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Economic Impact of Constructing and Operating a Hyatt
Hotel at the Seattle-Tacoma International Airport
Prepared for:
Access the USA, LLC, dba Washington Regional Center
665 Woodland Square Loop SE, Suite 100,
Lacey WA 98503
Prepared by:
Michael K. Evans
Evans, Carroll & Associates, Inc.
2785 NW 26th St.
Boca Raton, FL 33434
561-470-9035
April 14, 2013
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Table of Contents
1. Executive Summary 3
2. Tabulation and Summary of Principal Results 4
3. Introduction and Scope of Work 7
4. Brief Discussion of RIMS II Model and its Multipliers 8
5. Methodology for Calculating Indirect Jobs 11
6. Key Economic and Demographic Statistics for King County Group 16
7. Location of the Hotel, and Maps of the Area 23
8. Economic Impact of Construction of Airport Hotel 28
9. Economic Impact of Hotel Operations 37
10. Economic Impact of the Entire Project 43
Appendix: Resume of Dr. Michael K. Evans 45 3
1. Executive Summary
● Access the USA, LLC, dba Washington Regional Center, plans to finance the construction and operation of a Hyatt hotel located at 19518 International Blvd at the SeaTac International Airport, which will contain 152 units.
● This report contains the economic impact analysis for the construction and operation of this hotel. The economic impact projections for these facilities are based on the final demand multipliers from the RIMS II input/output model for the three major urban counties in Washington, which are King, Pierce, and Snohomish counties.
● Figures supplied by the developer (Table 8.1 below) indicate a total development budget of about $25 million. Of that amount, the eligible hard costs would be $14.89 million and eligible soft costs would be $0.89 million. Purchases of furniture, fixtures, and equipment, communications equipment, and supplies (FF&E) are expected to be $2.537 million; only indirect and induced jobs are counted for the latter. These figures do not have to be deflated to 2007 dollars because construction costs have declined since then, so they can be entered directly into the input/output model. The RIMS II final demand multiplier for construction for this three-county area is 17.3388, so hard construction costs would create a total of 258 permanent new jobs in this three-county area. The multiplier for architectural and engineering services is 16.0314, so expenditures for EB-5 eligible soft costs would add another 13 jobs. The multiplier for FF&E excluding direct jobs is 7.30, so those expenditures would add another 19 jobs, for a total of 290 jobs for all facets of construction. ● The airport hotel is expected to take 26 months, of which 6 months is for demolition, 18 months for primary construction, and 2 months for finishes. Hence direct as well as indirect jobs for construction and soft costs can be included except for purchases of FF&E. ● The developer has also indicated that total gross revenues in the first full year of operation of the hotel would be $5.223 million in 2015 dollars, or $4.836 million in 2007 dollars. Since the final demand multiplier is 18.86, that would mean about 91 new jobs would be created from hotel operations. ● Summing all these components, a total of 381 permanent new jobs would be created from the construction and operation of the Hyatt Sea-Tac Airport hotel.
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2. Tabulation and Summary of Principal Results The summary statistics in Tables A represent the total increase in revenue and employment that are expected to occur due to the construction and operation of the Hyatt Hotel at the SeaTac airport. These figures are based on the three-county RIMS II multipliers for the King county group. All jobs shown in Table A are permanent new jobs.
Table A. Summary of Revenue and Employment Effects for Hyatt Hotel at SeaTac Airport
Activity Revenue RIMS II Total $ millions Multiplier Jobs
Hard Construction Costs 14.891 17.3387 258.2 Architecture & Engineering 0.849 16.0314 13.6 Purchases of FF&E * 2.537 7.3001 18.5 Hotel operations 4.84 18.8612 91.3
Total Project 23.12 381.6
* Indirect and Induced jobs only All figures calculated from unrounded numbers
Table B-1 shows the 4-digit NAICS codes used in this report and the definitions from the NAICS code manual. Table B-2 contains print screen images of the exact multipliers used in this study.
Table B-1. NAICS Codes and Definitions Used in the Model
2362 Nonresidential Building Construction 4232 Furniture and Home Furnishing Merchant Wholesalers 4234 Professional and Commercial Equipment and Supplies Merchant Wholesalers 4236 Household Appliances and Electrical and Electronic Goods Merchant Wholesalers 5413 Architectural, Engineering & Related Services 7211 Traveler Accommodations
The print screen multipliers for this industry are shown below. Please note that for FF&E, the wholesale trade multiplier is used but only indirect and induced jobs are included, so the multiplier is calculated as 12.5811 * 1.3824/2.3824.
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Table B-2. Print Screen of Actual Multipliers Used for 3-County Region (1) (2) (3) (4) (5) (6) 230000 Construction 2.1886 0.7024 17.3388 1.1932 1.9247 2.1375 420000 Wholesale trade 1.9085 0.5843 12.5811 1.2335 1.8510 2.3824 541300 Architectural, engineering, and related services 2.1003 0.7002 16.0314 1.3005 1.8991 2.4538 7211A0 Hotels and motels, including casino hotels 1.9266 0.5692 18.8611 1.2114 1.9220 1.6682
The economic impact generated by these construction activities as measured by household earnings, demand for business services, utilities, maintenance and repair, and new supplier and vendor relationships is summarized in Table C.
Table C. Summary Measures of Economic Impact For Hotel Construction and Operation
All figures are in thousands of dollars Household Income from: Hard Construction Costs $10,458 Architecture & Engineering $594 Purchases of FF&E * $682 Hotel operations $2,754
Total these 6 categories $14,489
Demand (output) created by project Utility services $342 Maintenance and Repair Construction $224 Supplier/vendor links for manufacturing $3,403 Demand for professional and business services $5,139
Total these 4 categories $9,108
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Household Earnings (Labor Income)
The jobs created by the construction of this hotel will subsequently create new sources of household income. The household income for the jobs created by construction and operation of the hotel is about $14.5 million.
The details used to calculate these figures are given later in this report. Separate tables are provided for the total number of jobs created, the average earnings per new worker, and the total increase in earnings for construction for each phase of the construction projects. The RIMS II input/output model has been used to calculate the number of jobs in each major industrial classification, the average earnings per employee, and hence total earnings. The number of jobs by industrial classification is based on calculations imbedded in the RIMS II model for each of the activities as summarized in Table A and documented in detail throughout this report.
Demand for Business Services, Utilities, Maintenance and Construction, and New Supplier/Vendor Relationships Created with Manufacturers
The total economic impact of the regional center from construction and operation of this hotel would create approximately $9.1 million in additional economic activity across the region. These supplier purchases are calculated from the indirect increase in output generated by the RIMS II model. It should be noted that some of these supplier industries might potentially locate within the regional center, and their economic output is included in this total.
The estimate of supplier purchases is based on the commodity data in the RIMS II input-output model. This data specifies the amount and type of commodity input needed to maintain specific types of business operations. The model estimates the supplier purchases based on the types of jobs and number of jobs that will be created within the regional center. In addition, the model allocates the supplier purchases to businesses within the region, based on trade flow data from the U.S. Bureau of Economic Analysis.
The output figure for utilities represents the amount spent on electric power, natural gas, and water and sewer expenditures. These are based on the input/output coefficients showing how much the output of utilities would increase for each million dollars of final demand for each type of project. The economic impact for utility services totals about $0.34 million.
The RIMS II input/output model has only one category for construction expenditures. The figure shown in this category represents ongoing maintenance on a permanent basis; it does not include the original construction costs. The project would create an economic impact of about $0.22 million within these sectors in the region. Because this is a new building, the economic impact for maintenance and repair services is minimal for the first year of operation. 7
For the manufacturing sector, the increase in output depends on several factors. The amount of manufactured goods purchased for construction activity is based on the amount of locally produced and sold products and materials used in the project. For this project, new supplier/vendor relationships with manufacturers would create an economic impact of about $3.4 million, which represents the production of local parts and materials used in construction.
The figures for the output from business services are calculated from the rows in the RIMS II input/output table for professional and scientific services, management of companies, and administrative and waste management services. In general, the demand for professional and scientific services reflects the relative importance of these categories for each of the various economic activities. For the construction sector, for example, most of the jobs in this sector represent architects and engineers. The impact of this activity totals about $5.1 million, which represents the hiring of architects, engineers, and other professional services for the construction of the hotel.
The figures given in Table C represent only a brief summary of the detailed calculations that have been undertaken and are reported in tabular format throughout the report. The figure for utility output, for example, represents the sum of utility output for each of the categories of economic activity listed in Table A. For repair and maintenance construction office, this figure represents the amount spent times he input/output coefficient showing the total amount of output per $1 million of construction expenditures. The same methodology applies to all the other figures given in Table C. Detailed figures may be found in the tables in Sections (8) and (9), which provides estimates of direct, indirect and induced employment, output, and household earnings jobs by industry category.
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3. Introduction and Scope of Work
Access the USA, LLC, dba Washington Regional Center plans to finance the construction and operation of a Hyatt Hotel at the SeaTac airport. This report contains the economic impact analysis of this building, based on the final demand multipliers from the RIMS II input/output model for King, Pierce, and Snohomish counties. Section (4) contains a brief discussion of the RIMS II model and its final demand and employment multipliers, and Section (5) contains a more detailed description of how the indirect jobs are calculated in regional input/output models. Section (6) presents summary economic statistics for the three counties in Washington used for the multiplier calculations, and compares them to the statistics for the overall U. S. economy. Tables are presented for employment by occupation and industry, income distribution by deciles, mean and median household and family income, and poverty rates. Tables are also presented for labor force, unemployment, level and growth of population, and level and growth of personal income for the State of Washington; King, Pierce, and Snohomish counties; and the sum of those three counties. Section (7) describes the location of the hotel; it also shows the location of King, Pierce, and Snohomish, counties, and explains why these three counties are included in the multiplier analysis.
Section (8) contains the economic impact tables for the construction of the airport hotel. Separate sets of tables are presented for EB-5 eligible hard costs, EB-5 eligible soft costs, and purchases of FF&E; the latter category includes only indirect and induced jobs. Figures are calculated and presented for the increase in employment, output, and earnings, and the output and earnings per new worker, for the 20 major industrial classifications contained in the RIMS II model. Section (9) presents similar tables for the hotel operation. Section (10) presents the summary statistics for the entire project.
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4. Discussion of RIMS II Final Demand Methodology
The following material has been condensed from the RIMS II User Handbook.
Introduction and General Comments
Effective planning for public- and private-sector projects and programs at the State and local levels requires a systematic analysis of the economic impacts of these projects and programs on affected regions. In turn, systematic analysis of economic impacts must account for the inter-industry relationships within regions because these relationships largely determine how regional economies are likely to respond to project and program changes. Thus, regional input-output (I-O) multipliers, which account for inter-industry relationships within regions, are useful tools for conducting regional economic impact analysis.
In the 1970s, the Bureau of Economic Analysis (BEA) developed a method for estimating regional I-O multipliers known as RIMS (Regional Industrial Multiplier System), which was based on the work of Garnick and Drake. In the 1980s, BEA completed an enhancement of RIMS, known as RIMS II (Regional Input-Output Modeling System), and published a handbook for RIMS II users. In 1992, BEA published a second edition of the handbook in which the multipliers were based on more recent data and improved methodology. In 1997, BEA published a third edition of the handbook that provides more detail on the use of the multipliers and the data sources and methods for estimating them.
RIMS II is based on an accounting framework called an I-O table. For each industry, an I-O table shows the industrial distribution of inputs purchased and outputs sold. A typical I-O table in RIMS II is derived mainly from two data sources: BEA's national I-O table, which shows the input and output structure of nearly 500 U.S. industries, and BEA's regional economic accounts, which are used to adjust the national I-O table to show a region's industrial structure and trading patterns.
Using RIMS II for impact analysis has several advantages. RIMS II multipliers can be estimated for any region composed of one or more counties and for any industry, or group of industries, in the national I-O table. The accessibility of the main data sources for RIMS II keeps the cost of estimating regional multipliers relatively low. Empirical tests show that estimates based on relatively expensive surveys and RIMS II- based estimates are similar in magnitude.
BEA's RIMS multipliers can be a cost-effective way for analysts to estimate the economic impacts of changes in a regional economy. However, it is important to keep in mind that, like all economic impact models, RIMS provides approximate order-of- magnitude estimates of impacts. RIMS multipliers are best suited for estimating the impacts of small changes on a regional economy. For some applications, users may want to supplement RIMS estimates with information they gather from the region undergoing the potential change. To use the multipliers for impact analysis effectively, 10 users must provide geographically and industrially detailed information on the initial changes in output, earnings, or employment that are associated with the project or program under study. The multipliers can then be used to estimate the total impact of the project or program on regional output, earnings, and employment.
RIMS II is widely used in both the public and private sector. In the public sector, for example, the Department of Defense uses RIMS II to estimate the regional impacts of military base closings. State transportation departments use RIMS II to estimate the regional impacts of airport construction and expansion. In the private-sector, analysts and consultants use RIMS II to estimate the regional impacts of a variety of projects, such as the development of shopping malls and sports stadiums.
RIMS II Methodology
RIMS II uses BEA's benchmark and annual I-O tables for the nation. Since a particular region may not contain all the industries found at the national level, some direct input requirements cannot be supplied by that region's industries. Input requirements that are not produced in a study region are identified using BEA's regional economic accounts.
The RIMS II method for estimating regional I-O multipliers can be viewed as a three-step process. In the first step, the producer portion of the national I-O table is made region-specific by using six-digit NAICS location quotients (LQs). The LQs estimate the extent to which input requirements are supplied by firms within the region. RIMS II uses LQs based on two types of data: BEA's personal income data (by place of residence) are used to calculate LQs in the service industries; and BEA's wage-and- salary data (by place of work) are used to calculate LQs in the non-service industries.
In the second step, the household row and the household column from the national I-O table are made region-specific. The household row coefficients, which are derived from the value-added row of the national I-O table, are adjusted to reflect regional earnings leakages resulting from individuals working in the region but residing outside the region. The household column coefficients, which are based on the personal consumption expenditure column of the national I-O table, are adjusted to account for regional consumption leakages stemming from personal taxes and savings. In the last step, the Leontief inversion approach is used to estimate multipliers. This inversion approach produces output, earnings, and employment multipliers, which can be used to trace the impacts of changes in final demand on and indirectly affected industries.
Advantages of RIMS II
There are numerous advantages to using RIMS II. First, the accessibility of the main data sources makes it possible to estimate regional multipliers without conducting relatively expensive surveys. Second, the level of industrial detail used in RIMS II helps avoid aggregation errors, which often occur when industries are combined. Third, RIMS II multipliers can be compared across areas because they are based on a consistent set 11 of estimating procedures nationwide. Fourth, RIMS II multipliers are updated to reflect the most recent local-area wage-and-salary and personal income data.
Overview of Different Multipliers
RIMS II provides users with five types of multipliers: final demand multipliers for output, for earnings, and for employment; and direct-effect multipliers for earnings and for employment. These multipliers measure the economic impact of a change in final demand, in earnings, or in employment on a region’s economy.
The final demand multipliers for output are the basic multipliers from which all other RIMS II multipliers are derived. In this table, each column entry indicates the change in output in each row industry that results from a $1 change in final demand in the column industry. The impact on each row industry is calculated by multiplying the final demand change in the column industry by the multiplier for each row. The total impact on regional output is calculated by multiplying the final demand change in the column industry by the sum of all the multipliers for each row except the household row.
RIMS II provides two types of multipliers for estimating the impacts of changes on earnings: final demand multipliers and direct effect multipliers. These multipliers are derived from the table of final demand output multipliers.
The final demand multipliers for earnings can be used if data on final demand changes are available. In the final demand earnings multiplier table, each column entry indicates the change in earnings in each row industry that results from a $1 change in final demand in the column industry. The impact on each row industry is calculated by multiplying the final demand change in the column industry by the multipliers for each row. The total impact on regional earnings is calculated by multiplying the final demand change in the column industry by the sum of the multipliers for each row.
Employment Multipliers
RIMS II provides two types of multipliers for estimating the impacts of changes on employment: final demand multipliers and direct effect multipliers. These multipliers are derived from the table of final demand output multipliers.
The final demand multipliers for employment can be used if the data on final demand changes are available. In the final demand employment multiplier table, each column entry indicates the change in employment in each row industry that results from a $1 million change in final demand in the column industry. The impact on each row industry is calculated by multiplying the final demand change in the column industry by the multiplier for each row. The total impact on regional employment is calculated by multiplying the final demand change in the column industry by the sum of the multipliers for each row. 12
The direct effect multipliers for employment can be used if the data on the initial changes in employment by industry are available. In the direct effect employment multiplier table, each entry indicates the total change in employment in the region that results from a change of one job in the row industry. The total impact on regional employment is calculated by multiplying the initial change in employment in the row industry by the multiplier for the row.
Choosing a Multiplier
The choice of multiplier for estimating the impact of a project on output, earnings, and employment depends on the availability of estimates of the initial changes in final demand, earnings, and employment. If the estimates of the initial changes in all three measures are available, the RIMS II user can select any of the RIMS II multipliers. In theory, all the impact estimates should be consistent. If the available estimates are limited to initial changes in final demand, the user can select a final demand multiplier for impact estimation. If the available estimates are limited to initial changes in earnings or employment, the user can select a direct effect multiplier.
5. Methodology for Calculating Indirect Job Gains
In spite of the explanation of the RIMS II model given directly above, some USCIS adjudicators have asked for further clarification about how that model is used to determine the increase in the number of indirect jobs. That is an important issue because, unlike the direct job count, which can be verified by USCIS from various payroll and withholding documents, the calculation of indirect jobs cannot be verified directly but depends on mathematical calculations.
The general concept is based on the coefficients in the input/output model itself (the same methodology applies to RIMS II, IMPLAN, or any other generally recognized and accepted input/output model). In any given year, the government calculates how much input is used for a given production of output. The detailed figures are taken from the Economic Censuses taken once every five years; the figures are then updated from various annual supplements.
Basically the process has two steps, each of which is described next in greater detail. The first is to determine the amount of output, and hence the number of jobs, required to produce a given amount (say $1 million) of the final product or service. These are national coefficients. The second is to determine what proportion of those goods and services are purchased within the local region (the regional purchase coefficients, or RPCs).
In the case of a manufacturing process, the national coefficients are based on production functions: how much coke per ton of steel, how much steel per motor vehicle, how much flour for a loaf of bread, and so on. However, most of the jobs are created in the service sector, where Commerce Department data are used to determine, for example, how much restaurants spend on laundry services, how much airlines 13 spend for attorneys, and so on. These figures are based on information contained in the various Economic Censuses. The national coefficients would also determine, for example, how many architects and engineers would be hired for a construction project of a given scope and size, and how many new employees at financial institutions would be required to handle the additional cash flow generated by the new business. Both of these are discussed below in greater detail.
Even after these coefficients are determined, however, the regional purchase coefficients (RPC) must still be estimated. If, for example, a trucking firm spends 1% of its revenue on accountants, how much of that money is spent on local firms, and how much is spent outside the region?
That answer depends on various factors. The most important is the amount of the good or service produced within the region. If a trucking firm, for example, were located in a small county with no accountants, obviously it would not spend any of that money locally. That sets a lower limit but is not generally the case. Instead, a balancing algorithm is used.
Suppose, for example, that all the firms producing, distributing, or selling goods and services in a given county spent $10 million on accounting services. Also, suppose that total billings of all accountants in the county were $20 million. In that case, local accountants could handle all the local business, plus business from neighboring counties. If, on the other hand, total accountant billings in the county were only $5 million, local firms could not spend more than half of the money on local accountants.
Of course it is possible that there are adequate resources in the county but local firms choose to use companies outside the county; perhaps prices or service is better. No input/output model can account for such anomalies. On the other hand, given transportation costs, it would be highly unusual for a firm to be located in a given location and not serve the nearby businesses, instead choosing only those clients who were farther away.
The RIMS II model – and other regional input/output models – assigns regional purchase coefficients (RPCs) in all cases where the local industry purchases goods and services from local firms. This matrix could have as many as 406 * 406 = 164,836 elements, although in practice many of them are zero. Large counties with a wide variety of businesses have more non-zero elements than small counties with relatively few businesses.
In general, the RPCs tend to be close to zero for most manufactured goods, and close to unity for most services. While there are many exceptions to this rule, most firms will use financial, professional, business, and health care services that are located in that county or contiguous areas.
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To take just one example of many, consider the number of new jobs created by architects and engineers for a new construction project of any given size. Most construction cost manuals, such as those published by R. S. Means, indicate that those costs are usually about 5% to 9% of the total job. According to the national IMPLAN file, the figures are 9.2% for commercial construction and 4.5% for industrial construction.
These figures can be compared with the proportions of architects and engineers in the specific regional area, based on the RIMS II data that are used to determine the economic multipliers in the specific group. For this three-county group, the RIMS II model shows proportions of 8.4% for commercial and 4.3% for industrial construction, indicating that 91% of the architects and engineers for commercial jobs and 95% for industrial jobs are hired locally. These figures are fairly typical of other locations and regions; except for “signature” buildings designed by famous names, most architects and engineers live in the same region as the buildings that are being constructed.
To summarize to this point, the number of indirect jobs as a proportion of direct jobs depends on (a) the national relationships, and (b) the regional purchase coefficients. In our presentation for the businesses in this report, we provide further discussion of those industries with the largest number of indirect jobs. However, there are a few industries that produce relatively large numbers of jobs in almost all cases, and these can be generally discussed at this stage in order to avoid repeating this information several times. The industries discussed here include banking, real estate, legal and accounting, architects and engineers, other professional services, employment services, other business services, restaurants, and government. In all of these cases, the vast majority of workers are hired locally. Our comments for the rest of this section are based on the assumption of a $10 million investment; the results are linear.
Banking and credit: On an aggregate basis, for every $10 million in deposits, very broadly defined (M3), there is about 1 new banking employee. As a rough rule of thumb, the size of M3 is roughly equal to the size of GDP. Hence we would expect about 1 new banking employee for every $10 million increase in output, as calculated from the RIMS II model.
Real estate: Additional real estate employees are based on two factors. One is the leasing activity of the new building, and the other is the increase in residential real estate activity as people get new jobs, either within the area or by moving into the area. On a lease basis, a $10 million investment is likely to result in a building of 80,000 square feet. If it leases for $40/square foot, that would be $3.2 million in annual lease payments, and with a 6% commission would generate $192,000 in revenues, which would account for about 2 new real estate employees (the figure would be less for industrial buildings). The increase in employment would also result in some real estate activity as workers moved into better housing in the same location, or moved in from other areas. In a normal year, there are about 7 million sales of new and existing homes for a labor force of about 140 million, or 5%. Hence if the total increase in 15 employment were 200, that would imply 10 real estate transactions; if they average $200,000 at a 6% commission, that would be $12,000 per home or a total of $120,000, which would support approximately 3 new real estate jobs.
Legal & Accounting: Each of these accounts for about 1% of total employment; so if there were a total increase of 200 jobs, we would expect an average of 4 new employees in this classification.
Architects & Engineers: almost all of these jobs stem from the new construction activity. This category has already been discussed above; for a $10 million construction project, which would create about 80 new construction jobs, we would expect about 7 new jobs in architects and engineers for a commercial project and 3 to 4 new jobs for an industrial project.
Other professional services: This category includes employees in consulting, scientific research and development, advertising, and management, as well as several other smaller, specialized categories. In general, consulting, management, and the all other category each account for about 1% of total employment, and R&D and advertising account for about ½% of total employment, for a total of about 4% of total employment. This figure will vary widely depending on the degree to which consultants and R&D are used by the new business.
Employment services: On a national average basis, 1 out of every 45 people is employed by this industry. Here again, the figures will vary widely depending on (a) the proportion of people who are hired through employment agencies, and (b) the proportion of the work that is outsourced to employment services. Business support services include office management, travel arrangement, security, credit bureaus, telemarketing, and back-office jobs that are outsourced, such as direct mail, copying, and duplicating services. The back-office services would vary widely depending on the type of new business; retail stores, for example, would print and distribute more advertising brochures than a manufacturing operation. On a national average basis, these jobs account for about 2% of total employment.
Building support services, which includes janitorial services, lawn maintenance, and waste management. For an office building of 80,000 square feet, the cost would be approximately $2/sq ft per year for maintenance, or $160,000, which would support about 4 new jobs; here again, the figure would be lower for industrial buildings.
Restaurants: This category reflects business meals. Of course the number of business meals depends greatly on the type of business; lawyers, accountants, and consultants will have more business meals than manufacturing plants or water treatment facilities. On a national average basis, Commerce Department figures show that total restaurant sales in 2007 were $580 billion, while consumer expenditures at restaurants were $500 billion. However, that figure also includes tips, which are not included in restaurant sales. After subtracting 15% for tips, that indicates about $425 billion in food and beverage purchases by consumers, indicating about $155 billion for 16 business expenses. With a labor force of approximately 140 million, that is equivalent to about $1,100 per employee. Hence if 200 new jobs were created, business meal expenses would rise an average of $221,000, which would imply about 4.5 new indirect jobs in the restaurant industry. These figures are likely to be somewhat higher when direct jobs are created for office buildings and hotels.
Government: The increase in public sector employees represents the amount funded by increased real estate taxes. For a construction project with $10 million in hard costs, the total value is likely to be between $15 and $20 million when one includes furniture, fixtures, equipment, and land values. Using a national average property tax rate of 1%, that would raise $150,000 to $200,000, which would create 3 to 4 new jobs in the public sector.
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6. Key Economic and Demographic Statistics for King County Group
This section of the report presents and analyzes the key economic and demographic statistics for the three-county region used to calculate the multipliers for the Dexter Station project to be located in Seattle, WA, which is in King County. Table 6-1 shows employment by occupation and industry, income distribution by deciles, mean and median levels of household and family income, and poverty rates. All these figures are compared to U. S. averages. Table 6-2 shows labor market statistics for the State of Washington, each of the 3 counties, and the county group totals for each year from 2000 through 2010. Table 6-3 shows the level and growth rate of population for the same areas, and Table 6-4 presents figures for the level and growth rate of personal income for these areas.
Table 6-1. Key Economic and Demographic Statistics for King, Pierce, and Snohomish Counties, and Comparison to the U.S.
Category Snoho % King % Pierce % EMPLOYMENT STATUS mish Population 16 years and over 543,302 100.0% 1,552,600 100.0% 624,555 100.0% In labor force 380,201 70.0% 1,103,111 71.0% 417,959 66.9% Civilian labor force 376,365 69.3% 1,100,522 70.9% 394,189 63.1% Employed 343,323 63.2% 1,013,279 65.3% 352,932 56.5% Unemployed 33,042 6.1% 87,243 5.6% 41,257 6.6% Armed Forces 3,836 0.7% 2,589 0.2% 23,770 3.8% Not in labor force 163,101 30.0% 449,489 29.0% 206,596 33.1%
OCCUPATION Civilian employed population 16 + 343,323 100.0% 1,013,279 100.0% 352,932 100.0% Management & professional 124,078 36.1% 488,011 48.2% 115,503 32.7% Service occupations 54,622 15.9% 150,879 14.9% 65,494 18.6% Sales and office occupations 90,163 26.3% 223,639 22.1% 93,201 26.4% Farming, fishing, & forestry 1,580 0.5% 2,483 0.2% 909 0.3% Construction, maintenance, repair 37,238 10.8% 58,502 5.8% 33,428 9.5% Production & transportation 35,642 10.4% 89,765 8.9% 44,397 12.6%
INDUSTRY Civilian employed population 16 + 343,323 100.0% 1,013,279 100.0% 352,932 100.0% Agriculture & mining 3,312 1.0% 5,698 0.6% 2,459 0.7% Construction 31,535 9.2% 54,969 5.4% 25,463 7.2% Manufacturing 53,790 15.7% 113,742 11.2% 35,119 10.0% Wholesale trade 8,790 2.6% 33,351 3.3% 13,076 3.7% Retail trade 42,857 12.5% 103,352 10.2% 40,195 11.4% Transportation & utilities 17,392 5.1% 50,625 5.0% 22,358 6.3% 18
Information 9,058 2.6% 39,632 3.9% 4,536 1.3% Finance, insurance & real estate 24,305 7.1% 66,119 6.5% 24,304 6.9% Professional & administrative 35,619 10.4% 172,197 17.0% 35,283 10.0% Educational services & health care 59,571 17.4% 199,699 19.7% 84,076 23.8% Arts, entertain, hotel, food svcs 26,716 7.8% 93,934 9.3% 29,603 8.4% Other private services 14,905 4.3% 46,112 4.6% 16,209 4.6% Public administration 15,473 4.5% 33,849 3.3% 20,251 5.7%
INCOME AND BENEFITS Total households 263,539 100.0% 783,696 100.0% 297,952 100.0% Less than $10,000 13,337 5.1% 42,802 5.5% 17,125 5.7% $10,000 to $14,999 9,072 3.4% 28,452 3.6% 12,054 4.0% $15,000 to $24,999 18,208 6.9% 59,154 7.5% 29,023 9.7% $25,000 to $34,999 20,938 7.9% 61,024 7.8% 28,727 9.6% $35,000 to $49,999 36,120 13.7% 96,969 12.4% 45,289 15.2% $50,000 to $74,999 54,962 20.9% 139,521 17.8% 60,069 20.2% $75,000 to $99,999 40,919 15.5% 112,582 14.4% 42,152 14.1% $100,000 to $149,999 46,111 17.5% 130,314 16.6% 43,668 14.7% $150,000 to $199,999 15,881 6.0% 57,560 7.3% 12,598 4.2% $200,000 or more 7,991 3.0% 55,318 7.1% 7,247 2.4% Median household income (dollars) 64,658 128.7% 67,806 135.0% 55,980 111.5% Mean household income (dollars) 77,659 112.7% 89,343 129.6% 69,811 101.3%
Families 179,173 100.0% 454,248 100.0% 202,765 100.0% Less than $10,000 5,781 3.2% 11,543 2.5% 8,804 4.3% $10,000 to $14,999 3,781 2.1% 10,597 2.3% 5,693 2.8% $15,000 to $24,999 10,161 5.7% 22,220 4.9% 13,868 6.8% $25,000 to $34,999 11,552 6.4% 25,061 5.5% 16,734 8.3% $35,000 to $49,999 22,117 12.3% 44,881 9.9% 27,552 13.6% $50,000 to $74,999 35,822 20.0% 78,871 17.4% 40,182 19.8% $75,000 to $99,999 29,493 16.5% 74,452 16.4% 34,675 17.1% $100,000 to $149,999 40,390 22.5% 95,594 21.0% 37,628 18.6% $150,000 to $199,999 12,896 7.2% 46,147 10.2% 10,886 5.4% $200,000 or more 7,180 4.0% 44,882 9.9% 6,743 3.3% Median family income (dollars) 75,260 123.2% 86,064 140.9% 66,173 108.3% Mean family income (dollars) 87,934 109.7% 108,184 135.0% 80,312 100.2% Per capita income (dollars) 30,046 113.8% 37,403 141.6% 26,578 100.6%
Median earnings for workers 36,083 127.2% 37,964 133.8% 31,541 111.2% Median earnings for male full-time 56,453 124.1% 62,061 136.4% 48,264 106.1% Median earnings for female full-time 40,994 115.3% 45,417 127.8% 37,058 104.2%
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PERCENTAGE BELOW POVERTY LEVEL All families 6.8% 64.8% 5.8% 55.2% 8.5% 81.0% All people 9.8% 68.5% 9.7% 67.8% 12.3% 86.0%
Note: in these and similar succeeding tables, the percentage figures in black are proportions of the total in that category, while the percentage figures in red are relative to the U.S. figures.
These three counties represent the urban heart of Washington state, with Seattle in King County, Tacoma in Pierce County, and Everett in Snohomish County. All three counties have incomes that are well above average, and poverty rates that are well below average.
The economy of Snohomish County is dominated by Boeing, with 15.7% of the workforce employed in the manufacturing sector, compared to 10.5% nationally. Indeed, the manufacturing sector in the other two counties is also quite strong, at 11.2% for King and 10.0% for Pierce County. The proportions of workers in information, financial, and professional services in all three counties are close to the national average, with the single exception of professional services in King County, where the proportion is 17.0% compared to 10.6% nationally. The very large manufacturing workforce in Snohomish County is offset by a relatively small 17.4% in education and healthcare services, compared to 22.7% nationally. Snohomish County has 9.2% of the workforce in construction, compared to 6.8%, while that figure is only 5.4% for King County. The proportions for other sectors are close to the national averages.
All three counties have fewer than proportional households and families at the lower end of the income scale, and more at the upper end, but the proportion of high- income households and families is greatest in King County, pushing the median household and family income to 135% and 141% of the national average. The figures for Snohomish County are 129% and 123% respectively, and for Pierce County they fall off to 111% and 108%. The poverty rates show the same pattern in reverse; the rate for all families is lowest in King County, at 55% of the national average, followed by 65% for Snohomish County and 81% for Pierce County.
Table 6-2. Labor Market Statistics for State of Washington and 3 Counties
Labor Force Employed Unemployed Un Rate, %
Washington 2002 3,104,698 2,877,022 227,676 7.3 2003 3,146,154 2,913,230 232,924 7.4 2004 3,199,234 2,999,526 199,708 6.2 2005 3,255,527 3,075,972 179,555 5.5 2006 3,319,252 3,155,384 163,868 4.9 2007 3,386,775 3,232,652 154,123 4.6 20
2008 3,472,127 3,283,923 188,204 5.4 2009 3,522,803 3,193,293 329,510 9.4 2010 3,516,463 3,167,398 349,065 9.9 2011 3,484,814 3,165,348 319,466 9.2
King 2002 985,580 925,357 60,223 6.1 2003 989,564 928,428 61,136 6.2 2004 997,525 946,111 51,414 5.2 2005 1,012,944 965,308 47,636 4.7 2006 1,049,968 1,005,994 43,974 4.2 2007 1,068,967 1,030,025 38,942 3.6 2008 1,091,714 1,043,295 48,419 4.4 2009 1,115,973 1,020,085 95,888 8.6 2010 1,107,058 1,006,000 101,058 9.1 2011 1,105,546 1,015,967 89,579 8.1
Pierce 2002 347,203 319,255 27,948 8.0 2003 351,883 323,069 28,814 8.2 2004 359,953 334,820 25,133 7.0 2005 367,263 345,778 21,485 5.9 2006 371,218 352,360 18,858 5.1 2007 383,842 365,505 18,337 4.8 2008 393,078 370,570 22,508 5.7 2009 394,684 356,549 38,135 9.7 2010 392,441 352,258 40,183 10.2 2011 387,244 349,308 37,936 9.8
Snohomish 2002 333,169 309,729 23,440 7.0 2003 335,909 312,001 23,908 7.1 2004 339,050 319,436 19,614 5.8 2005 346,762 328,926 17,836 5.1 2006 360,391 343,723 16,668 4.6 2007 364,040 349,392 14,648 4.0 2008 373,782 354,331 19,451 5.2 2009 383,155 344,464 38,691 10.1 2010 387,139 346,210 40,929 10.6 2011 385,373 349,640 35,733 9.3
3 counties 2002 1,665,952 1,554,341 111,611 6.7 21
2003 1,677,356 1,563,498 113,858 6.8 2004 1,696,528 1,600,367 96,161 5.7 2005 1,726,969 1,640,012 86,957 5.0 2006 1,781,577 1,702,077 79,500 4.5 2007 1,816,849 1,744,922 71,927 4.0 2008 1,858,574 1,768,196 90,378 4.9 2009 1,893,812 1,721,098 172,714 9.1 2010 1,886,638 1,704,468 182,170 9.7 2011 1,878,163 1,714,915 163,248 8.7
This three-county area was not quite as hard hit by the recession as the overall state of Washington or the national economy, due in large part to a continued strong market for high-tech companies such as Microsoft and Amazon. Hence the unemployment rate in 2010 for King County at 9.1% was well below the national average rate of 9.6%, and the decline in 2011 to 8.1% was similarly well below the 8.9% national average. The rate for Pierce County in 2011 was 9.8%, with a 9.3% rate in Snohomish County. The 3-county average rate in 2010 was 8.7%, slightly below the national average, with more than 163,000 unemployed people in the region.
Table 6-3. Level and Growth Rate of Population for Washington State and 3 Counties
Washington King Pierce Snohomish 3 counties 2010 6,742,950 1,937,157 795,371 715,358 3,447,886 2009 6,667,426 1,912,012 796,483 706,302 3,414,797 2008 6,562,231 1,875,020 785,400 694,622 3,355,042 2007 6,461,587 1,847,986 772,484 683,997 3,304,467 2006 6,370,753 1,822,967 763,408 670,706 3,257,081 2005 6,257,305 1,795,268 748,148 654,849 3,198,265 2004 6,178,645 1,775,297 740,137 643,533 3,158,967 2003 6,104,115 1,763,440 733,969 635,612 3,133,021 2002 6,052,349 1,758,685 728,091 631,526 3,118,302 2001 5,985,722 1,754,090 716,447 622,390 3,092,927 2000 5,910,512 1,739,009 703,993 609,185 3,052,187
2010/09 1.13% 1.32% -0.14% 1.28% 0.97% 2009/08 1.60% 1.97% 1.41% 1.68% 1.78% 2008/07 1.56% 1.46% 1.67% 1.55% 1.53% 2007/06 1.43% 1.37% 1.19% 1.98% 1.45% 2006/05 1.81% 1.54% 2.04% 2.42% 1.84% 2005/04 1.27% 1.12% 1.08% 1.76% 1.24% 2004/03 1.22% 0.67% 0.84% 1.25% 0.83% 2003/02 0.86% 0.27% 0.81% 0.65% 0.47% 22
2002/01 1.11% 0.26% 1.63% 1.47% 0.82% 2001/00 1.27% 0.87% 1.77% 2.17% 1.33%
2010/00 1.33% 1.08% 1.23% 1.62% 1.23%
The growth in population in the 3-county area did not keep up with Washington State, although it did exceed the national growth rate of 0.93%. Growth in King County over the decade was moderate at 1.1%, although most of the slowdown occurred early in the decade, with the growth rate accelerating starting in 2005. The same pattern occurred in Pierce County. By comparison, growth in Snohomish County peaked in 2006 along with the construction boom and has moderated since then, although it still remains well above average.
Table 6-4. Level and Growth Rate of Personal Income, Billions of Dollars, for Washington State and 3 Counties
Washington King Pierce Snohomish 3 counties 2010 287.17 106.81 32.21 30.32 169.34 2009 278.94 104.24 31.40 29.61 165.24 2008 289.43 109.93 32.20 30.43 172.55 2007 272.62 106.69 30.16 28.33 165.19 2006 252.09 99.61 27.92 25.58 153.10 2005 230.06 89.43 25.58 23.20 138.21 2004 222.42 89.38 23.91 21.63 134.92 2003 206.98 80.13 22.79 20.63 123.55 2002 200.49 78.43 21.93 20.14 120.50 2001 197.32 77.99 21.21 19.83 119.03 2000 191.56 79.03 19.82 18.89 117.75
2010/09 2.95% 2.46% 2.60% 2.43% 2.48% 2009/08 -3.62% -5.18% -2.49% -2.70% -4.24% 2008/07 6.17% 3.03% 6.74% 7.41% 4.46% 2007/06 8.15% 7.11% 8.06% 10.75% 7.89% 2006/05 9.58% 11.38% 9.13% 10.25% 10.77% 2005/04 3.43% 0.06% 7.00% 7.25% 2.44% 2004/03 7.46% 11.55% 4.90% 4.87% 9.21% 2003/02 3.24% 2.16% 3.90% 2.44% 2.53% 2002/01 1.61% 0.56% 3.39% 1.56% 1.23% 2001/00 3.01% -1.31% 7.02% 4.94% 1.09%
2010/00 4.13% 3.06% 4.97% 4.84% 3.70%
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Growth in this 3-county region lagged behind the rest of Washington State, and was precisely the same as the U. S. growth rate throughout the decade, showing the same boom and bust pattern. The sector was hard hit by the 2001 recession, then zoomed during the housing boom, but showed a larger than drop than the national average in personal income in 2009, and a slower recovery in 2010.
Finally, we now turn to the commuting patterns of this region. In determining which counties should be included, there is always a tradeoff here in the following sense. The more contiguous counties that are included – i.e., the larger the overall area – the higher the multiplier is likely to be, and hence the higher the number for indirect and induced job creation. On the other hand, making the area larger than it should be overstates the result and vitiates its usefulness.
The Census publishes data on county-to-county workflow. In most cases, most of the people who work in a given county also live there. The question is how to identify those other counties that provide a significant proportion of the workers, because they will spend part of their paychecks at home, which means those counties should also be included in the multiplier calculations. In general, the multipliers are likely to be the most accurate when they are include those counties whose residents represent 90% to 95% of the county workforce. That is usually be two or three contiguous counties.
According to the Census data for commuting between counties, the total size of the workforce for King County in 2000 was 1,073,735, of which 849,709 actually lived in that county, 80,783 lived in Pierce County, and 103,334 lived in Snohomish County. The three counties we have included account for 96.3% of the workforce, which is slightly higher than the upper bound of 95%; however, to omit Snohomish County would bring the figure down to 86.7%, well below the lower boundary. The actual figures are shown in Table 6-5.
Table 6-5. Residence County of King County Workforce
Total workforce of King County 1,073,735 Number of employees living in: King County 849,709 Pierce County 80,783 Snohomish County 103,334
Total these 3 counties 1,033,826 Percent these 3 counties 96.3%
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7. Description and Location of Airport Hotel, and Maps of Area and Counties in the Regional Center
Figure 7-1. Location of Airport Hotel
25
Figures 7-2, 7-3 and 7-4 show the county maps for King, Snohomish, and Pierce Counties. Figure 7-5 shows the counties of Washington.
Figure 7-2. Map of King County
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Figure 7-3. Map of Snohomish County
Figure 7-4. Map of Pierce County
27
Figure 7-5. County Map of Washington State
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8. Economic Impact of Construction of Airport Hotel
The results in this section show the economic impact of the construction of the airport hotel. Table 8-1 shows the construction and development budget figures for the airport hotel. Separate calculations and sets of tables are prepared for hard costs, soft costs, and purchases of FF&E.
Table 8-1. Construction and Development Budget for Airport Hotel
29
In this table, the hard cost figure is the construction cost line of $14,890,888. EB- 5 eligible soft costs are the development fee, design and professional service fees, and project management, for a total of $849,437. Purchases of FF&E include the F&E line under hard costs, and the items for owner supply items, telecom, comm equipment, PMS/POS system, and supplies under soft costs, for a total of $2,537,550.
The table shown above indicates the cost per square foot calculated in the normal manner (i.e., including items that EB-5 does not count) is $174.33. According to R. S. Means, Square Foot Costs, 34th Annual Edition (2013), the average cost per square foot for low-rise hotels is $181.75, so these figures are clearly in line with industry standards.
Based on these calculations and decisions, these numbers can be entered into the RIMS II model to determine the detailed industry effects. Although the input/output multipliers and coefficients are based on 2007 data, these inputs do not have to be deflated because construction costs have declined since then, according to the Turner construction index, as explained next.
Turner has prepared the construction cost forecast for more than 80 years. Used widely by the construction industry and Federal and State governments, the building costs and price trends tracked by The Turner Building Cost Index may or may not reflect regional conditions in any given quarter. The Cost Index is determined by several factors considered on a nationwide basis, including labor rates and productivity, material prices and the competitive condition of the marketplace. This index does not necessarily conform to other published indices because others do not generally take all of these factors into account. Further information on this index is available at: http://www.turnerconstruction.com/cost-index
30
The Turner construction index was 854 in 2007. Assuming it grows at about the same rate for the next two and a half years as it has in 2011 and the first half of 2012, the index would rise to 838 in 2013 and 851 in 2014, still well below the 2007 value. Hence the figures given above are entered into the RIMS II model.
The next six tables show the detailed industry results for construction activity. The first two tables are for hard costs, the second two for purchases of FF&E, and the last two tables are for EB-5 eligible soft costs.
31
Table 8-3. Increase in Employment, Output, and Earnings for $14.43 Million of Hard Costs for Airport Hotel
Industry Group Employment Output Earnings Agriculture, forestry, fishing, 0.6 121 22 Mining 0.4 95 24 Utilities 0.4 195 40 Construction 121.8 15,021 5,481 Manufacturing 13.2 2,859 581 Wholesale trade 6.0 1,139 360 Retail trade 25.2 1,879 643 Transportation and warehousing 5.8 710 238 Information 3.1 919 195 Finance and insurance 7.0 1,581 429 Real estate and rental and leasing 8.8 2,461 194 Professional and scientific services 13.3 1,739 739 Management of companies 2.0 415 170 Admin and waste mgmt services 10.9 588 253 Educational services 3.2 158 63 Health care and social assistance 12.8 1,167 535 Arts, entertainment, and recreation 3.4 158 60 Accommodation 1.3 118 34 Food services and drinking places 9.9 502 158 Other services 7.7 762 226 Household 1.3 0 13
Total 258.2 32,589 10,458
Table 8-3 shows there will be a total of about 258 total new jobs created from the hard construction of the airport hotel. The increase in output will be about $32.6 million, and the annual increase in household earnings will be about $10.5 million. Table 8-4 shows that output per new worker would be about $126,200, with average annual earnings of about $40,500.
Table 8-4. Output and Earnings Per New Worker for $14.43 Million of Hard Costs for Airport Hotel
Industry Group Employment Output/Empl Earnings/Empl Agriculture, forestry, fishing, 0.6 216.6 40.1 Mining 0.4 233.6 58.4 Utilities 0.4 469.5 96.8 Construction 121.8 123.3 45.0 Manufacturing 13.2 217.2 44.1 32
Wholesale trade 6.0 189.3 59.9 Retail trade 25.2 74.6 25.5 Transportation and warehousing 5.8 121.7 40.8 Information 3.1 294.4 62.5 Finance and insurance 7.0 227.1 61.6 Real estate and rental and leasing 8.8 279.9 22.0 Professional and scientific services 13.3 130.6 55.5 Management of companies 2.0 208.8 85.3 Admin and waste mgmt services 10.9 54.0 23.2 Educational services 3.2 49.3 19.5 Health care and social assistance 12.8 91.0 41.7 Arts, entertainment, and recreation 3.4 46.2 17.4 Accommodation 1.3 88.9 25.9 Food services and drinking places 9.9 50.5 15.9 Other services 7.7 99.6 29.6 Household 1.3 0.0 10.3
Total 258.2 126.2 40.5
The airport hotel is expected to take 26 months, of which 6 months is for demolition, 18 months for primary construction, and 2 months for finishes. The quarterly construction expenditure figures are shown in Table 8-5.
Table 8-5. Construction Draws by Quarter
Quarterly Cumulative Cost Cost
1st qtr $4,186,575 $4,186,575 2nd qtr $1,519,452 $5,706,027 3rd qtr $4,493,886 $10,199,913 4th qtr $2,964,710 $13,164,623 5th qtr $1,505,073 $14,669,696 6th qtr $1,898,073 $16,567,770 7th qtr $2,877,451 $19,445,221 8th qtr $2,622,793 $22,068,013 9th qtr $2,887,290 $24,955,933
For equipment purchases, USCIS recently agreed to count jobs indirectly created outside the geographical boundaries of a Regional Center (RC) in determining whether the RC's business plan complies with EB-5 regulations. The policy change was 33 expressed in a December 3, 2010, letter from USCIS Director Alejandro Mayorkas in response to a letter from Senator Patrick Leahy, Chairman of the Senate Judiciary Committee.
Mayorkas wrote: "USCIS interprets the law to require that a regional center focus its EB-5 capital investment activities on a single, contiguous area within the defined geographic jurisdiction requested by the regional center. Nevertheless, we agree that the law does not further mandate that all indirect job creation attributable to a regional center take place within that jurisdiction. I will, therefore, ensure that USCIS policy reflects this understanding of the law."
In terms of the correct industry code, it is unlikely that the FF&E are produced in the Seattle area. However, they are purchased in this area, so we can use the indirect and induced jobs from wholesale trade for the economic impact calculations.
Table 8-6. Increase in Employment, Output, and Earnings for FF&E Purchases for the SeaTac Airport Hyatt Hotel, Indirect and Induced Effects Only
Industry Group Employment Output Earnings Agriculture, forestry, fishing, 0.0 5 1 Mining 0.0 1 0 Utilities 0.1 28 6 Construction 0.2 20 7 Manufacturing 0.7 162 32 Wholesale trade 0.9 165 52 Retail trade 2.2 167 57 Transportation and warehousing 1.4 147 56 Information 0.6 161 36 Finance and insurance 1.1 244 66 Real estate and rental and leasing 1.5 376 29 Professional and scientific services 1.5 179 81 Management of companies 0.6 132 54 Admin and waste mgmt services 2.2 116 50 Educational services 0.5 23 9 Health care and social assistance 1.8 164 75 Arts, entertainment, and recreation 0.5 25 9 Accommodation 0.2 16 5 Food services and drinking places 1.4 73 23 Other services 1.0 101 30 Household 0.2 0 2
Total 18.5 2,304 682
34
Table 8-6 shows there will be a total of 18.5 jobs indirect and induced jobs created from the purchase of FF&E for the airport hotel. The annual increase in output will be about $2.3 million, and the annual increase in household earnings will be about $0.7 million. Table 8-7 shows the average output per new worker will be about $124,400, while average earnings will be about $36,800.
Table 8-7. Output and Earnings Per New Worker for FF&E Purchases for the SeaTac Airport Hyatt Hotel , Indirect and Induced Effects Only Industry Group Employment Output/Empl Earnings/Empl Agriculture, forestry, fishing, 0.0 148.8 33.1 Mining 0.0 250.0 0.0 Utilities 0.1 468.6 96.2 Construction 0.2 122.8 44.7 Manufacturing 0.7 218.6 43.9 Wholesale trade 0.9 189.4 59.8 Retail trade 2.2 74.5 25.5 Transportation and warehousing 1.4 106.4 40.8 Information 0.6 274.4 61.8 Finance and insurance 1.1 228.8 62.1 Real estate and rental and leasing 1.5 258.1 20.2 Professional and scientific services 1.5 117.9 53.2 Management of companies 0.6 208.6 85.4 Admin and waste mgmt services 2.2 53.8 23.2 Educational services 0.5 49.1 19.6 Health care and social assistance 1.8 90.9 41.6 Arts, entertainment, and recreation 0.5 46.8 17.5 Accommodation 0.2 87.9 25.5 Food services and drinking places 1.4 50.5 16.0 Other services 1.0 101.8 29.8 Household 0.2 0.0 11.0
Total 18.5 124.4 36.8
The final two tables show the economic impact of expenditures for EB-5 eligible soft costs, which include architectural, engineering, and related services that were enumerated earlier in this section.
Table 8-8. Increase in Employment, Output, and Earnings for EB-5 Eligible Soft Costs for the SeaTac Airport Hyatt Hotel
Industry Group Employment Output Earnings Agriculture, forestry, fishing, 0.0 2 0 Mining 0.0 0 0 Utilities 0.0 10 2 35
Construction 0.1 9 3 Manufacturing 0.3 58 11 Wholesale trade 0.2 38 12 Retail trade 0.9 66 23 Transportation and warehousing 0.3 33 12 Information 0.2 62 14 Finance and insurance 0.5 112 30 Real estate and rental and leasing 0.6 150 12 Professional and scientific services 6.6 973 368 Management of companies 0.1 28 11 Admin and waste mgmt services 1.2 63 29 Educational services 0.2 9 4 Health care and social assistance 0.7 66 30 Arts, entertainment, and recreation 0.2 11 4 Accommodation 0.1 11 3 Food services and drinking places 0.8 42 13 Other services 0.4 41 12 Household 0.1 0 1
Total 13.6 1,783 594
Table 8-8 shows there will be a total of 13.6 jobs created from the expenditures for architectural, engineering, and related services used in the construction of the airport hotel. The annual increase in output will be about $1.8 million, and the annual increase in household earnings will be about $0.6 million. Table 8-9 shows the average output per new worker will be about $131,000, while average earnings will be about $43,700.
Table 8-9. Output and Earnings Per New Worker for EB-5 Eligible Soft Costs for the SeaTac Airport Hyatt Hotel Industry Group Employment Output/Empl Earnings/Empl Agriculture, forestry, fishing, 0.0 145.8 27.8 Mining 0.0 250.0 62.5 Utilities 0.0 469.4 98.0 Construction 0.1 123.1 45.0 Manufacturing 0.3 227.5 44.6 Wholesale trade 0.2 189.4 59.9 Retail trade 0.9 74.6 25.6 Transportation and warehousing 0.3 111.2 40.5 Information 0.2 289.2 63.5 Finance and insurance 0.5 226.4 61.3 Real estate and rental and leasing 0.6 257.4 20.0 Professional and scientific services 6.6 147.0 55.5 36
Management of companies 0.1 208.5 85.2 Admin and waste mgmt services 1.2 50.3 23.3 Educational services 0.2 48.9 19.7 Health care and social assistance 0.7 90.9 41.6 Arts, entertainment, and recreation 0.2 46.1 17.0 Accommodation 0.1 88.3 25.9 Food services and drinking places 0.8 50.5 16.0 Other services 0.4 100.8 29.5 Household 0.1 0.0 10.3
Total 13.6 131.0 43.7
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9. Economic Impact of Hotel Operations
The analysis in this section proceeds as follows. First, we determine the level of hotel service based on the hard construction cost per room. Second, two different surveys are used to indicate the average number of direct employees per room for this level of hotel service. Third, this figure is multiplied by the employment multiplier to determine the total number of jobs from hotel operations. Fourth, the annual revenues for the first year of operations are taken from the pro forma income statement and appropriately deflated. Fifth, this number is multiplied by the final demand multiplier to show that the results are the same using both methods. Sixth, the detailed industry tables from the RIMS II model are presented.
Table 9-1. Construction Cost per Hotel Room, Various Levels
Source: HVS International Hotel Survey, 2011 38
The construction figures given in the previous section indicate that hard costs are about $14.43 million for a 152-room hotel, or about $95,000 per room. The regional coefficient for commercial construction in Seattle, according to R. S. Means, is 1.05, so that would be equivalent to about $90,000 per room. That puts this hotel somewhat above the midscale hotels but below the full-service hotels.
According to various surveys of the hotel industry, the average number of employees per hotel room recently was 0.612. Furthermore, this figure has not changed very much over the years; earlier surveys also estimate it to be in the 0.6 to 0.7 range for U.S. hotels. However, while this survey covers a wide range of hotels, ranging from low-end motels to upscale hotels, and is a good starting point for our analysis, it does not fully apply to luxury resorts, where the ratio is 1.6 employees per room, as shown in a recent survey published in USA Today.
Evans, Carroll & Associates has calculated the likely breakdown of the 62 employees per 100 hotel rooms for standard and upscale hotels with a 75% occupancy rate, which is the national average; these figures can then be adjusted upward to reflect hiring practices at luxury hotels. The maid service figure is based on each maid cleaning 12 rooms per day; in some hotels, the figure is as high as 15 rooms per day, but that is unusual. This figure declines as the size of the room increases, and is estimated to be 8.3 rooms per day for luxury establishments. The front desk figure for a standard hotel assumes 3 people for each of 2 shifts and 2 people for the third shift; this figure also includes receptionists and people who answer the phone.
A recent survey of luxury hotel chains in the U.S., including the Mandarin Oriental, St. Regis Hotel, Ritz Carlton, and Four Seasons chains, found an average hotel room of 430 square feet, and an average of 1.7 employees per room except for the Ritz Carlton, which is slightly less luxurious at a rate of 1.2 employees per room. It is assumed that at a luxury hotel there are 6 people per shift at the front desk, and the addition of concierge and bellhop personnel throughout the day and night. The food service figures have been expanded to include room service and an extensive trade in meeting and banquet facilities. Three employees are available on each shift for customer relations instead of one. Similarly, three employees instead of two are available per shift for management, security, engineering, and inside maintenance, and janitorial services and outside maintenance, In addition, two employees per shift are added for reservations and for meeting and banquet facilities. All these results are summarized in Table 9-2.
Table 9-2. Hotel Employees Per 100 Rooms
Semi True Category Budget Standard Upscale Luxury Luxury Maid Service 6 7 8 10 12 Front Desk 6 7 8 12 15 Doormen 0 2 3 4 6 39
Bellhops 1 0 2 5 8 Parking Valets 0 0 2 4 8 Concierge 0 0 2 4 6 Food Service 3 14 20 30 45 Customer relations 3 3 5 7 9 Management 3 4 5 7 9 Security 3 5 6 7 9 Engineering/inside maint 4 5 6 7 9 Janitor/outside maint 3 4 5 7 9 Reservations 0 0 1 3 6 Meeting & Banquet 0 0 0 3 6
Total 32 51 73 110 160
Based on the classification of the hotel as close to the standard level would indicate 0.51 direct employees per room. However, that figure includes food service, which is not shown in the pro forma income statement given below; without food service, the figure declines to 0.37 employees per room. That figure is very close to the 0.35 figure given for limited service hotels as shown in Figure 9-1 below. We now compare this figure with the RIMS II model results.
Figure 9-1. Average Number of Direct Employees per Room by Hotel Service Category
40
Table 9-3 shows the pro forma income statement for the hotel for the first three years of operation. Note there are no revenues from food and beverage service.
Table 9-3. Pro Forma Income Statement for Hotel
The first year income figure of $5.223 million must be deflated to 2007 dollars. According to the BLS producer price index for hotel rooms, there was no increase at all from 2007 to 2011. However, prices are expected to rise an average of 2% per year from 2011 to 2015, so this revenue figure is deflated by 1.08. The resulting $4.836 million is entered into the RIMS II model and the results are shown in the next two tables.
41
Table 9-4. Increase in Employment, Output, and Earnings, Hotel Operations for 152-Room Airport Hotel
Industry group Employment Output Earnings Agriculture, forestry, fishing, 0.1 11 2 Mining 0.0 1 0 Utilities 0.2 108 23 Construction 0.5 66 24 Manufacturing 1.4 324 62 Wholesale trade 1.0 182 58 Retail trade 4.2 315 108 Transportation and warehousing 1.7 185 75 Information 1.3 348 81 Finance and insurance 2.1 466 127 Real estate and rental and leasing 2.8 714 57 Professional and scientific services 3.1 367 167 Management of companies 1.3 280 115 Admin and waste mgmt services 4.5 258 103 Educational services 0.8 42 16 Health care and social assistance 3.4 305 140 Arts, entertainment, recreation 1.3 59 22 Accommodation 55.1 4,872 1,443 Food services and drinking places 3.8 192 60 Other services 2.2 222 65 Household 0.3 0 4
Total 91.2 9,317 2,752
Table 9-4 shows that about 91 new jobs would be created by the hotel operations, of which about 54 are direct jobs (there are a small number of indirect and induced jobs included in the 55.1 figure). Total output would rise about $9.3 million, with earnings up about $2.75 million. Table 9-5 shows the average output per new worker at about $102,100, with average annual earnings of about $30,200.
Table 9-5. Output, and Earnings Per New Worker, Hotel Operations for 152-Room Airport Hotel
Industry group Employment Output/Empl Earnings/Empl Agriculture, forestry, fishing, 0.1 151.7 27.6 Mining 0.0 200.0 100.0 Utilities 0.2 464.7 97.5 Construction 0.5 123.2 45.3 Manufacturing 1.4 228.1 44.0 42
Wholesale trade 1.0 189.2 59.7 Retail trade 4.2 74.6 25.5 Transportation and warehousing 1.7 107.9 44.1 Information 1.3 260.1 60.8 Finance and insurance 2.1 226.7 61.6 Real estate and rental and leasing 2.8 254.6 20.2 Professional and scientific services 3.1 117.3 53.3 Management of companies 1.3 208.7 85.3 Admin and waste mgmt services 4.5 57.6 23.0 Educational services 0.8 49.0 19.4 Health care and social assistance 3.4 90.8 41.6 Arts, entertainment, and recreation 1.3 45.2 17.0 Accommodation 55.1 88.4 26.2 Food services and drinking places 3.8 50.5 16.0 Other services 2.2 102.2 30.0 Household 0.3 0.0 11.3
Total 91.2 102.1 30.2
As noted above, there are about 54 direct jobs for 152 rooms, which is 0.36 direct employees per room. The surveys given above show figures of 0.35 and 0.37 direct employees per room, which agree almost exactly with the RIMS II results.
43
10. Economic Impact of Construction and Operation of the Hotel
This section summarizes the results for the construction and operations of the hotel. These results are a summary of the figures given in Sections (8) and (9) so the individual cells in these tables are merely the sum (or average) of the results in those tables.
Table 10-1. Increase in Employment, Output, and Earnings for Construction and Operation of the Hotel
Industry Group Employment Output Earnings Agriculture, forestry, fishing, 0.7 138 26 Mining 0.4 97 24 Utilities 0.7 342 71 Construction 122.6 15,115 5,516 Manufacturing 15.6 3,403 687 Wholesale trade 8.1 1,525 482 Retail trade 32.6 2,428 831 Transportation and warehousing 9.2 1,075 382 Information 5.3 1,490 326 Finance and insurance 10.6 2,404 652 Real estate and rental and leasing 13.6 3,702 291 Professional and scientific services 24.6 3,258 1,354 Management of companies 4.1 856 350 Admin and waste mgmt services 18.8 1,025 436 Educational services 4.7 231 92 Health care and social assistance 18.7 1,703 780 Arts, entertainment, and recreation 5.5 253 95 Accommodation 56.8 5,020 1,486 Food services and drinking places 16.0 808 255 Other services 11.2 1,127 333 Household 1.9 0 20
Total 381.6 46,000 14,489
Table 10-1 shows there will be about 381 new jobs created by the construction and operation of the hotel. Total output will rise about $46.0 million, with household earnings up by about $14.5 million. Table 10-2 shows that the average output per new worker would be about $120,500, with average annual earnings of about $38,000.
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Table 10-2. Output and Earnings Per New Worker for Construction and Operation of the Hotel With Ancillary Facilities
Industry Group Employment Output/Empl Earnings/Empl Agriculture, forestry, fishing, 0.7 205.4 38.2 Mining 0.4 233.3 58.6 Utilities 0.7 467.9 97.0 Construction 122.6 123.3 45.0 Manufacturing 15.6 218.5 44.1 Wholesale trade 8.1 189.3 59.9 Retail trade 32.6 74.6 25.5 Transportation and warehousing 9.2 116.5 41.4 Information 5.3 283.2 62.0 Finance and insurance 10.6 227.1 61.6 Real estate and rental and leasing 13.6 271.4 21.4 Professional and scientific services 24.6 132.5 55.1 Management of companies 4.1 208.7 85.3 Admin and waste mgmt services 18.8 54.6 23.2 Educational services 4.7 49.2 19.5 Health care and social assistance 18.7 90.9 41.6 Arts, entertainment, and recreation 5.5 46.0 17.3 Accommodation 56.8 88.5 26.2 Food services and drinking places 16.0 50.5 15.9 Other services 11.2 100.3 29.7 Household 1.9 0.0 10.6
Total 381.6 120.5 38.0
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Resume of Dr. Michael K. Evans
CURRENT AND PREVIOUS POSITIONS
• Chairman, Evans, Carroll & Associates, Inc., 1980-present (previously Evans Economics)
Economic consulting firm specializing in EB-5 immigration analysis, economic impact studies of development projects and new construction, models of state and local tax receipts, impact of current and proposed government legislation, and construction of econometric models for individual industries and companies.
• Chief Economist, American Economics Group, 2000-present.
Built a comprehensive state modeling system that provides economic analysis for a variety of consulting projects (see below).
• Clinical Professor of Economics, Department of Managerial Economics and Decision Sciences (MEDS), Kellogg Graduate School of Management, Northwestern University, 1996-99.
Taught courses in macroeconomics and business forecasting. Wrote textbooks for both courses.
• Winner of Blue Chip Economic Indicator Award for most accurate macroeconomic forecasts during the past four years, November 1999
• Founder and President, Chase Econometric Associates, 1970-1980
• Assistant and Associate Professor of Economics, Wharton School, University of Pennsylvania, 1964-69. Co-developer of the original Wharton Model.
• Visiting Professor, Radford University, (Radford, VA), 1987
Chairman of Institute for International Economic Competitiveness
• Visiting Lecturer, Hebrew University (Jerusalem), 1966-67
Built econometric model of the Israeli economy
Ph. D. in Economics, Brown University. Dissertation, "A Postwar Quarterly Model of the United States Economy, 1948-1962". A. B. in Mathematical Economics, Brown University 46
PREVIOUS ACTIVITIES AND EDUCATION
• Contributing Editor, Industry Week
Wrote a column in each issue on economic and financial trends as they impact the manufacturing sector.
• Editor, The Evans Report
Weekly newsletter discussing economic trends and financial markets. Pioneered the concept of the Monthly Tracking Model to incorporate recent economic releases into the overall economic forecast, including methods to predict these economic data.
• Consultant, National Printing Equipment and Supply Association
Prepares quarterly forecasts of shipments of printing equipment and graphic arts supplies by product line, based on an econometric model constructed for NPES. Also prepares analysis and forecasts of exports and imports by principal product line.
• Consultant, APICS -- The Educational Society for Resource Management,
In 1993, designed and developed the APICS Business Outlook Index, which uses survey data collected by the Evans Group to measure current production, production plans, shipments, employment, new orders, unfilled orders, inventory stocks, and the comparison of the actual to desired inventory/sales ratio to predict short-term changes in manufacturing sector activity. The results of this survey appeared every month in APICS: The Performance Advantage
• Consultant, American Hardware Manufacturing Association
Wrote a separate weekly edition of the Evans Report analyzing recent trends in the hardware and housing industries, including forecasts of the hardware industry based on an econometric model developed for AHMA.
• Board of Economists, Los Angeles Times
Wrote column every 6 weeks (5 other economists on the Board)
• Columnist, United Press International
Wrote twice-weekly column, "Dollars and Trends"
• Consultant, Senate Finance Committee,
Built the first large-scale supply-side model of the U. S. economy 47
• Consultant, Environmental Protection Agency and Council on Environmental Quality
Estimated inflationary impact of government regulations
• Consultant, National Aeronautics and Space Administration
Estimate impact of R&D spending on productivity growth
• Consultant, U. S. Treasury
Estimated impact of investment tax credit and accelerated depreciation on capital spending by industry
• Consultant, U. S. Department of Agriculture
Built large-scale econometric model of agricultural sector of U. S. economy
• Consultant, Organization of Economic Cooperation and Development
Built econometric model of the French economy
SAMPLE OF RECENT CONSULTING PROJECTS
For more information on these projects, see www.evanseb5.com
Key to symbols: N, new regional center, E, extension of existing center
List is current as of April 1, 2010. Totals to date are 87 new regional centers, 58 extensions, and 7 new markets tax credits, for a total of 152 projects
A. Economic Impact of EB-5 Immigrant Investor Programs and New Markets Tax Credits
E● Calculated the economic impact of construction and operation of a new automobile assembly plant in Petersburg, VA
N● Calculated the economic impact of operating a call center for the U.S. government in Muskogee, OK
N● Calculated the economic impact of developing a mixed-use commercial and residential center in Scottsdale, AZ 48
N● Calculated the economic impact of constructing and operating a “Green Box” facility in New Jersey to process waste material on a pollution-free basis.
N● Calculated the economic impact of constructing and operating a “Green Box” facility in Washington State to process waste material on a pollution-free basis.
E● Calculated the economic impact of constructing and operating a new hotel in Coral Gables, FL
E● Calculated the economic impact of developing a new residential community in Brevard County, and retail stores and restaurants in St. Lucie County, FL
N ● Calculated the economic impact of a new business to store and process field crops in Madison, MS
N● Calculated the economic impact of operating food service establishments and assisted living centers in 40 counties in Texas.
E● Calculated the economic impact of developing a mixed-use commercial center in Miami, FL
N● Calculated the economic impact of renovating a theater in New York City to show film highlights of previous Broadway hits.
N● Calculated the economic impact of renovating and operating distressed buildings in the San Francisco Bay area.
E● Calculated the economic impact of a mixed-use commercial center in Montgomery County, TX
E● Calculated the economic impact of expanding a manufacturing facility to produce more energy-efficient lighting in Sarasota, FL
N● Calculated the economic impact of developing facilities for amateur sporting events in northern GA
N● Calculated the economic impact of developing a mixed-use commercial center in Missoula, MT
N● Calculated the economic impact of operating call centers in Las Vegas, NV, and other western Nevada counties
E● Calculated the economic impact of constructing and operating a proton cancer treatment center in Boca Raton, FL 49
E● Calculated the economic impact of constructing and operating a “Green Box” facility in Detroit to process waste material on a pollution-free basis.
E● Calculated the economic impact of renovating and expanding commercial property in Lower Manhattan
N● Calculated the economic impact of constructing student housing and retail stores in Davie, FL
E● Calculated the economic impact of constructing residential housing near Harvard University
E● Calculated the economic impact of developing mixed-use commercial centers in Broward County, FL
E● Calculated the economic impact of renovating a Dallas apartment building
E● Calculated the economic impact of renovating and operating a nursing home in Las Vegas, NV
E● Calculated the economic impact of constructing a hotel and shopping center in Miami, FL
E● Calculated the economic impact of developing a design center in Miami/Dade county, FL
E● Calculated the economic impact of developing and operating a chain of children’s playrooms and party facilities in South Florida
E● Calculated the economic impact of developing a new stadium for the Nets basketball team, to be located in Brooklyn, NY
E● Calculated the economic impact of developing a Hyatt hotel in Washington, D.C.
E● Calculated the economic impact of developing and operating a casino for foreign patrons in Las Vegas, NV
E● Calculated the economic impact of operating a series of yogurt fast-food restaurants in South Florida
E● Calculated the economic impact of constructing steel homes and commercial buildings in South Florida
N● Calculated the economic impact of construction and operation of a farm distillery in Vermont 50
N● Calculated the economic impact of purchase and renovation of deeply discounted residential properties in South Florida
N● Calculated the economic impact of a hotel to be built near LaGuardia Airport in Queens, NY
N● Calculated the economic impact for several mixed-use commercial and residential properties for a regional center covering southern Wisconsin and northern Illinois.
N● Calculated the economic impact for mixed-use commercial project in Flushing, NY
E● Calculated the economic impact for major new hotel near the Washington, D. C. conference center
N● Calculated the economic impact of renovating and operating an assisted living center in suburban Atlanta, GA
N● Calculated the economic impact of an office tower in mid-town Manhattan for the diamond trade
N● Calculated the economic impact of three mixed-use commercial and residential projects in Santa Clara County, CA
N● Calculated the economic impact of six mixed-use commercial and residential projects in Los Angeles, Orange, Riverside, and San Bernardino counties
N● Calculated the economic impact of operating a chain of pizza restaurants in southern Florida.
N● Calculated the economic impact of constructing and operating an assisted living facility in Atlanta, GA
E● Calculated the economic impact of constructing and operating an expansion of University Hospital in Cleveland, OH
E● Calculated the economic impact of a wastewater treatment plant in Victorville, CA
N● Calculated the economic impact of drilling for geothermal energy and constructing and operating power plants in several counties in Nevada
E● Calculated the economic impact of a vacation club operation in Orlando, FL
E● Calculated the economic impact of constructing and operating an extended-stay hotel in Boston, MA 51
E● Calculated the economic impact of constructing and operating an assisted living facility in Walton County, FL
N● Calculated the economic impact of manufacturing and constructing residential and commercial steel modular buildings in Lee County, FL
E● Calculated the economic impact of a chain of yogurt and juice stores and restaurants in southern Florida
E● Calculated the economic impact of two mixed-use commercial developments in Orange County, CA.
E● Calculated a Targeted Employment Area by census tracts for six counties in the Houston, TX metropolitan area
E● Calculated the expansion of new hybrid car manufacturing facility from Mississippi to Tennessee and Virginia.
E● Calculated the economic impact of construction and operation of a skilled nursing facility in Las Vegas, NV.
N● Calculated the economic impact of construction and operation of a proton cancer treatment center and medical offices buildings in Los Angeles County, CA.
E● Determined the economic impact of improving facilities at the Port of Baltimore in order to attract more shipping from the Panama Canal when the locks are widened.
N● Calculated the economic impact of a major hotel and resort area in Ft. Lauderdale, FL.
N● Calculated the economic impact of building steel homes in South Florida, including the local manufacture of steel fabricated parts.
E● Calculated the economic impact of constructing and operating a hotel at Times Square in New York City.
N● Calculated the economic impact of a mixed-used residential and commercial project in Atlanta, GA.
E● Calculated the economic impact of expanding and opening new restaurants in Dallas, TX. In a separate project, calculated the economic impact of renovating, refurbishing, and operating a boutique hotel in Dallas, TX.
E● Calculated the economic impact of building and operating low-income housing in Boston, MA. 52
N● Calculated the economic impact of constructing and operating assisted living facilities in eight rural Texas counties.
N● Calculated the economic impact of a mixed-use commercial project in Riverside County, CA.
E● Calculated the economic impact of opening a manufacturing plant for “green” motor vehicles in the Detroit, MI area. APPROVED
E● Calculated the economic impact of constructing and operating hotels and restaurants in Columbus, MS.
E● Calculated the economic impact of operating restaurants in the Hotel W in Hollywood, CA.
N● Calculated the economic impact of a mixed-use commercial project in McCook, IL (suburban Chicago).
N● Calculated the economic impact of constructing and operating a water-based amusement facility in San Diego, CA.
N● Calculated the economic impact of a mixed-use commercial facility in suburban Cincinnati, OH (project is in KY).
E● Calculated the economic impact of constructing and operating a casino, hotel, and restaurant in Las Vegas, NV.
N● Calculated the economic impact of a new academic institution for alternative energy in Santa Clarita, CA.
N● Calculated the economic impact of several mixed-used projects in San Francisco, Alameda County, Santa Clara County, and Fresno County.
N● Calculated the economic impact of a super energy store and solar farm in Riverside County, CA.
N● Calculated the economic impact of a prostate cancer treatment center in South Carolina.
E● Calculated the economic impact of refurbishing and expanding retail space at the George Washington Bridge in New York City.
E● Calculated the economic impact of building Atlantic Yards, new stadium for the New York Nets, in Brooklyn, NY APPROVED 53
N● Calculated the economic impact of an assisted living center and several mixed-use commercial facilities in the Reno, NV area.
E● Calculated the economic impact of buying residential properties at deep discount prices, refurbishing and selling them, in South Florida.
N• Calculated the economic impact for a fractional-ownership marina in Port Charlotte, FL, plus office space, retail stores, restaurants, and a home brokerage office. APPROVED
N• Calculated the economic impact of construction and operation of four retirement homes in Vermont.
E• Calculated the economic impact of an upscale retail shopping center in Vail, CO. and a medical office building in Edwards, CO (both in Eagle County). APPROVED
E• Calculated economic impact of a wind turbine manufacturing plant in Larimer County, CO APPROVED
N• Calculated economic impact of a hotel, retail stores, restaurants, office buildings, and bank facilities in Pasadena, CA
N• Calculated economic impact of a luxury hotel and condominiums in Destin, FL
N• Calculated economic impact of constructing and operating a mixed-use commercial project in Jupiter, FL APPROVED
E• Determined whether 17 possible restaurant locations in Miami/Dade and Broward Counties qualified as Targeted Employment Areas.
E• Determined the economic impact of opening and operating a slot-machine casino in Hanover, MD, as part of a proposed EB-5 regional center for the Baltimore metropolitan area.
N• Calculated the economic impact of renovating and expanding a restaurant on Martha’s Vineyard, MA, as part of an EB-5 regional center in that state.
N• Determined the economic impact of assembling and installing solar panels for residences in the state of LA.
E• Determined a Targeted Employment Area for Dallas, TX as part of a proposed EB-5 regional center for the Dallas area. APPROVED
N• Calculated the economic impact for various mixed used projects for a proposed regional center for the entire State of Texas, including shopping centers, office 54 buildings, restaurants, assisted living centers, medical technology facilities, and other personal and business services.
N• Calculated the economic impact for the construction and operation of several fast- food restaurants in 10 counties in central California.
N• Calculated the economic impact for the renovation and expansion of a shopping mall in Greenville, SC.
E• Calculated the economic impact of buying existing apartment buildings at deep discount prices, renovating and operating them, in 21 counties in FL.
N• Calculated the economic impact of building and operating an institute for proton cancer therapy for a proposed EB-5 regional center in Brooklyn, NY. APPROVED
N• Calculated the economic impact of building and operating a mixed-use facility with medical offices, hotels, and apartments for a proposed EB-5 regional center in Queens, NY. APPROVED
E• Determined a Targeted Employment Area for Philadelphia, PA as part of a proposed EB-5 regional center for the Philadelphia area. APPROVED
N• Calculated the economic impact of a proposed office building and mixed-use facility for an EB-5 regional center in Dallas, Texas APPROVED
N• Calculated the economic impact for various mixed-use projects for a proposed EB-5 regional center in the greater New York City area, including an extended stay hotel, urgent care center, financial lending firm for alternative assets, retail stores, apartments, office space, warehouses, industrial “flex” space, entertainment centers, restaurants, conference and convention centers, nursing home and assisted living facilities, medical offices, medical technology facilities, and high-tech manufacturing. APPROVED
N• Calculated the economic impact of “green” hotels in 10 counties in Central California. APPROVED
N• Calculated the economic impact of generic projects in manufacturing, financial services, health services, hotels, and restaurants for a proposed regional center for the state of Florida. APPROVED
E• Calculated the economic impact of 12 different types of economic activity for an expansion of the Palm Beach Regional Center to five contiguous counties. APPROVED
N• Calculated the economic impact of a new auto parts plant in Alabama to supply parts to Kia automobiles. APPROVED 55
N• Calculated the economic impact of opening fast-food restaurants in Miami/Dade and Broward counties in FL. APPROVED
N• Calculated the economic impact of a mixed-use commercial center in Flushing, Queens County, NY.
E• Calculated the economic impact of revitalizing and renovating part of the Brooklyn Navy Yard for “green” manufacturing facilities. APPROVED
E• Calculated the economic impact of 12 different types of economic activity for various counties in Charlotte and Sarasota counties, FL APPROVED
E• Calculated the economic impact of four new manufacturing and distribution companies in Palm Beach County, FL. APPROVED
N• Calculated the economic impact of developing a resort area and building residences in rural Tennessee.
N• Calculated the economic impact of developing and operating a resort area in Southern Arizona. APPROVED
N• Calculated the economic impact of revitalizing the depressed East Side of Cleveland, Ohio, with new commercial and industrial buildings. APPROVED
N• Determined the nationwide economic impact of a $1 billion investment in Mississippi for a new hybrid motor vehicle plant. APPROVED
N• Determined the economic impact of expanding a shipyard in Southeastern Louisiana. APPROVED
N• Calculated the economic impact of a new shopping center in Buena Vista, California, and two other generic shopping centers in Los Angeles and San Bernardino counties. APPROVED
E• Calculated the economic impact of enhancing resort areas in eight rural counties in Colorado. APPROVED
N• Calculated the economic impact of the rehabilitation of Fitzsimons Village in Aurora, Colorado, by adding an office building with medical labs, hotel, shopping center, and residences. APPROVED
E• Determined the economic impact of a mixed-use commercial center for the Kansas City metropolitan area.
N• Calculated the number of jobs created for a film production company in New York City. APPROVED 56
N• Calculated economic impact of small-scale rooftop solar panels in various counties in California.
N• Calculated economic impact of 7 different types of proposed businesses for a proposed regional center in the Bay Area of California. APPROVED
N• Determined the economic impact of a new biological research park, office building, and logistics center in Wooster, Ohio. APPROVED
E• Calculated the economic effect of a mixed-use urban renewal project in Cleveland, Ohio. APPROVED
N• Calculated economic impact of dairy farm and cheese processing plant in Northern California. APPROVED
N• Determined economic impact of a shipyard, food processing plant, and semiconductor plant for a proposed regional center in Louisiana and Mississippi. APPROVED
N• Calculated the economic impact of a new gaming casino in Natchez, Mississippi. APPROVED
N• Developed an Input/output Model for Guam, which was then used to calculate the economic impact of several generic projects. APPROVED
N• Calculated the economic impact of a retail shopping center in suburban Los Angeles County. APPROVED
N• Prepared an economic impact analysis for the “timber to homes” project for a proposed regional center in Colorado. APPROVED
N• Calculated the economic impact for a proposed regional center in Baltimore, Maryland that would include the rebuilding of depressed areas in East Baltimore and along the riverfront.
N• Prepared the economic analysis for a proposed EB-5 regional center for the entire state of Florida that included impact calculations for 14 different types of industries. APPROVED
N• Prepared the economic analysis for a proposed EB-5 regional center in the San Francisco Bay area that included calculations for 10 different types of industries. APPROVED
N• Prepared economic impact calculations for proposed EB-5 regional centers in New York City and Northeastern New Jersey. APPROVED 57
• Calculated the economic impact of a rehabilitated office building in Albuquerque, New Mexico, including the increase in high quality jobs. NEW MARKETS
• Calculated the economic impact of a rehabilitated skilled nursing center in East Los Angeles, California, including the impact on nearby census tracts. NEW MARKETS
N• Calculated the economic impact of development of warehouse and light industrial manufacturing space in Las Vegas, Nevada. APPROVED
N• Calculated the economic impact of rehabilitation and expansion of a vacation and health spa in Sharon Springs, New York
N• Calculated economic impact of revitalizing an old resort hotel and adding new facilities for Lake Geneva, WI.
• Calculated the employment and tax effects for a portfolio of projects undertaken under the New Market capital program. NEW MARKETS
E• Calculated generic employment changes for proposed EB-5 project for an Inland Port in Palm Beach County, FL APPROVED
N• Calculated the economic impact of construction of El Monte Village in El Monte, CA. APPROVED
• Calculated the economic impact of moving the Social Security Administration building in Birmingham, AL, and revitalizing the surrounding neighborhood. NEW MARKETS
• Calculated the economic impact of rehabbing and expanding the Everett Mall in Everett, WA. NEW MARKETS
• Determined the economic impact of building a new medical center in Charleston, SC NEW MARKETS
N• Calculated economic impact of expanding Sugarbush resort in VT. Study included expansion of existing facilities and addition of new facilities. APPROVED
• Calculated economic impact for new market tax credit program in Portsmouth, N.H. Study included both overall economic impact, and the increase in employment and income and the decrease in the unemployment rate and incidence of poverty in individual census tracts. NEW MARKETS
N• Calculated the economic benefits of EB-5 programs for foreign investors for a mixed-use construction project, including a hotel, retail stores, apartments, and a sports stadium in the Washington, D. C. metropolitan area APPROVED
58
N• Calculated the economic benefits of EB-5 programs for a mixed-used retail shopping center in the New York City metropolitan area. APPROVED
N• Calculated the economic benefits of EB-5 programs for foreign investors for proposed shopping centers in five separate counties in Southern California, including differential impacts of building the shopping centers in different counties. APPROVED
B. Projects for State and Local Governments
• Constructed an econometric model for the State of New York and determined the change in employment, labor income, and tax revenues for 43 different tax changes proposed by the Governor’s office.
• Constructed a detailed econometric model for the State of Pennsylvania to determine the economic impact of the complete panoply of state taxes levied; the model contains over 1,000 equations. In cooperation with American Economics Group, the model was developed to simulate the effect of changes in any state tax rate on households and businesses by income deciles, household status, age of individuals, size of households, and many other demographic variables. The change in business taxes can also be simulated for detailed industry classifications.
• Determined whether the Washington, D.C. water and sewer authority should accept a high bid for a new waste disposal system. Decision to reject has saved the authority over $200 million, as construction prices turned down sharply as predicted.
• Built an econometric model to determine the “tax gap” caused by Internet sales for the state of Minnesota.
• Determined appropriate levels of shelter grants individual counties in New York State, and for utility allowances in New York City. Reviewed and prepared testimony in ongoing court cases in these areas.
• Calculated the economic impact of the revitalization of downtown Milwaukee, Wisconsin.
C. Economic Impact of Casino Gaming
• Built an econometric model to predict the growth of the gaming industry over the next decade, and the economic impact of that industry on employment and tax revenues at the Federal and state levels.
• Estimated the economic impact of Indian casino gaming nationally and for the State of Wisconsin.
• Determined the economic impact of the Oneida Indian gaming casino on the Green Bay metropolitan area. 59
• Estimated the negative economic impact on the Milwaukee area if a new Indian gaming casino were to be built in Kenosha, Wisconsin.
D. Economic Impact of Smoking Bans and Higher Taxes
• Testified on economic impact of smoking bans in Canada; certified as an expert witness by the Court.
• Examined the impact of smoking bans on restaurant sales in several different locations in the U.S. to determine how much sales changed when these bans were imposed, and the differential effects depending on whether these bans were partial or total.
• Determined the cross-border effects on retail sales from differential rates in cigarette, gasoline, and alcohol excise taxes
• Determined the economic impact of higher cigarette taxes on minority group employment.
• Estimated the economic impact and loss of Federal and state tax revenues when higher cigarette prices lead to increased smuggling.
E. Consulting Projects for Travel and Tourism
• Built an econometric model to predict tourism trips and revenues for the major regions of the U.S. economy.
• Constructed econometric models to predict tourism in Las Vegas and Orlando.
• Using the IMPLAN model, predicted economic impact of tourism and travel expenditures for all counties in Pennsylvania.
F. Other Private Sector Consulting Projects
• Calculated the revenue gain at the Federal, state and local level generated by domestic manufacturing of Airbus parts and equipment.
• Calculated the economic impact of proposed EPA bans on fluoropolymer production.
• Estimated the size and economic importance of the fluoropolymer industry, and calculated economic impact of shutting down domestic production.
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• Built an econometric model to examine how U.S. tax and regulatory policies help determine whether the gold mining industry would invest in the U.S. or other countries. Testified before Congress to help defeat legislation inimical to the mining industry.
• Built an econometric model to predict consumer bankruptcies, based on recent growth in consumer credit outstanding, the overall economic environment, and recent changes in credit regulations
• Estimated the economic impact of the ethanol subsidy on the U.S. economy and Farm Belt States, including the impact on the balance of payments, employment, and tax receipts. Testified before Congress to help pass legislation to extent subsidies to the ethanol industry.
• Built an econometric model to determine the impact of updating and improving the system of locks on the Upper Mississippi River on corn prices and exports, farm income, and the overall economy.
BOOKS PUBLISHED
Macroeconomics for Managers, Blackwell, 2003
Practical Business Forecasting, Blackwell, 2002
Economic Impact of the Demand for Ethanol, Diane Publishing Company, 1998
How to Make Your Shrinking Salary Support You in Style for the Rest of Your Life, Random House, 1991
The Truth About Supply-Side Economics. Basic Books, 1983.
A Supply-Side Model of the U. S. Economy, mimeo (prepared for Senate Finance Committee), 1980.
An Econometric Model of the French Economy: A Short-Term Forecasting Model. O.E.C.D, March 1969.
Econometric Gaming (with L. R. Klein and M. J. Hartley). Random House, 1969.
Macroeconomic Activity: Theory, Forecasting and Control. Harper & Row, 1969.
The Wharton Econometric Forecasting Model (with L.R. Klein), Economics Research Unit, Wharton School: University of Pennsylvania Press, 1967. Enlarged edition, 1968.
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Over 30 articles in major academic journals and publications (list on request)
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