Condominium Conversions in San Francisco: GIS Analysis of Determinants by J. M. Pogodzinski, Economics Department and Urban and Regional Planning Department, San Jose State University Alicia T. Parker, Urban and Regional Planning Department, San Jose State University Tito Vandermeyden, GIS Manager, Nextbus, Inc.
Download Presentation Go to http://www.pogodzinski.net/ and click on ESRI UC 2007 Presentation
Overview Relationship to earlier paper Background Concerning Condominium Conversions in San Francisco Data about Condominium Conversions in San Francisco Economics of Condominium Conversions GIS Application to Condominium Conversions Supply-side Demand-side (in earlier paper)
San Francisco vs. San Diego
Relationship to Earlier Paper This paper extends the empirical/gis analysis to a longer period of time This paper examines supply-side in detail Earlier paper has detailed demandside theoretical development
Background Concerning Condominium Conversions in San Francisco San Francisco is the only City-County in California 177 Census Tracts in City-County of San Francisco; 575 Census Block- Groups 65% of housing units in San Francisco are rental units
What are condominium conversions? Rental Apartments Ownership Condominiums San Francisco: change in the type of ownership of real property to condominium, community apartment project or stock cooperative and in which two or more units of such projects are created within an existing structure
What are the public policy issues involving condo conversions? Condo conversions change the balance between rental and ownership housing Positive Aspects: Negative Aspects: + Provide affordable ownership housing + Improve the housing stock and increase property values through upgrades - Reduce the apartment rental inventory, thereby increasing rents - Limit housing options for low-income people
San Francisco Condo Conversion Ordinance Key features: Large complexes built for rental housing and occupied by tenants cannot convert Condo conversions are limited to owner-occupied buildings of six units or less and to only 200 applications (i.e. buildings) per year Lottery system designed to allocate approvals
San Francisco Data U.S. Census data on San Francisco census tracts and block groups includes data on Housing stock (rental and owner-occupied) Owner s assessment of house value, Renter s reported rent payments, median income, and demographic variables (including ethnic/racial classification) San Francisco Enterprise GIS website San Francisco Department of Public Works (for data about condominium conversions)
San Francisco Data
Economic Model (supply side) Model examines both supply-side and demand-side variables Supply side is significantly affected by regulation Limited land availability and limitations on conversion mean that supply of housing may not adjust quickly to increased demand (shortrun model)
Economic Model (demand side) Economic literature on tenure choice Tenure choice model focuses on comparing the satisfaction a household derives from owning vs. the satisfaction it derives from renting (Henderson and Ioannides [1983]) Tenure choice model can also be modified to apply to different types of ownership (e.g., tenants in common vs. individuated ownership) Tenure type within the same structure may yield different levels of satisfaction and be associated with different costs
Model Expectations (supply side) In the short run, more conversions should occur where the supply of potential conversions is the greatest Suggests looking at absolute or relative measures of supply of rental housing Measures include proportion of rental housing to owneroccupied housing and proportion of rental housing in one area relative to the city-wide stock of rental housing Suggests looking at more refined measure of rental housing in appropriate sized units Measures include the proportion of rental housing in buildings with a specified number of units in one area relative to the city-wide number of rental units in buildings with the specified number of units
Model Expectations (demand side) Whether owning or renting is better for a particular household depends on several factors Price vs. rent for comparable properties (mortgage interest rate affects the cost of owner-occupied housing) Expect price-to-rent ratio to be positively related with tendency to own; speculation on increase in house value also a possible explanation Income: owner-occupied housing is assumed to be a normal good, like HDTVs, unlike shoe repairs. Expect income to be positively related to tendency to own
Empirical Methods Create GIS layers of variables 1) Rental and Owner-occupied housing units 2) price-to-rent ratio 3) median income 4) percent Asian and 5) percent African American Geocode addresses of all conversions Geocode addresses for various years between 2000 and 2006 Map conversions and variables Create map overlays of condo conversions with layers representing the main variables of interest
Supply-side Variables Construction Costs Stock of Rental Housing Condo conversion Interest rates* Price-to- Rent Ratio * Interest rates affect both the demand side (mortgage interest) and the supply side (construction loans and discounting of income streams from rental properties.
Why these supply-side variables? The literature and the economic model support these variables as determinants of condo conversions: Construction Costs: condo conversions invariably require remodeling to comply with codes Interest Rates: reflected in cost of construction loans and used to assess the discounted value of a stream of rental payments Stock of Rental Housing (especially in appropriate size range): conversions occur of existing housing (short-run)
Rental vs. Owner-Occupied Housing by Census Tract Darkest: 100%-80% rental Lightest: 20%-8% rental
Distribution of the Rental Housing Stock (as percent of Total Rental Units) Darkest: 2.01%-1.19% Lightest: 0.24%-0.01%
Distribution of the Rental Housing Stock (as percent of Total Rental Units) Darkest: 2.01%-1.19% Lightest: 0.24%-0.01% Selected: tracts above Median (approx. 0.48)
Condo Conversions (2002 & 2003) and Distribution of Rental Housing Stock
Condo Conversions (2002 & 2003) and Census Tracts with More than Median Share of Rental Stock
Condo Conversions and Distribution of the Rental Housing Stock in Buildings with a Small Number of Units (as percent of Total Rental Units in such buildings) Darkest: 1.63%-1.07% Lightest: 0.21%-0.00%
Condo Conversions (2002 & 2003) and Census Tracts with More than Median Share of Small Unit Rental Stock
Demand-side Variables Percent Asian Median Income Condo conversion Percent African American Price-to- Rent Ratio* * The value used in calculations is more complex: {[(P*r)/12]R} where r is the annual mortgage interest rate, P is the median house value and R is the median rent. See the paper for details.
Why these demand-side variables? The literature and the economic model support these variables as determinants of condo conversions: Price-to-rent: the value of owning vs. renting is based on a comparison of the asset price to the cost of rental Median income: ownership is a normal good a good the demand for which increases when income increases Percent Asian/Percent African American: other factors held constant, Asians (especially Chinese) have a higher probability of being owner-occupiers, and African Americans have a lower probability of being owneroccupiers
Distribution of Price-to-Rent Ratio Census Block Groups in Price-to-Rent Ratio Categories (Total Number of Block Groups = 575) 160 140 120 100 80 60 40 20 0 Category containing median value median value 1.73 Number of Census Block Groups 0.01-1.00 1.01-1.50 1.51-2.00 2.01-2.50 2.51-3.00 3.01-3.50 3.51-4.00 4.01-4.50 4.51-5.00 5.01 plus Price-to-Rent Ratio
Distribution of Median Household Income Median Household Income 140 120 Category containing median value $59,351 100 80 60 40 Number of Census Block Groups 20 0 $0-$15,000 $15,001-$25,000 $25,001-$35,000 $35,001-$45,000 $45,001-$55,000 $55,001-$65,000 $65,001-$75,000 $75,001-$85,000 $85,001-$95,000 $95,001-$105,000 $105,001-$115,000 $115,001-$125,000 $125,001-$135,000 $135,001-$145,000 $145,001-$155,000 $155,001-$165,000 $165,001-$175,000 $175,001-$185,000 $185,001-$195,000 $195,001-$205,000
Higher price-to-rent ratio More condo conversions Price-to-Rent Ratio & Condo Conversions
Higher Income More condo conversions Median Household Income & Condo Conversions
575 Block Groups San Francisco Data 136 conversions in 2000 155 conversions in 2001 Frequency Distribution: Condo Conversions per Block Group in 2000 91% 34% Classes Frequency 0-29,999 1 30-49,999 11 50-69,999 78 70-89,999 29 90-109,999 8 110,000 and over 9 Total 136 Classes Frequency 0.00 2 0.01-1.00 2 1.01-1.50 5 1.51-2.00 36 2.01-2.50 35 2.51-3.00 23 3.01-3.50 16 3.51-4.00 11 4.01-4.50 6 4.51-5.00 0 5.01 and over 0 Total 136 Source: Authors calculations based on condo conversion data supplied by San Francisco Department of Public Works 93% 67%
San Francisco Data (cont.) Frequency Distribution: Condo Conversions per Block Group in 2001 86% 43% Classes Frequency 0-29,999 0 30-49,999 21 50-69,999 67 70-89,999 45 90-109,999 11 110,000 and over 11 Total 155 Classes Frequency 0.00 2 0.01-1.00 3 1.01-1.50 13 1.51-2.00 27 2.01-2.50 45 2.51-3.00 30 3.01-3.50 20 3.51-4.00 9 4.01-4.50 3 4.51-5.00 2 5.01 and over 1 Total 155 Source: Authors calculations based on condo conversion data supplied by San Francisco Department of Public Works 88% 71%
Results Greater relative supply More condo conversions Higher price-to-rent ratio More condo conversions Higher Income More condo conversions Higher Percentage Asian No strong correlation Higher Percent African American Fewer or no condo conversions
Weaknesses of the Analysis No characteristics data for condo conversions Using census tracts and block groups involves aggregation which destroys information The analysis is suggestive but more formal statistical analysis should be done
Comments Welcome J. M Pogodzinski jmp@pogodzinski.net Alicia Parker alitesus@yahoo.com Tito Vandermeyden tvandermeyden@nextbus.com