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The Impact of Apartment Complexes on Property Values of Single-Family Dwellings West of lnterstate-680 in Omaha, Nebraska
Lesli M. Rawlings University of Nebraska at Omaha
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Recommended Citation Rawlings, Lesli M., "The Impact of Apartment Complexes on Property Values of Single-Family Dwellings West of lnterstate-680 in Omaha, Nebraska" (2002). Student Work. 1145. https://digitalcommons.unomaha.edu/studentwork/1145
This Thesis is brought to you for free and open access by DigitalCommons@UNO. It has been accepted for inclusion in Student Work by an authorized administrator of DigitalCommons@UNO. For more information, please contact [email protected]. The Impact of Apartment Complexes on Property Values of
Single-Family Dwellings West of Interstate-680 in Omaha, Nebraska
A Thesis
Presented to the
Department of Geography and Geology
and the
Faculty of the Graduate College
University of Nebraska
In Partial Fulfillment
of the Requirements for the
Master of Arts Degree in Geography
University of Nebraska at Omaha
by
Lesli M. Rawlings
July 2002 UMI Number: EP73385
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Acceptance for the faculty of the Graduate College, University of Nebraska, in partial fulfillment of the Requirements for the degree Master of Arts, University of Nebraska at Omaha.
Committee
Chairperson ' 'N \ \ ^ ’s \ ' \ a «• c~\ The Impact of Apartment Complexes on the Property Values of
Single-Family Dwellings West of Interstate 680 in Omaha, Nebraska
ABSTRACT
Lesli M. Rawlings, M.A.
University of Nebraska, 2002
Advisor: Dr. Charles R. Gildersleeve
The literature concerning land use exhaustively describes why homeowners dislike apartment complexes, but fails to analyze the problem quantitatively. The objectives of this thesis were to determine if the selling price of single-family dwellings increased with increasing distance from the apartment complex and to determine if the selling price of single-family dwellings decreased with increasing structural density of apartment complexes in Omaha, Nebraska. Fifty apartments built in the year 2000 or before, and 1,665 single-family dwellings, which sold in 1999 and 2000 within 914.4 meters of an apartment complex, were geocoded by address. Data needed to test the hypotheses were sales price, structural characteristics of the dwellings, straight-line distance, and the number of apartments influencing each dwelling. Quantitative methods employed to test the hypotheses included a full and reduced attribute multiple regression model, factor analysis, and regression analysis using factor scores. The results indicated that sales price did increase with increasing distance from an apartment complex only with the utilization of factor analysis and regression using factor scores. However, the increased number of apartment complexes proved to decrease the sales price of single family dwellings when implementing all of the multivariate analyses. ACKNOWLEDGEMENTS
The purpose of a Master’s thesis is to illustrate a student’s capability to complete an original research project, which contributes to existing knowledge. For the most part, completing a Master’s thesis is not effortless or unproblematic, but rather it is a thought- provoking process, which requires a student to utilize knowledge acquired throughout her academic career in addition to learning and implementing new methods of analysis. And although I encountered typical problems associated with research such as sleepless nights, writer’s block, paper jams, corrupted data files, meticulously examining databases for errors, and receiving a white hair during the process; I feel that my pursuit to complete a Master of Arts degree in Geography was a rewarding experience. I have expanded my knowledge of geography in addition to meeting fellow geographers whom I consider friends.
I would like to thank several people for their valuable assistance, especially my thesis committee. I believe that my thesis advisor Dr. Charles Gildersleeve and committee members Dr. Karen Falconer Al-Hindi, Dr. Michael Peterson, and Dr. Louis
Pol form the ultimate “Thesis Committee Dream Team.” I thank Dr. Gildersleeve for his unwavering leadership as a thesis advisor who continually provided me with encouragement. Dr. Gildersleeve contributed with ideas concerning urban geography, land use studies, and his experience as a former Omaha Planning Board member. Dr.
Karen Falconer Al-Hindi provided invaluable assistance with this thesis, which included introducing me to the geographers’ perspective of New Urbanism (neo-traditionalism), a pictorial explanation of a reverse distance decay model, and suggesting an additional viewpoint relating to the significance of my thesis. Dr. Peterson gave constructive ideas with reference to measuring distance in Arc View and map development. I am grateful for Dr. Pol who offered statistical assistance on various occasions and contributed to the literature review with the article concerning the impact Section 8 housing on the value of single-family dwellings. I would also like to thank Dr. Rutherford Platt, from the
University of Massachusetts, who granted me permission to use his zoning hierarchy diagram, which I adapted for this thesis.
Former UNOmaha Geography students also played a crucial role with this thesis.
I would like to thank Shawn Simon, who assisted me with the data acquisition, answering my detailed questions regarding structural characteristics of housing, and recalling information from his extensive mental map of Omaha. Since the beginning of my graduate studies, Laura Banker has been my GIS mentor. She has provided me with insight regarding the creation of buffers in Arc View 3.2 and advising the implementation of using Avenue Scripts to measure straight-line distance.
I give a special thanks to the Graduate Studies Department for awarding me the
$1,000 Thesis Scholarship for the Spring 2002 semester. This scholarship paid for various expenses pertinent toward the completion of this thesis.
Last but not least, I would like to thank my parents Sonja and James Rawlings.
They have continuously supported my academic pursuits and without them I would not have excelled as a graduate student. I hope I will be able to make similar sacrifices if I have children. CONTENTS
THESIS ACCEPTANCE...... ii
ABSTRACT...... iii
ACKNOWLEDGMENTS...... iv
CONTENTS...... vi
LIST OF ILLUSTRATIONS...... viii
LIST OF TABLES...... ix
Chapter
1. INTRODUCTION...... 1
Nature of the Problem and Justification...... 4
Research Objectives...... 6
Hypotheses and Rationale...... 7
Significance of Research...... 9
Definition of General Terms...... 10
Study Area ...... 12
Chapter Summary...... 15
2. LITERATURE REVIEW...... 16
Undesired Residential Properties...... 16
The Market Value of New Urbanism...... 21
Price Distance Gradients with the Impact of Externalities...... 23
GIS Applications in Real Estate...... 25
Summary of Literature...... 26 3. METHODOLOGY 29
Data...... 29
Data Acquisition...... 31
GIS Procedures...... 32
Database Organization and Data Evaluation...... 35
Data Analysis...... 36
Summary of Methodology ...... 39
4. DISCUSSION OF RESULTS...... 41
Descriptive Statistics...... 41
Pearson Correlation...... 43
Multiple Regression ...... 45
Factor Analysis...... 48
Regression Utilizing Factor Scores ...... 54
Summary of Results...... 56
5. CONCLUSION...... 58
APPENDIX...... 61
A. ESRI Avenue Script...... 61
B. Scatter Plots: Sales Price Versus Housing Characteristics...... 65
C. Scatter Plots: Sales Price Versus Hypotheses Variables...... 69
D. Pearson Correlation Matrices...... 75
BIBLIOGRAPHY...... 80 LIST OF ILLUSTRATIONS
Figure Page
1. Hierarchy of Zoning ...... 2
2. Eagle Run Apartments Adjacent to Single-family Dwellings ...... 5
3. Conceptual Model of Distance ...... 8
4. Conceptual Model of the Number of Apartments ...... 8
5. Study Area...... 12
6. Typical Zoning Surrounding Apartments and Single-family Dwellings ...... 13
7. Heaviest Concentration of Apartments and Zoning ...... 14
8. Methodological Design ...... 30
9. ArcView 3.2 Interface ...... 34
10. Component Plot in Rotated Space ...... 53 LIST OF TABLES
Table Page
1. List of Variables ...... 31
2. Descriptive Statistics ...... 42
3. Descriptive Statistics for the Apartment Complexes ...... 43
4. Pearson Correlation Matrix ...... 44
5. Regression Results for Model 1 ...... 46
6. Regression Results for Model 2 ...... 47
7. Collinearity Statistics for Models 1 and 2 ...... 48
8. Communalities for Factor Analysis ...... 49
9. Total Variance Explained ...... 50
10. Unrotated Component Matrix ...... 52
11. Rotated Component Matrix ...... 52
12. Component Score Coefficient Matrix ...... 54
13. Regression Coefficients of Factor Scores for Model 3 ...... 55
14. Collinearity Statistics for Model 3 ...... 56 1
Chapter 1
INTRODUCTION
Court decisions, zoning laws, politicians, the federal government, and not-for- profit groups have all ingrained the idea that the American dream is to own a single family detached dwelling. During the 1930s, the federal government promoted homeownership when it entered the privatized housing industry by creating the Federal
Housing Administration and the Federal National Mortgage Association (Fannie Mae), which provide mortgage insurance (Levy 2000). In addition to these federal programs, favorable tax treatment is given to homeowners. For instance, homeowners may deduct their mortgage interest and property taxes from their taxable income, while renters cannot
(Levy 2000). Consequently, the federal government has contributed to the significant increase of home ownership rates from 1945 to 1980 through federal housing programs and tax policy (Hartshorn 1992 and Levy 2000). However by1980, the construction of multifamily dwellings was almost equivalent to the construction of single-family dwellings in the suburbs because of the elevated housing costs and the densification of the suburbs (Hartshorn 1992). In 1997, Omaha’s home ownership rate was 59 percent, which represented more than a 2 percent decrease from 1980 (Omaha Master Plan 1997).
Housing is the largest single type of land use in the United States. Housing has also been explained to represent a person’s socioeconomic status, power, and identity
(Adams 1984). The social factors of housing mentioned above should not be underestimated because it has been suggested that apartments adjacent to single-family neighborhoods challenge even threaten the so-called American dream (National 2
Apartment Association (NAA) 1997). Such occurs because the social fabric of the
American society considers home ownership higher on the social ladder than renting
(Adams 1987). In order to preserve this hierarchy, zoning ordinances protect single family dwellings. Thus, social inequalities exist where particular housing types are placed.
Zoning plays an important role because it dictates what specific types of land use can be developed in a particular area. Exclusionary zoning has protected detached single family residences from other land uses such as apartments, mobile home parks, businesses, and industries. Density factors, supposed aesthetic harm, and the lowering of property values are the most significant reasons given for zoning to exclude apartments
(Keating 1995). As a result, single-family dwellings are at the apex of the zoning hierarchy as show in Figure 1 (Adams 1987 and Platt 1996).
Single-family Dwelling
Apartment Complex
Commercial
Heavy Industry
Fig. 1. A hierarchy of zoning classifications adapted by permission, from Rutherford H. Platt, Land Use and Society: Geography, Law, and Public (Washington Policy D.C.: Island Press, 1996), 236. 3
Euclidean Zoning in effect prohibits the development of apartment complexes within single-family residential neighborhoods. However, there are some cases where developers submit proposals to rezone an area for apartment developments adjacent to single-family residences. In these instances, homeowners often request that the planning board vote against the proposal because they believe apartment complexes lower property values, are aesthetically displeasing, create a greater demand for goods and services, and increase noise pollution, crime rates, and traffic congestion in their neighborhoods.
A case study, which illustrates the opposition to apartments, comes from the July and August 2000 Omaha Planning Board meetings. Residents of the Stonybrook
Homeowner’s Association were vehemently opposed to the idea of apartments and instead favored a proposal for a light commercial development. Apparently, the commercial developer told residents if they did not accept his residential and commercial development, a high-density apartment complex would be in its place (Burbach 2000).
At any rate, residents told the Board that apartments would lower neighborhood property values because of their poor aesthetic quality and the enhancement of traffic congestion.
The irony is that planners have used apartments to buffer single-family residences from retail developments and major roads (Moudon and Hess, 2000).
Public hearings also suggest that homeowner activism at municipal planning board meetings is fueled by an alteration of their spatial vision. Purcell’s (2001) study concerning homeowner activism applies the concept of spatial visualization with a suburban homeowner’s view of what their landscape should look like. A suburban homeowner’s spatial vision can be described as a “bourgeois utopia” consisting primarily 4
of owner-occupied, single-family dwellings on large lots, occupied by nuclear families, and living in “harmony with nature” (Purcell 2001, 181). Land uses introduced in these areas such as malls, movie theatres, and multifamily dwellings disrupt the suburbanite’s spatial vision of a utopia landscape (Purcell 2001), which spurs their activism.
Nature of the Problem and Justification
The nature of the problem involves negative perception and litigation. The negative perception is clearly defined by John S. Adams’s statement that, “homeowners accept the idea of people living in apartments, but want them located someplace other than next door” (Adams 1987, 25). For the most part, the literature focuses on this perception, but fails to analyze the problem quantitatively. According to Omaha City
Acting Planning Manager Rod Phipps, there has been no quantitative study done for the
City of Omaha to determine if multifamily dwellings have an adverse affect on single family dwellings (Phipps 2000). This research study examines existing apartment complexes west of Interstate-680 and their impact on the sales price of single-family dwellings. Quantifying these results would help justify and confirm the homeowner’s negative perception, or dismiss it as a problem. 5
Fig. 2. A view of the Eagle Run Apartment Complex and adjacent to single-family dwellings.
In some cases homeowners who are near to higher-density housing or rental properties file lawsuits against the state if they believe their homes were over assessed for tax purposes. One such case involves a homeowner who filed an appeal with the
Wyoming State Board of Equalization challenging the appraiser’s market value assessment of their home in 1994. The owner claimed that the value of their home was depreciated by the lack of normal maintenance and its propinquity to condominiums
(Assessment Journal 1996). The county board declared that the assessor did not evaluate all features influencing value and concluded that the homeowner’s property fair market value was less than what was appraised (Assessment Journal 1996). Another case involves homeowners in Texas who filed a class action suit in 1986. Homeowners claimed that single-family rental properties present in their subdivision adversely affected their property values (Wang et al. 1991). 6
The Omaha City Master Plan strives for contiguous suburban growth for Omaha, where the development pattern:
“will be based on a series of high-density mixed use areas that contain, at a minimum, a combination of employment, shopping, personal services, ' open space, and multi-family uses in order to help relieve traffic congestion, allow for a more efficient use of mass transit, and help reverse the current pattern of strip commercial development” (Omaha Master Plan: “Land Use Element” 1997, 5).
Moudon and Hess’s (2000) study suggests that the nucleation of multifamily complexes should be viewed as positive suburban developments because high densities enable growth management, promote mixed land use, and social diversity (Moudon and Hess
2000). Again, the density related factor poses a problem for homeowners, who believe that apartment complexes require more goods and public services, thus increasing their property tax (NAA 1997). This research study analyzes the negative externalities, concerning high population densities to examine the effects they have on the selling price.
Research Objectives
The research study has two main objectives concerning single-family dwellings and their propinquity to apartment complexes west of Interstate-680 in Omaha, Nebraska.
Only existing apartment complexes were considered in this study and not condominiums and townhouses. Condominiums and townhouses are omitted because they can be rented or owned. Furthermore, this study does not take in consideration the potential effect minor roads and commercial properties have on the sales price of single-family dwellings. However, if the 914.4-meter (3,000-foot) radius of the apartment complex 7
extended across a major road where single-family dwellings sold, then it was assumed the particular apartment complex did not influence the dwellings’ sales price. Therefore, other studies are needed to differentiate the impacts of these land uses on the value of single-family dwellings.
• The first objective is to determine if proximity to apartment complexes has a
negative impact on the selling price of single-family dwellings.
• The second objective is to determine if the number of apartment complexes
influences the selling price of single-family dwellings.
Hypotheses and Rationale
If the assumptions were correct that apartment complexes increase population density, which result in a decrease of green space, promote traffic congestion, increase noise, or attract crime thus creating a negative externality, then the first hypothesis would be true (Refer to Figure 2). The second hypothesis should also be true because the increased number of apartment complexes is associated with the negative externality assumption described above (Refer to Figure 3).
• The selling price of single-family dwellings will increase with increasing
distance from the apartment complex.
• The selling price of single-family dwellings will decrease with increasing
numbers of apartment complexes. 8
Selling Price of Single-Family Dwellings
Distance from Apartments
Fig. 3. A conceptual model depicting reverse distance decay where the selling price of single-family dwellings increase with increasing distance from apartment complexes within a radius of 914.4 meters (3,000 feet).
Selling Price of Single-Family Dwellings
Number of Apartments
914.4 Meters
7ig. 4. A conceptual model where the selling price of single-family dwellings decrease with increasing numbers of apartment complexes within a radius of 914.4 meters (3,000 feet). 9
The rationale for the research is to test the hypotheses based on William Alonso’s location and land rent theory and Masahisa Fujita’s urban economic theory. Alonso used distance decay to illustrate bid-rent curves of land values. He claimed that land values are highest at the central business districts (CBDs), parks, or lakes, and would decrease with increasing distance (Alonso 1964). However, reverse distance decay is used for testing the hypotheses, where the sales price of single-family properties increases with increasing distance from an apartment complex (Figure 3). This application of reverse distance decay is justifiable, if it is assumed apartment development follows Fujita’s urban economic theory concerning neighborhood externalities. Fujita explains that households want low-density residential areas because they provide desired green space.
Conversely, higher densities decrease wanted green space, increase traffic congestion, increase noise levels, and crime, which in effect create negative externalities (Fujita
1989). Therefore, in this research study, apartments are assumed to create negative externalities, which are considered undesirable traits in a neighborhood, thus resulting in lower property values.
Significance of the Research
The placement of particular housing structures is an emotional issue. Often, purchasing a home is the most important investment of a lifetime (Adams 1984 and
Bourne 1981). Therefore this research contributes to the existing literature regarding land use, land rents, and property values. If the hypotheses are correct, city planners should regulate the development of apartment complexes in single-family neighborhoods in terms of size, design, and the like. If the hypotheses are incorrect, city planners can 10
strengthen their case to distribute affordable housing throughout the city. Consequently, there is a need for geographers to develop a theoretical model that can assist planners to determine how apartment complexes influence land values, property values, and the tax base.
General Definition of Terms
Address geocoding: A GIS procedure that finds a location on a map using an address
(ESRI1996).
Address matching: A GIS procedure that finds an address in a table, which corresponds
to an address attribute’s table of a theme (ESRI 1996).
Apartment complex: In this research study refers to as twenty or more dwelling units
under a single structure.
Collinearity: Where to two independent variables (multicollinearity more than two
variables) are highly correlated with each other, meaning their correlation
coefficients are close to 1.
Exclusionary zoning: Prevents the construction of mobile homes, or other types of
homes (multifamily housing) for middle income persons or racial minorities (Platt
1996).
Geographic Information Systems (GIS): Is the storage, retrieval, manipulation, and
display of spatial data.
Hedonic model: Similar to a multiple regression model, except that it applies to
housing (Eppli 2000). This model includes the structural and locational attributes
of housing to predict property values. 11
Multifamily dwellings: Five or more units under a single-structure (Van Vliet 1998).
Scripts: Programs written with Avenue, a programming language used by Arcview
(ERSI 1996).
Shapefile: A data format used in ArcView.
Standardized (Regression) Beta Coefficients: The transformation of raw data, where the
mean is zero, standard deviation is 1, and the constant, bo is 0. The purpose of the
standardized coefficient is to make variables with differing measuring units more
comparable to determine how each independent variable is influencing the
dependent variable (Hair 1987 and SPSS 2001).
Theme: A term referring to a spatial data layer containing a geographical feature and its
characteristics (ERSI 1996). The following themes generated for this research
study include: apartment complex and house location, streets, a city limit and a
study area boundary, and drainage.
Unstandardized Beta Coefficients: Are coefficients of the estimated regression model
(SPSS 2001). 12
Apartment Complexes and Single Family Dwellings West of Interstate-680
Ida Street
\V. Dodge
Center |
• Apartment Complex • Single-family Dndlins Street A / Study Area Biuimtaiy City Limits ■ Drainage 12 M iles
Fig. 5. The location of apartment complexes and single-family dwellings in the study area.
Study Area
The following streets and Interstate delimit the study area north, south, east, and west are Ida Street, Harrison, Interstate-680, and 180th Street respectively (Refer to
Figure 5). This area was chosen because most of the development occurred in the mid
1950s, after Euclidean zoning had been long implemented, which supposedly protects the residents and the valuation of single-family dwellings. Typical zoning in the study area consists of low-density, multi-family dwellings designated as R6 (apartment complexes), which are adjacent to high-density single-family dwellings, designated as R4. As seen in 13
Figure 6, two apartment complexes are buffering the negative externality effect of West
Maple Road and community commercial districts.
Typical Zoning Surrounding Apartment Complexes
Apartment Complex Single-family Dwellings | CC Community Commercial District DR Development Reserve District ■ |Q C General Commercial District ■ B G O General Office District LI Limited Industrial District R3 SFD Residential District (Medium Density) R4 SFD Residential District (High Density) R5 Urban Family Residential Blondo District R6 Low Density Multi-Family Residential District 1 Miles (Low Density)
Fig. 6. Typical zoning surrounding apartments and single-family dwellings in the study area.
The land use element of the Omaha City Master Plan (1997) explains that the ideal location for apartments would be adjacent to offices, commercial centers, and portions of civic centers designated for mixed-use (Refer to Figures 6 and 7). It designates West Maple, West Dodge, and West Center Roads for unlimited multifamily
(hereafter referred to as apartment) development within a quarter-mile north or south of these roads (Omaha City Master Plan 1997). If apartments are constructed outside of these corridors, then they are to be limited to 100 dwelling units, except if they are farther than a quarter-mile away from another apartment complex (Omaha City Master Plan 14
1997). Fifty apartment complexes were chosen in this region, some of which are adjacent to single-family dwellings. Figure 5 shows the general spatial concentration of apartment complexes are found along the western edge of Interstate-680 and the major roads.
However, Figure 7 depicts that the heaviest concentration of apartments occurs just south of the Interstate-680 West Maple Road off ramp. Most of the apartments are within close proximity to major thoroughfares and commercial developments because planners consider them buffers, thus protecting single-family dwellings. Moreover, apartment complexes situated next to these major thoroughfares provide “adequate transportation” for increased residential density (Moudon and Hess 2000, 253).
Heaviest Concentration of Apartment Complexes and Zoning in the Study Area
Apartment Complex Single-family Dwellings CC Community Commercial District DR Development Reserve District | GC General Commercial District ^ G O General Office District ^ ^ L l Limited Industrial District R3 SFD Residential District (Medium Density) R4 SFD Residential District (High Density) Blondg R5 Urban Family Residential District R6Low Density Multi-Family Residential District 1 Miles ■ ■ R7 Multi-Family Residential ■ Distict (Medium Density)
Figure 7. Depicts the heaviest concentration of apartments occurs just south of West Maple Road and Blondo. 15
Chapter Summary
The purpose of this thesis is to examine the impact of apartment complexes on the selling price of single-family dwellings by using distance and structural density as variables to test the hypotheses. Chapter 2 reviews the literature on housing value concerning undesired residential properties, New Urbanist developments, price-distance gradients, and the utilization of a GIS in Real Estate. Chapter 3 describes the methodological design, data collection, and analyses performed in the research study.
Chapters 4 and 5 provide an extensive discussion of the results and conclusions of the research study respectively. 16
Chapter 2
LITERATURE REVIEW
Minimal research has been done to examine the impact apartment complexes have on the value of single-family dwellings. This literature review offers a detailed description of the objectives, data characteristics, methodology, and conclusions found in previous studies indirectly related to the research. The summaiy of this Chapter provides justification why some of the procedures presented in this literature review support the methodology used in this research study.
Undesired Residential Properties
Homeowners view single-family rental properties, mobile home parks, and federally assisted housing as having a negative impact on the value of their homes. The literature for the most part supports this view. Wang et al. (1991) examined the impact of single-family rental properties on the market value of owner occupied single-family dwellings in San Antonio, Texas. Wang et al. (1991) tested three hypotheses concerning this issue. The first hypothesis suggested that higher percentages of rental properties present in a neighborhood would lower the sales price of single-family dwellings. The second hypothesis suggested sales price would decrease with increased numbers of surrounding rental properties. The third hypothesis suggested that poorly maintained properties decreased sales price.
The four main attributes that Wang et al. (1991) used which concern this research study were: housing quantity, housing quality, locational, and rental property variables.
Housing quantity variables were subdivided into square footage of living area, lot size, 17
number of garages, bedrooms, bathrooms, and other room types. Housing quality variables were categorized by the age of the structure, air conditioning, and number of fireplaces. The locational variables were dummy variables based on area designation.
The rental property attribute was subdivided into the number of rental properties present in 5-house and 8-house groupings, the sum of the rental properties in the 5 and 8-house groupings, and a variable concerning the percentage of rental properties for each subdivision. The sources for housing characteristic data came from the multiple listing services (MLS), Southwest Texas Data Bank, and Bexar County Appraisal District, while the rental variables were based on Wang et al.’s (1991) calculations.
The methods employed by Wang et al. (1991) were Pearson correlations and the following four hedonic models: a full attribute model, a second model eliminating the number of bathrooms, bedrooms, and other room types; a third model including the rental variables, that consisted of the number of rental properties within five and eight house groupings; and the fourth model, which only used the rental property variable that was the sum of the total rental properties within the five and eight house groupings.
Wang et al. (1991) regression results supported all three hypotheses, such that all rental property variables had the expected negative signs and were significant at the 90 percent or greater level. They found that homebuyers would pay 2 percent more if surrounding rental properties were maintained. They found that single-family rental properties have the same adverse effects as apartments or other “undesired properties”
(W angetal. 1991, 164). 18
Wang et al. (1991) differ from Beny and Bednarz (1975), which found that the percentage of apartments within census tracts have a positive effect on housing price.
They believed this to be a rational outcome because the presence of many apartment buildings symbolizes a more intensive land use, thus increasing the land value.
Cadwallader (1996) compares Wang et al. (1991) and Berry and Bednarz (1975) and suggests that multicollinearity in the independent variables probably caused the different outcomes between the two studies.
Berry and Bednarz (1975) developed a multivariate housing model for Chicago.
The dependant variable was the selling price of 275 single-family dwellings in the study area. The methods employed included simple correlation, a full attribute multiple regression model, and stepwise regression to create a reduced variable model. The four main attributes that Berry and Bednarz studied were housing characteristics, housing improvements, neighborhood characteristics, and accessibility. Housing characteristics were divided into floor area, age, and lot size. Housing improvements were also subdivided into air conditioning, garage, improved attic, improved basement, and number of bathrooms. Dummy variables represented all of the housing improvements, except for the number of bathrooms. Neighborhood characteristics contained the following variables: median family income, percentage of multiple family dwellings, and migration, which represented the percentage of family’s living in a different census tract five years before.
The multiple regression results show that all of the variables had the expected signs of the regression coefficients and were significant. Such that housing 19
characteristics floor space and lot size had a positive effect on sales price, while the effect of age was negative (Berry and Bednarz 1975). Neighborhood characteristics showed that sales price increased with higher median family income as well as with higher percentages of apartments.
Berry and Bednarz (1975) had problems with multicollinearity and attempted to resolve the issue by employing a preliminary analysis that tested a larger set of variables to determine which variables had collinearity. The variables having problems with collinearity were removed from the data, leaving the remaining dataset used in the study.
However, Berry and Bednarz explained that not all of the collinearities could be removed. For instance, large homes will usually occupy a large lot.
Munneke and Slawson (1999) examined the effect of proximity of mobile home parks on single-family property values in Baton Rouge, Louisiana. Fifty-eight mobile home parks were located in the study area. During the four-year study period, 3,025 single-family dwellings sales transactions were collected from a MLS.
Data used by Munneke and Slawson (1999) were price per square footage of living area, lot area, square footage of living area, age, presence of central air, and several straight-line distance measurements to central business districts, major intersections, nearest shopping center, distance to airport, presence of mobile home parks in a half- mile, and presence of mobile home parks greater than a half-mile away. The methods employed were descriptive statistics, a full probit model on the entire sample and a reduced-form probit equation. The results of the reduced-form probit model show that 20
mobile home parks had a negative effect on single-family dwellings less than one-half mile away.
There is the belief that federally assisted housing has a negative effect on the selling price of homes. Homeowners automatically assume that Section 8 housing will lower their property values (Babb et al. 1980). Babb et al. (1980) gathered data from the following sources: the 1970 U.S. Census of Population and Housing, 1980 Census of
Population and Housing, a computerized sales list, the location of public and federally assisted housing from the Memphis Housing Authority and the Memphis Department of
Housing and Community Development.
Eleven control neighborhoods without public and federally assisted housing were established to match the socioeconomic characteristics of those that did. Neighborhood boundaries were established after examining aerial photographs and land use maps.
Three dependant variables used in the study were selling price, the number of sales, and a ratio concerning neighborhood sale price to the home sale price for the city. The ratio had two purposes: one it adjusted for inflation, two it showed whether the valuation increased, lowered, and or remained stable during the 12-year time period. A t-test for dependent samples was used.
The results show that the average sales price in both groups increased during the time period. However, the rate of increase between both the control and federally assisted housing sites were not noticeable. The t-tests showed that the intra neighborhood differences in mean sales price and ratio were also not significant. Babb et al. (1980) conclude there is no evidence that the introduction of federal assisted housing 21
lowered the market value of owner-occupied homes in Memphis between 1970 through
1980.
The Market Value of New Urbanism
New Urbanism is based on the traditional neighborhood design where high urban density exists as a result of smaller lot sizes, thus supporting a mass transit orient design.
Other principles of design include the implementation of mixed land uses, such as apartments above stores, different housing types for varied income groups, within close proximity of each other; streets are based on the grid system, and the design promotes a pedestrian friendly atmosphere; retail, restaurants, parks, and civic centers are within a five minute walking distance.
Eppli and Tu (1999) conducted a study to determine if homebuyers would pay more for homes in New Urban communities than for adjacent suburban housing developments. The New Urban developments chosen for the study followed most of the principles of design as described in addition to having surrounding suburban developments for a control group. If the New Urban developments had the following characteristics: a “significant” number of multifamily dwellings, the presence of subsidized housing, were resort communities, and if either the New Urban or suburban housing communities had less than 150 sale transactions during the 1994 to 1997 study period, they were eliminated from the analysis (Eppli and Tu 1999, 22). It is important to note that the Eppli and Tu failed to explain why New Urban developments that contained a significant number of multifamily dwellings were excluded from the study; however, all other elimination criteria were discussed thoroughly. Six communities were 22
chosen for the study: Laguna West, California; Kentlands, Maryland; Southern Village,
North Carolina; Northwest Landing, Washington; and Celebration, Florida. The total transaction used in the study was 5,833 and 5,169 came from the suburban housing sales.
Eppli and Tu (1999) chose 32 attributes of housing characteristics collected from
First American Real Estate Solutions (FARES, previously known as Experian) and tax assessors’ office to explain the anticipated price differential. Each of the developments were analyzed separately and combined using several multiple regression models, with one New Urban variable added to each of the equations (parameter estimate).
The regression models developed for all of the communities explained 85 to 92 percent of the variance in sales price of the single-family dwellings. The authors claim that t-statistics was significant at the 99 percent level for the New Urban variable. They conclude that homebuyers are willing to pay more for homes in New Urban developments than suburban housing (Eppli and Tu 1999).
This study by Eppli and Tu (1999) that concerns New Urbanism was selected because the design elements of this paradigm allows for single-family dwellings to be within close proximity to a variety of land uses. Their results indicated homebuyers are willing to pay more for houses in these developments than typical suburban developments. If the hypotheses for this thesis are supported, then Omaha planners should consider adopting New Urbanism design principles in the Master Plan.
Furthermore, future New Urbanism studies should analyze the communities with a significant number of apartment complexes to determine their impact on sales price. 23
Price-Distance Gradients with the Impact of Externalities
Externalities are effects produced by a particular land use on adjacent areas (Platt
1994). Externalities are either positive or negative in relation to conforming land uses.
Platt (1994) notes that physical externalities include water and air pollution, noise, view, impact on wildlife, and dumping of wastes. He characterizes socioeconomic externalities as the loss of jobs, retail sales, taxes, increased use of goods and services, and traffic congestion. The studies reviewed in this section describe the impact of externalities using price-distance gradients on the value of homes.
Li and Brown (1980) examined micro-neighborhood externalities using a linear model to estimate hedonic housing prices in the southeast Boston metropolitan area. The sources of the 1970 data came from a MLS and census tract information. They sampled
781 sales of single-family homes in fifteen suburban communities. The variables were structural and site attributes, which used MLS data for the houses’ characteristics such as number of rooms, bathrooms, fireplaces, garage spaces, presence of basement, patio, and age. Neighborhood (census tract) attributes included median income for the study area and residential density. Local public services and cost attributes contained variables such as aesthetics, noise and air pollution, and proximity to determine housing prices. The dependant variable in the study was the selling price. Aesthetics was measured by people ranking a view on an index of one to five, one for the lowest and five as the highest.
The multivariate analysis employed was nonlinear least squares regression to lessen the sum of the squared residuals. Simple hedonic linear models were used to determine house price and size. Their findings suggest that accessibility had a positive 24
effect on sales price value, while external diseconomies such as higher density, which leads to traffic congestion, pollution, and poor aesthetics, were negative. The negative effects on sales price diminished more quickly with distance than the positive effects. Li and Brown’s (1980) findings support the rationale of the first hypothesis in this research study where sales price increases with increasing distance from an apartment complex.
Darling (1973) examined the impact of three California urban water parks on the surrounding property values of vacant lots, single-family dwellings, high-rise apartments, duplexes, and quadplexes. Darling’s main hypothesis was that property values would decrease with increasing distance from the urban water parks. He also examined how much property values would decrease with respect to distance and the rate this would occur.
The data used for this study came from the 1960 California Census, municipal records, county records, and MLS databases. Interviews were also used to construct a demand curve for explaining the utilization of the park. In this review, only the quantitative methods and results are discussed because these pertain to this research study. The independent variables used in the study were square footage of living area, square footage of lot area, number of rooms, and baths. Distance measurements were calculated within a 3,000-foot “arbitrary” boundary from the urban parks’ shoreline to the properties (Darling 1973, 25). Darling made the assumption that beyond the 3,000-foot boundary, the urban water park would not influence the properties’ value. Both the assessed value and sales price were used as dependent variables. 25
Full attribute regression models such as linear, double log transformation, and quadratic models were employed to test the hypothesis. However, only the linear model results were presented because of the space limitations in the journal (Darling 1973).
Resultant signs of the regression coefficients for distance were positive thus, proving their hypothesis to be valid where property values would increase with increasing distance from an urban water park.
GIS Applications in Real Estate
A GIS can be used in numerous applications pertaining to real estate and marketing. Bible and Hsieh (1996) used a GIS to generate regional variables that would affect the variation of apartment rents. The Metropolitan Statistical Area of Shreveport-
Bossier City, Louisiana was the location used to study non-subsidized apartment complexes.
The data characteristics for the apartment included age, number of bedrooms, square footage for each unit, whether a pool, fireplace, and tennis courts were present, and the number of total units. A database containing location and data characteristics were imported from a spreadsheet into a GIS. The address matching procedure in a GIS was used to locate the apartments on the TIGER Map files created by the United States
Census Bureau. The census information regarding housing and population on CD ROM disks were downloaded onto a spreadsheet and imported in a GIS, and merged with census tracts. On a separate theme, neighborhood characteristics were delineated with one-mile radius circles (buffers) for each apartment building. After these regions were identified, regional variables were generated. Bible and Hsieh (1994) explained that 26
numerous regional variables could be generated, however it would not be statistically correct to use all of them. Instead eleven variables were chosen and tested using the ordinary least squares regression analysis. The regression procedure employed by SAS showed that multicollinearity existed. To rectify this problem, the variables were grouped into five attributes of regional variables.
Six models were prepared to test for multicollinearity. Results of a correlation analysis indicated that age was negatively significant indicating that the older apartments had a lower rent per square foot. It also showed that apartments with a swimming pool and or a fireplace had a positive and significant correlation with apartment rents.
Summary of Literature
In summary, only a paucity of the literature concerning land use and property valuation directly examines the effect of apartment complexes on the selling price of single-family dwellings. The following summarizes the methods and results utilized in the reviewed literature and how it supports the methodological framework of this thesis through data selection, GIS procedures, data analysis, and the expected results.
In many of the studies presented the dependent variable was sales price while the independent variables were housing characteristics, various distance measurements, and some variable representing rental properties (Berry and Bednarz 1975; Eppli and Tu
1999; Li and Brown 1980; Munneke and Slawson 1999; and Wang et al. 1991). This research study selected similar housing structural characteristics and straight-line distance measurements because much of the variation influencing sale price was accounted for in 27
the literature. The selected data for these reviewed studies came from the following sources: county records, MLS, census data, Tiger map files, and data banks.
For the most part, many of the studies utilized the straight-line distance procedure to calculate distance measurements (Bible and Hsieh 1996; Darling 1973; Li and Brown
1980; and Munneke and Slawson 1999). However, only studies conducted in the 1990s employed a GIS, because GIS was not widely available earlier. All of the price distance gradient studies in this review captured the variance within a half-mile, 3000-feet, and or a mile. Their results justify that impact of both positive and negative externalities occur at a relatively short distance assumed in the rationale of this thesis.
The data analyses utilized in the literature were descriptive statistics (Li and
Brown 1980), Pearson correlation (Wang et al. 1991 and Berry and Bednarz 1975) multiple regression or hedonic pricing models (Berry and Bednarz 1975; Bible and Hsieh
1994; Eppli and Tu 1999; Li and Brown, 1975; and Wang et al. 1991). These statistical analyses were also used for this research.
The impact of undesired residential properties on the sales price of single-family dwellings examined in the literature review are single-family rental properties, apartments, mobile home parks, and Section 8 housing. Wang et al. (1991) studied the impact of single-family rental properties on the value of owner occupied single-family dwellings. They concluded that all of the rental property variables were significant and had the expected negative regression coefficient. Their study differed from the results of
Berry and Bednarz (1975) who developed a multivariate land use model for Chicago.
Their results indicated that higher percentages of multifamily dwellings within a census 28
tract had a positive effect on sales price. Munneke and Slawson (1999) examined the impact of mobile home parks on the sales price with relation to proximity using probit models. They explain that homes less one-half mile away from mobile home parks have a lower sales price than those farther away. Babb et al. (1984) determined if the introduction of Section 8 housing had a negative impact on sales price of existing owner- occupied housing. Their results indicate there was no evidence that the introduction of
Section 8 had a negative effect on sales price. The differing methodologies and results concerning the impact of these particular undesired land uses on sales price indicate that more rigorous quantitative analysis needs to be done. 29
Chapter 3
METHODOLOGY
The objective of this research study was to determine if the sales price of single family dwellings were influenced by proximity and structural density of apartment complexes. The hypotheses state that the sales price of a single-family dwelling would increase with increasing distance from apartment complex and decrease with increasing numbers of apartment complexes. As seen in Figure 8, the stages of the methodological design necessary to test the hypotheses were Data selection, Data Acquisition, Database
Organization and Evaluation, and Data Analysis. The subsequent sections provide the justification and explanation of the data and methods used for this research study.
Data
Since the research was completed at a micro-scale level, detailed data were selected to determine if distance and structural density of apartment complexes influenced the sales price of single-family dwellings. The selling price of the single family dwellings, which sold in 1999 or 2000, were chosen as the dependent variable because it reflected market conditions at the time better than assessed housing valuations.
The independent variables used for this study were housing characteristics, such as square feet, number of total rooms, bathrooms, bedrooms, age, and garage. These particular variables were selected because several studies have shown them to affect sales price (Berry and Bednarz 1975, Eppli and Tu 1999, Li and Brown 1980, Munneke and
Slawson 1999, Richardson et al. 1974, and Wang et al. 1991). The independent variables 30
distance and number of apartments were essential in ultimately testing the hypotheses.
A list of the variables utilized in the study as well as their sources is found in Table 1.
Methodological Design
Data (Necessary to test hypotheses)
GIS Procedures Data Acquisition (Data Generation) (Douglas County: Access to MLS)
Database Organization and Data Evaluation
Data Analysis
Quantitative and Statistical Presentation
Conclusions (Supported Hypotheses)
Fig. 8. Conceptual field methods adapted from Introduction to Scientific Geographic Research. Haring Boston: et al. WCB McGraw,1992. 31
Table 1. List of Variables (Dependent Variable is Sales Price)
Variable Description Sources
SP Sales Price (1999-2000) DCMLS Sq_Ft Total Square feet of living area DCMLS TotRm Total rooms excluding bathrooms DCMLS Bath Number of bathrooms DCMLS Bed Number of bedrooms DCMLS Age Age of the dwelling in years (2001-year built) DCMLS Garage Number of garages DCMLS Num_Apts Number of apartment complexes located within GIS 3,000 feet (914.4 meters) of a single-family dwelling Dist_one Distance in meters from the first closest apartment complex to the SFDs GIS Dist_two Distance in meters from the second closest apartment complex to the SFDs GIS Dist_three Distance in meters from the third closest apartment complex to the SFDs GIS Dist_four Distance in meters from the fourth closest apartment complex to the SFDs GIS Dist_five Distance in meters from the fifth closest apartment complex to the SFDs GIS Dist_six Distance in meters from the sixth closest apartment complex to the SFDs GIS Dist_seven Distance in meters from the seventh closest apartment complex to the SFDs GIS Dist_eight Distance in meters from the eighth closest apartment complex to the SFDs GIS Bed_Rnt The lowest one bedroom rent for an apartment Prty Man Apt_Age Age of the apartment complex in years (2001-year built) DCA Tot_Sqft Total square footage of the apartment DCA TotJUnits Total number of dwelling units in the apartment DCA Note: SFDs stands for single-family dwellings and Prty Man represents property managers.
Data Acquisition
The variables concerning sales price of single-family dwellings in 1999 or 2000 in addition to the structural attributes came exclusively through a MLS used by the Douglas
County Assessors Office (DCA). The DCA also provided the structural attributes of the apartment complexes located in the study area. The street, parcel, city limit boundary, zoning, and drainage shapefiles needed for locating apartment complexes and single family dwellings, data generation, and cartographic representations in a GIS came from the Omaha City Planning Department. It was decided to use these shapefiles because they were better maintained for address matching purposes as opposed to Tiger map files provided by the U.S. Census. 32
Several studies utilize the straight-line distance procedure to calculate distance measurements (Bible and Hsieh 1996; Darling 1973; Li and Brown 1980; and Munneke and Slawson 1999). However, utilizing a GIS to measure a straight-line distance provides the advantage of obtaining measurements between two absolute locations within a radius designated by the user (Bible and Hsieh 1996). As a result, the straight-line distance procedure in a GIS was chosen because of this advantage. In this study, a radius
(buffer) of 914.4 meters (3,000 feet) was generated around the center of each apartment complex. The designation of this particular radius of was used because the results of many price-distance gradient studies revealed that the impact of desired and undesired adjacent land uses on the valuation of single-family dwellings occurred within a half-mile to 3,000-feet (Darling 1973, Li and Brown 1980, and Munneke and Slawson 1999).
GIS Procedures
Fifty apartment complexes without the presence of condominiums or townhouses were considered for analysis within the delimited study area. As a result, the apartment complexes built in the year 2000 or before were geocoded by address using the street shapefile to determine their geographic location as a point. However, out of the fifty apartments only thirty-two were located using the geocoding procedure. This is because the City does not digitize (draw) and maintain the street attribute table concerning address ranges of private streets where many apartments are located as often as public streets. Other missing cases involved incomplete address ranges for particular streets where the apartments would be located. To rectify this problem, the parcel shapefile was used to locate the property. Basically, a parcel (lot) contains the following attribute 33
information: the owner’s name, address, the apartment complex name, and or the assessed valuation. Information from the parcel shapefile was then compared with the multifamily dwelling paper files to physically locate and attribute the missing apartment complex. Aalberts and Bible (1992) experienced similar problems with their research and resorted to this particular solution.
Once the locations of the apartment complexes were determined, buffers with a
914.4-meter (3,000-feet) radius were created around all apartment complexes (Bible and
Hsieh 1994). Only the sales of single-family dwellings that occurred within the 914.4 meters of an apartment complex were included because it was assumed that beyond this radius, the impact of an apartment would have a minimal effect. The streets that were encircled by the buffers were then selected using the shape button (Refer to Figure 9).
The street attribute table was opened to gather address ranges, which were entered at the
MLS online database to acquire the selling price and the necessary independent variables needed of the homes, which sold during the two-year period. During 1999 through 2000,
1,670 single-family dwellings sold within 914.4 meters of an apartment complex.
However, statistical analysis was only performed using 1,665 dwellings because five of the observations were outliers depicted in preliminary descriptive statistics and scatter plots. The subsequent section explains the elimination of the data in greater detail. The homes were geocoded by address using the street shapefile to determine their geographic location as a point. All of the homes had a seventy-five percent or greater success rate in being located in Arc View. 34
ES Ed* if«rr EFIeiv* U'4<>i'fCi 3^TdO*» U<* ...... W&& IB ! MSB] M M M ISO B OUIt>l b]>?^3l& iyi iiM XI-J ______\\ Swl-* 1 | 57.511
Shape ButtonDistance
Pud s ftp C~3
914.4 m. Radius Themes (Buffer)
Lake Zorinsky
TunquiHriSi
f»> utMfe 15m.
f uub-»j*l5mj
Fig. 9. ArcView 3.2 interface depicting the necessary themes and tools needed to complete the research.
After locating apartment complexes and single-family dwellings, distance from the apartment complex to the single-family dwelling was calculated. ArcView has the
capability to measure the distance between two point themes (spatial data layers). The point themes used to calculate distance were the apartment complex theme and the home
sales theme. A sample Avenue script, provided by the software package, calculated the
distance in meters from the selected apartment complex to the single-family dwellings as
seen in APPENDIX A.
However, two problems with calculating distance using this script occurred.
Consequentially, modifications were developed which solved these problems described
below. The first problem dealt with calculating distance from one apartment complex to 35
the all of the single-family dwellings, which sold in the study area. Thus, distance was calculated beyond the 914.4-meter buffer centering the apartment complex. The second problem was many houses were influenced by more than one apartment complex. In
Figure 9 it is apartment that two or more apartment buffers encircled the houses.
Therefore, more than one distance measurement needed to be calculated. To rectify these problems the home sales theme was subdivided into fifty shapefiles so that each group of houses was within 914.4 meters. The attribute table for each group contained the distance measurements, such as Dist one, representing the distance to the closest apartment, Dist two the second closest apartment, and so forth. The other solution to the distance problem as well as considering structural density was to determine the number of apartments within 914.4 meters for each house.
Database Organization and Data Evaluation
The database organization and data evaluation was completed before any of the statistical procedures were implemented. First, all 50 single-family dwelling shapefile attribute tables were merged together in one database file. An address sort was performed, which facilitated in the elimination of repetitive data entries. Distance measurements for each dwelling were sorted. During the data evaluation process, five unusual observations were eliminated because of outstanding age values, 71, 96, and 101; a dwelling with six bathrooms, and another dwelling with a six car heated garage. The abnormal data were eliminated because if included the statistical analyses it would have distorted results. 36
Data Analysis
Several valuation studies have used multivariate analysis, such as multiple regression, the hedonic model (Refer to General Definition of Terms), and factor analysis for predicting house price by examining how independent variables, such as structural characteristics of housing influence the dependent variable, sales price (Berry and
Bednarz 1975, Eppli and Tu 1999, Li and Brown 1980, Richardson et al. 1974, and Wang et al. 1991). These data for this research are analyzed by means of descriptive statistics,
Pearson correlation matrices, multiple regression and factor analysis to test the hypotheses whether distance or structural density influences the sales price of the single family dwelling. In the preceding sections, an explanation and justification is given for each statistical procedure utilized in the research.
Descriptive statistics were performed to summarize the continuous numeric variables. This also assisted with understanding the nature of data structure in addition to determining incorrect data entries within the maximum and minimum value range of the variables.
Pearson correlation matrices were developed to assist with examining bivariate correlations among the variables and to provide the direction of the expected signs of the regression coefficients in later analyses. Pearson correlation matrices were developed for all the attributes of the single-family dwellings, including all the distance measurements, in order to analyze the correlation coefficients linear relationship between two variables within the range of ± 1 (Refer to the APPENDIX D for the full variable correlation matrix). The value 1 in the Pearson correlation matrix signifies that there is always a 37
perfect linear relationship when a variable is compared with itself, such as square feet compared with square feet. If the coefficient is significant at the 2-tail level (p-value <
0.05), then the correlation is significant. The p-value = 0.000 indicates that the correlation is very significant.
Multiple regression analysis involves a single dependent variable that is related to several independent variables (predictors) (Hair et al. 1987). The purpose of multiple regression is to predict the response of the dependent variable through variations of the independent variables (Hair et al. 1987). Multiple regression was used to predict the housing price because it allows the measure of “consumer behavior” (Eppli and Tu 1999,
36). The distance from the apartment complex to the first single-family dwelling
(Dist one) and the number of apartments within a 914.4-meter radius (Num Apts) were variables included in the multiple regression model that essentially tested the hypotheses.
The Dist one variable was the only distance variable utilized in the multivariate analyses because it was the closest measurement from the apartment complex to each single-family dwelling in the area. Referring to Table 2 in Chapter 4, it is evident that two or more apartment complexes influenced very few single-family dwellings, thus providing justification of using just the first distance measurement. Furthermore, it would not be statistically accurate to implement multiple regression using all of the distance measurements. It is suggested as a “rule of thumb” that researchers should have
10 times more observations than the number of attributes in a multiple regression model
(Hair et al. 1987, 33). However, in practice most studies use 5 times the number of observations than attributes (Eppli and Tu 1999 and Hair et al. 1987). 38
In this research study, three multiple regression models were applied to the data to test both hypotheses. First, a full attribute multiple regression model was applied to the data (Berry and Bednarz 1973 and Wang et al. 1991). The first model multiple regression equation was:
(3.1) SP = /(Sq_Ft|, Tot_Rm 2 , Bath 3 , Bed4 , Age5 , Garage6, Num_Apt 7?Dist_one 8,)
The second model applied to the data was a reduced attribute model (Berry and
Bednarz 1973 and Wang et al. 1991). Some of the attributes were eliminated from Model
2 because of high correlations evident in the Pearson correlation matrix, unexpected signs with the coefficients, and if the p-values were greater than 0.05. The second reduced attribute model of the multiple regression equation was:
(3.2) SP = /(Tot_Rmj, Bed2, Age 3 , Garage^ Num_Apt 5)
Factor analysis is a method used to analyze several independent variables and decide whether they can be reduced and condensed into a smaller set of factors or components with a minimum loss of information (Hair et al. 1987). This analysis serves the purposes of identifying latent factors, reducing or removing multicollinearity, and the factor scores can be used in subsequent multiple regression analysis (Hair et al. 1987).
As a result, factor analysis was performed in this research study because severe multicollinearity existed in both the full and reduced attribute multiple regression models. 39
All of the variables were entered in the factor analysis and rotated with the orthogonal
Varimax rotation method. This rotation was utilized to uncorrelate all of estimated factor score coefficients to ameliorate the potential effect of multicollinearity (Hair et al. 1987 and SPSS 2001).
The factor scores were saved using the Anderson-Bartlet method to keep them uncorrelated in SPSS (SPSS 2001) so they could be used in a multiple regression analysis, as described in Model 3. The third model of multiple regression utilizing factor scores was:
(3.3) SP = / (Factori, Factor, Factor)
For the model equation 3.3, Factor 1, represents square footage, total number of rooms, bathrooms, and bedrooms; Factor 2, represents garage and age; while Factor 3 were the hypotheses variables number of apartments and distance.
Summary of Methodology
Chapter 3 concerned the methodological design utilized for the thesis as shown in
Figure 8. Data were acquired from various sources such as Douglas County Assessor’s office, Omaha City Planning Department, MLS, and GIS generated variables, such as distance and number of apartments. The problems that were encountered with data acquisition as well as the solution were discussed in this Chapter. Database organization was concerned with the ordering of distance measurements from the apartment complex to the first closest house, to the second closest house, and so forth. Data evaluation, such as eliminating repetitive data and correcting typographical errors were essential before performing statistical analyses. The Data processing consisted of descriptive statistics, 40
Pearson correlation matrices, multiple regression, and factor analysis. It was discussed that descriptive statistics assisted understanding the nature of the data structure and aided in the elimination of outlier data. Pearson correlation matrices were used to distinguish where potential multicollinearity existed, with the full attribute regression model. The reduced attribute multiple regression model was performed, but multicollinearity still existed. Factor analysis was then implemented and the factor scores were saved and utilized in a subsequent multiple regression model. The models of the multiple regression equations utilized in this research study are defined in this Chapter. Chapter 4 provides a quantitative and statistical presentation of the data and an explanation of the results. 41
Chapter 4
DISCUSSION OF RESULTS
In this Chapter an explanation is given for the results produced for the following statistical analyses: descriptive statistics, Pearson correlation matrices, multiple regression, factor analysis, and multiple regression utilizing factor scores. Each analysis proved useful in testing the research hypotheses.
Descriptive Statistics
The descriptive statistics in Table 2 shows the number of observations, the minimum and maximum, mean, and standard deviation values for each variable concerning single-family dwellings. It is evident in Table 2 that all of the variables had a broad range. For example, the sales price ranged from $66,150 to $752,500; square footage from 920 to 7,900; and total rooms from 3 to 15. The age of the dwellings ranged from 1 to 44 years with a mean age of 18.11 years. Furthermore, the number of apartment complexes within 914.4 meters of a house ranged from 1 to 8. The scatter plots in APPENDIX B clearly indicate that the maximum sales price $752,500 also contains the maximum values for square footage, total rooms, bathrooms, and bedrooms.
The scatter plots in APPENDIX C that show the impact the hypotheses’ variables
(number of apartments and distance) on sales price indicate that the majority of homes are within 914.4 meters (3,000 feet) of just one apartment. However, as the number of apartment complexes increase within the 914.4 meters of a single-family dwelling, the sales price decreases. 42
All of the homes in the study were within 914.4 meters of at least one apartment complex because of the selection method. The distance from the apartment complex to the closest group of houses was called Dist one. However, not all of the houses were within the radius of 2 or more apartments. For instance, Table 2 shows that Disttwo has
667 single-family dwellings that sold, while Dist eight only has 17, which explains why the mean number of apartments (Num Apts) within 914.4 meters of a single-family dwelling is 1.65.
Table 2. Descriptive Statistics for Single-Family Dwellings Variable N Minimum Maximum Mean Std. Deviation SP 1665 $66,150 $752,500 148558.61 63169.307 Sq_Ft 1665 920 7900 2050.54 696.104 T o tR m 1665 3 15 7.77 1.537 Bath 1665 1 8 2.73 .739 Bed 1665 1 6 3.35 .602 Age 1665 1 44 18.11 11.452 Garage 1665 0 4 2.10 .522 Num_Apts 1665 1 8 1.65 1.124 D istone 1665 12.7216 913.5065 548.244062 207.7627452 D isttw o 667 172.9067 912.9890 646.460752 180.7730136 D istthree 232 314.6783 912.5189 720.297664 144.1123990 Dist_four 72 450.3017 909.2468 696.196751 148.4509670 D istfive 51 478.0857 913.2174685.371157 138.2457610 D istsix 30 559.0625 881.1622 695.305003 70.1587240 D istseven 23 634.9855 891.1474 825.035161 58.4735467 Dist_eight 17 802.5654 911.2570 864.464706 28.4347109
Descriptive Statistics were also performed on all 50 apartment complexes located in the study area concerning their one bedroom rents, age, total square footage of the complex, and total units. The apartment variables were not implemented in multiple regression or factor analysis because the objective of the research focused on distance and structural density. However, the descriptive statistics and Pearson correlation results 43
for the apartment complexes found in Table 3 and APPENDIX D respectively provided some useful information, particularly with age. As seen in Table 3 the age range of the apartment complexes is from 1 to 35 years and the mean age is 16.84 years. When comparing the mean age in years of single-family dwellings to the apartment complexes, it is evident that apartment complexes are only slightly younger than all of the single family dwellings. Both means depict that construction occurred more or less at the same time.
Table 3. Descriptive Statistics for the Apartment Complexes N Minimum Maximum Mean Std. Deviation
Bed_Rnt 50 435 895 540.88 89.233
Apt_Age 50 1 35 16.86 11.254
Tot_Units 50 36 624 199.48 128.561
Tot_SqFt 50 33654 584432 204827.74 125418.974
Pearson Correlation
Two Pearson correlation matrices were developed for this research study. The first correlation matrix located in APPENDIX D contained all the distance measurements, while Table 4 used just the closest distance (Dist one) to the apartment complex. Table 4 shows the Pearson correlation coefficients of all the variables to have the expected signs and significant at the 0.01 level when compared with sales price. Such that the correlation coefficients for square footage, total number of rooms, bathrooms, bedrooms, garages, and distance are positive, while age and number of apartments are negative. 44
Table 4. Pearson Correlation Matrix
SP Sq_Ft TotRm Bath Bed Age Garage Num_Apts
Pearson .795** Sq_Ft Correlation Sig. (2-tailed) .000 Pearson .485** .717** Tot_Rm Correlation Sig. (2-tailed) .000 .000 Pearson .561** .699** .571** Bath Correlation Sig. (2-tailed) .000 .000 .000
Pearson .415** .607** .620** .510** Bed Correlation Sig. (2-tailed) .000 .000 .000 .000 Pearson -.527** -.200** -.033 -.162** -.065** Age Correlation Sig. (2-tailed) .000 .000 .182 .000 .008 Pearson .636** .480** .305** .383** .271** -.473** Garage Correlation Sig. (2-tailed) .000 .000 .000 .000 .000 .000 Pearson -.192** _141** -099** -.111** -.103** .130** -.090** Num_Apts Correlation Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .000 Pearson .086** .099** .070** .077** .056* .016 .034 -.341** D isto n e Correlation Sig. (2-tailed) .000 .000 .004 .002 .023 .505 .162 .000 Note: Pearson correlation matrix stops with Dist one. ** Coefficient is significant at the 0.01 level. * Coefficient is significant at the 0.05 level. For the full matrix see APPENDIX D.
It is important to note that correlations between the square footage variable with the number of total rooms, bathrooms, and bedrooms variables were relatively high. The
Pearson correlation coefficients between square footage and sales price, total rooms, bathrooms and bedrooms were 0.795, 0.717, 0.699, and 0.607 respectively. Thus, indicating that multicollinearity would present a problem in later multiple regression analyses. 45
Multiple Regression
Multiple regression was first performed on a full attribute model to test the hypotheses by including the number of apartments and distance variables. The t values for the independent variables represented in Model 1 should be below or above 2 in order to be considered good predictors (SPSS 2001). In Table 5 the t-values that did not meet these requirements in the model are number of bathrooms and distance. These same variables are also insignificant at the 0.05-level. In Model 1 the unstandardized beta coefficients for total rooms and bedrooms have unexpected negative signs. It was expected that these signs would be positive, since the Pearson correlation coefficients depicts them as such in Table 4. The variables had unexpected signs because multicollinearity existed in the model. The coefficient of determination R2, explains that
80 percent of the variation of sales price can be explained by the attributes used in this model. The adjusted R is also significant at 79.9 percent. 46
Table 5. Regression Results for Model 1 Variable Unstandardized Coefficients Std. Error Standardized Coefficients t B Beta (Constant) . 42778.102* 6024.669 7.100
Sq_Ft 65.884* 1.791 .726* 36.779
Tot_Rm -2896.283* (**) 699.407 -.070*(**) -4.141
Bath 62.867 1341.576 .001 .047
Bed -5795.447*(**) 1551.870 -.055*(**) -3.734
Age -1637.470* 70.329 -.297* -23.283
Garage 21638.556* 1693.732 .179* 12.776
Num_Apts -2567.602* 667.865 -.046* -3.844
Dist_one 1.515 3.575 .005 .424
R .894
R2 .800
Adj. R2 .799
Std. Error of 2845.437 The Estimate F Value 826.021* Note: The full attribute model used in multiple regression analysis had N= 1,665. * Significant 0.05 level. ** Coefficient is not with expected sign.
Table 6 shows the regression results for the reduced attribute model, which omits variables square footage, bathrooms, and distance. Model 2 excludes the variable square footage due to its high correlation with other housing characteristic variables evident in
Model 1. Bathrooms and distance were insignificant and were also removed from the model. In Table 6 the results of Model 2 in show that all the variables have the expected coefficient signs and are significant at the 0.05-level. The t-values were all well above or below 2 suggesting the independent variables were good predictors. However, with 47
this model coefficient of determination R2 only accounts for 60.1 percent of the total variation of the selling price.
Table 6. Regression Results for Model 2. Variable Unstandardized Std. Error Standardized t Coefficients Coefficients B Beta (Constant) -32210.358 7394.915 -4.356
TotRm 11982.617* 832.088 .292* 14.401
Bed 11413.341* 2088.077 .109* 5.466
Age -1838.226* 98.546 -.333* -18.653
Garage 42712.773* 2265.005 .353* 18.858
NumApts -4296.462* 883.527 -.076* -4.863
R 0.776
R2 0.601
Adj. R2 0.600 Std. Error of 39940.632 The Estimate
F Value 500.664* Note: The reduced attribute model used in multiple regression analysis had N=l,665. * Significant 0.05 level. All coefficients had expected signs.
Collinearity statistics in Table 7 show the tolerance and variation of inflation factors for Models 1 and 2. The tolerance statistic represents the percentage of variation in each predictor. If tolerances approach zero and if the variation of inflation factors are greater than 2, then multicollinearity is considered a problem in the model. Table 7 shows that multicollinearity exists in both models, especially Model 1. For instance, in 48
Model 1 all of the tolerances are slightly under 1 and the variation of inflation factors for the housing characteristics square footage, total rooms, and bathrooms are slightly greater than 2. It is evident that the offending variable causing the multicollinearity in Model 1 was square footage, which has the highest variance of inflation factor at 3.220. In Table
7 Model 2 is somewhat better, but the tolerance for all the variables is still less than 1 and less variation exists. The collinearity statistics performed in Model 2 explain that multicollinearity was reduced, but not significantly. As a result, factor analysis was performed on the data to remove multicollinearity.
Table 7. Collinearity Statistics for Model 1 and 2. Model 1 Model 2
Variables Tolerance VIF Tolerance VIF
Sq_Ft .311 3.220 —— Tot_Rm .418 2.393 .586 1.706 Bath .492 2.034 — Bed .553 1.808 .606 1.649 Age .744 1.343 .753 1.329 Garage .618 1.619 .686 1.458 Num_Apts .857 1.167 .972 1.029 Dist one .875 1.143 -- -- Note: — Indicates that the variables were removed from the analysis. VIF represents variation of inflation factors.
Factor Analysis
Factor analysis was employed to address the issue of multicollinearity. It does this by reducing and summarizing the data, such that original variables are grouped into factors with a minimum loss of information (Hair el al. 1987). All of the variables in
Model 1 were utilized in the factor analysis. The results of the factor analysis are presented in the following manner: communalities, total variance explained, unrotated 49
and rotated component matrix, a component plot in rotated space, and a component coefficient matrix. An explanation is given for each of the presented results.
Table 8. Communalities for Factor Analysis Initial Extraction Sq_Ft 1.000 .819 Tot Rm 1.000 .764 Bath 1.000 .664 Bed 1.000 .659 Age 1.000 .812 Garage 1.000 .714 Num_Apts 1.000 .673 Dist one 1.000 .693 Note: Extraction Method: Principal Component Analysis.
Communalities in factor analysis reflect the amount of variance for all the variables. For the principal component extraction method, all of the initial values are equal to 1. The extraction communalities estimate the variance for the factors used in the analysis. If the extraction values are small, then the factors should be removed from the analysis (Hair et al. 1987 and SPSS 2001). Table 8 shows that in this research study all of the extraction communalities are 0.659 or greater justifying the use of all the factors in the analysis. Table 9. Total Variance Explained using Factor Analysis 3 T 3 J3 .2 Pi GO "3 W GO J *-> cS o Vi 00 CD 3 > o CO 00 O n i VO O r 3 <4-H (N 'O O S . > 0 u fV* U 3 — ^ c3 S 4/ e w • • • r-~ co • o r Cw VOS T VO W I O < > o E Q. o d s- co co CN qcq cq CN co O 'O cn n i OV co vCO Ov O CN " t co O r ^ O - r VO O cq - r VO cd 1 ,— vd n i - r co vd in CO - r MH - c - co r-~ CO o oco co VO — —1 cq o In Table 9 the total variance explained provides the initial eigenvalues, extraction sums of squared loadings, and the rotation sums of squared loadings. Table 9 shows that in this analysis there are eight components that represent the variables. The total initial eigenvalues determine the amount of variance observed. Factor analysis is appropriate when only a few components that have eigenvalues greater than or equal to 1 explain most of the variance. As shown in Table 9, the first three components explain most of the variance, such that they account for 72.456 percent of the cumulative variability of the original variables. Since the principal component extraction method was used, the total extraction sums of squared loadings, total percent of variance, and cumulative percent column are the same as the initial eigenvalues. In Table 10 the unrotated component matrix shows that 3 components were extracted using principal component analysis. However, a Varimax orthogonal rotation was implemented to uncorrelate factor scores, spread the variation among of the variables evenly, and to assist with interpreting the results by grouping the factor loadings as seen in Table 11 (Hair et al.1987 and SPSS 2001). In Table 11 it is evident that variables in the rotated component matrix can be grouped by three distinct components and have their expected signs. Factor 1 consisted of square footage, total rooms, bathrooms, bedrooms; factor 2, age and garage; and factor 3, number of apartments and distance. 52 Table 10. Unrotated Component Matrix Component 1 2 3 Sq_Ft .899 9.294E-02 4.521E-02 Tot Rm .808 .222 .247 Bath .806 .110 5.344E-02 Bed .752 .206 .225 Age -.316 .367 .760 Garage .613 -.178 -.554 Num_Apts -.235 .747 -.246 Dist one .155 -.686 .445 Note: Extraction Method: Principal Component Analysis. 3 components extracted. Table 11. Rotated Component Matrix Component 1 2 3 Sq_Ft .864 .256 8.206E-02 Tot Rm .873 2.788E-03 4.079E-02 Bath .786 .209 5.587E-02 Bed .811 7.109E-03 3.731E-02 Age 2.136E-02 -.900 -4.443E-02 Garage .362 .763 1.416E-02 Num_Apts -5.834E-02 -.141 -.806 Dist_one 5.623E-02 -8.130E-02 .826 Note: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. 53 Component Plot in Rotated Space garage m tot distol 0.0 0.0 o.o -.5 Component 1 Component 3 Fig. 10. Component Plot in Rotated Space The component plot in rotated space in Figure 10 also shows the rotated component matrix. This plot serves three purposes, first it helps with interpretation of how the components were grouped, it shows the separation of age and garage, and thirdly it can be used to justify the signs of variables that test the hypotheses, such that the number of apartments variable is negative, while distance is positive. Garage and number of apartments are in the same space as the other variables because of their signs. 54 Table 12. Component Score Coefficient Matrix Component 1 2 3 Sq_Ft .286 .045 -.003 Tot Rm .332 -.138 -.025 Bath .265 .024 -.016 Bed .308 -.125 -.024 Age .154 -.655 -.001 Garage .017 .496 -.041 Num_Apts .052 -.063 -.603 Dist one -.018 -.099 .627 Note: Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. Component Scores. Regression Utilizing Factor Scores The factor scores used in the multiple regression analysis for Model 3 were saved in the factor analysis procedure using the Anderson-Rubins method. This method estimates factor score coefficients and keeps them uncorrelated. If the factor scores were saved as regression factor scores, then correlation could occur even with an applied orthogonal rotation (SPSS 2001). The three factor scores consisted of the following: factor 1: square footage, total rooms, bathrooms, and bedrooms; factor 2: age and garage; and factor 3: number of apartments and distance. The R and0*0 adjusted R are both at 70.6 and 70.5 percent respectively. As a result, the factors scores of Model 3 capture more of the variance than Model 2, therefore creating a better predicting model of sales price. The reason why the unstandardized and standardized beta coefficients are all positive is because when the rotation occurred during factor analysis, variables that contained positive coefficients were grouped with variables consisting of negative coefficients, thus 55 canceling each other out. However, the component plot illustrates that all of the variables had the expected signs, including those, which test the hypotheses, number of apartments and distance. Table 13. Regression Coefficients of Factor Scores for Model 3. Unstandardized Std. Error Standardized t Coefficients Coefficients B Beta (Constant) 148558.609 840.164 176.821 Factor 1: Housing 36826.739* 840.416 0.583* 43.820 Characteristics Factor 2: Age and Garage 37570.1758* 840.416 0.595* 44.704 Factor 3: Number of 7033.610* 840.416 0.111* 8.369 Apartments and Distance R 0.840 R2 0.706 Adj. R2 0.705 Std. Error of 3482.385 The Estimate F-Value 1329.558* Note: The multiple regression attribute model using factor scores saved using the Anderson-Rubins method had N =1,665. * Significant 0.05 level. Refer to Figure 10 and Table 11 for the signs of the factor score regression coefficients. As shown in Table 14 the collinearity statistics for Model 3 show that all of the factor groupings have a tolerance and variation of inflation factors of 1, indicating that multicollinearity was removed from the analysis. When comparing the collinearity statistics for Models 1 and 2 in Table 7 with Model 3 in Table 14 it is evident that multicollinearity was clearly eliminated. 56 Table 14. Collinearity Statistics for Model 3. Factors Model 3 Tolerance VIF Factor 1: Housing Characteristics 1.000 1.000 Factor 2: Garage and Age 1.000 1.000 Factor 3: Number of Apartments and 1.000 1.000 Distance Summary of Results The first hypothesis, where sales price of single-family dwellings increases with increasing distance, was supported only after performing factor analysis. This is because of severe multicollinearity existed among the variables in Models 1 and 2 as shown in Table 7. Although single-family dwellings that are closer to apartment complexes sell for a lower price and price increases with increasing distance, the relationship is strong at 0.627 only when analyzing component 3 in the component score coefficient matrix in Table 12. The subsequent regression utilizing the factor scores saved in the Anderson- Rubins method, indicated that factor 3: number of apartments and distance, are significant at the 0.05 level (Refer to Table 13). Although they are significant, the unstandardized and standardized coefficients are positive. This is because in the process of rotation in factor analysis grouped the variables NumApts and Dist one, with differing signs together, thus canceling the negative coefficient sign of Num Apts. However, using the component plot is essential to determine that the Num Apts variable is in fact negative, while Dist one is positive. 57 The second hypothesis, that dealt with the greater number of apartment complexes within 914.4 meters of a single-family dwelling, the lower the sales price was also supported when examining all of the analyses. For instance, the results of the scatter plot, the full and reduced attribute multiple regression model, the unrotated and rotated components, and the factors analysis scores (when analyzing the component loading plot) all had the expected negative coefficient. The results from testing the second hypothesis, show that the number of apartments variable has a stronger relationship than the distance variable, however only a few single-family dwellings are influenced by multiple numbers of apartment complexes as was seen in Table 2 and APPENDIX C. 58 Chapter 5 CONCLUSION The American dream is to own a single-family dwelling. It is evident that the federal government has supported this dream with the implementation of housing programs, favorable tax incentives, and zoning regulations. Furthermore, the placement of particular housing structures is an emotional issue. This is because owning a home signifies a persons’ placement in the social fabric of the American society as well as being an important investment. The literature and case study presented in this thesis explained that homeowners dislike apartment complexes because they lower property values, are aesthetically displeasing, create a greater demand for goods and services, increase noise pollution and crime rates, create traffic congestion, and disrupt their spatial visualization of what their landscape should look like. For the most part, the literature exhaustively explains the homeowners’ dilemma, but fails to analyze it quantitatively. The objective for this research study is to analyze the impact apartment complexes have on the sales price of single-family dwellings by using distance and structural density as factors. The two hypotheses determined if the selling price of single-family dwellings increase with increasing distance from an apartment complex and if the greater the number of apartment complexes within 914.4 meters (3,000 feet) of a single-family dwelling the lower the selling price. The first step in the methodological design determined the necessary data required to test the hypotheses. The data used in this thesis came from the Douglas County 59 Assessor’s office, the Omaha City Planning Department, and data generation by a GIS to obtain distance and number of apartment variables. The quantitative methods utilized were descriptive statistics, Pearson correlation matrices, multiple regression, factor analysis, and regression using factor scores. Descriptive statistics assisted with understanding the nature of the data structure. Pearson correlation matrices explained the bivariate relationship among all the variables to determine where multicollinearity appeared severe, while the multivariate analyses: multiple regression, factor analysis, and regression using factor scores were utilized to determine what model best predicted house price by examining how independent variables, such as structural characteristics of housing and the variables, which tested the hypotheses, number of apartments and distance, influenced the dependent variable, sales price. The results of the quantitative analysis performed on the data indicated that both the first and second hypotheses are supported. The selling price of single-family dwellings increased with increasing distance, but only after performing factor analysis and regression analysis utilizing factor scores. Regression using factor scores was utilized because severe multicollinearity existed in both the full and reduced attribute multiple regression models. However, the second hypothesis where selling price of single-family dwellings decrease with increasing numbers of apartment complexes was supported by all of the multivariate analyses including the full and reduced attribute multiple regression model, the Varimax rotated factor analysis scores, and regression utilizing factor scores. The regression coefficient for the number of apartments variable was negative for Models 1 and 2 and were significant at the 0.05-level. The number of 60 apartments variable had a positive coefficient when using factor scores in regression because it was grouped with distance. As a result, referring to both the rotated component matrix and the rotated component plot are essential when analyzing the factors scores because they showed that the number of apartments variable were in fact negative and distance positive. In other words, both hypotheses are supported, but the support of the second hypothesis is stronger. This is because the number of apartments variable had a negative coefficient in Models 1 and 2 and in the results presented in the tables for factor analysis. More research needs to be done to investigate the multifaceted effect apartment complexes have on the value of single-family dwellings. Future research could investigate various issues concerning the impact of apartment complexes on the property value of single-family dwellings. For instance, research could involve utilizing various distance measurements, such as a weighted inverse distance squared, measuring distance via a network of roads, straight-line distance, and zonal distance from an apartment complex to single-family dwellings to determine which method of measurement is most accurately represented in multivariate models to predict sales price. An additional study could differentiate the impact of various land uses, such as commercial centers, major and minor roads, and apartment complexes to determine which has the most influential effect on the sales price of single family dwellings. Still another potential study could involve whether the introduction of apartment complexes adjacent to existing neighborhoods comprised of single-family dwellings influenced sale prices. 61 APPENDIX A ESRI Avenue Script 62 ESRI ArcView 3.2 Sample Script: View.CalculateDistance The following comments regarding this Avenue script are quoted from (ESRI 1999) Title: Calculates distances from points in one theme to points in another Topics: Analysis Description: This script will prompt you for two point themes in the active view. The first is the point theme containing the selected points that you wish to calculate the distance FROM. The second is the point theme containing points that you wish to calculate the distance TO. You will also be prompted for an identifying field in the FROM theme. The value of this field will be used to name to distance field in the TO theme. A distance field will be added to the TO theme for each point selected in the FROM theme. This distance field will be populated with the distance between the selected From point and the To point. If the view is projected, the distance will be returned in distance units. Requires: This script should be attached to a button in the view GUI. To prepare to use this script: - 1. Add to your view two point themes. - 2. Determine which is to be theFromTheme and which is to be theToTheme. - 3. Determine which item is to be the identifying field. This is most likely a field that contains a key number or name. - 4. Create a selected set of points in theFromTheme, if you like. - 5. Attach this script to a button inthe Veiw GUI, if you like. Self: Returns: theThemeList = av.GetActiveDoc.GetThemes.Clone thePrj = av.GetActiveDoc.GetProjection ' Use these lines to hard-code the input parameters... 'theFromFtab = av.GetProject.FindDoc("Viewl ").FindTheme("FromTheme").GETFTab 'theFromlDField = theFromFtab.FindField(”Bnmb") ’theToTheme = av.GetProject.FindDoc(”Viewl ”).FindTheme("ToTheme") ’theToFtab = theToTheme.GetFTab 63 ' Use these lines to prompt the user for input parameters... theFromTheme = (MsgBox.List(theThemeList,"to calculate the distance FROM.","Please select a theme...")) if (theFromTheme = nil) then exit end theFromFtab = theFromTheme.GetFTab theThemeList.RemoveObj(theFromTheme) theToTheme = (MsgBox.List(theThemeList,"to calculate the distance TO.","Please select a theme...")) if (theToTheme = nil) then exit end theToFtab = theToTheme. GetFTab theFromlDField = MsgBox.ListAsString(theFromFtab.GetFields,"containing the point identifier.","Please select a field...") if (theFromlDField = nil) then exit end theFromShapeField = theFromFTab.FindField("Shape") theToShapeField = theToFtab.FindField("Shape") theT oFtab. SetEditable(true) for each f in theFromFtab.GetSelection theFromID Value = theFromFtab.RetumValueString(theFromIDField,f) theNewFieldName = "DistTo"+theFromIDValue ' If a distance field doesn't exist, add it... if (theToFtab.FindField(theNewFieldName) = nil) then ' Choose the field size commensurate with the view's projection, if (thePrj.IsNull) then theDistanceField = field.make(theNewFieldName,#field_decimal,8,4) else theDistanceField = field.make(theNewFieldName,#field_decimal,8,2) end theT oFtab.addfields( {theDistanceField} ) ' If a distance field does exist, clear it... else theToFtab. Calculate( "0 ",theToFtab.FindField(theNewFieldName)) theDistanceField = theToFTab.FindField(theNewFieldName) end 64 ' Get the point location you are measuring FROM. ' If the view is projected, get the the point location in projected units. theFromShape = theFromFTab.RetumValue(theFromShapeField,f) if (thePij.IsNull.Not) then theFromShape = theFromShape.RetumProjected(thePij) end for each t in theToFtab ' Get the point location you are measuring TO. ' Get it in projected units, if the view is projected. theT o Shape = theT oFT ab .Return V alue(theT oFtab .F indField(" Shape " ),t) if (thePij.IsNull.Not) then theToShape = theToShape.RetumProjected(thePij) end ' Calculate the distance between the two points. ' Add the value to the output (TO) branch table. theDistance = theFromShape.Distance(theToShape) theT oFtab. SetV alue(theDistanceF ield,t,theDistance) end end theT oFtab. SetEditable(false) av.ShowMsg(”Distance calculation complete") 65 APPENDIX B Scatter Plots: Sales Price Versus Housing Characteristics 800000 700000 600000 500000 400000 □ D □ cP 03 C/D 300000 200000 100000 0 1000 2000 3000 4000 5000 6000 7000 8000 Square Footage 800000 700000 600000 500000 o 200000 100000 2 4 6 8 10 12 14 16 Total Number of Rooms 800000 700000 600000 500000 400000 300000 200000 100000 0 0 2 4 6 8 10 Number of Bathrooms 800000 700000 600000 500000 400000 300000 200000 100000 0 0 1 2 3 45 6 7 Number of Bedrooms 800000 700000 600000 500000 400000 ■ □ § 300000 200000 ■ qO □ □ DS 100000 Age of the Dwelling in Years 800000 700000 ■ 600000 ■ 500000 . CL 400000 300000 ■ 200000 100000 Number of Garages 69 APPENDIX C Scatter Plots: Sales Price Versus Hypotheses Variables 800000 700000 600000 500000 O. 400000 on 300000 « 200000 100000 Number of Apartments Influence Single-family Dwellings 800000 ■ 700000 ■ 600000 ■ 500000 . 400000 300000 200000 100000 0 ^ ______0 200 400 600 800 1000 Dist one 71 400000 300000 ■ 200000 ■ 100000 400000 300000 200000 os C / 3 100000 ®,nS.D*a3l'£6£ 4 $ % 300 400 500 600 700 800 900 1000 Dist three 180000 160000 140000 cn 120000 « □ m 100000 ■ □ D □ □ 80000 500 600 700 800 900400 1000 Dist four 180000 160000 140000 120000 ■ 100000 ■ 80000 400 500 600 700 800 900 1000 Dist five 73 140000 130000 120000 O 100000 90000 80000 500 600 700 800 900 Dist six 140000 ’ □ 130000 ■ 120000 • □ 90000 « □ 80000 1 600 700 800 900 D istseven 120000 ■ 110000 100000 860 880 Dist eight 75 APPENDIX D Pearson Correlation Matrices Pearson Correlation M atrix CO (X Sq_Ft T o tR m Bath Bed Age Garage N u m A p ts Dist_one D isttw o D istjh re e D istfo u r Dist_five Dist_six Dist_seven Dist eight L______Os * * Os * oo - t T OO OS * Vi * SO ^ CN Pearson . p .636 .426 -.393 t-S * * * Correlation (**) (**) n c CU p p o o o o o o o p o o p o o o so o o o o p o m p o o OS p o p o o © o o Os VO CN Sig. 00 (2-tailed) Z VO so r*- (N m CN CN «Ti m CN tTi O 1665 1665 1665 1665 1665 1665 1665 1665 1665 w ^ * 00 CN - r i r~- £ * CN ^ o o vO * CN O —- m * m os W ^ r—s ^ Pearson .795 .699 .607 .099 -.049 .212 .326 * Correlation (**) (**) (**) (»*) (**) (**) Csj CN O © © © © © © © © © © © © © © © © © p O © © m © © © © © o in o O in Os Sq_Ft Sig. O .006 (2-tailed) Z CN c«"s cr> m CN CN SO VO r*- r- CN © in 1665 1665 1665 1665 1665 1665 1665 1665 1665 - Correlation n («) (**) (**) (*) © © © © © © 00 CN © © rn CN CN VO so 00 p © © © © © © © © © © p © CN oo © CN in oo OO © Tot_Rm Sig. m in (2-tailed) CN SO z CN m CN 1665 1665 1665 1665 1665 1665 1665 1665 1665 232 * n i * CN * * CN o © © m « ^ SO ^ CN w ® * o s r e o tj ^ - r n • * Tf ^ cn p o s © CN - * r- n •» in ^ in 2 CN o \ CN , m in os © SO so © r^ »’ ’ w i’ - - * * Pearson * * (**) w ^ ^ * * — Correlation , OO CN © © © © © © © © oo © © © © © © © © so © © oo © © in in © © © © CN p m r- rf r*» Age Sig. m .008 (2-tailed) Z SO SO CN cn CN CN r^* CN m o in r- 1665 1665 1665 1665 1665 1665 1665 1665 1665 77 * oo r~- 'S' 1>- r- © r r-* w (*) SO rs .280 .631 .295 .250 .019 .126 .026 -.537 p * r-* ^ © * © © 00 NO © cn Vi cn O cn * © cn © cn r- OS cn cn r*- cn rs © rS (**) O CS .574 cn -.263 r- O O n o ,,*r © r- rs © © © © rs © 2 r cn © © Os SO o vs SO SO © OO © rs ^ r- (**) © © .952 © .869 .052 so 9ZV cn N 4P so rs Tj* 46 © rs oo © rs 46 © rs rs © © *n cn p 46 © cn cn * © m © cn - cn © rrs © (**) © © p rs © rs rs © rs © rs rs .869 .838 r w Vi rs ^ © SO © © r^ © © 00 r- SO 46 r-* r- — 46 © r- rs © © SO Tf * © SO © * © so - SO vs cn SO p SO r** +_s p so SO © 232 rs © -.027 * r- V) .034 © © © 1665 1665 1665 © SO rs 46 o ^ o © 46 © cn ,s © rs ^ © NO ^ © o\ * o 4P © © 46 © rs SO 46 © rs © 46 © o p - cn ♦ © rs © vO © 46 © cn r- 46 © r-» SO 46 © 1665 1665 1665 so so rs o ^ © rs r^- SO SO rs rs Vi ON Os 46 © cn SO rs 00 r— *n rs V) NO rs SO TT - O * © p p so rs p cn p p © © 1665 1665 1665 r so rs * m ,—„ o © so vs 46 00 so cn NO cn t~~ * o © © © rs rs r- rJ" rs © rf p § r © © vs rs^ © so p r* cn © Vi r-* © © 1665 1665 so 1665 © rs o © cn 46 V) r- rs rs r- OO oo © o ♦ © rs 1 C=^ rs so © © cn m v> rs 00 cn fN^ iT —< * (*) r- Vi p © .056 © © rs © 1665 oo 1665 1665 © SO rs o © r-~ -—' rs so Os oo ^ os oo © — * © © cn r- © rs © rs S iP © o —- * r- I p cn so rs© * p cn °rn I © V) p (**) p o p .383 1665 1665 1665 r -w/ => ^ NO rs © Os ^ © tj- oo rs r- V i rs vs 00 © 00 o Os 46 © © cn rs m « cn m V i rs O n © o 46 p NO © r- (**) .070 cn (**) © rs .305 p © 1665 1665 1665 © rs o © CTv 'T' ' © o n vs r- rs V i o ^ 4P © Ov * © 'T t © o © rs © — 46 © © © SO cn © r- © (**) (**) p (**) p O SO o rs © VO © v> Os r- © rs vo © © r*1 * o On 46 © © m © © cn fN * © rs 2 r © SO * o -1—* * © o00 £ © © cn so rs © TT © r- in © 1665 1665 1665 so rs Z Z z Z Z Z Sig. Sig. Sig. Sig. Sig. Sig. Sig. Pearson Pearson Pearson Pearson Pearson Pearson Pearson (2-tailed) (2-tailed) (2-tailed) (2-tailed) (2-tailed) (2-tailed) (2-tailed) Correlation Correlation Correlation Correlation Correlation Correlation Correlation Garage Dist_one Disttwo Distfive Distfour Distthree Num_Apts 78 o r- o - r~- (M) o .416 .948 -.211 23 23 - 23 (**) .000 .906 .948 -.026 r^- 30 23 30 - .416 .906 -.026 -.211 on r-- O O' * o in V0 * o 30 23 .305 .278 .280 -.223 m o 2 ST o to 17 23 to «o 30 CN .870 p .295 -.036 CN 921' to r- 30 23 o P .631 .198 -.279 O o O O SO jp tO CO 23 to (**) .289 O .019 .200 -.734 r r^- ^ o Tf * o 30 23 tO p (*) .026 .207 .272 -.537 o TT m o 30 o 23 (**) o .574 os CN '55' r~- tO to 30 23 .120 .224 -.263 to ^ CO r— s OO onTt * -r- * CN * o r- tO in *_, 30 to * 23 p p .002 .007 021 On CN *0 to 30 23 ' .224 -.263 oo r- 30 23 (*) tO (*) o .147 .029 .272 -.567 -.456 00 r^- 30 to p 23 .095 .280 .055 -.473 -.235 r—N Z Z CO * IT) 30 SO * .006 .231 .219 .314 -.219 OO 3 CO tO 30 CN .276 .139 .462 -.393 z z z Z Sig. Sig. Sig. Pearson Pearson Pearson (2-tailed) (2-tailed) (2-tailed) Correlation Correlation Correlation Distsix Disteight ** Correlation is significant at the 0.01 the level (2-tailed). at is significant Correlation ** 0.05 the (2-tailed). level at is significant Correlation * a Cannot be computed because at least one of the variables is constant. variables of the one least at because computed be Cannot a Dist_seven 79 Pearson Correlation Matrix For Fifty Apartments in the Study Area BedRnt Age AptSqFt TotUnits Bed_Rnt Pearson Correlation 1 -.577** .184 -.052 Sig. (2-tailed) .000 .202 .718 N 50 50 50 50 Age Pearson Correlation -.577** 1 -.107 .006 Sig. (2-tailed) .000 .460 .967 N 50 50 50 50 Apt_SqFt Pearson Correlation .184 -.107 1 .945** Sig. (2-tailed) .202 .460 .000 N 50 50 50 50 TotU nits Pearson Correlation -.052 .006 .945** 1 Sig. (2-tailed) .718 .967 .000 N 50 50 50 50 Note: ** Correlation is significant at the 0.01 level (2-tailed). 80 BIBLIOGRAPHY Aalberts, R. J. and D.S. Bible, “Geographic Information Systems: Applications for the Study of Real Estate.” The Appraisal Journal 60:4(1992): 483-492. Adams, John S. “Residential Structure of the Midwestern Cities.” Annals o f the Association o f American Geographers 60, no.l (1970): 37-62 ______. “The Meaning of Housing in America.” Annals o f the Association of American Geographers 74, no.4 (1984): 515-26. ______. Housing America in the 1980s. New York: Russell Sage Foundation, 1987. Alonso, William. Location and Land Use; Toward a General Theory o f Land Rent. Cambridge: Harvard University Press, 1964. Asabere, Paul K. and Forrest E. Huffman. “Hierarchical Zoning, Incompatible Uses and Price Discounts.” Real Estate Economics 25, no. 3 (1997): 439-451. Babb, Carol E., Louis G. Pol, and Rebecca F. Guy. “The Impact of Federally-Assisted Housing on Single-Family Housing Sales: 1970-1980.” Mid-South Journal o f Business 4, no. 3 (1984): 13-17. Berry, Brian J.L. and Robert S. Bednarz. “A Hedonic Model of Prices and Assessments For Single-family Homes: Does the Assessor Follow the Market or the Market Follow the Assessor?” Land Economics 51, (1975): 21-40. ______,______, and H.M. Kim. “Challenges to the Monocentric Model.” Geographic Analysis 25, no.l (1993), 1-4. Bible, Douglas S. and Cheng-Ho Hsieh. “Applications of Geographic Information Systems for the Analysis of Apartment Rents.” The Journal o f Real Estate Research 12, no. 1 (1996): 79-87. Borchert, John R. “America’s Changing Metropolitan Region” Annals, Association of American Geographers 62, no.2 (1972): 353-73. Bourne, Larry S. Geography o f Housing. New York: Wiley, 1981. Burbach, Chris. “Board Votes to Rezone Stonybrook.” Omaha World-Herald. 3 August 2000, Thursday Sunrise Edition. 81 Cadwallader, Martin. Urban Geography: An Analytical Approach. Upper Saddle River: Prentice Hall, 1996. Can, A. “Measurement of Neighborhood Dynamics in Urban House Prices.” Economic Geography 66 (1990): 254-272. City of Omaha Planning Department. Omaha Master Plan. Omaha, 1997. Crone, Theodore M. and Richard P. Voith. “Risk and Return within the Single-Family Housing Market.” Real Estate Economics 27, no. 1 (1999): 63-78. Darling, Arthur H. “Measuring the Benefits Generated by Urban Water Parks.” Land Economics 49, (1973): 22-34. Dobriner, William M. Class in Suburbia. Englewood Cliffs: Prentice Hall, 1963. Downs, Anthony. Opening Up the Suburbs; An Urban Strategy For New America. Haven: Yale University Press, 1973. ______. Stuck in Traffic: Coping With Peak-Hour Traffic Congestion. Washington, D.C.: Lincoln Institute of Land Policy, 1992. Douglas County Assessors. “Douglas County Assessed Value Inquiry.” http://www.co.douglas.ne.us/dept/assessor/framevalinfo.htm Viewed April 17, 2001. Environmental Systems Research Institute, Inc. (ESRI). ArcView 3.2: Environmental Systems Research Institute, Inc. (ESRI). Using ArcView GIS. Redlands: Environmental Systems Research Institute, Inc., 1996. Fujita, Masahisa. Urban Economic Theory: Land Use and City Cambridge: Size. Cambridge University Press, 1989. Hair Joseph F. Jr., Rolph E. Anderson, and Ronald L Tatham. Multivariate Data Analysis: With Readings. 2d ed. New York: Macmillan Publishing Company, 1987. Haring, Lloyd L., John F. Lounsbury, and John W. Frazier. Introduction to Scientific Geographic Research. Boston: WCB McGraw-Hill, 1992. Harris, Richard and Robert Lewis. “Constructing a Fault(y) Zone: Misrepresentations of American Cities and Suburbs, 1900-1950.” Annals o f the Association o f American Geographers 88, no. 4 (1998): 622-639. 82 Hartshorn, Truman A. Interpreting the City: An Urban Geography. 2nd edition. New York: John Wiley and Sons, Inc., 1992. Keating, David C. “Exclusionary Zoning: In Whose Interests Should the Police Power Be Exercised?” Real Estate Law Journal 23, no. 4 (1995): 304-331. Li, Mingche M. and H. James Brown. “Micro-Neighborhood Externalities and the Hedonic Housing Prices.” Land Economics 56, no. 2 (1980): 124-141. Me Millen, Daniel P. and John Me Donald. “Population Density in Suburban Chicago: A Bid-Rent Approach.” Urban Studies 35, no.7 (1998): 119-131. Moudon, Anne V. and Paul M. Hess. “Suburban Clusters: The Nucleation of Multifamily Housing in Suburban Areas of the Central Puget Sound.” Journal o f the American Planning Association 66, no. 3 (2000): 243-264. Mundy, Bill. “The Scientific Method and the Appraisal Process.” The Appraisal Journal 60, no. 4 (October 1992): 493-499. Munneke, Henry J. and V. Carlos Slawson Jr. “A Housing Price Model with Endogenous Externalities and Location: A study of Mobile Home Parks.” Journal o f Real Estate Finance and Economics 19, no. 2 (1999): 113-131. Muth, Richard F. Cities and Housing; The Spatial Pattern o f Urban Residential Land Use. Chicago: University of Chicago Press, 1969. National Apartment Association. “National Apartment Association Zoning Moratorium White Paper.” (June 1997): 1-26. Omaha Planning Board Meeting, 12 July 2000. Omaha Planning Board Meeting, 2 August 2000. Phipps, Rod. Omaha City Planning Manager. Interview by author, 9 August 2000. Platt, Rutherford. Land Use and Society: Geography, Law, and Public Policy. Washington D.C.: Island Press, 1996. Purcell, Mark. “Neighborhood Activism Among Homeowners as a Politics of Space.” Professional Geographer 5, no. 2 (2001): 178-194. Richardson, Harry W., Joan Vipond, and Robert A. Furbey. “Determinants of Urban House Prices.” Urban Studies 11, (1974): 179-199. 83 Simons, Robert A., Roberto G. Quercia, and Ivan Marie. “The Value Impact of New Residential Construction and Neighborhood Disinvestment on Residential Sales Price.” The Journal o f Real Estate Research 15, no. 15 (1998): 147-161. Sweeny, J. L. “Housing Unit Maintenance Model of Tenure” Journal of Economic Theory 8, (1974): 111-138. Smith, Betty E. “A Review of Monocentric Urban Density” Journal o f Planning Literature 12, no. 2 (1997): 115-136. SPSS For Windows: Standard Evaluation Version, 11.0., 19 September 2001. [software on-line] available from: http://www.spss.com: Internet; accessed 1 May 2002. Waddell, Paul, Brian J.L. Berry, and Irving Hoch. “Housing Price Gradients: The Intersection of Space and Built Form.” Geographical Analysis 25, no. 1 (1993): 5- 19. Wang, Ko, V. Grissom, James Webb, and Lewis Spellman. “The Impact of Rental Properties on the Value of Single-Family Residences.” Journal o f Urban Economics 30, no. 2 (1991): 152-166. Van Vliet, Willem. The Encyclopedia o f Housing. Thousand Oaks: Sage, 1998.