FINAL REPORT - University of California, San Francisco · of space, a student housing complex containing 430 housing units , and a ... total hospital size to 550 beds and an ACC of

FINAL REPORT

COMMERCIAL LAND USE IMPACTS: UCSF MISSION BAY CAMPUS AND HOSPITAL Prepared for: University of California, San Francisco Prepared by: Economic & Planning Systems, Inc. December 2007 EPS #17030

TABLE OF CONTENTS

I. INTRODUCTION AND SUMMARY OF FINDINGS...........................................................1

Introduction ..............................................................................................................1 Project and Site Background....................................................................................5 Key Findings .............................................................................................................6

II. BIOTECHNOLOGY INDUSTRY OVERVIEW....................................................................9

Industry Description.................................................................................................9 Industry Size and Geographic Distribution..........................................................11 Determinants of Firm Location..............................................................................22

III. BIOTECHNOLOGY SPACE DEMAND..........................................................................28

Comparable Region Approach ..............................................................................28 Capture Rate Approach..........................................................................................29 Retail Associated with Biotech Employment........................................................32

IV. HOSPITAL‐RELATED SPACE AND DEMAND..............................................................35

Case Study Approach.............................................................................................35 Project Description Approach................................................................................46

V. IMPACT ON NEARBY NEIGHBORHOODS...................................................................50

Demand Summary..................................................................................................50 Supply Evaluation ..................................................................................................52 Analysis of Potential Land Use Impacts ...............................................................58

VI. BIBLIOGRAPHY..........................................................................................................63

APPENDICES 1. Case Studies of Biotechnology Clusters Anchored by Universities 2. Narrow and Broad Definitions of Biotech, by NAICS code 3. Detail on California Employment Development Department Job Counts,

by County 4. Detail on businesses/organizations counted as “health‐related” businesses 5. Map of San Francisco health‐related businesses, by type and size

LIST OF FIGURES AND TABLES

Figure 1A: Mission Bay Campus and Future Hospital Location.................................1

Figure 1B: Mission Bay Campus and Hospital Site .....................................................2

Figure 2: Framework for UCSF Mission Bay Impact Study......................................3

Figure 3: Illustration of Biotechnology Industry Definitions..................................11

Figure 4: Biotech Jobs Projection, Comparable Market Approach, San Francisco ..............................................................................................29

Figure 5: Health‐related Establishments near Alta Bates Medical Center.............38

Figure 6: Health‐related Establishments near Eden Medical Center .....................39

Figure 7: Health‐related Establishments near Sutter Medical Center....................40

Figure 8: San Francisco Hospitals and Health‐related Establishments..................41

Figure 9: Proposed Life Science /Medical Zoning Overlay.....................................54

Figure 10: Biotech Space: Various Demand Projections, 2005‐2030 .........................58

Figure 11: Medical ‐Related Space: Various Demand Projections............................59

Figure 12: Biotech, Medical‐Related Space: Demand and Supply Comparison......60

Figure 13: Retail Space: Demand and Supply............................................................61

Table 1: United States Biotechnology Industry Indicators, 1994‐2006 .................12

Table 2: Biotech Employment by Industry Subsector 2005 ...................................14

Table 3: Regional Biotech Industry, Public Companies and Total Capitalization, 2006 ..........................................................................15

Table 4: Biotechnology Case Studies Results, 2005................................................16

Table 5: Bay Area Biotech Employment by Region and Subsector, 2005.............18

Table 6: Bay Area Biotech Employment by Region, 1990‐2005.............................19

Table 7: Employment Projections, Bay Area ..........................................................21

Table 8: Mission Bay – Biotechnology Development.............................................23

Table 9: Comparable Market Approach: San Francisco Biotech Employment Projection, 2030...........................................................................................30

Table 10: Capture Rate Approach: San Francisco Projection, 2030.........................31

Table 11: Retail Space Associated with Biotech Employment, 2003 Projected ......34

Table 12: Hospital Beds, Four Selected Cities ..........................................................37

Table 13: Employment Surrounding Four Hospitals (Excludes hospital staff), 2007..............................................................................................................42

LIST OF FIGURES AND TABLES (continued)

Table 14: Case Study Approach, Key Calculations..................................................44

Table 15: Case Study Approach, Results ..................................................................45

Table 16: Project Description Approach: Physician Private Offices .......................47

Table 17: Project Description Approach: Hospital Suppliers..................................48

Table 18: Project Description Approach: Hospital Population Spending..............49

Table 19: Summary of Demand Projections .............................................................51

Table 20: Selected Eastern Neighborhoods Land Supply; Existing Uses...............53

Table 21: Projected Land Use (2000‐2025, Option B) ...............................................55

Table 22: Summary of Supply Options.....................................................................57

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I. INTRODUCTION AND SUMMARY OF FINDINGS

INTRODUCTION

Economic & Planning Systems (EPS) has been retained by University of California, San Francisco (UCSF) to evaluate the potential land use impacts of its Mission Bay research campus and hospital on the surrounding area (see Figures 1A and 1B for illustrations of the planned hospital location). This Study was requested by the Mission Bay Community Task Force, which was convened by UCSF to examine UCSF’s impact on the communities surrounding the Mission Bay campus. As part of this process, this Report seeks to:

• Estimate demand for building space from biotechnology businesses seeking proximity to the emerging Mission Bay campus;

• Estimate new demand for building space from businesses seeking proximity to the new UCSF hospital, such as medical suppliers, health care service providers, and private doctors offices; and

• Estimate the net land use impacts to the Eastern Neighborhoods of San Francisco, taking into account supply constraints on land such as zoning and existing uses.

Figure 1A: Mission Bay Campus and Future Hospital Location

FFuuttuurree HHoossppiittaall SSiittee

Figure 1B

2

Final Report Commercial Land Use Impacts: UCSF Mission Bay Campus and Hospital

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Figure 2 below further illustrates the framework and relationships evaluated in this Report. As shown, the new hospital is expected to generate demand for both suppliers and providers of health care products and services. Meanwhile, the medical research facility is expected to generate demand for biotechnology, or life science related firms. All of these activities will generate demand for retail space.

Figure 2: Framework for UCSF Mission Bay Impact Study

Hospital Medical Research

Health CareSuppliers


Health CareProviders


Life ScienceLife Science

RetailRetail

Hospital Medical Research





Life ScienceLife Science

RetailRetail

METHODOLOGY AND DATA SOURCES

EPS has used a variety of research methods and primary and secondary data sources to evaluate the relationships above. In addition, EPS has worked closely with UCSF staff, the City and County of San Francisco, including the San Francisco Planning Department staff, and community stakeholders throughout the study process to gain input and guidance on the sources and methodology used. Key assumptions and data are appropriately referenced throughout the Report. The primary information sources include, but are not limited to, the following:

• Academic and popular literature related to the size and location of biotechnology sector;

• Local and regional employment historic data and projections by industry sector;

• City and County of San Francisco development and land use data;

• Real estate broker reports (e.g., Collier’s International, CB Richard Ellis);

• Health‐related business/organization data including location, size, and employment information;

• Office of Statewide Health Planning and Development data on hospital usage and staffing;


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• Input on methodology and study direction by UCSF Mission Bay Community Task Force members; and

• Input from Mayors Office of Economic and Workforce Development.

One of the purposes of this analysis is to inform the rezoning process of the Eastern Neighborhoods, particularly around the issues of demand for biotech, medical and other types of space. In order to ensure that actual demand for these spaces does not exceed the supply projections contained in this Report, which may lead to land conflicts and land‐price inflation, the primary estimates provided include a low and high range. In addition, a variety of data sources have been used to verify and cross reference the results. The precise level of development that ultimately occurs will depend on a variety of factors, many of which cannot be predicted with certainty and/or are beyond the scope of this analysis. These include the intentions and capabilities of individual property owners or developers, the direction and timing of future business cycles, local land use policies and requirements, and State or Federal funding priorities and policies.

KEY TERMS

Several terms used throughout this report are described below to ensure the clarity of the document.

• In this report, the biotechnology industry is clarified as either referring to a broad definition of the industry (i.e., 23 NAICS1 codes included in the definition) or a narrow definition (i.e., 4 NAICS codes included).

• ”Life science” is a term often used in higher education to refer to the study of biochemistry, botany, cell biology, and molecular biology, which may or may not be focused on commercializing research products; it is similarly used in this report.

• The term Research and Development (R&D) is used throughout this Report to refer to the subset of the biotechnology industry which engaged in R&D activities. This portion of the industry is highlighted throughout the report because many biotechnology firms in the San Francisco Bay Area are focused on this area of the industry.

1 Stand for: North American Industrial Classification system. The NAICS system, originally the Standard Industrial Classification (SIC) codes, was developed to classify establishments by the type of activity in which they are primarily engaged.


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REPORT ORGANIZATION

The report is organized into five chapters. Chapter I provides an introduction to the project and the key findings of the study. Chapter II summarizes the biotechnology industry and relates important theories about the spatial distribution of biotech firms. Chapter III provides the methodology and results of the analysis of demand for biotech space. An analysis of organizations which tend to cluster around hospitals is described in Chapter IV, along with the results of the space demand analysis for these types of organizations. Chapter V summarizes supply for space which may accommodate the demand from the UCSF Mission Bay campus and compares each demand scenario with the potential supply.

PROJECT AND SITE BACKGROUND

The Mission Bay campus of the University of California at San Francisco (UCSF) is a 43‐acre development in the center of the 303‐acre Mission Bay redevelopment project area, located along San Francisco’s central waterfront, just south of the Giant’s ballpark. A significant portion of the UCSF Mission Bay campus has already been developed with nearly 1.5 million square feet of development that exist or are under construction. This development includes three research buildings with approximately 700,000 square feet of space, a student housing complex containing 430 housing units, and a campus community center with nearly 160,000 square feet of space, as well as parking for the on‐site daily population which averages 3,200 people. The San Francisco Redevelopment Agency’s plan for the Mission Bay Redevelopment Area includes space for UCSF’s campus (totaling 2.65 million square feet) and up to 6 million square feet of flexible commercial space (office, life science, and technology development types are allowed), as well as residential, retail, and hospitality space. 1.6 million square feet of this flexible space is planned to be devoted to new facilities for the UCSF Medical Center at Mission Bay immediately adjacent to the original 43‐acre UCSF campus site. In January 2007, UCSF acquired 14 acres of land, adjacent to the Mission Bay campus’s south end, as the future site of a Mission Bay hospital facility. The planned hospital will integrate multiple, specialty hospitals (women, children, and cancer) with the goal of accelerating UCSF’s translational research—which involves increasing the pace at which a new discovery is brought to patients through a diagnostic technique or a drug—by co‐locating basic scientists, clinical researchers, and physicians and patients. Plans for the Mission Bay hospital include two phases of development, with the actual timing of construction dependent on fundraising efforts. Preliminary planning for Phase I includes a 289‐bed hospital for children, women, and cancer services along with approximately 240,000 square feet dedicated to an ambulatory care center (ACC). Phase II includes an additional 261 beds and 196,500 square feet of ACC space, bringing the total hospital size to 550 beds and an ACC of 436,500 square feet.


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KEY FINDINGS

1. The total increase in demand for building space accommodating biotechnology companies that seek to locate in San Francisco because of the presence of the UCSF Mission Bay campus, or other factors, is estimated to range from 840,000 to 3.6 million square feet over the next 10 to 20 years.

The actual amount of future growth in San Francisco’s biotechnology sector is difficult to estimate with precision because the City does not currently have an established track record of success in attracting these firms and the industry itself is relatively difficult to define. However, it is generally expected that the presence of a prestigious and high‐profile research campus at Mission Bay will serve as a major competitive asset to the City, creating “spin‐off” entrepreneurial activity in the biotech sector as well as attracting existing firms who seek proximity to UCSF scientists, facilities, and research. This is further supported by the biotechnology sector’s high dependence on cutting‐edge research and tendency to co‐locate or “cluster” in specific geographic locations. Given the high level of uncertainty associated with projecting biotech growth in San Francisco, EPS has used several approaches and data sources to create a range of likely outcomes. The low‐end, 840,000‐square foot estimate (about 1,900 net new employees) is based on an approach which more than doubles the rate at which San Francisco currently captures Bay Area biotech employment and uses a narrow definition of the industry. Using a broader definition of the industry and significant growth in Bay Area employment (roughly double the existing total employment), about 2.9 million square feet is estimated in San Francisco (about 6,400 new employees). Finally, a “comparable market,” approach, which assumes that San Francisco’s biotech employment will experience growth rates similar to San Mateo County at the height of its biotech expansion, as well as a broad definition of the industry, yields a high‐end estimate of 3.6 million new square feet in the City over the next 20 years (about 8,100 new employees). To ensure an overestimate rather than an underestimate of potential demand near Mission Bay, this study assumes that all San Francisco biotech growth locates in Mission Bay and the Central Waterfront. 2. The total demand for building space accommodating firms providing health‐

related products and services that seek proximity to UCSF’s new hospital at Mission Bay is likely to range between 450,000 and 915,000 square feet, depending on the size of the hospital.

It is a widely observable phenomenon that hospitals tend to be surrounded by health‐related establishments such as physicians’ private medical offices, pharmacies, alternative health providers, and other organizations that provide products and services to the hospital and its patients. In order to quantify the amount of health‐related businesses that might seek to locate near the new UCSF hospital, EPS conducted case studies of hospitals in four Bay Area communities. The case studies of these hospitals provide a range for the spatial distribution of non‐hospital, health‐related employees.


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Based on this analysis, EPS estimates that health‐related business will demand between 450,000 and 915,000 square feet of space within one‐half mile radius of the UCSF hospital. 3. The total demand for new retail space because of the daily expenditures of new

employees in both the biotech industry and health‐related organizations is likely to range between 54,000 and 200,000 square feet, depending on the size of the hospital and the growth in the San Francisco biotech industry.

The above estimates of new demand for retail space are based on the expected daily spending of employees and patients as well as assumptions regarding typical sales per square foot needed to support retail establishments. The range in demand is based on likely biotech employment and the size of the hospital.2 4. There are a variety of sites near the Mission Bay campus that are likely to be

available to accommodate the building space demand estimated above, including areas in the Central Waterfront and Mission Bay.

The primary sources of potential supply of space are (1) entitled land in Mission Bay, (2) areas of likely redevelopment such as Pier 70 and Seawall Lot 337, (3) infill locations on vacant or underutilized sites, and (4) parcels which are expected to be converted to either office or medical space under the rezoning options now being considered in these neighborhoods. Mission Bay has a large amount of entitled land which is zoned flexibly and is a logical location for biotech, medical space, and retail. The Planning Department has also proposed a Life Science/Medical zoning overlay in a portion of the Central Waterfront which allows more flexibility in development of these uses than has previously been proposed. A third source of potential supply in this area are Port of San Francisco properties including Seawall Lot 337 (near‐term supply and Pier 70 (long‐term supply)Finally, conversions of production, distribution, and repair (PDR) space projected in the rezoning plan for the Eastern Neighborhoods will also serve as a source of potential supply. 5. Even under the most aggressive assumptions regarding the impact of the new

Mission Bay campus on the demand for space in adjacent neighborhoods, it appears that sufficient development opportunities will be available to accommodate this demand without “crowding out” other land uses.

Supply of land in Showplace Square/Potrero Hill, the Central Waterfront, and Mission Bay meets projected demand from biotech, health‐related, and retail establishments

2 The 54,000 to 200,000 square foot estimates the lowest and highest projection for retail space. The chart illustrating this demand projection includes the middle projection for retail because of biotech employment expansion and the highest projection for retail to serve employment because of an increase in health‐related establishments. See Table 19 and Figure 13.


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attracted to the Mission Bay hospital. The supply sources at Mission Bay and Pier 70 for biotechnology space may provide 4.5 million square feet compared to the 3.6 million (high‐end) demand projection.3 Vacant and underutilized acreage within the Life Science/Medical overlay area combined with projected conversions to office/medical space under Option B of the Eastern Neighborhoods rezoning and space at Pier 70 may be developed into more than 2.6 million square feet of space, nearly three times the 915,000 of square feet of demand estimated for the larger‐sized hospital.

3 Any redevelopment which may occur at Pier 70 or the Mirant site is likely to include a mix of uses. With both areas included in the proposed Life Science/ Medical overlay, either biotech or medical development may occur at these locations. To match supply with demand by type of space, these two locations are assumed to provide half of the available space to biotech and half to medically related spaces.


II. BIOTECHNOLOGY INDUSTRY OVERVIEW

As noted at the outset, biotechnology firms are expected to be a key tenant type seeking space adjacent to the emerging Mission Bay campus. Consequently, this chapter evaluates the conditions and trends in the biotechnology industry as they relate to the type and amount of future development that may be attracted to the Mission Bay area.

INDUSTRY DESCRIPTION

The biotechnology industry is comprised of a collection of firms that use cellular or molecular processes, particularly recombinant DNA technology, to produce goods or provide services.4 Because of the nature of the technology and practices which cut across traditional industry‐lines, biotechnology is not a neatly defined or quantified industry sector. Economic data, such as employment, number of firms, and output, are typically tracked through the North American Industrial Codes System (NAISC), formerly the Standard Industrial Classification (SIC), and through the Standard Occupational System (SOC).5 However, since biotechnology is defined by the underlying process—recombinant DNA—which cuts across many industries, rather than a set of products or services, the collection and interpretation of economic data for the sector can be difficult. Depending on a particular study’s aim, geographic boundaries, or depth, either broad or limited definitions of the biotechnology industry have been applied. These firms generally fall into one or several of the following four categories (which can be further subdivided into smaller categories):

• Biotechnology Research. This includes firms or other entities that conduct basic or applied research in the biotechnology field. This research includes (1) Genomics, the study of structure/function of genes; (2) Proteomics, the study of structure/function of proteins); (3) Bioinformatics, the use of computer technology in biotechnology; and (4) Pharming, the production of pharmaceuticals derived from genetically engineered plants.

• Biotech Drugs and Therapeutic Approaches. This includes enterprises involved in the development of drugs or therapies to advance human health such as gene therapy, stem cell therapy, and therapeutic proteins.6

4 Definition used in the context of this study from “The Dynamics of California’s Biotechnology Industry,” Public Policy Institute of California, 2005. 5 This data is collected and transmitted through the Bureau of Labor Statistics (BLS). BLS data summarized by the California Economic Development Department by counties is cited as such throughout the report. 6 Includes therapeutic proteins, monoclonal antibodies, gene therapy, stem cell therapy and personalized medicine.


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• Agricultural Biotech. This includes enterprises involved in genetic engineering of new plants and animals with characteristics desired by producers and/or consumers. These include disease resistant crops and genetically modified crops.

• Industrial Biotech. This area refers to biotechnology techniques applied to areas outside the traditional sectors of medicine, health care, and agriculture. It includes research and development in the energy, environmental technology, and special materials fields. Areas of exploration in these fields includes, respectively, biofuels, waste management and hazardous materials abatement, and research into the genetic codes of animals and plants which provide clues on how to synthetically replicate the strength of a spider’s webs or the fineness of silk from a silkworm.

Figure 3 illustrates several definitions of biotechnology and how each relates to industry size. As shown, the industry may be defined broadly, including surgical and medical instrument manufacturing and even certain types of food processing, or it may be defined more narrowly, excluding many industries which have biotechnology companies among them. The Biotech Industry Group has developed a list of 28 NAICS codes which it tracks as part of its work providing information on the industry.7 The San Diego Workforce Partnership developed a definition of the biotechnology industry (by trackable NAICS code) through its National Biotechnology Advisory Committee which includes 23 NAICS codes and estimates that the total U.S. biotechnology employment is more than 1.4 million. Conversely, Ernst & Young’s global biotechnology reports, which have been published annually for 30 years, estimates U.S. biotechnology employment is about 181,000, based on a narrow definition of applicable NAICS codes.8 9 This study reports information on all the industry definitions for which data is available, with references provided to clearly identify the application, in order to capture the range of potential outcomes.

7 See “Growing the Nation’s Bioscience Sector: A Regional Perspective,” prepared by Batelle, January 2007. 8 The actual NAICS codes used by Ernst & Young are proprietary and have not been disclosed to the author. 9 “Beyond Borders, Global Biotechnology Report 2007,” Ernst & Young.


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Figure 3: Illustration of Biotechnology Industry Definitions

* Job counts from 2005.

Biotech Industry Group Definition28 NAICS Codes

Includes wet corn milling, soybean processing, ethyl alcohol manufacturing, medicinal and botanical manufacturing, pharmaceutical manufacturing, irradiation apparatus manufacturing,

and physical/engineering/ biological research and development.

Biotech Workportal Definition23 NAICS Codes

Includes cotton farming, flour milling, ethyl alcohol manufacturing, surgical and medical instrument manufacturing, pharmaceutical manufacturing, and

physical/engineering/ biological research and development.

Narrow Definition5 NAICS Codes

1. Medicinal and botanical manufacturing, 2. Pharmaceutical preparation manufacturing, 3. In-vitro

diagnostic substance manufacturing, 4. Biological product manufacturing, and 5. Physical/engineering/ biological

research and development.

1.4 million jobs in the U.S.

68,000 jobs in the Bay Area

Roughly 180,000 jobs in the U.S.


U.S. 1.7 million


INDUSTRY SIZE AND GEOGRAPHIC DISTRIBUTION

The size, location and future growth in the biotechnology industry will have important implications on its potential expansion in Mission Bay. This section examines these trends at the national, regional, and local level.

NATIONAL

Just over 30 years old, the biotechnology industry has matured from a relatively obscure field of molecular biology to an industry with revenues of nearly $60 billion and investments of more than $27 billion in research and development (R&D).10 Table 1 reports key statistics from 1994 through 2006 for the industry. Revenue in this industry has grown from just over $11 billion in 1994 to $58.8 billion in 2006 and employment has grown by about 75 percent, from 103,000 jobs in the United States in 1994 to 181,000 in 2006. However, the industry is still fairly small compared to large U.S. industries such 10 Ernst & Young, Beyond Borders, Global Biotechnology Report 2007

Table 1United States Biotechnology Industry Indicators, 1994-2006Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Indicator1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Total Total % Annual

Rate %

Revenues (billions, nominal$) 11.2 12.7 14.6 17.4 20.2 22.3 26.7 29.6 29.6 39.2 43.8 50.7 58.8 47.6 425% 15%

R&D Expenditure 7.00 7.7 7.9 9.0 10.6 10.7 14.2 15.7 20.5 17.9 19.6 20.8 27.10 20.1 287% 12%(billions, nominal$)

No. of companies 1,311 1,308 1,287 1,274 1,311 1,273 1,379 1,457 1,466 1,473 1,346 1,475 1,452 141.0 11% 1%

No. pubic companies 265 260 294 317 316 300 339 342 318 314 331 331 336 71.0 27% 2%

Market Cap (billions, nominal$) 45.0 41.0 52.0 83.0 93.0 137.9 353.5 330.8 225.0 206.0 336.8 408.4 392.4 347.4 772% 20%

No. of employees (thousands) 103 108 118 141 155 162 174 191 195 198 n/a 171 181 78.0 76% 5%

Sources: Ernst & Young, Global Biotechnology Reports, 1994-2007; Economic & Planning Systems

1994-2006 ChangeYear

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as computer and information technology sectors. For example, the market capitalization—the aggregate amount of the member companies’ stocks—of the biotech industry in 2006 was less than the total for two of the computer technology sector’s leaders, Microsoft ($269 billion) and Intel ($149 billion). 11 There is much debate around the relative strengths of particular metropolitan areas in terms of job creation, human capital, venture capital, and absolute economic growth. One measure of the existing distribution of biotechnology clusters around the country is the relative level of employment. Table 2 provides 2005 estimates of jobs in the industry, with the twelve regions with the largest number of employees listed in descending order. The New York‐New Jersey region has the largest concentration of employment with about 126,000 jobs followed by the Bay Area with 68,100 employees (based on 2005 data using the broad definition of the industry). A second measure of the size of a region’s biotech cluster is the number and size of large, public companies in a region. Table 3 shows the number of public companies in 16 regions in the United States, the number of public companies in the region, and value of the companies’ market capitalization. According to this measurement, the Bay Area is home to the highest number of public companies of any region with 69 firms and the highest valued, market capitalization with 37 percent of the total value of public, biotech companies in the country. As part of this analysis EPS conducted a number of case studies of biotechnology clusters around the country which are situated near a leading, medical research university similar to UCSF. EPS summarizes the results of this study in Table 4. Examining the metropolitan regions surrounding Johns Hopkins, Colorado State University, UCLA, and UCSD, the case studies documented the number of biotechnology firms, total biotech employment, total building square footage related to biotechnology uses, and other indicators of the cluster. As shown, the areas evaluated housed between 2.4 million square feet to 17.6 million square feet of space devoted to biotechnology. Although these locations have similarities to the Bay Area’s biotech cluster, quantitative differences—such as large size of the building base in the Bay Area and qualitative ones, including differences in policies towards attracting biotech businesses and the interaction effect of other universities in an area—render the case studies’ use as a basis for predicting future growth in San Francisco‘s biotech cluster insufficient. Details describing each case study are provided in the Appendix for reference.

11 Market capitalization refers to a company’s total stock value. It is calculated by multiplying a company’s stock price by the total number of outstanding shares.

Table 2Biotech Employment by Industry Subsector 2005

Region Total 1 % of totalEmployment

New York-New Jersey 126,329 9%San Francisco Bay Area 68,068 5%Los Angeles-Long Beach-Riverside 61,488 4%Washington DC-Baltimore-N. Virginia 53,340 4%Chicago 52,778 4%San Diego 34,716 2%Minneapolis-St. Paul 31,943 2%Philadelphia 29,261 2%San Juan 28,025 2%Detroit 24,312 2%Raleigh-Durham 18,582 1%Boston 16,563 1%

Subtotal 545,405 39%

Rest of U.S. 865,249 61%

USA Total 1,410,654 100%

Sources: www.biotechwork.org, Economic & Planning Systems, Inc.

[1] Includes employment in research and development, pharmaceuticals, medical equipment, medical devises, agricultural biotech, and industrial biotech. 23 NAICS codes are represented.

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Table 3Regional Biotech Industry, Public Companies and Total Capitalization, 2006Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Regionnumber % millions$ %

San Francisco Bay Area 69 21% 145,553 37%New England 1 60 18% 62,936 16%San Diego 38 11% 20,916 5%New Jersey 28 8% 28,556 7%Mid-Atlantic 2 23 7% 17,111 4%Southeast 3 19 6% 5,301 1%New York State 17 5% 8,893 2%Mid-West 4 8 2% 1,161 0%Pacific NW 5 15 4% 4,928 1%Los Angeles/ Orange County 11 3% 81,585 21%North Carolina 9 3% 2,017 1%Pennsylvania/ Delaware Valley 12 4% 7,140 2%Texas 11 3% 1,495 0%Colorado 6 2% 1,847 0%Utah 2 1% 1,454 0%Other 6 8 2% 1,526 0%

336 100% 392,419 100%

[2] Includes Maryland, Virginia, and Washington D.C.

[4] Includes Illinois, Michigan, Ohio, and Wisconsin.[5] Includes Oregon and Washington.[6] Other includes Hawaii, Minnesota, Missouri, Montana, Nebraska, Oklahoma, and South Dakota.

Sources: Ernst & Young, Beyond Borders, Global Biotechnology Report, 2007; Economic & Planning Systems

Market CapitalizationPublic Companies

[1] New England region includes the following states: Maine New Hampshire, Vermont, Massachusetts, Connecticut, and Rhode Island.

[3] Includes Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Tennessee, and South Carolina.


Table 4Biotechnology Case Studies Results, 2005Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Category Greater Washington DC 1 Los Angeles Metro 2 San Diego Metro 3 Denver Metro 4 Bay Area 5

University Johns Hopkins UCLA UCSD CU Boulder UCSFStart of Biotech 6 1985 1980 1978 1980 1976Establishments 7 1,472 1,807 843 536 1,648Life Science Employment 53,340 61,488 34,716 9,781 68,068Biotech Spin-Offs from University 8 33 18 60 n/a 60NIH Funding by University (2005, Million) $607.2 $385.8 $309.4 $41.5 $452.2Biotech Space (sq.ft.) 9,10 7,174,673 5,220,000 8,053,431 2,400,000 17,600,000Vacancy Rate 18.7% 8.8% 13.2% n/a 7.9%Annual Absorption Rate 11 358,734 208,800 298,275 96,000 606,897

Total Annual Average Absorption 313,700

[1] Greater Washington DC is comprised of the Washington-Baltimore-Northern Virginia CSA.[2] Los Angeles Metro is comprised of Los Angeles-Long Beach-Riverside CSA [3] San Diego Metro is comprised of San Diego-Carlsbad-San Marcos MSA.[4] Denver Metro is comprised of Denver-Aurora-Boulder CSA.[5] Bay Area is comprised of San Jose-San Francisco-Oakland CSA[6] Reflects the year in biotech-companies began locating in proximity to the University.

[9] Information obtained through Colliers Alchemy Report 2005. Total inventory includes lab and office space.[10] Information on inventory for the Denver area is not tracked by the Colliers Alchemy Report. Information is reported from Denver Business Journal article "Biotech Takes Roots Northern 'Burbs" October 28,2005[11] Calculated by dividing total Biotech Space (sq.ft.) row by the years since the start of biotech in the area.

Sources: www.Biotechwork.com, NIH Awards to All Institution FY 2005, Colliers Alchemy Report 2005, Denver Business Journal.

[7] Number of biotech companies, as defined by the "broad" definition of the industry. Includes employment in research and development, pharmaceuticals, medical equipment, medical devises, agricultural biotech, and industrial biotech. 23 NAICS codes are represented, see Appendix for list.[8] Reflects number of biotech companies each respective University claims as a "spin-off". Common criteria for claiming a company is a direct spin-off of a particular university are: Founded or co-founded by faculty or former students; Utilizes technology or research developed by university; etc.


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BAY AREA

As the birthplace of the biotechnology, the Bay Area has been a leading locale for the industry right from the start. With clusters of biotechnology firms on the Peninsula (South San Francisco and throughout San Mateo County), in the East Bay (Emeryville, Richmond, and Hayward), and in Santa Clara County (Palo Alto and Mountain View), firms in the Bay Area are representative of the overall industry with giants such as Amden, Genentech, Chiron/Novartis, and Johnson & Johnson as well as startups such as Nuon Therapeutics, which are located throughout the region. The Bay Area contains nearly 18 million square feet of biotechnology space with an average vacancy rate near 7 percent.12 As described earlier, tracking employment within the biotechnology industry is done by selecting NAICS industry codes as aligned as possible with biotech firms. Table 5 shows biotech employment in the Bay Area, by region. Defined using the traditional fields of biological research and pharmaceuticals Bay Area employment in these industries totals 40,600, with the Peninsula and East Bay containing about a quarter each of the total employment while Silicon Valley employees make up 45 percent of the total. With three elite research universities ringing the San Francisco Bay (UCSF, UC Berkeley, and Stanford), the region is heavily focused on Research & Development. Unlike biotechnology employment nationally, of which about 36 percent is research focused, more than half of all biotechnology jobs in the Bay Area are in the research sector.13 Also shown on the table are job counts using a broad definition of biotech (28 NAICS codes included) which leads to a total of more than 68,000 jobs. Using this broader definition of biotechnology, Santa Clara County has nearly half the total employment in the Bay Area, with San Mateo County dropping to 18 percent, the East Bay at 27 percent, and San Francisco and the North Bay Area with 2 and 4 percent, respectively. Including a broader number of industries in the biotech definition increases the number of jobs in the Bay Area by about 80 percent. In San Francisco, this broader definition increases the job count from 1,100 to about 1,500. This larger number includes nearly 100 jobs in the surgical appliance/supplies manufacturing, more than 100 jobs in diagnostic imaging centers, and about 30 jobs in medical laboratories, all categories which are excluded from the narrow definition of the industry.

Historic Job Counts

Between 1990 and 2005, the Bay Area’s biotech sector has grown roughly 44 percent. Using the narrow definition of biotech, the industry grew from 28,300 jobs in 1990 to 40,300 jobs in 2005. (See Table 6 for job counts by county.) Similarly, the biotech sector using the broad definition grew from 48,200 jobs in 1990 to 68,100 jobs in 2005. 12 As reported by Colliers Alchemy Report, 2007. 13 Counting all 23 NAICS codes, the U.S. has about 508,500 jobs in the research industry code, of the 1.4 million biotech jobs in the country. The Bay Area has about 37,000 jobs of its 68,000 jobs in this job code.

Table 5Bay Area Biotech Employment by Region and Subsector, 2005Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Location

Drugs and Pharmaceuticals

Research Testing and Medical Laboratories

Total, Both Types Areas

% of Bay Area

Biotech Industry Group Definition5

% of Bay Area

Peninsula (San Mateo County) 282 9,832 10,114 25% 12,014 18%

Silicon Valley (Santa Clara County) 1,626 16,793 18,419 45% 33,260 49%

1,334 8,122 9,456 23% 18,657 27%

San Francisco 0 1,064 1,064 3% 1,539 2%

58 1,522 1,580 4% 2,663 4%

Total Bay Area 3,300 37,333 40,633 100% 68,133 100%% of Total

[1] NAICS codes included in total are: 325411 Medicinal and Botanical Manufacturing325412 Pharmaceutical Preparation Manufacturing325413 In-Vitro Diagnostic Substance Manufacturing325414 Biological Product (except Diagnostic) Manufacturing541710 Research and Development in the Physical, Engineering, and Life Sciences

[2] See Appendix for NAICS codes included in broad, biotech industry organization count.

Sources: California Employment Development Department; Economic & Planning Systems

North Bay (Marin, Napa, Solano, and Sonoma Counties)

East Bay (Alameda and Contra Costa C i )

Biotechnology - Narrowly Defined NAICS Codes1Broad Definition, 28 NAICS

Codes2


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Table 6Bay Area Biotech Employment by Region , 1990-2005Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

County Name

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Total %Rate of Change

Narrow Industry Definition2

Alameda 5,665 5,359 4,362 5,372 5,529 5,834 5,597 5,719 5,876 6,346 7,202 8,244 8,857 7,662 7,938 8,029 42% 2%Contra Costa 516 674 652 746 870 939 681 731 708 674 775 878 817 757 1,304 1,427 177% 7%Marin 305 302 275 309 320 275 311 211 260 357 454 544 611 597 726 781 156% 6%Napa 14 10 12 25 10 9 13 51 64 84 118 145 0 150 0 0 -100% -100%San Francisco 1,253 1,205 1,199 952 1,250 1,331 1,351 1,285 1,294 1,469 1,562 1,188 1,160 1,544 1,451 1,064 -15% -1%San Mateo 3,382 4,942 5,034 5,283 5,739 5,763 5,714 6,033 6,426 7,272 8,061 9,510 10,678 9,498 9,567 10,114 199% 8%Santa Clara 17,159 16,177 16,276 17,887 18,409 18,309 17,635 18,266 19,714 20,136 20,709 21,431 19,261 18,633 18,554 18,419 7% 0%Solano 0 0 0 0 0 0 0 0 0 0 0 1,289 0 0 0 0 -- --Sonoma 0 0 0 0 0 0 0 0 0 0 0 331 337 648 718 799 -- --

28,294 28,669 27,810 30,574 32,127 32,460 31,302 32,296 34,342 36,338 38,881 43,560 41,721 39,489 40,258 40,633 44% 2%1% -3% 10% 5% 1% -4% 3% 6% 6% 7% 12% -4% -5% 2% 1%

Broad Industry Definition3

Alameda 8,701 8,255 7,649 9,118 9,356 10,098 10,140 10,608 10,980 11,636 13,057 14,515 15,281 15,131 15,094 15,332 76% 4%Contra Costa 1,781 1,698 1,612 1,756 1,857 1,911 1,708 1,775 1,760 1,945 2,088 2,257 2,270 2,453 3,617 3,322 87% 4%Marin 371 367 335 364 379 343 377 266 320 410 502 641 711 664 852 901 143% 6%Napa 27 24 27 39 26 26 30 70 86 108 143 167 23 171 22 18 -33% -3%San Francisco 1,992 1,983 1,976 1,591 1,887 1,926 1,917 1,858 1,861 2,002 2,042 1,733 1,748 2,114 1,961 1,539 -23% -2%San Mateo 5,712 7,334 7,676 7,562 8,193 8,297 8,385 8,937 9,211 9,904 10,710 14,728 15,200 11,510 11,727 12,014 110% 5%Santa Clara 29,021 29,845 30,455 33,328 33,720 33,328 34,078 36,369 39,550 40,044 40,570 40,417 37,967 35,501 34,344 33,263 15% 1%Solano 83 114 113 112 110 140 163 272 271 229 237 1,530 175 206 142 211 154% 6%Sonoma 528 534 536 533 472 473 470 449 458 478 531 885 940 1,032 1,425 1,533 190% 7%

48,216 50,154 50,379 54,403 56,000 56,542 57,268 60,604 64,497 66,756 69,880 76,873 74,315 68,782 69,184 68,133 41% 2%4% 0% 8% 3% 1% 1% 6% 6% 4% 5% 10% -3% -7% 1% -2%

NAICS Code325411 Medicinal and Botanical Manufacturing325412 Pharmaceutical Preparation Manufacturing325413 In-Vitro Diagnostic Substance Manufacturing325414 Biological Product (except Diagnostic) Manufacturing541710 Research & Dev. - Physical, Engineering,& Life Sciences

1990-2005 Change

Sources: California Economic Development Dept.; Economic & Planning Systems

[1] Job counts from California EDD; Job totals undercount employment slightly as numbers which would compromise privacy are excluded. "S" Indicate revealing employment count may violate privacy of individual firms and is not published.

Description

[3] See Appendix for list of NAICS codes which make up this definition.

Total Bay AreaBay Area, Annual % Change

Total Biotech Employment, by year1

Total Bay AreaBay Area, Annual % Change

[2] Total employment includes selected North American Industrial Classification System (Codes) as follows:


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The greatest numeric increases in jobs occurred in Santa Clara, San Mateo, and Alameda counties. Under both definitions, San Francisco experienced modest job growth in the sector from 1990 through 2000. Since that time, enough jobs were lost during the economic bust between 2000 and 2003 to drive the total job count in 2005 below the level it was in 2000 in the City, under both the narrow and the broad definition of biotech.

Employment Projections

The California Economic Development Department (EDD) provides employment projections for 10‐year periods for individual counties or for groups of counties, segmented by NAICS code. EPS reports the analysis of these projections in Table 7, as they pertain to the biotech industry. The EDD projects that between 2004 and 2014, the total number of jobs in the Bay Area will grow by about 9 percent, from 3.5 million to 3.8 million jobs. For the NAICS codes which most closely match the biotech industry, the Marin/San Francisco/ San Mateo counties projection, jobs are projected to grow 38 percent over the 10‐year period, from 25,300 to 34,900 while the rest of the Bay Area is expected to grow 15 percent, from 97,000 to 111,500.14 EDD projections are not yet available beyond 2014. In order to provide one employment projection in the biotech sector for the Bay Area, the weighted‐average percentage growth for the 10‐year period is applied to 2005 job counts in the Bay Area. As shown, the weighted average growth over the 10‐year period is about 20 percent. Applying this 20 percent growth every 10 years between 2005 and 2030 increases the number of biotech jobs under the narrow definition by 23,300 jobs, from 40,600 to 64,000. The number of jobs under the broad definition increases from 68,000 to 107,300 jobs, or 39,100 jobs.15

Mission Bay

An approximately 303‐acre redevelopment project area in San Francisco, Mission Bay’s redevelopment plan includes up to 6 million square feet of flexible commercial space (office, biotech, and technology allowed) along with the 43‐acre UCSF campus. 1.6 million square feet of this flexible space is planned for new UCSF Medical Center facilities. In terms of its success in attracting private, biotech development, the Mission Bay area is showing strong signs that it will outperform predictions of a 30‐year buildout. Nine years after the adoption of the area plan, the first private, life science building in the project area is complete, Alexandria’s 1700 Owens Street building. In addition, the first

14 These job counts are higher than the counts on the previous table (Bay Area Job Counts, 1990‐2005) because the projections are not provided for each individual NAICS code (at the fifth‐level), as they are provided for historic job counts. 15 A second employment projection is used later on in the analysis, as described in Chapter V, which provides a larger growth projection.

Table 7Employment Projections, Bay AreaCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Job Type Item

2004 2005 2010 2014 2015 2020 2025 2030 # %

EDD PROJECTIONS

Bay Area Employment Projection (all jobs)1 3,505,200 -- -- 3,807,400 -- -- -- -- 302,200 9%

Marin, San Francisco, San Mateo Biotech County ProjectionChemical Manufacturing2 6,600 -- -- 13,000 -- -- -- -- 6,400 97%Other Prof., Scntfc. and Tech. Svcs.3 18,700 -- -- 21,900 -- -- -- -- 3,200 17%Total 25,300 -- -- 34,900 -- -- -- -- 9,600 38%

Alameda, Contra Costa, Napa, Santa Clara, Solano, Sonoma County Biotech ProjectionChemical Manufacturing2 55,100 -- -- 58,800 -- -- -- -- 3,700 7%Other Prof., Scntfc. and Tech. Svcs.3 41,900 -- -- 52,700 -- -- -- -- 10,800 26%Total 97,000 0 0 111,500 -- -- -- -- 14,500 15%

Bay Area 10-Year Projected Rate of GrowthChemical Manufacturing2 16%Other Prof., Scintfc. and Tech. Svcs.3 23%

Wtd. Average 10-Year Growth Rate 20%

ANALYSIS OF EDD PROJECTIONS

Bay Area Biotech Estimate1

Jobs Based on Narrow Indst. Definition4 -- 40,633 44,636 -- 48,640 53,432 58,225 63,962 23,329 57%Jobs Based on Broad Indst. Definition5 -- 68,133 74,846 -- 81,559 89,595 97,631 107,250 39,117 57%

[2] Includes NAICS 325--[3] Includes NAICS 5414, 5417, & 5419

[5] See Appendix for NAICS codes.

Source: California Economic Development Department; Economic & Planning Systems

Total Change for Period ShownYear

[1] The California Economic Development Office provide employment projections by industry code for 2004 through 2014 and in some cases 2012 through 2012. Several NAICS codes were selected representing the biotechnology industry. Growth rates for these NAICS codes over the 10 year period are analyzed and applied to the base, biotech employment in the Bay Area, either utilizing the narrow, 5-NAICS code definition or the broader, 20+ NAICS code definition.

[4] See Table 6 for NAICS codes.


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of two buildings at 409‐499 Illinois Street is planned for 2008. Table 8 illustrates the existing and planned private development at Mission Bay related to biosciences. Within ten to fifteen years, total biotech development at Mission Bay may top 3 million square feet.16 For the period of this study (2005‐2030), the likely total private biotech development likely is 2.8 million square feet, including 2.5 million square feet to be developed by Alexandria Equities and half a million square feet under construction by Shorenstein Properties.17

DETERMINANTS OF FIRM LOCATION

As part of this analysis EPS reviewed the extensive body of academic literature related to high‐technology clusters and “spin‐offs” from large research facilities.18 This effort was designed to ascertain data and information on the location patterns and trends associated with high‐tech firms in general and biotech firm in particular. (See Appendix for summary.) This research can shed light on the degree to which the UCSF Campus is likely to produce, attract, and/or retain a cluster of biotech related firms. Specific issues investigated through this literature review include (1) the degree to which high technology firms cluster around high profile research institutions, especially those in a similar field, (2) what factors determine if “spin‐offs” are likely to remain near their parent institutions or move away, and (3) what other location criteria or factors attract biotech firms to a particular neighborhood or district.

CONCEPTUAL FRAMEWORK FROM LITERATURE REVIEW

Although the existence of biotech clusters is an empirical fact, the basis for their development has been the subject of numerous studies and analysis. In general, the research points to a number of interrelated factors that contributes to the creation of high‐technology geographic clusters, especially near prestigious research institutions. These include:

• Knowledge and technology transfer: One explanation for clustering is that cutting‐edge knowledge is most effectively transmitted through face‐to‐face interaction and is not easily spread beyond the environment in which it is

16 Other areas, such as Block 40 at Mission Bay, may eventually be developed with some biotech space included or as office space. 17 Employment counts and projections in the remainder of the report have a time horizon beginning in 2005 through 2030. The amount of biotech space at Mission Bay is therefore counted from 2005 rather than 2007, which is why the total “remaining” square footage at Mission Bay is shown at 2.95 million. 18 As in Table 4 (case studies of comparable universities), companies within technology clusters include both those spun off from the university itself as well as companies which locate in the area benefit from the pool of talent and to collaborate with one another.

Table 8Mission Bay - Biotechnology Development Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Completed Prior to Completed inDevelopment Total SF 2006 2006 2007-2009 2010-2017

Non-Profit BiotechGladstone (1650 Owens)1 180,000 180,000 -- -- --

Private Biotech/ Other Development1700 Owens Building2,3 165,000 -- 165,000 -- --1500 Owens 165,000 -- -- 165,000 --Buildings TBD 2,370,000 -- -- -- 2,370,000

Total Entitlement 2,700,000 0 165,000 165,000 2,370,000

Shorenstein Properties409-499 Illinois4 450,000 -- -- 450,000 --

Total 3,330,000 180,000 165,000 615,000 2,370,000Total, remaining private dev.5 3,150,000 0 0 615,000 2,370,000

[3] Sirna leases about 70,000 SF reported at $36 to $40 per SF annually.[4] Fibrogen has signed a lease for about half of the space at this site and holds an option on the other half.

Sources: San Francisco Redevelopment Agency; Economic & Planning Systems

[5] Total includes Alexandria square footage plus Shorenstein total. Because employment projections is shown throughout the report from 2005-2030, the supply of land at Mission Bay for biotech uses is also shown from 2005 going forward. Other potential development types in Mission Bay such as retail and medical are not shown here.

Expected Opening

[1] Completed in 2004, the Gladstone Institutes is sited on 1.1 acres, the building footprint is 33,000 square feet, the Institute's construction cost was $74M, while the completed cost was $145M which included land purchase and development and financing.

[2] Lessees include Sirna Therapeutics, Pharmion Corp., Versant Ventures, Novo Ventures, and Arch Venture Partners.


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developed and applied (Feldman 2000).19 Proximity to prominent academic institutions is especially vital in biotech industry because of the “knowledge intensive” nature of the field and its reliance on scientific research. Consequently, biotech firms “cluster” in close proximity to one another and to related research institutions because the information diffusion that occurs in these environments are key to their economic existence. Such environments not only benefit existing firms but also help create new ones (e.g., “start‐ups” or “spin‐offs”) that capitalize on the steady flow of ideas and innovations that emerge from nearby universities or by “parent” firms.

• Access to specialized labor and other inputs: In addition to scientists, biotech

firms generally require specialized inputs from other fields such as finance, law, accounting, management, and real estate. Although these fields exist in most regions, the unique attributes of the biotech sector related to its R&D intensive nature, long product cycles, and specialized space requirements, for example, demand a more specialized set of support services and products. The suppliers of these inputs, in turn, tend to locate in the regions where their services and products are most needed. For example, venture capital firms, patent lawyers, and suppliers of specialized R&D space and equipment tend to flourish in areas with strong biotech sectors.20 This set of symbiotic relationships and professional networks reinforces the trend toward co‐location and clustering.

• Academic Relationships and Affiliations: Given the industry’s high

dependence on scientific research and validation, many biotech firms have extensive links with academic‐research institutions, especially in early stages of a product life cycle (e.g., R&D phase).21 Indeed, many of the key employees in a biotech firm have or formerly held prestigious academic posts. For professional, economic, and personal reasons biotechnology scientists are often reluctant to

19 “The mysteries of trade become no mysteries; but are as it were in the air …. “ (Alfred Marshall, 1890). 20 See, Bowie, Koput, Powell, and Smith‐Doerr, “The Spatial Clustering of Science and Capital: Accounting for Biotech Firm‐Venture Capital Relationships,” National Science Foundation, September 2001. This article notes that access to local venture capital is cited most frequently in the literature as being a major factor influencing the location of biotech firms. This is especially true for young, start‐up companies, which are more likely to gain support from local sources. 21 “The biotech ‐ university connection is reinforced by the often‐cited list of founders of some of the key biotech firms created in the late 1970s and 1980s: Genentech (Herbert Boyer, University of California – San Francisco), Biogen (Walter Gilbert, Harvard), Hybritech (Ivar Royston, University of California – San Diego), Genetics Institute (Mark Ptashne, Harvard), Systemix (David Baltimore, MIT and Whitehead Institute), and Immulogic (Malcolm Gefter, MIT).1 All of these eminent scientists retained their university affiliations, often full‐ time. They were able, so to speak, to have their cake and eat it too, precisely because their universities had created rules and routines that enabled technology transfer and faculty entrepreneurship. There are many regions where there is scientific excellence but not the requisite infrastructure to capture the rents from knowledge spillovers.” From Powell, Walter W., et al.


December 10, 2007


give up their university positions even after they pursue a career with a private sector biotech firm. In addition, these dual allegiances facilitate the knowledge transfer rationale described above.

• Founder/Workforce Preferences: Whether from inertia or preference or both,

entrepreneurs and their employees often seek to remain in the environment in which they got their start and where they have grown accustomed (either as students or professors). This tendency may also be reinforced by the need to maintain social and professional ties in a heavily knowledge‐based industry. In any case, given that many biotech entrepreneurs and employees often have a relatively scarce and/or irreplaceable set of skills, they tend to have more control over their workplace location than other fields.

Despite the numerous factors that contribute to the clustering of the biotech industry near major life sciences research institutions, many of biotech firms are not located in these environments. Conversely, not all high‐profile “life sciences” research institutions have extensive biotech clusters nearby.22 This suggests that there is a variety of other factors important to the location of biotech firms that in many cases override the factors described above. These factors included the following:

• Lack of available real estate: Regardless of firm or employee preference, if a prominent research institution does not have sufficient land or building space nearby, because of land use regulatory practices, infrastructure, or geography, a biotech cluster cannot develop nearby.

• Policies and orientation of university: Not all universities embrace the link to

commercialization. In some cases, this is regarded as a distraction to “basic research” or even a conflict of interest. In addition, the “publish or perish” pressures of some universities make it difficult for academics to perform the dual role of research and entrepreneur.

• Local regulatory policies and political environment: In some cases, biotech

companies are involved in highly controversial research and may attract significant neighborhood opposition to proposals to locate nearby existing residential and commercial areas. Some local policy environments are not as

22 One case in point is Atlanta, Georgia, where there is a major research center, the Center for Disease Control; a technology‐based university, Georgia Tech; and one of the top medical schools in the country at Emory University. The metropolitan area is reasonably well‐ to‐do and well‐educated, and a number of Fortune 500 firms are headquartered there. But there is little in the way of commercial biotechnology, despite abundant intellectual resources. From Powell, Walter W., et al University of Michigan is another example. The University is rich in biotechnology expertise but the surrounding area has a dearth of biotech firms (from Audretsch and Stephan).


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accepting of biotech companies as others. By way of example, many of the biotech spin‐off firms from UC Berkeley have located in the adjacent City of Emeryville where the policy environment was viewed as more favorable.

• Need for privacy and employee retention: Some biotech firms prefer to locate in

more exclusive settings away from established clusters of biotech activity precisely to avoid the potential for knowledge transfer and employee exchange. Given the importance of both workforce and R&D‐based knowledge to the success of a biotech firm, the transfer of either of these to competitors is often a major concern.

EMPIRICAL RESULTS FROM LITERATURE REVIEW

The empirical evidence is mixed regarding the role of large research institutions in generating “spill‐over” economic activity immediately adjacent to their campuses. Although it is generally accepted that these institutions can have a positive impact on local economies in general, data on the geographic proximity of these impacts are less clear. In any case, the literature does not provide quantitative measures or relationships on the level of “spin‐off” activity or business development likely to occur adjacent to R&D institutions nor rules of thumb regarding optimal distance. Most of the studies on agglomeration focus at the County or even regional level (e.g., MSA) rather than the City, let alone zip code, level. Nevertheless, the academic literature does provide valuable information on firm location trends in the biotech industry relevant to the type and level of development that may occur near the Mission Bay campus. Two key conclusions include:

1. The degree to which geographic proximity influences the location of biotech firms may be inversely related to firm maturity.

A number of academic studies have found that proximity to universities (and academic research institutes) was central to the formation of the early biotechnology start‐ups and continues to be influential.23 However, older or more mature biotech firms, especially those focused on commercial application, appear to be less dependent on or attracted to these locations and in many cases actually perform better in more separated environments.

23 Kenney, Martin and Donald Patton, “Supporting the High‐Technology Entrepreneur: Support Network Geographies for Semiconductor, Telecommunications Equipment, and Biotechnology Start‐Ups,” Berkeley Roundtable on the International Economy, University of California, Berkeley, April 29, 2004.


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2. The role and importance of geographic clusters of biotech activity may wane, or at least change, as the industry itself grows and evolves.

A number of academic studies have also noted that the location dynamics of the biotech industry as a whole are evolving with a wider geographic dispersion corresponding to an increase in availability of the expertise, services, and facilities that support it. This phenomenon was described by one author as follows:

“In its earliest years, the biotech industry grew up around university star scientists who licensed innovations to companies. Today, there are many initiatives that attempt to build biotechnology clusters around universities using formal technology transfer mechanisms. Further, as biotechnology moves out of the lab, out of small single technology based start‐ups and into new commercial applications, the location dynamics of the industry are evolving …. We may expect that as an industry develops and science is translated into commercial applications the locational dynamics may change to emphasize industrial and technological attributes. While science resources may be most important in the earliest stages of the industry development, technology resources may become more important as the industry develops.”24 25

24 Ibid. 25 Feldman, Maryann, “The Locational Dynamics of the U.S. Biotech Industry: Knowledge Externalities and the Anchor Hypothesis,” Johns Hopkins University, August 15, 2002.


III. BIOTECHNOLOGY SPACE DEMAND

This chapter estimates the demand for biotechnology space generated by firms seeking proximity to the UCSF Mission Bay campus. In addition, it estimates the retail space associated with this new biotech employment. Two separate approaches are used in order to provide a range of potential outcomes, (1) an approach based on the performance of comparable regions and (2) an approach based on projected regional biotech employment. Based on the methodologies developed for this study, the demand projections for biotechnology space include all demand likely for the City as a whole.

COMPARABLE REGION APPROACH

The “comparable region approach” assumes that the historical growth of the biotech industry in other high‐performing Bay Area locations can provide a good indication of the growth that might occur in San Francisco. It is based on the notion that the UCSF campus and life science buildings at Mission Bay signify the start of a new biotechnology cluster in the City. The analysis looks at the biotechnology cluster in San Mateo County, originated with the founding of Genentech in South San Francisco in 1976, as a comparable model. As a potential business location for a biotechnology firm, San Francisco County and San Mateo County share certain similarities. Both have access to pools of highly‐skilled labor with sufficient transportation infrastructure to draw labor from other parts of the Bay Area (San Francisco actually has a greater diversity of mass transit options). Both are also associated with high‐quality research institutions, San Francisco as the host city to UCSF, and San Mateo through its proximity to Stanford University and through Genentech, which has strong ties to UCSF. Finally, both have a high cost of living, which may deter certain firm types from seeking to locate in either place. San Mateo’s biotech employment growth between 1990 and 2005 is documented in Chapter II. As noted, the County experienced a period of high employment growth during the 1990s. Over this period, the rate of employment growth was between 5 and 8 percent per year, depending on whether a narrow or broad definition of the industry is applied. Based on this information, the “comparable region approach” assumes that San Francisco will experience an annual growth rate of 10 percent for 15 years (2010‐2025) followed by a more stable annual growth rate of 4 percent for 10 years (2005‐2009 and 2026‐2030).


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Table 9 and Figure 4 estimate employment growth in San Francisco based on the methodology described above. As shown, EPS projects an increase of between 5,600 jobs and 8,100 jobs, depending on how the industry is defined. This translates into a need for between 2.5 million and 3.6 million square feet of space, assuming each employee requires 450 square feet.26

Figure 4: Biotech Jobs Projection, Comparable Market Approach, San Francisco

1,0641,539

6,663

9,638

01,0002,0003,0004,0005,0006,0007,0008,0009,000

10,000

Jobs

2005

2008

2011

2014

2017

2020

2023

2026

2029

NarrowDefinitionBroadDefinition

CAPTURE RATE APPROACH

The “capture rate approach” is based on regional biotech employment projections and assumes that San Francisco will capture a higher rate proportion of these jobs than it currently does because of the presence of the UCSF Mission Bay campus. The benefit of this approach is that the results are scaled to actual biotech‐related employment projections for the Bay Area. However, as described further, these projections vary substantially depending on the source. Table 10 summarizes the results from the “capture rate approach”. As shown, the current number of biotech jobs in the Bay Area ranges from 40,600 to 80,000, depending on the definition used to define the industry. San Francisco currently captures between

26 Assumption from CB Richard Ellis Biotech Real Estate Survey, 2006. It reports that, depending on a firm’s status in terms of size from start‐ups to mature companies, the square footage of space per employee ranges from 400 to 475.

Net Change (2005-2030) Narrow: 5,600 jobs; 2.5 million Sq.Ft. Broad: 8,100 jobs; 3.6 million Sq.Ft.

Table 9


ProjectionsItem 2005 2010 2015 2020 2025 2030

Narrow Definition 1,064 1,343 2,163 3,484 5,610 6,663Net Change (2005-2030) 5,599Sq.Ft.2 2,519,700

Broad Definition 1,539 1,943 3,129 5,039 8,115 9,638Net Change (2005-2030) 8,099Sq.Ft.2 3,644,500

Sources: California Economic Development Department; Economic & Planning Systems

Comparable Market Approach: San Francisco Biotech Employment Projection, 2030

Total Employment for Selected Years1

[2] Total Sq.Ft. @ 450 Sq.Ft./Emp, based on real estate survey of biotech firms in Bay Area.

[1] Calculated from 2005 San Francisco employment base, expected to experience a "high" rate of growth of 10% per year for 15 years (beginning 2010) and a stable, 4% annual growth rate for all other years.


30

Table 10Capture Rate Approach: San Francisco Projection, 2030Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

ItemNarrow Broad Other 2 Narrow Broad Other 2

Jobs Projection, 20303 63,962 107,250 160,000 -- -- --

Bay Area Biotech Jobs (2005) 40,633 68,133 80,000 -- -- --

Bay Area Net New jobs 23,329 39,117 80,000 -- -- --

@ 3% capture 611 1,024 2,095 274,900 460,900 942,700

@ 6% capture 1,400 2,347 4,800 629,900 1,056,200 2,160,000

@ 10% capture 2,333 3,912 8,000 1,049,800 1,760,300 3,600,000

[3] See Table 7.

Sources: California Economic Development Department; Census County Business Patterns; San Francisco Biosciences Task Force Report (2005); The Future of Bay Area Jobs (A.T. Kearney); Economic & Planning Systems

Employment Projection by Biotech Definition Sq. Ft. (By Biotech Definition) 1

[1] Total Sq.Ft. @ 450 Sq.Ft./Emp. per survey of Bay Area biotech real estate.

[2] Several publications seeking to analyze and plan for the growth of the life science sector in the Bay Area note a growth potential which would roughly double a very broad definition of life science employees in the region. These documents consider not only the growth potential inherent in the industry's underlying economics, but also consider policies which seek to attract and retain life science companies in the area. ("Very broad" definition of this sector does not include specialized professional service provides such as venture capitalists or attorneys, but does include a wider range of medical jobs than the "broad" definition used in the previous column does.) See Biosciences Task Force Report (2005) and "The Future of Bay Area Jobs", produced for Bay Area Economic Forum, JointVenture Silicon Valley, and SPRIE.

San Francisco Net New Biotech Jobs/ Space


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2 and 3 percent of those jobs.27 Future projections range from growth rates of nearly 60 percent between 2005 and 2030 to about 100 percent between 2005 and 2015. The first projection is from the California Economic Development Department for the 2004 to 2014 period, extrapolated to provide a longer term horizon. The second projection is based on information in the recent San Francisco Bioscience Task Force Report (2005), as well as other publications.28 The “capture rate approach” assumes that San Francisco will capture between 3 and 10 percent of future employment growth through 2030. Three percent corresponds to the current capture rate and represents the low‐end of likely outcomes. The high‐end projection assumes a tripling of the existing City capture rate to 10 percent based roughly on capture rates experienced by San Mateo between 1990 and 2005.29 A capture rate double San Francisco’s existing rate is shown as the middle range projection. These three projections, calculated for three definitions of the biotech industry, provide space projections ranging from 275,000 to 3.6 million square feet. The average projection is 1.3 million square feet.

RETAIL ASSOCIATED WITH BIOTECH EMPLOYMENT

In addition to the space needed to accommodate job growth in the biotech industry, new employees will spur demand for retail space through their spending in nearby neighborhoods.30 A calculation of the amount of new retail space resulting from biotech related employment is provided in Table 11 for the two approaches described above.

27 This capture rate is calculated by dividing the total number of jobs in biotech in San Francisco, by the total number of jobs in the Bay Area, under the identical biotech industry definition. A “capture rate” of 2 percent is not intended to imply that the same types of jobs are located within each County. For example, the overwhelming majority of jobs in the biotech industry are in the “54140‐Research and Development – Physical, Engineering, & Life Science” NAICS code while the majority of biotech jobs in Sonoma County, under the broad definition of biotech, are in NAICS code”541380‐Testing Laboratories”. See Appendix for comprehensive list of NAICS job counts by County. 28 See “The Future of Bay Area Jobs”, produced for Bay Area Economic Forum, JointVenture Silicon Valley and SPRIE (2006). Study notes biotech growth in the Bay Area is expected to average 5‐9% annually for 10 years. 29 Over the 15 year period, San Mateo County doubled its capture rate of Bay Area biotech jobs from 12 percent to 25 percent. Given that San Francisco’s capture rate in 2005 begins at a low‐base (about 3 percent) and that our projection period is 25 years, we estimated a tripling of the capture rate from 3 to 10 percent is a reasonable, higher‐end projection, based on San Mateo County’s experience. 30 Additional economic activity associated with new biotech companies located in San Francisco may also include office supplies, janitorial services, professional services, etc. The precision with which these “spin‐offs” and even the “spin‐offs” of the “spin‐offs” may be quantified and spatially distributed diminishes as the impact examined occurs further downstream from the initial impetus of the impact. Our study is constrained to evaluating space for biotech companies in San Francisco and space demanded related to the Mission Bay hospital development. Onsite retail to serve the employees in the biotech companies and


December 10, 2007


As shown, the “comparable market approach” estimates between 5,600 and 8,100 net new employees. Using a typical assumption about employee‐spending near their places of work, total annual expenditures on retail goods is estimated at $25.7 million to $37.1 million, enough to support between 73,000 and 106,000 square feet of retail space.31 The “capture rate approach” generates between 1,400 and 4,800 new employees. Applying the same spending assumptions as discussed above, between 18,000 and 63,000 square feet of retail is likely to be demanded.

hospital‐related health organizations is closely tied with these two impacts we were directed to study and is therefore included in the projection. 31 One publication, “Office Worker Retail Spending Patterns (2003, International Council of Shopping Centers)”documents surveys of office workers’ and their spending habits. A typical office worker in an urban area of the western U.S. spends a little more than $25 per working day near their place of work.

Table 11Retail Space Associated with Biotech Employment, 2030 ProjectedCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

ItemNarrow Broad Other Narrow Broad Other

Projection - Biotech (Jobs & Sq.Ft.)Comparable Market Approach1 5,599 8,099 -- 2,519,700 3,644,500 --Capture Rate Approach2 1,400 2,347 4,800 629,900 1,056,200 2,160,000

Retail Spending/Space Associated with Jobs Projection3

Comparable Market Approach $25,659,000 $37,113,000 -- 73,000 106,000 --Capture Rate Approach $6,414,000 $10,755,000 $21,996,000 18,000 31,000 63,000

Total Biotech Projection (Biotech Space + Retail Space)Comparable Market Approach -- -- -- 2,592,700 3,750,500 --Capture Rate Approach -- -- -- 647,900 1,087,200 2,223,000

[1] See Table 9, "Net Change" in biotech employment between 2005 and 2030. [2] See Table 10, row showing 6% capture rate.

Sources: Office Worker Retail Spending Patterns (2003, International Council of Shopping Centers); Economic & Planning Systems

Employment by Biotech Definition Sq. Ft. (By Biotech Definition)

[3] Retail space is estimated by calculating spending by office employees per year and dividing by $350 per square foot of retail space, the gross level of retail sales needed to support 1 square foot of retail space. Typical spending by office workers is based on Office Worker Retail Spending Patters publication.


34


IV. HOSPITAL‐RELATED SPACE AND DEMAND

This chapter examines the extent to which health‐related organizations may seek to locate near the planned UCSF Mission Bay hospital. Four types of space impacts are evaluated: (1) Demand for doctors offices both from physicians employed at UCSF and those affiliated with the hospital; (2) Space demands from businesses which provide supplies to the hospital; (3) Demand from businesses which rely on hospital patients and employees, as a primary source of business; and (4) Space impacts because of retail businesses locating nearby to serve the hospital‐related population (e.g., doctors, staff, and patients). Demand for hospital‐related space is estimated using two approaches. One approach, the “case study approach,” relies on the experience of other Bay Area hospitals. It takes a broad look at hospital impacts, including the observed phenomenon of health‐related businesses clustering around hospitals. The second approach, the “project description approach,” analyzes the preliminary plan for the Mission Bay to estimate the demand for space generated by the hospital population and its direct suppliers.

CASE STUDY APPROACH

Hospitals across various locales tend to be surrounded by health‐related establishments such as physicians’ private medical offices, pharmacies, alternative health providers, and other organizations that provide products and services to the hospital and its operations. In order to quantify this phenomenon, EPS selected four hospitals in the Bay Area and examined the number of health‐related businesses within walking distance and in each city as a whole.

INFORMATION ON SELECTED CITIES

The four case studies evaluated in the study are: Alta Bates, in Berkeley; Eden Medical Center, in Castro Valley; Sutter Hospital, in Santa Rosa, and UCSF’s Mount Zion, in San Francisco.32 The case studies represent: (1) large and small hospitals located within large and small communities, (2) cities with multiple as well as single‐hospitals; and (3) cities with various levels of development intensity within their borders. Each of the individual case studies offers unique programs and circumstances that impact the number of health‐related businesses that cluster around the hospitals.33

32 We also examined data on health‐related businesses throughout each of the cities. For San Francisco in particular, every hospital in the city is cited in the pertinent figure to highlight areas where a cluster of health‐related businesses may exist. 33 These four case studies were selected to represent a variety of types of hospitals and settings. Having a variety of sizes of hospitals and different, local land use conditions was important at selecting case studies at the outset of our study‐process because we theorized that the patient‐traffic – that is, the number of times a patients enters and leaves a hospital – would play a role in the extent to which businesses outside the


December 10, 2007


Table 12 provides details on the relative sizes of the hospitals, measured by the number of inpatient beds, a typical measure of hospital size tracked annually through the State’s Office of Health Planning and Development. Alta Bates is the only hospital in Berkeley, making up 100 percent of the beds within the city, while beds at Mount Zion make up only about 1 percent of the San Francisco total. The number of beds at Eden Medical Center and Sutter Medical Center comprise 88 percent and 24 percent, respectively, of the total hospital beds in each city. Figures 5 to 8 illustrate the locations and employment levels of health‐related organizations within the case study cities.34 For Figures 5 to 7, the businesses are plotted by aggregated categories and sizes based on employment onsite. For Figure 8, which shows each hospital in San Francisco as well as health‐related locations within the City, only the locations of each organization are shown for simplification purposes. 35 (See Appendix for detailed map.) As shown, these maps illustrate the extent to which these types of establishments tend to cluster around local hospitals. To quantify the clustering affects illustrated above, EPS compared health related employment levels within one‐half and one‐mile of each hospital (the counts exclude employment in the hospital itself). A concentration exists if the amount of employment within these radii exceeds the level that would be expected based on its relative size within the cities evaluated. For example, 25 percent of a city’s total medical‐related employment located within 5 percent of the city’s land area would be considered a concentration or cluster.36 Table 13 provides counts of health‐related employment within one‐half, and one‐mile of each hospital. As shown, of the four case studies examined, the analysis indicates a concentration of health‐related businesses proximate to hospitals appears to exist in all but one case. No concentration of health‐related businesses is found around Sutter Medical Center.

hospital would be supported by the presence of the hospital. The relationships examined in the case studies are those which occur outside the hospital – that is, tracking the amount and location of economic activity generated by patients of the hospitals. As is discussed in the analysis of the data from these hospitals and others within each of the four cities, the economic relationships between patients and non‐hospital, health‐related establishments, are generally consistent across all four case studies as well as the 15 other hospitals in the four cities. 34 See Appendix for the list of NAICS/ SIC codes for which information on health‐related businesses is reported in the maps and tables in this section. They generally include the types of businesses from which a hospital may purchase supplies or services (laundry or medical instruments) and businesses whose primary customer base is made up of patients (doctor’s offices, chiropractors, long‐term care facilities, etc.). 35 The boundaries of two Eastern Neighborhoods – Showplace Square/Potrero Hill and the Central Waterfront are within roughly one mile of the site of the hospital. In addition, available space at Mission Bay is within this one‐mile radius. These sources of land are used in the supply analysis. 36 This methodology assumes that a city’s geographic area is relatively similar throughout its jurisdiction in terms of its development potential. This assumption is appropriate for urbanized areas but would be less applicable in jurisdictions with significant land reserved for non‐development purposes.

Table 12Hospital Beds, Four Selected CitiesCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Hospital Name/ Location Certified State Hospital % of City's TotalBeds (2007) Beds

BerkeleyAlta Bates Summit (Alta Bates and Herrick) 553 100%

Castro ValleyEden Medical Center 234 88%Laurel Grove 31 12%

Total 265 100%

San FranciscoCalifornia Pacific Medical Center 1,044 19%Chinese Hospital 54 1%Hebrew Home for the Aged Disabled 491 9%Kaiser (Geary) 247 4%Laguna Honda 1,457 26%SF General Hospital 598 11%St. Francis Memorial 356 6%St. Luke's 260 5%St. Mary's Medical 403 7%UCSF - Langley 67 1%UCSF - Medical Center 566 10%UCSF Mount Zion 72 1%

Total 5,615 100%

Santa RosaKaiser 117 17%Santa Rosa Memorial (Fulton, Montgomery, Sotoyome) 345 50%Sutter Medical Center 163 24%Sutter Warrack 63 9%

Total 688 100%

Source: Office of Statewide Health Planning and Development, Hospital Beds Licensed June 30, 2007; Economic & Planning Systems


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Figure 5: Health-related Establishments near Alta Bates Medical Center

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Figure 6: Health-related Establishments near Eden Medical Center


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CALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTRCALIFORNIA PACIFIC MED CTR DAVIES CAMPUS DAVIES CAMPUS DAVIES CAMPUS DAVIES CAMPUS DAVIES CAMPUS DAVIES CAMPUS DAVIES CAMPUS DAVIES CAMPUS DAVIES CAMPUS

LANGLEY PORTERLANGLEY PORTERLANGLEY PORTERLANGLEY PORTERLANGLEY PORTERLANGLEY PORTERLANGLEY PORTERLANGLEY PORTERLANGLEY PORTERPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTEPSYCHIATRIC INSTITUTE

UCSF MEDICAL CENTERUCSF MEDICAL CENTERUCSF MEDICAL CENTERUCSF MEDICAL CENTERUCSF MEDICAL CENTERUCSF MEDICAL CENTERUCSF MEDICAL CENTERUCSF MEDICAL CENTERUCSF MEDICAL CENTER

ST. MARY'SST. MARY'SST. MARY'SST. MARY'SST. MARY'SST. MARY'SST. MARY'SST. MARY'SST. MARY'S

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ST. FRANCIS ST. FRANCIS ST. FRANCIS ST. FRANCIS ST. FRANCIS ST. FRANCIS ST. FRANCIS ST. FRANCIS ST. FRANCISMEMORIAL HOSPITALMEMORIAL HOSPITALMEMORIAL HOSPITALMEMORIAL HOSPITALMEMORIAL HOSPITALMEMORIAL HOSPITALMEMORIAL HOSPITALMEMORIAL HOSPITALMEMORIAL HOSPITAL

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UCSF Medical Centerat Mission Bay (2014)

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Figure 8: San Francisco Hospitals and Health-related Establishments


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41

Table 13Employment Surrounding Four Hospitals (Excludes hospital staff), 2007Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Facility Type/ Area# % # % # % # %

Senior CareTotal 616 100% 616 100% 1,816 100% 4,514 100%0-1/2 Mile Radius 100 16% 275 45% 70 4% 523 12%0-1 Mile Radius 432 70% 338 55% 83 5% 1,976 44%

PharmaciesTotal 300 100% 181 100% 382 100% 2,028 100%0-1/2 Mile Radius 24 8% 7 4% 0 0% 66 3%0-1 Mile Radius 92 31% 121 67% 50 13% 245 12%

Other Patient-Serving Orgs.Total 593 100% 113 100% 1,900 100% 5,978 100%0-1/2 Mile Radius 183 31% 16 14% 0 0% 357 6%0-1 Mile Radius 316 53% 100 88% 15 1% 2,125 36%

Doctor's OfficesTotal 3,625 100% 448 100% 2,661 100% 13,059 100%0-1/2 Mile Radius 954 26% 347 77% 237 9% 1,777 14%0-1 Mile Radius 2,401 66% 423 94% 490 18% 4,131 32%

DentalTotal 454 100% 241 100% 805 100% 4,082 100%0-1/2 Mile Radius 8 2% 145 60% 0 0% 86 2%0-1 Mile Radius 126 28% 218 90% 30 4% 492 12%

Alternative HealthTotal 240 100% 67 100% 300 100% 1,331 100%0-1/2 Mile Radius 37 15% 13 19% 0 0% 178 13%0-1 Mile Radius 135 56% 43 64% 9 3% 343 26%

All Other1

Total 949 100% 118 100% 2,803 100% 7,609 100%0-1/2 Mile Radius 111 12% 27 23% 0 0% 189 2%0-1 Mile Radius 404 43% 105 89% 21 1% 774 10%

TOTAL ALLTotal 6,777 100% 1,784 100% 10,667 100% 38,601 100%0-1/2 Mile Radius 1,417 21% 830 47% 307 3% 3,176 8%0-1 Mile Radius 3,906 58% 1,348 76% 698 7% 10,086 26%

[1] This category is defined in the Appendix. Sources: InfoUSA; Economic & Planning Systems

Alta Bates Eden Medical Sutter Mount Zion



December 10, 2007


FINDINGS FROM CASE STUDY APPROACH

This analysis uses data on the relationship between hospital size (e.g., number of patients) and the level of health‐related employment from the case studies above to quantify that amount of health‐related space expected to locate in or near the new UCSF facility. The analysis is based on data from all hospitals in San Francisco plus the three other case studies (for a total of 19 hospitals). Table 14 summarizes the two primary inputs needed to quantify the approach outlined above. The total number of annual patient‐visits (both inpatient and outpatient visits) to hospitals is provided by hospital, based on California Office of Statewide Health Planning and Development (OSHPD) data for year 2005. Berkeley, Santa Rosa, Castro Valley, and San Francisco handle between 162,000 and 2.5 million patient‐visits per year.37 Although these service‐populations range widely in size, the ratios of patients to hospital full‐time equivalent (FTE) employees in these four cities is relatively constant, between 118 patients per FTE to 155 patients per FTE.38 The second ratio is the number of non‐hospital health‐related employees within each of the four cities, compared to the number of patient‐visits. Across the four cities, the case study data indicates that there are between 11 and 30 non‐hospital, health‐related jobs in the city, for every 1,000 patient‐visits. These two average relationships – (1) Patient‐visits per hospital‐FTE and (2) Non‐hospital, health‐related employees per 1,000 patient‐visits – are used in Table 15 to calculate expected space demands related to the hospital development.39 As shown, the net new FTEs expected at the Mission Bay hospital is used to estimate the annual expected patient‐visits (97,000 and 150,000 patient‐visits per year, depending on the size of the hospital). Based on the total annual patient‐visits, EPS estimates the number of non‐hospital, health‐related employees of UCSF at between 2,150 and 4,350, depending on the hospital size. Assuming approximately 350 square feet per new employee, totals of 750,000 square feet of space related to the 289‐bed hospital and 1.5 million square feet related to the 550‐bed hospital are expected.

37 The term “patient‐visits” used in this report is distinct from the number of individual patients. This term includes the number of discharges (inpatients, outpatients, and nursery discharges are included) the hospital makes each year. For example, a patient may enter the hospital and remain an inpatient for 10 days, resulting in a single patient‐visit. Conversely, the same patient may enter the hospital on 10 separate days, being admitted and discharged 10 times, resulting in 10 patient‐visits. This accounting method is used because economic activity related to the hospital (but outside the hospital) is expected to occur each time the patients enters/exits the hospital. Therefore, the number of visits a patient makes to the hospital is the number used to project health‐related employment outside the hospital. 38 The average calculated for the four cities is 139 patients per FTE. This is consistent with data for the entire State of California, which averages roughly 145 patients per FTE. 39 These space demands include all types of space except retail, serving the hospital daily population. This square footage is included in the summary tables.

Table 14Case Study Approach, Key CalculationsCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Item Berkeley Santa Rosa Castro Valley San Francisco Average

Patient-Visits (Citywide) CalculationOutpatients 253,608 321,332 143,947 2,330,559 --Inpatient Discharges 26,966 29,562 16,386 115,661 --Nursery Discharges 6,836 4,107 852 9,823 --

Total Patient Visits1 287,410 355,001 161,185 2,456,043 --

Relationships Used for Projection CalculationHospital FTEs2 1,855 3,017 1,212 16,294 --

Patients per FTE 155 118 133 151 139

Non-Hospital, health employment (by City)3 6,777 10,667 1,784 38,601 --24 30 11 16 20Health Employees per 1,000 patients

Sources: Office of Statewide Health Planning and Development - Dataset: "Hospital Annual Financial Data for California" 2005; Economic & Planning Systems

[1] Emergency visits are included in outpatient numbers, according to Office of Statewide Health Planning and Development.

[2] Full-time equivalents accounts for all hospital staff, including support staff who do not work directly with patients. When this average relationship is applied to the UCSF Mission Bay hospital, all staff expected at the Mission Bay site is similarly accounted for.

[3] The average of 20 non-hospital, health-related employees per 1,000 patient-visits is somewhat higher than San Francisco's actual ratio of 16 employees per 1,000 patient-visits (within the health-related employment category) while the average "Patients per FTE" is somewhat lower than San Francisco's actual average.


44

Table 15Case Study Approach, ResultsCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Projection Formula 289-Bed Hospital 550-Bed Hospital

Hospital FTEs 1 1,095 2,224(Staff Relocated, not backfilled) 1 (329) (667)Net New FTEs a 767 1,557

Expected PatientsAverage Patient visits per Hospital FTE b 2 139 139Expected Patients c = a * b 106,607 216,523

Expected Non-Hospital Health Employeesd 3 20 20

New Employees e = d * (c / 1,000) 2,143 4,353

@ 350 Sq.Ft. 4 f = e * 350 750,090 1,523,470.Neighborhood Capture Rate

450,100 914,100

150,000 304,700

[1] Per UCSF estimate, including both hospital and ambulatory care center employees.[2] See Table 14 - "Patients per FTE" average.[2] See Table 14 - "Health Employees per 1,000 patients" average.[4] EPS assumption based on evaluation of the types of businesses included in health-related businesses data.

Sources: UCSF; Office of Statewide Health Planning and Development - dataset: "Hospital Annual Financial Data for California" 2005; Economic & Planning Systems

@ 60% Capture Rate (one-mile)5

@ 20% Capture Rate (one-half mile)5

Health Employees per 1,000 patients

[5] See Table 13 for range of neighborhood capture rate of health-related businesses within one-half, and one-mile of the four hospitals examined under the case study approach.


45


December 10, 2007


The extent to which this demand will be captured near the UCSF hospital versus elsewhere in the City will be dependent both on land availability and the degree to which hospital proximity is necessary to a firm’s business. Based on the analysis of the four hospital case studies, between 20 percent and 60 percent of employment is estimated to be captured within one‐half mile and one‐mile, respectively from the hospital.40 This low and high neighborhood capture rate results in demand for between 150,000 square feet and 450,000 square feet for the 289‐bed hospital and between 305,000 square feet and 914,000 square feet for the 550‐bed hospital.

PROJECT DESCRIPTION APPROACH

The “project description approach” estimates the demand for space based on the economic activity and population stemming from the Mission Bay hospital operations. This approach provides a low‐end projection because it does not account for health‐related organizations, unaffiliated with the Mission Bay hospital itself, attracted to the location. As calculated in Tables 16 through 18 and summarized below, space demand is assumed to be generated from a number of distinct sources.

• Private office space for UCSF physicians: As shown in Table 16, UCSF physicians will demand between 75,300 and 140,000 square feet of private office space, for the 289‐bed and 500‐bed hospital, respectively. The hospital itself may also drive space demands because of its own spending including purchases of hospital supplies, information technology and software, transportation and professional services.

• Building space for UCSF hospital suppliers: As shown in Table 17, firms that

supply the new hospital with goods and services will require 43,700 and 95,700 square feet of space near the facility for the 289‐bed and 500‐bed hospital, respectively assuming a neighborhood capture rate of 60 percent.

• Demand for retail space: Table 18 estimates the demand for retail space

supported by the spending of the hospital‐related population (e.g., employees, patients and visitors). Depending on the size of the hospital, between 35,800 and 69,400 square feet in retail space may be required.

40 This range was examined by disentangling the effect on the case studies of nearby hospitals and by considering zoning restrictions or lack of available space around the hospitals. It is roughly equal to the findings from the Alta Bates example, which is the only case study with only one hospital in the city.

Table 16Project Description Approach: Physician Private OfficesCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Item289 Beds 550 Beds

Attending Physicians1 279 517Proportion with private offices in City2 36% 36%Neighborhood capture3 75% 75%Building Sq.Ft./ Physician4 1,000 1,000

Demand for physician office space 75,300 139,600

[1] Planning document, UCSF.

[2] From UCSF review of existing physicians and residents.

[4] EPS estimate based on prior studies.

Sources: UCSF, InfoUSA, Economic & Planning Systems

[3] Review of doctor's office in San Francisco indicates that 74% are located within one-half mile of one of San Francisco's 12 hospital locations.

Hospital Size


Table 17Project Description Approach: Hospital SuppliersCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Item 289 Beds 550 Beds

Employment Associated with Hospital Spending on Supplies1 208 456

Neighborhood capture2

14,600 31,90043,700 95,700

Sources: IMPLAN, Economic & Planning Systems

@ 20% capture@ 60% capture

Hospital Size

[2] Neighborhood capture rates shown based on an evaluation of the industries most affected by an increase in spending related to the hospital, and whether close proximity to the hospital would be an important advantage to the business. Assumes 350 square feet per employee.

[1] Employment total estimated using input-output model, IMPLAN which models changes in a local economy, by industry.


48

Table 18Project Description Approach: Hospital Population SpendingCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Individual

Average WeekdayStaff/ Visitor Population1

Annual Spending

Days2

Daily Spending/

Person3Neighborhood

Capture4 Annual Spending Supportable Sq. Ft.5

a b c d e = a * b * c * d @$350 sales/ Sq.Ft.

289-Bed HospitalPhysicians 279 235 $26.44 85% $1,464,800 4,200Hospital Staff 644 235 $26.44 86% $3,441,200 9,800Intern/Student 172 172 $12.30 90% $326,500 900Inpatients 246 365 $5.00 86% $387,200 1,100Outpatients 1,560 260 $5.00 86% $1,744,100 5,000Visitors 2,230 260 $10.44 86% $5,182,200 14,800

Square Feet 35,800

550-Bed HospitalPhysicians 517 235 $26.44 85% $2,714,400 7,800Hospital Staff 1,379 235 $26.44 86% $7,368,700 21,100Intern/Student 328 172 $12.30 90% $622,600 1,800Inpatients 468 365 $5.00 86% $736,700 2,100Outpatients 2,838 260 $5.00 86% $3,172,900 9,100Visitors 4,138 260 $10.44 86% $9,616,100 27,500

Square Feet 69,400

[1] Provided by UCSF.

[4] Neighborhood capture rate based on assumption that 25% of those who drive to work will go outside the neighborhood for their work-hour expenditures.

Sources: Office Worker Retail Spending Patterns (2003, International Council of Shopping Centers); UCSF Parking Demand Study for Mission Bay Hospital, April 20, 2007; Bureau of Labor Statistics

[2] Estimated based on number of working days and school days for hospital staff and students. Inpatients beds are assumed to be equally occupied regardless of the day of the year. Outpatients and visitors are assumed to be onsite during the work-week.[3] Spending per person is estimated based on a number of sources: Hospital staff spending is based on Office Worker Retail Spending Patters publication; Interns/ student spending is based on UCSF's estimated student budget; Inpatients are assumed to spend little outside of the hospital, with the $5 assumption included as a parking allocation; and Outpatient/Visitor spending is based on average consumer spending for the San Francisco MSA, adjusted for the likely number of waking hours outpatients/visitors will spend near the hospital.

[5] Assumes 350 square feet per retail employee.


49


V. IMPACT ON NEARBY NEIGHBORHOODS

This chapter uses the space demand estimates from the previous chapters to evaluate the potential impact of the Mission Bay campus on nearby neighborhoods. Specifically, it compares the estimated demand for space because of (1) biotech spin‐offs and (2) hospital‐related business with the supply of land in neighborhoods adjacent to the campus. The areas examined in this study are: Showplace Square/Potrero Hill; Central Waterfront; and Mission Bay.41 A one‐mile radius around the hospital site and existing UCSF Mission Bay campus coincides with available data on land supply and may be considered within “walking distance” of the hospital and UCSF Mission Bay campus.42

DEMAND SUMMARY

The projected demand for building space from new commercial tenants seeking proximity to the Mission Bay campus is summarized in Table 19 based on the analysis in the preceding chapters. The information is summarized below for each demand driver.

• Biotech space demand. As shown, the projected range of demand for biotech space is between 840,000 and 3.65 million square feet, depending on the definition with which the industry is defined and on the aggressiveness of the assumptions underlying the projection. The highest demand projection results from the “comparable market approach” using the broad definition of the sector. The low end of the scale is based on the narrow definition and “capture rate” approach.

• Health‐related space demand. The range of the health‐related space demand

depends primarily upon the size of the hospital examined and whether the “case study approach” or “project description approach” is more likely. The “case study approach” yields the highest result with about 450,100 square feet of health care‐related space under a 289‐bed hospital and 914,100 square feet of space with a 550‐bed hospital.

• Retail space demand. The retail space demand projection is primarily driven by

the number of new employees in biotech and the health‐related fields, including those in the hospital. Thus, under the smallest biotech employment scenario and

41 Although East SoMA and the Mission are included in the Eastern Neighborhoods Rezoning Area, the idea driving the demand projection is that organizations will be drawn to either the UCSF hospital or the UCSF Mission Bay campus to benefit from interactions with hospital patients or UCSF students and staff. These interactions are most likely to take place within walking distance of either of these two drivers of “spin‐offs”. 42 Other distances for walking used in planning include one‐quarter mile and one‐half mile.

Table 19Summary of Demand ProjectionsCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Demand Projection

Biotech Spin-Offs Narrow Definition Broad Definition Other Projection(through 2030) (through 2030) (through 2020)

Biotech Employee SpaceComparable Market Approach 1 2,520,000 3,645,000 --Capture Rate Approach2 840,000 1,408,000 2,880,000

Health Related Space Demand 289-Bed Hospital 550-Bed Hospital

Project Description ApproachHospital physician, private office space 3 75,300 139,600Hospital supplier space4 43,700 95,700

Total Project Description Approach 119,000 235,300

Case Study ApproachHealth-related Businesses5 450,100 914,100

Retail Space

Retail related to biotech employmentComparable Market Approach6 73,000 106,000 --Capture Rate Approach6 18,000 31,000 63,000

Retail related to Hospital-related Demand employmentProject Description Approach7 35,800 69,400Case Study Approach8 48,700 95,500

Retail, lowest projection 53,800Retail, highest projection 201,500

[1] See Table 9.

[3] See Table 16.[4] Reflects higher of two neighborhood capture rates shown (60 percent). See Table 17.[5] Reflects higher of two neighborhood capture rates shown 60 percent. See Table 15.[6] See Table 11. Fill-formatting indicates number is used in chart illustrating retail demand.[7] See Table 18.

Source: Economic & Planning Systems

Scenario Analyzed

[2] Average of 6 and 10 percent projected capture rate, reflecting expectation that San Francisco will increase its rate of capture of Bay Area biotech employment, which is currently about 3 percent. (See Table 10.)

[8] See Table 15 for number of new, non-hospital, health-related employees. Retail demand from spending due to this new employment is added to the retail demand for hospital employees. Fill-formatting indicates projection is used in chart illustrating retail demand.



December 10, 2007


using the 289‐bed hospital scenario, demand for new retail space is estimated to be about 53,800 square feet. Using the largest biotech employment demand and the 550‐bed hospital scenario (“case study approach”), 202,000, square feet is projected.

SUPPLY EVALUATION

Evaluating supply of land in the City and in proximity to the Mission Bay hospital requires estimating the quantity of vacant or underutilized land and the amount of it that may be converted to biotech, medical, retail, and/or office space based on zoning and other factors. This information is derived from several sources, including the Environmental Impact Report (DEIR) for the Eastern Neighborhoods Rezoning, data from ongoing planning efforts for large underutilized areas on nearby properties, and information from the City of San Francisco Planning Department. Information on the amount and sources of land supply estimated in this analysis is presented in Tables 20 to 22 and summarized below by category. Vacant land. The Eastern Neighborhoods Rezoning DEIR provides an estimate of the acres of vacant land in East SoMa, Mission, Showplace Square/Potrero Hill, and the Central Waterfront. Table 20 reports figures for Showplace Square/Potrero Hill and the Central Waterfront, the two neighborhoods within roughly one‐mile of the Mission Bay campus/future hospital site. As shown, 23 acres of land within these two neighborhoods are vacant/surface parking lots. This estimate is refined to provide an estimate of supply for medical and biotech uses by focusing on the proposed Life Science/Medical overlay in the Central Waterfront just south of Mission Bay. (See Figure 9 for an illustration of the location of the proposed Life Science/ Medical Overlay.) A Planning Department review of the vacant and utilized parcels within the proposed overlay estimates that there are 14.32 acres of land which may serve as potential infill, redevelopment areas. 43

43 Rather than using the full, 23 vacant acres shown in the Eastern Neighborhood Option B rezoning plan as a supply source, this smaller, 14.32 acres designation is used in the analysis as the potential supply of space to meet demand projections.

Table 20Selected Eastern Neighborhoods Land Supply; Existing UsesCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Use

Acres % of Total Acres % of Total Acres % of Total

Residential 169 43% 7 41% 9,774 45%PDR - Production, Distribution, Repair 81 21% 362 38% 1,582 7%Cultural/ Institutional 45 11% 1 8% 1,293 6%Mixed Uses 34 9% 2 3% 1,176 5%Public/Open Space 21 5% 3 2% 6,097 28%Vacant 19 5% 4 2% 551 3%Retail 13 3% 4 1% 512 2%Office 9 2% 0 1% 363 2%Residential Mixed 5 1% 2 4% 222 1%Hotel/ Visitor Retail 0 0% 0 0% 67 0%Total 396 100% 385 100% 21,638 100%

Sources: San Francisco Planning Department Eastern Neighborhoods Rezoning and Area Plans, Draft Environmental Impact Report; Economic & Planning Systems

Showplace Sq. & Potrero Hill Central Waterfront Total City


53


December 10, 2007


Figure 9: Proposed Life Science /Medical Zoning Overlay

Land use conversions/new development related to rezoning. The DEIR for the Eastern Neighborhoods also provides an estimate of the likely land use changes which will take place under the rezoning plan, through 2025, by neighborhood. In the Central Waterfront, a total of 52,600 square feet of additional space in the Medical and Office use categories is expected (under Option B, rezoning option), as shown in Table 21.

Port projects. The Port of San Francisco is currently undertaking planning processes for Seawall Lot 337 (SWL 337) and Pier 70. SWL 337 is a 16‐acre site between the Ballpark and Mission Bay which is used as a surface parking lot. The Port submitted a Request for Proposals for the site to the development community in October 2007. The RFP does not define a density parameter for the site; however, using assumptions about surrounding density, and assuming both commercial and non‐commercial may ultimately be proposed for the site, about 600,000 square feet of commercial space is estimated for the site. The Port is also undertaking a planning process for Pier 70, a 65‐acre location, just south of Mission Bay and in

Table 21Projected Land Use (2000-2025, Option B)Commercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Item 2000 2025 Change % Change

Central WaterfrontResidential Units 798 1,922 1,124

Cultural/ Institutional 52,500 67,500 15,000 29%Medical 2,700 3,300 600 22%Office (MIPS)1 1,057,800 1,109,819 52,019 5%PDR 3,569,371 3,666,765 97,394 3%Retail/Entertainment 167,400 184,500 17,100 10%Visitor 30,600 30,900 300 1%Total SF 4,880,371 5,062,784 182,413 4%

[1] MIPS = Management Informational and Professional Services

Source: Eastern Neighborhoods Draft EIR, June 20, 2007; Economic & Planning Systems



December 10, 2007


close proximity to the Mission Bay hospital. Development of this site is in earlier stages than SWL 337. Preliminary estimates of the development potential discussed during a public meeting in November 2007 indicates that between 1.5 and 2.6 million square feet of commercial development may be proposed for the site as well as the rehabilitation of several hundred thousand square feet of existing building space.

Excluded supply: Mirant and Rest of City. The Mirant Power Plant, located just south of Pier 70, is a 27‐acre waterfront site. The operator recently signed an agreement with the City indicating its intention to close the Plant when the State’s Independent System Operator and the Federal Energy Commission grants permission to do so. Though this property presents a significant amount of potentially redevelopable property, because of the regulatory hurdles which need to be crossed before any development, as well as the likelihood of the need for contamination clean up, it is not included as land supply in this analysis.

In addition, for the purposes of presenting a conservative analysis of the extent to which demand may strain available supply, all biotech demand projected for the City in the study is compared with supply of land only in Mission Bay and the Central Waterfront. It is likely that a portion of this demand will be met elsewhere in the City. For example, there are a number of retail sites available in the Central Waterfront and Showplace Square neighborhoods that may be well‐positioned to absorb increased demand for retail locations.

Table 22 summarizes the supply options. Supply from vacant and underutilized land along with expected conversions to medical and office space in Central Waterfront neighborhood totals 988,300 square feet. Supply at Mission Bay, likely to be developed for biotech uses, adds about 3.15 million square feet of supply. A significant amount of retail may still be developed at Mission Bay, according to the Redevelopment Plan for the area (about 495,000 square feet). In addition, the preliminary plan for the 289‐bed hospital will provide about 23,000 square feet of retail space, onsite (food‐related). The final sources of supply in the area are the Port’s SWL 337 and Pier 70 properties. Based on conservative assumptions about density and a mix of uses at the site, 600,000 square feet of commercial development is assumed for SWL 337. Assessing the potential development now being discussed at Pier 70, 2.25 million is included in the supply analysis for this site. The total supply of potential development and conversion is 9.0 million square feet.

Table 22Summary of Supply OptionsCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Potential Land Supply Central Waterfront Mission Bay Total Sq. Ft.

Eastern Neighborhoods, Option BVacant/"Soft Sites", Life Sciences/Medical Overlay 1 935,700 -- 935,700Medical 600 -- 600Office 52,000 -- 52,000Subtotal 988,300 -- 988,300

Planned DevelopmentsMission Bay - Biotech2 -- 3,150,000 3,150,000Mission Bay - Retail3 -- 495,000 495,000Hospital, Onsite Retail -- 23,000 23,000Subtotal -- 3,668,000 3,668,000

Other Planning SitesSWL 337 4 600,000 -- 600,000Pier 705 2,650,000 -- 2,650,000

Total 4,238,300 3,668,000 9,035,000

[3] Retail square footage remaining within entitlement under the Redevelopment Plan.

Source: Economic & Planning Systems

[1] Planning Department assessment of vacant acreage and "soft" sites within the proposed Life Science/ Medical overlay, not including Pier 70 and Mirant. Potential building square footage calculated assuming a the floor area ratio of 1.5 for the 14.3 acres. [2] Total development entitlement under ownership of developers likely to build biotech and mixed office space.

[5] The high and low potential building square feet presented by the Port of San Francisco on the preliminary, master planning efforts for this site included estimates of 1.5 and 2.6 million square feet. This estimate reflects the average of these two new development potential estimates, plus about 600,000 square feet of existing building space.

[4] Seawall Lot 337 is a 16-acre, mixed use opportunity site owned by the Port of San Francisco. The October 30, 2007 Request for Proposals for the site does not propose a maximum density for the site. The total square footage shown assumes about 30 percent of the site is used for streets and public uses, that the floor-area-ratio for the site averages 2.5, and about half the site is commercial development.


57


December 10, 2007


ANALYSIS OF POTENTIAL LAND USE IMPACTS

This analysis evaluates the potential land use impact of the Mission Bay campus on surrounding neighborhoods by comparing projected market demand with market supply. If estimated demand equals or exceeds supply, one would expect the potential for land use conflicts and significant price appreciation. On the other hand, if supply appears abundant relative to Mission Bay related demand, one would expect plenty of opportunity for other land uses to thrive. In other words, if Mission Bay‐generated demand is significant relative to the number of development opportunities in surrounding neighborhoods, than these uses may “crowd‐out” or dominate other land uses, especially those that cannot compete on price. Figures 10 to 13 provide graphical illustrations of the market dynamics described above by applying the supply estimated in Table 22 with the demand summarized in Table 19. Because each use may have disparate impacts on the neighborhoods and because supply of land for one use may not be suitable for another, the projected demand and supply dynamics are evaluated separately below.

SUPPLY‐DEMAND COMPARISON: BIOTECH AND HEALTH‐RELATED

As shown in Figure 10, the highest projected demand is for biotech space (3.65 million square feet). Unlike projections for medical‐related uses and retail, the demand projection for biotech includes all demand likely for the City as a whole. In reality, it is likely that some of the biotech‐related demand will occupy space outside the neighborhoods evaluated in this study, thus reducing demand pressures.

Figure 10: Biotech Space: Various Demand Projections, 2005‐2030

2,520,000

3,645,000

840,000

1,408,000

2,880,000

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

Build

ing

Squa

re F

eet

1 2 3 4 5

DemandEach bar represents an individual projection for the period, they are not cumulative.

Demand Projections Definitions1. Comparable Market Approach, Narrow Definition of Biotech

2. Comparable Market Approach, Broad Definition of Biotech

3. Capture Rate Approach, Narrow Definition of Biotech

4. Capture Rate Approach, Broad Definition of Biotech

5. Capture Rate Approach, Published accounts of bitoech growth


December 10, 2007


Demand for medical‐related space could be as high as the 450,000 to 914,000 square foot range, depending on the size of the hospital. Figure 11 illustrates these projections.

Figure 11: Medical ‐Related Space: Various Demand Projections

450,100

914,100

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

Bui

ldin

g Sq

uare

Fee

t

289 Bed-CaseStudy 550 Bed-CaseStudy

Demand

Projection of demand for space outside the hospital. Demand projections are not cumulative, they represent individual projections, based on the size of the Mission Bay hospital.


December 10, 2007


Figure 12 illustrates the comparison between demand for biotech and health‐related space with supply in the Mission Bay and Central Waterfront areas. As shown, the lowest demand projection – adding demand for health‐related establishments from the 289‐bed hospital to the biotech space projection using the capture rate approach (narrow definition of biotech industry) – totals 1.3 million square feet. Also shown is the highest demand projection estimated by adding demand for health‐related establishments from the 550‐bed hospital to the biotech projection using the comparable market approach. This high‐end projection totals 4.6 million square feet.

Figure 12: Biotech, Medical‐Related Space: Demand and Supply Comparison

840,000450,100

3,645,000

914,100

3,150,000

600,000

935,700

52,000

2,650,000

0

1,000,000

2,000,000

3,000,000

4,000,000

5,000,000

6,000,000

7,000,000

8,000,000

Demand, LowestBiotech + 289-Bed

Hospital

Demand, HighestBiotech + 550-Bed

Hospital

Supply, by type

DemandMedical-Related

Biotech

SupplyPier 70

Office conversion

Life Science/ Medical Overlay (vacant, soft sites)

SWL 337

Mission Bay1,290,10

4,559,10

7,387,700

Compared with entitled supply for office and lab space at Mission Bay, the low‐end demand projection can be more than met with the 3.1 million square feet of supply at Mission Bay. For the higher‐end projection, land‐supply from various sources including Mission Bay, SWL 337, and the conversion and redevelopment of sites in the Life Science/Medical overlay in the Central Waterfront would need to be drawn upon to meet the demand projection of 4.6 million square feet. As long as the demand projection does not exceed supply projections, it is unlikely that a space shortage will occur. These comparisons of demand and supply indicate that sufficient supply exists within the identified sources, with excess supply available for other uses. In other words, the amount of potential land in nearby neighborhoods that can be used for biotech uses appears sufficient to accommodate the demand generated from Mission Bay campus.44

44 Our analysis does not consider the differing levels of price sensitivity between land uses and potential tenants. For example, our projection of biotech employment is based on the range of establishment types


December 10, 2007


SUPPLY‐DEMAND COMPARISON FOR RETAIL SPACE

The total estimated amount of retail space generated from Mission Bay related activities is between 111,700 and 158,500 square feet, depending on the size of the hospital.45 This demand is slightly less than 40 percent of the total potential retail supply available at Mission Bay and on site at the hospital itself. In other words, the Mission Bay project is not expected to result in a shortage of retail space in adjacent neighborhoods.

Figure 13: Retail Space: Demand and Supply

111,700 158,500

0

100,000

200,000

300,000

400,000

500,000

600,000

Hosp+Biotech Retail,289-Bed

Hosp+Biotech Retail,550-Bed

Supply

518,000

Two demand projections are individual assessments of demand,; they are not cumulative.

Demand

Supply - Onsite Hospital Space

Supply - Mission Bay

SUMMARY OF POTENTIAL IMPACTS

One of the primary purposes of this analysis is to inform the rezoning process of the Eastern Neighborhoods, particularly around the issues of demand for biotech and medical space. In order to ensure that actual demand for these spaces does not exceed the projections contained in this report, which may lead to land conflicts and land‐price inflation, many of the assumptions used in calculations are conservative, aimed at providing projections which will lead to sufficient zoning for each of the uses. To that

which would benefit from proximity to a hospital, but does not consider the disparate levels of lease payments which are economical for each establishment type. The extent to which the costs to operate a business in the Mission Bay and Central Waterfront areas may exceed a particular type of establishment’s ability to pay may lessen demand for the use. 45 See “grey” boxes from demand projection, Table 19, reflects medium retail projected to be demanded because of biotech employment, and the higher demand projection for retail for both the 289‐bed and the 550‐bed hospital.


December 10, 2007


end, the comparison of supply and demand discussed in the previous section compared the highest projection of demand with available supply. Using the high‐end projections, there appears to be sufficient supply to meet likely demand for all of the land uses evaluated. In other words, even under the most aggressive assumptions regarding the impact of the new Mission Bay campus on the demand for space in adjacent neighborhoods, it would appear that sufficient development opportunities will be available to accommodate this demand without “crowding out” other land uses. Although, clearly land uses attracted by proximity to the new UCSF Campus and hospital will affect supply‐demand dynamics in surrounding neighborhoods, these uses are not expected to dominate the real estate market. Rather, there would appear to be adequate supply of land to accommodate a healthy mix of land uses and tenant types. The conclusions above are based on a description of the amount of land uses and facilities needed to accommodate all of UCSF’s activities and programs. If additional UCSF programs and activities not accounted for here are expected to locate in or around the Mission Bay campus, these conclusions would need to be adjusted accordingly.46

46 At the time of publication, the UCSF real estate office indicated that they were looking at a variety of sites for their Orthopaedic Institute, estimated to be about 60,000 square feet. However, given indications that this space may be included within the Mission Bay campus itself and the preliminary nature of the plans at this time, the corresponding space needs have been excluded from the analysis above.

63 P:\17000s\17030UCSF_MB\Report\17030_Bibliography.doc

VI. BIBLIOGRAPHY

Title / Author(s) Publication / Source Notes "2005 Bay Area Biotechnology Real Estate Survey: Defining the Market for Lab Space," Davis, Douglas Brad Werner

Bay Bio & CB Richard Ellis, September 2006

This document reports the experiences of biotech companies and their local market conditions of firms throughout the bay area, including lease rates, workforce, community support, and amenities such as proximity to academic institutions. Companies located in the Mid Peninsula and Silicon Valley North reported highest satisfaction of proximity to academic institutions due to their proximity to Stanford. Companies in the Northern Peninsula reported the second highest satisfaction rating.

"A Good Fit: North Carolina's Place in the Biotechnology Value Chain," Armstrong-Hough, Mari

Department of Sociology, Duke University, January 2006

Biotech firms tend to cluster geographically to take advantage of the increased efficiency, tacit knowledge, and shared local assets associated with doing so. Feldman asserts that "the anchor firm acts as an agglomerative force." Sorenson expands on this notion contributing that "start-ups seek information and resources and will relocate near these tangible inputs" often provided by existing firms.

"Biopharmaceutical Contributions to State and U.S. Economics"

Milken Institute, October 2004 This report looks at the performance and impacts of different biopharmaceutical clusters across the U.S. The article mentions that biotech firms tend to locate in close proximity to each other and start-ups generally locate around leading research universities. Elements that contribute to a cluster's success include long-term capital, quality research facilities, biopharmaceutical firms, highly skilled workers, and catalyst organizations.

Final Report Bibliography Commercial Land Use Impacts: UCSF Mission Bay Campus and Hospital

December 2007


Title / Author(s) Publication / Source Notes "Clustering in the Biotechnology Industry," Schweitzer, Stuart, et al.

Department of Statistics, University of California, Los Angeles, March 4, 2004

This article utilizes the Poisson regression to examine locational tendencies of biotech firms. It finds that while proximity to population centers as well as colleges and universities are influential, the affect of research-based universities is particularly significant. The location of research-based universities is important, because start-up firms often develop as spin-offs of the university and the university attracts and trains a highly skilled labor force.

"Commercialisation of Knowledge - A Conceptual Framework," Spilling, Olav

Department of Innovation and Economic Organisation, BI Norwegian School of Management, May 2004

This paper discusses an approach for analyzing the processes of knowledge commercialization and identifying significant barriers hindering different stages of the commercialization process. The concept of knowledge refers to that developed in academic institutions like universities and research institutions.

"Company-Scientist Locational Links: The Case of Biotechnology," Audretsch, David B., and Paula E. Stephan

Academic Economic Review 86 (3): 641-652, 1996

This document examines the geographic influence of ties between biotech firms and university-based scientists. It finds that because meetings with university scientists are generally planned, the influence of proximity is not overwhelming. Proximity does matter when knowledge spillovers are informal. The article states that proximity to existing biotech clusters and research institutions plays a significant role influencing firm location.

"Dynamics of Industry and Innovation: Organizations, Networks and Systems," Brouwer, Maria

Department of Economics, University of Amsterdam, March 2005

???

"Entrepreneurship, Cluster Formation and the Strategic Management of Places," Audretsch, David

Max Planck Institute of Economics, March 7, 2005

This paper examines the impact of the degree of diversity or specialization of economic activities on the level of innovation. It finds that specialization of economic activity does not promote innovative output. Rather, diversity across complementary economic activities sharing a common knowledge is more conductive to innovation." It finds that knowledge created in university laboratories contributes to the generation of innovation by private firms.


December 2007


Title / Author(s) Publication / Source Notes "Firm-specific characteristics of R&D collaborators and non-collaborators in US biotechnology clusters and elsewhere," Sharmistha Bagchi-Sen

International Journal of Technology and Globalisation 2004 - Vol. 1, No.1 pp. 92 - 118

While this study primarily examines the difference between collaborators and noncollaborators, it also addresses the role of a firm’s location as it relates to innovation. Location is important, especially for start-ups, for various reasons including its image as well as proximity to producers sharing tacit knowledge and inputs necessary to the innovation process.

"Intellectual Human Capital and the Birth of U.S. Biotechnology Enterprises," Zucker, Lynne G., et al.

American Economic Review 88 (1): 290-306, 1998

Intellectual human capital variables play a strong role in determining where biotech industry developed during the 1980's. Star scientists play a crucial role in spillover and geographic agglomeration above what would be predicted based on university reputation and scientists supported by federal grants alone. But there is a declining value over time of intellectual human capital with "stars" less likely to result in birth of firms after 1985 than before.

"Location Decisions of Spin-Offs from Public Research Institutions," Egeln, Jürgen

Industry & Innovation, Volume 11, Issue 3, pages 207 - 223, September 2004

"Mind to Market: A Global Analysis of University Biotechnology Transfer and Commercialization," by DeVoll, Armen Bedroussian, et al.

Milken Institute, September 2006 This study examines the biotech transfer process from knowledge creation at universities to technology transfer and commercialization. This report provides rankings for top universities' biotechnology publications (UCSF ranks 4th), biotech patents (UCSF ranks 2nd behind University of Texas), and transfer and commercialization (University of California System ranks 2nd behind MIT). Clusters of biotechnology are listed as one of the most significant factors influencing successful commercialization of university research. "Research suggests that biotech clusters that overcome geographical borders may be best positioned to leverage the nature of advanced scientific research."


December 2007


Title / Author(s) Publication / Source Notes "New technology-based firms in science parks. A study of resources and absorptive capacity," Löwegren, M.

Lund University, 2003

"Signs of Life: The Growth of Biotechnology Centers in the U.S.," Cotright, Joseph and Heike Mayer

The Brookings Institution Center on Urban and Metropolitan Policy, 2002

This report surveys biotech research and commercialization in the U.S., and it discusses locations and key elements facilitating its development. All nine of the identified biotechnology centers have access to venture capital, high levels of NIH funding, and a highly ranked medical research institution. "Biotech firms generally grow by drawing on access to local inputs such as talented workers, research, and venture capital."

"Supporting the High-Technology Entrepreneur: Support Network Geographies for Semiconductor, Telecommunications Equipment, and Biotechnology Start-Ups," Kenney, Martin and Donald Patton.

Berkeley Roundtable on the International Economy, University of California, Berkeley, April 29, 2004

This report finds that in general biotech networks are more dispersed as compared to those of other high-technology industries. It notes that the determining factor influencing the location of early biotech start-ups is proximity to star scientists and, thus, the university. But, as these firms mature, the locational significance of being in close proximity to universities, incumbent firms, and venture capital declines.

"Tacit knowledge and the economic geography of context, Or the undefinable tacitness of being (there)," Gertler, Meric S.

Journal of Economic Geography, Vol. 3, Iss. 1, January 2003

This document identifies tacit knowledge as a significant factor influencing a firm's level of innovation, but problems exist in disseminating this tacit knowledge. Three distinct ways of sharing tacit knowledge are examined. The first necessitates the need to share through face-to-face contact, the second emphasizes the central role of "communities of practice" rather than physical proximity, and the third suggests that the circulation of "knowledge enablers" can act as agents diffusing the tacit knowledge through an organization.


December 2007


Title / Author(s) Publication / Source Notes "Taking Action for Tomorrow," Bay Area Life Sciences Strategic Action Plan"

Bay Area Council, 2003 Seeking to understand the Bay Area's prospects for attracting and retaining life sciences employment in the future, this document examines the key factors business and institutional leaders consider when making business-location decisions. Each of the nine Bay Area Counties are rated by their perceived ranking, according to these factors. Information on the bioscience industry specific to the Bay Area is included here.

"The Dynamics of California's Biotechnology Industry," Nikesh Patel and Junfu Zhang

Public Policy Institute of California, 2005

California's biotechnology industry is examined, focusing on research institutions and the venture capital that spurs new biotech businesses and associated job growth. California's largest advantage in biotech is its world-renowned universities. Along with its tradition of venture capital investment and high quality labor pool, the state is expected to continue to lead the nation in this industry. The study provides detailed information on California's biotech history by 3 regions (Bay Area, San Diego, LA), venture capital in the US and CA by industry over time, and statistics on biotech firms in California.

"The Geography of Opportunity: Spatial Heterogeneity in Founding Rates and the Performance of Biotechnology Firms," Stuart, Toby and Olav Sorenson

Research Policy 32: 229-253, 2003

Geographical factors that support biotech start-ups and spin-offs do not necessary promote firm performance and maturation.

"The knowledge Filter and Economic Growth: The Role of Scientist Entrepreneurship," Audretsch, David, Taylor Aldridge and Alexander Oettl.

Draft WP, March 2006 "This study examines the prevalence and determinants of the commercialization of research by the top twenty percent of university scientists funded by grants from the National Cancer Institute (NCI)." This paper finds that "scientist entrepreneurship is the sleeping giant of commercializing university research. More than one in four patenting NCI scientists have started a new firm," but it does not address where these firms tend to locate geographically.


December 2007


Title / Author(s) Publication / Source Notes "The locational dynamics of the US biotech industry: knowledge externalities and the anchor hypothesis," Feldman, Maryann

Industry and Innovation, September 2003

This paper explores the locational patterns and evolution of firms in the biotech industry. In its early years, biotech grew up around university star scientists who licensed innovations to companies, but today many cluster around universities using formal technology transfer mechanisms. The locational dynamics of the biotech industry are evolving, and the location of anchor firms is likely to increasingly influence the formation of clusters.

"The Spatial Clustering of Science and Capital: Accounting for Biotech Firm-Venture Capital Relationships," Powell, Walter W., et al.

Regional Studies 36 (3): 291-305, 2002

The article, which primarily examines the locational relationship between biotech firm formation and venture capital, finds that start-ups generally receive support from local venture capital sources, while mature firms are more likely to receive non-local support. It identifies the role of the research university as necessary, but not sufficient and that the support of venture capital firms is crucial.

"The territorial development of innovation support assets through university/business interactions," Benneworth, Paul, et al.

Centre for Urban and Regional Development Studies, Newcastle University, March 2004

"What's in a location? Science parks and the support of new technology-based firms," Ferguson, R.

Department of Economics, Uppsala, Swedish University of Agricultural Sciences, 1998

"Why Does an Entrepreneurial Manufacturer Spin Off and Locate in a Cluster," Lee, In Hyeock (Ian)

Business Economics & Public Policy, Kelley School of Business, Indiana University

Focusing San Francisco's Economic Strategy; From Goals to Strategic Priorities, ICF International

Mayor's Office of Economic and Workforce Development


December 2007


Title / Author(s) Publication / Source Notes San Francisco Biosciences Task Force Report to the San Francisco Board of Supervisors and the Planning Commission

SF Biosciences Task Force, February 15, 2005

APPENDICES

1. Case Studies of Biotechnology Clusters Anchored by Universities 2. Narrow and Broad Definitions of Biotech, by NAICS code 3. Detail on California Employment Development Department Job Counts,

by County 4. Detail on businesses/organizations counted as “health‐related” businesses 5. Map of San Francisco health‐related businesses, by type and size

A‐1 P:\17000s\17030UCSF_MB\Report\17030_AppendCaseStudies.doc

APPENDIX 1. CASE STUDIES

JOHNS HOPKINS

Always ranking as a top 20 university in the US New & World Report, John Hopkins University was founded in 1876 and is among the nationʹs most respected universities. And while John Hopkins has many top ranking programs and departments it is best know for its medical program. Over the past 25 years Johns Hopkins has been credited for two of the most far reaching medical advances, restricting enzymes which made possible the genetic engineering industry and the discovery of the brainʹs natural opiates which has triggered an explosion of interest in neurotransmitter pathways and functions. John Hopkins Hospital also enjoys the moniker of top hospital in the country for the past 17 years according to US News & World Report and ranks as one of the top schools in biotechnology. From 1998‐2002 Johns Hopkins ranked seventh in the number of biotechnology citations and third in biotechnology patents between 2000 and 20041. It ranks first among all US universities for overall R&D expenditures and receives the most funding from the National Institute of Health in the form of $607.2 million dollars2. Over the years Johns Hopkins and the Baltimore region have become one of the few biotechnology clusters in the country. Biotechnology in Baltimore began in 1985 with establishment of Martek, which grew out of the incubator at the University of Maryland, College Park. In 1993 Guilford Pharmaceuticals (now Mgi Pharma) was founded based on technology researched at Johns Hopkins. Since 1985 the biotechnology industry has grown in the Greater Washington DC area boasting 1,472 biotechnology companies with 53,340 of employees as of 20053. Big research institutes and universities such as the National Institute of Health, the Food and Drug Administration, Johns Hopkins and University of Maryland attract and train the scientist who “seed” the market with new firms started by these same scientists. As of 2005, the Baltimore metro area has a total of 2.88 million square feet of biotech space and a vacancy rate of 18.7 percent4. Currently, there are plans to expand the amount of biotech space in the area with the development of The Science + Technology Park at Johns Hopkins and BioPark at the University of Maryland Baltimore. Both developments plan to add over one million square feet each of life sciences/office space and wet lab space.

1 Bedroussian, Armen and Ross DeVol, “Mind to Market: A Global Analysis of University Biotechnology Transfer and Commercialization” Milken Insitute, September 2006 2 “Biosciences in Greater Baltimore”, Economic Alliance of Greater Baltimore, June 2007 3 www.biotechwork.org, Greater Washington DC Area Overview. 4 Colliers International, “Annual Review and Analysis of Real Estate Trends in the Life Sciences Industry”, Alchemy 2005

Final Report Appendix 1 Commercial Land Use Impacts: UCSF Mission Bay Campus and Hospital

December 2007


UNIVERSITY OF CALIFORNIA, SAN DIEGO

A relatively new university, the University of California at San Diego was founded in 1960 but has rapidly attainted a status of one of the top research institution in the nation. It has especially excelled in the field of biotechnology ranking 6th among the world universities in its biotechnology research publication between 1998 and 2002 and 8th in terms of biotechnology patents from 2000 to 20045. By 2005, UC San Diego ranked 14th in terms of receiving awards from the National Institute of Health totaling $309 million6. In terms of invention disclosures from 2000‐2004 the UC System ranked 2nd with 927 inventions with UC San Diego producing the most inventions 260 and ranking 8th overall in terms of invention disclosures7. UC San Diego is also known for its technology transfer to commercialization which includes the number of spin off companies and patents issued. It is estimated that since 1960 more than 220 companies have been spun off from UCSD faculty, staff, or alumni and around 60 have been biotechnology companies. The San Diego metro area is known as one of the premiere and most established biotechnology clusters in the country anchored by world class research institutes such as The Scripps Research Institute, The Salk Institute, The Burnham Institute, and The University of California, San Diego. The regions biotechnology business emerged with the formation of Hybritech in 1978 by two researchers from UCSD and has grown with the support of UCSD’s Connect program, which encourages networking among researchers and businesses. Over time the San Diego has built up an experienced network of supporting services such as venture capitalist, headhunter, and patent attorneys specializing in the biotechnology industry enabling the area to house 843 biotechnology companies and employ 34,716 workers8. As of 2005, San Diego has over eight million square feet of lab space with a vacancy rate of 13 percent making it one of the largest and most dense biotech clusters in the nation9.

UNIVERSITY OF CALIFORNIA, LOS ANGELES

Founded in 1919 the University of California, Los Angeles (UCLA) has earned the reputation of being one of the nation’s finest universities with a wide breadth of top academic programs. Among one of the best programs UCLA offers is its medical and biotechnology program. For the past 18 years UCLA Medical Center has been ranked as 5 Bedroussian, Armen and Ross DeVol, “Mind to Market: A Global Analysis of University Biotechnology Transfer and Commercialization” Milken Insitute, September 2006 6 NIH Awards to All Institutions by Rank Fiscal Year 2005 7 Bedroussian, Armen and Ross DeVol, “Mind to Market: A Global Analysis of University Biotechnology Transfer and Commercialization” Milken Insitute, September 2006 8 www.biotechwork.org, San Diego Overview 9 Colliers International, “Annual Review and Analysis of Real Estate Trends in the Life Sciences Industry”, Alchemy 2005


December 2007


the number one hospital in the West by US New and World Report. UCLA has also ranked 10th in biotechnology publications between 1998 and 2002 it and ranked 25th in the number of biotechnology patents from 2000 to 200410. It also ranked 8th in grants and awards provided by the National Institute of Health (NIH) receiving $385.8 million.11 And although UCLA has been very prolific in its research there have only been 18 spin offs as of 2005. Los Angeles has a large biotechnology base located in a series of small clusters focused around major universities and research institutes such as UCLA, University of Southern California, Cal Tech, the Jet Propulsion Laboratory, UC Irvine, Cedars Sinai Medical Center, City of Hope Cancer Center, and Huntington Medical Research Institute. Over the years, the Los Angeles market has been recognized for its strength in medical device manufacturing but it also hosts the largest biotechnology company in the nation, Amgen. The foundation of Amgen in 1980 gave birth to the biotechnology market in Los Angeles which has grown to 1,807 companies employing 61,488 workers.12 This makes the Los Angeles market the second highest biotechnology market in terms of employees. Spread out over the region these employees work in over 5.2 million square feet of lab space and future biotech developments include a 215,000 square foot USC Biomedical Research Park, the 50,000 square foot Alexandria Technology Center, and the City of Hope Life Science Campus with its size yet to be determined.13

UNIVERSITY OF COLORADO, BOULDER

Founded in 1867, the University of Colorado at Boulder is the University of Colorado’s flagship campus. Noted for its many strong academic programs their biotechnology research is one of the most respected producing many spin off companies. Amgen used the CU Boulder facilities to commercialize inventions that came from Marv Caruthers lab, who pioneered chemical synthesis of DNA and out of his lab produced various spin offs such as Array Bio Pharma, Dharmoacon, Ancora, and Mementa, Bio Sstar, and BaroFold. Tom Cech, Noble laureate in biochemistry, established Boulder as the center of RNA research for his discovery of ribozymes and developing technologies surrounding the manufacture and use of RNA. Other researchers such as Scoggins and Hoff man achived grate success developing recombinant hemoglobin which they sold to Baxter in 1998. And although many there have been many discoveries and spin off from

10 Bedroussian, Armen and Ross DeVol, “Mind to Market: A Global Analysis of University Biotechnology Transfer and Commercialization” Milken Insitute, September 2006 11 NIH Awards to All Institutions by Rank, Fiscal Year 2005 12 www.biotechwork.org, Los Angeles Overview 13 Colliers International, “Annual Review and Analysis of Real Estate Trends in the Life Sciences Industry”, Alchemy 2005


December 2007


The University of Colorado only ranked 75th in biotech patents world wide between 2000‐2004 14. Not always recognized and sometimes overlooked as a major biotech cluster, the Denver biotech industry has a diverse cluster of pharmaceuticals, biotechnology, and medical device and instrument companies. As of 2005, the Denver Metro area counted 536 biotechnology establishments and 9,781 employees15. With renowned research institutions and universities such as the University of Colorado at Denver and Boulder, National Jewish Medical Research Center, The Children’s Hospital Research Institute, University of Colorado Denver Health Sciences Center, and Eleanor Roosevelt Institute to support the biotechnology cluster annual biotechnology employment growth rates have outpaced in the national biotechnology employment growth rate between 1999 and 200416. With 2,400,000 square feet of biotechnology space in 200517 and considerable growth in the industry, the 578 acre Fitzsimons site in Aurora in under going a transformation into a one square mile life sciences community. Established as a Redevelopment area in 1996, the former Fitzsimons Army Medical Center is to be anchored by a 227 acre University of Colorado Health Sciences Center and Hospital and a 160 acre Colorado Bioscience Park Aurora. At build out, in 2016, the master plan calls for over 15 million square feet of new construction. According to Battelle’s study in 2006 “Growing the Nation’s Bioscience Sector: State Bioscience Initiative 2006” almost 3 million square feet of research and educational space should have been built by 2006.

14 Bedroussian, Armen and Ross DeVol, “Mind to Market: A Global Analysis of University Biotechnology Transfer and Commercialization” Milken Insitute, September 2006. 15 www.biotechwork.org, Denver Overview. 16 Development Research Parteners, Inc.“Bioscience Metro Denver Industry Cluster Profile”, Meto Denver Economic Development Corporation, October 2005. 17 Fletcher, Amy “Biotech takes root in northern ‘burbs” Denver Business Journal Oct.31, 2005

Appendix 2Total Employment by NAICS CodeCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

Total Industry BiotechNAICS Description Employment Group Work Portal EPS-Defined

Narrow

111191 Oil seed and grain combination 6,191 x111421 Nursery and tree production 100,453 x111920 Cotton farming 18,860 x111998 All other miscellaneous crop farming 43,172 x311211 Flour milling 13,166 x311221 Wet corn milling 8,294 x311222 Soybean processing 11,388 x311223 other oilseed processing 2,168 x325193 Ethyl alcohol manufacturing 5,024 x x325199 All other basic organic chemical manufacturing 32,169 x x325221 Cellulosic organic fiber manufacturing 10,348 x x325222 Noncellulosic organic fiber manufacturing 22,460 x325311 Nitrogenous fertilizer manufacturing 8,020 x325312 Phosphatic fertilizer manufacturing 7,908 x325314 Fertilizer (mixing only) manufacturing 8,325 x325320 Pesticide and other agricultural chemical mfg. 16,223 x325411 Medicinal and botanical manufacturing 23,215 x x x325412 Pharmaceutical preparation manufacturing 225,364 x x x325413 In-vitro diagnostic substance manufacturing 15,290 x x x325414 Other biological product manufacturing 24,286 x x x325611 Soap and other detergent manufacturing 24,181 x325612 Polish and other sanitation good manufacturing 27,084 x325613 Surface active agent manufacturing 5,039 x334510 Electromedical apparatus manufacturing 56,386 x x334516 Analytical laboratory instrument manufacturing 31,300 x x334517 Irradiation apparatus and furniture manufacturing 11,531 x x339111 Laboratory apparatus and furniture manufacturing 13,842 x x339112 Surgical and medical instrument manufacturing 105,996 x x339113 Surgical appliance and supplies manufacturing 86,768 x x339114 Dental equipment and supplies manufacturing 15,599 x339115 Ophthalmic goods manufacturing 31,080 x339116 Dental laboratories 49,521 x541380 Testing laboratories 141,967 x541710 Physical, engineering and biological research 508,529 x x x621511 Medical laboratories 136,529 x621512 Diagnostic imaging centers 59,192 x

Total Bio.org Employment 1,646,262Total Biotechwork.org Employment 1,410,654Total Narrow Employment 796,684Total Employment in all NAICS codes 1,906,868

Source: Bureau of Labor Statistics, Economic & Planning Systems, Inc.

Economic & Planning Systems, Inc. 11/9/2007 P:\17000s\17030UCSF_MB\Report\Appendix_Biotech_Emp.xls

Appendix 3


County NameNAICS Code Industry

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

San Mateo 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 0 0 0San Mateo 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSan Mateo 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Mateo 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 70 61 S S 59San Mateo 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSan Mateo 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 0 0 0San Mateo 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 101 142 230 251 240 223San Mateo 334510 Electromedical and Electrotherapeutic Apparatus Manufac 581 472 476 499 589 613 685 721 734 710 711 605 609 356 263 110San Mateo 334516 Analytical Laboratory Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 1,944 2,003 S S SSan Mateo 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 5 7 13 16San Mateo 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 0 S S SSan Mateo 339112 Surgical and Medical Instrument Manufacturing 430 561 644 631 743 786 857 1,002 884 598 576 700 741 838 811 910San Mateo 339113 Surgical Appliance and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 717 S S 199 SSan Mateo 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 14 16 17San Mateo 339115 Ophthalmic Goods Manufacturing 0 0 0 0 0 0 0 0 0 0 0 31 S S S 8San Mateo 339116 Dental Laboratories 135 136 130 133 124 122 114 106 106 119 138 122 120 130 121 121San Mateo 541380 Testing Laboratories 317 263 506 435 495 471 479 585 590 711 677 632 624 378 426 382San Mateo 541710 Research and Development in the Physical, Engineering, 3,382 4,942 5,034 5,283 5,739 5,763 5,714 6,033 6,426 7,272 7,960 9,298 10,387 9,247 9,327 9,832San Mateo 621511 Medical Laboratories 509 574 619 497 457 498 477 423 361 347 402 320 199 229 256 286San Mateo 621512 Diagnostic Imaging Centers 358 386 267 84 46 44 59 67 110 147 145 147 221 60 55 50

San Francisco 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSan Francisco 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 S S S 0 0San Francisco 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 SSan Francisco 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSan Francisco 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S 0San Francisco 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 0 0 0San Francisco 334510 Electromedical and Electrotherapeutic Apparatus Manufac 0 0 0 0 0 0 0 0 0 0 0 S S S S SSan Francisco 334516 Analytical Laboratory Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSan Francisco 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0San Francisco 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 S S SSan Francisco 339112 Surgical and Medical Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSan Francisco 339113 Surgical Appliance and Supplies Manufacturing 140 129 129 130 134 118 110 112 130 75 66 71 73 80 79 92San Francisco 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 17 S SSan Francisco 339115 Ophthalmic Goods Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 0 0 0San Francisco 339116 Dental Laboratories 153 157 144 129 131 135 139 141 138 130 116 115 114 130 148 136San Francisco 541380 Testing Laboratories 123 116 139 161 163 131 123 177 157 162 177 217 251 212 157 101San Francisco 541710 Research and Development in the Physical, Engineering, 1,253 1,205 1,199 952 1,250 1,331 1,351 1,285 1,294 1,469 1,562 1,188 1,160 1,544 1,451 1,064San Francisco 621511 Medical Laboratories 237 302 308 169 166 172 161 122 117 133 74 81 88 62 27 33San Francisco 621512 Diagnostic Imaging Centers 86 74 57 50 43 39 33 21 25 33 47 61 62 69 99 113

Average Employment

Detail on EDD Job Counts

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Appendix 3



1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average Employment


Alameda 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Alameda 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 S S S S 0Alameda 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 S S S S SAlameda 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Alameda 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 44 S S SAlameda 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Alameda 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Alameda 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Alameda 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Alameda 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SAlameda 325411 Medicinal and Botanical Manufacturing 0 0 0 265 292 296 307 317 293 279 218 213 238 238 S 164Alameda 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SAlameda 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 51 47 148 404 411 513 523 502 462 423 472Alameda 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SAlameda 334510 Electromedical and Electrotherapeutic Apparatus Manufac 0 0 221 196 169 164 156 173 124 138 208 210 140 472 535 725Alameda 334516 Analytical Laboratory Instrument Manufacturing 373 210 181 172 193 229 252 285 261 193 230 235 520 509 495 699Alameda 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SAlameda 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 0 0 SAlameda 339112 Surgical and Medical Instrument Manufacturing 662 786 854 864 824 1,108 1,131 1,283 1,418 1,492 1,761 1,749 1,766 2,007 2,110 2,270Alameda 339113 Surgical Appliance and Supplies Manufacturing 435 508 581 593 566 431 416 428 453 415 356 377 409 404 392 371Alameda 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 51 54 SAlameda 339115 Ophthalmic Goods Manufacturing 135 63 69 69 59 76 90 106 125 152 197 220 218 255 198 188Alameda 339116 Dental Laboratories 338 365 333 348 348 411 510 582 692 706 742 743 652 643 639 593Alameda 541380 Testing Laboratories 697 611 634 735 758 846 961 1,030 1,109 1,279 1,330 1,698 1,620 1,791 1,658 1,489Alameda 541710 Research and Development in the Physical, Engineering, 5,665 5,359 4,362 5,107 5,237 5,487 5,243 5,254 5,179 5,656 6,471 7,508 8,117 6,962 7,515 7,393Alameda 621511 Medical Laboratories 262 186 315 705 842 938 992 984 902 880 995 936 924 983 871 838Alameda 621512 Diagnostic Imaging Centers 134 167 99 64 68 61 35 18 20 35 36 103 131 354 204 130

Contra Costa 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S SContra Costa 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Contra Costa 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 S S 0 0 0Contra Costa 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Contra Costa 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SContra Costa 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Contra Costa 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SContra Costa 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Contra Costa 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Contra Costa 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0 0Contra Costa 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 0 S SContra Costa 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S 682 698Contra Costa 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SContra Costa 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0 0Contra Costa 334510 Electromedical and Electrotherapeutic Apparatus Manufac 0 0 0 0 0 0 0 0 0 0 0 0 0 S S SContra Costa 334516 Analytical Laboratory Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SContra Costa 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Contra Costa 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 30 40 S 47 SContra Costa 339112 Surgical and Medical Instrument Manufacturing 54 59 62 74 77 85 102 117 116 327 315 306 300 252 262 267Contra Costa 339113 Surgical Appliance and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 10 9 S S SContra Costa 339114 Dental Equipment and Supplies Manufacturing 70 68 70 69 90 91 89 95 119 109 103 99 100 84 74 75Contra Costa 339115 Ophthalmic Goods Manufacturing 169 69 67 75 64 54 51 63 65 56 50 45 43 48 S 44Contra Costa 339116 Dental Laboratories 317 258 252 239 248 229 230 233 230 251 265 273 282 275 791 260Contra Costa 541380 Testing Laboratories 655 570 509 553 508 513 555 536 522 528 580 616 679 844 857 952Contra Costa 541710 Research and Development in the Physical, Engineering, 516 674 652 746 870 939 681 731 708 674 775 878 817 757 622 729Contra Costa 621511 Medical Laboratories 0 0 0 0 0 0 0 0 0 0 0 S S 165 187 173Contra Costa 621512 Diagnostic Imaging Centers 0 0 0 0 0 0 0 0 0 0 0 S S 28 95 124


Appendix 3



1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average Employment


Marin 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S 0 0Marin 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S 54 58Marin 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S 0 0Marin 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S 0 0Marin 334510 Electromedical and Electrotherapeutic Apparatus Manufac 0 0 0 0 0 0 0 0 0 0 0 0 0 S S 0Marin 334516 Analytical Laboratory Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SMarin 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Marin 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0 0Marin 339112 Surgical and Medical Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S 0Marin 339113 Surgical Appliance and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 S S SMarin 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0 0Marin 339115 Ophthalmic Goods Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SMarin 339116 Dental Laboratories 66 65 60 55 59 68 66 55 60 53 48 51 54 54 54 50Marin 541380 Testing Laboratories 0 0 0 0 0 0 0 0 0 0 0 46 46 13 72 70Marin 541710 Research and Development in the Physical, Engineering, 305 302 275 309 320 275 311 211 260 357 454 544 611 597 672 723Marin 621511 Medical Laboratories 0 0 0 0 0 0 0 0 0 0 0 S S S S SMarin 621512 Diagnostic Imaging Centers 0 0 0 0 0 0 0 0 0 0 0 S S S S S

Napa 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 S S 0 0Napa 334510 Electromedical and Electrotherapeutic Apparatus Manufac 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0 0Napa 334516 Analytical Laboratory Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 339112 Surgical and Medical Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 339113 Surgical Appliance and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 339115 Ophthalmic Goods Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Napa 339116 Dental Laboratories 13 14 15 14 16 17 17 19 22 24 25 22 23 21 22 18Napa 541380 Testing Laboratories 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 541710 Research and Development in the Physical, Engineering, 14 10 12 25 10 9 13 51 64 84 118 145 S 150 S SNapa 621511 Medical Laboratories 0 0 0 0 0 0 0 0 0 0 0 S S S S SNapa 621512 Diagnostic Imaging Centers 0 0 0 0 0 0 0 0 0 0 0 S S S S 0


Appendix 3



1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average Employment


Solano 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0 0Solano 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 1,289 S S S SSolano 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S SSolano 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 334510 Electromedical and Electrotherapeutic Apparatus Manufac 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 334516 Analytical Laboratory Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 339112 Surgical and Medical Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 S S SSolano 339113 Surgical Appliance and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Solano 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 S S SSolano 339115 Ophthalmic Goods Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 339116 Dental Laboratories 0 0 0 0 0 0 0 0 0 0 0 S S 48 43 46Solano 541380 Testing Laboratories 83 114 113 112 110 140 163 272 271 229 237 241 175 158 99 96Solano 541710 Research and Development in the Physical, Engineering, 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 621511 Medical Laboratories 0 0 0 0 0 0 0 0 0 0 0 S S S S SSolano 621512 Diagnostic Imaging Centers 0 0 0 0 0 0 0 0 0 0 0 S S S S 69

Sonoma 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 325211 Plastics Material and Resin Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 S S SSonoma 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSonoma 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSonoma 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S 98 SSonoma 325412 Pharmaceutical Preparation Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 38 S SSonoma 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S 0Sonoma 334510 Electromedical and Electrotherapeutic Apparatus Manufac 0 0 0 0 0 0 0 0 0 0 0 S S S S 111Sonoma 334516 Analytical Laboratory Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSonoma 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Sonoma 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSonoma 339112 Surgical and Medical Instrument Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSonoma 339113 Surgical Appliance and Supplies Manufacturing 0 0 0 0 0 18 19 19 19 0 20 20 50 68 87 261Sonoma 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S SSonoma 339115 Ophthalmic Goods Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSonoma 339116 Dental Laboratories 128 139 146 153 164 171 164 169 189 189 201 194 178 154 153 144Sonoma 541380 Testing Laboratories 288 268 233 194 167 170 152 100 84 80 91 104 105 162 194 218Sonoma 541710 Research and Development in the Physical, Engineering, 0 0 0 0 0 0 0 0 0 0 0 331 337 610 620 799Sonoma 621511 Medical Laboratories 93 106 69 98 112 102 123 149 152 174 186 213 237 S 217 SSonoma 621512 Diagnostic Imaging Centers 19 21 88 88 29 12 12 12 14 35 33 23 33 S 56 S


Appendix 3



1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Average Employment


Santa Clara 311221 Wet Corn Milling 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Santa Clara 311222 Soybean Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Santa Clara 311223 Other Oilseed Processing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Santa Clara 325193 Ethyl Alcohol Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Santa Clara 325199 All Other Basic Organic Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 12 S S SSanta Clara 325221 Cellulosic Organic Fiber Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Santa Clara 325311 Nitrogenous Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S 0 0 0 0Santa Clara 325312 Phosphatic Fertilizer Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S 0Santa Clara 325314 Fertilizer (Mixing Only) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSanta Clara 325320 Pesticide and Other Agricultural Chemical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0Santa Clara 325411 Medicinal and Botanical Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S S S SSanta Clara 325412 Pharmaceutical Preparation Manufacturing 3,769 3,936 4,271 3,939 3,445 3,201 2,995 2,475 2,351 2,481 2,558 2,629 2,579 1,515 1,401 1,489Santa Clara 325413 In-Vitro Diagnostic Substance Manufacturing 0 0 0 0 0 0 0 0 0 0 0 S S 122 102 137Santa Clara 325414 Biological Product (except Diagnostic) Manufacturing 0 0 0 0 0 0 0 0 0 0 0 547 S S S SSanta Clara 334510 Electromedical and Electrotherapeutic Apparatus Manufac 2,676 3,788 4,003 3,990 3,851 4,017 4,175 4,275 4,311 3,810 4,199 4,557 3,992 3,423 3,145 3,159Santa Clara 334516 Analytical Laboratory Instrument Manufacturing 2,062 2,816 2,806 3,062 3,061 2,839 3,070 3,426 3,807 4,103 3,997 4,184 4,322 4,230 3,786 3,421Santa Clara 334517 Irradiation Apparatus Manufacturing 0 0 0 0 0 0 0 0 0 0 0 71 53 50 94 54Santa Clara 339111 Laboratory Apparatus and Furniture Manufacturing 0 0 0 0 0 0 0 0 0 0 0 41 S S 30 33Santa Clara 339112 Surgical and Medical Instrument Manufacturing 2,617 2,468 3,227 3,263 3,239 2,937 3,400 3,572 3,806 4,265 4,242 3,734 4,331 4,000 4,054 4,185Santa Clara 339113 Surgical Appliance and Supplies Manufacturing 436 363 326 374 394 320 292 438 488 446 213 222 264 461 436 363Santa Clara 339114 Dental Equipment and Supplies Manufacturing 0 0 0 0 0 0 0 0 0 0 0 52 S S 18 30Santa Clara 339115 Ophthalmic Goods Manufacturing 236 245 134 154 173 150 167 159 498 318 222 102 59 47 25 25Santa Clara 339116 Dental Laboratories 334 332 321 335 324 349 352 371 386 413 388 392 406 425 445 436Santa Clara 541380 Testing Laboratories 1,957 2,015 2,111 3,361 3,264 3,366 3,947 4,838 5,543 5,496 5,681 4,741 4,472 3,446 2,934 2,259Santa Clara 541710 Research and Development in the Physical, Engineering, 13,390 12,241 12,005 13,948 14,964 15,108 14,640 15,791 17,363 17,655 18,151 18,255 16,682 16,996 17,051 16,793Santa Clara 621511 Medical Laboratories 1,324 1,289 876 674 859 888 883 869 807 853 706 702 682 668 734 743Santa Clara 621512 Diagnostic Imaging Centers 220 352 375 228 146 153 157 155 190 204 213 188 113 118 89 136


Appendix 4Detail on Health-Related Businesses Included in Counts of Organizations Around HospitalsCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

SIC code Description NAICS code Description Smaller Categorization

8021 Offices and Clinics of Dentists 62121 Offices of Dentists Dental8031 Offices and Clinics of Doctors of Osteopathy Doctor's Office8031 Offices of Doctors of Osteopathy, Except Mental Health 621111 Offices of Physicians (except Mental Health Specialists) (pt) Doctor's Office8031 Offices of Doctors of Osteopathy, Mental Health Specialists 621112 Offices of Physicians, Mental Health Specialists (pt) Doctor's Office8041 Offices and Clinics of Chiropractors 62131 Offices of Chiropractors Alternative 8042 Offices and Clinics of Optometrists 62132 Offices of Optometrists Doctor's Office8043 Offices and Clinics of Podiatrists 621391 Offices of Podiatrists Doctor's Office8049 Offices and Clinics of Health Practitioners, NEC 62133 Offices of Mental Health Practitioners (except Physicians) Other Patient-Serving Orgs

62134 Offices of Physical, Occupational and Speech Therapists, and Audiologists Other Patient-Serving Orgs621399 Offices of All Other Miscellaneous Health Practitioners Other Patient-Serving Orgs

3582 COMMERCIAL LAUNDRY & DRYCLEANING MACHS 333312 Commercial Laundry, Drycleaning, and Pressing Machine Manufacturing All Other3821 LABORATORY APPARATUS & FURNITURE 339111 Laboratory Apparatus and Furniture Manufacturing (pt) All Other3826 LABORATORY ANALYTICAL INSTRUMENTS 334516 Analytical Laboratory Instrument Manufacturing All Other3827 OPTICAL INSTRUMENTS & LENSES 333314 Optical Instrument and Lens Manufacturing All Other3841 Surgical and Medical Instruments and Apparatus 339112 Surgical and Medical Instrument Manufacturing (pt) All Other3842 Orthopedic, Prosthetic, and Surgical Appliances and Supplies 322291 Sanitary Paper Product Manufacturing (pt) All Other

339113 Surgical Appliance and Supplies Manufacturing All Other334510 Electromedical and Electrotherapeutic Apparatus Manufacturing (pt) All Other

3843 Dental Equipment and Supplies 339114 Dental Equipment and Supplies Manufacturing Dental3844 X-Ray Apparatus and Tubes and Related Irradiation Apparatus 334517 Irradiation Apparatus Manufacturing (pt) All Other3845 Electromedical and Electrotherapeutic Apparatus 334517 Irradiation Apparatus Manufacturing (pt) All Other3851 Ophthalmic Goods 339113 Surgical Appliance and Supplies Manufacturing (pt) All Other5047 MEDICAL DENTAL & HOSPITAL EQUIPMENT 42145 Medical, Dental, and Hospital Equipment and Supplies Wholesalers All Other5048 OPHTHALMIC GOODS 42146 Ophthalmic Goods Wholesalers All Other5122 DRUGS PROPRIETARIES & SUNDRIES 42221 Drugs and Druggists' Sundries Wholesalers All Other7213 LINEN SUPPLY 812331 Linen Supply (pt) All Other7218 INDUSTRIAL LAUNDERERS 812332 Industrial Launderers All Other7261 FUNERAL SERVICE & CREMATORIES 81222 Cemeteries and Crematories (pt) All Other

81221 Funeral Homes All Other8011 Offices and Clinics of Doctors of Medicine 621493 Freestanding Ambulatory Surgical and Emergency Centers Doctor's Office

621491 HMO Medical Centers Doctor's Office621112 Offices of Physicians, Mental Health Specialists (pt) Doctor's Office621111 Offices of Physicians (except Mental Health Specialists) (pt) Doctor's Office

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Appendix 4Detail on Health-Related Businesses Included in Counts of Organizations Around HospitalsCommercial Land Use Impacts: Mission Bay Campus and Hospital; EPS #17030

SIC code Description NAICS code Description Smaller Categorization

8051 Continuing Care Retirement Communities 623311 Continuing Care Retirement Communities (pt) Senior Care8052 Intermediate Care Facilities Senior Care8059 Nursing and Personal Care Facilities, NEC Other Patient-Serving Orgs8071 Medical Laboratories 621512 Diagnostic Imaging Centers All other

621511 Medical Laboratories All other8072 Dental Laboratories 339116 Dental Laboratories All other8082 Home Health Care Services 62161 Home Health Care Services Other Patient-Serving Orgs8092 Kidney Dialysis Centers 621492 Kidney Dialysis Centers Other Patient-Serving Orgs8093 Specialty Outpatient Facilities, NEC 62141 Family Planning Centers (pt) Other Patient-Serving Orgs

62142 Outpatient Mental Health and Substance Abuse Centers Other Patient-Serving Orgs621498 All Other Outpatient Care Centers Other Patient-Serving Orgs

8099 Health and Allied Services, NEC 621991 Blood and Organ Banks Other Patient-Serving Orgs54143 Graphic Design Services (pt) Other Patient-Serving Orgs541922 Commercial Photography (pt) Other Patient-Serving Orgs62141 Family Planning Centers (pt) Other Patient-Serving Orgs621999 All Other Miscellaneous Ambulatory Health Care Services Other Patient-Serving Orgs

5912 Drug Stores and Proprietary Stores 44611 Pharmacies and Drug Stores Pharmacies8322 Individual and Family Social Services 62411 Child and Youth Services Other Patient-Serving Orgs

62421 Community Food Services Other Patient-Serving Orgs624229 Other Community Housing Services Other Patient-Serving Orgs62423 Emergency and Other Relief Services Other Patient-Serving Orgs62412 Services for the Elderly and Persons with Disabilities Other Patient-Serving Orgs624221 Temporary Shelters Other Patient-Serving Orgs92215 Parole Offices and Probation Offices Other Patient-Serving Orgs62419 Other Individual and Family Services Other Patient-Serving Orgs

8361 Residential Care 623312 Homes for the Elderly Senior Care62322 Residential Mental Health and Substance Abuse Facilities Other Patient-Serving Orgs62399 Other Residential Care Facilities All Other

7352 Home Health Furniture and Equipment Rental and Leasing 532291 Home Health Equipment Rental All Other7352 Medical Machinery Rental and Leasing 53249 Other Commercial and Industrial Machinery and Equipment Rental and Leasing (pt) All Other

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0 0.75

miles

Half mileradius

One mileradius

1.5

UCSF Mount Zion

Establishment Type


Employees on Site

2,500 to 5,000

1,000 to 2,500

500 to 1,000

250 to 500

100 to 250

50 to 100

25 to 500 to 25

Figure 3: Health-related Establishments near UCSF Mount Zion Medical Center


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FINAL REPORT - University of California, San Francisco · of space, a student housing complex containing 430 housing units , and a ... total hospital size to 550 beds and an ACC of