THE ROLE OF INSTITUTIONAL FACTORS IN MODELING THE LOCATION OF
URBAN INFILL HOUSING DEVELOPMENT IN DECLINING U.S. CITIES:
A STUDY OF CLEVELAND, OHIO
A Dissertation
Presented to
The Graduate Faculty of The University of Akron
In Partial Fulfillment
of the Requirements for the Degree
Doctor of Philosophy
Jung-Wook Kim
August 2006
THE ROLE OF INSTITUTIONAL FACTORS IN MODELING THE LOCATION OF
URBAN INFILL HOUSING DEVELOPMENT IN DECLINING U.S. CITIES:
A STUDY OF CLEVELAND, OHIO
Jung-Wook Kim
Dissertation
Approved: Accepted:
______Advisor Department Chair Richard E. Klosterman Charles B. Monroe
______Committee Member Dean of the College Ashok K. Dutt Ronald F. Levant
______Committee Member Dean of the Graduate School Ralph P. Hummel George R. Newkome
______Committee Member Date Robert B. Kent
______Committee Member Robert A. Simons
ii
ABSTRACT
The main purpose of this study is to contribute to the field of urban modeling by identifying factors associated with the location of infill housing development in declining
American cities. The focus of this research is on the institutional factors that have not been considered in traditional urban land use models. The two main research questions were: (1) are the institutional factors significantly associated with the location of urban infill housing development in declining American cities? and (2) are the institutional factors more important than non-institutional factors, i.e., site and neighborhood characteristics, in determining the location of infill housing development? The study analyzed housing development patterns in City of Cleveland, Ohio, during the 1990s.
Most of the institutional factors were found to be significantly related to infill housing development. The land bank and Neighborhood Reinvestment Agreement
(NRA) programs were found to be particularly important in guiding the process of the infill housing development. Several non-institutional factors such as the quantity of vacant residential land were also found to be significantly related to infill housing development. Other non-institutional factors that have been traditionally used in urban models such as accessibility and proximity to amenities were found to not be significantly related to the location of infill housing development. Interestingly, the minority-concentrated, poverty-stricken, crime-ridden, and population-losing
iii neighborhoods which urban modelers have traditionally assumed would have little new housing experienced the most housing development activities. These rather surprising results reflect the fact that these neighborhoods were the focus of governments’ and other supporting institutions’ efforts to revitalize depressed urban neighborhoods.
The study has several important implications for urban modelers and urban planners. It first suggests that urban models for the residential development in struggling older cities should consider the important role played by a city’s institutional support systems. Second, it suggests that these models should pay particular attention to land availability factors such as land bank programs and the amount of residential vacant land.
Third, it suggests that urban modelers interested in urban infill residential development should not be as concerned with accessibility to employment or shopping centers as they are with other locational factors. Lastly, it indicates that governmental and institutional interventions can effectively guide the location of infill housing development in a weak inter-urban housing market.
iv
ACKNOWLEDGEMENTS
The completion of this dissertation was made possible by the encouragement and support rendered by my dissertation committee who guided me with great patience and understanding, many practitioners in the fields of urban planning and development in
Cleveland, and my families who always remembered me in their prayers.
Thanks to my dissertation committee. My deep gratitude goes to my mentor Dr.
Richard E. Klosterman. He has inspired, encouraged, challenged, corrected, supported, and collaborated with me in every aspects of the dissertation study. His pioneering research work in urban models and planning support systems has become one of my permanent research themes and, unsurprisingly, a key motive for this study. Dr. Robert
Simons has always been willing to make a time for my visit to his Cleveland office and has been passionate in our discussions about my dissertation research and any relevant issues of real estate development in American cities and Cleveland. Dr. Ashok Dutt has been one of my most generous supporters of my academic life at the University of Akron.
Whenever I met him at the school in the evening after work, Dr. Robert Kent greeted me with a big smile and handsome voice and never forgot to give me encouraging words.
Dr. Ralph Hummel gave me the warmth of a respectfully gentle intellectual and gave me sincere advice on my research. Dr. Gasper Garofalo, as an urban economist, gave me valuable insights on urban economic system in the inception of my dissertation research.
v Many individuals outside my dissertation committee provided practical insights and information for my dissertation research. During the initial stage of my study, I met and inquired with leading scholars and practitioners in the area of inner city housing and neighborhood redevelopment in Cleveland. Dr. Dennis Keating, Professor Norman
Krumholz, and Mr. Philip Star of Cleveland State University provided me with their insightful analyses of the neighborhood housing development market in Cleveland. Dr.
Tom Bier shared with me his recent research on the housing development in Cleveland.
Dr. Mark Salling and Mr. Charlie Post of NODIS were supportive enough to give me a huge set of GIS and tax record data for the City of Cleveland, which became the backbone of my research analysis. Ms. Wendy Sattin at Cleveland Neighborhood
Development Coalition explained the trend of community development projects of the
1990s in Cleveland. The Cleveland city government staff was very kind and patient with my nonstop inquiries and requests for a variety of information and data. Among them were Mr. Robert Brown, Mr. Martin Cader and Mr. Scott Frantz (City Planning
Commission), Mr. Bill Resseger, Ms. Rebecca Lombardo and Ms. Estella Loar
(Department of Community Development), Ms. Evelyn Sternard (Land Bank), and Ms.
Onelia Cedeno (Department of Building and Housing).
Appreciation is also extended to Mr. Anthony O’Leary and Mr. Michael
Blakemore, my respected bosses at my workplace, Akron Metropolitan Housing
Authority. Thankfully, they have trusted and offered me an enormous opportunity of being involved in two neighborhood revitalization projects called HOPE VI since the summer of 2001. My work experience at the AMHA surely opened my eyes as to how a
vi real world community development evolves and comes to fruition, which in turn provided a valuable insight for my dissertation.
My spiritual brothers and sisters prayed diligently for my faith and the success of this dissertation research. Pastor Kim in Phila and Deacon Choi and all the members of
Troas Mission Church have always showed their sincere love and care for my family.
A penultimate thanks goes to my parents and parents-in-law and my brothers and sisters in Korea. Their love for my family and unshakable faith in my ability to complete my doctoral research gave me extraordinary courage everytime I had a doubt about myself.
My final, and most heartfelt, acknowledgment must go to my wife Soo-Hyoun and my three children, Hae-In, Paul, and Grace. Soo-Hyoun’s steadfast love always encouraged and sustained me all through my long journey to the Ph.D. more than anyone else. Therefore, she is the rightful co-worker and co-author of all the work in this study.
And there is no need to mention how much joy my three children brought to my everyday life all through my lengthy research period. The nine years of my family life in Akron, an old central city, planted in my mind a special affection for American cities.
vii
TABLE OF CONTENTS
Page
LIST OF TABLES ...... xiii
LIST OF FIGURES...... xv
CHAPTER
I. INTRODUCTION ...... 1
1.1 Research Subject ...... 1
1.1.1 Definitions...... 2
1.1.2 Geographic Scale of Urban Modeling...... 2
1.1.3 Location of Urban Infill Housing Development ...... 3
1.1.4 Weak Market and Institutional Intervention ...... 4
1.1.5 Factors: Site, Neighborhood, and Institutional...... 5
1.2 Research Methods ...... 6
1.3 Research Questions ...... 8
1.4 Significance of the Research...... 9
1.5 Organization of the Dissertation ...... 11
II. THORIES AND MODELS OF RESIDENTIAL DEVELOPMENT ...... 12
2.1 Introduction...... 12
2.2 Urban Economic Theories...... 13
2.2.1 Classical Bid-Rent Theory...... 13
viii 2.2.2 The Polycentric Model...... 15
2.2.3 The Changing Utility of Work Trips...... 15
2.2.4 Workplace and Residential Location Theories ...... 16
2.2.5 Residential Location Choice Theory...... 16
2.2.6 Hedonic Price Models...... 18
2.3 Urban Models...... 19
2.3.1 Current Urban Modeling Approaches...... 21
2.3.2 Factors of the Spatial Distribution of Urban Land Development ...... 26
2.4 Determinants of Land Developability...... 32
2.5 Summary ...... 34
III. THE ROLE OF INSTITUTIONAL FACTORS IN THE URBAN
REDEVELOPMENT PROCESS...... 38
3.1 Introduction...... 38
3.2 Institutional Partnerships for Urban Infill Housing Development...... 40
3.3 Institutional Factors...... 44
3.3.1 The City Comprehensive Plan...... 44
3.3.2 The Community Reinvestment Act...... 46
3.3.3 The Land Reutilization Program...... 48
3.3.4 The Cleveland Neighborhood Partnership Program ...... 51
3.3.5 Empowerment Zones...... 52
3.3.6 Development Equity Funds and Debts...... 53
3.3.7 Ward Politics...... 54
3.4 Summary ...... 55
ix IV. THE URBAN DEVELOPMENT HISTORY OF CLEVELAND...... 57
4.1 Introduction ...... 57
4.2 Ups and Downs of the Urban Development...... 60
4.2.1 A Thriving Industrial Central City...... 60
4.2.2 Deindustrialization and Suburbanization...... 61
4.2.3 Segregated Neighborhoods ...... 64
4.2.4 Housing Surplus, Abandonment, and Massive Demolition...... 66
4.3 Urban Redevelopment...... 68
4.3.1 The Redevelopment of Downtown and Subcenters...... 70
4.3.2 The Redevelopment of Residential Neighborhoods ...... 72
4.3.3 Institutional Supporting System for Neighborhood Redevelopment...77
4.4 Summary ...... 79
V. CONCEPTUAL FRAMEWORK AND HYPOTHESES ...... 81
5.1 Introduction...... 81
5.2 Conceptual Framework ...... 82
5.2.1 Institutional Factors...... 85
5.2.2 Non-institutional Factors...... 87
5.3 Hypotheses ...... 88
5.3.1 Central Hypotheses ...... 89
5.3.2 Institutional Factor Hypotheses...... 90
5.4 Summary ...... 92
VI. RESEARCH METHOD AND DATA SOURCES...... 94
6.1 Introduction...... 94
x 6.2 Analysis Methods...... 95
6.2.1 Neighborhood-level Analysis...... 96
6.2.1 Parcel-level Analysis...... 99
6.3 Data ...... 100
6.3.1 Some Issues...... 100
6.3.2 Variables...... 101
6.3.3 Filtering: Finding Developable Parcels...... ……..104
6.3.4 Sampling...... 105
6.4 Summary ...... 106
VII. ANALYSIS OF THE NEIGHBORHOOD-LEVEL FACTORS ...... 107
7.1 Introduction...... 107
7.2. Spatial Description...... 110
7.3. Zero vs. Non-zero Development Rates ...... 113
7.4. Regression and Supplemental Statistical Analyses...... 115
7.4.1 Analysis of the Non-Institutional Factors ...... 116
7.4.2 Analysis of the Institutional Factors ...... 124
7.5. Correlation Between the Institutional and Non-institutional Variables...... 127
7.6. Cluster Analysis ...... 131
7.7. Summary ...... 134
VIII. ANALYSIS OF THE SITE-LEVEL FACTORS...... 136
8.1 Introduction...... 136
8.2 Logit Regression Analysis ...... 139
8.2.1 Physical Features...... 140
xi 8.2.2 Accessibility...... 141
8.2.3 Amenities ...... 143
8.2.4 Disamenities...... 143
8.2.5 Site Assembly Potential ...... 147
8.2.6 Other Positive Externalities...... 148
8.2.7 Institutional Factors...... 148
8.3. Summary ...... 150
IX. CONCLUSIONS...... 157
9.1 Summary ...... 157
9.2 Implications...... 160
9.2.1 Implications for Urban Modelers...... 160
9.2.2 Implications for Urban Planners ...... 163
9.3 Future Research...... 164
BIBLIOGRAPHY ...... 166
APPENDIX. MAPS OF INSTITUTIONAL AND NON-INSTITUTIONAL FACTORS...... 177
xii
LIST OF TABLES
Table Page
2.1 Site and Neighborhood Factors in Urban Models...... 36
3.1 Target Neighborhoods for Neighborhood Reinvestment Agreements ...... 48
3.2 Number of Land Banking Sites, 1990 – 2000...... 51
3.3 Institutional Factors Affecting the Location of Residential Development ...... 56
4.1 Cleveland’s Population, 1800 – 1990 ...... 58
4.2 Comparison of Population between the City of Cleveland and Suburbs ...... 62
6.1 Neighborhood-level Institutional Factors...... 101
6.2 Neighborhood-level Non-Institutional Factors ...... 102
6.3 Parcel-level Institutional Factors...... 102
6.4 Parcel-level Non-Institutional Factors ...... 103
7.1 Neighborhood-level Institutional Factors...... 108
7.2 Neighborhood-level Non-institutional Factors...... 109
7.3 Summary of Neighborhood-level Hypotheses ...... 111
7.4 Zero vs. Non-zero t statistics...... 114
7.5 Multiple Regression Analysis of Non-institutional Factors...... 117
7.6 Correlation Analysis of Non-institutional Factors, 1990-1995...... 119
7.7 Correlation Analysis of Non-institutional Factors, 1995-2000...... 120
7.8 Regression Analysis of Institutional Factors...... 125
xiii 7.9 T test analysis of Institutional Factors...... 125
7.10 Correlation Analysis of Percent Land Banking Areas ...... 125
7.11 Regression Analysis for Wards...... 126
7.12 Correlation between Institutional and Non-institutional Variables: 1991-1995 .....129
7.13. Correlation between Institutional and Non-institutional Variables: 1996- 2000 ...130
7.14 Neighborhood Clustering and Variables...... 133
7.15 Summary of Neighborhood-level Hypotheses Test Results ...... 134
8.1 Summary of Parcel-level Hypotheses ...... 137
8.2 Parcel-level Institutional Variables ...... 138
8.3 Parcel-level Non-institutional Variables ...... 138
8.4 Logit Regression Analysis of Site Physical Factors...... 140
8.5 Logit Regression Analysis of Accessibility Factors ...... 141
8.6 Logit Regression Analysis of Amenity Factors ...... 144
8.7 Logit Regression Analysis of Disamenity Factors...... 145
8.8 Logit Regression Analysis of Site Assembly Potential and Other Positive Externality Factors ...... 147
8.9 Logit Regression Analysis of Institutional Factors...... 149
8.10 Summary of Hypotheses Test Results...... 151
8.11 R-Square Changes of Logit Regression Block-Submodels...... 152
8.12 Ranks of Parcel-level Factors in Cluster-1 Neighborhoods...... 153
8.13 Ranks of Parcel-level Factors in Cluster-2 Neighborhoods...... 154
8.14 Ranks of Parcel-level Factors for City...... 155
xiv
LIST OF FIGURES
Figure Page 4.1 Location of the City of Cleveland ...... 58
4.2 Concentration of the Poor and Black Population in Cleveland ...... 65
4.3 Special Planning Areas Boundaries ...... 67
4.4 Vacant Residential Land Distribution at the Neighborhood-level, 1990...... 68
4.5 Urban Renewal Areas...... 70
4.6 Important Large-scale Residential Projects in the 1990s...... 74
4.7 Newly Built Shopping Centers in the 1980s and 1990s...... 76
5.1 Conceptual Framework of the Location Choice of Urban Infill Housing...... 84
7.1 Spatial Pattern of Housing Development, 1991-1995...... 112
7.2 Spatial Pattern of Housing Development, 1996-2000...... 112
7.3 Neighborhood Clusters...... 132
A.1 NRA Minority Target Neighborhoods in 1994 ...... 178
A.2 NRA Low-Income Target Neighborhoods in 1994...... 178
A.3 Percent Land Bank Sites in 1990-1995 and 1995-2000...... 179
A.4 Service Areas of CNPP Grant Recipients in 1990-1995 and 1995-2000...... 180
A.5 Empowerment Zone in 1994 ...... 181
A.6 Cleveland Ward Boundary in 1990...... 181
A.7 Average Commuting Time by Census Tract in 1990...... 182
A.8 Percent White Population by Census Tract in 1990...... 182 xv A.9 Population Growth Rates by Census Tract between 1980 and 1990 ...... 183
A.10 Percent College Graduates by Census Tract in 1990 ...... 183
A.11 Average Median Household Income by Census Tract in 1990...... 184
A.12 Population Density (People Per Acre) by Census Tract in 1990 ...... 184
A.13 Criminal Incidents Per 1,000 Residents by Census Tract in 1990 and 1995 ...... 185
A.14 Percent Residential Vacant Land by Census Tract in 1990 and 1995...... 186
A.15 Percent Residential Land Use by Census Tract in 1990 and 1995...... 187
A.16 Percent Residential Development in 1986-1990 and 1991-1995...... 188
A.17 Land Bank Sites in 1990-1995 and 1995-2000...... 189
A.18 Potential Development Sites Identified in City Plan in 1990...... 190
A.19 Employment Centers and Transit Stations in 1990...... 190
A.20 Neighborhood and Local Shopping Centers in 1990 and 1995...... 191
A.21 Recent Housing Developments in 1986-1990 and 1991-1995...... 192
A.22 LUST, TRI and Heavy Industrial Sites in 1990 and 1995 ...... 193
A.23 Railroads in 1990...... 194
A.24 Historic and Landmark Districts, Parks and Recreation Centers in 1990...... 194
xvi
CHAPTER I
INTRODUCTION
1.1 Research Subject
Many central cities of America began to experience witness growing urban development activities as a result of the rising interest in an urban life style among many singles, young couples, and empty-nesters during the 1990s (Suchman & Sowell, 1997).
Also in the mid 1990s, the Smart Growth movement, a nationwide campaign for balanced regional growth, called for the revitalization of existing urban areas, including urban infill housing development, as a desirable way to accommodate future growth. Urban modelers, who have developed advanced methods for modeling land use changes, should respond to this renewed interest in old urban areas by developing and improving land use change models for the urban residential development of the old and often struggling
American cities.
An important question to be answered in order to improve models of urban infill housing development is: which factors are significantly associated with the location of urban housing development, especially in declining cities? The variables used by traditional urban models may not effectively explain the location of urban housing
1 development in declining cities. Therefore, this study aims to promote the urban modeling of infill housing development by identifying factors which are significantly related to the location of urban infill housing development in declining central cities.
1.1.1 Definitions
In this study, the key word “urban infill housing development” is defined as new housing construction in urbanized areas, especially in the built-up central cities of metropolitan areas. That is, in this study, urban infill housing development does not include the rehabilitation or adaptive reuse of existing buildings. This study also does not differentiate between the types and value of infill housing development. Instead, it uses an operational definition of the urban infill housing development as a land parcel that is developed for a residential use in an existing built-up urban area. Also, other terms such as “urban housing development” and “urban residential development” will be interchangeable with the term “urban infill housing development” in this study.
“Institutional factors” are defined as policies and programs of a variety of public and private organizations that regulate or support urban infill housing development.
The institutional factors included in this study such as land bank program have not been considered in urban land use models. “Non-institutional factors” are defined as site and neighborhood characteristics such as accessibility and median household income that have been used in most urban models as essential drivers for urban residential development.
1.1.2 Geographic Scale of Urban Modeling
Compared to their efforts of developing regional-scale urban models, urban modelers have paid little attention to the spatial process of urban infill housing
2 development in American central cities. This is due in part to the fact that these cities have experienced little housing development compared to suburban and greenfield development for the past several decades. Policy makers and planners have also generally focused on measures for controlling growth and preventing urban sprawl. As a result, urban modelers have seldom focused on modeling the spatial process of the urban housing development in urban built-up areas, especially, in declining American cities with weak housing markets.
However, responding to the rising importance of the urban infill development in the efforts to balance regional growth, this study attempts to promote the development of city-level residential location models by identifying factors that are significantly associated with the location of urban infill housing development. It suggests that unlike regional scale models based on market-driven growth, urban infill development models should focus on governmental and institutional factors because many older declining cities need some types of institutional support and intervention. 1
1.1.3 Location of Urban Infill Housing Development
This study will investigate the spatial pattern of urban housing development within existing urban areas whose physical, economic, and social capital have been depressed. Therefore, the findings of this study will help urban modelers, planners and
1 Many complex factors dive the inner city infill development. Compared to market-driven suburban development, which has been the focus of traditional urban models, the inner city development seems to be dependent upon political will, governmental subsidies, and community-based movement rather than the market. Because of the complex institutional factors and the relatively short development history, there have been few academic theories on the location of urban infill housing developments. The situation seems to have hampered the development of those urban models that effectively address the spatial pattern of inner city infill housing development. 3 policy makers to better understand and manage the spatial pattern and associative factors of the urban housing development. Urban planners need to understand the limitations and potentials of urban housing markets in order to create strategic plans for supporting and attracting development activities into their cities. They should also know which locations are attractive to infill housing developers and understand the characteristics of attractive and unattractive locations.
1.1.4 Weak Market and Institutional Intervention
In general, four important elements affect the location and rate of residential development. These elements are buildable land, adequate financial resources, developers’ capacity, and market demand. Compared to inner city infill development, housing developments on greenfields are generally blessed with readily buildable and inexpensive lands, enough investors, and sufficient market demand.
Compared to suburbs and booming cities, many American central cities are weak with regard to all of those elements and need substantial institutional support. First of all, many properties are underutilized or abandoned in most central cities, but, at the same time, are difficult to acquire because of problems such as complicated title issues and hard-to-find absentee landlords. Also, many abandoned properties are brownfields which are – or may be – contaminated from current or past land uses. Therefore, cities may need to provide regulatory and monetary support in order for developers to have easy access to developable land.
Urban housing development projects also may not be able to support their financing without external financial assistance. Land acquisition and site preparation costs are often high and market rate values and rents are low. As a result, construction
4 costs become prohibitive and operating cash flows do not provide sufficient financing for urban residential development. As a result, completing an urban housing development project is very difficult without financial assistance from governments and other institutions.
In addition, there are a limited number of experienced housing developers in many declining cities. Because of the problems mentioned above most developers choose to work on suburban development projects. Community-based nonprofit organizations such as community development corporations or CDCs 2 often do not have sufficient development experience and organizational and technical capacity to plan, develop and manage infill housing development. Therefore, governments and other institutions should provide development and operating capital and technical assistance and encourage creative partnerships with developers. Lastly, although some cities claim comebacks, many cities continue to loose their population base and are unattractive places to live for many people.
1.1.5 Site, Neighborhood, and Institutional Factors
Traditional urban models have focused on the site and neighborhood characteristics that have been assumed to be influential for the location of housing and households. Site characteristics such as lot size, land value, and environmental constraints have been used to measure the land development capacity in many urban models. Neighborhood-level factors such as population growth, land use mix, and socioeconomic characteristics have also frequently been used to measure the
2 Legally, a community development corporation is “an incorporated, non-profit agency empowered to purchase, develop and manage residential and commercial property, or to provide loans and technical assistance to other organizations doing these same things” (Robinson, 1996, p. 1652) 5 attractiveness of neighborhoods in traditional urban models. In this study, the site and neighborhood characteristics are collectively referred to as the non-institutional or market factors.
However, whether these market-driven site and neighborhood characteristics are as important factors for urban infill housing development as for the housing development in suburbs is questionable. It is hard for development activities in declining cities to generate as much profits as suburban developments. As a result, all levels of governments and other institutions may need to intervene in the vulnerable urban housing markets. Therefore, this study assumes that urban models for urban infill residential development should consider the association between institutional factors and the location of urban housing development.
1.2 Research Methods
This study will use quantitative methods to investigate the association between traditional and non-traditional factors and the location of urban housing development in an old, struggling central city, Cleveland, Ohio. Regression and correlation analyses will be the main techniques for examining the association at both neighborhood and site levels. The details of the research method are discussed in Chapter 6. Also, geographic information system (GIS) technique will be heavily used for the spatial analysis of a variety of data and the descriptive analysis of the data.
This study will identify traditional non-institutional site and neighborhood factors from the literature review of urban modeling and urban economics related to the location of residential development. Institutional factors to be considered in urban infill housing
6 development will be drawn from the literature of urban planning and housing studies as well as from the urban development history of the study area.
This study will analyze the spatial pattern of inner city infill housing development and its association with the institutional and non-institutional factors in Cleveland during the 1990s. The institutional and traditional or non-institutional factors will then be grouped into two geographic levels, i.e., neighborhood and parcel levels. Also, the study will examine the 10-year period during the 1990s. The study period will be divided into two periods with five years for each, to look for temporal changes in the association of the factors and the location of urban housing development in the study area.
Although the statistical techniques used in this dissertation are not new, the logistic or logit regression technique used for the parcel-level analysis has some particular points which should be considered. The dependent variable for the parcel level analysis is a binomial dummy variable indicating whether a parcel was developed as infill housing or not. Therefore, the binomial logit or logistic regression 3 technique will be used to regress the dependent variable against a set of potential explanatory variables.
The basic unit of the spatial analysis will be a land parcel. Conceptually, the logit regression model of this study resembles a “discrete change” technique used in land use change modeling. The discrete change modeling technique is an alternative to “discrete choice”4 modeling technique. The concept of the discrete change model was first
3 Logistic regression is the most popular regression technique for modeling dichotomous dependent variables (Kleinbaum, 1998). 4 These models attempt to simulate the discrete choices of agents such as developers, households, and commuters on development location, housing location, and travel mode. UrbanSim (Waddell, 2002) is a recent example of the behavioral discrete choice models. 7 developed by John Landis at the University of California at Berkeley who developed the
CUF II (Landis, 2001; Landis & Zhang, 1998) and CURBA models (Landis, 2001;
Landis et al., 1998). In the face of the lack of data on the characteristics of individuals
(i.e., property owners), Landis and his colleagues used the discrete change model to simulate land use changes on the basis of the attributes of each individual site. That is,
CUF II and CURBA models used only site characteristics to estimate and project the location of new development and redevelopment, without taking into account the characteristics and motivations of property owners. This concept will be adapted to the parcel-level logit regression analysis of this study.
1.3 Research Questions
As noted above, this research will attempt to explain the spatial pattern of urban infill housing development and the important locational factors associated with this development in Cleveland during the decade of the 1990s. The factors to be considered in the analysis include site, neighborhood, and institutional factors. However, this study places a particular emphasis on the policies of governments and private and nonprofit institutions and their association with the location of urban infill housing development.
The major research questions to be addressed are as follows.
1. Which institutional factors are significantly associated with the location of
urban infill housing development?
2. Are the traditional site and neighborhood factors of urban models associated
with the location of urban infill housing development?
8 3. Are institutional factors more significantly associated with the location of
urban housing development than non-institutional factors?
1.4 Significance of the Research
The field of urban modeling has established an extensive body of theory about where greenfield development will occur. Urban modelers have focused primarily on suburban or rural greenfield development or urban sprawl phenomenon (see US EPA
2000). However, because of the importance of urban infill housing development in urban planning as discussed above, the field needs to expand its knowledge base to better understand which locations inside a city are likely to be developed. In order to do so, this study will analyze the factors that are important to the spatial pattern of urban housing development in a central city of the 1990s.
The main contribution of this study to urban modeling will be its consideration of institutional factors including public-private partnerships and governmental policies.
The study assumes that housing development in urban neighborhoods is much more sensitive to (or dependent upon) institutional support and subsidies than to traditional or non-institutional factors, i.e., site and neighborhood characteristics. That is, it suggests, urban models for urban residential development in weak housing market should be more sensitive to the institutional factors than other models dealing with resource-abundant suburbs with a strong market. Moreover, the institutional factors examined in this study have not been considered by existing urban models.
Academic studies on infill housing development have focused mainly on institutional networks (Bogart, 2003; Keyes et al., 1996; Liou & Stroh, 1998), financial
9 policies (Schwartz ,1998), or CDCs’ capacity (Vidal, 1992; Walker, 1993; Walker, 2002).
This study will therefore examine the spatial process of an important aspect of urban development, which has rarely been examined systematically by the fields of urban modeling, urban economics, geography, and real estate development.
In many parts of this country, managing inner city infill development has become a more important policy issue than managing suburban growth. Many of American central cities, including Cleveland, need better land use planning and more effective political mobilization to prevent further contraction and decline. The findings of this study may therefore help to develop a strategic plan for improving the housing and living environment quality of inner city neighborhoods.
Inner city infill housing development is a critical course of action for achieving
“smart” or balanced regional growth. Urban housing development in older cities helps revitalize blighted urban neighborhoods by providing decent and affordable houses which, in turn, help induce social and economic reinvestment in declining central cities. Infill housing development can also reduce urban sprawl 5 by accommodating a significant portion of a region’s future growth within urbanized area served by existing infrastructure and urban services (Wheeler, 2002). This dissertation will therefore help urban and regional planners understand where the opportunities for urban housing development lie within older cities with weak housing markets. By providing knowledge about the
5 Sprawl is defined as residential and nonresidential development that expands in an unlimited and noncontiguous (leapfrog) way outward from the built-up core of a metropolitan area (Burchell et al., 1998, p. 6). Sprawl has been criticized as a cost-ineffective way of urban development noticeably since The Costs of Sprawl (RERC, 1974). Smart growth movement originated by American Planning Association is also closely related to the efforts to prevent the sprawl from damaging the economic and social health and natural resources of cities and regions (Lorentz & Shaw, 2000; Weiss, 2001). 10 association between particular institutional and non-institutional factors and the location of urban residential development, this study will also provide useful research findings for the urban planners and policy makers who make and implement plans for infill housing development.
1.5 Organization of the Dissertation
The dissertation will address the main research questions by testing hypotheses that will be developed in the literature review. Chapter Two will review the literature to identify factors that have been used by urban models and urban economic theories to project the location of future residential development. Chapter Three will review the practical experiences with urban neighborhood and housing redevelopment to identify institutional factors that can be assumed to have an impact on the location of urban infill housing development. Chapter Four will review the development history of Cleveland in order to understand the context of Cleveland’s infill housing development during the
1990s. These three chapters will identify possible locational factors, which provide the basis for the research hypotheses. Chapter Five will lay out the conceptual framework and hypotheses of this study. Chapter Six will discuss the research method and test variables. Chapter Seven will present a neighborhood-level statistical analysis of the spatial pattern of the explanatory factors and the location of infill housing in Cleveland during the 1990s. Chapter Eight will describe a parcel-level statistical analysis.
Chapter Nine will summarize and discuss the research findings and their implications for urban modelers and planners.
11
CHAPTER II
THEORIES AND MODELS OF URBAN RESIDENTIAL DEVELOPMENT
2.1 Introduction
Computer-based urban models, stochastic and mathematical representations of urban spatial systems, have generally not paid as much attention to urban redevelopment, and urban infill housing development in particular, as they have to suburban or greenfield development. The main purpose of this study is to contribute to the field of urban modeling by enhancing the field’s knowledge base on the factors associated with the location of urban infill housing development. To this end, this study first identifies the factors traditionally used by urban models to account for urban residential development.
The factors identified in this review will be tested in the remainder of the dissertation for their association with the location of urban infill housing development in the study area.
Importantly, most urban models rely on their own calibration process, either stochastic or deterministic, to derive the parameters for each assumed causal factor.
Thus, urban modelers generally do not clarify their assumptions about the effects of the various factors as much as their interest in the combined effect of several factors.
However, the selection of many modeling factors is implicitly or explicitly rooted in
12 urban economic theories. Therefore, the following review of urban economic theories will support the discussion of the assumed urban modeling factors.
This chapter first reviews the most important urban economic theories concerning residential development and residential location choice. It then reviews several important urban models, focusing on the theoretical framework and location factors used by the models. Finally, it reviews land supply monitoring theory and practices that have been recently developed to identify developable sites in urbanized areas.
2.2 Urban Economic Theories
This section will briefly discuss the theories of urban economics that have significantly shaped the traditional urban modeling theory. This discussion will help understand the theoretical background of some of the important urban modeling factors.
The urban economic theory identifies various factors that have been assumed to affect the utility of urban land for infill housing development. This review will discuss four major theories related to urban modeling theory: (1) classical bid-rent theory, (2) contemporary bid-rent theory, (3) household or residential location choice theory, and (4) housing hedonic price models.
2.2.1 Classical Bid-Rent Theory
Ricardo’s (Ricardo, 1817) and Von Thünen’s (Hall, 1966) land rent models were the first bid-rent models. Extending von Thunen’s model, Alonso (1960) developed an urban bid-rent model which was subsequently tested and refined by Mills (1967), Muth
(1969), and Evans (1973). The main idea of the classical bid-rent theory was that the rent for a land site was determined by subtracting the transportation cost from output
13 revenues or land users’ utilities. Also, the classical models were based on a monocentric city in which economic activities were assumed to be concentrated in the central core area (DiPasquale & Wheaton, 1996, chapters 3 and 4).
Von Thünen’s model (Hall, 1966) incorporated important theoretical assumptions later adopted by Alonso (1960). The most significant aspect of his model was the assumption that land rents were determined mainly by transportation costs or the distance to the market center. In this, he recognized the importance of accessibility of land, which has a negative linear relationship with land rent (O’Sullivan, 1993, p. 183).
Alonso (1960) adopted von Thünen’s model assumptions and framework to create a sophisticated bid-rent model of urban land, substituting residential and industrial land use for von Thünen’s farmland use. Alonso’s bid-rent model maintained that land rents fell with increasing distance. A single urban center or central business district (CBD) was also assumed to be the location of all the jobs and sales. The main assumptions of the theory were: (1) employment was located at the center of the city; (2) each household had one worker; (3) only work-trip travel was considered; (4) housing was homogeneous; and
(4) location was the only distinguishing factor.
In Alonso’s theory, the rent gradients of all land users are negatively sloped against the mono-center (i.e., the CBD). Land users who pursue utility maximization under predetermined utility level at each location are assumed to be willing to pay a certain amount of land rent according to the distance from the urban center. Importantly, as Mills (1972a, p. 67) mentions, the distance from the urban center or CBD was the only relevant dimension in the model.
14 Many urban economists have improved on the classical bid-rent model by using more realistic assumptions and observations. They have found that the assumption of monocentric city in the classical models is a poor representation of contemporary U.S. metropolitan areas. Two important consequences of these theoretical arguments are, first, the abandonment of monocentric-city model and, second, the weakening of transportation-land rent link. As a result, the important factors to be considered in analyzing residential location choices for contemporary bid-rent theory are decentralized employment centers, complex transportation-land use link, and a variety of neighborhood and site characteristics (Cropper & Gordon, 1991). This theory and its assumed causal factors have been incorporated in most contemporary urban models.
2.2.2 The Polycentric Model
Many studies have observed the unrealistic assumption of the monocentric city, i.e., the existence of polycentric employment centers or subcenters (for example, Anas et al., 1998; Giuliano & Small, 1991; Helsley & Sullivan, 1991). As a result, economists have started to relax the assumption of monocentric city or region (for example,
Brueckner, 1979; White, 1988). For example, Heikkila et al., (1989) found that the effect of the distance to CBD on land value was not significant and that the distance from employment subcenters had positive effects. Furthermore, other researchers such as
McDonald & McMillen (1990) have argued that access to employment subcenters was not necessarily a significant determinant of residential values.
2.2.3 The Changing Utility of Work Trips
In his Wasteful Commuting argument Hamilton (Hamilton, 1982; Hamilton, 1989) criticized the assumption of standard monocentric models that land rent was a function
15 mainly of accessibility to the CBD. He maintained that the monocentric models significantly under-predicted commuting distances compared to actual commuting distances. Other researchers (e.g., Clark and Burt, 1980; Small and Song, 1992; Giuliano and Small, 1993) have found that the link between accessibility to workplace and workers’ residential location choice has become weaker over time, which suggests the inclusion of a variety of non-transportation factors in location economic models.
2.2.4 Workplace and Residential Location Theories
Workplace location has generally been treated as an exogenous variable in residential location choice theories and early gravity-based models (e.g., the Lowry model). That is, residential location was assumed to be the function of workplace location, not vice versa. However, Waddell (1993) proved that the location of residence was better estimated by using a joint choice specification of residential and job locations.
As a result, a predetermined residential location can be assumed to influence workplace location as much as the other way.
2.2.5 Residential Location Choice Theory
When bid-rent models were used to account for residential land rent/use, they provided the basis for a residential or household location choice models (see, Anas, 1987;
Pollakowski, 1982; Waddell, 1997). According to Balchin, et al. (2000, pp. 179-183), there are four major theories of household’s residential location choices and associated critics: (1) travel cost minimization theory, (2) trade-off theory, (3) maximum housing expenditure theory, and (4) racial prejudice theory.
16 2.2.5.1 Travel Cost Minimization Theory
The travel cost minimization theory argues that both the utility (or profit) maximization behavior of households (or housing developers) is spatially relevant only with respect to the distance or accessibility to the CBD. However, as income increases, families prefer lower-density, spacious housing located far from the CBD since they become afford higher travel cost.
2.2.5.2 Trade-off Theory
Trade-off theory suggests that households would freely relocate as long as they pay the same aggregate costs under their income limits. However, housing relocation is costly and occurs in the long-term. In addition, there are many other factors (e.g., job changes, change in family size, and mortgage availability) that may play more important roles in a household’s location choice than the travel-housing costs trade-off.
2.2.5.3 Maximum Housing Expenditure Theory
Maximum housing expenditure theory argues that residential location is a function of household income and the condition of mortgage financing. However, there are other factors influencing location choice, such as environmental and social factors that are positively correlated with capital appreciation.
2.2.5.4 Racial Prejudice
Racism or racial prejudice has been a significant factor for the residential location choice theory because households often consider the racial composition of a neighborhood an important amenity (Smith, 1982). An “amenity model of racial prejudice” suggests that (Yinger, 1976; Yinger, 1979), households have had racial prejudice (usually white against Black or non-white) and the prejudice makes certain
17 levels of neighborhood racial composition either disamenities (in case of predominantly non-white neighborhoods) or amenities (in case of white-dominant neighborhoods).
2.2.6 Hedonic Price Models
A hedonic price model is an equation which relates housing attributes or services to their market prices. The housing services include housing structural characteristics
(e.g., building size) and location amenities 6 (e.g., adjacency to lakes) (DiPasquale &
Wheaton, 1996, p. 182). In particular, hedonic housing price models attempt to estimate the demand for and the value of these location amenities (Quigley, 1979, pp. 399-411).
The hedonic models have incorporated a variety of locational factors or amenities into housing valuation models. Importantly, most of these locational amenities are neighborhood characteristics (Diamond & Tolley, 1982a; Freeman, 1979). A variety of location amenity factors have been tested including air pollution (Linnenman, 1982), noise pollution (Vaughan & Huckins, 1982), view amenities (Pollard, 1980; Benson, et al., 1998), proximity to parks and lakeside (Vaughn, 1981; Palmquist, 1991; Pollakowski,
1977), and proximity to suburbs (Berry and Bednarz, 1979).
Furthermore, Ding, Simons, and Baku (2000) examined the value appreciation effects of community reinvestments, i.e., housing new construction and rehabilitation, on nearby property values. They found that the reinvestments had a positive influence on housing values. However, the influences had a few important conditions. First, the effects of the reinvestments were geographically limited. Second, the positive impacts were more significant in low-income and low-minority areas. Third, reinvestment projects had to be of sufficient scale to have a significant price effects on nearby housing.
6 “Location amenity can be defined as location-specific good” (Diamond & Tolley, 1982, p. 5). 18 In addition, new construction had a larger impact on nearby property values than rehabilitation did.
2.3 Urban Models
By using mathematical equations and statistical techniques, an urban model simplifies and simulates a real-world urban (sub)system, comprised of social, organizational, physical, cultural, and economic subsystems 7. Projecting land use patterns and transportation interaction has been the major subject of urban models for more than forty years. The goal of urban modeling is to help planners, policymakers, and communities describe, predict, and prescribe urban system and invent urban futures
(Batty, 1979, p. xx).
American city planners, transportation engineers, and social scientists has been interested in systematic knowledge and modeling of the spatial organization and development of their cities since the 1950s. The professionals began to develop land use and transportation models based on the similarities and regularities of spatial process between the cities, which were expressed in mathematical formulas. The early modelers wanted to improve and develop more accurate tools for analyzing population distribution, employment, economic factors, social patterns, and travel habits which could be used to prepare land use and transportation plan (Harris, 1985; Voorhees, 1959).
Later, the field of urban modeling became more rigorous and diverse. Urban modelers used the more rigorous analytic techniques of urban economics, regional science, and quantitative geography. Modelers have also begun to deal with diverse
7 Wilson (2000, chapters 1 and 2) presents the subsystems of the complex urban system. 19 urban spatial activities including residential location, urban industrial location, retail trade location, business service location, and public facility location. Also, urban models have become more inclusive or comprehensive by incorporating the multiple subsystems of urban activities into a single large-scale urban model (Harris, 1985a, 1985b;
Klosterman, 1994). Along with modern computer technology, the urban models have become more and more operational and integrated, contrary to the bleak views of the
“Requiem for Large-Scale Models (Lee, 1973),” which criticized the discrepancy between the models and planning context (Batty, 1994; Wegener, 1994).
There are many comprehensive, systematic reviews of urban models. 8 Therefore, the purpose of this literature review is not to thoroughly review the status of the urban modeling field, but rather to identify the factors that are widely used by the current generation of urban models to account for the location of residential land development.
This review is based on four important types of operational urban models, which have been applied to real planning processes. 9 The main models to be reviewed in this chapter include: (1) associative econometric models; (2) spatial interaction models; (3) discrete choice models; and (4) rule-based allocation models. These types of urban models have been widely researched and reviewed as the most popular models in the current urban land use modeling literature (Brail & Klosterman, 2002; US EPA, 2000).
The main models to be reviewed in this section include: (1) the California Urban Future model II (CUF-2) as an econometric model, (2) the Disaggregated Residential Allocation
8 See for example, Agarwal, 2002; Anas, 1987; Bertuglia et al., 1987; de la Barra, 1989; EPA, 2000; Harris, 1985a, 1985b; Lowry, 1965; Kain, 1985; Klosterman et al., 1994; Miller, Kriger, & Hunt, 1998; Oppenheim, 1986; Parsons Brinkerhoff, 1998; Southworth, 1995. 9 There are many review papers and books on operational urban models (for example, Foot, 1981; Klosterman, 1994; Miller, Kriger, & Hunt, 1999; EPA, 2000) 20 Model (DRAM) as a spatial interaction model, (3) UrbanSim as a discrete choice model; and (4) What if? as a rule-based allocation model. This section will discuss: (1) the four modeling approaches and (2) the modeling factors which the models assume to shape the spatial pattern of housing development.
In addition, a relatively new field, GIS-based land supply and capacity monitoring system will be reviewed. Recycling vacant and underused land for housing (i.e., infill housing development) is one of the important elements of urban redevelopment and the focus of these land supply monitoring system. Urban land use planners have used the
GIS-based land supply monitoring system to monitor and analyze infillable lands in urban area (Knaap, 2001). Moreover, rule-based land monitoring systems have become an increasingly popular method for recording and analyzing the development potential of each piece of land.
2.3.1 Current Urban Modeling Approaches
There are several different approaches for developing urban models or land use change models. However, most models have been based on a few principal approaches with respect to their underlying theoretical and conceptual framework. Therefore, the models to be considered here are grouped into: (1) associative econometric models using regression and econometric techniques; (2) Lowry-style spatial interaction models based on the gravity model; and (3) discrete choice models based on random utility theory.
New types of urban models also emerged in the 1990s using advanced GIS technology.
These include rule-based allocation model, cellular automata, and artificial intelligence models such as neural network and genetic algorithmic models. Among them, the rule- based allocation model s deserve a special attention in this study along with the three
21 main model approaches noted above, because of their uniquely transparent and operational model structure.
2.3.1.1 Associative Econometric Models
This type of urban model is comprised of one or more multivariate econometric equations or multiple regression models. The associative models are also called urban linear models or econometric models (Foot, 1981, Chapter 6; Putman, 1979, Chapter 2;
Waddell, 1997, pp. 14-17). For example, EMPIRIC, the most widely applied associative econometric model in the 1960s and 70s, was called a non-behavioral model because the model was not based on rigorous theoretical causal relationships (Brand et al.,
1967; Hill et al., 1965; Putman, 1979; Waddell, 1997, p. 15).
CUF-2 (California Urban Future Model-2) developed by Landis & Zhang (1998a and 1998b) is a recent example of associative econometric modeling, even though it used multinomial logit regression rather than simultaneous linear regression that EMPIRIC utilized. CUF-2 projected the discrete change of land uses without specifying the demand and supply subsystems. This econometric model incorporates both demand- and supply-related independent variables into a single multinomial logit regression equation. The logit regression model used a variety of independent variables to estimate the probabilities of the discrete land use changes (EPA, 2000; Landis, 2001; Landis &
Zhang, 1998a, 1998b). The seminal model was applied in San Francisco and California
Bay Region (EPA, 2000, P. 43).
2.3.1.2 Spatial Interaction Models
Spatial interaction models are based on an analogy to a physical law, Newton’s law of gravity, which assumes that the interaction between two bodies is directly related
22 to their masses and inversely related to the distance between them (Wilson, 1974, p. 39).
Location scientists (e.g., Dodd, 1943; Isard, 1960; Lowry, 1964; Reilly, 1931; Ravenstein,
1885; Hansen, 1959) started to apply the principal assumption of the law of gravity to activity modeling of urban systems.
The conceptual framework of the residential location choice in spatial interaction models assumes that employees working in employment zones choose particular residential zones (or neighborhoods) in order to minimize their work-trip cost (i.e., increasing their accessibility) and maximize neighborhood characteristics called attractiveness. In essence, spatial interaction models allocate regional total households or employment to the zones in a region by comparing the accessibility and attractiveness between zones.
The DRAM/EMPAL (Disaggregated Residential Allocation Model/Employment
Allocation Model), an integral part of Integrated Transportation and Land Use Package developed by Putman (1983), is the most widely applied spatial interaction model in U.S.
The main assumption of the DRAM/EMPAL is that commuting accessibility and zonal attractiveness influences the number of households of different types in each zone. The location of employment centers is an exogenous variable for DRAM/EMPAL. The model adds zonal attractiveness variables to accessibility-based spatial interaction modeling (EPA, 2000; Putman, 1983; Putman 2001; Putman & Chan, 2001). The model is one of the most popular large-scale urban models (Wegener, 1994); application sites include Southern California, Atlanta Region, Northeast Illinois, North Central Texas, and Kansas City (EPA, 2000, p. 57).
23 2.3.1.3 Discrete Choice Models
Discrete choice urban models employ random utility maximization theory, which was originally developed by McFadden (1974), to simulate the location choice behavior of land market agents (e.g., land owners, households, and developers). Random utility theory considers the utility maximization behavior of the agents to be a stochastic or probabilistic process, not a deterministic process. Based on an assumed probability distribution, the discrete choice model incorporates a randomized bidding process which simulates the competition between different types of households for each location
(Waddell, 1997, 2002).
UrbanSim, a dynamic large-scale urban model developed by Paul Waddell and others (2000), is one of the most advanced discrete choice models and has been applied to several metropolitan areas (USEPA 2000; Waddell, 2001). UrbanSim simulates the land use choices of disaggregate groups of land users (i.e., households and businesses) for relatively small geographic units such as one-hectare grid cells. The model is comprised of discrete location-choice (sub)models for households, businesses, and real estate developers. The model simulates the land use demand (i.e., household and business location choice) and supply (i.e., real estate development) subsystems separately. It is designed to predict future real estate development ranging from urban sprawl to infill and redevelopment activities. The model operates at a regional scale, covering both urban and non-urban areas. The model has been applied to many regions including Honolulu, HI,
Eugene-Springfield, OR, Salt Lake City, UT, and Seattle, WA (EPA, 2000, p. 137).
24 2.3.1.4 Rule-based Allocation Models
Rule-based allocation models such as What if? use an explicit set of allocation rules to allocate exogenously projected demand (e.g., projected population and employment) to available (e.g., suitable or developable) locations. The models do not rely on traditional statistical or stochastic calibration approaches to determine the model parameters. Instead, they rely on a set of practical assumptions and local knowledge to identify important factors that are assumed to determine the suitable location for a particular land use and to allocate exogenously projected demand to suitable location.
What if? is an exemplary rule-based urban model.
What if?, developed by Klosterman (1999), is a supply-oriented model which allocates projected regional population and employment totals to locations according to site characteristics (e.g., suitability and regulation conformity). What if? considers mainly environmental and policy factors. The model uses and combines a variety of map layers for those environmental and policy factors by using GIS overlay function
(EPA, 2000; Klosterman, 1999; Klosterman, 2002; Klosterman, et al., 2002). The overlay process creates a single layer with numerous irregular-shaped polygons called
“uniform analysis zones” (or UAZs). This allows users to easily score each UAZ with its suitability for accommodating different land uses. The model then allocates forecasted regional population and employment totals (i.e., future land demand) to appropriate location by considering UAZs’ suitability scores subject to user-specified allocation rules and policies. The model has been widely used by modelers across the
United States and overseas, including Australia and South Korea (EPA 2000; Klosterman, et al. 2002; Klosterman, et al. 2006; Kweon and Kim 2004; Pettit 2002)
25 2.3.2 Factors of the Spatial Distribution of Urban Land Development
This section identifies the major factors that have been employed by the land use change projection models described in the previous section.
2.3.2.1 Associative Econometric Models: CUF-2
This model considers the following types of factors: (1) site accessibility; (2) physical and cost constraints; (3) relationship to neighboring sites; (4) inter-use externalities; and (5) policy constraints (Landis & Zhang, 1998a; Landis & Zhang,
1998b).
Site Accessibility: The model measured the accessibility (i.e., Euclidian or airline distance) from each development site to the nearest city center. It also measured the distance from every site to the closest freeway interchange and transit station. The model views the city centers as the locus of economic activity bearing the highest rent.
Also, the model considers proximity to transportation facilities (e.g., highway interchanges and public transit stations) as a driver for land (re)development.
Physical and Cost Constraints: The model assumes that natural constraints such as steep slopes increase development costs and, thus, discourage land development on these sites. Also, land developments are assumed to be located within or near existing developed areas to tap into existing infrastructure services, which would reduce the cost of site and infrastructure improvements.
Relationship to Neighboring Sites: The land use mix of surrounding or neighboring sites is assumed to affect the land uses of the surrounded site because neighboring sites are assumed to have similar types of land use. To measure the effect, the model used the percentage of surrounding sites in different land uses.
26 Inter-use Externalities: The model assumed that positive or negative effects exist between different land uses, called “inter-use externalities.” Three variables were employed to test this assumption: the distance to the nearest industrial use, the distance to the nearest commercial use, and the distance to the nearest public use. The model, however, did not have any specific assumptions about the externality effect of adjacent land uses.
Policy Constraints: Several governmental policies were considered in the model.
Zoning was a typical land use control with a simplistic assumption that a parcel of land will only be developed for the uses permitted by the zoning regulation. The model examined whether or not sites were located within a municipality’s growth boundaries or
“sphere-of-influence” because land (re)developments were required to be within these boundaries. Environmental policies such as prime farmland and wetland preservation were also assumed to restrict development activities in certain areas.
2.3.2.2 Spatial Interaction Model: DRAM/EMPAL
Commuting cost: The DRAM/EMPAL model assumes that the employment center is the center of gravity in urban spatial system. The model also assumes that households choose residential locations based on the time or cost spent for work trips.
The impedance (i.e., travel time or trip costs) between residential and employment zones has been the central variable for many spatial interaction models. 10 As a result, the primary factor influencing residential location choice of an employee is the utility
10 The single factor, in Lowry model (1964), which affects the employee’s residential location choice, is the distance between an employment zone and a residential zone. Later, the distance was often replaced by travel cost and time. 27 determined by commuting cost which is assumed to inversely affect the location benefit of households.
Zonal Attractiveness: Although DRAM/EMPAL is based on the gravity model which focuses on commuting costs, the model improved the traditional urban gravity model by using an additional attractiveness component comprised of many variables that were assumed to change the location utility or benefit enjoyed by households (Putman,
1983). Like many urban models, the model relies on a statistical calibration method to determine the importance of parameters for the different variables. As a result, the model does not state the assumed effects of the variables on the residential location choice. Instead it uses two types of attractiveness factors, i.e., the inherent attractiveness of a zone and the land development capacity of the zone, as described below (Putman, 1983, chapter 7).
First, the model uses the income distribution of a zone to represent characteristics of the zone that are assumed to influence the location choice of different income levels of households. Second, the model assumes that the attractiveness of a zone is a function of the amount of vacant developable land and the zone’s residential density (e.g., households per acre). That is, more land development activities is assumed to take place in zones with more vacant land and lower residential densities.
Policy Factors: DRAM/EMPAL can develop different scenarios by incorporating land use development control policies such as municipal zoning and comprehensive land use planning, which influence the capacity of a zone to accomdate additional residential demand. In general, these policies influence the development density, the amount of developable land, and the permissible land use designation in each zone.
28 Also, the model can incorporate transportation policies that would change trip costs (Putman, 1983, chapter 9). It was assumed that increasing region-wide transportation costs resulting from rising energy-costs and taxes would result in denser development pattern around central business districts and sub-centers.
2.3.2.3 Discrete Choice Model: UrbanSim
The Household location choice and real estate development submodels incorporated in the UrbanSim model are reviewed briefly below. The first set of factors, which is related to household location choice model, includes site and neighborhood characteristics such as accessibility and neighborhood socioeconomic status. The second set of factors used in the real estate development model includes a variety of variables such as neighboring land use mix and environmental constraints.
Household location choice submodel
Accessibility: The model incorporates a traditional accessibility variable such as travel time to the CBD, employment centers, and regional shopping centers. Sites with good accessibility are assumed to have high land rent and, consequently, more intense land uses and higher high building densities.
Neighborhood Land Use Mix: A residential dominant neighborhood is assumed to be compatible with residential use. Industrial land use is used as a proxy for less desirable land use characteristics (Urban Simulation Project 2000) and thus is assumed to be incompatible with residential use.
Neighborhood Housing Age: In general, households would prefer newer housing. However, some households choose to live in old but historic, wealthy
29 neighborhoods. Therefore, the model uses nonlinear or dummy variables for unique neighborhoods.
Real Estate Development Submodel
The model uses a number of variables that are assumed to affect developers’ location choice decisions. Using a nested logit regression, the model estimates the development probability of a site and the probable land use type of the development.
The independent variables of the regression model include: (1) proximity to CBD and shopping centers, (2) neighborhood land use mix and property values, (3) recent development in neighborhood, and (4) environmental constraints . The assumed effects of these variables correspond to the traditional urban economic theory discussed earlier.
Policy factors: The policies used by the model include environmental preservation policies (e.g., wetland preservation) and regional growth control boundaries
(e.g., the urban growth boundary or UGB). Advocating sustainable regional land development, the model uses regional scale land use control measures that either restrict development in certain areas such as wetland and wildlife protection areas or promote future land development in designated urban growth areas. The model can also incorporate other policy instruments such as density controls, impact fees, transportation policy and land use plan.
2.3.2.4 Rule-based Models: What if?
Natural Suitability: The suitability submodel of What if? incorporates a spatial analytic process that identifies the relative suitability of UAZs for each type of land use.
The submodel generally uses natural suitability factors such as slopes, soils, and wetlands,
30 which allows development to avoid locations with environmentally sensitive or hazardous features. The model can also incorporate other suitability factors such as the proximity to roads and the availability of public facilities.
Policy Factors: All three submodels (i.e., suitability, demand, and allocation scenarios) of What if? use some policy factors. The suitability submodel can use local or regional environmental preservation policies to identify the suitability factors and their relative importance for particular land uses. For example, the community may choose to protect environmentally sensitive area or to provide developers with more freedom regarding possible development locations.
The allocation submodel allocates future land demand to suitable location based in part on policy-based restrictions such as infrastructure control. If these control policies exist, model users can identify both existing and future boundaries for infrastructure supply (e.g., water and sewer services). Then, users can specify the infrastructure requirement for each land use restricting future development to areas that are provided with the infrastructure that is assumed to be required for each land user.
2.4 Determinants of Land Developability
Like rule-based allocation models such as What if?, land supply monitoring systems use rule-based methods and geographic information system (GIS) technology to identify suitable land development areas. Increasingly available parcel-level data provided with tax records and GIS database management technology comprise a crucial part of the method. The purpose of rule-based land supply monitoring system is to identify land that is available, suitable, permissible, economically-underutilized, and so
31 on (Godschalk 1986; Kaiser et al., 1995; Knapp 2001; Moudon & Hubner 2000; Northam,
1971).
The rule-based land supply method helps understand how governmental regulations and other site characteristics affect the quantity and the spatial pattern of available redevelopment lands. By using simple spatial queries based on alternative regulatory and empirical rules, the rule-based methods yield a set of possible developable lands. That is, the rule-based land monitoring methods use predefined rules comprised of market assumptions and policy constraints to eliminate land that is not suitable for future infill developments.
Urban land use planning agencies can use a set of locally defined rules to define their land capacity monitoring system. This section will identify the rules which can be applied to identify developable or infillable parcels. The exemplary rules used by Metro, a regional government in Portland, Oregon (Hall 2001) and a research team at the
University of California (Landis 2001) are as follows.
Metro’s inventory system and the University of California model use a similar method of identifying buildable parcels eliminating not-buildable land from comprehensive land inventories.
o The Metro system eliminates vacant lots that have the following attributes: (1)
public land, (2) streets, (3) schools, (4) parks, (5) places of worship, (6) streams,
(7) rivers, (8) wetlands, (9) floodplains, (10) 200-ft. riparian buffer, (11) steep
slopes, (12) zoning, and (13) outside urban growth boundary (Hall, 2001, pp.
63-65)
32 o The University of California model eliminates vacant lots that have the
following attributes: (1) small lot sizes, (2) environmentally inappropriate, and
(3) not economically underutilized (Landis, 2001, pp. 25)
The “economically underutilized” attribute was measured by the improvement-to- land value ratio that is explained in detail below.
Focusing on housing redevelopment, Moudon (2001) defined nonvacant land as the opposite of vacant land. That is, nonvacant land can be simply defined as developed
(partially, underutilized, or fully utilized) land. Redevelopable land is a subset of nonvacant land. This section will review rules that were delineated by Moudon (2001) to separate redevelopable land parcels from nonvacant or developed land.
Lots were excluded from redevelopment if they had following attributes: (1) recently built structures (i.e., buildings built within 30 years) or historic structures; (2) smaller-than-minimum lot size; (3) steep slopes (e.g., higher than 25%); or (4) high assessed values (e.g., higher than $300,000).
Nonvacant parcels are assumed to be more likely to be redeveloped if they had significantly low improvement-to-land value ratio compared to neighboring parcels
(Moudon 2001: 118). This improvement-to-land value ratio has been employed by urban land monitoring systems to identify economically underutilized properties, which have a higher redevelopment potential (Landis, 2001, p. 26; Moudon, 2001, pp. 122-123).
When studying the infill capacity of San Francisco Bay area, Landis considered sites with improvement-to-land value ratio lower than 0.9 as economically underutilized land.
33 Portland Metro 11 used an advanced measure of improvement-to-land ratio, i.e., a certain percent (e.g., 50%-70%) of the mean improvement-to-land ratio of surrounding properties within a certain distance (e.g., 500 feet), to identify redevelopable sites
(Moudon, 2001, p. 115). In essence, they both assumed that the economically underutilized land implied a certain level of in improvement-to-land ratio was redevelopable. This suggests that nonvacant sites 12 can be redeveloped if they contain economically underutilized structures.
2.5 Summary
This chapter has identified the factors employed by the most widely used urban modeling theories to project the location of future residential development. Although the urban models used different dependent variables or outputs (e.g., site-level land use change by type, neighborhood-level housing development area by type, or households by income), they all identified factors that were assumed to attract residential development to certain sites or neighborhoods. This chapter also supplemented the discussion of the urban modeling factors by reviewing important urban economic theories that set the theoretical ground for urban modeling.
Urban models have used a diverse set of site and neighborhood factors to identify areas suitable for development. These factors can be grouped into several categories,
11 Portland Metro is “the directly elected regional government that serves more than 1.3 million residents in Clackamas, Multnomah and Washington counties, and the 24 cities in the Portland, Oregon, metropolitan area.” (http://www.metro-region.org/) 12 Nonvacant site is defined as the site with structure(s). 34 i.e., accessibility, land use mix, development capacity, amenity, socioeconomic/demographic, and suitability as shown in Table 2.1.
Accessibility is a measure of trip-generated cost involved in commuting and shopping trips. A location with good accessibility is assumed to attract residential land development.
Land use mix is assumed to lead to inter-use externalities in that some land uses are either compatible or incompatible to each other. Thus, for example, a neighborhood’s residential land use area would be proportionately associated with neighborhood’s infill housing development rate.
Development capacity represents the amount of developable land in a neighborhood or the potential of land development of a site. This suggests that neighborhood served by capable community development corporations and developers would provide more infill housing.
Amenity is a label for a variety of factors creating externality effects, either positive or negative, on household’s residential location choice. For example, a neighborhood park is an amenity creating a positive externality and housing located nearby the park would enjoy the amenity.
Demographic/socioeconomic factor is an important neighborhood-level feature for household residential location choice. For example, urban models generally assume that neighborhoods with low-income, minority residents and high crime rate would have lower market demand and less housing development.
35 Table 2.1 Site and Neighborhood Factors in Urban Models
Correlation with the Factors Urban Models probability of residential development Accessibility Accessibility to employment centers or shopping CUF-2, DRAM, Negative centers UrbanSim Accessibility to transportation stations (e.g., CUF-2 Negative distance to highway interchanges or public transit stations) Land Use Mix of Neighboring Sites Similar or compatible land use (e.g., residential CUF-2, UrbanSim Positive development in residential area) Incompatible land use (e.g., residential land use vs. CUF-2, UrbanSim Negative heavy industrial land use) Site Physical Features and Development Capacity Environmentally vulnerable land or sites with CUF-2, UrbanSim, Negative natural constraints (e.g., steep slope) What if? Vacant land DRAM, UrbanSim Positive Residential density DRAM Negative Percentage of recent development in neighborhood UrbanSim Positive Amenity/Disamenity Distance from natural or man-made amenities Typical* Negative Distance from natural or man-made disamenities Typical* Positive Socioeconomic/ Demographic Income DRAM, UrbanSim Non-linear** Residential density (e.g., households per acre) DRAM Negative Racial majority Typical* Negative Crime rate Typical* Negative
* “Typical” means that the variables are not used in the four urban models but are so typical or almost classic variables used in many other urban models. Therefore, the variables will be considered in statistical analyses under the categorized factors. ** According to the urban models, the price of housing will be compatible with neighborhood’s average household income. Therefore, low-income neighborhood would encourage affordable housing while upper income neighborhood would induce high-end housing.
The suitability factor is closely related to the developability of a site which is often determined by natural or environmental constraints. That is, land with high level of suitability is assumed to have better chance to be developed.
The review indicates that urban modelers have paid attention to governmental or institutional policy factors. However, they have employed a limited set of policy measures such as zoning and most of them are of regional scale, which is not suitable for a city-scale urban housing model. Also, the existing urban models failed to incorporate 36 the institutional policy factors that are designed uniquely for urban redevelopment and infill housing development.
This chapter also reviewed an emerging GIS-based land use planning tool known as land supply monitoring systems. Using parcel-based GIS information and a set of land policy rules, the systems have been developed to identify buildable or developable land in urbanized areas and suburban greenfields. As a result, these models have been used increasingly to estimate the infill housing development potential of urban areas.
37
CHAPTER III
THE ROLE OF INSTITUTIONAL FACTORS
IN THE URBAN REDEVELOPMENT PROCESS
3.1 Introduction
This chapter reviews the studies of urban planning, housing policy, and real estate development that are relevant to urban redevelopment and infill housing. The focus is on location-based institutional policy factors that are associated with the location of urban housing development.
As pointed out in the previous chapter, the importance of some institutional policy factors has been appreciated by urban modelers. In order for urban models to be useful in real world planning processes the models should be able to identify the impacts of important policy variables on the urban spatial system (Agarwal et al., 2002, p. 1).
Therefore, since the earliest days of urban modeling, modelers have attempted to develop policy-responsive urban models that can explain and predict the effects of policy factors on urban spatial structure (Brand et al., 1967; Foot, 1981, chapters 1 and 2; EPA, 2000;
Miller et al., 1998; Putman, 1979; Waddell, 2002). In this sense, some have called urban models “policy-oriented models” (Mohan, 1979).
38 However, urban models have generally incorporated only a limited number of policy variables such as environmental protection areas, growth management boundaries, transportation improvements, and local zoning. In addition, the majority of the policy measures used by existing urban models are of regional scale and regional influence.
This is largely due to the fact that urban models are often developed to address regional planning and policy issues such as regional transportation policy, growth management, and smart growth initiatives. As a result, urban modelers have failed to incorporate into their models a variety of institutional policy factors that are important for describing urban housing development.
This chapter reviews the urban housing industry and identifies institutional factors that are important in U.S. and in Cleveland in particular. First, it describes the institutional players and their roles in the American urban housing development industry.
Second, it describes important institutional factors that are assumed to be associated with the location of housing development in American market and Cleveland in the 1990s.
These factors will be analyzed for their importance by the statistical analysis in later chapters of the dissertation.
This chapter and the entire study focus on supply-side or development-oriented policies and programs (e.g., land banking) rather than a demand-side or household- oriented ones (e.g., homebuyer tax abatements). This is due to the three presumptions of this study: (1) there is generally more demand than supply in urban infill housing markets; (2) the will and capacity of urban housing developers are very important for the development of urban infill housing; and (3) supply-side, place-oriented institutional
39 factors are more associated with the spatial pattern of urban housing development than demand-side, people-oriented factors.
3.2 Institutional Partnerships for Urban Infill Housing Development
Many American cities have suffered from urban problems. American cities have suffered from urban problems detrimental to their housing market: racial prejudice; central city neighborhoods characterized by poor and minority population; weakening tax bases; aging housing stock without proper funding for rehabilitation and redevelopment; urban sprawl boosted by federal highway construction; federally-backed mortgage insurance activities concentrated in suburbs; ill-prepared urban renewal; and the 1980s’ federal cutbacks (Byrum, 1992, chapter 2). These problems have made it very difficult for housing developers to generate a positive cash flow from housing development, which, in turn, makes it difficult to obtain conventional development financing. This makes urban infill housing development very risky for the developers in many urban areas. Assembling the multiple sites needed to create a housing development of adequate scale is often a very costly and tedious process for a private developer without governmental support, let alone paying for any required the site and infrastructure improvements.
Problems with the land assembly in urban areas include: hard-to-find absentee owners, complex chains of title, costly environmental remediation, and land speculation.
Also, urban housing developers often have to go through a good deal of governmental red-tape, numerous regulations, community oppositions, and conventional lenders’ reluctance, which are of less concern for suburban housing developers.
40 In a nutshell, the majority of U.S. urban infill housing developers has faced one or more of the unmanageable risks resulting from societal, financial, and institutional problems. In order to overcome these difficulties, developers have had to forge creative partnerships with a variety of institutions including city governments, community-based development organizations, local banks, philanthropic foundations, and socially- responsible financial intermediaries (Suchman, 2002; Suchman & Sowell, 1997).
Since the urban renewal era of the 1950s and 60s, governments have joined forces with corporations and businessmen interested in downtown revival and urban renewal.
Although the partnerships have been inherently biased in favor of the business sector
(Squires, 1989; Keating et al., 1996), public-private partnerships have become a systematic and powerful force for urban redevelopment in the U.S. (Suchman et al.,
1990). In the midst of the federal cutbacks in many urban programs that addressed urban low income housing production, the partnerships between state and local governments and public and private institutions have become a crucial element for producing urban affordable housing (Keyes, 1996).
Urban housing development faces many obstacles (Farris, 2001). Private developers often hesitate to invest in inner city neighborhoods because of many perceived risks such as high development costs, complex development financing structures, and land assembly problems. These obstacles are difficult to overcome with only the conventional knowledge and practices of real estate development and financing that have been used in suburban communities. Therefore, more often than not, urban housing developers collaborate, or partner with, non-profit or limited-profit development corporations that have access to a variety of financial subsidies and incentives. As a
41 result, partnerships between public agencies, private lenders, private foundations, intermediaries, and nonprofit organizations have created an important civic infrastructure for housing projects in U.S. cities (Bogart, 2003; Bright, 2000a; Vidal, 1996; Yin, 1998;
Keyes et al., 1996; Rubin, 2000; Walker, 1993).
Spawned in the 1960s, when private developers largely abandoned American cities, community-based development organizations such as Community Development
Corporations (CDCs) 13 have been successful institutional models for initiating the housing and economic development in many urban neighborhoods (Grogan, 2000, chapter 4; Sullivan, 2001, chapter 4; Keating & Krumholz, 1999). CDCs have played a strong and positive role in revitalizing housing and jobs for urban neighborhoods
(Keating & Krumholz, 1999). However, resource-poor CDCs have relied on subsidies not only for development capital but also for operational resources that are closely related to their organizational, political, and technical capacities (Goetz, 1992).
Governments provide tax incentives (e.g., local property tax abatement and state
Low Income Housing Tax Credits) and neighborhood and housing development grants
(e.g., Community Development Block Grants or CDBG and the Home Investment
Partnership Program or HOME). Also, governments provide regulatory support (e.g.,
13 Legally, a CDC is “an incorporated, non-profit agency empowered to purchase, develop and manage residential and commercial property, or to provide loans and technical assistance to other organizations doing these same things” (Robinson, 1996, p. 1652). Community development corporations (CDCs) have played an important role in their service neighborhoods as a community-controlled, comprehensive redevelopment organizations pursuing economic revitalization and affordable housing development. In most cities, CDCs focus on their own neighborhoods (Walker, 1993, p. 370; Keyes et al., 1995, p. 207). A CDC is usually based on a geographically-defined neighborhood. In this study, “CDC” will refer to a community-based development organization. 42 the Community Reinvestment Act of 1977) for community redevelopment by eliminating the discriminatory financing practices of private financial institutions. Governments provide not only monetary support but also development incentives such as target neighborhood redevelopment, land banking, and site preparation.
Non-profit intermediaries and foundations, both local and national, play an important role in gathering and packaging a variety of public and private capital (so called “patchwork financing”) and providing soft development capital such as organizational development and capacity building for urban housing developers including the CDCs (Keating, 2003; Liou & Stroh, 1998; Rubin, 2000, pp. 99-132). National- level intermediaries such as the Local Initiatives Support Corporation (LISC) and the
Enterprise Foundation and local ones such as Cleveland’s Neighborhood Progress, Inc.
(NPI) and the Cleveland Neighborhood Development Coalition (CNDC) have helped the developers tap into a variety of mixed-financing deals and gain development capacity.
In addition, the intermediaries and foundations empowered many undercapitalized CDCs with administrative grants, predevelopment funds covering architecture and engineering, site controls, feasibility analysis costs, and technical expertise (Walker, 1993, 389-390).
Community-based nonprofit development corporations and for-profit housing developers are generally the lead partners in the urban housing development projects while governments and other institutions are generally the major supportive partners.
With these partnerships, urban housing developers often initiate and lead the complex and creative financing structure and implement the details of the development processes for urban revitalization and housing development. Governments, non-profit intermediaries, and foundations also provide monetary and political help for urban infill
43 housing development. This kind of partnership creates an institutional support system for urban housing development in which the partnering institutions provide CDCs and private development partners with financial support, technical expertise, and political support thereby reducing investment risks for urban redevelopment and infill housing projects (Bogart, 2003; Vidal, 1996).
3.3 Institutional Factors
This section discusses the institutional programs and policies that were used to deal with the imperfect urban housing market in the last decade. The section focuses on the principal institutional policy factors that are assumed to be closely related to the location of urban housing development. With some exceptions, the discussion of each institutional factor starts with a general explanation of the factor, followed by the application of the factor to Cleveland’s urban housing development in the 1990s. Some factors (e.g., CNPP and ward politics) are unique for Cleveland while others (e.g.,
Federal tax incentives and development grants) are more generally applicable.
3.3.1 The City Comprehensive Plan
To promote infill housing development, some city plans have called developers’ attention to specific locations and provided general information on available sites
(Suchman, 2002, p. 46). Cleveland did this with its Civic Vision 2000 Citywide Plan .
In 1991, Cleveland published Cleveland Civic Vision 2000 Citywide Plan (Cleveland
City Planning Commission, 1991), a 10-year land use plan for the city. While the
“Downtown Plan” of 1989 focused on Cleveland’s central business district, the
44 “Citywide Plan” focused on neighborhood revitalization. The Citywide Plan contained comprehensive, but clearly defined, goals and recommended courses of action for neighborhood revitalization, including housing redevelopment in particular. An important map labeled “Potential Housing Development Sites” showed the potential sites for housing development for the next ten years. Another important element of the plan was the future land use maps for eight planning regions covering the entire city. Also, the policy goals and statements indicated location preferences for infill housing development, which will be reviewed in the following section. Together the map and policies made it possible to identify the proposed future land use changes roughly at the parcel level (Figure A.16).
The Plan’s policy goals specified a set of potential sites for housing developments.
The planning goals suggested the following criteria for locating housing development:
“1. Re-use large vacant sites in City neighborhoods for the construction of comprehensively-planned residential developments that are competitive with suburban alternatives. 2. Actively promote development …. in proximity to major centers of employment and recreational or entertainment activity” (Cleveland City Planning Commission, 1991, p. 9).
Moreover, the planners proposed lists of potential sites for future housing developments for the eight planning regions. As urban housing development is often supported in the context of the larger community plan, the city government goals for
Cleveland’s housing development can be assumed to be based on the housing plan of the
Citywide Plan in the 1990s.
45 3.3.2 The Community Reinvestment Act
Neighborhood life-cycle theory 14 influenced the financial institutions’ underwriting system that refused to provide mortgage loans in “redlined” areas 15 , which are generally low-income and African-American neighborhoods. This planned disinvestments of governments and private lenders and developers in poor minority urban neighborhoods resulted in “planned abandonment” (Metzger, 2000).
The Community Reinvestment Act of 1977, as amended (CRA), was enacted by
Congress to overturn these discriminatory lending practices. The purpose of the CRA is to help promote reinvestment in lower-income and minority neighborhoods that were previously excluded from private financial institutions and underserved in the mortgage market. More specifically, it attempted to reverse the banks’ discriminatory lending practices against racial and economic minorities in inner city neighborhoods. The CRA requires depository institutions insured by Federal Deposit Insurance Corporation (FDIC) to meet the credit needs of their service communities, including low- and moderate-
14 The neighborhood life-cycle theory was first developed in 1935 (Home Owners’ Loan Corporation,
1935). Although there have been different versions of the theory (for example, Hoover & Vernon, 1959;
Real Estate Research Corporation, 1975), they commonly propose a four- or five-stage process of neighborhood change. The crux of the theory is that a neighborhood inevitably goes through a series of declining stages, which are associated with the changes to the character of people, including racial and income changes more than anything (Metzger, 2000, pp. 8-10)
15 The discriminatory lending practice by financial institutions called “redlining,” among many other reasons, caused a segregated housing market (Burchell & Listokin, 1995, p. 585). “Redlining” refers to a discriminatory lending practice of financial institutions that excluded minority and poor urban neighborhoods by drawing the neighborhoods boundaries with red-colored pen.
46 income neighborhoods. Federal regulators regularly assess the fair lending performance of the insured depository institutions to see if the lenders meet the credit needs of their entire service communities (for details, see FFIEC, 2003).
Many financial institutions have established CRA-based reinvestment agreements with local communities to demonstrate their conformity to CRA. Under the agreements, the lenders commit a targeted level of loans to designated minority and lower-income neighborhoods. Noncredit provisions (e.g., opening branches or investment in community redevelopment projects) can also be used as an alternative to lending in the targeted communities (Bostic & Robinson, 2003).
The Cleveland city government started to aggressively enforce the CRA in the late 1980s in order to increase banks’ lending for redevelopment projects in the previously redlined neighborhoods. The city and area lenders entered into
Neighborhood Reinvestment Agreements16 with the banks which specified future investment levels in previously red-lined areas among many other details.
The Neighborhood Reinvestment Agreements (NRA) with each bank specified target neighborhoods for reinvestments such as home purchase and improvement loans.
They also identified a list of target neighborhoods based on their income and racial composition in order to address the urgent reinvestment needs of the poor and racially segregated neighborhoods (Krumholz, 1997, p. 66; Loar, 2003; Schwartz, 1998) (Table
16 The Cleveland Neighborhood Reinvestment Agreements were enforced by the city in order to increase lending of area banks in neighborhoods that had been discriminated and neglected by the area’s lenders. Lending targets include home purchase, home improvement, small business, and community development loans. The first agreement was made between the City and Society National Bank in December, 1991. By 2000, seven additional banks and a national mortgage lender, Fannie Mae, have joined the agreements with the promised loans of more than $3 billion (The City of Cleveland, 2001). 47 3.1 and Figures A.1 and A.2). The Agreements designated 11 Spatial Planning Areas or
SPAs and 20 SPAs as target reinvestment areas based on low income and minority concentration rates, respectively. Nine of them were targeted in the basis of both criteria.
By mandating that banks to invest into to particular neighborhoods, the NRA program is assumed to increase housing development activities in these neighborhoods.
Table 3.1 Target Neighborhoods for Neighborhood Reinvestment Agreements
Criteria Neighborhoods (SPAs) Low household- Central , Corlett, Fairfax , Forest Hills , Glenville , Hough , Kinsman , Lee Miles, Mt. income Pleasant , Union Miles Park , Woodland Hills (Total 11 areas) Brooklyn Center, Central , Clark-Fulton, Detroit-Shoreway, Fairfax , Forest Hills , Minority- Glenville , Goodrich-Kirtland Park, Hough , Kinsman , Mt. Pleasant, N. Broadway, concentrated Ohio City, South Broadway, South Collinwood, St. Clair/Superior, Tremont, Union Miles Park , University, Woodland Hills , (Total 20 areas) Note: Neighborhoods targeted by both income and racial criteria are underlined. Source: Loar (2003)
3.3.3 The Land Reutilization Program
Finding buildable sites at low cost is a daunting problem for developers in urban areas because: (1) it is often difficult to find owners and to obtain a clean title of land, and (2) sites often have structures and environmental contamination that require demolition and remediation which load developers with unreasonably high development costs. However, recycling or reusing vacant abandoned land is considered to be an essential element for achieving the goal of urban community revitalization (Bright,
2000b). As a result, government programs such as land banking have been started to help ease the burden for urban infill housing developers.
There have been many efforts to convert underutilized properties into productive resources for communities (see for example, Accordino & Johnson, 2000; Bright, 2000b;
US Department of Interior, 1979). However, there are also many obstacles to be
48 overcome in reusing urban land. The abandoned and tax-delinquent sites often have complex titles (e.g., it is often difficult to even find owners) to be cleaned, deteriorated building to be demolished, and environmental pollution to be remediated (Farris, 2001).
Therefore, land development projects in urban communities often need to be subsidized in some ways to reduce the cost of site acquisition and preparation. This kind of land policy is considered to be an effective and efficient tool for converting abandoned land into revenue-generating properties. These programs have significantly reduced the development costs and time for infill housing development (Bright, 2000a; Bright ,
2000b; Keating & Sjoquist, 2001; Krumholz, 2002).
Since the mid-1970s, Cleveland’s land bank program has acquired tax delinquent vacant lands and provided them to infill housing developers at nominal costs (Simons &
Sharkey, 1997, p. 147). In order to reutilize the nonproductive land or tax delinquent properties, Cleveland has also established a “land bank” land reutilization program under
Chapter 5722 of Ohio Revised Code in 1976.
The land banking procedure works as follows. Tax delinquent properties that were foreclosed on by Cuyahoga County are offered at tri-annual county sheriff’s sales.
Before the sales, the County sends a list of the foreclosed properties to the City. The
City’s Department of Community Development examines the list to see if any of them are clean and vacant 17 and in accordance with city’s neighborhood redevelopment plans.
If the foreclosed properties are not sold in two consecutive offerings at the sheriff’s sales, the city then acquires the properties they are interested in.
17 According to Sternad (May 28, 2003), the city prefers vacant land with no contamination because of the costs involved in cleaning up the land and buildings. 49 The properties are classified into buildable (i.e., equal to or larger than 40 feet x
120 feet) and non-buildable (smaller than 40 x 120 feet) sites. Most non-buildable sites are offered to adjacent property owners for a nominal price of $1. Buildable lands are sold to any new construction proposal including infill housing (for $100) that were appropriate to the City’s land use and neighborhood plans (Bright, 2000a; Bright, 2000b;
Keating & Sjoquist, 2001; Krumholz, 2002; Sternad, 2003). The City strongly recommends that developers on land bank sites assemble adjacent land if the land is either another land banking site or a tax delinquent site. This recommendation is included in the review of development proposals for the land bank sites.
Cleveland is one of the first cities in US to adopt a land-banking program. The municipal land bank program has been a major catalyst of Cleveland’s infill housing development. Several studies (e.g., Blackwell, 2003; Bright, 2000a; Rosan, 2001; and
Keating & Sjoquist, 2001) suggest that a significant association exists between the land banking program and the location of infill housing development in Cleveland during the
1990s.
According to Sternad (2003), the land bank system started to actively acquire land in 1990, one year after Mayor White took office. The land acquisition program was very active in the early and mid 1990s (Table 3.2). Between 1990 and 2000 the land bank acquired a total of 17,426 parcels. Sternad (2003) estimated that a quarter of the sites were non-buildable sites.
The growing program benefited Cleveland housing developers by proving clean, vacant residential development sites at a minimal price. Therefore, the land bank sites are assumed to be targeted for a new housing site by urban infill housing developers.
50 Like Neighborhood Reinvestment Program target neighborhoods, the land bank sites are located largely in the east (Figures A.3 and A.17).
Table 3.2 Number of Cleveland Land Bank Sites, 1990 – 2000
Number of Sites Year Acquired Each Year 1990 2,226 1991 2,945 1992 1,457 1993 1,524 1994 2,462 1995 1,136 1996 1,554 1997 1,641 1998 826 1999 944 2000 711 Total 17,426 Source: Provided by Evelyn Sternad (Lank Bank Program Administrator, City of Cleveland)
3.3.4 The Cleveland Neighborhood Partnership Program
The Neighborhood Progress, Inc. (NPI), one of the largest intermediaries in
Cleveland, recently created a management consulting and training program called
Quantum Leap (Nye & Glickman, 2000). More importantly, since the late 1980s, the
NPI joined with the Cleveland Neighborhood Partnership Program (CNPP) to award operating grants to competent CDCs for a three-year grant cycle (Yin, 1998). The grant proposal form required CDCs to demonstrate their achievements over the past three years and outline a three-year future plan. The CNPP awards significant operating funds ranging from $30,000 to $75,000 to CDCs on a competitive basis (Rohe and et al., 2003).
It can therefore be assumed that the CDCs that received CNPP grants were more competent than other CDCs in Cleveland. The service areas of the more competent
CDCs are therefore assumed to contain more infill housing units than areas for the less competent CDCs (Appdendix, p. 183). 51 3.3.5 Empowerment Zones
The empowerment zone (EZ) program was created by the federal and state governments in 1994 to provide special tax incentives and other funds and grants to encourage businesses to revitalize blighted, distressed, and poor communities in inner cities (Berger 2001; Keating et al. 1996; Keating & Krumholz 1999). It provides a variety of funding mechanism including money for the new construction and renovation of buildings, property acquisition, business retention, and job training. Coupled with the target neighborhood redevelopment programs, the EZ programs are important place- based policies for aiding the poorest neighborhoods of inner cities. Although the EZ program is business-oriented, it can be part of comprehensive community development efforts to increase the opportunities for residential and commercial infill development within the selected zones or communities. Unlike the CDBG and HOME grant programs, the EZ program funds geographically defined areas in each city.
Cleveland’s Empowerment Zone program received $117 million for a 10-year program from 1995 to 2005 (Krumholz, 1999). The Cleveland EZ is comprised of three neighborhoods, Hough, Glenville, and Fairfax, and a special business zone called
MidTown. The Cleveland EZ is known for its job matching program that provides an array of job matching, job training, and employment supportive services to the residents of the distressed neighborhoods. It also supports a variety of community revitalization activities such as the new construction and renovation of buildings, business retention, and even neighborhood drug patrols (Murphy, 1998). As a result, this place-based federal funding program will be assumed to promote housing development activities as
52 well as general economic development programs in the targeted neighborhoods (Figure
A.5).
3.3.6 Development Equity Funds and Debts
A federally-funded and state-run tax credit program, Low Income Housing Tax
Credit (LIHTC), has been one of the most successful programs for financing affordable housing developments in depressed inner city neighborhoods (Green & Haines, 2002, pp.
121-122; Rubin, 2000). Created by the Tax Reform Act of 1986, the LIHTC has been a significant resource for low- and moderate-income housing by providing tax credits for tax credit equity investment for affordable housing development, generally in the form of limited partnerships. The LIHTC program has some spatial implications. Because of its program goal, the majority of the tax credit-funded development goes to low-income neighborhoods. In addition, the scoring system for the tax credit application gives an additional points for a development located in qualified census tracts (QCTs) that are usually lowest-income neighborhoods in each city.
The federally-funded and locally-run Home Investment Partnership (HOME) program is designed to provide housing development funds to low-income housing developers and rental and homeownership assistance to low-income households. The
HOME investment program provides housing developers with gap financing to fund the development or rehabilitation of affordable housing. The assistance provided by the
HOME funds for homebuyers and renters includes down payment assistance and rental gap payments.
53 The Community Development Block Grant (CDBG) is another important federal grant program run by local governments to support urban infill housing development
(Galster, et al. 2004). Although the spending priority of cities using the CDBG varies, local governments generally use the grants for activities related to affordable housing development such as site and infrastructure improvement and construction loans.
Although there is no stated preference concerning the appropriate geographic target area, the developments targeted by the grants are often located in needy or low-income and minority neighborhoods.
Local governments have used many other financing tools to encourage urban housing development and, especially, affordable housing. These tools include development cost write-downs, tax abatement, tax increment financing, and low-interest loans. Local governments have used these types of subsidies by drawing on their own discretionary funds, with some help from state and federal governments (Conley, 1976;
Vliet, 1997). Because these programs do not have clear location implications, this study does not include these in the analysis.
3.3.7 Ward Politics
Although no literature has pointed it out, professionals and scholars in the field of urban affairs in Cleveland suggested that ward politics, i.e., the decision making of 21 councilpersons, might be an important factor affecting the location of the infill housing development. Cleveland’s major community development funds, consisting of the
HOME and CDBG programs, are evenly distributed to the 21 councilpersons at their disposal. As a result, the Ward councilmen can use their political influence to steer this money, either toward housing development or non-housing development activities.
54 They may also use their political networks to obtain other subsidies for infill housing development within their wards (Author’s interview with Krumholz, Keating, & Star,
May 13, 2003). In addition, the councilmen were the final decision makers for the construction plan on a land banking site (Author’s interview with Sternad, May 28, 2003).
As a result, the geographical boundary of the wards will be assumed to be significantly associated with the location of urban infill housing development
3.4 Summary
In the majority of American cities, the costs and risks of urban infill housing development are often prohibitive and unmanageable without some kinds of public- private partnerships and significant institutional support. From land assembly and financing to political leadership and community acceptance, there are many challenges, risks, and costs involved in urban housing development, which are often not found in suburban housing development. Therefore, the efforts of declining American cities to improve decades-old, abandoned, and underutilized urban infill sites and neighborhoods often require strong institutional support from governments, community-based development organizations, nonprofit and private foundations, financial intermediaries, and private lenders.
This chapter has reviewed the location-based or geographically targeted institutional factors that are assumed to have significant association with the location of urban housing development in urbanized areas with weak housing markets. The review also examined prominent institutional factors in Cleveland (Table 3.3). Those factors included the the city comprehensive plan, neighborhood reinvestment program, land
55 reutilization program or land banks, the Cleveland Neighborhood Partnership Program
(CNPP), the Empowerment Zone, tax incentives and development grants. By targeting specific sites or neighborhoods, the institutional programs addressed important barriers against urban infill housing developers such as discriminatory lending practices, costly land acquisition and site preparation, and depressed neighborhood economies. In addition, as the interview with experts suggested, Ward politics can also be assumed to have an important role of locating the development activities in different political districts.
Table 3.3 Institutional Factors Affecting the Location of Residential Development
Correlation with the Institutional Factors probability of residential development City Comprehensive Plan (CivicVision 2000 in Positive Cleveland): Potential Residential Development Areas Community Reinvestment Act (Neighborhood Positive Reinvestment Agreement in Cleveland): Target Reinvestment Neighborhoods Land Reutilization Program or Land Bank Sites Positive Cleveland Neighborhood Partnership Program: Positive Neighborhoods Targeted by CDCs Receiving the Program Funds Empowerment Zone Positive Ward Politics Various
56
CHAPTER IV
THE URBAN DEVELOPMENT HISTORY OF CLEVELAND
4.1 Introduction
The City of Cleveland grew as a major trade center of the country and of its region largely because of its location adjacent to Lake Erie and the Cuyahoga River. The City of Cleveland is a major central city with the largest population in northeast Ohio (Figure
4.1). Like most central cities in this country, the Cleveland has gone through a course of development and decline with population growth and reduction over the last two centuries (Table 4.1). Cleveland started as a mercantile city in the early 1800s and bloomed as a leading industrial center of the U.S. in the late 1800s and the early 1900s
(Miller & Wheeler, 1997; Condon, 1976, pp. 55-92). However, in the aftermath of
WWII, the City started to experience a dramatic loss of manufacturing plants and affluent residents, resulting from deindustrialization and suburbanization. As a result, Cleveland has suffered from a plethora of urban problems such as rising unemployment rate, rundown housing stock, dwindling middle-income population, a waning tax base, and poverty-stricken and racially segregated neighborhoods.
57 Ohio Cleveland and the Northeast Ohio
Northeast Ohio
Figure 4.1 Location of the City of Cleveland
Table 4.1 Cleveland’s Population, 1800 – 1990
Year Population Rank in nation 1800 7 1850 17,034 1900 381,768 7th 1950 914,808 7th 1990 505,616 23 rd 2000 478,393 33 rd Source: A Bicentennial Timeline of Cleveland. (The Encyclopedia of Cleveland History. http://ech.cwru.edu/timeline.html)
58 Since the 1950s, Cleveland has tried to reestablish its glorious old days as a regional and national center of political power, employment, shopping, and art and cultural entertainment. The downtown district has been the target of the City’s urban redevelopment programs which were “privately controlled and often publicly subsidized”
(Keating et al., 1989, p. 121). In particular, the City government and the business community created public-private partnerships combining government’s tax abatement and grants, corporations’ investment, and corporation-led growth coalition’s funds for downtown revitalization projects including new building construction (e.g., shopping malls, hotels, offices, convention centers, entertainment districts, and stadiums) and transportation projects (airports, subways, and other public transit systems).
However, the City’s focus on Downtown led to a neglect of the chronic problems of its depressed neighborhoods such as poverty concentrations, racial segregation, dilapidated houses, and high unemployment rates. It seemed to prove that the problems of depressed urban neighborhoods were more complex and difficult to deal with than those of the downtown district (Miller & Wheeler, 1997, p. 189). In other words, it seems to be easier to mobilize the monetary and political resources for the downtown’s physical and economic revitalization projects than for neighborhoods’ social and economic redevelopment.
In response to this situation, community-based development organizations started to help their own communities by tapping into a variety of public and private resources – monetary, political, and technical – especially for housing rehabilitation programs and new infill housing construction. These community-based developments have been assisted and guided by local government, financial intermediaries, and foundations.
59 Since the 1990s, Cleveland’s community development corporations and community development support network have been recognized as one of the most innovative and effective community development systems in the nation.
This chapter discusses the historical background of urban decline and redevelopment of Cleveland, Ohio, mainly from 1900 to the present. First, it describes the urban problems and its spatial pattern. Then, focusing on housing development, it looks at the institutions and players in the City’s neighborhood redevelopment.
4.2 Ups and Downs of Cleveland Development
4.2.1 A Thriving Industrial Central City
The Cuyahoga River, which runs through the heart of Cleveland, and its surrounding site were praised as the excellent transportation node of the Western Reserve by Moses Cleaveland who was dispatched in 1796 by Connecticut Land Company to select a site for the capital of the Western Reserve. Over the next half century, a small village on the west side of the river became a booming commercial city and a vibrant national economic center with its prominent Ohio & Erie Canal and later with railroads which made Cleveland a shipment center for agricultural and manufactured goods.
Cleveland became an important industrial city with iron and steel and oil manufacturing companies in the second half of the 1800s. During that period, Euclid
Avenue, once known as Millionaire’s Row and one of the most beautiful streets in
America, was built up with homes of wealthy industrialists and bankers and financiers including John D. Rockefeller, whose oil refinery company controlled 90% of the
60 nation’s refining capacity in the 1880s. The period also provided work to numerous immigrants who flooded to Cleveland from all over the Europe. The industrial boom lured not only European immigrants but also Southern Blacks to Cleveland in the early
1900s. The ambitious Group Plan, which envisioned a grand cluster of public and governmental buildings in the northeast of the Public Square, began to be implemented during that period. In sum, the economic and population growth of the City was at its top speed until the great stock market crash on October 29, 1929.
That was about the time when upper income families started to abandon the homes of Euclid Avenue to move to a newly emerging suburb called Shaker Village in order to escape from the invasion of the poor and minority immigrants. And that was the time when the foundations of the City’s economic structure began to crack.
4.2.2 Deindustrialization and Suburbanization
Suburbanization was first reported in Cleveland in 1941 (Miller & Wheeler, 1997, p. 147), but its beginning could be observed even at the turn of the century 18 (Bier, 1999, p. 62). Affluent Clevelanders started to flee to suburbs as a result of the excessive commercial and retail development that was occurring near their residential neighborhoods and the urban problems such as overcrowded neighborhoods, the concentration of poor immigrants of color, and a declining public school system. At the same time, new large-scale housing projects booming in suburbs started to meet the pent-
18 “At the turn of the twentieth century, downtown Cleveland was in a process of turning residential neighborhood’s downtown into a office and commercial district. It was a radical physical change that caused the city’s wealthy families on the residential boulevards to flee into new suburban settlements such as Bratenahl on Lake Erie, Shaker Heights, Gates Mills, Cleveland Heights, East Cleveland, Lakewood, and Rocky River. (Condon, 1976, p. 57)” 61 up housing demand, caused by the influx of Blacks and Appalachians who came to the city for job opportunities during WWII and service men and women who came back to their hometown seeking jobs and housing (Bier, 1988, p. 1; Miller & Wheeler, 1995, p.
43).
The engine of suburbanization was also fueled by federally-subsidized interstate highway construction, federal mortgage insurance, federal income tax law, and the suburbs’ exclusionary zoning regulation (Bier, 1991; Condon, 1976, p. 56; Miller &
Wheeler, 1997, p. 158). Consequently, the wealthy suburbs virtually locked poor families in the rundown inner city neighborhoods that the well-to-do could afford to leave behind for their new homes in suburbs.
The ratio of the Cleveland’s population to Cuyahoga County’s has declined dramatically since the 1910s (Table 4.2). Many inner-city neighborhoods also experienced population loss due to interstate highway construction that tore down many houses. For example, I-90, which was completed in the late 1970s, demolished thousands of housing units in Cudell, Detroit-Shoreway, Jefferson, Kamms Corners, Ohio
City, and so on. Not only inner city neighborhoods but also businesses and churches were destroyed by the inner-belt highway system (Miggins, 1995, p. 198)
Table 4.2 Comparison of Population between the City of Cleveland and Suburbs
Year Cleveland City Cuyahoga County Ratio of City to County 1800 7 - - 1850 17,034 48,099 35% 1900 381,768 439,120 87% 1950 914,808 1,389,532 66% 1990 505,616 1,412,140 36% 2000 478,393 1,393,978 34% Source: A Bicentennial Timeline of Cleveland. (The Encyclopedia of Cleveland History. http://ech.cwru.edu/timeline.html) 62 Meanwhile, Cleveland, once one of the leading industrial cities in the nation, could not resist the Rust Belt’s deindustrialization during the mid 1900s. Cleveland’s manufacturers started to close down their plants as they moved to the suburbs and to the
Sun Belt states and foreign countries where they could find cheaper land and labor and growing markets. Cleveland lost 130,000 jobs from 1958 to 1977 and 86,100 jobs from
1970 to 1985 (Miller & Wheeler, 1997, pp. 171 and 183). Manufacturing employment fell from 223,000 in 1947 to 82,000 in 1986, while service sector employment in the metropolitan area climbed from 434,000 in 1965 to 629,000 in 1985 (Cleveland City
Planning Commission, 1991). Although Cleveland’s economy shifted toward the service sector 19 , the new service jobs did not pay as much as the manufacturing ones did.
As Clay (1988, p. 30) points out, Cleveland residents who held the service-oriented low- skill jobs that were available since the 1970s could not match the incomes of the City’s previous industrial workers. Adjusted for inflation, Cleveland’s household income fell by 30 % between 1950 and 1980 (Cleveland City Planning Commission, 1991, p. 21).
This job loss and the City’s overall economic decline placed a heavy damage upon the city’s poor residents 20 who started to experience the devastating effects of suburbanization such as a waning local tax base, abandoned housing, plummeting property values, and decaying neighborhoods (Krumholz & Hexter, 1999). Poverty and troubled inner city neighborhoods became a lingering image of Cleveland.
19 Since this period, Cleveland’s economic base has become dominated by service industry structured by governments, financial services, education, retailing, and corporate hospitals. 20 In the late 1980s, 40 percent of Cleveland’s households were in poverty status. (Miggins, 1995, p. 198) 63 4.2.3 Segregated Neighborhoods
Although Cleveland had been a popular destination for many immigrants of diverse backgrounds since the late 1800s, the City regrettably failed to fully integrate its new Black residents into its white communities. Since the mid 1900s, many neighborhoods in the city’s east side have been segregated from the rest of the city (i.e., west side and periphery) as they became the primary destination for low-income Black families. In Figure 4.2, a neighborhood map displaying Special Planning Areas or SPAs, shows the spatial pattern of the concentration of the poor Black population in the east side of Cleveland in 1990.
In the mid 1900s, as Blacks started to be concentrated in certain neighborhoods within the City, the racial transition was dramatic in many neighborhoods. A salient example was the Hough neighborhood whose Black population was less than 5% during the 1950s but became 74% in the 1960s, as a result of urban renewal projects and realtors’ blockbusting (Stakes, 1995, p. 43; Miller & Wheeler, 1997, pp. 166-167). Not only racial prejudice but also governmental projects such as urban renewal stimulated the racial concentration in some neighborhoods. One of the urban renewal project’s goals was to deconcentrate or reduce the Black population in the renewal districts because their increasing Black population concentrations were perceived to be undesirable for private investment and the City’s cooperation with suburbs (Cleveland Metropolitan Services
Commission, 1959, pp. 64-65). Also, the interstate highway system destroyed many poor Black neighborhoods. As a result, the displaced poor Black families moved into some neighborhoods such as Hough, Mt. Pleasant, Miles Heights, and other nearby neighborhoods (Leahy & Snow, 1984; Miller & Wheeler, 1997, p. 166-167). 64
Figure 4.2 Concentration of the Poor and Black Population in Cleveland (Source: 1990 Census of Population and Housing)
65 The racial segregation ended up dividing the city’s neighborhoods largely into east and west sides as Black residents were segregated into the east side of the City
(Keating, 1995, p. 301; Miggins, 1996, pp. 20-21). Refer to Figure 4.3 to locate these neighborhoods.
4.2.4 Housing Surplus, Abandonment, and Massive Demolition
A housing research report prepared by Cleveland City Planning Commission
(1972a) suggested that the City’s decreasing population and increasing surplus of housing units increased the number of vacant units, creating an excess supply of housing. The surplus housing stock, combined with the high poverty rate, reduced housing rents to the point that it was impossible for landlords to profitably operate standard housing units.
In other words, many landlords who could not collect enough rent to adequately maintain their units had little choice but to watch as their houses became vacant, deteriorated, and eventually abandoned.
According to another report (Planning, 1972b) 21 , Cleveland had over 2,200 abandoned units as of 1972 and the rate of abandonment was expected to increase. The abandoned units had also become geographically widespread while the abandoned buildings lured drug dealers and forced families out of neighborhoods. As a result, many neighborhoods were plagued by abandoned housing and rampant vandalism. To address the problem, the city initiated massive housing demolition projects in the 1960s
21 The report was based on a housing abandonment survey undertaken by the City Planning Commission in 1972. They surveyed 11 neighborhoods (i.e., Statistical Planning Areas) that were labeled “abandoned neighborhoods with highest propensity to abandonment” including Near West Side, Tremont, Norwood, Glenville, Forest Hills, West Hough, East Hough, East Central, West Central, Kinsman, and East End. 66 (Stakes, 1995, p. 43). By the late 1980s, the city had demolished 75,000 properties, which resulted in many scattered vacant sites (Cleveland City Planning Commission,
1991, p. 30). Figure 4.4 shows the amount of vacant residential land by neighborhood in 1990.
Figure 4.3 Special Planning Areas Boundaries Note: Dashed line is East-West divide, along Cuyahoga River
67
Figure 4.4 Vacant Residential Land Distribution at the Neighborhood-level, 1990 (Source: Cuyahoga County tax records from Northeast Ohio Data and Information Service. Data was compiled by the author)
4.3 Urban Redevelopment
When the Housing Act of 1949 enabled American central cities to initiate large- scale urban renewal projects, Cleveland, one of the largest urban renewal program grantees, bulldozed approximately 6,000 acres of land including many Black, low- income residential neighborhoods to prepare sites for potential economic development projects. Cleveland’s urban renewal program, led by the Cleveland Development
68 Foundation, 22 provided huge quantities of vacant lands in the 1950s and the 1960s for rehabilitating the City’s east side including Downtown, employment centers, and residential neighborhoods including Garden Valley, Longwood, East Woodland,
University-Euclid, St. Vincent Center, Gladstone, and Erieview (Figure 4.5).
The massive slum-clearance program was assumed to induce private commercial, office, and market-rate housing development. The following decades of the 1970s and
1980s were devoted largely to economic development under the name of public-private partnerships (Keating et al., 1995, p. 309). Meanwhile, in the 1970s, the Famicos
Foundation 23 initiated a small community-based housing development in the Hough neighborhood. Also, the 1980s and 1990s were a blooming period for neighborhood redevelopment, which was supported by a variety of public and private institutions more systematically than ever before (Yin, 1998). Cleveland had several positive factors which stimulated the physical renewal of its neighborhoods such as numerous vacant parcels, downtown revitalization, and rejuvenating economic, cultural, and entertainment centers. In addition, the City’s aging housing stock 24 encouraged housing redevelopment projects (Cleveland City Planning Commission, 1991).
22 The Cleveland Development Foundation was a key institution consisted of some 100 corporation leaders for the urban renewal projects. It was formed in 1954 to assist private developers involved in urban renewal projects (Cleveland City Planning Commission, 1964, p. 8; Cleveland Metropolitan Services Commission, 1959, pp. 56-60; The Foundation Center-Cleveland, 2003). 23 Famicos (Family Cooperators) Foundation was the earliest neighborhood-based redevelopment organization in Cleveland which Sister Henrietta and Bob Wolf founded to promote homeownership opportunities in Hough neighborhood. This organization inspired and directed the formation of Cleveland Housing Network (Keating et al., 1996, p. 205). 24 By 1985, the typical single-family house in Cleveland dated from 1924, while the typical suburban house had been built in 1953 (Cleveland City Planning Commission, 1991, p. 29). 69
Figure 4.5 Urban Renewal Areas (Source: Planning in Cleveland: 1903-1963 , Cleveland Planning Commission, 1964)
4.3.1 The Redevelopment of Downtown and Subcenters
In the mid 1900s, the private sector in the City (e.g., the Cleveland Development
Foundation) wanted to make Downtown Cleveland once again the vibrant economic center for northeast Ohio and the nation. At the same time, the city government supported slum-clearance programs in the Downtown and nearby residential neighborhoods, hoping that private developers would develop the cleaned-up vacant lots
70 into profitable tax-generating land uses. As a result, the urban renewal projects were aimed primarily at Downtown and other cultural and industrial clusters including hospitals, education institutions, commercial and industrial interests and many service organizations (Miller & Wheeler, 1997, chapter 12).
After the urban renewal, tax abatement supported flagship projects in Downtown.
Governmental subsidies and civic boosterism (e.g., CDBG and tax abatement) and growth coalitions’ funds (e.g., Cleveland Tomorrow and Greater Cleveland Growth
Association) attracted a large amount of private capital for revitalization projects in
Downtown and other subcenters (Bartimole, 1995). In the late 1980s and the early
1990s, four major downtown revitalization or renovation projects were underway: the
Gateway project, Tower City Center, Playhouse Square, and North Coast Harbor
(American Institute of Architects, 1992). Many other economic, cultural, and entertainment redevelopments were located in and near downtown, Public Square, East
9th Street Corridor, the Flats, lakefront, and Euclid Avenue between downtown and cultural/medical areas around University Circle. Many obsolete, but structurally sound warehouses and factories were reused as condominiums and apartments in the Flats and the Downtown district.
71 4.3.2 The Redevelopment of Residential Neighborhoods 25
Housing redevelopment has been one of major elements of neighborhood redevelopment programs in Cleveland, as in many other inner cities, since the 1990s.
The housing redevelopment projects encompassed housing rehabilitation and infill housing development at both the small and the large scales. These projects were supported by the city government’s target area policies as well as other programs.
Neighborhood shopping centers were also promoted as an important tool for revitalizing neighborhoods. According to a city report (The City of Cleveland, 2001), during the
12-year period between 1990 and 2001) when longest-serving Cleveland Mayor Michael
White was in office, 2,303 new homes were built in neighborhoods and 36,674 homes were painted and renovated. Ten shopping centers and 5 grocery stores were also built or renovated in the 1980s and 1990s.
The most successful housing rehabilitation program in Cleveland was the
Cleveland Housing Network’s 26 (CHN) lease-purchase or rent-to-buy program. Under this program, the CHN purchased vacant single-family houses with sound structural foundations and rehabilitated them, either substantially or moderately, using subsidies from local foundations, the Local Initiative Support Corporation (LISC), the Enterprise
25 There are three institutions that provide comprehensive and detailed profiles for each of Cleveland’s neighborhoods (i.e., SPAs) on the World Wide Web. Those are NeighborhoodLink of Cleveland State University (http://www.nhlink.net/neighborhoodtour/nt.php), City of Cleveland (http://www.city.cleveland.oh.us/around_town/map/neighborhood/neighborhood.html), and Center on Urban Poverty and Social Change of Case Western Reserve University (http://povertycenter.cwru.edu/urban_poverty/dev/research/view_publications.asp?id=33&context=change). 26 CHN is an umbrella organization made up of many community-based development organizations in Cleveland. The organization provided member organizations with technical, financial, and managerial assistance (Krumholz, 1997). 72 Foundation, commercial banks, and the city (e.g., CDBG) and state (e.g., Low Income
Housing Tax Credit) governments (Krumholz, 1997, pp. 60-64; Krumholz, 1997, pp. 55-
57).
Many neighborhoods have made steady progress in the construction of infill housing over the last two decades. By the late 1990s, residents and City’s private developers started to realize a potential housing redevelopment market existed in
Cleveland’s neighborhoods (Lombardo, 2003; Sattin, 2003). Since the late 1980s, custom housing and small-scaled infill housing development have been gradually established as an important approach to neighborhood housing redevelopment process in
Cleveland. Individual homeowners were also encouraged to build their own homes by governmental financial incentives such as 15-year tax abatement (10 dollars a month) and land bank’s inexpensive vacant sites that were readily available (Lombardo, 2003).
Hough, Broadway, Ohio City, Union Miles, and more other neighborhoods have experienced significant housing redevelopment projects (Grogan & Priscio, 2000, pp. 79-
80). For example, in the Hough area, the local CDCs helped build 2,400 new housing units in the 1990s (ibid, p. 57). In the 1990s, landmark neighborhood housing development projects inspired other neighborhoods and community developers to pursue housing projects throughout the city. Hough was the most salient model of the transformation from a decaying slum to a multi-income vibrant neighborhood (Keating &
Smith, 1996, pp. 35-36). However, other important large-scale housing development projects completed in the 1990s include Lexington Village (277 dwelling units) in the
Hough neighborhood, Beacon Place (92 unit) and Bicentennial Village (118 dwelling units) in the Fairfax neighborhood, North Park Place in the Glenville neighborhood,
73 Central Commons (60 dwelling units) in the Central neighborhood, Mill Creek (217 units) in the Broadway neighborhood (EcoCity Cleveland, 2004; NeighborhoodLink 2004)
(Figure 4.6). 27
Figure 4.6 Important Large-scale Residential Projects, in the 1990s
The Cleveland city government targeted the most blighted neighborhoods for inner city redevelopment in the early 1980s. Five neighborhoods (i.e., SPAs) were identified by the City as the most distressed areas and designated as a target investment
27 These projects were supported and developed mainly by Neighborhood Progress, Inc. (the largest private non-profit development organization in Cleveland) and Zaremba, Inc. (the largest local housing developer in Cleveland). 74 areas. These areas were the Central, Hough, Fairfax, Kinsman, and University neighborhoods all of which were located east of downtown and the industrial valley . A consultant group from the Urban Land Institute (ULI) found both good and bad factors in these neighborhoods for neighborhood redevelopment (ULI, 1983, p.26-31). The positive location factors were: (1) important employment centers, i.e., Cleveland State
University, the Cleveland Clinic, and University Circle area; (2) close proximity to downtown; (3) a strong institutional and cultural base (i.e., Playhouse Square, the Flats, the Warehouse District, and Public Square); (4) abundant vacant land; and (5) existing community-based organizations with a strong commitment for neighborhood redevelopment. The negative factors included: a high crime rate and a lack of jobs.
Moreover, the ULI group found some subareas in the target neighborhoods had a good potential for housing redevelopment. They recommended in particular that the Hough and Fairfax neighborhoods should be targets for residential redevelopment in the immediate future (ULI, 1983, p. 26).
Neighborhood commercial and retail development was also seen as an important element for improving the quality of life in inner city neighborhoods. Cleveland’s neighborhoods, however, still suffered from a lack of adequate neighborhood retail stores.
Therefore, the City made a significant effort to attract and retain neighborhood retailers and subsidized the construction of neighborhood shopping centers over the past two decades (City of Cleveland, 2001, p. 12). Shopping centers developed in the 1980s and
1990s includes Brooklyn Center Shoppes (1993) in Brooklyn Center, Buckeye Commons
(1990) in Woodland Hills, Church Square (1993) in Hough, Five Points Shopping Plaza
(the 1980s) in South Collins, Glenville Plaza (1991) and East Side Market (the 1980s) in
75 Glenville, Market Plaza (1989) in Ohio City, Midtown Square (the 1980s) in Hough,
Miles Avenue Shopping Plaza (1990) in Corlett, and Westown Square Shopping center
(The 1980s) in Cudell (Figure 4.7). Seven of the ten shopping centers were located in the east side.
Figure 4.7 Newly Built Shopping Centers in the 1980s and 1990s Source: Civic Vision 2000 Citywide Plan . (Cleveland City Planning Commission, 1991) Cleveland’s neighborhoods in 1990s and beyond (The City of Cleveland, 2001)
During the 1980s and the 1990s, great strides were made for Cleveland’s neighborhood redevelopment by creative and effective public-private partnerships between CDCs, governments, financial institutes, and foundations, which together
76 created an institutional network or a civic infrastructure for neighborhood redevelopment.
The institutional network initiated and provided a variety of subsidies and supports for urban housing development. This institutional support system is reviewed in the next section.
4.3.3 Institutional Support System for Neighborhood Redevelopment 28
Since the 1980s, CDCs 29 and their nationally acclaimed institutional network including governments, private lenders, financial intermediaries, foundations, and corporations have made significant contributions to Cleveland’s neighborhood redevelopment and housing development (Bogart, 2003). These institutions supported community-based housing developers, financially, politically, and technically. The CDCs were pioneers and major players that attempted to address the problem of the inner city neighborhood revitalization movement of inner city neighborhoods with many market, social, and political failures (Vidal, 1995, p. 208). Unlike for-profit developers, the
28 Rubin (2000, chapter 5) discussed the institutional support systems for community-based development organizations. The system included financial intermediaries, foundations, and governmental agencies across the country, which he called “support organizations.” This section describes the same types of institutional network involved in Cleveland neighborhood redevelopment. 29 Community development corporations (CDCs) were generally called community-based development organizations (CBDOs) by Rubin (2000). There were about 40 CDCs in Cleveland in the 1990s. They have acted either as developers or as mediators between financial sources and developers. They have received both operational and capital funds from government, private foundations, or intermediates. CDCs have also collaborated with neighborhoods and city planners to develop neighborhood plans focusing on housing redevelopment and economic development. The CDCs were pioneers and major players in the revitalization of inner city neighborhoods with many market, social, and political failures (Vidal, 1995, p. 208). Unlike for-profit developers, the socially-responsive CDCs worked in distressed urban neighborhoods with no visible housing market and economic strength (Sattin, 2003). 77 socially-responsive CDCs worked in distressed urban neighborhoods with no visible housing market and economic strengths (Sattin, 2003).
In the 1980s, responding to the federal government’s budget cutback for low- income housing assistance, an innovative institutional environment emerged in Cleveland for providing public and private funds for neighborhood revitalization and housing development (Darwiche, 1995, pp. 29-32; Keating et al., 1989, pp. 133-136; Keyes et al.,
1996; Suchman et al., 1990; Vidal, 1995, p. 218). The institutional support system for
Cleveland’s neighborhood revitalization was stabilized rapidly in the 1990s.
The CDCs played a major role in this effect by providing affordable housing for communities that had been abandoned and ignored by for-profit developers, investors, and absentee landlords (Grogan & Proscio, 2000, pp. 79-80). In addition, the city, along with the state and federal governments, subsidized the Cleveland’s housing development industry in many ways ranging from land banking and infrastructure improvement to tax incentives and CRA enforcement.
Foundations such as Cleveland Tomorrow and the Cleveland Foundation also provided financial, technical, and political support for neighborhood redevelopment largely through the CDC-led projects (Rubin, 2000, pp. 86-88). A variety of financial and technical assistance programs were brought to the CDCs and private infill housing developers by national or local foundations and non-profit financial intermediaries such as the Local Initiative Support Corporation (LISC), the Enterprise Foundation,
Neighborhood Progress, Inc. (NPI), the Cleveland Housing Network (CHN), and the
Cleveland Neighborhood Development Coalition (CNDC) (Darwiche, 1995; Keating,
Krumholz, & Metzger, 1995; Walker, 1993; Warren, 1995; Yin, 1998).
78 The NPI 30 distributed operating funds to CDCs according to the CDCs’ capacity.
Also, NPI provided development financing and partnered with CDCs and private developers for large-scale residential development throughout the City. National intermediaries such as the LISC and the Enterprise Foundation also provided significant support, i.e., loans, grants, technical assistance, training, and information, to the CDCs and private developers in the City.
4.4 Summary
This chapter has reviewed the process and spatial pattern of Cleveland’s decline and redevelopment efforts at the city and neighborhoods level. It also discussed the
City’s institutional support system for neighborhood redevelopment and housing development.
Cleveland is a prototypical central city in the rustbelt of industrial heart of
America. Like many post-industrial rustbelt cities, Cleveland has struggled to recover from a declining employment and population base since the mid 1950s. The City suffered from devastating processes of suburbanization, urban renewal program, and racial segregation. As a result, many of the City’s residential neighborhoods suffered from decaying and abandoned housing stock and the concentration of its minority and poor population. The City’s east-west racial divide was a spatial reflection of the urban decline and suburbanization. The urban decline process also resulted in a vast amount
30 NPI, a nonprofit neighborhood development organization, was originally supported by BP America; Cleveland Foundation, George Gund Foundation, Ford Foundation, and Cleveland Tomorrow (Krumholz, 1997, p. 61). The NPI played dual roles, both a financial intermediary and a developer, in many neighborhood redevelopment projects in Cleveland. 79 of abandoned homes and vacant properties, many of which have been acquired and cleared by the City in order to create real estate development opportunities.
Cleveland’s major urban redevelopment projects started with a federally-fund
Urban Renewal program in the 1950s. The urban renewal program and subsequent urban revitalization projects focused primarily on business-oriented economic development. However, in the 1970s strong neighborhood-based organizations started to promote low-income neighborhood redevelopment and affordable housing development. Cleveland’s infill housing development started with custom houses in some neighborhoods such as Hough and Ohio City neighborhoods in the late 1980s.
After discovering the feasibility of urban infill housing market in those neighborhoods, developers started projects in many other neighborhoods, and began affordable or even market-rate housing development projects. In the late 1990s, many neighborhoods had gained significant momentum for residential development, both rehabilitation and new construction.
Meanwhile, during the 1980s and 1990s, the City established an important institutional supporting system for community redevelopment consisting of private developers, community-based development organizations, governmental agencies, financial intermediaries, private lenders, and philanthropic foundations. The institutional support has been a very important element of Cleveland’s urban redevelopment and housing development because decades-old urban problems and disinvestment could not be overcome by private developers, neighborhood activists, or the government. The details of the institutional support system were reviewed in the
Chapter 3.
80
CHAPTER V
CONCEPTUAL FRAMEWORK AND HYPOTHESES
5.1 Introduction
The main research question of this study is identifying the traditional urban modeling factors and non-traditional or institutional factors which had a significant association with the location of urban infill housing development in the City of Cleveland during the decade of the 1990s. To identify these factors, this study first critically reviewed the theories of urban modeling and urban economics which proposed a variety of factors determining the spatial pattern of urban and regional development and land use changes. It then reviewed practical theories and the history of urban redevelopment and housing development with a particular emphasis on the study area, the City of Cleveland.
The review suggested that urban models and economic theories have not adequately incorporated institutional policy factors that can be assumed to be associated with the location of the urban housing development.
Based on the preceding chapters, this chapter presents a conceptual framework for analyzing the factors that are associated with the spatial pattern of urban housing development. The framework is based on the standard location theories of urban
81 models and economics and the urban redevelopment experience of Cleveland of the
1990s. Finally, it presents a series of research hypotheses.
5.2 Conceptual Framework
In general, the location of residential real estate development depends on the market demand for a particular neighborhood and on the suitability of a particular site within that neighborhood. Features such as accessibility, amenities, land use mix, socioeconomic status, and site characteristics are assumed to determine the attractiveness and suitability of neighborhoods and sites, which in turn determine the location of housing development. This is an underlying assumption of traditional urban location models. It is typically the case in market-driven areas in growing urban areas and in suburban greenfields.
However, in many stagnant or declining cities of the U.S., the neighborhood and site characteristics may not be the major elements in selecting the location of infill housing development. This may be the case because many cities do not have strong market demand, which makes development financing very difficult under banks’ conventional underwriting methods. Site acquisition and assembly is another obstacle for urban housing developers, because it is often very hard to find absentee landowners, to obtain free and clear title, and to assemble an adequately large development site. In
American cities experiencing urban problems such as population and job loss, deteriorating housing stock, and decaying neighborhoods, many neighborhoods become unattractive housing locations, while their individual sites have many shortcomings such
82 as inadequate size, entangled title issues, contaminated soil, and high purchase price, which together challenge the housing development in those cities.
These obstacles make it difficult for urban infill housing developers to make money from new housing development. The costs for correcting these problems are often beyond the project revenue for many urban housing development projects, eventually creating negative operating cash flow.
As a result, urban housing developers need financial support from a variety of institutions at various levels including governments, nonprofit community-based organizations, for-profit lenders, philanthropic foundations, and nonprofit financial intermediaries to make urban housing development feasible. Urban housing development projects often rely on public-private partnerships to tap into a variety of political and financial resources from government, nonprofit, and for-profit institutions.
Overall, urban housing development has become as much a function of institutional policy factors as it is of market factors. Therefore, institutional factors are assumed to have significant implications for the location of urban housing development as shown in the conceptual framework presented in Figure 5.1.
Moreover, urban housing developers and their partners in struggling cities often have a unique motive for providing the urban housing in addition to the traditional profit- making motive. This motive is the vision of revitalizing distressed urban communities by improving their living environments and economies in order to benefit the lives of the residents in the communities. Providing decent affordable housing is a very important issue on which a variety of organizations collaborate to mobilize political and financial resources. That is why the public-private partnerships of urban housing development
83 focus on weak-market neighborhoods experiencing significant disinvestment and political and or social disadvantages. They strive to empower the failing communities and mobilize a variety of resources for improving the physical, social and economic status of those communities.
Demand-Side Supply-Side Institutional
Location Factors Location Factors Support
Location Developer’s Capacity Operating Funds (e.g., Accessibility, Proximity CNPP) to Amenities (Externalities), Neighborhood Development Capital Neighborhood Characteristics Construction and Permanent Reinvestment Financing Agreement Empowerment Zone LIHTC, CDBG, HOME Developable Land Site Acquisition (Price, Title Transfer, etc.), Site Assembly Potential, Extraordinary Site Land Bank Program Costs (e.g., Soil Local Land Use Plan Contamination)
LOCATION OF URBAN INFILL HOUSING
Key : Arrows show the linkages between factors or between a factor and the location of urban infill housing.
Figure 5.1 Conceptual Framework of the Location Choice of Urban Infill Housing
84 5.2.1 Institutional Factors
Some of the institutional factors assumed to be significantly associated with the location of urban infill housing development are listed below. The list does not include important policy factors which do not have geographically explicit impacts on the location choice of infill housing development, such as resident tax abatements, lower- than-market-rate mortgages or soft subordinate mortgage loans, and government’s development cost write-downs. Within the parentheses are the assumed correlation between the factors and the neighborhood-level infill housing development rate or parcel- level development potential.
I. Community Reinvestment Act : Under the CRA, local banks are obliged to provide
loans and investments for urban neighborhoods in which housing has been
disinvested as a result of discriminated lending practices based on race and income.
Therefore, the regulations target and encourage new housing development in
neighborhoods with minority and low-income concentration. The location of these
areas in Cleveland is shown in Figures A.1 and A.2. (Positive association)
II. Land Reutilization Program or Land Banking : Some cities acquire and clean up
abandoned or vacant properties in order to make them ready for real estate
development at a minimal price. As a result, the land bank sites in Cleveland are
location-specific programs as shown in Figure A.3, which encourage new urban
housing development in locations that are designated as land bank sites. (Positive
association)
III. City-wide Land Use Plan : Local governmental land use plans are meant to guide
and lead the future development of a city including housing development. As a
85 result, the Cleveland city plan would influence the spatial pattern of urban housing
development in as shown in Figure A.18. (Positive association)
IV. Operating Fund Subsidy : Some organizations provide operating funds for
community-based development organizations. In Cleveland, the Neighborhood
Progress, Inc. (NPI) administers an important operating grant program named the
Cleveland Neighborhood Partnership Program (CNPP). The program awards
significant operating fund to CDCs that have strong capacity and development plans.
The CDCs that receive the competitive operating support assumed to be active in
housing development than other CDCs. The CDCs in Cleveland have their own
service boundaries as shown in Figure A.4. Therefore, the service areas of the
CNPP grant recipients can be assumed to experience more housing development
than other areas. (Positive association)
V. Empowerment Zone : Empowerment Zone (EZ) is an economic development
program which provides special tax incentives and other funds to encourage
businesses to revitalize blighted, distressed, and the poorest areas in inner cities.
The federal economic development support can be assumed to encourage and
support a variety of industrial and development activities including housing
development within the grant zone in Cleveland as shown in Figure A.5. (Positive
association)
VI. Ward Politics : Wards are the primary political divisions within an American
municipality. Cleveland has 21 ward districts as shown in Figure A.6. As
discussed before, some scholars suggested the inclusion of wards on an assumption
that the councilmen’s decision-making power and political influence were
86 important in determining the allocation of ward funds and other subsidies for
residential development within their districts. (Unknown)
5.2.2 Non-institutional Factors
An underlying assumption of traditional urban location model and economic theory is that neighborhood and site characteristics determine the market demand for a neighborhood and the development feasibility of a site. Supply is assumed to follow demand. The following list identifies variables that have been frequently employed by the urban land use change models. Since those factors were examined in detail in the
Chapter 2, this section will present only a brief list of the factors.
5.2.2.1 Neighborhood Characteristics
I. Accessibility a. Proximity to employment centers or central business district, as shown in Figure A.7 (Negative association) II. Land use mix a. Percent vacant developable land, as shown in Figure A.14 (Positive association) b. Percent residential land, as shown in Figure A.15 (Positive association) c. Percent recent development activities, as shown in Figure A.16 (Positive association) III. Demographic/Socioeconomic/Housing condition a. Percent White population, as shown in Figure A.8 (Positive association) b. Population increase, as shown in Figure A.9 (Positive association) c. High education graduation rate, as shown in Figure A.10 (Positive association) d. Median household income, as shown in Figure A.11 (Positive association) e. Population density, as shown in Figure A.12 (Positive association) f. Crime rate, as shown in Figure A.13 (Negative association)
87 5.2.2.2 Site Characteristics
I. Site Physical Features a. Lot size (Positive association) b. Residential vacant land (Positive association) II. Accessibility a. Proximity to city center or central business district, employment centers, and shopping centers, as shown in Figure A.19 (Negative association) b. Proximity to transit station, as shown in Figure A.19 (Negative association) III. Amenities/Disamenities a. Proximity to recreational facilities, as shown in Figure A.24 (Negative association) b. Environmental or cultural amenities, as shown in Figure A.24 (Negative association) c. Proximity to disamenities such as noise, pollution, heavy manufacturing, and landfill, as shown in Figures 22 and 23 (Positive association) IV. Site Assembly Potential: a. Adjacency to land bank sites or vacant land (Positive association) V. Other Positive Externality a. Proximity to recent housing development sites as shown in Figure A.21 (Positive association)
5.3 Hypotheses
According to traditional urban modeling theory, urban infill housing developers would select neighborhoods that have the attractive features needed to draw sufficient demand for urban housing. The theories also assume that developers will select sites that have good access to amenities and only modest site preparation costs. However, this study assumes that institutional factors are also significantly associated with the
88 location of urban housing development and more so than the neighborhood and site characteristics assumed by traditional urban models.
5.3.1 Central Hypotheses
As reviewed in Chapter 2, urban models usually emphasize neighborhood and site characteristics and some regional-scale policy factors. However, this study assumes that the traditional factors of the urban models are not sufficient for modeling the location of urban housing development in cities such as Cleveland. Therefore, this study focuses on two central hypotheses as follows.
Central Hypothesis 1 :
H0: There is no association between the institutional factors and the location of
urban infill housing development.
H1: There is a significant association between institutional factors and the
location of urban infill housing development.
This hypothesis simply states that urban housing development projects require a variety of resources from local governments and many other organizations in order to supplement market-driven private investment. This assumption reflects that fact that political, financial, and technical support from governments and other organizations is very helpful for the urban housing developers who face many obstacles such as weak market demand and complicated land assembly. With its focus on the location of development, the study looks at those institutional factors that have location-specific implications such as city land use plan, land bank program, and target neighborhood reinvestment program. It is assumed that these institutional policies or programs would significantly affect the location of urban housing development.
89 Central Hypothesis 2 :
H0: The association between the location of urban housing development and institutional factors is equal to that between the location of urban infill housing and non- institutional factors.
H1: The location of urban housing development is more significantly associated with its institutional factors than with the non-institutional ones.
The second hypothesis focuses on the comparison between institutional and non- institutional factors with regard to their association with the location of urban housing development. The basis of the hypothesis is an assumption that the needs for the subsidies and incentives from governments and other institutions are crucial for urban housing development in weak urban markets. Therefore, the institutional factors are assumed to be more strongly associated with the location of the urban housing development than the market-driven or non-institutional factors.
5.3.2 Institutional Factor Hypotheses
While the central hypotheses reflect the main research questions of this study, this section explains the assumptions about the individual institutional factors specifically applied to the study area, Cleveland. Chapters 3 and 4 provided an extensive discussion of these institutional policies and programs. The conceptual framework of this chapter summarized the assumptions about the institutional factors. As a result, this section provides a brief statement of each hypothesis.
90 5.3.2.1 City Plan
H0: There is no correlation between the location of the potential residential development sites identified in the city land use plan and the location of urban housing development.
H1: Parcels that are located in the potential residential development sites identified in the city land use plan are more likely to be developed than those in other areas.
5.3.2.2 Land Bank
H0: There is no association between the location of land bank sites and that of urban infill housing development.
H1: Land bank sites and sites that are adjacent to land bank sites are more likely to be developed than other sites.
5.3.2.3 Neighborhood Reinvestment Agreement (NRA)
H0: There is no association between the location of NRA target neighborhoods and that of urban infill housing sites.
H1: Parcels that were located in the neighborhoods targeted by the NRA are more likely to be developed than other areas.
5.3.2.4 Cleveland Neighborhood Partnership Program (CNPP)
H0: There is no association between the location of CNPP recipients’ service neighborhood and urban infill housing development.
H1: Parcels located within the service areas of CDCs funded by CNPP grants are more likely to be developed than other sites.
91 5.3.2.5 Empowerment Zone (EZ)
H0: There is no association between the location of the EZ targeted neighborhoods and the location of urban housing development.
H1: Parcels located within the EZ target neighborhoods are more likely to be developed than other sites.
5.3.2.6 Ward
H0: There is no association between the location of wards and urban infill housing development.
H1: There is a significant association between wards and urban infill housing development. The correlation may be positive in some wards and negative in other wards.
5.4 Summary
Based on the literature review in the preceding chapters, this chapter provided a conceptual framework for the location of urban housing development in weak market cities such as Cleveland. As observed in the urban redevelopment history of Cleveland, the declining city relied heavily on public subsidies, public-private partnerships, nonprofit intermediaries, local foundations, and community-based development organizations in revitalizing its troubled neighborhoods and houses.
Urban housing developers need a high level of commitment to go through complex mixed-financing deals that are often crucial for urban housing development.
Other difficult issues such as land acquisition/assembly, environmental problems, and discriminatory lending practices have also created significant roadblocks to urban housing development. Many urban housing developers face unusually high risks of 92 doing business in weak-market with many challenging problems compared to the greenfield developers. In many American cities, the extra development costs such as high land value, onerous land acquisition process, and environmental remediation could hardly be compensated for the income stream drawn from low or moderate-income urban housing buyers or renters.
Institutional intervention in the failing urban housing market is therefore thought to be critical for resource-poor urban housing market. Therefore, the major hypotheses regarding the study area are: (1) non-traditional institutional factors are assumed to be closely associated with the location of urban housing development in Cleveland in the
1990s and (2) the institutional factors are assumed to be more significantly associated with the location of the urban housing development than the traditional non-institutional factors. These hypotheses will be examined in Chapters 7 and 8.
93
CHAPTER VI
RESEARCH METHOD AND DATA SOURCES
6.1 Introduction
The purpose of this research is to identify institutional and non-institutional factors that were significantly associated with the location of urban housing development in declining cities such as Cleveland, Ohio, during the 1990s. This study also compares the importance of traditional urban modeling factors (i.e., neighborhood and site characteristics) and institutional factors regarding their effect on the location of urban infill housing development. Given the nature of the research objectives and the urban modeling discipline, this study used quantitative research methods including standard statistics such as multiple regression, logit regression, correlation analysis, means difference test, and cluster analysis. The location of the urban housing development is analyzed at two geographic levels, i.e., at the neighborhood and the site levels, which represent the geographic units of the majority of urban models. The multiple regression and other supplementary statistics are used to measure the association between the percentage of neighborhood (or census tract) land area developed for housing and the values of the neighborhood-level factors. The site level analysis uses logit regression
94 method to examine the association between the development potential of a parcel and parcel-level factors.
In order to further examine the association between the study factors and the location of urban housing development, this study will examine two time periods, 1990-
1995 and 1995-2000. The later half of the 1990s witnessed more housing development activities. This was probably because of the momentum gained by successful housing development in some neighborhoods and the strong engagement of the city government and public and private partnerships of the late 1980s and early 1990s. Also, many
CDCs seemed to have gradually built up their development capacity over the study period while private developers probably learned that there were profitable underserved housing markets in many urban neighborhoods. The growing maturity of institutional programs and the renewed appreciation for the location advantages of urban neighborhoods may also have influenced the development patterns in the later half of the
1990s.
The study uses a variety of spatially-referenced data. This chapter briefly describes the variables and their sources.
6.2 Analysis Methods
At the neighborhood level, the study will use t-test and multiple regression techniques to investigate the association between neighborhood-scale factors and neighborhood-level housing development rates. At the parcel level, the study will use the binary logit regression method to examine the relation between parcel-level factors and parcel’s development possibility. 95 6.2.1 Neighborhood-level Analysis
In this study census tracts are used as a proxy for neighborhoods. In Cleveland, there were many neighborhoods without any urban infill housing development in both study periods. Accordingly, the dependent variable, the neighborhood’s housing development rate, includes many zero observations. 135 of the 224 neighborhoods had zero housing development rates (i.e., no housing development) between 1991 and 1995 while 88 neighborhoods had no development between 1996 and 2000. There are several possible several explanations for these patterns. A major reason is that the urban housing development market was immature in Cleveland during the study period. As a result, not every neighborhood was appreciated by developers. That is, even though many undeveloped neighborhoods may have been appropriate for housing development, they had little chance to appeal to housing developers. Also, many stable affluent neighborhoods would not need any new housing development. As a result, in this situation of, any statistical analysis at the citywide level may underestimate the importance of some important factors.
The many zero values may also make the regression statistical analysis of neighborhood housing development rates significantly skewed. Therefore, the study will use two steps to address the problem of many-zero-valued dependent variable. First, a means difference test (i.e., a t-test) will be utilized to compare the neighborhood-level factors between the neighborhoods with zero development rates and those with non-zero rates. The main reasons for using the t-test instead of a binary logit regression are that the means difference test is easy to interpret and a preliminary analysis using the logit regression did not provide a statistically significant model fit. Second, the study will
96 use the multiple regression method only for the non-zero neighborhoods. In addition, as the focus of this study is on individual factors, the study will use other standard statistical tests to supplement the regression analyses.
Lastly, the study will conduct a spatial cluster analysis using a number of significant factors to visualize the clusters of neighborhoods based on the factors. It will then compare the factor-based spatial clusters with the observed urban housing development distribution to visually evaluate the association between the statistically significant factors and the neighborhood housing development rates.
6.2.1.1 T-Test
This study will use the simple statistical technique, the standard t-test, to evaluate the difference between the zero and non-zero neighborhoods. The research design for the t-test is relatively simple. First, the neighborhoods will be divided into two groups, i.e., those with no housing development and those with housing development (or zeros and non-zeros). Then, the means of the factors between the two groups of neighborhoods will be compared. If the means difference is found to be significant, it can be assumed that the factors help differentiate between the zero and non-zero neighborhoods.
6.2.1.2 Regression Analysis
The study will use multiple regression analysis to examine the neighborhoods with non-zero housing development rates. There were 88 non-zero neighborhoods in the first study period (1990-1995), and 137 non-zero neighborhoods in the second period
(1995-2000).
97 The study expects there to be a high correlation between some of the institutional and non-institutional factors, a multicollinearity problem. For example, the minority- based target neighborhoods of NRA program and the minority-concentrated neighborhoods should be highly related. Therefore, the study will conduct separate regression models for the institutional and non-institutional factors in order to reduce the multicollinearity problem. Variance influence factor (VIF) and tolerance are common indices for multicollinearity. As a rule of thumb, VIF values larger than 10 and tolerance smaller than 0.1 are considered problematic (Myers 1990). However, Allison
(1999) consider VIF of 2.5 and tolerance of 0.4 to be problematic in multiple regression.
Garson (2005) also sets a different cutoff for VIF, i.e., 4.0. Therefore, this study will not rely too much on the traditional tests of multicollinearity. Instead, the study will supplement the regression analysis with bivariate correlation statistics in order to address potential distortion of regression statistics resulting from significant multicollinearity.
6.2.1.3 Cluster Analysis
The study will use k-means cluster analysis technique which is frequently used in spatial cluster research. The study will attempt to identify three spatial clusters of neighborhoods by applying the method. The cluster map will be compared to the maps of the distribution of urban housing development. Visual inspection will therefore help verify the significance of the associations between the neighborhood-level factors and the neighborhood-level distribution of housing development. Moreover, the parcel-level analysis of the next chapter will be conducted for each neighborhood cluster in order to eliminate the need for taking the neighborhood variables and their interaction effects into consideration in the parcel-level regression models.
98 6.2.1 Parcel-level Analysis
Since this study uses a binary dependent variable (i.e., developed or undeveloped) for the parcel-level analysis, it employs the logit regression method, which is the most popular regression technique for modeling dichotomous dependent variables (Kleinbaum,
1998, p. 656). The regression analysis technique will help identify factors which were significantly associated with the parcels’ binomial probabilities of being developed with infill housing in the 1990s in Cleveland. Again, the dependent variable of the regression model is each parcel’s binomial changes, i.e., developed or not developed as urban housing.
The logit regression of this study can be expressed as follows.
Prob(infill) Z = log= a + b X + b X i 1− Prob(infill) i i j j
Zi : the odds that a parcel would be developed as an infill housing Prob(infill): the probability that a parcel would be developed as an infill housing a : intercept bi , bj : coefficients
Xi: a vector of parcel-level institutional factors Xk: a vector of parcel-level non-institutional factors
The model originally assumes a linear relationship between the odds and the factors. The vector of institutional factors includes four variables, i.e., whether the parcel is located in: (1) potential residential development areas identified by city-wide land use plan; (2) service areas of CNPP grant recipient CDCs; (3) land bank program; and (4) NRA’s target neighborhoods. The vector of non-institutional factors includes a variety of accessibility, land use mix, demographic condition, and amenity variables.
99 As in the neighborhood-level analysis, the parcel-level data has a problem with the data. Of the total 175,993 parcels, only 1,835 parcels (1.04%) were developed in the study period. As a result, the study used a sampling method to address the data’s skewed distribution. The sampling process is discussed in detail later in this chapter.
6.3 Data
6.3.1 Some Issues
This section discusses some important considerations in gathering and using the data sources. Parcel boundaries in the study area have changed during the 1990s.
However, Cuyahoga County and the City of Cleveland have not tracked and recorded the changes in these GIS data layers since the 1995. In fact, the only year for which parcel- level GIS data are available for Cleveland is 1994. Time and cost constraints prohibited the study from attempting to correct the spatial information for every parcel of the city.
The study considered the spatial pattern of the parcels with missing information (e.g., parcels with no land use information) to be random.
The data used in this study were spatially-referenced (or geocoded) and stored and managed in a geographic information system (GIS) using a standard GIS software package 31 . Some map layers (e.g., historical districts) were digitized. Other layers
(e.g., parcel boundary, neighborhood and political boundaries, and physical and natural features) were collected in their original GIS data format. All the data were processed to summarize it for different spatial units, i.e., the site and neighborhood levels
(Appendix).
31 This study used popular GIS software packages, i.e., ArcGIS 8.3 and ArcView 3.3 of ESRI, Inc. 100 Data were also gathered from a variety of secondary data sources including the
U.S. Bureau of Census, the Cleveland State University GIS data center, local governmental agencies, and community data warehouse. 32 Basic parcel-based information, i.e., the parcel boundary and parcel attribute tables between 1990 and 2000, were provided by Northeast Ohio Data and Information Service (NODIS) at Cleveland
State University. The map layers of all factors are presented in Appendix.
6.3.2 Variables
As noted above, factors are categorized into two geographic levels, i.e., parcel and neighborhood levels. Tables 6.1 through 6.4 show the variables to be analyzed in the following analysis chapters.
Table 6.1 Neighborhood-level Institutional Factors
Variable Description Unit Year NRA_MINOR NRA target census tracts: minority concentration Yes (1) or no (0) 1994 NRA_INCOME NRA target census tracts: poverty concentration Yes (1) or no (0) 1994 P_LBANK90 (95) Land bank sites Yes (1) or no (0) 1990 (1995) CNPP_90 (_95) Service areas of CNPP-assisted CDCs Yes (1) or no (0) 1990 (1995) EMPZONE Empowerment Zone Yes (1) or no (0) 1994 WARD# 21 Wards, 20 dummy variables Yes (1) or no (0) 1990
32 The study used three major online data warehouses: NeighborhoodLink ( http://www.nhlink.net/ ); Case Western Reserve University CAN DO (http://cando.cwru.edu/ ); and CleveInfo ( http://www.cleveinfo.com ). 101 Table 6.2 Neighborhood-level Non-Institutional Factors
Vectors/Variables Description Unit Year Accessibility TRAVELTIME Average commuting time for census tracts minutes 1990 Land Use Mix P_VACANT90 Percentage of residential vacant land out of total % 1990 (1995) (_95) census tract land area P_RESD90 (_95) Percentage of residential land out of total census % 1990 (1995) tract land area P_NEW8690 (9195) Percentage of recent housing development area out % Between of total census tract land area, developed between 1986 and 1986 and 1990 (1991 and 1995) in census tracts 1990 (1991 and 1995) Demographic 8090POPGRO Population growth rate % Between 1980 and 1990 P_WHITE Percent white in census tracts % 1990 P_COLLEGE Percent college graduates in census tracts % 1990 MED_INCOME Average median household income in census tracts currency ($) 1990 DENSITY Population per acre in census tracts number of 1990 people per acre CRIME90 (95) Incidents of violent crimes per 100,000 people in number 1990, 1995 census tracts
Table 6.3 Parcel-level Institutional Factors
Vectors/Variable Description Unit Year PLANRES_1 Potential residential development sites identified Yes (1) or no (0) 1990 by CivicVision 2000: Within 0.25mile of the potential sites PLANRES_2 Ditto: Within 0.5 mile of the potential sites Yes (1) or no (0) 1990 L_BANK90(95) Land bank sites between 1990 and 1995 (1995 and Yes (1) or no (0) between 1990 2000) and 1995 (1995 and 2000) ADJ_LBANK90(95) Adjacency to land bank sites Yes (1) or no (0) between 1990 and 1995 (1995 and 2000)
102 Table 6.4 Parcel-level Non-Institutional Factors
Vectors/Variables Description Unit Year Site LOTSIZE90 (95) Square feet of each parcel in 1990 (1995) Square feet 1990, 1995 VACANT Residential vacant land in 1990 (1995) Yes (1) or No (0) 1990, 1995 Accessibility TRANSIT_1 Being located within 0.1 mile from rapid transit Yes (1) or No (0) 1990 stations TRANSIT_2 Being located within 0.25 mile from rapid transit Yes (1) or No (0) 1990 stations D_CBD Distance from central business district Feet 1990 D_EMPLOY_1 Being located within 0.5 mile from employment Feet 1990 centers including Metrowest, W117/Brook Park, University Circle, and Hopkins Airport D_EMPLOY_2 Being located within 1 mile from the employment Feet 1990 centers L_SHOP90_1 (95) Being located within 0.1 miles from local, medium Yes (1) or No (0) 1990 (1995) shopping centers L_SHOP90_2) (95) Being located within 0.25 miles from local, Yes (1) or No (0) 1990 (1995) medium shopping centers N_SHOP90_1 (95) Being located within 0.1 miles from local, medium Yes (1) or No (0) 1990 (1995) shopping centers N_SHOP90_2 (95) Being located within 0.25 miles from local, Yes (1) or No (0) 1990 (1995) medium shopping centers Amenity D_RECREAT_1 Being located within 0.1 from recreational centers Yes (1) or no (0) 1990 D_RECREAT_2 Being located within 0.25 from recreational centers Yes (1) or no (0) 1990 D_RECREAT_3 Being located within 0.5 from recreational centers Yes (1) or no (0) 1990 PARK_1 Being located within 0.1 mile from local parks Yes (1) or no (0) 1990 PARK_2 Being located within 0.25 mile from local parks Yes (1) or no (0) 1990 PARK_3 Being located within 0.5 mile from local parks Yes (1) or no (0) 1990 HISTORIC_1 Being located within 0.1 mile from national historic Yes (1) or no (0) 1990 districts and Cleveland landmark districts HISTORIC_2 Being located within 0.25 mile from national Yes (1) or no (0) 1990 historic districts and Cleveland landmark districts HISTORIC_3 Being located within 0.5 mile from national historic Yes (1) or no (0) 1990 districts and Cleveland landmark districts Disamenity D_RAIL_1 Being located within 0.1 mile from railroad tracks Yes (1) or no (0) 1990 D_RAIL_1 Being located within 0.25 mile from railroad tracks Yes (1) or no (0) 1990 LUST90 (95) Being located within 250 feet from leaking Yes (1) or no (0) 1990 underground storage tanks TRI90 (95) Being located within 250 feet from Toxic Release Yes (1) or no (0) 1990 Inventory sites INDUST90_1 (95) Being located within 0.1 mile from heavy industrial Yes (1) or no (0) 1990 (1995) land use INDUST90_2 (95) Being located within 0.25 mile from heavy Yes (1) or no (0) 1990 (1995) industrial land use Site Assembly Potential ADJ_RVAC90 (95) Being adjacent to residential vacant lots Yes (1) or no (0) 1990 (1995)
103 Table 6.4 Parcel-level Non-Institutional Factors (Continued)
Vectors/Variables Description Unit Year Other Positive Externality RES8690_1 Being located within 0.1 mile from the closest Yes (1) or no (0) 1986-1990 recent housing units developed between 1986 and 1990 RES8690_2 Being located within 0.25 mile from the closest Yes (1) or no (0) 1986-1990 recent housing units developed between 1986 and 1990 RES8690_1 Being located within 0.1 mile from the closest Yes (1) or no (0) 1991-1995 recent housing units developed between 1991 and 1995 RES8690_2 Being located within 0.25 mile from the closest Yes (1) or no (0) 1991-1995 recent housing units developed between 1991 and 1995
6.3.3 Filtering: Finding Developable Parcels
A considerable number of parcels were not developable in the City during the study period. Therefore, before conducting the regression analysis, the study filtered out parcels that were not buildable during the 1990s by using a GIS-based land supply monitoring technique (refer to Chapter 2). Using the GIS-based land supply monitoring system, the study eliminated parcels that were not-developable in terms of natural hazards, topography, suitability, and the like. As a result, this method reduced the number of variables that would affect the spatial pattern of urban housing development.
Therefore, the process attempted to reduce a frequent error of regression model specification, i.e., omitting relevant variables.
The filtering system eliminated the parcels with the following characteristics.
1) Parks
2) Water
3) Industrial and offices
4) Steep slopes (i.e., higher than 20%)
104 5) Economically well-utilized (Improvement-to-land-value ratio of more than 1.5)
6) Recently built on land (i.e., built on within 30 years)
This filtering process produced two sampling populations for the two study periods. Among the 175,993 parcels, 79,966 parcels (12,891 acres) were developable in
1990 and 78,972 parcels (12,784 acres) were developable in the 1995.
6.3.4 Sampling
The number of developed parcels was quite small compared to the total number of parcels in the study area. The housing development sites also comprised only a very limited proportion of the total 175,993 parcels in the City. Among them were 79,966 buildable or developable parcels in 1990 and 78,972 in 1995. In the first five-year period (1991-1995), 627 parcels comprising 0.4% (190 acres) of the City’s total area were developed as infill housing. In the second period (1996-2000), 1,089 parcels were developed and the total size was 283 acres, 0.6% of the City’s total area. The study used 627 and 1,089 developable, yet not developed, parcels in each study period as the comparison groups, as described below.
This situation makes the regression analysis underestimate the importance of some factors, especially when examining a broad geographic area for a short period of time. In order to minimize this problem, the study sampled the same number of not- developed 33 parcels as the developed parcels (1,835 parcels). The study used a geographically stratified sampling for the not-developed parcels by selecting non-infill
33 The parcels are developable, but “not developed” during the 1990s. Furthermore, the infill housing development parcels in this study mean the parcels that were actually “developed” in the 1990s.
105 parcels from each neighborhood cluster in proportion to the distribution of the housing parcels of each neighborhood cluster. The neighborhood clusters are discussed in detail in the cluster analysis section of the Chapter 7. Again, the study had to draw samples proportionately from each of the three neighborhood clusters defined in the Chapter 7.
The same number of not-developed parcels was sampled from each neighborhood cluster as that of developed parcels within each cluster. For example, if the developed parcels in the neighborhood cluster 1 comprise 10% of all infill parcels, then the 10% of total samples of not-developed parcels were randomly selected from the neighborhood cluster
1.
6.4 Summary
This study uses standard methods such as regression and correlation techniques.
Both the institutional and non-institutional factors were grouped into two geographic levels, neighborhood and parcel. The study then conducts separate statistical analyses for each geographic unit.
This study conducted developability analysis for parcel-level analysis in order to make a reasonable set of sampling population. It was done by using a rule-based land supply monitoring based on a parcel-based geographic information system. Then, a stratified random sampling method was implemented to produce testable sets of sample parcels, i.e., the same number of not-developed parcels as that of developed parcels for each study period.
106
CHAPTER VII
ANALYSIS OF THE NEIGHBORHOOD-LEVEL FACTORS
7.1 Introduction
The objective of this study is to contribute to urban modeling theory and practice by identifying factors that are significantly associated with the location of urban infill housing development in declining American cities. Moreover, the focus of the study is on the effect of institutional and non-institutional factors on the location of urban infill housing development at both the neighborhood and site (or parcel) level. Neighborhood and site characteristics are examined to assess the usefulness of traditional (or non- institutional) factors of urban models in predicting the spatial pattern of urban infill housing development. However, the study pays a particular attention to institutional policy factors at both the neighborhood and parcel levels, which have been rarely employed by existing urban models.
This chapter examines the spatial pattern of urban housing development at neighborhood-level in the City of Cleveland between 1990 and 2000. Overall, three groups of factors were considered in this study, that is, neighborhood characteristics, site characteristics, and institutional factors. For the convenience of the following statistical
107 analyses, these factors were reclassified into two groups based on their geographic scale, i.e., neighborhood- and parcel-level. The neighborhood characteristics were dealt with at the census tract level and the site characteristics were considered at the parcel level.
The neighborhood-level institutional factors were examined at the census tract level and site-level institutional factors were analyzed at the parcel level. Chapter 7 describes the spatial pattern of neighborhood-level factors and their association with neighborhood- level infill housing development rate while Chapter 8 deals with the parcel-level site characteristics and their association with infill housing development.
The following Tables 7.1 and 7.2 are reproduced from Chapter 6 to provide a convenient reference for the variables used in the analysis.
Table 7.1 Neighborhood-level Institutional Factors
Variable Name Description NRA_INCOME Income-based neighborhood reinvestment target neighborhood (dummy) NRA_MINOR Minority-based neighborhood reinvestment target neighborhood (dummy) P_LBANK90 (95) Percentage of each census tract’s total land area that is land bank site CNPP_90 (_95) Service neighborhood of CDCs that received CNPP funds in 1990 (1995) (dummy) WARD1 (through 20 dummy variables for 21 wards (dummy) WARD21)
The neighborhood-level study looked at two periods: 1990-1995 and 1995-2000.
1990 and 1995 were the base years for each study period. The dependent variable was the percentage of a census tract’s land area that was developed for residential use. The study examined the neighborhood-level housing development rates for 5-year periods, first from 1991 to 1995, and second from 1996 to 2000.
108 Table 7.2 Neighborhood-level Non-institutional Factors
Variable Name Description First Study Period Second Study Period ACCESSIBILITY TRAVELTIME Average commuting time 21.72 minutes (6.26) (minutes) in 1990 LAND USE MIX P_VACANT90 (95) Percentage of each census tract’s total land area that is residential 2.99% (3.28) 2.86% (2.85) vacant land in 1990 (1995)* P_RESD90 (95) Percentage of each census tract’s total land area that is residential 35.91% (21.65) 35.30% (21.89) land use in 1990 (1995)* P_NEW8690 (9195) Percentage of each census tract’s total land area that is recently improved as residential use, 0.66% (3.01) 1.83% (6.82) developed between 1986 and 1990 (1991 and 1995)* DEMOGRAPHIC/SOCIOECONOMIC CHARACTERISTICS P_WHITE Percent white population in 1990 46.38% (41.13) 8090POPGROW SPA-level population growth rate -12.91% (7.81) between 1980 and 1990 P_COLLEGE Percent college graduates in 1990 15.37% (9.11) MED_INCOME Average median household income $15,970 (8,482) in 1990 CRIME90 (95) Incidents of crimes per 1000 241 crimes per 403 crimes per 1000 people in 1990 (1995)* 1000 residents residents (939) (571) DENSITY Population density (people per 31.34 people per acre (99.27) acre) in 1990
NOTES: 1. Values in parentheses indicate one-standard deviation for each variable. N = 224 2. One standard deviation is show within parenthesis
As discussed in Chapter 6, the urban modeling factors at neighborhood-level are categorized into three groups, which have been used in urban models to predict neighborhood-level land use location choice (Table 7.2). Those are (1) accessibility, (2) land use mix, and (3) demographic/social factors.
Table 7.3 summarizes the hypotheses about the neighborhood-level variables and the assumptions about the sign of the relationship between the variables and the neighborhood’s infill housing development rate. These hypotheses are described in 109 detail in Chapter 5. A neighborhood’s proximity to employment centers (or short commuting time) was assumed to have a negative correlation with neighborhood’s infill housing development rate. Assumptions for land use mix factor stated that neighborhoods with more residential vacant land, more developable land, more residential land use, and more recently built housing will have more infill housing development. The size of neighborhood’s population in general and neighborhood’s percent white population in particular were also assumed to be positively associated with a neighborhood’s infill development rate. Neighborhood residents’ education level also was assumed to be positively associated with a neighborhood’s infill housing development rate. A higher crime was assumed to discourage infill housing development in a neighborhood.
7.2. Spatial Description
During the first study period, Cleveland experienced urban infill housing development throughout the City. However, more infill housing development occurred in the eastern part of the city than in the western part. During the period, the total area of new infill housing development of the west was 16.7 acres (14% of total infill housing development for the period), while the total new infill housing area of the east was 105.9 acres (86%).
The difference between the west and east was remarkable considering the population difference (the west contains 214,379 people or 42%; the east contains 291,408 people or 58%) and total area difference (the west comprises 25,972 acres or 52% of the city; the east contains 23,556 acres or 48%). The locational skew was clearly evident in the
110 choropleth map showing the darker or higher percentage of infill housing development in the east (Figures 7.1 and 7.2).
Table 7.3 Summary of Neighborhood-level Hypotheses
Factors Sub-factors Hypothetical Sign * Institutional Neighborhood reinvestment program Positive for low-income and minority- concentrated areas Percent land bank area Positive Service neighborhood of CNPP- Positive funded CDCs Empowerment Zone Positive Wards Unknown Accessibility Distance to employment centers Negative Land Use Mix Percent residential vacant land Positive Percent residential land Positive Percent recent housing development Positive Demographic/ Percent white population Positive Socioeconomic Population growth Positive Percent high-education graduates Positive Median household income Positive Crime rate Negative Population density Positive
Note: * The signs indicate the direction of correlation between the neighborhood-level housing development rate and the independent variables.
The following statistical analyses help identify important institutional and non- institutional factors. As discussed later, the significance of the factors was assumed to change between the two study periods, 1990-1995 and 1995-2000. The comparison of the temporal changes provides a useful insight into the factors affecting the location of urban infill housing development.
The second half of the 1990s experienced more urban infill housing development than the first half of the decade. In addition, the infill housing development occurred more in the eastern part of the city than in the west. The infill housing development for 111 1996-2000 comprised 33.3 acres (21% of total infill housing development for the period) in the west, while it comprised 123.5 acres (79%) in the east. The locational skew seems to be unchanged over the study periods (Figure 7.2).
Figure 7.1 Spatial Pattern of Housing Development, 1991-1995
Figure 7.2 Spatial Pattern of Housing Development, 1996-2000
112 7.3. Zero vs. Non-zero Development Rates
As discussed in the Chapter 6, there were many neighborhoods with no urban housing development during the study period, 1990-2000, which makes a standard regression analysis problematic. Therefore, the study examined the difference of in the neighborhood-level factors between neighborhoods with zero housing development rates and those with non-zero rates by using a means difference t test. The multiple regression analysis was then applied only to the non-zero neighborhoods.
To conduct the t test, the neighborhoods with non-zero rates were grouped and coded as 1 and the remaining neighborhoods were coded as 2. There were 88 neighborhoods in Group 1 and 136 in Group 2 in the first study period and 137 neighborhoods in Group 1 and 87 in Group 2 in the second period. Then, each factor was examined by examining the means differences for each of the factors in the two neighborhood groups. If there is the two groups of neighborhoods were significantly different regarding a factor, the factor can be considered significantly associated with the zero vs. non-zero neighborhood-level housing development rates. Moreover, if the t value is positive, the study concludes that the neighborhoods with more of the factor are likely to have some infill housing development. If the value is negative, the neighborhoods with more of the factor would be assumed to have no infill residential development.
Except for the Empowerment Zone and Ward, other institutional variables
(NRA_INCOME, NRA_MINOR, P_LBANK, and CNPP) had consistently positive significant associations with the neighborhoods’ non-zero development rates in both periods. If p-value of 0.10 is allowed, even the Empower Zone factor was significant 113 throughout the two study periods. Therefore, neighborhoods with NRA’s designation, higher proportion of land bank areas, CNPP recipients, and Empowerment Zone designation were more likely to experience some residential development in the study area during the 1990s (Table 7.4).
As for the non-institutional factors, the amount of commuting time
(TRAVELTIME), residential vacant land (P_VACANT90), and residential land
(P_RESD90) were all positively associated with the neighborhood development in both study periods. In other words, neighborhoods with longer commuting time and a higher proportion of residential vacant land and residential land were more likely to have some housing development. In contrast, neighborhoods with higher population density and crime rates were less likely to have housing development (Table 7.4).
Table 7.4 Zero vs. Non-zero t statistics
Factors 1990-1995 1995-2000 Variables t Significance T Significance NRA_MINOR 3.35** 0.00 2.29* 0.02 NRA_INCOME 2.36* 0.02 3.83** 0.00 P_LBANK90(95) 3.44** 0.00 4.17** 0.00 CNPP_90(95) 2.99** 0.00 3.83** 0.00 EMPZONE 2.10* 0.04 1.91 0.06 WARD1 - 0.86 0.39 0.84 0.40 WARD2 1.23 0.22 - 0.63 0.53 WARD3 2.16* 0.03 2.71** 0.01 WARD4 0.29 0.77 0.37 0.71 WARD5 - 0.08 0.94 - 1.38 0.17 Institutional WARD6 - 1.13 0.26 0.49 0.63 WARD7 1.78 0.08 1.84 0.07 WARD8 0.14 0.89 1.11 0.27 WARD9 2.40* 0.02 1.81 0.07 WARD10 0.17 0.86 0.59 0.56 WARD11 0.17 0.86 2.71** 0.01 WARD12 1.80 0.07 2.26* 0.02 WARD13 - 2.39* 0.02 - 3.26** 0.00 WARD14 0.40 0.69 0.20 0.84 WARD15 - 2.06* 0.04 0.37 0.71
114 Table 7.4 Zero vs. Non-zero t statistics (Continued)
Factors 1990-1995 1995-2000 Variables t Significance T Significance WARD16 - 0.86 0.39 0.10 0.92 WARD17 - 0.86 0.39 0.83 0.41 Institutional WARD18 - 0.86 0.39 - 2.41* 0.02 WARD19 0.17 0.86 - 0.98 0.33 WARD20 - 0.61 0.54 - 0.98 0.33 Accessibility TRAVELTIME 3.20** 0.00 3.46** 0.00 Land Use Mix P_VACANT90(95) 4.92** 0.00 6.85** 0.00 P_RESD90(95) 3.54** 0.00 4.44** 0.00 P_NEW8690 0.99 0.33 1.61 0.11 Demographic 8090POPGRO - 1.18 0.24 0.26 0.80 P_WHITE - 2.34* 0.02 - 1.06 0.29 P_COLLEGE 0.83 0.41 1.25 0.21 DENSITY - 2.26* 0.03 - 2.17* 0.03 CRIME90 - 2.43* 0.02 - 2.70** 0.01 MED_INCOME 0.05 0.96 1.12 0.26 NOTE: Group 1: Neighborhoods with non-zero development rates Group 2: Neighborhoods with zero development rates Therefore, the negative t values indicate that the mean of the Group 2 was larger than that of the Group 1. ** significant at p=0.01 level * significant at p=0.05 level
7.4. Regression and Supplemental Statistical Analyses
More sophisticated statistical analyses were conducted for the “non-zero” neighborhoods. Two statistical methods were used for the analysis of the non- institutional factors. The study analyzed the non-institutional factors with multiple regression technique. However, it was found that there was a significant problem of multicollinearity. The significant correlations between the factors or independent variables seemed to lower the t-values for individual factors as well as the R square for the overall model. The study did not cure the collinearity problem by reducing the factors because the focus of this study was on individual neighborhood-level factor rather than
115 on model fitting per se. Therefore, the study used an alternative method, a bivariate correlation analysis, i.e., Pearson Correlation, to supplement the regression analysis.
The study used multiple regression and t test analysis techniques as for the institutional factor analysis at the neighborhood level. The reason for using additional t test was to supplement the regression analysis which involved a significant multicollinearity among the institutional factors. The correlation between the independent variables was significant and thus distorted the regression statistics, lowering the significance of individual factors and the model itself. Also, t test analysis was used because the independent variables were all dummy variables except for percent land bank area for which a correlation analysis was done.
7.4.1 Analysis of the Non-Institutional Factors
The regression model is specified as:
Y = β0 + β1 x1 + β2 x2+ . . . + βn xn + u
Where Y is the dependent variable (the natural log of the percentage of each neighborhood’s total land that was developed as infill housing during the study period), the β weights represent the coefficient values for the various independent demographic variables ( x), and u represents the error term. The independent variables used in this analysis include neighborhood-level factors consisting of land use mix and demographic/socioeconomic characteristics (see Table 7.2).
The multiple regression analysis (Table 7.5) showed that three independent variables – percent recent housing developments (NEW_8690), percent residential land use (P_RESD90), and percent college graduates (P_COLLEGE) – were significantly associated with the neighborhood development rates during the first study period. Three 116 other variables – percent recent housing developments (P_RESD95), average household median income (MED_INCOME), and percent college graduates (P_COLLEGE) – were significant during the second period. However, as noted above, many more independent variables were found to be significantly correlated each other although the multicollinearity indices such as Tolerance and VIF did not indicate any serious mutual dependence between the variables.
A Pearson Correlation analysis revealed significant correlations between most of the variables in both study periods (Tables 7.6 and 7.7). Therefore, the neighborhood- level analysis of the non-institutional factors are based on both the regression and the correlation analysis, focusing on the one-to-one correlation between the dependent variable and each independent variable.
Table 7.5 Multiple Regression Analysis of Non-institutional Factors
1990-1995 1995-2000
Variables Parameter Collinearity Statistics Parameter Collinearity Statistics t-value t-value Estimate Estimate Tolerance VIF Tolerance VIF TRAVELTIME 0.040 1.055 0.353 2.832 0.015 -0.418 0.381 2.624 P_VACANT90 (95) 0.036 1.024 0.563 1.775 0.074 1.834 0.541 1.847 P_RESD90 (95) 0.015 2.000* 0.479 2.089 0.040 3.356** 0.423 2.365 P_NEW8690 (9195) 0.088 8.330** 0.930 1.076 0.013 1.055 0.813 1.230 P_WHITE -0.006 -1.371 0.291 3.441 -0.002 -0.459 0.299 3.346 8090POPGRO -0.027 -1.505 0.574 1.742 -0.006 -0.354 0.545 1.835 DENSITY 0.011 1.206 0.637 1.570 -0.012 -1.475 0.616 1.624 CRIME90 0.001 0.821 0.605 1.652 0.260 0.008 0.709 1.411 MED_INCOME -0.470 -1.736 0.290 3.450 -0.700 -3.005** 0.320 3.123 P_COLLEGE 0.031 2.022* 0.578 1.731 0.028 2.120* 0.616 1.624 Note: Adj. R square (1990-1995) = .651, Adj. R square (1995-2000) = .441 *p = 0.05, ** p = 0.01
117 Together the regression and correlation analyses showed that the neighborhoods had significant differences in their infill housing development rates with regard to some of the accessibility variables, land use mix variables, and demographic variables at the significance level of 0.05. In particular, the correlation analysis revealed an association between the neighborhood-level housing development rates and some of the independent variables. However, not all the assumptions about the signs of the correlations were upheld. Also, the significance level of individual variables has changed and the model fit (adjusted R square) decreased over the 10-year study period. Following is a detailed interpretation of the analysis in line with the research assumptions.
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Table 7.6 Correlation Analysis of Non-institutional Factors, 1990-1995
Dependent Accessibility Land Use Mix Demographic/Social Variable
LN_INFIL90 TRAVELTIME P_VACANT90 P_RESD90 P_NEW8690 P_WHITE 8090POPGRO DENSITY P_COLLEGE MED_INCOME CRIME90 1 LN_INFIL90 1.000 0.437** 0.397** 0.214* 0.124 -0.571** -0.339** 0.172 0.017 -0.297** 0.242* 2 Sig. 0.000 0.000 0.044 0.245 0.000 0.001 0.106 0.874 0.005 0.023 TRAVELTIME 1.000 0.379** 0.509** -0.066 -0.713** -0.055 -0.100 -0.104 -0.121 0.226* Sig. 0.000 0.000 0.537 0.000 0.607 0.349 0.334 0.260 0.033 P_VACANT90 1.000 0.046 -0.040 -0.425** -0.437** 0.151 -0.204 -0.497** 0.401** Sig. 0.672 0.713 0.000 0.000 0.157 0.055 0.000 0.000 P_RESD90 1.000 0.107 -0.308** 0.270* -0.355** 0.204 0.331** -0.171 Sig. 0.317 0.003 0.011 0.001 0.055 0.002 0.110 P_NEW8690 1.000 0.172 0.051 -0.031 0.464** 0.234* -0.113 119 Sig. 0.107 0.633 0.771 0.000 0.027 0.290
P_WHITE 1.000 0.333** -0.158 0.217* 0.449** -0.387** Sig. 0.001 0.140 0.041 0.000 0.000 8090POPGRO 1.000 -0.405** 0.153 0.531** -0.290** Sig. 0.000 0.152 0.000 0.006 DENSITY 1.000 -0.224* -0.469** 0.019* Sig. 0.035 0.000 0.863 P_COLLEGEM 1.000 0.592** -0.362** Sig. 0.000 0.001 MED_INCOME 1.000 -0.466 Sig. 0.000** CRIME90 1.000
Note: 1. The first row shows R square. 2. The second row shows P-value **Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed). 119
Table 7.7 Correlation Analysis of Non-institutional Factors, 1995-2000
Dependent Accessibility Land Use Mix Demographic/Social Variable LN_INFIL95 TRAVELTIME P_VACANT95 P_RESD95 P_NEW9195 P_WHITE 8090POPGRO DENSITY P_COLLEGE MED_INCOME CRIME95 1 LN_INFIL95 1.000 0.308** 0.519** 0.169** 0.196* -0.439** -0.375** 0.022 -0.185* -0.484** 0.240** 2 0.000 0.000 0.049 0.022 0.000 0.000 0.798 0.032 0.000 0.005 TRAVELTIME 1.000 0.302** 0.373** 0.101 -0.724** -0.070 -0.083 -0.072 -0.133 0.066 0.000 0.000 0.243 0.000 0.417 0.339 0.404 0.123 0.444 P_VACANT95 1.000 0.560** 0.346** -0.393** -0.396** 0.022 -0.255** -0.431** 0.243** 0.000 0.000 0.000 0.000 0.801 0.003 0.000 0.004 P_RESD95 1.000 0.131 -0.528** -0.463** -0.011 -0.381** -0.560** 0.251** 0.128 0.000 0.000 0.901 0.000 0.000 0.003 P_NEW9195 1.000 -0.105 -0.016 -0.021 0.109 0.029 -0.020
120 0.224 0.854 0.806 0.208 0.735 0.817 P_WHITE 1.000 0.394** -0.121 0.142 0.395** -0.332**
0.000 0.162 0.099 0.000 0.000 8090POPGRO 1.000 -0.282** 0.208* 0.503** -0.432** 0.001 0.015 0.000 0.000 DENSITY 1.000 -0.113 -0.448** 0.038 0.189 0.000 0.658 P_COLLEGEM 1.000 0.572** -0.175* 0.000 0.042 MED_INCOME 1.000 -0.351** 0.000 CRIME95 1.000
Note: 1. The first row shows R square. 2. The second row shows P-value **Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).
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7.4.1.1 Accessibility Factors
Accessibility in urban models usually measures a residential area’s proximity to employment centers. Traditional urban location models generally treat accessibility as one of the most significant factors affecting on the location of residential land development.
The study used each tract’s average commuting time (TRAVELTIME) to measure the neighborhoods’ the accessibility, as measured by the 1990 Census of Population and
Housing, to represent each neighborhood’s accessibility. The correlation analysis result indicated that accessibility had a significant correlation with a neighborhood’s housing development rate. That is, neighborhoods near to employment centers had less infill residential development.
However, contrary to the conventional assumption of urban models, neighborhoods with longer average commuting time had more infill housing development in both study periods. The neighborhoods with relatively longer commuting time and fewer white residents (or larger minority population) were spatially clustered in the East
Cleveland.
7.4.1.2 Land Use Mix Factors
Neighborhood land use characteristics have been important factors in many urban models. This study used three measures of the neighborhood land use characteristics: percent residential vacant land (P_VACANT90 and P_VACANT95), percent residential land use (P_RESD90 and P_RESD95), and percent recently developed land
(P_NEW8690 and P_NEW9195). All of these variables were found to be significantly associated with a neighborhood’s infill housing development rate.
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The supply of residential vacant land seems to be strongly related to the neighborhood-level infill housing development rates. This is reasonable because the site acquisition cost is a significant part of the development cost in urban areas. As a result, vacant land would be much cheaper to prepare for infill development than improved sites that often require demolition, relocation, and so on.
Neighborhoods with a larger proportion of residential land use also tended to have more housing developments. Traditionally, urban modelers assumed that similar land uses are more likely to be located near each other. The test results agreed with this in that the neighborhoods with higher percentage of residential land use were found to attract more new residential development. However, another explanation might be the high correlation between percent vacant residential land (P_VACANT) and proportion of residential land use (P_RESD). That is, neighborhoods with the larger percentage of residential land use also had the larger percentage of residential vacant land.
Neighborhoods with more residential land and more recent residential development seemed to become increasingly attractive to infill developers over the study period. This could result from the cumulative effort of infill developers (i.e., CDCs and for-profit infill developers) to revitalize their residential neighborhoods.
The variables were not only significant, but the importance of land availability on neighborhood’s infill development rates increased over the study period. According to city planners, infill housing developers became more aware of the easy access to inexpensive vacant developable land, much of which had been prepared by the City.
Also, the City experienced a strong momentum of infill housing development during the
1990s, increasing the demand for readily developable sites.
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7.4.1.3 Demographic/Socioeconomic Characteristics Factors
In urban models, neighborhood-level demographic/socioeconomic variables have been frequently assumed to be associated with residential development. Those variables were also found to be significant in this study. As in the traditional urban models, racial composition, population growth, average household income, educational achievement and crime rate variables were all associated with the neighborhood-level infill housing development rates.
However, the signs of the relationship were contrary to the assumptions of traditional urban models. That is, urban infill housing development was more likely to occur in neighborhoods with a lower percent white population (P_WHITE), lower population growth (8090POPGRO), a lower proportion of college graduates
(P_COLLEGE), lower area median household income (MED_INCOME), and higher crime rates (CRIME90 and CRIME95). That is, somewhat surprisingly, population loss, lower educational achievement rates, lower average median incomes, and high crime rates do not seem to discourage infill housing development.
The effect of percent white population and the average median household income may be related to an institutional policy, i.e., neighborhood reinvestment agreement
(NRA) programs which target lower income and minority neighborhoods. This suggests that the unconventional correlation results for those variables may be explained by the
NRA program.
This suggests that urban infill housing does not necessarily reflect the logic of market efficiency. Instead, it seems to occur more often in places where market failure such as disinvestment and undervaluation are evident. This suggests the importance of
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non-market or institutional policy factors’ attempt to correct for the market failures, in locating urban infill housing development.
7.4.2 Analysis of the Institutional Factors
The analysis of the institutional factors supplemented the regression statistics with t-test (for the dummy variables) and correlation analysis (for the non-dummy variables).
In the first period (1990-1995), neighborhoods in the minority and low-income target reinvestment program (NRA_MINOR and NRA_INCOME) and Empowerment Zone
(EMPZONE), and with more land bank sites (P_LBANK90) had higher infill housing development rates. Neighborhood-level infill housing development rates were lower if the neighborhoods were located in wards 11, 12, 13, and 20 (Tables 7.8, 7.9, 7.10, and
7.11).
In the second period (1995-2000), neighborhoods in NRA reinvestment target areas (NRA_MINOR and NRA_INCOME), Empowerment Zone (EMPZONE) and
Wards 3 through 10 and with more land bank sites (P_LBANK90) had higher infill housing development rates. The neighborhood-level infill housing development rates decreased if neighborhoods were located in CNPP recipients’ service areas and in ward
21 (WARD21) (Tables 7.8, 7.9, 7.10, and 7.11).
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Table 7.8 Regression Analysis of Institutional Factors
1990-1995 1995-2000 Variables Collinearity Collinearity Parameter Parameter t-value Statistics t-value Statistics Estimate Estimate Tolerance VIF Tolerance VIF CNPP_90(95) -0.044 -0.411 0.652 1.534 0.156 1.980* 0.751 1.331 NRA_MINOR 0.543 4.997** 0.646 1.549 0.246 2.939** 0.664 1.505 NRA_INCOME 0.130 1.200 0.646 1.548 0.158 2.013* 0.752 1.329 P_LBANK90(95) 0.071 0.538 0.439 2.278 0.299 3.160** 0.520 1.924 EMPZONE -0.085 -0.727 0.562 1.781 0.147 1.637 0.574 1.743 Note: N = 224, Adjusted R square (1990-1995) = .330, Adjusted R square (1995-2000) = .374 *p = 0.05, ** p = 0.01
Table 7.9 T test analysis of Institutional Factors
CNPP90(95) NRA_MINOR NRA_INCOME EMPZONE Dependent Variables t sig. t sig. t sig. t sig. Ln_Infill90 (1990-1995) -0.530 .597 6.820** .000 2.958** .004 2.705** .008 Ln_Infill95 (1995-2000) -5.516** .020 6.337** .000 4.824** .000 5.652** .000 *p = 0.05, ** p = 0.01
Table 7.10 Correlation Analysis of Percent Land Banking Areas
1990-1995 1995-2000 P_LBANK90 P_LBANK95 LN_INFIL90 0.387** LN_IFIL95 0.515** Sig. 0.000 Sig. 0.000 Note: **Correlation is significant at the 0.01 level (2-tailed). *Correlation is significant at the 0.05 level (2-tailed).
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Table 7.11 Regression Analysis for Wards
1990-1995 1990-2000 Collinearity Collinearity Parameter Parameter t-value Statistics t-value Statistics Variables Estimate Estimate Tolerance VIF Tolerance VIF Constant -0.416 -0.490 -2.672 -3.872** ward1 -0.093 -0.793 0.511 1.955 0.140 1.121 0.349 2.868 ward2 -0.074 -0.487 0.303 3.303 0.186 1.677 0.442 2.265 ward3 -0.001 -0.005 0.268 3.730 0.275 2.100* 0.316 3.162 ward4 -0.094 -0.663 0.349 2.865 0.256 2.052* 0.349 2.868 ward5 0.082 0.485 0.241 4.146 0.387 2.827** 0.290 3.451 ward6 -0.190 -1.342 0.349 2.865 0.476 3.230** 0.249 4.015 ward7 0.023 0.129 0.220 4.551 0.440 2.984** 0.249 4.015 ward8 -0.021 -0.141 0.303 3.303 0.482 3.387** 0.268 3.735 ward9 0.031 0.181 0.241 4.146 0.365 2.785** 0.316 3.162 ward10 -0.196 -1.510 0.414 2.416 0.031 0.264 0.389 2.569 ward11 -0.275 -2.116* 0.414 2.416 0.035 0.268 0.316 3.162 ward12 -0.442 -2.600* 0.241 4.146 0.179 1.259 0.268 3.735 ward13 -0.392 -2.203* 0.220 4.551 0.298 1.893 0.219 4.559 ward14 -0.152 -1.002 0.303 3.303 0.246 1.878 0.316 3.162 ward15 -0.138 -1.354 0.674 1.483 0.080 0.639 0.349 2.868 ward16 -0.286 -2.453 0.511 1.955 0.095 0.806 0.389 2.569 ward17 -0.152 -1.168 0.414 2.416 0.192 1.401 0.290 3.451 ward18 -0.208 -1.783 0.511 1.955 -0.051 -0.605 0.756 1.324 ward19 -0.233 -1.794 0.414 2.416 0.033 0.319 0.511 1.956 ward20 -0.354 -3.030** 0.511 1.955 -0.022 -0.213 0.511 1.956 Note: N = 224, Adj. R 2 (1990-1995) = .386, Adj. R 2 (1995-2000) = .268 *p = 0.05, ** p = 0.01
Since the late 1980s, the City of Cleveland has strongly and effectively enforced the Community Reinvestment Act, which requires local banks to make loans equally available to all neighborhoods, especially to traditionally redlined neighborhoods. Since
1994, the Neighborhood Reinvestment Program has targeted two types of neighborhood groups, i.e., minority-concentrated (NRA_MINOR) and low-income neighborhoods
(NRA_INCOME). The analysis suggests that these geographically targeting programs have been very effective in locating infill housing development at the neighborhood level.
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The analysis also showed that the Land Reutilization Program, often known as land banking, was also significantly associated with infill housing development. The more land bank sites a neighborhood had, the more infill housing development the neighborhood attracted. The neighborhood’s land bank area percentage became significantly more associated with its infill housing development rate during the study period.
The Cleveland Neighborhood Partnership Program (CNPP) of Neighborhood
Progress, Inc. (NPI) has been an important subsidy program for community development corporations’ operating fund since 1989. NPI awards CNPP operating subsidy grants to well-performing and well-planning CDCs. Therefore, the study assumed that the neighborhoods served by CDCs receiving the CNPP subsidy would have more infill housing development.
Due to the lack of a good measure for the political influence of the ward councilmen, the study used 21 dummy variables for the twenty-one wards of the City.
The test result showed that the impact of the ward geography on the spatial pattern of the infill housing development at neighborhood-level was neither consistent nor easy to interpret.
7.5. Correlation Between the Institutional and Non-institutional Variables
As was shown in the previous analysis, the non-institutional or market factors had a significant association with infill housing development in the study area. However, the association of the accessibility and the demographic variables were contrary to the
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assumptions of traditional urban modeling theory. As was pointed out above, these findings may be explained by institutional factors.
These results demanded a close examination of the relationship between the non- institutional and institutional factors. A Pearson correlation analysis was conducted for the significant institutional variables and non-institutional variables (i.e., the accessibility and demographic/socioeconomic factors). The analysis showed that the correlation between the two groups of factors was very high (Tables 7.12 and 8.13). The correlations between the two groups are highlighted by bold borders in the Tables. That is, the most significant non-institutional variables were highly correlated with one or more institutional variables.
During the first study period, longer commuting time neighborhoods
(TRAVELTIME) were likely to be in minority-concentrated reinvestment target areas
(NRA_MINOR), to have a larger percentage of land bank area (P_LBANK), and to be located in an Empowerment Zone (EMPZONE). All the institutional factors, including the low-income reinvestment target areas (NRA_INCOME), were significantly correlated with the other demographic/socioeconomic variables (P_WHITE, MED_INCOME,
8090POPGRO). For the second study period, the correlation analysis added crime rates
(CRIME95) and CNPP (CNPP_95) variables. Both variables were not consistently correlated with the other institutional or non-institutional variables. However, the other variables showed correlation results similar to those of the first study period.
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Table 7.12 Correlation Between Institutional and Non-institutional Factors, 1990-1995
INSTITUTIONAL FACTORS ACCESSIBILITY & DEMOG/SOCIOECONOMIC FACTORS NRA_MINOR NRA_INCOME P_LBANK90 EMPZONE P_WHITE TRAVELTIME 8090POPGRO MED_INCOME NRA_MINOR 1.000 0.276** 0.492** 0.520** -0.817** 0.446** -0.441** -0.334** 0.000 0.000 0.000 0.000 0.000 0.000 0.000 NRA_INCOME 1.000 0.354** 0.267** -0.352** 0.130 -0.416** -0.596** 0.000 0.000 0.000 0.074 0.000 0.000 P_LBANK90 1.000 0.535** -0.500** 0.326** -0.470** -0.410** 0.000 0.000 0.000 0.000 0.000 EMPZONE 1.000 -0.437** 0.250** -0.376** -0.353** 0.000 0.001 0.000 0.000 P_WHITE 1.000 -0.440** 0.447** 0.429** 129 0.000 0.000 0.000 TRAVELTIME 1.000 -0.031 0.069 0.674 0.343 8090POPGRO 1.000 0.510** 0.000 MED_INCOME 1.000
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Table 7.13 Correlation Between Institutional and Non-institutional Factors, 1995-2000
INSTITUTIONAL ACCESSIBILITY & DEMOG/SOCIOECONOMIC FACTORS NRA_MINOR NRA_INCOME P_LBANK95 EMPZONE CNPP_95 MED_INCOME P_WHITE 8090POPGRO TRAVELTIME CRIME95 NRA_MINOR 1.000 0.278** 0.536** 0.519** -0.156* -0.333** -0.814** -0.440** 0.445** 0.087 0.000 0.000 0.000 0.029 0.000 0.000 0.000 0.000 0.220 NRA_INCOME 1.000 0.358** 0.263** 0.152* -0.599** -0.347** -0.409** 0.113 0.090 0.000 0.000 0.032 0.000 0.000 0.000 0.114 0.208 P_LBANK95 1.000 0.522** -0.265** -0.413** -0.543** -0.455** 0.343** 0.078 0.000 0.000 0.000 0.000 0.000 0.000 0.272 EMPZONE 1.000 -0.277** -0.342** -0.435** -0.362** 0.250** 0.088 0.000 0.000 0.000 0.000 0.000 0.218 CNPP_95 1.000 0.129 0.117 0.158* 0.060 -0.147* 130 0.070 0.100 0.026 0.401 0.038
MED_INCOME 1.000 0.433** 0.521** 0.079 -0.259** 0.000 0.000 0.266 0.000 P_WHITE 1.000 0.447** -0.440** -0.146* 0.000 0.000 0.040 8090POPGRO 1.000 -0.020 -0.293** 0.776 0.000 TRAVELTIME 1.000 -0.013 0.858 CRIME95 1.000
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The correlation analysis showed that important accessibility and demographic factors were highly connected with the institutional ones. This suggests that the unconventional effects of the non-institutional factors on the location of infill housing development are closely related to the spatial match between non-institutional factors and the institutional factors. That is, the analysis suggests that the institutional efforts to support infill housing development were concentrated in distressed and disinvested urban neighborhoods, which are often minority- and poverty-concentrated and had many vacant and buildable land prepared by the city government.
7.6. Cluster Analysis
The study used k-means cluster analysis to classify the neighborhoods (i.e., census tracts) into three groups. Ten neighborhood-level institutional and non-institutional factors which were identified in the regression and correlation analyses in this chapter, were used for the cluster analysis. The cluster analysis showed that the important factors used in the cluster analysis were reliable because the east-west divide of the neighborhood clustering is closely aligned with the spatial pattern of neighborhood-level infill housing development rates. The parcel-level analysis of the next chapter will be conducted for each neighborhood cluster to eliminate the effect of the neighborhood variables in the parcel-level regression models.
The cluster analysis identified three types of neighborhoods (Table 7.14).
Cluster-2 contained the largest number of infill housing parcels and Cluster-1 the next highest number. The Cluster-3 neighborhoods were scattered around the city center and the eastern periphery and had the least infill housing development (Figure 7.3).
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Figure 7.3 Neighborhood Clusters
While the Cluster-2 neighborhoods were located mainly in the eastern part of the city, the Cluster-1 neighborhoods were mainly in the west. The spatial clustering pattern changed little in the 1990s (Figure 7.3). The cluster analysis seems to be valid because the cluster map shows the spatial disparity in the amount of infill housing development between the west and the east of Cleveland, which was shown in the maps of neighborhood infill housing development rates, earlier in this chapter.
As for the institutional factors, the Cluster-2 neighborhoods were more likely to be located in minority- and low-income-based reinvestment target area and to have more land bank sites than to other Clusters. Cluster-1 neighborhoods had the lowest percentage of total land that were registered in the land bank program and were least likely to be in the NRA target areas.
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Table 7.14 Neighborhood Clustering and Variables
Mean Values Variables Cluster 1 Cluster 2 Cluster 3 NRA_MINOR 0.00 0.80 0.13 NRA_INCOME 0.50 0.79 0.75 Institutional P_LBANK90 0.61 4.78 1.60 P_LBANK95 0.69 5.79 2.13 EMPZONE 0.00 0.30 0.00 P_VACANT90 1.80 4.30 2.73 P_VACANT95 1.98 3.95 2.16 Non-Institutional P_WHITE 88.28 3.04 45.25 TRAVELTIME 19.75 24.21 19.95 8090POPGRO -9.45 -16.52 -12.92 Number of Neighborhoods 102 98 24 Average % Housing Development ,1991-1995 .06% .66% .17% Average % Housing Development, 1996-2000 .24% .75% .11% Average Square Feet of Housing Development, 1991-1995 8,133 44,042 8,172 Average Square Feet of Housing Development, 1996-2000 17,703 49,365 7,767 Total Number of Parcels Developed, 1991-1995 143 491 41 Total Number of Parcels Developed, 1996-2000 278 778 35
The Cluster-2 neighborhoods had more residential vacant land and a much lower white population than the other two clusters. They also had the longest average commuting time and the highest rate of population loss among the three clusters. On the contrary, the Cluster-1 neighborhoods had the highest average percent white population, the lowest percent of total land that were vacant lots, the lowest rate of population loss, and the shortest commuting time among the three clusters.
Therefore, the Cluster-1 neighborhoods are characterized as white and affluent communities with the best accessibility to employment centers. The Cluster-2 neighborhoods can be described as minority-concentrated, low-income community with an abundant supply of vacant land and land bank sites. The Cluster-3 neighborhoods
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are in the middle range of the characteristics that distinguished the Cluster-1 and Cluster-
2 neighborhoods.
7.7 Summary
Multivariate regression analysis and correlation statistics were used in this chapter to test a set of hypotheses on the association between the neighborhood-level factors and the neighborhood-level infill housing development rates. The statistical tests supported some of the hypotheses and disapproved others (Table 7.15).
Table 7.15 Summary of Neighborhood-level Hypotheses Test Results
Factors Sub-factors Hypothetical Sign Test Result of Correlation Institutional Neighborhood reinvestment target Positive Positive neighborhood (both income- and minority- based) Percent land bank area Positive Positive Service neighborhood of CNPP-funded Positive Positive CDCs Empowerment Zone Positive Positive Ward Unknown Mixed results Accessibility Distance to employment centers Negative Positive Land Use Mix Percent residential vacant land Positive Positive Percent residential land Positive Positive Percent recent housing development Positive Positive Demographic/ Percent white population Positive Negative Socioeconomic Population growth Positive Negative Population density Positive Not significant Average median household income Positive Negative Percent college graduates Positive Negative Crime rate Negative Positive
NOTE: The grey shading indicates the variables were significant and consistent with research hypotheses.
Although many of the non-institutional factors that have been used by many urban models were found to be significant, the relationship was often found to be contrary to 134
the assumptions of traditional land use modeling. For example, the minority- concentrated neighborhoods gained more urban infill housing development than others.
In fact, almost every conventional urban modeling assumption was violated at the neighborhood level. This suggests an alternative explanation for the urban infill development pattern, which highlights the important role of institutional and governmental support in determining the location of urban infill housing development.
That is, neighborhoods with high minority population rates received special attention from the government and other socially-responsible institutional players in the locality. Neighborhoods with more residential vacant land and more developable parcels also gained more urban infill housing development. However, those variables were also found to be significantly associated with an institutional factor (i.e., the percent land bank area). As a result, the institutional factors had a very strong effect on the infill development pattern which was often reflected in counter-intuitive relationship with the traditional non-institutional factors.
Overall, the study result upheld the hypothesis that neighborhood-level institutional factors were positively associated with neighborhood-level housing development rates. Moreover, the land bank program became more significant over the study period. This suggests that the spatial pattern of neighborhood-level infill housing development is more closely related to the institutional factors than to the non- institutional factors. This hypothesis will be examined further in the next chapter.
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CHAPTER VIII
ANALYSIS OF THE SITE-LEVEL FACTORS
8.1 Introduction
The objective of this study is contributing to the field of land use modeling by identifying factors associated with the location of urban infill housing development in declining American cities. More specifically, the study tests the hypotheses for institutional factors that have not been used by existing urban models and for the traditional non-institutional factors that have been frequently used by urban models.
Following the neighborhood level analysis in Chapter 7, this chapter examines the association between the institutional and non-institutional factors and the location of infill housing development at the parcel level. In order to identify significant factors, the study used logistic or logit regression analysis that is a widely used calibration method for urban models.
By analyzing each neighborhood group or cluster, this chapter concentrates on the parcel-level factors and the parcel-level hypotheses. Table 8.1 summarizes the hypotheses with the hypothesized correlation signs discussed in the Chapter 5.
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Table 8.1 Summary of Parcel-level Hypotheses
Factors Variables Hypothetical Sign of Correlation with Parcel’s Housing Development Probability SITE PHYSICAL Size of parcels Positive FEATURES Residential vacant land (1=yes, 0=no) Positive ACCESSIBILITY Distance from rapid transit stations Negative Distance from central business districts Negative Distance from other employment centers Negative Distance from local, medium shopping centers Negative Distance from neighborhood shopping centers Negative AMENITIES Distance from recreation centers Negative Distance from parks Negative Distance from historic or landmark districts Negative DISAMENITIES Distance from railroads Positive Distance from leaking underground storage tanks Positive Distance from Toxic Release Inventory sites Positive Distance from heavy industrial land use Positive SITE ASSEMBLY Adjacency to residential vacant lots (1=yes, 0= no) Positive POTENTIAL OTHER Distance from the recently built housing units developed Negative EXTERNALITY for the last 5 years INSTITUTIONAL Land bank site (1=yes, 0= no) Positive Adjacency to land bank sites (1=yes, 0= no) Positive Distance from potential residential development Negative identified in city’s comprehensive plan
The institutional parcel-level factors included the parcel’s being a land bank site or in a city plan-based potential residential development site (Table 8.2). As discussed in Chapter 5, the parcel-level non-institutional variables were grouped into five classes that have been frequently used in urban land use location models: (1) site physical features; (2) site accessibility; (3) site amenities; (4) site disamenities; (5) site assembly potential; and (6) other externalities (Table 8.3). Many urban models have assumed that these factors have either a positive or negative effect on the location of different land uses.
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Table 8.2 Parcel-level Institutional Variables
Variable Description L_BANK90 (95) Land bank sites between 1990 and 1995 (1995-2000) (dummy) ADJBANK90 (95) Adjacency to land bank sites between 1990 and 1995 (1995-2000) (dummy) PLANRES_01 (_02) Proximity to potential residential development area identified in the City’s master plan published in 1990: Within 0.25 mile of the potential area; within 0.50 mile
Table 8.3 Parcel-level Non-institutional Variables
Variable Description SITE PHYSICAL CHARACTERISTICS LOTSIZE90 (95) Size of parcels in 1990 (1995) (square feet) R_VACANT90 (95) Residential vacant land in 1990 (1995) (dummy), excluding land bank sites ACCESSIBILITY TRANSIT_1 (_2) Within 0.1 mile (0.25 mile) from rapid transit stations in 1990 D_CBD Distance (feet) from central business district in 1990 D_EMPLOY_1 (_2) Within 0.5 miles (1 mile) from employment centers in 1990 L_SHOP90_1 (_2) Within 0.1 miles (0.25 miles) from local, medium shopping centers in 1990 (95) (1995) N_SHOP90_1 (2) Within 0.1 miles (0.25 miles) from neighborhood shopping centers in 1990 (95) (1995) AMENITIES D_RECREAT_1 (_2 Distance to closest recreation centers in 1990 (Within 0.1 mile; Within 0.25 and _3) mile; Within 0.50 mile) PARK_01 (_02 and Proximity to parks: Within 0.1 mile; Within 0.25 mile; Within 0.50 mile in _03) 1990 HISTORIC_01 Proximity to historic and landmark districts: Within 0.1 mile; Within 0.25 mile; (_02, _03) Within 0.50 mile in 1990 DISAMENITIES D_RAIL_1 (_2) Proximity to railroads: Within 0.1 mile; Within 0.25 mile in 1990 LUST90 (95) Proximity to leaking underground storage tanks: within 250 feet between 1990 (1995) TRI90 (95) Proximity to Toxic Release Inventory sites: within 500 feet in 1990 (1995) D_HINDUST90_1 Distance from heavy industrial land use (Within 0.1 mile; Within 0.25 mile) in (_2) (95) 1990 (1995)
SITE ASSEMBLY POTENTIAL ADJ_RVAC90 (95) Adjacency to residential vacant lots in 1990 (1995) (dummy) OTHER POSITIVE EXTERNALITY RES8690_1 (_2) Distance from the closest new housing units developed between 1986 and1990 (9195) (1991-1995) Within 0.1 mile; Within 0.25 mile
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8.2 Logit Regression Analysis
The dependent variable for the logit regression analysis is dichotomous, i.e., 1 if a parcel was developed between 1991 and 2000 and 0 if a parcel was not developed. This parcel-level logit regression analysis was conducted for each neighborhood cluster, minimizing the needs of taking into consideration the interaction effects between the neighborhood and parcel-level variables in the regression models. However, the study also ran a comprehensive regression analysis for the entire city. This was useful for identifying variables that were significantly associated with infill housing development for the different types of neighborhoods.
Tables 8.4 and 8.9 show the parameter estimates for logit regression analysis.
A positive sign for B (the regression coefficient) means the variable had a positive effect on the probability of infill housing development. While the B value clearly shows the sign of the relationship between the dependent and independent variables, Exp(B) provides statistics that are easier to interpret than B. Exp(B) is the ratio-change in the odds of infill development for a one-unit change in the independent variable. For example, let the probability of a parcel’s infill development be 0.50 (i.e., odds of infill development = 1) without considering a variable named X. Then, let Exp(B x) be 10.
This implies that the odds of infill housing development with one more unit of variable X will be 10. The corresponding probability of infill housing development will increase to .909 (90.9 percent), an increase by .409 (40.9 percent). In simple terms, if the Exp(B) of a variable is larger than 1, then a one-unit increase in the variable raises the probability of parcel’s infill housing development; if Exp(B) is smaller than 1, a one-unit increase in the variable reduces the probability. 139
Neighborhood Cluster 3 was not analyzed due to the very small number of cases, only 82 and 70 cases for the two study periods.
8.2.1 Physical Features
The analysis of the parcels’ physical features (Table 8.4) showed that urban infill housing development was more likely to occur on vacant residential land (R_VACANT) than on non-vacant land. Overall, this was the most significant non-institutional variable in both neighborhood clusters for both study periods.
Vacant residential land was also a very important factor for both types of neighborhoods. The test result was consistent with a traditional assumption of urban models that residential development is more likely to occur on residentially-zoned vacant land. The association between the parcel’s size (LOTSIZE) and its probability of housing development was insignificant in both periods for all clusters. This may be due to the fact that housing development in urban built-up areas often requires a land assembly that combines small parcels to make sufficient size of developable land.
Table 8.4 Logit Regression Analysis of Site Physical Factors
Variable Cluster Period B Exp(B) 1990-1995 .00 1.00 1 1995-2000 .00 1.00 1990-1995 .00 1.00 LOTSIZE90 (95) 2 1995-2000 .00 1.00 1990-1995 .00 1.00 All 1995-2000 .00 1.00 1990-1995 1.94*** 6.99 1 1995-2000 2.20*** 9.07 1990-1995 1.50*** 4.49 R_VACANT90 (95) 2 1995-2000 1.07*** 2.93 1990-1995 1.49*** 4.46 All 1995-2000 1.11*** 3.03 * Significant at p=0.1 level ** Significant at p=0.05 level *** Significant at p=0.01 level 140
8.2.2 Accessibility
The accessibility factors measured by proximity to work and shopping have traditionally been assumed to have a significant negative correlation with residential land development. That is, it is generally assumed that the closer a parcel is to employment and shopping centers, the more likely it is to be developed as residential land use.
Table 8.5 Logit Regression Analysis of Accessibility Factors
Variable Cluster Period B Exp(B) 1990-1995 -18.69 .00 1 1995-2000 1.64 5.18 1990-1995 -19.52 .00 TRANSIT_01 2 1995-2000 -20..18 .00 1990-1995 -18.94 .00 All 1995-2000 -.56 .56 1990-1995 .305 1.35 1 1995-2000 -2.08 .12 1990-1995 .05 1.05 TRANSIT_02 2 1995-2000 -.39 .67 1990-1995 -.21 .81 All 1995-2000 -.59* .55 1990-1995 .00 1.00 1 1995-2000 .00 1.00 1990-1995 .00 1.00 D_CBD 2 1995-2000 .00 1.00 All 1990-1995 .00 1.00 1995-2000 .00 1.00 1990-1995 -.65 .51 1 1995-2000 -2.50 .08 1990-1995 -16.77 .00 D_EMPLOY_1 2 1995-2000 -17.77 .00 1990-1995 -1.11 .32 All 1995-2000 -1.81 .16 1990-1995 -1.76** .17 1 1995-2000 -.42 .65 1990-1995 -.87** .41 D_EMPLOY_2 2 1995-2000 -.92*** .39 1990-1995 -.95*** .38 All 1995-2000 -.81*** .44 * Significant at p=0.1 level ** Significant at p=0.05 level *** Significant at p=0.01 level 141
Table 8.5 Logit Regression Analysis of Accessibility Factors (Continued)
Variable Cluster Period B Exp(B) 1990-1995 -.61 .54 1 1995-2000 -.01 .98 1990-1995 -.60 .54 L_SHOP90_1 (95) 2 1995-2000 -.43 .64 1990-1995 -.57 .56 All 1995-2000 -.12 .88 1990-1995 .26 1.30 1 1995-2000 .03 1.03 1990-1995 -.19 .82 L_SHOP90_2 (95) 2 1995-2000 .15 .85 1990-1995 -.04 .95 All 1995-2000 .19 1.21 1990-1995 .36 1.43 1 1995-2000 -.44 .64 1990-1995 -.10 .90 N_SHOP90_1 (95) 2 1995-2000 -.24 .78 1990-1995 -.04 .95 All 1995-2000 -.40*** .67 1990-1995 -.84 .42 1 1995-2000 .47 .62 1990-1995 -.17 .84 N_SHOP90_2 (95) 2 1995-2000 -.02 .97 1990-1995 -.19 .82 All 1995-2000 -.03 .96 * Significant at p=0.1 level ** Significant at p=0.05 level *** Significant at p=0.01 level
Interestingly, almost none of the accessibility variables were significantly associated with parcels’ probabilities of infill housing development in this study (Table
8.5). Accessibility to transit stations (TRANSIT_1 and TRANSIT_2), the central business district (D_CBD), and neighborhood and local shopping centers (N_SHOP# and
L_SHOP#) were not significantly associated with the parcels’ infill housing development in all of the neighborhood clusters and both study periods. D_EMPLOY_2 (being located within 1 mile from major employment centers) showed significant association.
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However, its inverse correlation was inconsistent with the conventional hypothesis, in suggesting that more housing developments took place more than one mile from the employment centers than within one mile. Therefore, the analysis results failed to support the traditional hypothesis about the effect of accessibility on residential development patterns.
8.2.3 Amenities
Proximity to amenities such as recreation centers has generally been considered to be an important positive externality for residential development. However, the results of the regression analysis are vague and do not support the hypotheses very well. Only
10 out of 36 tests of the individual variables show a significant association between the amenity variables and the parcel’s development potential (Table 8.6).
These results suggest that urban housing development is generally not always situated in the close proximity to the amenities. Overall, the hypotheses about the importance of the amenities in locating urban development cannot be supported by the analysis results.
8.2.4 Disamenities
Urban models often consider negative externalities for residential land use, such as proximity to pollutants or noise to discourage housing development. In this analysis, the disamenity variables were found to have a limited association with housing development (Table 8.7). The relationship between the proximity to railroads and development potential seemed to be inconsistent over the study period and across the neighborhood clusters. Therefore, the parcels located closer to the railroads were not consistently less likely to be developed than those located further away.
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Table 8.6 Logit Regression Analysis of Amenity Factors
Variable Cluster Period B Exp(B) 1990-1995 -19.29 .00 1 1995-2000 -19.09 .00 1990-1995 -.30 .73 D_RECREAT_1 2 1995-2000 -.63 .52 1990-1995 -.41 .65 All 1995-2000 -.62 .53 1990-1995 -.95 .38 1 1995-2000 -.25 .77 1990-1995 -.62* .53 D_RECREAT_2 2 1995-2000 -.16 .83 1990-1995 -.83** .43 All 1995-2000 -.12 .88 1990-1995 .63 1.87 1 1995-2000 -.62 .53 1990-1995 .43 1.55 PARK_01 2 1995-2000 .68*** 1.97 1990-1995 .39 1.48 All 1995-2000 .35* 1.42 1990-1995 .88 2.42 1 1995-2000 .48 1.62 1990-1995 .69*** 2.00 PARK_02 2 1995-2000 -.48** .61 1990-1995 .93 2.54 All 1995-2000 -.22 .79 1990-1995 1.75** 5.75 1 1995-2000 .19 1.21 1990-1995 -1.59*** .20 HISTORIC_01 2 1995-2000 -.51 .59 1990-1995 .17 1.89 All 1995-2000 .38 1.47 1990-1995 1.33* 3.78 1 1995-2000 .40 1.50 1990-1995 -.51 .60 HISTORIC_02 2 1995-2000 .08 1.09 1990-1995 .45 1.56 All 1995-2000 .63*** 1.87 * Significant at p=0.1 level ** Significant at p=0.05 level *** Significant at p=0.01 level
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Table 8.7 Logit Regression Analysis of Disamenity Factors
Variable Cluster Period B Exp(B) 1990-1995 2.09** 8.15 1 1995-2000 -31 .72 1990-1995 -.03 .96 RAIL_01 2 1995-2000 -.14 .86 1990-1995 .29 1.34 All 1995-2000 -.15 .85 1990-1995 -2.61*** .07 1 1995-2000 -.32 .72 1990-1995 -.66** .51 RAIL_02 2 1995-2000 -.23 .79 1990-1995 -.88*** .41 All 1995-2000 -.00 .99 1990-1995 -18.24 .00 1 1995-2000 .61 1.84 1990-1995 -.86 .42 LUST9095 (9500) 2 1995-2000 -1.70 .18 All 1990-1995 -1.63* .19 1995-2000 -.13 .87 1990-1995 .58 1.79 1 1995-2000 -.55 .57 1990-1995 -2.17*** .11 TRI9095 (9500) 2 1995-2000 -.96** .38 1990-1995 -1.65** .19 All 1995-2000 -1.01** .36 1990-1995 -2.66 .07 1 1995-2000 -.88 .41 D_HINDUS90_1 1990-1995 -.24 .78 2 (95) 1995-2000 -1.43*** .23 1990-1995 -.51 .59 All 1995-2000 -.84** .43 1990-1995 -.03 .97 1 1995-2000 -.38 .68 D_HINDUS90_2 1990-1995 -.71** .48 2 (95) 1995-2000 -40* .66 1990-1995 .03 1.03 All 1995-2000 -.11 .89 * Significant at p=0.1 level ** Significant at p=0.05 level *** Significant at p=0.01 level
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The analysis seemed to suggest that parcels located within 250 feet of the
Leaking Underground Storage Tanks or LUSTs (LUST9095) tended to have a lower probability of infill housing development only citywide for the first study period.
However, locations within 500 feet of Toxic Release Inventory or TRI sites (TRI9095 and TRI9500) were negatively associated with the development potential in Cluster-2 neighborhoods and citywide during both study periods. The TRIs seemed to have a larger negative effect on the development locations than the LUSTs. It could be understandable if you recognize that LUSTs usually do not have spatially extensive contamination beyond the site while the effect of TRI sites extends beyond the toxic release facilities.
The 0.1-mile proximity to heavy industrial land uses (D_HINDUST95_1) seemed to significantly decrease a parcel’s development potential in Cluster-2 neighborhoods and the entire city only in the second study period. Also, the parcels located within 0.25 miles of the heavy industrial sites (D_HINDUST90_2 and
D_HINDUST95_2) were less likely to be developed in the Cluster-2 neighborhoods in both study periods. The negative externality of the proximity to heavy industry apparently discouraged any residential development.
Disamenities such as railroad tracks, TRI, LUST, and industrial sites have negative externality effects on the surrounding area, lowering the development potential in neighboring sites. Although the association was somewhat limited, the analysis results showed that the direction of association (i.e., inverse correlation) was consistent with the hypothesis on the disamenity factor.
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8.2.5 Site Assembly Potential
The site assembly potential factor is assumed to be positively associated with a parcel’s probability of being developed. The analysis shows that parcels adjacent to vacant residential parcels (ADJ_RVAC90) were more likely to be developed in the
Cluster-2 neighborhoods and citywide during the first study period (Table 8.8).
However, the variable was not significant in other areas and other time periods. This suggests that being located adjacent to residential vacant land does not always increase the parcel’s development potential. The analysis fails to support the hypothesis about the site assembly potential factor.
Table 8.8 Logit Regression Analysis of Site Assembly Potential and Other Positive Factors
Variable Cluster Period B Exp(B) 1990-1995 -.73 .47 1 1995-2000 .47 1.60 1990-1995 .48** 1.62 ADJ_RVAC90 (95) 2 1995-2000 .00 1.00 1990-1995 .42** 1.52 All 1995-2000 .07 1.07 1990-1995 -.63 .53 1 1995-2000 1.28*** 3.29 1990-1995 2.12*** 8.35 D_RES8690_1 (9195) 2 1995-2000 .34** 1.41 1990-1995 1.52*** 4.58 All 1995-2000 .43*** 1.54 1990-1995 .66 1.94 1 1995-2000 .15 1.16 1990-1995 .63*** 1.87 D_RES8690_2 (9195) 2 1995-2000 .44** 1.55 1990-1995 .74*** 2.09 All 1995-2000 .19 1.22 * Significant at p=0.1 level ** Significant at p=0.05 level *** Significant at p=0.01 level
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8.2.6 Other Positive Externalities
Parcels located near recently-built residential development were assumed to have a higher probability of infill housing development than other parcels. This study used two distance measure, 0.1 and 0.25 miles from recently developed parcels, to test this hypothesis. In both study periods, the positive externality was significant in Cluster-2 neighborhoods and citywide (Table 8.8). The Cluster-1 neighborhoods were significant at only 0.1-mile distance during the second period. Therefore, the hypothesis about the positive externality of recent housing developments was supported in Cluster-2 neighborhoods and citywide and partly supported in the Cluster-1 neighborhoods.
8.2.7 Institutional Factors
According to the logit regression analysis (Table 8.9), the land banking program
(LBANK_#) was the most significant factor among all of the non-institutional and institutional factors in all neighborhood clusters and the entire city during both study periods. Parcels in the land bank program were more likely to be developed than any other parcels, ceteris paribus . The significance of this factor also became larger over the two study periods.
The land bank variable was a much more important factor in the Cluster-1 neighborhoods than in the Cluster-2 neighborhoods. This may be due to the fact that the
Cluster-1 neighborhoods have a limited amount of non-land-bank developable land (e.g., vacant residential lots) compared to the Cluster-2 neighborhoods. Therefore, the land bank sites would be more valued by infill developers in the Cluster-1 neighborhoods than in the Cluster-2 neighborhoods.
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Table 8.9 Logit Regression Analysis of Institutional Factors
Variable Cluster Period B Exp(B) 1990-1995 4.24*** 69.42 1 1995-2000 4.69*** 109.20 1990-1995 1.83*** 6.24 LBANK_90 (95) 2 1995-2000 2.98*** 19.21 1990-1995 1.82*** 6.22 All 1995-2000 2.65*** 14.20 1990-1995 -.24 .78 1 1995-2000 1.14** 3.14 1990-1995 .17 1.19 ADJ_BANK90 (95) 2 1995-2000 .91*** 2.50 1990-1995 -.02 .97 All 1995-2000 .61*** 1.85 1990-1995 2.00** 7.45 1 1995-2000 .68 1.98 1990-1995 1.21*** 3.36 PLANRES_1 2 1995-2000 .57** 1.77 All 1990-1995 1.38*** 4.00 1995-2000 .73*** 2.08 1990-1995 .60 1.83 1 1995-2000 .14 1.15 1990-1995 -.11 .89 PLANRES_2 2 1995-2000 -.41* .65 1990-1995 .05 1.06 All 1995-2000 -.38** .68 * Significant at p=0.1 level ** Significant at p=0.05 level *** Significant at p=0.01 level
Parcels adjacent to land bank sites (ADJBANK_95) were likely to be developed more than other parcels in the Cluster-1 and Cluster-2 neighborhoods and the entire city.
This factor, however, was only significant in the second study period.
Parcels within 0.1 miles of a potential residential development area identified in the citywide comprehensive plan, CivicVision 2000 , (PLANRES_1) were very likely to be developed in both neighborhood clusters and citywide during both study periods except in the Cluster-1 neighborhood during the second study period. The city comprehensive plan seemed to significantly draw attention from the housing developers 149
in the study area. However, the significance of the plan seemed to be diminished in the second study period.
8.3. Summary
This chapter examined the association between the parcel-level factors and the probability of infill housing development in Cleveland between 1990 and 2000. The parcel-level analysis was done for three neighborhood clusters and for the entire city to consider the effect of parcel-level factors for different types of neighborhoods.
Table 8.10 summarizes the test results in light of the research hypotheses. The table indicates the number of regression models in which the variables were proved to be significant. Bold characters shows that the hypotheses that were supported by the analysis.
The institutional factor was consistently significant throughout the neighborhood clusters and during both study periods. Most of the hypotheses about the institutional factor were supported by the test results.
In general, the analysis results upheld the hypotheses for the four non- institutional factors, i.e., site physical feature, disamenity, site assembly potential, and other positive externality factors. The test results of other non-institutional factors such as accessibility and amenity were inconsistent and, in many cases, contrary to the traditional hypotheses of urban models.
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Table 8.10 Summary of Hypotheses Test Results
Factors Variables Hypothetical Sign Test Result of Correlation with Parcel’s Development Probability SITE PHYSICAL Size of parcels Positive N/S FEATURES Residential vacant land (1=yes, 0=no)****** Positive Positive ACCESSIBILITY 0.1 miles from rapid transit stations Positive N/S 0.25 miles from rapid transit stations* Positive Negative Distance from central business districts Negative N/S 0.5 miles from other employment centers Positive N/S 1 mile from other employment centers***** Positive Negative 0.1 miles from local, medium shopping centers Positive N/S 0.25 miles from local, medium shopping centers Positive N/S 0.1 miles from neighborhood shopping centers* Positive Negative 0.25 miles from neighborhood shopping centers Positive N/S AMENITIES 0.1 miles from recreation centers Positive N/S 0.25 miles from recreation centers** Positive Negative 0.1 miles from parks* Positive Positive 0.25 miles from parks** Positive Inconsistent 0.1 miles from historic or landmark districts** Positive Inconsistent 0.25 miles from historic or landmark districts** Positive Positive Distance from the recently built housing units developed for the last 5 years DISAMENITIES 0.1 miles from to railroads* Negative Positive 0.25 miles from to railroads*** Negative Negative 250 feet from LUSTs* Negative Negative 500 feet from TRIs**** Negative Negative 0.1 miles from heavy industrial land use** Negative Negative 0.25 miles from heavy industrial land use** Negative Negative SITE Adjacency to residential vacant lots (1=yes, 0= no)** Positive Positive ASSEMBLY POTENTIAL OTHER Distance from the recently built housing units Positive Positive POSITIVE developed for the last 5 years (1986-1990)***** EXTERNALITY Distance from the recently built housing units Positive Positive developed for the last 5 years (1991-1995)*** INSTITUTIONAL Land bank site (1=yes, 0= no)****** Positive Positive Adjacency to land bank sites (1=yes, 0= no)**** Positive Positive 0.1 miles form potential residential development Positive Positive identified in city’s comprehensive plan***** 0.25 miles from potential residential development Positive Negative identified in city’s comprehensive plan**
NOTE: 1. The grey shading indicates the variables were significant and consistent with research hypotheses. 2. The number of * symbols shows the number of the logit regression models in which the variables were found to be significant 3. The “Test Result” column shows the analysis results based on the significant variables at p=0.1 level. If the result of a variable says inconsistent , the sign shows both positive and negative. 4. “N/S” in the Test Result column indicates that the variable was not significantly associated with parcel’s development potential in any clusters.
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In order to compare the importance of the institutional and non-institutional factors (i.e., the second main hypothesis), the study examined two submodels within each logit regression model for each study period. The first-submodel block included either the institutional or non-institutional factors and the second submodel included all the factors. By examining the R squares (Nagelkerke) of the submodels, the study could determine the influence of each set of factors on the probability of infill housing development.
Table 8.11 shows the R squares values for the submodels. In the first study period, the block of non-institutional factors was more significantly associated with the probability of infill development than the block of institutional factors. In the second period, the block of institutional factors had a more significant association than the non- institutional factors.
Table 8.11 R Square Changes of Logit Regression Block-Submodels
Influence of Institutional Factors Influence of Non-institutional Factors Blocks R square Blocks R square Non-institutional .351 Institutional Factors .298 Factors 1991-1995 Plus Institutional .486 Plus Non-institutional .486 Model Factors Factors Difference .135 Difference .188 Non-institutional .254 Institutional Factors .388 Factors 1996-2000 Plus Institutional .475 Plus Non-institutional .475 Model Factors Factors Difference .221 Difference .087
As noted earlier, the influence of the institutional factors, especially the land bank program, became stronger in the second half of the 1990s. The land bank program seemed to become a critical factor inducing urban infill housing development over the
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decade. In addition, the influence of the institutional factors of the second half of the
1990s was greater than that of non-institutional factors of the first half of the decade.
The following tables (Tables 8.12, 8.13, and 8.14) show the significant variables at p=0.1 level by neighborhood cluster and by study period and, the ranking of variables based on the degree of correlation. The main purpose of this ranking of individual variables is to compare institutional and non-institutional factors regarding their association with parcel-level development potential.
The analysis proved that land bank program (LBANK_#) was most significantly associated with the parcel-level likelihood of housing development across the neighborhood types and throughout the study period (except for the Cluster 2 neighborhoods in the first study period). Moreover, the degree of the association increased during the study period. Vacant residential land (R_VACANT#) was the only non-institutional factor that was statistically significant for all the clusters and study periods. However, its significance became weaker over the study period.
Table 8.12 Ranks of Parcel-level Factors in Cluster-1 Neighborhoods
Period Ranking Factor Variable B 1 Institutional LBANK_90 4.24*** 2 Disamenity RAIL_2 -2.61*** 3 Disamenity RAIL_1 2.09** 4 Institutional PLANRES_1 2.00** 1990-1995 5 Site Physical R_VACANT90 1.94*** 6 Accessibility D_EMPLOY_2 -1.76** 7 Amenity HISTORIC_1 1.75** 8 Amenity HISTORIC_2 1.33* 1 Institutional LBANK_95 4.69*** 2 Site Physical R_VACANT95 2.20*** 1995-2000 3 Other Externality D_RES9195_1 1.28*** 4 Institutional ADJBANK_95 1.14**
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Table 8.13 Ranks of Parcel-level Factors in Cluster-2 Neighborhoods
Period Ranking Factor Variable B 1 Disamenity TRI9095 -2.17*** 2 Other Externality D_RES8690_1 2.12*** 3 Institutional LBANK_90 1.83*** 4 Amenity HISTORIC_1 -1.59*** 5 Site Physical R_VACANT90 1.50*** 6 Institutional PLANRES_1 1.21*** 7 Amenity PARK_3 0.90*** 1990-1995 8 Accessibility D_EMPLOY_2 -0.87** 9 Disamenity D_HINDUS90_2 -0.71** 10 Amenity PARK_2 0.69*** 11 Disamenity RAIL_2 -0.66** 12 Other Externality D_RES8690_2 0.63*** 13 Amenity D_RECREAT_2 -0.62* 14 Assembly ADJ_RVAC90 0.48** 1 Institutional LBANK_95 2.98*** 2 Disamenity D_HINDUS95_1 -1.43*** 3 Site Physical R_VACANT95 1.07*** 4 Disamenity TRI9500 -0.96** 5 Accessibility D_EMPLOY_2 -0.92*** 6 Institutional ADJBANK_95 0.91*** 7 Amenity PARK_1 0.68*** 1995-2000 8 Institutional PLANRES_1 0.57** 9 Amenity D_RECREAT_3 0.51*** 10 Amenity PARK_2 -0.48** 11 Other Externality D_RES9195_2 0.44** 12 Institutional PLANRES_2 -0.41* 13 Disamenity D_HINDUS95_2 -0.40* 14 Other Externality D_RES9195_1 0.34**
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Table 8.14 Ranks of Parcel-level Factors for the City
Period Ranking Factor Variable B 1 Institutional LBANK_90 1.82*** 2 Disamenity TRI9095 -1.65** 3 Disamenity RAIL_2 -1.63* 4 Other Externality D_RES8690_1 1.52*** 5 Site Physical R_VACANT90 1.49*** 1990-1995 6 Institutional PLANRES_1 1.38*** 7 Accessibility D_EMPLOY_2 -0.95*** 8 Disamenity RAIL_2 -0.88*** 9 Amenity D_RECREAT_2 -0.83** 10 Other Externality D_RES8690_2 0.74*** 11 Site Assembly ADJ_RVAC90 0.42** 1 Institutional LBANK_95 2.65*** 2 Site Physical R_VACANT95 1.11*** 3 Disamenity TRI9500 -1.01** 4 Amenity HISTORIC_03 0.93*** 5 Disamenity D_HINDUS95_1 -0.84** 6 Accessibility D_EMPLOY_2 -0.81*** 7 Institutional PLANRES_1 0.73*** 1995-2000 8 Amenity HISTORIC_2 0.63*** 9 Institutional ADJBANK_95 0.61*** 10 Accessibility TRANSIT_2 -0.59* 11 Other Externality D_RES9195_1 0.43*** 12 Accessibility N_SHOP95_1 -0.40*** 13 Institutional PLANRES_2 -0.38** 14 Amenity PARK_1 0.35*
The importance of the institutional factors was almost consistently important for the different types of neighborhoods. In particular, the land bank program appeared to provide a significant incentive to infill housing developers by providing clean, vacant, low-cost, developable land which is usually difficult to find in built-up, declining urban areas. Also, the citywide master plan seems to strongly influence the location of infill housing development at the parcel level.
The analysis suggests that urban modelers should also consider some traditional
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variables. Site physical features (e.g., residential vacant land), disamenities (e.g., proximity to Toxic Release Inventory sites), and other positive externality factors (e.g., proximity to recent housing development) were more consistently and significantly related to infill housing development than other non-institutional factors. However, accessibility to employment and shopping centers and other externality factors did not have a consistently significant influence on the location of the urban housing development at the parcel level.
Finally, the study suggests that urban modelers would benefit from the neighborhood cluster-based analysis. As shown in this chapter, each neighborhood cluster has particular characteristics and parcel-level variables have different effects in different neighborhood clusters.
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CHAPTER IX
CONCLUSIONS
9.1 Summary
The main purpose of this study is to contribute to the field of urban modeling by identifying factors associated with the location of infill housing development in older
American cities with weak housing markets. The focus of this research was on the institutional factors that were assumed to provide support for urban housing development that had not been considered in current urban models. This study also examined the effect of traditional site and neighborhood factors of contemporary urban models.
Therefore, the factors to be considered in this analysis included both institutional factors and traditional market or non-institutional factors.
The study placed a particular emphasis on the policies and programs of governments, private and nonprofit institutions which were assumed to play significant roles in promoting the housing development in the declining American cities. This assumption was based on the premise that the market alone does not provide sufficient resources and motivation to help struggling and worn-down urban neighborhoods.
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Urban infill housing development has been promoted by policy makers and planners as a tool for revitalizing urban neighborhoods and for reducing urban sprawl.
Realizing the importance of the housing development in urban built-up areas, this study attempts to improve the knowledge base for urban modeling that can be used as an effective tool for planning and managing urban infill housing development. To do so, this study examined the usefulness of existing urban modeling theories for the older urban areas and attempted to identify gaps between these theories and the current development activities in urban areas. It did this by closely examining the role of non- traditional institutional factors and the traditional market-oriented factors (i.e., site and neighborhood characteristics). This study analyzed the City of Cleveland, Ohio, during the 1990s.
The first main questions of this study were: (1) Which institutional factors are significantly associated with the location of urban infill housing development in declining
American cities? and (2) Do non-institutional factors, i.e., site and neighborhood characteristics, matter to the location of residential development in urban built-up areas?
Most of the institutional factors were found to be significant. The land bank program was found to provide very significant incentives to developers and, consequently, played an important role in guiding the process of the infill housing development. Low- income and minority-concentrated neighborhoods targeted by the Neighborhood
Reinvestment Agreements received better chances to secure loans for development and home buying activities, which in turn increased housing development in those areas.
Places identified as potential residential development sites in a comprehensive plan and neighborhoods within Empowerment Zones also had significant association with
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residential development. Ward politics seemed to influence the neighborhood-level development rates although there was inconsistent on pattern during the study period.
Several traditional modeling factors also showed significant correlation with infill housing development pattern. Vacant residential land was found to be an attractive location for housing development. Neighborhoods with more residential land use and residential vacant land gained more housing developments than other neighborhoods.
Parcels located near recent housing development were also more likely to be developed.
Interestingly, minority-concentrated, poverty-stricken, crime-ridden, population-losing neighborhoods experienced the most housing development activities. However, these unexpected results were found to be closely related to the institutional factors. This reflects that fact that governments and other supporting institutions had a mission of revitalizing depressed neighborhoods and their programs targeted disenfranchised neighborhoods. Finally, the accessibility factor, a leading factor of urban modeling, did not have a strong and consistent association with the location of urban residential development. In particular, accessibility to the CBD and shopping areas seems to have little influence on the location of infill housing development.
The second main question was: Which factors are more significantly associated with the location of housing development in older, weak market cities? According to the neighborhood-level correlation analysis and parcel-level logit regression analysis, the land bank and NRA programs institutional factors were the most significant factors among all the factors considered in this study. It was evident that the institutional support system was the critical factor in promoting housing developments in the decades- old deprived urban neighborhoods. Almost as important as the two institutional factors
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was the availability of vacant residential land. Acquiring developable land in blighted urban neighborhood is a daunting task in older cities with many abandoned and tax delinquent properties that also have entangled title issues. Developable vacant residential land, much of which were prepared by the City, was thus an oasis for most infill developers.
9.2 Implications
The findings of this study have important implications for urban modelers and planners. In particular, the study identified several important variables for urban models of infill housing development and examined the spatial process of urban infill housing development in old declining American city, which have been rarely researched by urban modelers and planners.
9.2.1 Implications for Urban Modelers
Urban models for the residential development in struggling older cities should understand and take the city’s institutional support system seriously.
Housing development projects in cities with weak housing markets require a variety of resources from local governments and many other organizations. Political, financial, and technical support such as gap financing and land assembly from governments and other institutions can help urban housing developers overcome many obstacles. Public and private institutions also provide a range of political, financial and operational supports for urban infill housing developers. Some of these policies and programs are location-explicit such as the land bank program and target neighborhood reinvestment program.
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This study suggests that the institutional support system significantly affects the location of urban housing development in old declining cities. In the 1990s, the City of
Cleveland had a number of major location-specific institutional factors such as the land reutilization program, the Community Reinvestment Act-based neighborhood reinvestment program, an operational funding program, and empowerment zones. The target location of those programs was found to be closely associated with the location of urban residential development at both the neighborhood and parcel levels.
In order to build location models for urban residential development for a struggling urbanized area, urban modelers should pay particular attention to institutional factors. Without doubt, the need for the subsidies and incentives from governments and other institutions is much higher for the urban housing development in a weak urban market than in a growing suburban market. As a result, the institutional factors were as important – and often more important – than the traditional or non-institutional factors
(i.e., site and neighborhood characteristics).
Urban models of infill housing development for urban built-up area should consider factors related to land availability such as the land bank program and residential vacant land.
It is difficult to find and acquire developable, i.e., clean and inexpensive, land in built-up older central cities. Although many cities have many abandoned and underutilized properties, the cost of site acquisition, assemblage, and demolition and remediation are often prohibitively high. Therefore, developers often prefer well- prepared, inexpensive land bank sites or and residential vacant lots. Vacant or abandoned houses are also preferred because relocation payments that are required by
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governmental regulations such as Uniform Relocation Program are not required. In sum, urban modelers interested in urban infill housing development should pay particular attention to the issues of land availability.
Urban modelers interested in urban infill residential development may care less about general accessibility to employment or shopping centers than other factors.
In a citywide context, the general accessibility to employments and shopping facilities was found to have a small association with the location of infill housing development. Urban modelers have used accessibility as one of the most critical factor in the location decision of residential households. However, the accessibility factor is apparently not an important factor for the location of infill housing development, especially, in the cities with easy assess to the CBD, employment centers, and shopping centers from any location.
Urban models of infill housing development for older American cities should pay attention to the interaction effects between neighborhood and site characteristics.
This study found that the parcel-level location factors, both institutional and non- institutional, of urban infill housing development had different levels of and association with the development potential of different types of neighborhoods. In other words, there were some significant interaction effects between the neighborhood and site factors.
For example, in neighborhoods that had well-maintained housing stock (Cluster-1 type in this study), residential vacant land seemed to be more important than in the neighborhoods of other types. Similarly, the land bank program sites were less important in neighborhoods that had sufficient supply of residential vacant land (Cluster-
2 type in this study).
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9.2.2 Implications for Urban Planners
The location of housing development can be guided by governmental and institutional interventions in a depressed, weak housing market
Many neighborhoods in struggling older cities have been neglected and abandoned by residents, absentee landlords, developers, and investors. However, the experience in Cleveland suggests that those disadvantaged neighborhoods experienced a significant amount of development in the 1990s. As described above, difficult neighborhoods (e.g., poverty- and minority concentrated neighborhoods) were targeted and the housing developers in the neighborhoods were aided by institutional factors such as neighborhood reinvestment agreement and land bank programs. The effects of these efforts were reflected in the significant spatial correlation between some of the market and institutional factors.
This suggests that planners should have confidence in the effectiveness of institutional factors in guiding inner city housing development. However, urban planners should focus on collaborating with other institutional players and explicitly targeted locations for their efforts. In particular, coordinated efforts to support geographically targeted areas are more likely to achieve the desirable or planned spatial pattern of infill housing development. Planners should also pay attention to different types of neighborhoods because, as this study indicated, institutional factors perform somewhat differently in the different types of neighborhoods.
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9.3 Future Research
This study attempted to identify factors associated with the location of urban infill housing development in the context of urban modeling. The findings of this study will certainly make a contribution to the knowledge base of urban modelers and planners.
However, the topic, the research methods, and outcome of this study also point to improvements that could be made and new areas that could be studied by future research.
First, a logical sequence of this study suggests an analysis of the next 5-year trend of infill housing development in Cleveland, i.e., between 2000 and 2005. The sequential analysis will supplement this study by looking into the influence of institutional factors over a longer period.
Second, more cities should be examined to broaden our understanding of the spatial process of urban residential development in American older cities. This suggests that future studies should create a typology of factors for different types of cities and neighborhoods. Different types of cities and neighborhoods may have different associations of which the factors with the location of housing redevelopment. It might also be useful to categorize different types of institutional players and their unique agendas and influences regarding the geographic targets for their efforts. This will help urban modelers and planners extend and improve their knowledge on the effects of different factors, both institutional and non-institutional.
Third, future studies should look at the location of infill houses with different sales prices. This study treated all the new housing development equally on the assumption that most of the housing developments are affordable-rate housing.
However, cities like Cleveland have also experienced a significant momentum for
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market-rate housing developments. According to urban modeling and economic theories, houses with different values are likely to be segregated. Also, the institutional and non-institutional factors would probably have different location effects on the houses with different price ranges. This type of research will provide a more sophisticated and realistic understanding of the spatial process of urban residential development.
Fourth, other types of institutional factors such as home buyer assistance programs should be researched in order to expand the knowledge base on the effects of institutional factors on the location of urban infill housing development. This study focused on institutional supports that had a direct impact on the supply side of housing development. However, there are other important institutional factors such as below- market-rate mortgage and tax abatements that do not have clear locational preferences.
It would be worthwhile to study the spatial pattern of the infill housing subsidized by the demand-oriented, home buyer assistance programs.
Lastly, future studies are should also consider the spatial process of other important forms of urban housing redevelopment such as housing rehabilitation and adaptive reuse in older cities. In many cities, housing rehabilitation has been an important way of preserving existing housing stock and providing decent and affordable housing for urban residents. Adaptive reuse of commercial or industrial buildings for apartments or condominiums has also become popular in many central cities for both affordable and market-rate housing. As a result, in addition to considering new construction of infill housing, future studies should also examine housing rehabilitation and reuse which are important housing mechanisms for many older urban areas. There are few spatial modeling studies of these increasingly important housing activities.
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APPENDIX
MAPS OF INSTITUTIONAL AND NON-INSTITUTIONAL FACTORS
This Appendix contains the map layers of the institutional and non-institutional factors that were used in the statistical analyses of this study. The figures from A.1 to
A.16 show neighborhood-level factors, while the rest from A.17 to A.24 show site-level ones.
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Figure A.1 NRA Minority Target Neighborhoods in 1994
Figure A.2 NRA Low-Income Target Neighborhoods in 1994
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Figure A.3 Percent Land Bank Sites in 1990-1995 and 1995-2000
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Figure A.4 Service Areas of CNPP Grant Recipients in 1990-1995 and 1995-2000
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Figure A.5 Empowerment Zone in 1994
Figure A.6. Cleveland Ward Boundary in 1990 185
Figure A.7 Average Commuting Time by Census Tract in 1990
Figure A.8 Percent White Population by Census Tract in 1990
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Figure A.9 Population Growth Rates by Census Tract between 1980 and 1990
Figure A.10 Percent College Graduates by Census Tract in 1990
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Figure A.11 Average Median Household Income by Census Tract in 1990
Figure A.12 Population Density (People Per Acre) by Census Tract in 1990
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Figure A.13 Criminal Incidents Per 1,000 Residents by Census Tract in 1990 and 1995
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Figure A.14 Percent Residential Vacant Land by Census Tract in 1990 and 1995
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Figure A.15 Percent Residential Land Use by Census Tract in 1990 and 1995
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Figure A.16 Percent Residential Development in 1986-1990 and 1991-1995
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Figure A.17 Land Bank Sites in 1990-1995 and 1995-2000
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Figure A.18 Potential Development Sites Identified in City Plan in 1990
Figure A.19 Employment Centers and Transit Stations in 1990
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Figure A.20 Neighborhood and Local Shopping Centers in 1990 and 1995
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Figure A.21 Recent Housing Developments in 1986-1990 and 1991-1995 196
Figure A.22 LUST, TRI and Heavy Industrial Sites in 1990 and 1995 197
Figure A.23 Railroads in 1990
Figure A.24 Historic and Landmark Districts, Parks and Recreation Centers in 1990
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