail.
for Housing (a)
Principal & Property Property
Interest Insurance Taxes
Mortgage Total Monthly Down- Affordable
Insurance Payment Payment Home Price
Condo -Strong
Townhouse: Moderate
Townhouse: Strong
Single -Family. Moderate
Single-Family:Strong
$2,499 $1,570
$2,493 $1,567
$3,117 $1,958
$3,400 $2,136
$6,044 $3,798
Ownership Cost Assumptions (b)
$94
$93
$117
$127
$226
$303
$303
$378
$413
$733
% of Income for Housing Costs
Down payment
Annual interest rate
Loan term
Upfront mortgage insurance
Annual mortgage insurance
Annual property tax rate
Annual hazard insurance
30% of gross annual income
3.50% of home value
4.25% fixed
30 years
0.00% of home value
2.00% of mortgage
1.10% of home value
0.34% of home value
$532
$531
$663
$724
$1,287
$2,499
$2,493
$3,117
$3,400
$6,044
$11,576
$11,550
$14,438
$15,750
$28,000
$330,750
$330,000
$412,500
$450,000
5800,000
Notes:
(a) Represents 30 percent of monthly household income.
(b) Based on a low down payment conventional loan.
Sources: Grand County, 2017; Insurance.com, 2017; Bankrate.com, 2017; BAE, 2018.
Step 4: Analyze Projected Spending Patterns for Households In New
Market -Rate Units
New households boost spending within an economy. As these new households spend money
on retail goods, food, and health, personal, professional, and educational services, they
support job growth in these and other sectors.
To estimate the effect of new household spending on employment generation, this Phase II
nexus study uses IMPLAN ("Impact analysis for Planning"), a widely -accepted and utilized
software model. At the heart of the model is an input-output dollar flow table. For a specified
region, the input-output table accounts for all dollar flows between different sectors of the
economy. Using this information, IMPLAN models the way income injected into one sector is
spent and re -spent in other sectors of the economy, generating waves of economic activity, or
so-called "economic multiplier" effects. Appendix A contains a more detailed overview of
IMPLAN.
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The IMPLAN model is also able to estimate the number of direct, indirect, and induced jobs
generated by a given economic "event." Once the economic events have been entered into
the model, IMPLAN reports the following types of impacts:
• Direct Impacts. Direct impacts refer to the set of producer or consumer expenditures
applied to the predictive model for impact analysis. It is the amount of spending
available to flow through the local economy. IMPLAN then displays how the local
economy will then respond to these initial changes. The direct impacts may equal the
amount of spending input into the model, depending on a variety of factors.
• Indirect Impacts. The indirect impacts refer to the impact of local industries buying
goods and services from other local industries. The cycle of spending works its way
backward through the supply chain until all money leaks from the local economy, ,
either through imports or by payments to income and taxes. For capital projects this
would include payments for construction inputs such as wood, steel, office supplies,
and any other non -labor payments that a construction firm would purchase in the
building process. Since IMPLAN is only used for the housing analysis for this report to
assess the impacts of new resident household expenditures, there are no indirect
impacts to assess as there are no industry expenditures as inputs to the model.
• Induced Impacts. The induced impacts refer to an economy's response to an initial
change (direct impact) that occurs through re -spending of income according to
household spending patterns. When households earn income, they spend part of that
income on goods and services, such as food and healthcare. IMPLAN models
households' disposable income spending patterns and distributes them through the
local economy.
For the purpose of this analysis, the economic "event" is the household spending by occupants
of new residential units in Grand County. By IMPLAN definition these expenditures are direct
impacts, and the resulting spending generates induced impacts. For instance, the household
expenditures generate jobs for cashiers and baggers at grocery stores patronized by the new
households. The process initiated by household expenditures continues as these workers and
the businesses they work for spend money in subsequent transactions, supporting
employment at places other than the initial point of sale, such as wholesalers supplying retail
stores, or truck drivers delivering goods to those stores. In turn, these businesses and workers
spend money to generate additional activity in the local economy of Moab and Grand County.
These are all part of the induced impacts linked to the household expenditures.
Table 9 above shows the income levels typically required to support the purchase of each of
the housing prototypes under consideration. After adjustment for FICA taxes, IMPLAN uses
these income levels to estimate expenditures within income categories encompassing each of
the income levels. Since the income of an individual household does not generate enough
expenditures to be recognized by the regional IMPLAN model, the total income analyzed is as
14
Page 25 of 46
assumed for 1,000 households at that level; IMPLAN scales the expenditures in a linear
manner. The results are then ultimately divided by 1,000 to show the estimated impact of a
single household.
Step 5: Estimate New Worker Households by Household Income
The analysis uses a data set published by the U.S. Census (the Public Use Microdata Sample
or PUMS) to estimate the household income distribution among the worker households
derived from Step 4.
Worker households4 often have more than one employed person. As discussed previously in
the non-residential nexus analysis section, this analysis makes use of a detailed and rich data
set published by the U.S. Census known as the Public Use Microdata Sample (PUMS) from the
2012 to 2016 period for Carbon, Daggett, Duchesne, Emery, Grand, San Juan, Uintah, and
Wasatch Counties to estimate household income distributions for worker households by major
industry group. The results are shown in Table 10. The income limits used were from 2016, in
order to match the source data.
4 A worker household is defined as a household with one or more employed persons. They may be wage and salary
workers, or self-employed/sole proprietors.
15
Page 26 of 46
Table 10: Income Level by Industry, Working Persons by 2016 Household Income
Limits
FOR HOUSING ANALYSIS Estimated Household Income as a Percent of AMI
Extremely Above
NAICS Code Industry Low Very Low Low Moderate Moderate Total
Private Sector
11, 21 Agriculture & Natural Resources 3.7% 1.6% 6.9% 16.6% 71.1% 100.0%
23 Construction 6.0% 4.9% 20.9% 27.1% 41.1% 100.0%
31-33 Manufacturing 5.1% 5.0% 8.2% 22.1% 59.6% 100.0%
42 Wholesale Trade 8.5% 2.0% 9.9% 16.8% 62.8% 100.0%
44-45 Retail Trade 8.0% 4.8% 15.7% 23.9% 47.6% 100.0%
48-49, 22 Transportation, Warehousing, & 1.5% 1.1% 12.7% 22.9% 61.8% 100.0%
Utilities
51 Information 6.0% 12.1% 8.9% 19.4% 53.6% 100.0%
52-53 Finance, Insurance, & Real Estate 5.9% 3.8% 12.7% 26.2% 51.3% 100.0%
54-55 Professional, Scientific, & Technical 7.4% 1.6% 9.7% 21.5% 59.9% 100.0%
Services, & Mgmt of Companies
56 Admin, Support, & Waste Mgmt Srvcs 21.4% 0.7% 18.5% 26.2% 33.2% 100.0%
61 Educational Services 1.6% 6.2% 17.4% 24.2% 50.6% 100.0%
62 Health Care & Social Assistance 2.3% 4.4% 13.6% 24.6% 55.0% 100.0%
71-72 Leisure'& Hospitality 7.6% 11.1% 15.6% 18.9% 46.8% 100.0%
81 Other Services Except Public Admin 4.5% 5.9% 25.0% 23.5% 41.1% 100.0%
All Govemment Employment 3.0% 2.7% 11.9% 22.3% 60.1% 100.0%
FOR HOTEL ANALYSIS Estimated Household Income as a Percent of AMI (a)
NAICS Code Land Use
Private Sector Only
721 Hotel/Motel
Extremely Above
Low Very Low Low Moderate Moderate Total
3.2% 5.3% 20.0% 17.4% 54.1 % 100.0%
Notes:
Based on a cross tabulation of Public Use Microdata Samples (PUMS) from the 2012-2016 American Community Survey.
These incomes were compared to household income limits published by HUD to determine the percentage of households
falling into each income category. The analysis controlled for household size, to address the varying HUD income limits for
each household size.
Sources: Census, American Community Survey Public -Use Microdata Sample (PUMS) 2012-2016; HUD; BAE, 2018.
Housing need is based on the number of households rather than the number of jobs. As such,
jobs are translated into households by dividing the number of jobs by the average number of
workers per worker household for each income category. Applying this factor to the IMPLAN
output for each housing type, the following table summarizes estimate jobs supported per 100
units of housing. Detail on the calculations can be found in Appendix B.
As shown in the next table (Table 11), the total number of Grand County worker households
supported by a 100-unit development ranges from 30 to 40 households; the affordable
housing need,5 based on households earning up to and including moderate incomes, ranges
from 17 to 23 units per 100 new housing units.
5 The affordable housing need is based on the number of rental housing units demanded by worker households
estimated to have a gap between the cost to build and the financing supported by rents.
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Table 11: Summary of Worker Households Supported by New Market -Rate Units
Based on 100 Units
Total
Total Affordable
Worker Households by Percent of AMI (a)
House- Housing Extremely Very Above
Housing Type holds Need Low Low Low Moderate Moderate
Condominium - Strong Market 33.2 18.9 2.9 2.2 6.1 7.7 14.3
Townhome - Moderate Market 30.2 17.2 2.6 2.0 5.6 7.0 13.0
Townhome - Strong Market 37.0 21.0 3.2 2.5 6.8 8.5 15.9
Single Family Detached - Moderate Market 40.3 22.9 3.5 2.7 7.4 9.3 17.4
Single Family Detached - Strong Market 38.5 21.9 3.3 2.6 7.1 8.9 16.6
Notes:
Estimates based on 100 units of housing for each type. See Appendix B for detail on calculations.
(a) Based on 2016 HUD Income Limits.
Sources: American Community Survey, 2012-2016, including the Public User Microdata Sample; HUD; IMPLAN; BAE,
2018.
In addition to providing information necessary to calculate affordable housing in -lieu fees, the
figures shown in Table 11 provide data that quantify the nexus between new market rate
housing units and potential assured housing policies that would require new market rate
housing developments to incorporate housing units to meet demand for affordable housing
that is generated by the new market rate units. For example, the nexus analysis indicates that
for every 100 new condominiums (strong market) there would be a need for 18.9 additional
affordable housing units, including 2.9 extremely low-income housing units, 2.2 very low-
income housing units, 6.1 low-income housing units, and 7.7 moderate -income units.
Step 6: Calculate Financing Gap per Affordable Unit
This step determines the per unit "financing gap" that housing developers encounter when
securing a permanent loan for their projects. This step has been completed as Step 5 in the
commercial fee analysis and is described in the preceding chapter of this report.
To summarize, the financing gap per affordable unit ranges from $172,000 for extremely low-
income units to only $4,076 for moderate -income units. For convenient reference purposes,
the table showing these calculations is repeated here as Table 12.
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Table 12: Financing Gap Analysis
Household Income Limit (a)
Maximum Affordable Monthly Rent per Unit (b)
Monthly Operating Expenses (c)
Vacancy(d)
Net Operating Income per Unit (e)
Operating Subsidy from Other Sources (f)
Monthly Supportable Debt Service per Unit (g)
Loan Amount (h)
Financing Gap per Affordable Unit (i)
Assumptions
Income Group
Extremely Low Very Low Low Moderate
$20,420 $30,500 $48,750 $73,200
$418 $670 $1,126 $1,737
$458 $458 $458 $458
5% 5% 5% 5%
-$62 $178 $611 $1,192
$62 $0 $0 $0
$0 _ $142
$0 $25,036
$172,000 $146,964
$489 $953
$86,107 $167,924
$85,893 $4,076
Total Affordable Unit Development Costs (j)
$172,000
Financing Terns
Debt Coverage Ratio 1.25
Interest Rate 5.50%
Tenn of Loan (years) 30
Notes:
(a) Based on a 3-person household, HUD, 2017.
(b) 30% of income to rent and utilities.
(c) Based on proforma analysis from Phase I.
(d) Standard required assumption for financing applications.
(e) Affordable Monthly Rent less Operating Expenses & Vacancy.
(f) Operating subsidy is necessary for units with negative NOI.
(g) Net Operating Income plus Operating Subsidy, divided by Debt Coverage Ratio.
(h) Based on financing terms assumptions.
(i) Total Development Costs less Loan Amount.
(j) Based on proforma analysis from Phase I.
Sources: HUD, 2017; Southeastem Utah Housing Authority; BAE, 2018.
Step 7: Calculate the Maximum Justifiable Fee per the Nexus Analysis
The final step in calculating the impact fee is to apply the financing gap per unit for each
income level (from Step 6) to the total housing need by income level from new market -rate
units (from Step 5).
The results of the calculations are shown in Table 13. Per this nexus analysis, the maximum
justifiable fees range from $5.31 per square foot for single-family detached homes in a strong
market scenario to $10.19 per square foot for condominiums in a strong market scenario.
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Table 13: Maximum Justifiable Fee per Residential Unit
Condominium Townhome Townhome Single -Family Single -Family
Worker Households by Income Level Strong Moderate Strong Moderate Strong
Extremely Low Income (up to 30% AMI) 2.9 2.6 3.2 3.5 3.3
Very Low Income (31-50% AMI) 2.2 2.0 2.5 2.7 2.6
Low Income (51-80% AMI) 6.1 5.6 6.8 7.4 7.1
Moderate Income (81-120% AMI) 7_7 7.00 885 993 8_9
Subtotal - Affordable Housing Need (Units) 18.9 17.2 21.0 22.9 21.9
Above Moderate Income (over 120% AMI) 14.3 13.0 15.9 17.4 16.6
Total Housing Need 33.2 30.2 37.0 40.3 38.5
Financing Gap (a)
Extremely Low Income Units $492,760 $448,054 $545,290 $594,862 $569,233
Very Low Income Units $327,280 $297,587 $366,587 $399,913 $377,941
Low Income Units $524,732 $477,125 $586,455 $639,770 $610,361
Moderate Income Units $31,269 $28,432 $34,765 $37,925 $36.250,
Total Financing Gap per 100 Units $1,376,040 $1,251,199 $1,533,097 $1,672,469 $1,593,785
Maximum Impact Fee per Unit $13,760 $12,512 $15,331 $16,725 $15,938
Unit Size (b) 1,350 1,650 1,650 2,250 3,000
Maximum Impact Fee per Square Foot $10.19 $7.58 $9.29 $7.43 $5.31
Notes:
(a) The financing gap is calculated by multiplying the number of employee households at each income level by the financing
gap per unit (from Step 7) at each affordability level.
(b) Per the Phase I analysis, based on an assumed average unit size of 1,000 sq. ft.
Source: BAE, 2018.
Step 8: Comparison of Nexus and Financially Feasible Fees
A comparison of the maximum justifiable fee per the nexus analysis to the maximum
financially feasible fees for the various residential scenarios shows that the nexus fee is higher
than the financially feasible fee for any of the market scenarios, especially for the single-family
homes. As a result, if the City and the County choose to implement a residential in -lieu fee,
the level of appropriate fees might be constrained by market conditions as indicated by the
maximum financially justifiable fee levels shown in Table 14.
Table 14: Summary of Residential Impact Fee Analysis
Fee per Square Foot
Condominium Townhome Townhome Single -Family Single -Family
Strong Moderate Strong Moderate Strong
Maximum Financially $5.18 $4.64 $8.77 $1.13 $1.62
Feasible Fee
Maximum Justifiable Fee $10.19 $7.58 $9.29 $7.43 $5.31
Source: BAE, 2018, based on sources as described in previous tables.
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CONSIDERATIONS FOR IMPLEMENTATION
Following is a more detailed discussion of some key factors to take into account in
implementation of affordable housing impact fees for Moab and Grand County, including
issues discussed previously in Phase I.
Maximum Justifiable Fees vs. Maximum Financially Feasible Fees
As noted in the Introduction, the maximum justifiable fees have been derived from the nexus
analysis, and thus represent the maximum fee based on the demand for additional affordable
housing driven by new commercial and residential development. However, as indicated in the
analysis here and in Phase I, applying a fee to some commercial and residential development
might result in projects which would no longer generate high enough returns to be financially
feasible, and for projects where a fee might be feasible, the maximum feasible fee might be
lower than the maximum justifiable fee as determined by the nexus analysis.
As shown in the analysis, for residential uses, the maximum financially feasible fees were
lower than the maximum justifiable fees from the nexus analysis, indicating that the City and
County should consider setting fees at or below the maximum financially feasible fees. This is
unlike the hotel/commercial fees, where the maximum financially feasible fees are higher than
the maximum justifiable fees.
It should be noted that when setting the in -lieu fee at a level that is less than the maximum
justifiable fee, the funds collected would be lower than necessary to subsidize the needed
amount of affordable housing, meaning that the fee proceeds would need to be leveraged with
other sources of subsidy to produce the desired level of affordable units.
Market Conditions
Changes in the economy, locally or nationally, could impact both the financial feasibility and
the justifiable nexus fees for the different development types. The analysis here presents two
scenarios, categorized as moderate market conditions and strong market conditions. Changes
in economic conditions that could influence feasibility of different fee levels would include
interest rates for development and for mortgages, changes in rents, home sale prices, land
costs, operating expenses, acceptable rates of return for developers, and other factors.
While these factors are interdependent, the following illustrative example shows a simple
sensitivity analysis for a change in just the interest rate for a developer loan for affordable
rental housing. The financing gap analysis above shows the per unit subsidy required to
support a loan at a 5.5 percent interest rate based on rents affordable to extremely low- to
moderate -income households, with a per unit financing gap ranging from $4,076 for moderate
income units to $172,000 for extremely low-income units. A one percent increase in interest
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rates to 6.5 percent leads to a higher financing gap, as shown below in Table 15, to $21,153
for moderate income units to $172,000 for extremely low income units.6 The higher interest
rate leads to an increase of approximately 14 percent in the maximum justifiable nexus fees,
since the cost of providing affordable units has increased (see Table 16 below). 7
Table 15: Financing Gap Analysis — Comparative Interest Rates
Income Group
Extremely Low Very Low Low Moderate
Household Income Limit (a) $20,420 $30,500 $48,750 $73,200
Maximum Affordable Monthly Rent per Unit (b) $418 $670 $1,126 $1,737
Monthly Operating Expenses (c) $458 $458 $458 $458
Vacancy (d) 5°% 5% 5% 5°%
Net Operating Income per Unit (e) -$62 $178 $611 $1,192
Operating Subsidy from Other Sources (f) $62 $0 $0 $0
Monthly Supportable Debt Service per Unit (g) $0 $142 $489 $953
Loan Amount (h) $0 $22,490 $77,350 $150,847
Financing Gap per Affordable Unit @ 6.5%
Interest Rate (I) $172,000 $149,510 $94,650 $21,153
Financing Gap per Affordable Unit @ 5.5%
Interest Rate (I)
Assumptions
$172,000 $146,964 $85,893 $4,076
Total Affordable Unit Development Costs 0) $172,000
Financing Terms
Debt Coverage Ratio 1.25
Interest Rate 6.50%
Term of Loan (years) 30
Notes:
(a) Based on a 3-person household, HUD, 2017.
(b) 30% of income to rent and utilities.
(c) Based on proforma analysis from Phase I.
(d) Standard required assumption for financing applications.
(e) Affordable Monthly Rent less Operating Expenses & Vacancy.
(f) Operating subsidy is necessary for units with negative NOI.
(g) Net Operating Income plus Operating Subsidy, divided by Debt Coverage Ratio.
(h) Based on financing terms assumptions.
(i) Total Development Costs less Loan Amount.
(j) Based on proforna analysis from Phase I.
Sources: HUD, 2017; Southeastem Utah Housing Authority; BAE, 2018.
In fact, the Phase I analysis presented two scenarios, for a moderate market based on
conditions in 2014 and a strong market scenario based on 2017 conditions. Generally, the
6 The financing gap for the extremely low income units is the same as the subsidy required in both cases is the
entire cost of building the unit.
7 The hypothetical interest rate change here does not change the maximum financially feasible fee. In real world
conditions, interest costs for market rate development might also rise, but for the sake of simplicity that possibility
is not considered in this hypothetical scenario.
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moderate market scenarios present a lower fee. To be more conservative, the City and County
could choose the fee levels associated with the moderate market scenario, making it less
likely that the fee structure would need to be adjusted as often to account for changing market
conditions. However, when economic conditions reflect the strong market scenario, those
conditions result in higher per unit costs and make it more expensive for the City and County
to use in -lieu fees to subsidize an affordable development. As discussed in further detail
below, it is recommended that the technical analysis underpinning assured housing policies
and establishment of jobs -housing linkage fees and affordable housing in -lieu fees be updated
on a periodic basis, to account for changing conditions.
Table 16: Impact Fee Analysis — Comparative Interest Rates
Fee per Square Foot
Hotel Hotel
Moderate Strong
Maximum Financially Feasible Fee $30.86 $54.14
Maximum Justifiable Fee @ 5.5% Interest $15.57 $15.57
Maximum Justifiable Fee @ 6.5% Interest $17.71 $17.71
Maximum Financially
Feasible Fee
Maximum Justifiable Fee
@ 5.5% Interest
Maximum Justifiable Fee
@ 6.5% Interest
Fee per Square Foot
Condominium Townhome Townhome Single -Family Single -Family
Strong Moderate Strong Moderate Strong
$5.18 $4.64 $8.77 $1.13 $1.62
$10.19 $7.58 $9.29 $7.43 $5.31
$11.60 $8.63 $10.58 $8.46 $6.05
Source: BAE, 2018, based on sources as described in previous tables.
Phase -In of Requirements
As discussed in the Phase I study, when adopting a fee or inclusionary policy, some
communities do so with a phase -in schedule. For instance, when first adopting a policy like
this, some jurisdictions set a future date for its implementation, and define how to treat
current "pipeline" projects that would have been started without knowledge of this fee. A
phase -in allows developers to adjust their bidding for development sites with the knowledge of
how the applicable requirements affect the residual land value that they can afford to pay for a
site and achieve financial feasibility. In the case of Moab, we understand that there have been
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public discussions about establishing assured housing requirements for the last couple of
years, in which case a phase -in feature may not be as important.
Fees per Unit versus Fees per Square Foot
When inclusionary requirements or in -lieu fees are fixed on a "per unit" basis, rather than
varying by the size of the market rate units, this creates an incentive for builders to maximize
the size of their market rate units, so that they can spread the cost of compliance over a
greater quantity of saleable square footage, making market rate housing units less attainable
to middle -income households. This report recommends tying the fee to square feet instead of
units.
Policy Flexibility
During economic downturns, some jurisdictions have either created special deferral programs
or lowered fees across the board. Some places have built-in mechanisms that require the fees
or inclusionary policy to be re -analyzed at defined time intervals or when there are substantial
changes in economic indicators such as interest rates or development costs. These
approaches demonstrate that the requirements can be customized to adapt to changes in
economic conditions. Because there are many constantly changing variables that influence
affordable housing needs, costs of providing affordable housing units, and feasibility for
market rate development, best practices dictate that analysis underpinning affordable housing
requirements should be updated on a periodic basis, to determine if changes to policy or
program parameters are appropriate.
Another important component of policy flexibility is to offer builders a range of options to
comply with assured housing requirements. Every development project has a unique set of
financial circumstances, and while a given project may not be able to afford to pay adopted in -
lieu fees, there may be other options that would be feasible, such as providing on -site
affordable units, dedicating land that could be utilized by others to construct affordable
housing, or potentially complying with a reduced affordable housing requirement if a reduction
could be justified, based on a finding of reduced affordable housing need or a lack of financial
feasibility to meet the full requirements. An important caveat is to avoid making any of the
options inherently more economically attractive than others, to avoid encouraging builders to
all select the same compliance option. For example, when in -lieu fees are set at levels that
are substantially below the actual cost to subsidize affordable housing units, developers will
rarely build affordable units onsite. To achieve better economic parity between payment of in -
lieu fees and production of below market rate units onsite, if an in -lieu fee is set at less than
the full rate justifiable by the nexus analysis, then the City and County should also consider
reducing the assured housing inclusionary percentages commensurately. As previously noted,
when requirements are set below the maximum justifiable levels identified in the nexus
analysis, the City and County might have to leverage other sources of financing to support
development of affordable housing in quantities sufficient to mitigate the impacts.
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Finally, the feasibility analysis and the nexus analysis conducted for prototype development
projects in this study cannot
Revenue Estimate
For projects where a linkage fee was feasible, the maximum potentially feasible fee levels
were applied to historic building permit data to estimate revenue that could potentially be
generated from an in -lieu fee program. To partially take into account the variation in feasibility
due to fluctuations in economic conditions over time, the assumed fees were rounded down to
the nearest dollar, and were based on the moderate market scenario, with the exception of
condominiums, where the fee for the strong market was used since a fee was deemed not
feasible under the moderate market scenario.
This assumed fee structure could generate an estimated average annual revenue of
approximately $1.3 million if applied in both the City of Moab and Grand County, assuming the
same rate of development as between 2010 and 2017.8 The City could be expected to
generate substantially more revenue from hotel development than from residential
development, while slightly more than half of Grand County's revenue would come from
residential projects. The City's annual projected share is slightly less than $800,000, and the
County's share is estimated at about $523,000. These average annual revenue estimates
may under- or overstate actual revenue in any given year, depending on the overall economic
cycle.
Table 17: Annual Estimated Fee Revenue Based on Historic Permit Activity
Proposed Est. Annual
Fee City of Moab Grand County Revenue
Residential Protects
Single -Family Detached $ 1.00 $ 31,898 $
Townhomes ! SFR Nightly Rentals $ 4.00 $ 64,763 $
Condominiums $ 5.00 $ 5,159 $
Apartments
Annual Revenue, Residential Projects (a) $ 101,819 $
44,796 76,694
82,891 147,653
150,105 155,264
277,791 $ 379,611
Commercial Proiects
Retail $ - $ $
Office (b) $ - $ - $
Hotel $ 15.00 $ 694,714 $ 245,010
Annual Revenue, Commercial Projects (a) $ 694,714 $ 245,010
939,724
939,724
Annual Revenue by Place $ 796,533 $ 522,801 $ 1,319,334
Notes:
(a) The annual revenue is based the average annual square feet permitted between 2010 and 2017 in the City of Moab and
Grand County. Revenue will vary year to year based on actual development activity.
8 These calculations assume that all assured housing obligations are met by payment of fees, rather than
construction of inclusionary housing units.
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(b) The building permit data did not contain square footage data for newly constructed office projects. Each office project
was estimated at 8,000 square feet based on the recently built office buildings profiled in the Phase I study.
Sources: City of Moab, 2017; Grand County, 2017; BAE, 2018.
Summary
The analysis here and in the Phase I study of this Assured Housing Study indicates that
although charging an affordable housing nexus fee increases the cost to build, the fee can be
set at a reasonable level for some land uses by estimating a maximum financially feasible fee
as well as a maximum justifiable fee, and ensuring that the fee is below the lower of these two
thresholds. In establishing a policy for such a fee, the City and County could build in some
flexibility by considering a gradual phase -in, and monitoring market conditions on an ongoing
basis. Assuming current development trends continue, a nexus fee could bring in over $1
million annually to assist in the production of affordable housing units.
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APPENDICES
Appendix A: Overview of IMPLAN
This appendix provides additional clarification of the workings of the IMPLAN input-output
model. It provides a step-by-step account of how IMPLAN estimates economic impacts using
new residential development as an illustrative example. Definitions of key italicized terms are
provided in footnotes for the benefit of the reader. This section begins with an overview of the
data that IMPLAN uses internally, and moves forward through the process of how the model
estimates the impacts of new commercial and housing projects.
What is 1MPLAN?
IMPLAN is an input-output model that estimates the total economic implications of new
economic activity within a specified geography. The model uses national industry data and.
county -level economic data to generate a series of multipliers, which in turn estimate the total
economic implications of economic activity.
At the heart of the model is a national input-output dollar flow table called the Social
Accounting Matrix (SAM). Unlike other static input-output models, which just measure the
purchasing relationships between industry and household sectors, SAM also measures the
economic relationships between government, industry, and household sectors, allowing
IMPLAN to model transfer payments such as unemployment insurance. Thus, for the specified
region, the input-output table accounts for all the dollar flows between the different sectors
within the economy.
National Industry Data. The model uses national production functions for 536 sectors to
determine how an industry spends its operating receipts to produce its commodities. The
model also uses a national matrix to determine the byproducts9 that each industry generates.
To analyze the impacts of household spending, the model treats households as an "industry"
to determining their expenditure patterns. IMPLAN couples the national production functions
with a variety of county -level economic data to determine the impacts for our example.
County -Level Economic Data. In order to estimate the county -level impacts, IMPLAN combines
national industry production functions with county -level economic data. IMPLAN collects data
from a variety of economic data sources to generate average output, employment, and
productivity for each of the industries in a given county. It also collects data on average prices
for all of the goods sold in the local economy. In this analysis, IMPLAN uses economic data for
Grand County. IMPLAN gathers data on the types and amount of output that each industry
generates within the region. In addition, the IMPLAN model uses county -level data on the
prices of goods and household expenditures to determine the consumption functions of
9 The byproducts refer to any secondary commodities that the industry creates.
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regional households and local government, taking into account the availability of each
commodity within the specified geography.
Multipliers. IMPLAN combines this data to generate a series of SAM-type multipliers for the
local economy. The multiplier measures the amount of total economic activity that results
from an industry (or household) spending an additional dollar in the local economy. Based on
these multipliers, IMPLAN generates a series of tables to show the economic event's direct,
indirect, and induced impacts to gross receipts, or output, within each of the model's 536
sectors. These outputs are described below:
■ Direct Impacts. Direct impacts refer to the dollar value of economic activity available
to circulate through the economy. In the case of new residential development, the
direct impacts are equal to the new households' discretionary spending. The direct
impacts do not include household savings and payments to federal, state, and local
taxes, as these payments do not circulate through the economy.
It should be noted that impacts from retail expenditures differ significantly between
the total economic value of retail and the amount available to circulate through the
local economy. The nature of retail expenditures accounts for this difference. The
model assumes that only the retail markup impacts the local economy, particularly for
industries heavily populated with national firms such as gas stations and grocery
stores. Since local stores buy goods from wholesalers and manufacturers outside of
the area, and corporate profits also leave the local economy, only the retail markup will
be available for distribution within the local economy. To the extent that retailers'
headquarters are located within the county or region, the model allocates their
portions of the impacts to the local economy.
■ Indirect Impacts. The indirect impacts refer to the inter -industry impacts of the input-
output analysis. Since IMPLAN is only used for the housing analysis for this report to
assess the impacts of new resident household expenditures, there are no indirect
impacts to assess as there are no industry expenditures as inputs to the model.
■ Induced Impacts. The induced impacts refer to the impacts of household spending by
the employees generated by the direct and indirect impacts. In other words, induced
impacts result from the household spending of employees of business establishments
that the new households patronize (direct) and their suppliers (indirect). The model
accounts for local commute patterns in the geography. For example, if 20 percent of
construction workers who work in the region live outside of the region, the model will
allocate 80 percent of labor's disposable income into the model to generate induced
impacts. The model excludes payments to federal and state taxes and savings based
on the geography's average local tax and savings rates. Thus, only the disposable
incomes from local workers are included in the model.
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Specifying the "Event' and Running the Model
Once the model is built for the specified geographies, it is time to specify the "event" that the
model will analyze and run the model.
Specifying the 'Event.' The "event" refers to the total economic value of industry output that
we are interested in analyzing. In the case of the ongoing economic impacts of a new
residential development, the "event" would be the total household incomes of the households
that buy or rent the homes.
Running the Model. Once the event is specified, IMPLAN runs the event through the model to
generate the results. IMPLAN applies the local data on average output per worker and
compensation per worker to determine the direct impacts. It then applies the value of the
event to the national production functions and runs a number of iterations of this value
through the production functions for the local economy to determine the indirect and induced
impacts. For each iteration, the model removes expenditures to government, savings, and for
goods bought outside of the local economy so that the results only include those dollars that
impact the local economy.
Summarizing the impacts
Once the model is run, IMPLAN generates a series of output tables to show the direct, indirect,
and induced impacts within each of the model's 536 sectors. IMPLAN generates these tables
for three types of impacts: output, employment, and value added. This nexus study is
concerned with the employment impacts.
• Output refers to the total economic value of the project in the local economy.
• Employment shows the number of employees needed to support the economic activity
in the local economy. It should be noted that for annual impacts of ongoing
operations, the employment figure shown represents the amount of employment
needed to support that activity for a year. Furthermore, IMPLAN reports the number of
jobs based on average output per employee for a given industry within the geography.
This is not the same as the number of full-time positions.
• Value Added shows the total income that the event generates in the local economy.
This income includes:
o Employee Compensation - total payroll costs, including benefits
o Proprietary Income - payments received by self-employed individuals as
income
o Other Property Type Income - payments for rents, royalties, and dividends
o Indirect Business Taxes - excise taxes, property taxes, fees, and sales taxes
paid by businesses. These taxes occur during the normal operation of
businesses, but do not include taxes on profits or income.
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Appendix B: Detailed Calculation of Employment Supported by New
Market -Rate Housing
Appendix B -1: Induced Employment Generation from Household Expenditures
NAICS
Code
11, 21
23
31-33
42
44-45
48-49, 22
51
52
53
54-55
56
61
62
71-72
81
0
Industry
Natural Resources
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation, Warehousing, &
Utilities
Information
Finance & Insurance
Real Estate & Rental & Leasing
Professional & Technical Services;
Management of Companies &
Enterprises
Administrative & Waste Services
Educational Services
Health Care & Social Assistance
Arts, Entertainment & Recreation;
Accommodation & Food Services
Other Services, except Public
Administration
Govemment
Total Jobs
Number of Jobs per 100 Housing Units (a)
Condominium Townhome Townhome Single -Family Single -Family
Strong Moderate Strong Moderate Strong
0.07 0.06 0.08 0.09 0.08
0.74 0.68 0.85 0.92 0.87
0.04 0.03 0.04 0.04 0.04
1.00 0.91 1.11 1.21 1.11
9.46 8.60 10.42 11.37 11.02
1.39 1.27 1.60 1.75 1.86
0.64
1.54
4.93
3.17
2.05
1.19
10.87
11.67
0.58
1.40
4.49
2.88
1.87
1.08
9.89
10.61
0.68
1.63
4.71
3.57
2.29
1.63
12.37
13.22
0.74
1.77
5.14
3.89
2.50
1.78
13.50
14.42
6.98 6.35 7.87 8.59
0.65 0.59 0.78 0.85
56.42 51.30 62.85 68.56
0.63
1.22
4.75
3.95
2.74
3.21
12.33
13.33
7.54
0.78
65.45
Note:
(a) Job generation is output of the IMPLAN model, and shows induced employment generated by household spending per 100 new
housing units.
Sources: IMPLAN; BAE, 2018.
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Appendix B - 2: Employment Generation by Income Level from New Condominium
Housing by Income Level, per 100 Units (Strong Market)
Estimated Jobs by Percent of AMI (a)
Total Extremely Very Above
NAICS Code Industry Jobs (b) Low Low Low Moderate Moderate
Private Sector
11, 21 Agriculture & Natural Resources 0.07 0.00 0.00 0.00 0.01 0.05
23 Construction 0.74 0.04 0.04 0.16 0.20 0.31
31-33 Manufacturing 0.04 0.00 0.00 0.00 0.01 0.02
42 Wholesale Trade 1.00 0.09 0.02 0.10 0.17 0.63
4445 Retail Trade 9.46 0.76 0.45 1.49 2.26 4.50
Transportation, Warehousing, & 1.39 0.02 0.02 0.18 0.32 0.86
48-49, 22 Utilities
51 Information 0.64 0.04 0.08 0.06 0.12 0.34
52-53 Finance, Insurance, & Real Estate 6.47 0.38 0.24 0.82 1.70 3.32
54-55 Professional, Scientific, & Technical 3.17 0.23 0.05 0.31 0.68 1.90
Services, & Mgmt of Companies
Admin, Support, & Waste Mgmt 2.05 0.44 0.01 0.38 0.54 0.68
56 Srvcs
61 Educational Services 1.19 0.02 0.07 0.21 0.29 0.60
62 Health Care & Social Assistance 10.87 0.25 0.48 1.48 2.68 5.99
71-72 Leisure & Hospitality 11.67 0.89 1.29 1.82 2.20 5.47
81 Other Services Except Public Admin 6.98 0.32 0.41 1.75 1.64 2.87
All Government Employment 0.65 0.02 0.02 0.08 0.15 0.39
Total Jobs
Workers per Households (c)
Number of Households
56.42 3.51 3.19 8.82 12.96 27.93
1.70 1.22 1.43 1.44 1.69 1.95
33.17 2.86 2.23 6.11 7.67 14.30
Notes:
(a) Based on 2016 HUD Income Limits.
(b) Job estimates are the output of the IMPLAN model, and shows employment generated by household spending.
Columns to right may not sum to Total Jobs due to independent rounding.
(c) Average number of workers per worker household calculated based on American Community Survey PUMS Analysis,
2012-2016.
Sources: American Community Survey, 2012-2016, including the Public User Microdata Sample; HUD; IMPLAN; BAE,
2018.
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Appendix B - 3: Employment Generation by Income Level from New Townhome
Housing by Income Level, per 100 Units (Moderate Market)
Estimated Jobs by Percent of AMI (a)
Total Extremely Very Above
NAILS Code Industry Jobs (b) Low Low Low Moderate Moderate
Private Sector
11, 21 Agriculture & Natural Resources 0.06 0.00 0.00 0.00 0.01 0.05
23 Construction 0.68 0.04 0.03 0.14 0.18 0.28
31-33 Manufacturing 0.03 0.00 0.00 0.00 0.01 0.02
42 Wholesale Trade 0.91 0.08 0.02 0.09 0.15 0.57
44-45 Retail Trade 8.60 0.69 0.41 1.35 2.05 4.09
Transportation, Warehousing, & 1.27 0.02 0.01 0.16 0.29 0.78
48-49, 22 Utilities
51 Information 0.58 0.03 0.07 0.05 0.11 0.31
52-53 Finance, Insurance, & Real Estate 5.89 0.35 0.22 0.75 1.54 3.02
54-55 Professional, Scientific, & Technical 2.88 0.21 0.05 0.28 0.62 1.72
Services, & Mgmt of Companies
Admin, Support, & Waste Mgmt 1.87 0.40 0.01 0.34 0.49 0.62
56 Srvcs
61 Educational Services 1.08 0.02 0.07 0.19 0.26 0.55
62 Health Care & Social Assistance 9.89 0.23 0.44 1.34 2.44 5.44
71-72 Leisure & Hospitality 10.61 0.81 1.17 1.66 2.00 4.97
81 Other Services Except Public Admin 6.35 0.29 0.38 1.59 1.49 2.61
All Government Employment 0.59 0.02 0.02 0.07 0.13 0.36
Total Jobs
Workers per Households (c)
Number of Households
51.30 3.19 2.90 8.02 11.79 25.40
1.70 1.22 1.43 1.44 1.69 1.95
30.16 2.60 2.02 5.55 6.98 13.00
Notes:
(a) Based on 2016 HUD Income Limits.
(b) Job estimates are the output of the IMPLAN model, and shows employment generated by household spending.
Columns to right may not sum to Total Jobs due to independent rounding.
(c) Average number of workers per worker household calculated based on American Community Survey PUMS Analysis,
2012-2016.
Sources: American Community Survey, 2012-2016, including the Public User Microdata Sample; HUD; IMPLAN; BAE,
2018.
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Appendix B - 4: Employment Generation by Income Level from New Townhome
Housing by Income Level, per 100 Units (Strong Market)
Estimated Jobs by Percent of AMI (a)
Total Extremely Very Above
NAICS Code Industry Jobs (b) Low Low Low Moderate Moderate
Private Sector
11, 21 Agriculture & Natural Resources 0.08 0.00 0.00 0.01 0.01 0.06
23 Construction 0.85 0.05 0.04 0.18 0.23 0.35
31-33 Manufacturing 0.04 0.00 0.00 0.00 0.01 0.02
42 Wholesale Trade 1.11 0.09 0.02 0.11 0.19 0.70
44-45 Retail Trade 10.42 0.84 0.50 1.64 2.49 4.96
Transportation, Warehousing, & 1.60 0.02 0.02 0.20 0.37 0.99
48-49, 22 Utilities
51 Information 0.68 0.04 0.08 0.06 0.13 0.36
52-53 Finance, Insurance, & Real Estate 6.33 0.38 0.24 0.80 1.66 3.25
54-55 Professional, Scientific, & Technical 3.57 0.26 0.06 0.34 0.77 2.14
Services, & Mgmt of Companies
Admin, Support, & Waste Mgml 2.29 0.49 0.02 0.42 0.60 0.76
56 Srvcs
61 Educational Services 1.63 0.03 0.10 0.28 0.39 0.83
62 Health Care & Social Assistance 12.37 0.28 0.55 1.68 3.05 6.81
71-72 Leisure & Hospitality 13.22 1.01 1.46 2.06 2.49 6.19
81 Other Services Except Public Admin 7.87 0.36 0.47 1.97 1.85 3.23
All Government Employment 0.78 0.02 0.02 0.09 0.17 0.47
Total Jobs
Workers per Households (c)
Number of Households
62.85 3.88 3.58 9.86 14.41 31.12
1.70 1.22 1.43 1.44 1.69 1.95
36.96 3.17 2.49 6.83 8.53 15.93
Notes:
(a) Based on 2016 HUD Income Limits.
(b) Job estimates are the output of the IMPLAN model, and shows employment generated by household spending.
Columns to right may not sum to Total Jobs due to independent rounding.
(c) Average number of workers per worker household calculated based on American Community Survey PUMS Analysis,
2012-2016.
Sources: American Community Survey, 2012-2016, including the Public User Microdata Sample; HUD; IMPLAN; BAE,
2018.
32
Page 43 of 46
Appendix B - 5: Employment Generation by Income Level from New Single Family
Detached Housing by Income Level, per 100 Units (Moderate Market)
Estimated Jobs by Percent of AMI (a)
Total Extremely Very Above
NAICS Code Industry Jobs (b) Low Low Low Moderate Moderate
Private Sector
11, 21 Agriculture & Natural Resources 0.09 0.00 0.00 0.01 0.01 0.06
23 Construction 0.92 0.06 0.05 0.19 0.25 0.38
31-33 Manufacturing 0.04 0.00 0.00 0.00 0.01 0.03
42 Wholesale Trade 1.21 0.10 0.02 0.12 0.20 0.76
44 45 Retail Trade 11.37 0.91 0.54 1.79 2.71 5.41
Transportation, Warehousing, & 1.75 0.03 0.02 0.22 0.40 1.08
48-49, 22 Utilities
51 Information 0.74 0.04 0.09 0.07 0.14 0.39
52-53 Finance, Insurance, & Real Estate 6.91 0.41 0.26 0.88 1.81 3.55
54-55 Professional, Scientific, & Technical 3.89 0.29 0.06 0.38 0.84 2.33
Services, & Mgmt of Companies
Admin, Support, & Waste Mgmt 2.50 0.53 0.02 0.46 0.66 0.83
56 Srvcs
61 Educational Services 1.78 0.03 0.11 0.31 0.43 0.90
62 Health Care & Social Assistance 13.50 0.31 0.60 1.83 3.33 7.43
71-72 Leisure & Hospitality 14.42 1.10 1.60 2.25 2.72 6.75
81 Other Services Except Public Admin 8.59 0.39 0.51 2.15 2.01 3.53
All Government Employment 0.85 0.03 0.02 0.10 0.19 0.51
Total Jobs
Workers per Households (c)
Number of Households
68.56 4.23 3.90 10.76 15.72 33.95
1.70 1.22 1.43 1.44 1.69 1.95
40.32 3.46 2.72 7.45 9.30 17.38
Notes:
(a) Based on 2016 HUD Income Limits.
(b) Job estimates are the output of the IMPLAN model, and shows employment generated by household spending.
Columns to right may not sum to Total Jobs due to independent rounding.
(c) Average number of workers per worker household calculated based on American Community Survey PUMS Analysis,
2012-2016.
Sources: American Community Survey, 2012-2016, including the Public User Microdata Sample; HUD; IMPLAN; BAE,
2018.
33
Page 44 of 46
Appendix B - 6: Employment Generation by Income Level from New Single Family
Detached Housing by Income Level, per 100 Units (Strong Market)
Estimated Jobs by Percent of AMI (a)
Total Extremely Very Above
NAICS Code Industry Jobs (b) Low Low Low Moderate Moderate
Private Sector
11, 21 Agriculture & Natural Resources 0.08 0.00 0.00 0.01 0.01 0.06
23 Construction 0.87 0.05 0.04 0.18 0.24 0.36
31-33 Manufacturing 0.04 0.00 0.00 0.00 0.01 0.02
42 Wholesale Trade 1.11 0.09 0.02 0.11 0.19 0.70
4445 Retail Trade 11.02 0.88 0.53 1.73 2.63 5.24
Transportation, Warehousing, & 1.86 0.03 0.02 0.24 0.43 1.15
48-49, 22 Utilities
51 Information 0.63 0.04 0.08 0.06 0.12 0.34
52-53 Finance, Insurance, & Real Estate 5.97 0.35 0.23 0.76 1.57 3.07
54-55 Professional, Scientific, & Technical 3.95 0.29 0.06 0.38 0.85 2.37
Services, & Mgmt of Companies
Admin, Support, & Waste Mgmt 2.74 0.58 0.02 0.50 0.72 0.91
56 Srvcs
61 Educational Services 3.21 0.05 0.20 0.56 0.78 1.62
62 Health Care & Social Assistance 12.33 0.28 0.55 1.67 3.04 6.79
71-72 Leisure & Hospitality 13.33 1.02 1.48 2.08 2.51 6.24
81 Other Services Except Public Admin 7.54 0.34 0.45 1.89 1.77 3.10
All Government Employment 0.78 0.02 0.02 0.09 0.17 0.47
Total Jobs
Workers per Households (c)
Number of Households
65.45 4.05 3.69 10.26 15.03 32.43
1.70 1.22 1.43 1.44 1.69 1.95
38.48 3.31 2.57 7.11 8.89 16.60
Notes:
(a) Based on 2016 HUD Income Limits.
(b) Job estimates are the output of the IMPLAN model, and shows employment generated by household spending.
Columns to right may not sum to Total Jobs due to independent rounding.
(c) Average number of workers per worker household calculated based on American Community Survey PUMS Analysis,
2012-2016.
Sources: American Community Survey, 2012-2016, including the Public User Microdata Sample; HUD; IMPLAN; BAE,
2018.
34
Page 45 of 46
BUREAU OF LAND MANAGEMENT
CASE RECORDATION
(LIVE) SERIAL REGISTER PAGE
Run PateRime: 04/17/18 04:09 PM
01 10-21-1976;090STAT2776;43USC1761 Total Acres
Case Type 287001: ROW -WATER FACILITY 7.600
Commodity 971: NON -ENERGY FACILITIES
Case Disposition: AUTHORIZED Case File Juris: MOAB FIELD OFFICE
Serial Number: MU--- - 062491
Name & Address Int Rel • %Interest
Page 1 of 1
GRAND COUNTY
126 E CENTER ST MOAB UT 84532 HOLDER 10100000000
MerTwp Rng Sec Tyype Nr Suff Subdivision
Serial Number: WM--- - 062491
District/Resource Area County Mgmt Agency
26 0250S 0220E 031 LOTS 7,8;
26 0260S 0220E 005 LOTS 18;
Act Date Code
Action
MOAB FIELD OFFICE
MOAB FIELD OFFICE
GRAND BUREAU OF LAND MGMT
GRAND BUREAU OF LAND MGMT
Serial Number: MU--- - 062491
Action Remarks Pending Office
11127M987 124
01/19M988 307
0201n988 600
01/13/1989 974
01/0112017 853
01/18/2018 763
APLN RECD
ROW GRANTED -ISSUED
RECORDS NOTED
AUTOMATED RECORD VERIF
COMPL/REVIEW DUE DATE
EXPIRES
Line Nr Remarks Serial Number: 13T17--- - 062491
0001 76 T CATaMENTS OF APPROX 0.10 ACRE EACH;
0002
0003
PROVIDES FLOOD CONTROL FOR THE CITY OF MOAB;
RENTAL EXEMPT;
/ t ysJ
NO WARRANTY IS MADE BY BLM
FOR USE OF THE DATA FOR
PURPOSES KgeN AD BY BLM