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Condominium Conversions in San Francisco: GIS Analysis ofDeterminants

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”

OverviewRelationship to earlier paperBackground Concerning Condominium Conversions in San FranciscoData about Condominium Conversions in San FranciscoEconomics of Condominium ConversionsGIS Application to Condominium Conversions

Supply-sideDemand-side (in earlier paper)

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San Francisco vs. San Diego

Relationship to Earlier Paper

This paper extends the empirical/GIS analysis to a longer period of timeThis paper examines “supply-side” in detailEarlier paper has detailed “demand-side” 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

3

What are condominium conversions?

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

Ownership Condominiums

Rental Apartments

San Francisco:

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

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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 websiteSan 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 variablesSupply side is significantly affected by regulationLimited land availability and limitations on conversion mean that supply of housing may not adjust quickly to increased demand (short-run model)

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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 owner-occupied 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

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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 for various years between 2000and 2006

Create map overlays of condo conversions with layers representing the main variables of interest

Geocode addresses of all conversions

Map conversions and variables

Supply-side Variables

Stock of Rental

Housing

Price-to-Rent Ratio

Construction Costs

Interest rates*

Condo conversion

* 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 thesevariables 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)

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Rental vs. Owner-Occupied Housing by Census Tract

Darkest: 100%-80% rentalLightest: 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 aboveMedian (approx. 0.48)

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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%

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Condo Conversions (2002 & 2003) and Census Tracts with More than Median Share of Small Unit Rental Stock

Demand-side Variables

Median Income

Price-to-Rent Ratio*

Percent Asian

Percent African

American

Condo conversion

* 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 thesevariables 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 owner-occupiers

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Distribution of Price-to-Rent RatioCensus Block Groups in Price-to-Rent Ratio Categories

(Total Number of Block Groups = 575)

020406080

100120140160

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 p

lus

Price-to-Rent Ratio

Num

ber o

f Cen

sus

Blo

ck

Gro

ups

Category containing median valuemedian value 1.73

Distribution of Median Household Income

Median Household Income

0

20

40

60

80

100

120

140

$0-$1

5,000

$15,0

01-$2

5,000

$25,0

01-$3

5,000

$35,0

01-$4

5,000

$45,0

01-$5

5,000

$55,0

01-$6

5,000

$65,0

01-$7

5,000

$75,0

01-$8

5,000

$85,0

01-$9

5,000

$95,0

01-$1

05,00

0

$105

,001-$

115,0

00

$115

,001-$

125,0

00

$125

,001-$

135,0

00

$135

,001-$

145,0

00

$145

,001-$

155,0

00

$155

,001-$

165,0

00

$165

,001-$

175,0

00

$175

,001-$

185,0

00

$185

,001-$

195,0

00

$195

,001-$

205,0

00

Num

ber o

f Cen

sus

Blo

ck G

roup

s

Category containing median value$59,351

Price-to-Rent Ratio & Condo Conversions

Higher price-to-rent ratio More condo conversions

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Median Household Income & Condo Conversions

Higher Income More condo conversions

San Francisco Data

Frequency Distribution: Condo Conversions per Block Group in 2000

Classes Frequency0 - 29,999 130 - 49,999 1150 - 69,999 7870 - 89,999 29

90 - 109,999 8110,000 and over 9

Total 136

Classes Frequency0.00 2

0.01 - 1.00 21.01 - 1.50 51.51 - 2.00 362.01 - 2.50 352.51 - 3.00 233.01 - 3.50 163.51 - 4.00 114.01 - 4.50 64.51 - 5.00 0

5.01 and over 0Total 136

Source: Authors’ calculations based on condo conversion data supplied by San Francisco Department of Public Works

575 Block Groups

136 conversions in 2000

155 conversions in 2001

91%

34%

93%

67%

San Francisco Data (cont.)

Frequency Distribution: Condo Conversions per Block Group in 2001

Classes Frequency0 - 29,999 030 - 49,999 2150 - 69,999 6770 - 89,999 45

90 - 109,999 11110,000 and over 11

Total 155

Classes Frequency0.00 2

0.01 - 1.00 31.01 - 1.50 131.51 - 2.00 272.01 - 2.50 452.51 - 3.00 303.01 - 3.50 203.51 - 4.00 94.01 - 4.50 34.51 - 5.00 2

5.01 and over 1Total 155

Source: Authors’ calculations based on condo conversion data supplied by San Francisco Department of Public Works

86%

43%

88%

71%

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Results

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

Greater relative supply More condo conversions

Weaknesses of the AnalysisNo 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 Pogodzinskijmp@pogodzinski.net

Alicia Parkeralitesus@yahoo.com

Tito Vandermeydentvandermeyden@nextbus.com