ROOM TO GROW: ESTIMATING HOUSING CAPACITY IN SAN FRANCISCO
Kasey Klimes UC Berkeley Fall 2014
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INTRODUCTION
It is news to no one in the Bay Area that San Francisco is in the midst of a severe housing affordability crisis. While the economy has boomed, the population drawn to San Francisco has pressurized the housing market in a manner unprecedented. New housing development has failed to meet demand, driving property values and rents ever higher. In 2012, the number of building permits was up 7% over 2011, but down 9% from 2003 . A common refrain is that land-locked San Francisco is simply “out of room� for more housing, built to the maximum possible amount of housing that could possibly fit on the tip of a hilly peninsula. This study sets out to challenge this assumption and discover how much housing San Francisco could still build under current zoning regulation.
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NEIGHBORHOODS IN SAN FRANCISCO
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THE MARKET IN CONTEXT
San Francisco’s high rate of rental occupancy makes it particularly vulnerable to major fluctuation in the housing market and therefore low-income displacement. Among American cities with populations greater than 300,000 it ranks 4th with a rental occupancy rate of 64%.
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Furthermore, rents in San Francisco have soared in recent years, taking the median gross rent to approximately $1500/month following the recession*. These prices reflect not only a high demand for rental housing, but specifically a high demand from those with the means to afford higher rents.
* The 2013 American Community Survey 3 Year estimate has been chosen specifically for its sampling time-frame following the great recession, which ended in June 2009 according the National Bureau of Economic Research.
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THE MARKET IN CONTEXT
Rental vacancy rates can serve as a proxy for the degree to which rental housing demand is outpacing rental housing supply. Unsurprisingly, San Francisco’s vacancy rate is among the lowest in the nation at approximately 3%. Contrast this with rust belt cities like Detroit (10.6%) or cities like Phoenix (10.2%) where the suburban housing boom fell to the foreclosure crisis. While one end of the spectrum indicates economic stagnation, the other suggests an incredibly pressurized housing market.
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Given the astronomical rents and highly competitive rental market in San Francisco, one might expect the average San Franciscan to be sacrificing an excessively high percent of their income on rent. In fact, the opposite is true–Fewer San Franciscans spend more than 30% of their income on rent than nearly any other city in the country*. Note that where previous charts have consistently placed New York and San Francisco side by side, the two notoriously expensive cities make a dramatic departure from one another along this metric. While rents stand above the rest of the nation in San Francisco, incomes are even higher, suggesting a market that bars and potentially displaces low-income households. While only about 46% of renteroccupied households pay more than 30% of household income on rent–among the smallest shares of any major city–this metric does not take into account living conditions (such as 5 people sharing a 2 bedroom apartment). Furthermore, it does not illustrate the potential severity of economic conditions among those who are paying more than 30% of income on rent. The income gap in San Francisco is the fastest growing in the nation, an inequality already on par with that of Rwanda (Knight, 2014).
* The 30% threshold marks the definition of housing affordability according to the Department of Housing and Urban Development.
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THE MARKET IN CONTEXT
The above chart shows the makeup of existing housing by decade built across six major west coast cities, controlling for each city’s population in order avoid distortions in the trend lines due to varying sizes. This data suggests that San Francisco has consistently built less housing for its population than any of its peer cities since 1960. Meanwhile, Seattle built twice as much housing per thousand residents in the 2000’s, and continues to outpace San Francisco in this decade. While Seattle’s vacancy and income-dependent affordability rates stand next to San Francisco’s, San Francisco’s median rents are approximately 35% higher than Seattle’s. According to the National Low Income Housing Coalition (2014), the hourly wage required to afford a 1-bedroom apartment in San Francisco was 178% higher than local minimum wage, compared to a difference of only 17% in Seattle.
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9
A REVIEW OF THE LITERATURE
This increased demand in the San Francisco housing market is a combination of an influx in economic opportunity, a shift in preference for dense urban cities, and the increasingly appeal of urban redevelopment. Glaeser et al. (2001) points to the growing demand for lifestyle amenities and cultural experiences that is bringing households– and in particular highly educated and wealthy households–back cities from the suburbs. A number of scholars have explored the relationship between supply and demand on housing prices in urban centers. When development of housing in a city curtails–be it for reasons of geography or regulation–and becomes an inelastic supply, increased demand leads to higher home values (Roback, 1982). A look at recent data indicates that San Francisco is now head and shoulders above any other major US city with a median home value over $150,000 greater than the next most expensive housing market*. San Francisco now stands on one extreme end of the spectrum amidst a nation-wide spatial
*Statistically significant at the 95% confidence level.
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stratification of wealth. The average house price in San Francisco in 1950 was 37% higher than the national average of metropolitan areas–by 2000 that difference stood at 218% (Sinai, 2007). In that time, the number of housing units increased by 30%, but this increase was negated by a decrease in family households and an overall decrease in average household size. Between 1950 and 2000, San Francisco experienced a population growth of only 0.2%. New housing development did not keep up with consumer preferences or demand, thereby pushing home values and rents higher while keeping population stagnant. Housing in San Francisco has become an effectually inelastic supply, thereby leaving its population vulnerable to turbulent swings of boom and bust economics and continually shifting the city’s income distribution upwards. Gyourko, Mayer, and Sinai (2006) demonstrate that high demand cities with inelastic housing supplies generate bidding wars in which high-income households outbid low-income households for their preferred urban location. As population
grows with increased economic activity, this competition intensifies and the amount of wealth required to ‘play the game’ increases, leading to a city in which even middle-class households are unable to secure housing.
San Francisco is simply not as dense as it is often conceptualized–the city is far from the “Manhattanization” that some fear. Consider, for example, that 36% of non-public land is currently zoned for single family homes.
In many ways, this ongoing dynamic has already made San Francisco a citadel for the wealthy. School teachers, waiters, bus drivers, and even some tech employees can no longer afford to live within the city limits of their employment. While it would be myopic to consider the city of San Francisco’s housing market without consideration for the greater Bay Area, the city does have a responsibility to address the rising inequality and displacement occurring within its jurisdiction.
Salam makes the case for supply-side economics, but is San Francisco a unique exception to economic rule? Calvin Welch (2013) makes the case that home values have trended in correlation with new construction in San Francisco, not against it. There are only 13 square miles available for housing in San Francisco, Welch contends–and all 13 miles are fully developed. In theory Salam’s argument for densification may be sound, but if all available land is fully developed and monopolized with no ceiling on price then there is no incentive for owners to demolish and rebuild more densely. “There is no ‘free land’ in San Francisco,” argues Welch, “The owners have total ‘market power’ over its price.”
In a recent article, Reihan Salam (2014) argues that providing more housing for San Francisco would decrease rents. In particular, he points to the housing stock of Tokyo for comparison, which grew at twice the clip as San Francisco over the last decade and where rents actually decreased slightly over the same period. In response to those who argue San Francisco has already reached its maximum density he calls on other comparisons: San Francisco’s population is about 825,000. If it had the same population density as my hometown, New York City, it would instead have a population of 1.2 million. Note that I’m referring to the population density of all five boroughs of New York City, including suburban Staten Island and the low-rise outer reaches of Brooklyn, Queens, and the Bronx. A San Francisco of 1.2 million would not be a Blade Runner–style dystopia in which mole people were forced to live cheek-by-jowl in blighted tenements. San Francisco at 1.2 million people would still be only half as dense as Paris, a city that is hardly a Dickensian nightmare.
Unless San Francisco isn’t out of room. Plan Bay Area calls on San Francisco to build 92,410 new housing units by 2040, a quantity that would begin to approach the aforementioned densities of New York City. This study seeks to answer the question of whether this much housing can be built in San Francisco, and if so where?
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ROOM TO GROW
SINGLE FAMILY ZONING IN SAN FRANCISCO
36%
of non-public land is currently zoned for single family homes
This study seeks to identify capacity for further residential unit development in San Francisco at varying hypothetical thresholds. They are as follows:
•The estimated housing capacity available across currently vacant parcels if built to the maximum allowable building envelope and residential unit density allowable under current zoning code.
• The estimated housing capacity available if currently existing residential buildings were further developed to the maximum allowable building envelope and residential unit density allowable under current zoning code.
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ROOM TO GROW
VACANT LAND IN SAN FRANCISCO
6.8% 14
of San Francisco’s land currently stands vacant
The San Francisco Planning Department’s database includes data regarding vacancy by parcel. This includes wholly undeveloped land as well as parcels containing nothing but surface parking lots under the planning department’s vacancy data definition. This study will proceed by this definition under the notion that surface parking is an inefficient use of urban land and that the removal of the parking lot for development is a negligible cost in the potential development of housing. The definition of vacancy in this study does not include the hundreds of acres of land currently dedicated to golf courses in San Francisco, though this land may warrant further study in the future.
While this data is incomplete and slightly outdated (from 2011), it is useful in directing attention to potential areas for further housing development. According to this data, approximately 6.8% of the city’s land (2.7% of parcels) is vacant. This would not suggest that San Francisco is out of room, but the question then remains; how much more housing could be built on this land under current zoning regulation? Furthermore, how much housing could be built if currently built parcels were densified to the maximum allowable number of housing units?
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METHODOLOGY
BUILDINGS IN SAN FRANCISCO
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DATASETS Name
Geometry
Scope
Attributes
SFLandUse2011
Polygon
Parcel Map
Number of Residential Units, Sto- SF Planning (via ries, Land Use, Building Area (sq ft) Aksel Olsen)
Height and Bulk Districts Zoning Districts State Highways Buildings
District Type, Maximum Allowed Height (ft) Zoning Code, District Name
SF Planning
Highway Name
National Highway Service Open Street Maps
Height_and_ Polygon Bulk_Districts Zoning_DisPolygon tricts NHS_Map21 Lines san-francisco Polygon _california. osm-buildings
Name, Building Area
Source
SF Planning
Note: Due to the limited scope of available data, Treasure Island will not be included in this study.
NATIONAL HIGHWAYS IN SAN FRANCISCO
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METHODOLOGY
PROCESS Because I am interested in capacity on the parcel level, SFLandUse2011 (henceforth referred to as the ‘parcel map’) will be the primary layer that I will use to identify most existing conditions and contrast against zoning districts and height and bulk districts. Parcel Map Among the parcel map’s most valuable attributes to my process is “Land Use”, which identifies vacant parcels (among residential, industrial, mixed use, etc). Many of these parcels, however, are in fact land used for state highways. IDENTIFYING HIGHWAY PARCELS To identify and separate these parcels I have performed a spatial query, selecting all parcels that intersect the lines of the state highway shapefile and creating a new binary attribute, “hwy_plot” in which ‘1’ indicates highway parcels and ‘0’ indicates non-highway parcels *. These parcels will not be considered available capacity for residential development in this study. VERIFYING VACANCY STATUS The land use attribute of this data set is useful but not perfectly reliable, in particular in regards to identifying vacant land (which in this case includes parking lots and land dedicated exclusively to parking garages). While I have manually combed through identified vacant parcels and compared them to both OpenStreetMaps buildings layer and Google satellite/streetview imagery to verify or correct their vacancy status, it is likely that there remain many parcels that are incorrectly identified. Unfortunately there is no cost-effective means of measuring the accuracy of this data without a citywide vacancy audit.
* It was also necessary to first manually remove portions of the state highways that tunnel below buildable parcels, such as at the Transbay terminal.
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PARCELS INTERSECTED BY HIGHWAY
Zoning Map I next needed to identify the thresholds of allowable residential density across the many zoning districts. The difficulty in this is that allowable density is discerned in different ways. For example, in district code RH-1(D) (Residential – House, One Family, Detached), which makes up about 10.5% of San Francisco’s non-public land, only one residential unit is allowed per parcel regardless of size. In others, like RM-1 (Mixed, Low Density) through RM-4 (Mixed, High Density), one residential unit is allowed for a set lot area (800 sq ft through 200 sq ft, respectively) in correspondence with increasing density limits. Others still do not have unit caps, and are only limited by the maximum allowable building envelope determined by the height and bulk district.
CODING DENSITY LIMITS Using basic SQL commands (see: Appendix, fig 1), I created a new field to include the minimum lot area permitted per residential unit where applicable . With this method I was able to determine the minimum lot area per residential unit for approximately 88% of residentially zoned land. This field is called “MaxUnitDen”. By comparing this to the
RESIDENTIALLY ZONED PARCELS
area of a parcel (with controls for limitations on building footprints such as setbacks and backyards) I will be able to determine the maximum permitted residential units for these parcels. The maximum permitted residential units for parcels that do not fall into this category–the other 12%– will be calculated later on. I have also created an attribute that reduces zoning code down to a simple binary, “Z_Res”, in which “1” indicates permitted housing, and “0” indicates no permitted housing. Joining Zoning & Building Envelope Data to the Parcel Map Using spatial joins, I merged the attribute data from the zoning district map and the height and bulk map to the parcel map, creating a parcel database that includes all of the aforementioned
attributes on the parcel level. Once this was accomplished, I had a few new attributes to create in order to set up my database to produce the analysis needed to answer my research questions. The “MaxStories” field provides the estimated maximum number of stories allowed on a given parcel. It is a derivative of the height limits, providing a conservative estimate of 12ft per story. For parcels zoned within districts with residential unit density limits set to parcel area, I created a new field called “Max_Units”. This attribute divides parcel area by the previously created “MaxUnitDen” figure to produce the maximum allowed number of residential units in any given parcel for zoning districts where maximum allowed residential unit density is related to lot size. For parcels in other zoning districts (Urban Mixed Use, Mixed Use Residential, and Neighborhood Commercial District being the most common) however, it is necessary to estimate the maximum allowed residential density a different way. In these parcels residential density limits do not relate to parcel area but instead to height and bulk limits. Before estimating the number of potential residential units in these districts, however, I need to find the average unit sizes in each of them. To generate average unit size by zoning district, I have first created the new field “AvgUnitSize”. This field is the total building’s square footage divided by the number of residential units it contains. Using a pivot table in Microsoft Excel, I then produced the average unit size (or more accurately the average square footage of building per residential unit) for each zoning district. In some instances, this produced an average unit size that was smaller than the minimum allowed unit size in San Francisco **. By joining this average unit size by zoning district data back into my parcel data I could generate the main field of interest for my study; the maximum allowed residential units by parcel. ** 220 sq ft as of 2012. The minimum unit size was previously 290 sq ft.
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METHODOLOGY
To do this I identified residential parcels in which I had not already calculated the maximum number of residential units and where height and bulk limits are not restricted to open space. Within these parcels, I used the general building footprint rule which limits building to 75% of parcel area to generate buildable area, then multiplied this by the maximum number of stories to find the maximum building square footage***. By dividing this quotient by the average unit size for the parcel’s zoning code, I could estimate the maximum allowable residential units for each parcel (see: Appendix, fig. 2). Manual Adjustments for Large Development Areas Large development areas like Mission Bay and Bayview Hunters point currently have specific plans for allowed residential units. Since these numbers have been determined outside the scope of standard zoning practice, I have calculated their maximum allowed residential units based on information from the specific plan. MISSION BAY The number of residential units permitted for the Mission Bay redevelopment (which makes up nearly 2% of San Francisco’s non-public land) has been determined block by block for a total of 7,536 units . The site is a project of the Mission Bay Redevelopment Group. Because my parcel data does not use the same block number coding as the redevelopment plan, I cannot assign these unit limits block by block. I can, however, determine the average number of units allowed per 1,000 sq ft (17.3 units) and use this to extrapolate for the area (see: Appendix, fig. 3). Though I can’t provide an exact estimate of allowed units for each block, the total allowed units for the area is the most important statistic for my purposes.
MISSION BAY REDEVELOPMENT PLAN AREA
BAYVIEW HUNTERS POINT A manual calculation is also in order for the Bayview Hunters Point development, which falls *** 25% of parcel area must be backyard in most zoning districts, in other instances this provides a conservative estimate of allowable building area.
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BAYVIEW HUNTERS POINT PLAN AREA
MISSION BAY http://news.theregistrysf.com/equity-residential-nibbi-brothers-celebrate-topping-azure-mission-bay/
under the jurisdiction of a site-specific plan which has approved a total of 10,500 units . The site is under development by Lennar Urban. According to my parcel data, the total area of land under this plan’s zoning code is 465 acres. I have calculated this into my database much in the same way that I did for Mission Bay (see: Appendix, fig. 4). While again this does not provide us with accurate parcel figures, it does produce the correct number of allowed units for the overall development. INDIA BASIN Next door to the Bayview Hunters Point development is a much smaller planned development on the only remaining vacant and privately-owned waterfront site in San Francisco. This land was recently purchased for development by Build Inc. According to Gehl Studio, the lead urban design
INDIA BASIN PLAN AREA
firm on the project, 1,041 residential units have been approved by the city. After creating a new zoning code for the parcels within this site, ‘IB’, I calculated the average number of units per 1,000 and applied it to each parcel (see: Appendix, fig. 5). PARK MERCED Current plans for the Park Merced area have projected a total of 8,900 residential units for the site, up from about 3,200 today. It is important to note that this site is already a residential development and is simply poised for densification. I have calculated this area into the maximum residential unit figures in the same way as the other large developments, except that in this case the zoning code specifies sub-plan codes for residential and mixed use zones (“PM-R” and “PM-MU1”, respectively), allowing me a more accurate estimate (see: Appendix, fig. 6).
PARK MERCED PLAN AREA
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METHODOLOGY
LAND AVAILABLE FOR HOUSING DEVELOPMENT IN SAN FRANCISCO
1,861
of San Francisco’s parcels are available for housing development under current zoning code
Conclusive Queries Once my parcel database was structured with all the necessary components, answering my research required only a few basic SQL queries. CALCULATING POTENTIAL UNITS ON VACANT LAND To identify parcels available for housing development under zoning code, I queried for parcels that: 1. Were identified as vacant or under development. 2. Were not occupied by highway infrastructure. 3. Were zoned for housing of any type. 4. Possessed an estimated maximum residential unit limit greater than 0. (For syntax see: Appendix, fig. 7) As seen in the map, there remain few large areas open for housing development in San Francisco (aside from Bayview Hunters Point and Mission Bay which are already under development). There are, however, a sizable number of smaller available parcels scattered throughout the city; over 1,800 of them by this study’s estimate.
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MAPPING OPPORTUNITY
HOUSING UNIT CAPACITY BY AVAILABLE PARCEL (Grouped by Census block group)
Using the maximum permitted residential units per parcel, I can now calculate the answer to my first research question; the estimated housing capacity available across currently vacant parcels if built to the maximum allowable building envelope and residential unit density allowable under current zoning code. The map shown groups these parcel unit capacities by census block group. Approximately 39,821 housing units could be built on vacant land in San Francisco under current zoning code.
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39,821
housing units could be built on vacant land in San Francisco under current zoning code.
MAPPING OPPORTUNITY
HOUSING UNIT CAPACITY BY NEIGHBORHOOD (Total indicates 100 residential units)
14,678
housing units can be built in Bayview, while 8,651 can be built in SoMa.
Broken down by neighborhood, we see that the largest concentration of these potential residential units is in the Financial District, South of Market (SoMa), and Downtown/Civic Center areas, while the neighborhood with the largest capacity overall is Bayview. Neighborhoods to the west such as Parkside, Lakeshore, and Outer Richmond have a very low density of potential units due to a combination of few vacant parcels and zoning code that severely restricts density. Nonetheless, this analysis suggests a capacity of over 200 units remaining under current zoning code in these three neighborhoods.
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MAPPING OPPORTUNITY
BUILT PARCELS WITH POTENTIAL FOR FURTHER DENSIFICATION
313,459
potential housing units could be built with further densification of existing built parcels under current zoning code.
CALCULATING TOTAL UNIT CAPACITY BEYOND EXISTING Just as I have calculated the total residential capacity on vacant land in San Francisco, I can use the data I have acquired and generated to calculate the total estimated capacity for the entire city under current zoning code (regardless of what is currently built). This many have implications for further densification of the city. To identify parcels available for housing densification under zoning code, I queried for parcels that: 1. 2. 3. 4.
Were not identified as vacant. Were not occupied by highway infrastructure. Were zoned for housing of any type. Possessed an estimated maximum residential unit limit greater than 0.
By subtracting the existing residential units from the maximum permitted number of units for each parcel, I can identify and map potential for densification on already-built parcels. According to my analysis, approximately 313,459 additional units could be added to existing built sites . While the scope of this figure brings into question the complete reliability of the datasets, it indicates and reaffirms the hypothesis that San Francisco has considerable room for densification even under current zoning codes.
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MAPPING OPPORTUNITY
LAND ZONED FOR 4 OR MORE STORIES IN SAN FRANCISCO
17.7% 30
of non-public land is currently zoned for four or more stories.
The estimated capacity for new housing on vacant land identified here aligns closely with current figures for projected housing development over the coming decades, around 36,200 units . Beyond this, San Francisco will need to either densify existing developments and/or loosen density restrictions on current zoning code. The below map of parcels zoned for more than 4 stories will likely need to become much more expansive窶田urrently only 17.7% of non-public land is available for taller buildings.
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IDENTIFIED SPACES FEW OPPORTUNITIES FOR LARGE SCALE DEVELOPMENT
This 45,000 sq ft parking lot along the Embarcadero could hold 54 housing units under current zoning code.
This parking lot near Candlestick Park is over 300,000 sq ft, with an estimated 200 potential housing units available even under current low-density zoning for the area. Here one can see neighborhood children using the land to play catch in the empty lot. 32
A street view look of available parcels identified in this study confirms the suspected inefficiency of San Francisco land, especially that used for surface parking. While the few large vacant lots are an easy win for large-scale development, the city also has many small vacant parcels scattered throughout residential neighborhoods that could have a meaningful contribution to achieving a sufficient amount of housing.
Further study might also consider the potential housing units available were the highways removed from the city, if more industrial land were unlocked for residential purposes, or were current zoning amended by various thresholds. It should also consider the utility of surface parking in relation to housing developments in a more nuanced manner.
Over 500 potential residential units could fit in this 7,500 sq ft SoMa parking lot under current zoning code. 33
IDENTIFIED SPACES
MANY OPPORTUNITIES FOR SMALL SCALE DEVELOPMENT
This 3,000 sq ft lot in Richmond could hold up to 5 residential units under current zoning code.
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This 7,800 sq ft lot in the Mission could also hold 5 units.
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CONCLUSION
It is not true that San Francisco is out of room. The nearly 40,000 potential housing units on vacant land estimated in this study could meet current demand, and likely would have had they been developed years ago. If the average number of persons per household remains relatively consistent, these units could house approximately 90,000 people. Based on Plan Bay Area population projections, however, these 40,000 potential units on vacant land would only meet the demand of growth to the mid-2020’s. Much is already under construction or planned for development. To meet demand beyond it is highly likely that San Francisco will need to densify currently built property and change zoning code to optimize potential density where possible. The political question of regulation around development in San Francisco comes into focus on this point. It is not uncommon for residential projects to spend many years –up to a decade–in San Francisco’s planning process, and often under scrutiny of density above all else. There are many hurdles to ending San Francisco’s housing crisis, but it remains unquestionable that the city must build more housing–and as this study has demonstrated, the opportunity is available.
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APPENDIX
Fig. 1 CASE WHEN “ZONING_SIM” = ‘RH-1(D)’ THEN 3000 WHEN “ZONING_SIM” = ‘RH-1’ THEN 3000 WHEN “ZONING_SIM” = ‘RH-1(S)’ THEN 3000 WHEN “ZONING_SIM” = ‘RH-2’ THEN 1500 WHEN “ZONING_SIM” = ‘RH-3’ THEN 1000 WHEN “ZONING_SIM” = ‘RM-1’ THEN 800 WHEN “ZONING_SIM” = ‘RM-2’ THEN 600 WHEN “ZONING_SIM” = ‘RM-3’ THEN 400 WHEN “ZONING_SIM” = ‘RM-4’ THEN 200 WHEN “ZONING_SIM” = ‘RC-3’ THEN 400 WHEN “ZONING_SIM” = ‘RC-4’ THEN 200 WHEN “ZONING_SIM” = ‘RED’ THEN 400 WHEN “ZONING_SIM” = ‘RTO’ THEN 600 WHEN “ZONING_SIM” = ‘NC-1’ THEN 800 WHEN “ZONING_SIM” = ‘NC-2’ THEN 800 WHEN “ZONING_SIM” = ‘NC-3’ THEN 600 WHEN “ZONING_SIM” = ‘NC-S’ THEN 800 WHEN “ZONING_SIM” = ‘C-2’ THEN 800 WHEN “ZONING_SIM” = ‘C-3’ THEN 125 WHEN “ZONING_SIM” = ‘C-3-G’ THEN 125 WHEN “ZONING_SIM” = ‘C-3-O’ THEN 125 WHEN “ZONING_SIM” = ‘C-3-O(SD)’ THEN 125 WHEN “ZONING_SIM” = ‘C-3-R’ THEN 125 WHEN “ZONING_SIM” = ‘C-3-S’ THEN 125 WHEN “ZONING_SIM” = ‘C-M’ THEN 200 WHEN “ZONING_SIM” = ‘CRNC’ THEN 200 WHEN “ZONING_SIM” = ‘CVR’ THEN 200 WHEN “ZONING_SIM” = ‘CCB’ THEN 200 WHEN “DISTRICTNA” = ‘BROADWAY NEIGHBORHOOD COMMERCIAL’ THEN 400 WHEN “DISTRICTNA” = ‘CASTRO STREET NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘INNER CLEMENT STREET NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘CASTRO STREET NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘OUTER CLEMENT STREET NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘FILLMORE STREET NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘HAIGHT STREET NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘UPPER MARKET STREET NEIGHBORHOOD COMMERCIAL’ THEN 400 WHEN “DISTRICTNA” = ‘NORTH BEACH NEIGHBORHOOD COMMERCIAL’ THEN 400 WHEN “DISTRICTNA” = ‘POLK STREET NEIGHBORHOOD COMMERCIAL’ THEN 400 WHEN “DISTRICTNA” = ‘SACRAMENTO STREET NEIGHBORHOOD COMMERCIAL’ THEN 800 WHEN “DISTRICTNA” = ‘UNION STREET NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘VALENCIA STREET NEIGHBORHOOD COMMERCIAL TRANSIT’ THEN 600 WHEN “DISTRICTNA” = ‘24TH-MISSION NEIGHBORHOOD COMMERCIAL TRANSIT’ THEN 600 38
WHEN “DISTRICTNA” = ‘24TH STREET- NOE VALLEY NEIGHBORHOOD COMMERCIAL’ THEN 600 WHEN “DISTRICTNA” = ‘WEST PORTAL AVENUE NEIGHBORHOOD COMMERCIAL’ THEN 800 WHEN “DISTRICTNA” = ‘INNER SUNSET NEIGHBORHOOD COMMERCIAL’ THEN 800 WHEN “DISTRICTNA” = ‘PACIFIC AVENUE NEIGHBORHOOD COMMERCIAL’ THEN 800 WHEN “DISTRICTNA” = ‘RESIDENTIAL TRANSIT ORIENTED- MISSION’ THEN 600 WHEN “DISTRICTNA” = ‘CHINATOWN- RESIDENTIAL- NEIGHBORHOOD COMMERCIAL’ THEN 600 END Fig. 2 CASE WHEN “Z_res” = 1 AND “Max_Units” IS NULL AND “HEIGHT” != ‘OS’ THEN (“MaxStories”* (“AREA”*.75) )/ “AverageUni” ELSE “Max_Units” END Fig. 3 CASE WHEN “ZONING_SIM” = ‘MB-RA’ THEN (“AREA” /1000)*0.625068066 END Note: The allowed residential unit density according to the redevelopment plan is 17.3 units/1000 ft of designated housing area. 0.625068066 was produced using the total area of redevelopment (276.7 acres) and the total permitted residential units (7,536) due to the inability to identify parcels within designated housing area blocks. Fig. 4 CASE WHEN “ZONING_SIM” = ‘HP-RA’ THEN (“AREA” /1000)* 0.51821762 END Note: The figure 0.51821762 units/1000 sq ft was produced using the total area of redevelopment (465.1 acres) and the total permitted residential units (10,500) due to the inability to identify parcels within designated housing area blocks. Fig. 5 CASE WHEN “ZONING_SIM” = ‘IB’ THEN (“AREA” /1000)* 1.384857297 END Note: The figure 1.384857297 units/1000 sq ft was produced using the total area of redevelopment (75,100 sq ft) and the total permitted residential units (1041) due to the inability to identify parcels within designated housing area blocks. Fig. 6 CASE WHEN “ZONING_SIM” = ‘PM-R’ OR “ZONING_SIM” = ‘PM-MU1’ THEN (“AREA” /1000)* 2.868967794 END Note: The figure 1.868967794 units/1000 sq ft was produced using the total area of residentially zoned redevelopment (71.2 acres)and the total permitted residential units (8,900) due to the inability to identify parcels within designated housing area blocks. Fig. 7 “LANDUSE” = ‘VACANT’ AND “hwy_plot” = 0 AND “Z_res” =1 AND “HEIGHT” != ‘OS’ AND “ZMAXUNITS” != 0 39
BIBLIOGRAPHY
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San Francisco Planning Department, “DENSITY OF RESIDENTIAL USES IN MB-R AND MB-NC DIS TRICTS.” Text. Accessed December 13, 2014. http://planning.sanfranciscocode.org/9/920/. San Francisco Planning Department, “Zoning Use District Summaries”. http://www.sf-planning.org/in dex.aspx?page=1580 San Francisco Planning Department, “2012 San Francisco Commerce and Industry Inventory”, 2013. Sinai, Todd. “Urban Housing Demand.” New Palgrave Dictionary of Economics, March 8, 2007. Welch, Calvin. “SF Controller Shows Supply and Demand Does Not Work.” Scribd. Accessed September 24, 2014. http://www.scribd.com/doc/217112947/SF-Controller-Shows-Supply-and-Demand- Does-Not-Work.
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