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Geospatial Procedures for Identifying a Prospective Development Location

Permanent Link: http://ufdc.ufl.edu/UFE0020342/00001

Material Information

Title: Geospatial Procedures for Identifying a Prospective Development Location Apartment Origin Walkable Destination Scenario
Physical Description: 1 online resource (101 p.)
Language: english
Creator: Bolden, Gabriel
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: algorithm, analysis, apartment, area, estate, hedonic, heuristic, location, market, pipeline, psychographics, real, regression, trade
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: We set forth a set of procedures for determining the best location for a new apartment complex in Gainesville, Florida. An apartments' location is central to its economic performance, thus it is important to find a location that would be optimal for a new apartment complex. We reviewed literature on apartment complexes and set forth a set of procedures for locating an apartment complex. It adds to the existing literature on apartments, and apartment markets, and can help to improve the decision making process for real estate professionals. Different methods were used to determine the proper site of a new apartment complex. Psychographics were used to determine the demand and trade areas of apartments in Gainesville. Hedonic modeling was used to determine the characteristics that go into rent in the Gainesville market. Once everything was analyzed, an algorithm was developed to determine the best site for a new apartment complex. The algorithm has seven steps for identifying a potential location for development of an apartment complex, these steps are composed by analyzing the Demand Generator, Population Identification, Hedonic Pricing Model, Pipeline Construction, Locational Amenities, Locational Necessities, and finally Site Availability. This paper is the first to set out a specific list of rules for locating a new building.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Gabriel Bolden.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Thrall, Grant I.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0020342:00001

Permanent Link: http://ufdc.ufl.edu/UFE0020342/00001

Material Information

Title: Geospatial Procedures for Identifying a Prospective Development Location Apartment Origin Walkable Destination Scenario
Physical Description: 1 online resource (101 p.)
Language: english
Creator: Bolden, Gabriel
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2008

Subjects

Subjects / Keywords: algorithm, analysis, apartment, area, estate, hedonic, heuristic, location, market, pipeline, psychographics, real, regression, trade
Geography -- Dissertations, Academic -- UF
Genre: Geography thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: We set forth a set of procedures for determining the best location for a new apartment complex in Gainesville, Florida. An apartments' location is central to its economic performance, thus it is important to find a location that would be optimal for a new apartment complex. We reviewed literature on apartment complexes and set forth a set of procedures for locating an apartment complex. It adds to the existing literature on apartments, and apartment markets, and can help to improve the decision making process for real estate professionals. Different methods were used to determine the proper site of a new apartment complex. Psychographics were used to determine the demand and trade areas of apartments in Gainesville. Hedonic modeling was used to determine the characteristics that go into rent in the Gainesville market. Once everything was analyzed, an algorithm was developed to determine the best site for a new apartment complex. The algorithm has seven steps for identifying a potential location for development of an apartment complex, these steps are composed by analyzing the Demand Generator, Population Identification, Hedonic Pricing Model, Pipeline Construction, Locational Amenities, Locational Necessities, and finally Site Availability. This paper is the first to set out a specific list of rules for locating a new building.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Gabriel Bolden.
Thesis: Thesis (M.S.)--University of Florida, 2008.
Local: Adviser: Thrall, Grant I.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2008
System ID: UFE0020342:00001


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GEOSPATIAL PROCEDURES FOR IDENTIFYING A PROSPECTIVE
DEVELOPMENT LOCATION: APARTMENT ORIGIN WALKABLE DESTINATION
SCENARIO




















By

GABRIEL KENNETH BOLDEN


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2008


































2008 Gabriel Kenneth Bolden


































To my Mom, Colin, Grandma and Grandpa, Aunt Kathy, Great Grandma and the rest of my
family.









ACKNOWLEDGMENTS

I thank Dr. Thrall for his patience and guidance. I would like to thank my family for all

of their support. I would also like to thank Richard Sheffler for all of his statistical

recommendations. Finally, I would like to thank all of my friends here in Florida, without whom

I never would have made it.









TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ..............................................................................................................4

L IS T O F T A B L E S ........................................................................................................................... 7

LIST OF FIGURES .................................. .. ..... ..... ................. .8

L IST O F A B B R E V IA T IO N S .................................................................. .............................. 10

A B S T R A C T ......... ....................... ............................................................ 1 1

CHAPTER

1 INTRODUCTION ............... .............................. ............................ 12

2 L ITE R A TU R E R E V IE W ......................................................................... ........................ 14

P sychographics .............................. .......................................................14
Apartm ent Studies: H edonic Pricing M odels ........................................ ............................ 15

3 APPLICATION OF ALGORITHM ......................................................... ..............20

P rocedu ral Issu es ................................................................2 0
Heuristic Algorithm .............. ................. .................................... ... 20
D em and G enerator........... ...... ... .... .... ............................ .... .... ..... ...... 21
Population Identification .............. .. .............................................. ............................ 25
Iterative Procedure for Calculating Trade Area and Apartment Demand ............................26
Geospatial Characteristics of UF's L1-High Society Students.......................................27
A part ent Supply ........... ........... .. .................. ....... .......... ................. 29
D evelopm ent of a H edonic M odel......... ................. ................... ................. ............... 31
D ata........................... .................................. . ........ 32
H edonic M odel T est R result ......... ..... ............ ................. .................................................35
P ip e lin e .............. ...... ............................................................. 3 9
Qualitative Analysis........... .... ..... .......... ............... 42

4 ALGORITHM SUMMARY AND SITE SELECTION.......... ... ................. 44

5 C O N C L U S IO N ............................................................................................. .... .............. 5 2

APPENDIX

A PSYCHOGRAPHIC SUBMARKETS...........................................................................53

B R E G R E SSIO N T A B L E S............................................................................. .....................57

C AFTERW ORD ..................... .. ..................... ......... ..........93









L IST O F R E F E R E N C E S .............................................................................. ...........................96

B IO G R A PH IC A L SK E T C H .............................................................................. .....................99





















































6









LIST OF TABLES

Table page

3-1 2004-2005 Change in ESRI tapestry lifemode groups for University of Florida
stu d e n ts ........................................................ ...................................2 6

3-2 V variable table ............................................................... ..... ..... ........ 36

4-1 Score card: comparison and ranking of three prospective sites.......................................48

4-2 Score card: comparison and ranking of three prospective "finalist" sites .........................51

B-1 Notes .........................................57

B-2 D descriptive statistics. ................................ .. .. ................. ......... 59

B -3 C o rre latio n s .................................................................................................................. 6 0

B -4 V variable entered and rem oved. ........................................ ............................................63

B-5 M odel summary ..................................... ..... .......... .......... .... 65

B-6 ANOVA table. ................................ .. .... .. .... .... ................. 66

B-7 Coefficient table ....................................................... ........... ........... .... 68

B -9 C oefficient correlations........................................................................... .....................8 1

B -10 C ollinearity diagnostics .......................................................................... .....................86

B -11 R esiduals statistics ............................................... .... .. ............ ............ .. 89









LIST OF FIGURES

Figure page

3-1 Student population by distance from campus........................................ ............... 24

3-2 Location of UF students with LifeM ode L ........................................... ............... 28

3-3 Primary trade area for university of Florida student campus addresses, and locations
of apartm ents w ith that trade area....................... ................................... ............... 30

3-4 Apartment rent per bedroom by location, Gainesville FL ...........................................34

3-5 Map of apartments and condominiums in the pipeline for 2005 and 2006 .....................41

3-6 Map of apartments and condominiums in the pipeline for 2007 ....................................41

3-7 Map of grocery stores in Gainesville, Florida. .............. ......... .................... 42

4-1 Three prospective apartment development locations............................................. 45

4-2 Site A -U university Avenue and 6th Street.................................. ........................ .. ......... 46

4-3 Site B M ain Street and W illiston Road ........................................ ....................... 46

4-4 Site C 13th Street and 7th A venue.......................................................... ............... 47

4-5 Site B Replacement of Site B with 1216 SW 2nd Avenue .............. ............... 49

4-6 Photo of replacement site B at 1216 SW 2nd Avenue............. .................50

A-i Map of L1 students ......................................... .......... 53

A-2 M ap of L2 students. ....................... ........ .. ... ... .. .................. 53

A-3 M ap of L3 students ........................ ........ .. ... ... .. .................. 54

A-4 M ap of L4 students. ....................... ........ .. ... ... .. .................. 54

A-5 M ap of L5 students. ....................... ........ .. ... ... .. .................. 55

A -6 M ap of L 6 students. ................................................................................. ................ .. 55

B -i H istogram ...............................................................................................90

B-2 Normal P-P plot of regression standardized residual ............. .........................................91

B -3 S matter p lo t ............................................................................. 9 2









C-1 13th Street and NW 7th Avenue location. ........................................ ....................... 93

C-2 13th Street and NW 7th Avenue. ............................................... .............................. 94

C-3 Trophy shop surrounded by new complex at NW 7th Avenue............................... 94

C-4 SW 2nd Avenue and 6 Street........................................................ ... ............. 95

C-5 Alternate shot of SW 2nd Avenue and 6th Street. .................................... .................95









LIST OF ABBREVIATIONS

DG Demand Generator

GIS Geographical Information System

LSP Life Style Segmentation Profile

SFCC Santa Fe Community College

UF University of Florida









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

GEOSPATIAL PROCEDURES FOR IDENTIFYING A PROSPECTIVE
DEVELOPMENT LOCATION: APARTMENT ORIGIN WALKABLE DESTINATION
SCENARIO

By

Gabriel Kenneth Bolden

August 2008

Chair: Grant Ian Thrall
Major: Geography

We set forth a set of procedures for determining the best location for a new apartment

complex in Gainesville, Florida. An apartments' location is central to its economic performance,

thus it is important to find a location that would be optimal for a new apartment complex. We

reviewed literature on apartment complexes and set forth a set of procedures for locating an

apartment complex. It adds to the existing literature on apartments, and apartment markets, and

can help to improve the decision making process for real estate professionals.

Different methods were used to determine the proper site of a new apartment complex.

Psychographics were used to determine the demand and trade areas of apartments in Gainesville.

Hedonic modeling was used to determine the characteristics that go into rent in the Gainesville

market. Once everything was analyzed, an algorithm was developed to determine the best site

for a new apartment complex. The algorithm has seven steps for identifying a potential location

for development of an apartment complex, these steps are composed by analyzing the Demand

Generator, Population Identification, Hedonic Pricing Model, Pipeline Construction, Locational

Amenities, Locational Necessities, and finally Site Availability. This paper is the first to set out

a specific list of rules for locating a new building.









CHAPTER 1
INTRODUCTION

The location of a new apartment development can mean the difference between successful

business investments, versus investments that may not even break even. These are the steps that

an analyst can follow to identify and evaluate the best prospective locations for new apartment

developments. Data for Gainesville, Florida, were used to illustrate the steps. The procedures

introduced here can be replicated for any location if the appropriate data is available.

Among the data required for the study is the location and amount of competitive supply of

apartments, and historic trends of apartment construction, property values, and rental rates. In

addition to current supply, apartment development scheduled for construction and therefore

adding to future supply must be considered; this is known as the supply "pipeline" which is

assembled from building permits. This information is compiled to predict future values and

rental rates by location, a necessary component for making business decisions that will lead to a

higher likelihood of generating a profitable return.

Often ignored in apartment studies, is the human component of a successful apartment

development. People will be living at the location. The location and the amenities of the

apartment development must be amenable with the people making the choice to live there. A

contribution of this study is the use of psychographic measurements which will identify the type

of person most likely to find the amenities and location suitable. Conversely, with the target

psychographic group identified, the appropriate location and amenity bundle can be selected by

the developer. Psychographic measurements are also known as Life-style Segmentation Profiles

(LSPs). Here, ESRI's Tapestry LSPs are used which breaks the population into twelve Life

Mode Groups. Tapestry also offers a more granular set of 65 LSP groups. The more granular the

segmentation, the more narrow the demographic profile. The benefits of narrowing the









demographic profile are that the selection of amenities and therefore costs of development can

more closely fit the target population. A disadvantage of a highly granular segmentation

approach, however, is that the developer might appeal to only a very small demographic market

leading to a thin demand and therefore higher risk. Balancing these tradeoffs, this study therefore

uses the boarder 12 Tapestry segments.

Hedonic modeling is used to determine the relationship between rent, and apartment

location and their amenities. An algorithm assembling the above information is introduced to

evaluate the geographic trend of apartment rents and thereby prospective rents for the new

apartment development. Following Grant Thrall's "hierarchical categories of geographic

reasoning," the penultimate stage of geospatial reasoning is "judgment" (Thrall, 2002).

Judgment is the "so what" stage of analysis. Improving the judgmental decision is the ultimate

objective of business geography. Therefore, a recommendation will be made for a new apartment

complex. The primary data used for the analysis was compiled through 2 years of research

beginning in 2005. Development in the pipeline and development that has actually occurred

since the termination date of the data collection is used to document if this study actually

anticipated future development trends.

Thesis Questions: Where are the locations that can best take advantage of the increasing

student market demand for housing? What are the characteristics of the UF student market; in

other words, what can we learn from the students' psychographic LSP profile that can inform the

developer of the composition of amenities that are best included in a new apartment complex

targeted to this market?









CHAPTER 2
LITERATURE REVIEW

Psychographics

Psychographics provide a complete description of consumer behavior based on

demographics but also components of consumer behavior not captured by demographics alone.

Reynolds and Darden (1972) used psychographics to determine the trend of women shoppers in

Dublin, Georgia. Through traditional methods and the use of psychographics they were able to

determine which type of women is more likely to shop outside Dublin. These researchers

revealed that women who are more likely to shop greater distances outside the local area tended

to have more wealth, be more highly educated, and tended to be middle aged in comparison to

women that shopped predominantly in the local area (1972). This pioneering study corroborates

the above finding and helps explain why UF's young student population is tightly clustered near

the core campus area. Tassiopoulos, Nuntsu and Haydam (2004) used psychographics to

determine the makeup of the average wine tourist in South Africa. Their research showed that

the average wine tourist for that region is a young professional woman without children. The

current generation that fits that profile are referred to in the popular press as a "Generation Xer"

(Tassiopoulos, Nuntsu, Haydam, 2004).

Psychographics have been used to identify the characteristics of people that visit specific

areas. Dutta-Bergman (2002) was able to extrapolate the picture of the average internet user.

Dutta-Bergman discovered that the average internet user is a young person who exercises, is a

crafty consumer, and is "innovative" (2002). This research has allowed web-masters to design

internet pages better more specific and relevant to the needs of the average web site viewer.

Psychographics are an important area of study because it allows an individual to explore









variables not covered by conventional demographics and thereby get a better understanding of

demand and prospective changing demand.

Apartment Studies: Hedonic Pricing Models

Hedonic pricing models have been used on all areas of the real estate market to evaluate

property value or rental rates, it involves running a regression to capture trends in the real estate

market of interest. Stephen Malpezzi states that, "The method of hedonic equations is one way

expenditures on housing can be decomposed into measurable prices and quantities, so that rents

for different dwellings or for identical dwellings in different places can be predicted and

compared. At its simplest, a hedonic equation is a regression of expenditures (rents or values) on

housing characteristics" (Malpezzi, 2002). Hedonic modeling has been used to calculate the

impact that "quality of life" has on rental rates. John Kain, and John Quigley (1970), used

hedonic modeling to determine how the quality of housing affects value; their findings were that

the quality of a dwelling had as much impact on property value as does other standard

components of a hedonic model, such as square footage, age, number of bathrooms, fireplace,

and bedrooms (Kain and Quigley, 1970).

Determinants of market housing rent have focused on how various amenities affect

valuation. Hedonic modeling is used to distinguish the impact of how the various amenities

affect rent. Pagliari and Webb (1996) successfully modeled monthly rent as a function of

occupancy rates and rental incentives. They were also able to set the appropriate rental rate

through the results of their regression. Benjamin, Sirmans, and Sirmans (1989) studied the effect

of amenities, services and external factors on apartment rental rates. They used 188 observations

from 92 apartment complexes in Lafayette, Louisiana. They demonstrated that size of the

apartment, size of the complex, and age of the complex were the primary factors that affected

rent. Amenities such as 'covered parking, all utilities paid and a modem kitchen' were highly









prized in their sample, as were swimming pools. Amenities internal to the apartment complex

affected rent the most. Only one external factor affected rent in their sample: the degree of

congestion was the main external determinant affecting rent. Bible and Hsieh's (1996) study of

apartments in the Shreveport-Bossier, Louisiana, used GIS to explore the spatial relationships of

the local market. They found that the age of an apartment building and the size of the apartments

have a negative impact on apartment rent, while the presence of swimming pools and fireplaces

result in higher apartment rents. They also found that the distance to a college or university has a

negative impact on rent. The conclusion of these studies is that location is an amenity, and that

the amenity of location matters.

The impact of absolute location versus relative location has been examined by Frew and

Wilson (1990). Their hedonic model calculated a rent gradient for apartments in Portland,

Oregon. They included amenities of the apartment complex as well as dummy variables to

represent distance relative to the downtown. They found that location was crucial in determining

land values and rent. "The results clearly show that rent values drop substantially for the first ten

miles outside the city center, indicating that the downtown area is the central urban hub" (Frew

and Wilson, 1990, pp. 22). Their results also revealed that a 'rent gradient ridge occurs' around

freeway corridors; they hypothesized the ridge was attributable to accessibility to transportation.

The highest apartment rents do not necessarily concentrate around the center of the city; local

maximums can occur in other areas because of neighborhood (location) effects, and amenities of

the apartment complex itself. Nevertheless, the general trend is for rent to decline away from a

central node, while rising with proximity to other albeit comparatively minor nodes. Twelve

years following their publication on apartment rents, Frew and Wilson revisited their earlier

study and confirmed that the highest rents are not solely concentrated in the downtown area, but









that different areas command different rents based on their access to transportation corridors.

Their model showed that rent was highly correlated to access to a "highway" and "highway

intersection" (Frew and Wilson, 2002). Frew and Wilson confirmed that apartment rent varies

with location to different nodes and access to transportation. Therefore, minimizing distance to a

central node and good access to transportation is important when choosing a location for a new

apartment complex. 1

Asabere and Huffman (1996) evaluate the actual value of the apartment building rather

than apartment rents. Their analysis concludes that proximity to freeways, and "thoroughfares"

in Philadelphia correlates with higher property values, as does proximity to the central business

district. Benjamin and Sirmans too looked at the impact of proximity to transportation on

apartment rent. Their study incorporated 250 apartments in the Washington D.C area that were

near 'Metrorail Stations.' They found that rent decreases as distance from a 'Metrorail station'

increases. Internalizing Benjamin and Sirmans' conclusions into framework for improving

apartment location decisions would suggest that developers give higher priority to locations near

to transportation hubs. If locating near the desired central node is not possible, then a second best

solution would be to locate near a transportation hub that accesses that specific node.2 Song and

Garrit-Jan (2003) in their study of willingness to pay for "new urbanist" features found that

people are willing to pay more for access to transportation, and better walking access to

commercial uses; however, these same people consider disamenities to include higher density,

nearby multifamily housing, and proximity of commercial real estate.


1 Access to transportation or transportation corridors is important for people commuting to work on a daily basis.
Most people would rather live closer to their occupation but when that is not an option those same people prefer to
live where it is easiest to get to work. Researchers have used hedonic modeling to assess the distance of
transportation hubs to apartments and the resultant affect on their rental rates.
2This result was proven in Thrall (1988, ch. 7). Thrall demonstrates that as transportation cost and time decrease,
access to a transportation hub, or the central node accessed via the hub becomes equivalent.









The literature indicates that there are a wide variety of destinations that people want to

access (Cervero and Duncan, 2003). Generally, places of work and shopping rank as the most

important destinations. Destinations affecting apartment rents include hospitals, downtown city

centers, shopping locations, and universities. Each destination requires unique analysis to capture

the characteristics of the population placing high priority on accessing the specific destination.

The importance of transportation access cannot be underestimated. Is the destination a Walkable

(Cheadle, Garvin, Johnson, Lee, Lin, Moudon, Schmid, Weathers, 2006) place, with public

transit access, or is the destination a car environment accessed only by private transportation?

The type of destination and the mode of travel linking apartment origin and destination must be

considered. The example of this thesis is the walkable environment of University of Florida

which has limited automobile parking; therefore, the transportation linkage between proposed

apartment development and the UF destination is critical to the success of the development.

A university's effect on rental rates has been studied by Ogur (1973). His study evaluated

the rental housing market in comparison to the population of students enrolled in school versus

employment in the manufacturing sector. The study consisted of information gathered from 62

counties in New York State in 1960. His hedonic regression documented that a university has a

significant impact on nearby rental housing. His results further revealed that there was no clear

distinction between rents that "non-whites versus whites" pay. Instead, it was the presence of a

university that incremented rents to a higher level (Ogur, 1973).

Distance to a university is important to the students attending the university, and

consequently may be willing to pay more than others for premium university access.

Guntermann and Norbinn (1987) demonstrated that variation in rents in Mesa and Tempe

Arizona depended on access to the university. Their primary data base of 104 apartments was









created via personal contact with property managers. Their hedonic model revealed that

university students rank as high priority both access to the university and the condition of the

apartment. Condition of the apartment was not depending upon age; rather, condition as

measured by Guntermann and Norbinn (1987) was dependent upon the characteristics of the

amenities of the apartment and apartment complex. Therefore, good access to University of

Florida is hypothesized to be the most important factor when selecting the best locations for new

apartment complexes targeted to UF students. UF is the primary destination for the target rental

population. Walking and bicycling proximity to UF is one measure of access; availability of

public transportation is a second best location. The bundle of amenities offered is hypothesized

to also important to the UF student when choosing an apartment.









CHAPTER 3
APPLICATION OF ALGORITHM

Procedural Issues

The procedure followed here for the large part follows the general framework introduced

by Thrall (2002); namely, first calculate trade area; second calculate demand; third, calculate

competitive supply; and fourth, integrate these findings to make a recommendation. However, to

calculate the relevant trade area, the target student population will need to be derived, and this

implicitly calculates demand. So the order of Thrall's (2002) procedure will need to be

rearranged, and indeed even iterated between steps.

Heuristic Algorithm

The Heuristics algorithm allows for a standardized set of procedures for locating an

apartment complex, for our purposes the algorithm addresses the needs that must be met to locate

a prospective apartment site. Seven heuristic steps or rules are introduced to identify prospective

sites for a new apartment complex.

1. Demand Generator (DG) identify a demand generator and the area surrounding the node
that prospective renters are willing and able to commute between the demand generator and a
prospective apartment complex location. The distance will vary by demand generator,
population characteristics, and transportation access.

2. Population Identification identify the psychographic LSP group to target. Confirm the
current demand of the target LSP accessing the DG. Identification of the target LSP group
gives rise to necessary amenities and affordability requirements of the new housing. Project
the increase in target LSP population accessing the DG. If there is no increase in target LSP
population, then the new development must compete solely on the basis of price, higher level
amenities, and superior location. The developer must be so forewarned and then calculate if
the proposed development with the necessary characteristics and price is financially viable.

3. Hedonic Pricing Model- Construct a database of rents and amenities by location (address).
Thematically map the current rents adjusted by bedrooms and other amenities. Derive the in
apartment surface, including distance decay of rents from the DG.

4. Pipeline Analysis- Construct a database of competitive pipeline supply. Identify locations of
clusters of competitive supply. Avoid the location if the competitive supply will require the
new development to compete on the basis of price in order to achieve occupancy over 94%









(Thrall, 2002). Hot spot development areas can also serve as gravitational attractants, as
"the" place to reside.

5. Locational Amenities- identify second order locational amenities that will piggy back onto
the primary DG. Secondary locational amenities include good proximity to restaurants,
nightlife, and so on.

6. Locational Necessities- A successful development will also have available those necessities
that prospective renters expect to obtain on a daily or weekly basis. If the apartment complex
and the DG are walking environments, then the nearby necessities must also be available via
waking or public transportation. If the target population group is likely to have children, then
the quality of schools must be assessed. The crime rate must be considered. While all desire a
low crime rate, the rate is more important when making a location decision for some LSP
groups than others.

7. Site Availability identify sites that can be acquired to support the development, and
estimate their necessary cost of acquisition. Without adequate prospective development sites,
there can be no new development.

Demand Generator

Florida is the third fastest growing state in the nation. Even during the recent downturn in

the economy and migration to Florida, "despite a recent slowing in the rate of growth in new

residents, Florida remains on of the fastest growing states in the nation" (Schenker, 2007). As

our economy slows down Florida remains an affordable place to live: which makes it an

attractive destination. "During the 1990's, Florida's population increased by 3 million. Only

California and Texas experienced population increases equal to or exceeding Florida during the

decade" (Schenker, 2007). Florida's population increase is attributable to in-migration from

other states and other nations; natural increase, the difference between birth and death rates,

contributed less in population increase. Most of the population increase has been in the

southeastern region of Florida however, this population increase has implications for north

central Florida, particularly Gainesville Florida. As the state's population increases, the increase

translates into greater enrollments at University of Florida and Santa Fe Community College. In

1997, UF's student population equaled 42,053, and SFCC equaled 12,486. UF's student numbers









in 2008 exceed 51,000 (http://www.ir.ufl.edu/factbook/i-05.a hist.xls), and SFCC exceeds

16,000 (Jim Lewis, Office of Institutional Research, SFCC). The surge in increase in population

results in an increase in the demand for student housing.


Gainesville's economic base remains concentrated in education and government services,

most of which are located in the downtown to UF campus corridor, an exception being the SFCC

main campus in NW Gainesville1. Gainesville's metropolitan area hosts a population projected at

251,332 people (www.ams.usda.gov), while 108,655 people reside within the city limits

(www.bestplaces.net). Gainesville's spatial population trend has been westward, away from the

historic central downtown and toward the Interstate and its concentration of retail shopping

opportunities. (see for example, Smersh, Smith and Schwartz, 2004)2.

In 2004, the University of Florida admitted 46,441, and in 2005, the university admitted

49,780, resulting in a net increase of student enrollment at UF of 3,339 students

(http://www.ir.ufl.edu/factbook/i-05.ahist.xls). Assuming that students were adequately

housed in 2004, and that the market over time had adjusted so that there was not an oversupply

of student housing, then an increase of 3,339 students represents a significant increase in demand

for additional student housing. To accommodate and take advantage of the growth in student

population, where should new student housing be located? Answering that question is the

central focus of this thesis. Applying a gamut of geospatial procedures, that question is answered

here.




1 According to ESRI's business analyst, 19% of Alachua County's working population is engaged in education
based upon NAICS employment data.
2It is conventional in Real Estate Market Analysis to provide an overall description of trade area that places the
population in context.









The registrar at University of Florida allowed Dr. Grant Thrall to have access to the

student records. The confidentiality and sensitivity of accessing individual student records

necessitated that Dr. Thrall perform the basic geospatial analysis, the results of which were

provided to me for subsequent analysis and synthesis. Among the information in the student

database is permanent address and address on campus, as well as personally identifying

demographic characteristics such as age, gender, year in school, and other educational

curriculum information. Dr. Thrall's and my analysis determined that 80% of the students at UF

have campus addresses within 3.65 miles of the core UF campus as defined by the intersection of

University Avenue and 13th Street, this area makes up the University of Florida's primary trade

area3. The University of Florida's trade area is the entire state of Florida and to a lesser extent

the entire nation, and even globally; however the primary trade area is concentrated around the

university4. Grant Thrall explains states that, "The primary trade area is the geographic core from

which a real estate project will get the majority of its business. Applebaum (1996) used

supermarket data for metropolitan areas to reveal that the core trade area accounted for 60-70

percent of the real estate project's customers. Analysts today often define the primary trade area

as having 80 percent of the customers" (2002), this rule still remains relevant for defining the

University of Florida's primary trade area5. The University Avenue and 13th Street location is

adjacent to UF's College of Business and near to the College of Liberal Arts and Sciences. Both

colleges account for over 40% of total student enrollment at UF (www.ir.ufl.edu/factbook/i-

01.ahist.xls). The intersection of University Avenue and 13th Street was chosen because the


6 The highest density of students is at the North East comer of the campus. The North East comer was selected
because that is the destination for the majority of the student population.
4Using the primary trade area allows the largest and most dominant group to be identified, and this revealed group
is then used to establish the value platform of the apartment complex including its location.
The whole issue of calculating trade areas is very debatable. Using any locus will enter bias into the analysis. The
NE comer as a locus will capture the general market trend.










University is so expansive that if we were to use the entire circumference of the University you

would get apartments that were within in a mile of the university but outside a walkable distance

to the core. University Avenue and 13th Street represents a focal point as to base walking

distance. The 3.65 mile UF primary trade area, as shown in Figure 3-1, provides a spatial

delineation of what distance a student targeted apartment complex should not exceed from UF6



100.0
80.0


S40.0
20.2 20.1
20.0 15.5 15.1 10.0 10.0 3.0
0.0
1.05 1.5T 2.11 2.33 3.65 4.51 8.14
| I ODistaMic from Site
m of Customcr5 -*- Cumulative % of Customer

Figure 3-1. Student population by distance from campus

Taking the overall population of Gainesville (108,655) and the student population of the

university (51,599) reveals that about 48% of the Gainesville's population is made up of

university students. Given the historic increases in student population, and large student

population base, it is reasonable for this demonstration to restrict the example to decisions on

prospective locations for new apartment complexes that are targeted to UF students. Thrall

(2002) states that:

Apartments are targeted to a specific target population niche. Competing
apartments can be identified in a variety of ways, including geocoding databases
of apartment addresses, visualizing the resulting mapped data, and calculating the
lifestyle segmentation profiles (LSPs) [Life Mode Group] of apartments that are
derived using addresses of the apartments. The LSPs (Life Mode Group) can be
used as surrogate measures of apartment type and revealed target demographic
niche (2002).

6 This figure is automatically generated by ESRI's Community Coder when geocoding the data set. It allows the
input of only one locus for the market core. The graphical output has no user options.









Population Identification

The derivation of UF students' psychographics is preliminary to calculation of trade area

and demand. Psychographics allows researchers to evaluate trends in human behavior not

accounted for by demographics alone.

UF student information was used to calculate the students' psychographic profile by

submarket within UF's larger 3.65 mile primary campus trade area. This is accomplished by first

calculating each student's LSP based upon their permanent address. That derived characteristic

was then mapped using each student's campus address. ESRI's Tapestry twelve category Life

Mode LSP was used for the study. A life mode group is a combination of the more granular 65

category ESRI Tapestry LSP database. A student's permanent address is generally their

parent's addresses. This allows calculation of their propensity to consume. Without their parent's

addresses, commercial LSP databases like Tapestry merely list the student as an unknown

"student" consumption profile. Students at UF have different propensity to consume than SFCC,

and other colleges and universities. Subsequent to analysis of the students' LSP profiles, it is

possible to determine which LSP group comprises the largest increase in student population,

thereby revealing a granular insight into what type of housing is demanded, and where.

Life mode groups "arise via geo-statistical procedures that allow myriad descriptive

measurements to be combined into a small set of commonalities that explain the variation among

the population subgroups" (Thrall, 2002). Life mode groups range from the elite at the top of the

society to the poverty stricken at the bottom of society. Once it is determined which Life Mode

Group represents the largest increase in students, that group can be mapped out to discover any

trends associated with that Life Mode Groups housing habits.










Iterative Procedure for Calculating Trade Area and Apartment Demand


ESRI's Business Analyst (www.esri.com) was used to calculate the number of UF

students by their Life Mode Groups, and the change in their numbers between years. This is

shown in Table 3-1.

Table 3-1: 2004-2005 Change in ESRI tapestry lifemode groups for University of Florida
students
C change In Students % Of
Lifemode Segment 2004 to 2005 Student Change By
By Lifemode Lifemode Segment
L1: High Society 1,364 44%
L2: Upscale Avenues 247 8%
L3: Metropolis 88 3%
L4: Solo Acts 52 2%
L5: Senior Styles 324 11%
L6: Scholars & Patriots -20 -1%
L7: High Hopes 69 2%
L8: Global Roots 91 3%
L9: Family Portrait 461 15%
L10: Traditional Living 120 4%
L11: Factories & Farms 13 0%
L12: American Quilt 262 9%
Student Record Change B 371
Lifemode Segment 3
Not accounted for 268
Total Change In Student 3339
Records


Lifemode" (www.esribis.com ) psychographics of UF students for 2004 and 2005 are calculated,
and the difference by year derived for count of students by Lifemode segment.

The largest change in Life Mode Group between 2004 and 2005 was the LI: High

Society life mode group with 44% of the total incoming student population. Second ranked was

the L9 Family Portrait group with 15%, the third ranked was the L5 Senior Styles life mode

group with 11%. Because L1 life mode group represents the largest segment of incoming UF

students, that group will be the focus for deriving prospective apartment demand7.


It is assumed that investment would have lower risk by targeting the largest market, which the North East corer of
campus and the L1 segment group.









Geospatial Characteristics of UF's L1-High Society Students

People in the L1 category tend to enjoy fine dining, the arts, social activities and travel.

These people place importance on family and leisure time, they are also considered active

investors and savvy financial planners8. Lis are at the top of the financial ladder. Ll's have a

median household income of $185,415 dollars a year. Ll's own at least one home, the average

median home value of people in the L1 life mode group is $1,078,501 dollars. This life mode

group will not just live anywhere; instead, the apartment in which they reside must have certain

amenities or the L1 population will not be attracted to that apartment complex. Ll's are used to

a certain lifestyle that they will not want to part with once they get to college9. Neighborhood is

also important to the L1 student. The L1 neighborhood must provide good access to UF, and also

be clean, prestigious, and safe (http://www.esri.com/library/brochures/pdfs/community-tapestry-

handbook.pdf).

Following the derivation of the Life Mode segment for each student based upon their

permanent address, the campus address locations of students with L1 Life Mode segments was

mapped to reveal which neighborhoods L1 students choose. It was found that L1 students

primarily resided to the north and west of the university. This is documented in Figure 2-1.

Each dot in Figure 3-2 represents the location of one ore more students with the L1

characteristic. The map that results from the application of this procedure identifies the campus

locations of students with the L1 characteristic, and therefore the primary trade area of L1

students attending UF.



8There is an implicit assumption made and am aware of it, but I am trying to capture the general market trend.
The methodology employed follows standard business geography procedures. People can be segmented into
segments (LSPs, psychographics, demographics), parents have behavioral traits that are passed down to their
children, however, they may not always be the same.



















.'.me*
6
4S


a.-



C




i'
a.
-* .


I'


. $


i

. Gauisesyill


Figure 3-2. Location of UF students with LifeMode L1. Note: Because many students reside in
apartment complexes, and the geocoding procedure assigns a geographic coordinate
based upon address, and also since each apartment complex is assigned only one
address, therefore many students may be positioned at the same geographic
coordinate and represented by the same dot in the map.

The trade area is visualized in Figure 3-2. The L1 trade area is within the 80% primary trade area

for all UF students; namely, within 3.65 radial miles from the NE corer of campus. Now that it


* d4! *


9 elope










has been determined where L1 students live, it is important to find a suitable location for an

apartment marketed toward L1 students10.

Apartment Supply

The next step in conducting geospatial market analysis for a new development (Thrall,

2002) is to calculate competitive supply. The existing apartments in the city of Gainesville need

to be enumerated, along with their locations. Also, the known future supply of apartments needs

to be considered when calculating competitive supply".

Existing apartment supply was compiled from information gathered from three local

apartment guides; Apartment Finder (2007), Gainesville Apartment and Condominium Guide

(2007), and Gainesville 's Premier Apartment Guide (2007). All three publications are marketed

both to university students and the general population. These guides were used instead of the

property assessment data files because they contained more information relevant to this study

than is available in the assessment property records. 12 The database comprised 109 apartments.









10A question was raised as to "that segment of L1 students whose parents believe they should make it on their
own." To this I respond by stating that we do make certain assumptions, however, in all likelihood things will
continue to stay the same. L1 dominance is assumed to continue. The assumption is not heroic that they will
conform to behavior associated with the L1 life mode group.

1A question was raised as to why single family homes were not included in this study. The reason single family
dwellings were not included in the study is because we are not competing with single family housing. We are
looking at multi-family housing. Also, it does not make sense to try and develop affordable housing around UF
because of high property costs. The objective was to have a location close enough to be able to walk, bike or take a
short bus ride to the Demand Generator. Few Houses are walkable to the core of our primary trade area.


12 To view the property assessment data records for any property in Alachua County, see
hup \ \" \ .acpafl.org/search.html









The map in Figure 3-3 reveals that the majority of apartments listed in the apartment

guides are located within the 3.65 mile primary trade area13. Figure 3-3 provides the analyst the

opportunity to visualize the location of relevant existing supply of apartments.


Figure 3-3. Primary trade area for university of Florida student campus addresses, and locations
of apartments with that trade area

To calculate the proportionate share of apartments in Gainesville that fall within UF's 80% trade

area for resident students, the count of apartments within the trade area were divided by the total

number of apartments in the Gainesville. In Gainesville 72% of the apartments are located


13 Note: The trade area calculated here is 3.65 radial miles. The map projection used in Business Analyst distorts the
circular trade area to instead appear egg shaped.










within the University of Florida's 3.65 mile primary trade area14. Developers, therefore,

consider proximity to the university to be a key aspect of apartment location in Gainesville.

Most apartment market analysis studies are proprietary to the client and the analyst,

including studies specific to market analysis of apartments targeted to university students. 15 For

that reason, with the exception of Thrall (2002), few articles have been published specifically on

campus area apartment market analysis. Generally, there is a lack of research into the housing

side of the University experience. The publications readily available to those researching real

estate have been concerned with the presence of Universities on property values (See Jaffe and

Bussa, 1977, for example). These publications rely upon hedonic modeling to extrapolate the

relationship between rent and amenities.

Development of a Hedonic Model

According to Rogerson the regression equation used in SPSS is:


= a+ b2+x +b2aX+ .... + bpp (1)
A
where y is the predicted value of the dependent variable, with a given set of

observations on the dependent variable (y) and the independent variables (x)

(2004).




The dependent variable is monthly rent and the independent variables are made up of

variables based on different amenities as well as dummy variables.



14The fact that 72% of UF apartments are within the 3.65 mile marker, which makes it the place for straight line
analysis, is confirmatory.

15 For example, PricewaterhouseCoopers' Financial Advisory Services Real Estate division produces market
analyses of student apartments for their clients. PwC's procedure is very different from that which is introduced
here. PwC relies on survey questionnaires administered to prospective tenants.









Data

An experiment is conducted to document the effect of distance from UF upon rental rates,

and to document the effect of amenities on rental rates. A primary data base of 109 apartments

was created. Three locally published and readily available apartment guides were used to

establish the addresses, names, rental rates and amenity bundles of each apartment. Different

sized apartments were maintained in the database as separate observations, resulting in 350

observations for 109 apartments. The database included number of bedrooms, number of

bathrooms, whether it was a studio or not, whether the apartment complex had a clubhouse or

not, and if the apartment unit was a townhouse. Number of bedrooms was used for an

approximation of size because square footage was not available for all of the records in the

database. "Dummy" variables were created identifying the characteristics of amenities

hypothesized to be relevant, including Pets Allowed, Furnished, Patio, Balcony, Dishwasher,

apartment complex Laundry, Washer andDryer in the unit, apartment complex Pool, apartment

complex Tennis Court, apartment unit Fireplace. The apartment complex's age was included in

the database as a measure of condition or architectural style.

The actual driving distance from the apartment to the University's Admissions Office

was taken and then divided into five concentric zones in order to create "dummy" variables

representing which mile marker the apartment complex is located in.16 The Admissions

administrative office was chosen to represent the campus location because that is a location that

all students must periodically access, and that location is nestled in between the main classrooms

of the College of Business and the College of Liberal Arts and Sciences; together representing



16 Each one mile increment is expected to be correlated with a different mode of transportation: walking, bicycling,
bus, automobile, and beyond that locations are competing with different markets. The objective is to capture the
general market trend.









40% ofUF students' major colleges (www.ir.ufl.edu). Subsequent to documenting that distance

is significant, and then dummy variables representing concentric circles of a mile each around

the university will be used to determine differences by distance from the university, if any.

Segmenting the market by concentric circles arises from the work of William Applebaum and his

Customer Spotting Technique. Applebaum used concentric circles around grocery stores to

capture potential "drawing power" (1966). The concentric circle method allows for the

determination of which distance has the greatest impact on rent1. The "dummy" variables were

created by designating 1 for the apartment if it fell with in the corresponding mile range, and a 0

if not. The 1 mile variable included every apartment that was less than or equal to one mile, this

process was followed for the 2 mile, 3 mile, 4mile, until the 4plus mile variable which consisted

of all of the other apartments within the Gainesville. Prior to executing the analysis it was

hypothesized that monthly rents decline with increasing distance from the university; the

experiment is designed to allow for this hypothesis to be tested.

Figure 3-4 shows a map of apartment location and amount rent per bedroom. Rent is the

rate, while bedroom is an approximation for the size of the apartment. While the resulting map

displayed in Figure 3-4 is descriptive, it does convey that there is a relationship between

availability of apartment supply and proximity to UF. Also, the higher rental rates are close to

UF.

It is expected that each of the variables collected in the primary database will affect

monthly rent. However, it is not known how each characteristic affects apartment rent in the





1 The rational for choosing the NE comer as an origin for the concentric circles surrounding the campus is because I
am not addressing housing for people working at Shand's Hospital but rather I am addressing housing that is
targeted to students.









Gainesville market. Regression analysis is used to calibrate the hedonic model and thereby reveal

how each variable affects monthly rent.












O ?
.. .. .. ....
Ga nesf.diie AxPColl'o Da'a :; ^'^C

: ,-._-.- ..---- --... ....... ..-. -....---

0 41- 575
i -s iss -- -- --^- .--..--- g.----....







UF





9 .4- 'i'
37


Figure 3-4. Apartment rent per bedroom by location, Gainesville FL. Data updated as of 21
August 2007

It is expected that number of bedrooms will have a significant positive impact on rent

because the more bedrooms the larger the apartment, hence higher rent. The relationship

between bathrooms and rent is less clear, however it is expected to be positive because all

apartments contain at least one bathroom, people probably would not live in an apartment









without a bathroom. The relationship between clubhouses, townhouses, and rent is not clear. It

is expected that a pool will have a positive impact on rent and thus be significant, as well as the

presence of a washer and dryer in the unit. The proximity to UF variable is expected to have a

significant positive relationship with the price of monthly rent. Moreover it is expected that the

distances within the three mile range of the university will also have a significant positive impact

on rent, while anything over four miles will have less.

Hedonic Model Test Result

The Hedonic Model Test modifies the distance variable so that it is partitioned into five

zones of decreasing proximity to UF's core NE campus. With an F-Statistic of 110.894 the

regression equation can be accepted as greater than 99.99% confidence level as generating

significant explanatory power. The multiple correlation coefficient, R is .864, demonstrating a

strong relationship between rent and the hypothesized variables model18. The coefficient of

determination, r2, is equal to .746, which means that about 34 of the variation in price is explained

by the model. The histogram and P-P plot show that the model fits the assumptions of

normality. There is a high co-linearity with bed, and price, as well as, bathroom and price based

on VIF score of 3.377 and 3.626 respectively. The VIF is the "variance inflation factor," a score

greater than 5 for the VIF indicates problems with multi-colinearity, the VIF is also the

reciprocal of the tolerance (Rogerson, 2004). This is expected as bedrooms and bathrooms are

the two most dominant features in apartments. The Step Wise regression returned ten models,

although the ninth was the one selected to use. The ninth model returned from the model was

used because the condition index for Townhouses was greater than 15, which indicates a possible

problem with co-linearity. In model 9 the highest condition index returned was 14.929, which


18 Thereby confirming the hypothesized reasonableness of assuming five rings.









being less than 15 was acceptable for this study. The tables and charts for this regression are in


Appendix B.


Table 3-2 Variable table
Mean
Price $950.89
Studio .03
Bed 2.04
Bath 1.80
TownHouse .18
PatioBalcony .89
Pet .86
Furnished .31
Dishwasher .91
LaundryRoom .80
WasherDryer .45
Fireplace .18
Pool .89
Tennis .34
Clubhouse .53
Age 14.73
OneMile .08
TwoMile .16
ThreeMile .17
FourMile .19
FourPlusMiles .39


Std. Deviation
$379.355
.182
.975
.800
.382
.315
.344
.465
.289
.401
.498
.385
.308
.473
.500
10.395
.272
.364
.380
.396
.489


The first variable19 that is returned in the regression report is the bathrooms variable. The

bathroom variable has an expected value of 149.872 (with a confidence interval between 100.97

and 198.78 at the 95% confidence level). Adding an extra bathroom will command $149.87

more dollars to the monthly rent on average.



19 In forward stepwise regression, the first variable returned is the most significant in explaining the variation in the
dependent model.









The next variable, as in the first model, was Pet; it was negative as well. The model

returned an expected value of negative 138.781, (with a confidence interval between -73.43 to -

204.13 at the 95% confidence level). This is interpreted as apartments that allow pets are less

costly per month than places that do not allow pets. The presence of a club house returned an

expected value of 123.529, (with a confidence interval between 74.37 and 172.69 at the 95%

confidence level). Apartment complexes that have clubhouses (basically common areas,

different clubhouses have different amenities associated with them but this was not factored in

here, only whether or not it had one), command an extra $138.78 dollars on average.

The next variable returned in the model was the dummy variable representing less than or

equal to 1 mile. The expected value for the 1 mile variable was 329.435, which was the highest

expected value in the model (with a confidence interval of 247.37 to 410.90 at the 95%

confidence level). Apartments within the one mile radius fetch $329.43 dollars on average than

those outside that range.

The bedroom variable came up next in the model, the addition of an additional bedroom

to an apartment unit will increase the monthly rent by $145.41. The expected value of the model

was 145.407 with a confidence interval of 106.69 to 184.13 at the 95% confidence level. The

age variable followed bedroom and this has negative significance associated with it. The

expected value for the age variable was -4.566 with a confidence interval between -2.23 to -6.9

at the 95% confidence level. Thefurnished variable returned an expected value of 97.566

meaning that on average, apartments that are furnished cost $97.57 more a month than non-

furnished apartments. The furnished variable had a confidence interval between 49.62 and

145.52 at the 95% confidence level.









The 4 mile variable was next. This is interesting because it suggests that there is a rent

gradient ridge in Gainesville. The expected value for the 4 mile variable is 76.682 (with a

confidence interval at the 95th percentile at 23.44 to 129.92). Apartments at the four mile zone

command $76.68 more dollars a month in rent than apartments in the other zones except for the

one mile zone. This reinforces previous literature on rent gradient ridges by Frew and Wilson

who found that rent increases around "freeway corridors" (1990). The four mile zone

corresponds to the 1-75 freeway and shopping at Gainesville's two highest order retail centers

(Butler Plaza, Oaks Mall). Access to transportation and other important destinations increase

rent20

The final variable in the model was the fireplace variable. The expected value for this

variable was 67.851 (with a confidence interval of 12.99 to 122.71 at the 95% confidence level).

An apartment with a fireplace can command $67.85 more dollars a month than apartments that

do not have a fireplace. The fireplace variable was not expected to significant however, it makes

sense that a fireplace would contribute greatly to rent because it is considered a luxury in

apartment living.

Using the predicted values and residuals at the 95% level will reveal what the predicted

rent of an apartment should be. For a two bedroom, two bathroom apartment, that does not have

a townhouse or allow pets, that is furnished with a patio or balcony, a dishwasher and a

communal laundry room, that is a within a mile of the school and five years old, returns a

predicted monthly rent at the 95% confidence level between $1207.06 and $1444.43 dollars a

month. Predicted values can reveal where properties are properly priced. Comparing the



21 For documentation on the theory and standard model, see Thrall, Grant I., 1998, Land Use and Urban Form: The
Consumption Theory ofLand Rents; Rutledge/Methuen: London and New York.









predicted monthly rent to the actual monthly rent will reveal which apartments in Gainesville are

priced properly. This regression allows for the formation of a rental platform that can be used to

calculate the proper amount of rent to charge for a new apartment complex in Gainesville. The

variables returned in the model show what variables or rather amenities are important to rent

valuation and that the variables selected should be used in the development of a new apartment

building.

Pipeline

To get an idea of what was being planned on being built in Gainesville projects in the

development pipeline were considered. Thrall (2002) states that "the pipeline is real estate at

various stages of construction from conceptualization to the awarding of a certificate of

occupancy. What is in the pipeline can be used as a measure of the prospective increase in the

supply of real estate." Both condominiums and apartments are considered the competitive

supply. Single family dwellings while part of housing supply are not competitive in most

circumstances with condominiums and apartment.

Visual display of the locations of L1 students revealed (see Figure 1) the existing L1

submarket. The question arises "is the supply in the pipeline focused on L1 students in their

revealed trade area?" The pipeline data will also give insight to whether the L1 apartment

market will soon be saturated.

To obtain the pipeline information in Gainesville, a visit to the Alachua County Building

Department "First Step" Program was required. Developers applying for building permits must

begin the permitting process at "First Step." The developments recorded at First Step are either

in the planning phase, construction phase, or are finished but yet to receive a certificate of

occupancy (CO). First Step records are held in a folder containing forms of "completed"

building permit applications. Regrettably, the permitting office prior to 2007 neither required that









the forms be filled out in their entirety, nor required information be updated as plans became

more specific. The First Step Program has not yet gone digital so the person assembling the data

must hand transcribe the records, and then later convert them to digital format. Because the

proposed construction is in the planning phase when First Step begins its process, information is

often missing, including proposed number of apartment units, number of floors, and square

footage. As plans solidify, the First Step forms are not updated. Accurate and complete

information is not publically available until after the project has been completed and a CO

awarded. The pipeline supply is important for the big spatial picture of future competition by

location, and which sub markets are receiving developer's attention; however, the pipeline

supply data for Alachua County is not sufficiently accurate for statistical analysis.

While 2005 and 2006 pipeline data was of a quality that restricted its use to being purely

qualitative and visual, pipeline for 2007 was much improved as compared to recent previous

years. Pipeline supply for 2007 is shown in Figure 3-6.

The pipeline supply collected for 2007 more complete, allowing for density plots of

planned apartments and condominiums by the number of units planned. Considering the

information in the pipeline supply allows the analyst to guide the developer away from small

submarkets that might in the near future have an over supply of competing units, and at the same

time provide guidance as to the near future locations of high levels of development before

construction is readily apparent on the ground. The benefits of agglomeration might guide the

development toward others in the pipeline; avoidance of high levels of competitive supply might

guide the development away from concentrations of new development in the pipeline.






































Figure 3-5. Map of apartments and condominiums in the pipeline for 2005 and 2006. Pipeline
data obtained from Alachua County Building Permits office "First Step." Data
accurate as of 24 August 2006.


* crrre~t

* &4d

LOI.-
IAt
wr:~c



o 2.




s


Figure 3-6.Map of apartments and condominiums in the pipeline for 2007. Pipeline data
obtained from Alachua County Building Permits office "First Step." Data accurate as
of 24 August 2007.


. .-











Qualitative Analysis

The objective of business geography is for geospatial analysis to improve the business


decision (Thrall, 2002). Not all real estate development decisions can be based solely on


quantitative analysis. Qualitative analysis based upon reliable data is equally if not more


important than quantitative analysis. When locating an apartment complex qualitative analysis,


which includes a large measure of what is often referred to as "common sense," should be drawn


upon to account for goods and services that potential renters will need on a regular basis.


Without large pantries for storage, the apartment dweller will need either to go frequently to the


grocery store or regularly to nearby restaurants. Renters value good proximity of grocery stores


and pharmacies, as well as good proximity to retail shops offering goods and services consumed


on a daily or weekly basis.


-.[ J.t J,,
-..i Jr.i

<25


Grocery Store Map
iffmlDenry _












S.. Gnesyve

1 : ; ", ;


I-)


i., ... ... O '
S.-..... ," ... ----






.. X

Figure 3-7. Map of grocery stores in Gainesville, Florida.

Figure 3-7. Map of grocery stores in Gainesville, Florida.


I..-I..



Publix

Albertson's

Ward's

Kash'N' Karry
Winn Dixie


., Ii)1.llr


=,, ,,.^









This map allows for the visualization of grocery stores across the city of Gainesville. The

map shows a lack of grocery stores concentrated in the downtown and eastside of Gainesville.

This area may be in need of a new grocery store to meet the needs of a larger population. Publix,

grocery store, is the franchise that has the most stores in Gainesville, the distribution of Publix'

stores seem dispersed enough to cover every section of Gainesville, except the eastside.

Also, the size of the lot that you are trying to build on should also be considered.

Although not impossible, getting parcels rezoned is a hurdle that does not need to be encountered

during the process of locating an apartment, choosing the proper parcel size for avoid going

through the hassle of rezoning. It is difficult to get permission to build a large apartment building

where it is zoned for a single family development and adjacent to existing single family homes.









CHAPTER 4
ALGORITHM SUMMARY AND SITE SELECTION

The above seven step algorithm is followed for identification of a set of prospective sites for new

apartment development.

* Demand generator: In Gainesville the most important DG is the University of Florida. The
target population for the new development is students attending UF.

* Population identification: Given the UF student population as the target renters of the new
development, the L1 LSP segment has the greatest base at UF, and is expected to increase
more than other LSP groups in the near future. Therefore, the target group is the L1 students.

* Hedonic pricing model: The regression model revealed that

a. pets should not be allowed
b. a clubhouse designed for student use should be included in the development
c. location should be within a mile of UF

* Pipeline analysis: The pipeline for Gainesville revealed that potential market saturation is
occurring along 13th Street north and south of University Avenue, as well as SW Archer
Road east and west of the interstate, and along SW 24th Avenue between SW 34th Street and
the interstate.

* Locational amenities: L1 students are financially able to enjoy nightlife and fine restaurants.
The student oriented nightlife in Gainesville is clustered near the downtown and midtown
areas. Therefore, proximity to both UF and downtown must be considered as a primary
locational asset.

* Locational necessities: A map of grocery stores in Gainesville reveals that downtown and
the east side of UF are not served by grocery stores. This is both an opportunity for new
grocery store development, and a prerequisite for a successful new apartment development.

* Site availability: the analyst is at times required to perform "muddy boots geography" and
qualitatively assess sites visually, as well as within a layer of parcels displayed within a GIS.
Three sites were identified and will be compared.

Three Sites:Three prospective sites for the new apartment development were identified by

viewing the Alachua County Land Parcel Database (http://www.acpafl.org/search.html) within a

GIS, and by personal inspection, based on the visualization of the sites, on a map, and in real life.

The three sites were chosen based upon their close location to UF, proximity to other L1










students, proximity to downtown, proximity to shopping, and size of the parcel. These three

sites are then ranked based upon the above discussed criteria. The locations of the three sites are

displayed in Figure 4-1. Images of the three sites are displayed in Figures 4-2, 4-3 and Figure 4-

4.


Site C: 13th and

Northwest 7th

Street



Site A: University

and 6th Street








Site B: South Main

Street


Mi ^ :i i '
I s P w qT_ ,
t 777r


"-^~~~~~ f* 4ji wT y ,c
>^*.LT *^.*' .~~~r i | *ta .ii


I.-i R i.

I;. ft [ i

i 9 9*.5 f
r3^ ~.wf. 1S. Pa,, .- ^^
Be' \ I *



JLI
2 rlf PyP
T rrrr~ 1 v < B

f4 nb l-".C. f IMlJ .P


i f I-- v 4
I 3ts ri



-i i p s AI
5- -_ s-^^ P I i 5 ..r
^"'~ ^. if rja" Rd,


Figure 4-1. Three prospective apartment development locations. Selection completed September
14, 2005.

Initially, site selection was based on the availability of land, the size of the parcel and the

location of the parcel. This type of strategy is commonly applied in real estate market analysis.

Site A is currently occupied by Central Florida Office Supply. This two acre parcel lies

within UF's 3.65 mile trade area, and within a mile of the downtown district. Zoned as a

redevelopment district by the City of Gainesville, this site has the potential to hold up to 300

units.


i



**lk


I i [
I
i *
LlkriL


.s 1iI

t
.5 .211
4,-











'A
"I-"


Figure 4-2. Site A -University Avenue and 6th Street. Photograph taken February 3, 2008.

This site was chosen for two reasons. First is its proximity to campus and the downtown area.

Though not within the established L1 student trade area, L1 students are expected to find the

location attractive because of its access to both UF and downtown. It is on a public transportation

route, which UF students can ride at no additional charge beyond their required student fees.

Second is to avoid the saturated submarkets of new apartment complexes.

















Figure 4-3. Site B Main Street and Williston Road. Photograph taken February 3, 2008.









Site B is a two acre assembled lot that provides great access to major roads, such as: SR

20, SR 24, US 441, and SR 301. Site B also lies within the 3.65 mile trade area of UF, and is 1.5

miles from downtown. This location was chosen because a new mixed use development (Thrall,

2002) combining housing and retail on this site could provide both the site and situation needs

required by the target renters.
















..... ...........
.... .- .. .. .. .. ... .. ... ......


Figure 4-4. Site C 13th Street and 7th Avenue

The site C location is within the University's trade area. It is located near the future

mixed use development of University Corners (www.universitycorners.com) which is under

construction and will contain over 150 square foot of retail within the one-million square foot

development. This site is located relatively close to housing in the pipeline, which can provide

agglomeration benefits as well as competitive supply. Therefore, the developer must include site

amenities within the apartment complex to insure its competitive position. Site C is presently

occupied by a scuba dive shop. The area has potential to be developed into a complex that

would be suitable for Ll's.









Table 4-1. Score card: comparison and ranking of three prospective sites

Site A Site B Site C Best


Proximity to UF 1 3 2 Site A


Proximity to L s 2 3 1 Site C



Land parcel size 1 1 2 Site A & B


Proximity to downtown 1 2 3 Site A



Proximity to Mid-Town 2 3 1 Site C



Proximity to shopping 2 3 1 Site C



The three sites are compared and ranked from 1 to 3 according to which was qualitatively

judged to be the best for the specific criterion. There are six criterion listed in Table 3 which

were also discussed above.

Site A and C tied for the best development sites. Both sites A and C are located within

one mile of UF. Site B greatest disadvantage is that it is outside the desired one mile distance

from UF, and not close to existing shopping or transportation to compensate. A fourth site was

then chosen to substitute for site B, and to confirm more closely to the desired criteria. The site

chosen was the present location of College Manor Apartments, which is directly across the street

from UF, on SW 13th Street. The apartment complex is older and suffering from lack of recent

renovation. The site is suitable for demolition and rebuilding to a higher then current density.









1- i f MIfmt -i
A
Site C: 13thand
Northwest 7th 9 21 ; .
Street --.



and 6th Street .
3 V e '
S-0. I Z is no t. 1h *



Site B: Southwest --

2nd Avenue and "
12th Street '



Figure 4-5. Site B Replacement of Site B with 1216 SW 2d Avenue

The new site to replace site B is located at the corner of Southwest Second Avenue, and
Southwest 12th Street. This site was selected because of its relative proximity to the university,

its relative proximity to other L1 students, as well as its relative proximity to Downtown, as well

as good proximity to everyday shopping needs with the development completion of University

Corners. The site is the second oldest apartment complex in Gainesville; the Age of a building

has a negative affect on rent. The age of the current apartment building is 37 years old. Because

there is already an apartment complex at this location, the predictive model from the regression
can be used to evaluate whether the property is being utilized properly. The predicted monthly

rent interval for this apartment complex is between $378 and $1352 dollars a month at the 95%

confidence level, the actual price per month for this apartment complex is $580 dollars a month.

The actual monthly rent falls on the smaller side of the prediction interval. Plugging the










variables for this location into the regression equation gives you a monthly rent of $739 dollars a

month for a one bedroom, one bath apartment. Plugging in variables for a new apartment




.'x
i -I















:::::. .... ....









Figure 4-6. Photo of replacement site B at 1216 SW 2nd Avenue. Photograph taken February 3,
2008.

complex at this location gives a predicted monthly rent of $1165 dollars, this results from

changing the age of 37 to one, signifying a new development, also a clubhouse was included, and

pets not allowed, as the regression results showed that the age of a building as well as the

allowance of pets are variables that affect rent negatively. A new apartment complex at this

location can command rent much greater than it currently receives. A financial analysis would

need to be performed, working backwards from the target (the predicted monthly rent interval)

$378 and $1352 dollars a month, cost of acquisition, demolition and construction.









Table 4-2. Score card: comparison and ranking of three prospective "finalist" sites

Site A Replacement Site C Best
Site B'


Proximity to UF 3 1 2 Site B'

Proximity to L s 3 2 1 Site C

Land Parcel Size 2 1 3 Site B'

Proximity to Downtown 1 2 3 Site A


Proximity to Mid-Town 3 2 1 Site C

Proximity to shopping 2 3 1 Site C


After substituting the new Site B' for the previous Site B, the results of Table 4 indicate that Site

C should be considered by developers as a prospective location for a new apartment complex.

Site C is located at 13th Street and Northwest 7th Avenue. It is within the L1 student trade

area, and offers L1 type amenities nearby. It is within the premium one mile zone distant from

UF. The site is large enough to support the development of an apartment complex and to provide

adequate onsite parking. The property would need to be rezoned from retail to either mixed use

or high density housing. The disadvantage of Site C is that it is least proximate to downtown

than the two other competitive locations. The pipeline analysis shows that other new apartments

or condos are planned to be constructed in the area. The competitive supply might be an

agglomerative benefit since the site is at the periphery of the L1 trade area. It can be stated, that

this is the best location for a new apartment building. (A discussion of developments in the

market that took place after this study was concluded can be found in Appendix C).









CHAPTER 5
CONCLUSION

This analysis introduces a seven step procedure for identifying prospective sites for

development. The seven steps are:

1. Demand Generator identification
2. Population Identification
3. Hedonic Pricing Model
4. Pipeline Analysis
5. Locational Amenities
6. Locational Necessities
7. Site Availability

The seven steps were applied to select a location for a new apartment complex development.

The analysis of the seven steps is used to justify the selection of the finalist site. Step 3 was a

hedonic model is used to calculate potential rents for a site. This is valuable because the

geospatial analyst is responsible for data creation that is input to the financial analysis. The

difference between potential and realized rents is the opportunity cost of not developing or

redeveloping a site. Psychographic lifestyle segmentation profiles (LSP) were used to obtain the

characteristics of current and future demand; among the characteristics are the type of amenities

that need to be included in the new development, and establishing constraints on location.

Pipeline analysis reveals potential overdevelopment, or possible locations that might benefit

from agglomeration. These seven steps will assist real estate professionals in their ranking

properties for construction or acquisition. The analysis supports qualitative judgment with

geospatial procedures. The seven steps will reduce the risk of making a bad judgment, and to

increase the likelihood making a successful business investment.












APPENDIX A
PSYCHOGRAPHIC SUBMARKETS


Mapping the location of students in Gainesville by Life Mode Group allows for the visualization
of submarkets demarcated by their psychographic profile.


0
A, J
o' y: -u


* S


*. .


Figure A-1. Map


of L1 students.


The L1 population groups predominately to the North and West of the University.


N


U. S
*
*


DC.
-S
%. +.


Figure A-2. Map of L2 students.


*












L2 students do not have the same numbers as the L1 students but one can see a pattern emerge as
to where the L2 students live.


*. *

**:\


a .*


* .


i..


Figure A-3. Map of L3 students


L3 Students group to the North and North East of the University.


* ^ **


"V.


Figure A-4. Map of L4 students.


Q mO










L4 Students seem to cluster around Main Street and around the 1-75 interchange.






^S.
















Figure A-5. Map of L5 students.

L5 Students are concentrated to the North of the University like the L1 students', however, L5
students have a greater concentration while having less of a concentration in the Northwest.










I*













Figure A-6. Map of L6 students.









L6 Students are concentrated along 13th Avenue as well as scattered across the University itself.
Looking at these maps allows one to see the different psychographic groups and where they live
in Gainesville. These maps reinforce the belief that similar people will group together, there is
overlap in every psychographic market, however, each psychographic group has its own unique
pattern of residential behavior. Some groups are willing to live in areas other groups are not,
reinforcing the idea that individuals that are similar in psychographic make-up make decisions
that are indistinguishable from one another.









APPENDIX B
REGRESSION TABLES

Table B-1. Notes
Output Created 27-Jan-2008 19:07:57
Comments
Input Data C:\Documents and Settings\Gabriel
Bolden\Desktop\aptList.sav
Active Dataset DataSetl
Filter
Weight
Split File
N of Rows in Working Data
File
Missing Value Handling Definition of Missing User-defined missing values are
treated as missing.
Cases Used
Statistics are based on cases with no
missing values for any variable used.

Syntax REGRESSION
/DESCRIPTIVES MEAN STDDEV
CORR SIGN
/MISSING LISTWISE
/STATISTICS COEFF OUTS CI
BCOV R ANOVA COLLIN TOL
ZPP
/CRITERIA=PIN(.05) POUT(.10)
CIN(95)
/NOORIGIN
/DEPENDENT Price
/METHOD=STEPWISE Studio Bed
Bath TownHouse PatioBalcony Pet
Furnished Dishwasher LaundryRoom
WasherDryer Fireplace Pool Tennis
Clubhouse Age OneMile TwoMile
ThreeMile FourMile FourPlusMiles
/SCATTERPLOT=(* SDRESID
*ZPRED)
/RESIDUALS HIST(ZRESID)
NORM(ZRESID)
/SAVE ZPRED COOK LEVER
MCIN ICIN ZRESID.









Table B-1 Continued.
Resources






Variables Created or
Modified


Processor Time
Elapsed Time
Memory Required
Additional Memory
Required for Residual Plots
ZPR 4
ZRE 4
COO 4
LEV 4
LMCI 4

UMCI 4

LICI 4

UICI 4


00:00:02.016
00:00:01.579
14076 bytes

760 bytes

Standardized Predicted Value
Standardized Residual
Cook's Distance
Centered Leverage Value
95% Mean Confidence Interval Lower
Bound for Price
95% Mean Confidence Interval Upper
Bound for Price
95% Individual Confidence Interval
Lower Bound for Price
95% Individual Confidence Interval
Upper Bound for Price


__









Table B-2. Descriptive statistics.
Mean Std. Deviation N
Price $950.89 $379.355 35C
Studio .03 .182 35C
Bed 2.04 .975 35C
Bath 1.80 .800 35C
TownHouse .18 .382 35C
PatioBalcony .89 .315 35C
Pet .86 .344 35C
Furnished .31 .465 35C
Dishwasher .91 .289 35C
LaundryRoom .80 .401 35C
WasherDryer .45 .498 35C
Fireplace .18 .385 35C
Pool .89 .308 35C
Tennis .34 .473 35C
Clubhouse .53 .500 35C
Age 14.73 10.395 35C
OneMile .08 .272 35C
TwoMile .16 .364 35C
ThreeMile .17 .380 35C
FourMile .19 .396 35C
FourPlusMiles .39 .489 35C













Table B-3. Correlations.
Thre FourPlu
Studi TownHous PatioBalcon Furnishe Dishwashe LaundryRoo WasherDry Fireplac Tenni Clubhous One Two e Four s
Price o Bed Bath e y Pet d r m er e Pool s e Age Mile Mile Mile Mile Miles


Pearson Price
Correlatio
n Studio

Bed


.049 .151 .275

.075 -.191 -.134

-.073 .215 .213


Bath


TownHouse
PatioBalcony
Pet

Furnished
Dishwasher
LaundryRoo
m
WasherDryer
Fireplace
Pool

Tennis
Clubhouse
Age

OneMile

TwoMile

ThreeMile


FourMile

FourPlusMil
es


.379 -.338 .133 -.126 -.067 .078

-.200 .156 .234 -.038 -.045 -.013

.204 -.156 -.077 -.050 .059 -.035


-.0951 .073


-.019 .181 -.135 -.198


.117 -.149 -.145 -.212


-.135 -.145


.153 -.2131-.238 -.348 -.371


.136 -.207 -.210

-.080 .123 -.175


-.039 .060 .166


-.042 -.160 -.014

.033 .106 .154


.160 -.398

-.083 -.016

.061 -.214


-.096 -.097

.153 .126


-.077 .052


.082 -.098

-.067 -.001


.126 -.038


.067 -.045


.078 -.013

.013 -.088













Table B-3. Continued
One Two Three Four FourPlus
Price Studio Bed Bath TownHouse PatioBalcony Pet Furnished Dishwasher LaundryRoom WasherDryer Fireplace Pool Tennis Clubhouse Age Mile Mile Mile Mile Miles
Sig. (1- Price .000 .000 .000 .159 .001 .000 .000 .000 .000 .000 .182 .002 .000 .000 .000 .006 .009 .105 .072 .402
tailed) Studio .000 .000 .000 .462 .061 .382 .132 .000 .039 .023 .080 .000 .006 .000 .002 .000 .238 .200 .403 .051
Bed .000 .000 .000 .018 .127 .000 .000 .000 .001 .000 .087 .000 .000 .000 .002 .075 .175 .137 .257 .099
Bath .000 .000 .000 .001 .093 .000 .000 .000 .000 .000 .497 .000 .000 .000 .000 .318 .083 .406 .390 .044
TownHouse .159 .462 .018 .001 .177 .013 .025 .003 .201 .001 .252 .019 .268 .118 .020 .005 .008 .001 .000 .016
PatioBalcony .001 .061 .127 .093 .177 .124 .323 .000 .088 .305 .001 .121 .000 .000 .047 .036 .002 .076 .063 .105
Pet .000 .382 .000 .000 .013 .124 .000 .371 .044 .002 .143 .006 .276 .020 .000 .035 .009 .167 .033 .491
Furnished .000 .132 .000 .000 .025 .323 .000 .008 .125 .013 .076 .006 .000 .000 .000 .118 .023 .290 .318 .036
Dishwasher .000 .000 .000 .000 .003 .000 .371 .008 .001 .001 .035 .003 .000 .000 .000 .048 .006 .104 .285 .085
LaundryRoom .000 .039 .001 .000 .201 .088 .044 .125 .001 .000 .290 .244 .155 .031 .000 .246 .357 .002 .420 .002
WasherDryer .000 .023 .000 .000 .001 .305 .002 .013 .001 .000 .021 .118 .001 .000 .000 .156 .025 .019 .446 .000
Fireplace .182 .080 .087 .497 .252 .001 .143 .076 .035 .290 .021 .115 .023 .318 .078 .005 .068 .235 .216 .270
Pool .002 .000 .000 .000 .019 .121 .006 .006 .003 .244 .118 .115 .000 .000 .291 .000 .011 .132 .001 .023
Tennis .000 .006 .000 .000 .268 .000 .276 .000 .000 .155 .001 .023 .000 .000 .007 .000 .001 .001 .396 .002
Clubhouse .000 .000 .000 .000 .118 .000 .020 .000 .000 .031 .000 .318 .000 .000 .000 .000 .037 .363 .015 .002
Age .000 .002 .002 .000 .020 .047 .000 .000 .000 .000 .000 .078 .291 .007 .000 .000 .086 .000 .003 .000
OneMile .006 .000 .075 .318 .005 .036 .035 .118 .048 .246 .156 .005 .000 .000 .000 .000 .009 .006 .003 .000
TwoMile .009 .238 .175 .083 .008 .002 .009 .023 .006 .357 .025 .068 .011 .001 .037 .086 .009 .000 .000 .000
ThreeMile .105 .200 .137 .406 .001 .076 .167 .290 .104 .002 .019 .235 .132 .001 .363 .000 .006 .000 .000 .000
FourMile .072 .403 .257 .390 .000 .063 .033 .318 .285 .420 .446 .216 .001 .396 .015 .003 .003 .000 .000 .000
FourPlusMiles .402 .051 .099 .044 .016 .105 .491 .036 .085 .002 .000 .270 .023 .002 .002 .000 .000 .000 .000 .000













Table B-3. Continued
One Two Three Four FourPlus
Price Studio Bed Bath TownHouse PatioBalcony Pet Furnished Dishwasher LaundryRoom WasherDryer Fireplace Pool Tennis Clubhouse Age Mile Mile Mile Mile Miles
N Price 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Studio 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Bed 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Bath 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
TownHouse 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
PatioBalconv 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Pet 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Furnished 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Dishwasher 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
LaundrvRoom 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
WasherDrver 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Fireplace 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Pool 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Tennis 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Clubhouse 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
Age 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
OneMile 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
TwoMile 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
ThreeMile 350 350 350 350 350 350350 50 350 350 350 350 350 350 35035050 350 350 350 350
FourMile 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350
FourPlusMiles 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350









Table B-4. Variable entered and removed.
Variables Variables
Model Entered Removed Method
1 Stepwise
(Criteria:
Probability-of-
Bath F-to-enter <=
Bath .
.050,
Probability-of-
F-to-remove >=
.100).
2 Stepwise
(Criteria:
Probability-of-
Pet F-to-enter <=
.050,
Probability-of-
F-to-remove >=
.100).
3 Stepwise
(Criteria:
Probability-of-
Clubhouse F-to-enter <=
.050,lubhouse
Probability-of-
F-to-remove >=
.100).
4 Stepwise
(Criteria:
Probability-of-
OneMile F-to-enter <=
OneMile .
.050,
Probability-of-
F-to-remove >=
100).
5 Stepwise
(Criteria:
Probability-of-
Bed F-to-enter <=
.050,
Probability-of-
F-to-remove >=
.100).









Table B-4 Continued.


Age








Furnished








FourMile








Fireplace


TownHouse


a. Dependent Variable: Price


Stepwise
(Criteria:
Probability-of-
F-to-enter <=
.050,
Probability-of-
F-to-remove >=
.100).
Stepwise
(Criteria:
Probability-of-
F-to-enter <=
.050,
Probability-of-
F-to-remove >=
.100).
Stepwise
(Criteria:
Probability-of-
F-to-enter <=
.050,
Probability-of-
F-to-remove >=
.100).
Stepwise
(Criteria:
Probability-of-
F-to-enter <=
.050,
Probability-of-
F-to-remove >=
.100).
Stepwise
(Criteria:
Probability-of-
F-to-enter <=
.050,
Probability-of-
F-to-remove >=
.100).



























a. Predictors:
b. Predictors:
c. Predictors:
d. Predictors:
e. Predictors:


(Constant), Bath
(Constant), Bath, Pet
(Constant), Bath, Pet,
(Constant), Bath, Pet,
(Constant), Bath, Pet,


Clubhouse
Clubhouse, OneMile
Clubhouse, OneMile, Bed


f. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age
g. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age,
Furnished
h. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age,
Furnished, FourMile
i. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age,
Furnished, FourMile, Fireplace
j. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age,
Furnished, FourMile, Fireplace, TownHouse
k. Dependent Variable: Price


Table B-5. Model summary
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .759a .576 .575 $247.287
2 .783b .613 .611 $236.634
3 .803c .644 .641 $227.217
4 .824d .678 .675 $216.411
5 .844e .713 .709 $204.815
6 .851' .725 .720 $200.755
7 .8589 .736 .730 $197.067
8 .861h .741 .735 $195.156
9 .864' .746 .739 $193.742
10 .865J .749 .741 $192.878









Table B-6. ANOVA table.
Model Sum of Squares df Mean Square F Sig.
1 Regression 2.894E7 1 2.894E7 473.328 .000a
Residual 2.128E7 348 61150.620
Total 5.022E7 349
2 Regression 3.079E7 2 1.540E7 274.968 .000b
Residual 1.943E7 347 55995.868
Total 5.022E7 349
3 Regression 3.236E7 3 1.079E7 208.942 .000O
Residual 1.786E7 346 51627.625
Total 5.022E7 349
4 Regression 3.407E7 4 8516782.277 181.852 .000d
Residual 1.616E7 345 46833.527
Total 5.022E7 349
5 Regression 3.579E7 5 7158823.445 170.654 .000e
Residual 1.443E7 344 41949.357
Total 5.022E7 349
6 Regression 3.640E7 6 6066829.415 150.533 .000f
Residual 1.382E7 343 40302.389
Total 5.022E7 349
7 Regression 3.694E7 7 5277560.853 135.895 .000s
Residual 1.328E7 342 38835.585
Total 5.022E7 349
8 Regression 3.724E7 8 4654678.070 122.215 .000h
Residual 1.299E7 341 38085.840
Total 5.022E7 349
9 Regression 3.746E7 9 4162498.765 110.894 .000'
Residual 1.276E7 340 37535.903
Total 5.022E7 349









Table B-6 Continued.
10 Regression
Residual
Total


a. Predictors:
b. Predictors:
c. Predictors:
d. Predictors:
e. Predictors:
f. Predictors:
g. Predictors:
h. Predictors:


(Constant), Bath
(Constant), Bath, Pet
(Constant), Bath, Pet, Clubhouse
(Constant), Bath, Pet, Clubhouse, OneMile
(Constant), Bath, Pet, Clubhouse, OneMile, Bed
(Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age
(Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished
(Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile


i. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile,
Fireplace
j. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile,
Fireplace, TownHouse
k. Dependent Variable: Price










Table B-7. Coefficient table.
Unstandardized Standardized 95% Confidence Collinearity
Coefficients Coefficients Interval for B Correlations Statistics
Lower Upper Zero-
Model B Std. Error Beta t Sig. Bound Bound order Partial Part Tolerance VIF

1 (Constant) 302.255 32.613 9.268 .000 238.112 366.398
Bath 360.182 16.555 .759 21.756 .000 327.621 392.743 .759 .759 .759 1.000 1.000
2 (Constant) 540.397 51.871 10.418 .000 438.376 642.419
Bath 333.471 16.510 .703 20.198 .000 300.999 365.943 .759 .735 .674 .921 1.086
Pet
-220.244 38.319 -.200 -5.748.000 -295.611 -144.877 -.398 -.295 .921 1.086
.192
3 (Constant) 500.173 50.339 9.936 .000 401.164 599.182
Bath 311.241 16.358 .656 19.027 .000 279.067 343.415 .759 .715 .610 .865 1.156
Pet
-212.492 36.821 -.193 -5.771 .000 -284.913 -140.071 -.398 -.296 .919 1.088
.185
Clubhouse 139.182 25.260 .183 5.510 .000 89.499 188.864 .379 .284 .177 .928 1.078
4 (Constant) 437.632 49.052 8.922 .000 341.153 534.111
Bath 310.272 15.581 .654 19.913 .000 279.626 340.918 .759 .731 .608 .865 1.157
Pet
-186.499 35.333 -.169 -5.278 .000 -255.994 -117.003 -.398 -.273 .906 1.104
.161
Clubhouse 177.821 24.896 .234 7.143 .000 128.854 226.788 .379 .359 .218 .866 1.154
OneMile 267.933 44.398 .192 6.035 .000 180.607 355.259 .133 .309 .184 .922 1.084
5 (Constant) 410.120 46.622 8.797 .000 318.421 501.820
Bath 175.434 25.673 .370 6.834 .000 124.939 225.929 .759 .346 .197 .285 3.505
Pet
-191.481 33.449 -.174 -5.725 .000 -257.271 -125.691 -.398 -.295 .905 1.105
.165
Clubhouse 186.867 23.604 .246 7.917 .000 140.440 233.293 .379 .393 .229 .863 1.158
OneMile 297.635 42.274 .213 7.041 .000 214.487 380.782 .133 .355 .203 .911 1.097
Bed 131.116 20.435 .337 6.416.000 90.923 171.310 .715 .327 .185 .303 3.299










Table B-7 Continued.
6 (Constant) 486.623 49.769 9.778 .000 388.733 584.513
Bath 164.851 25.311 .347 6.513 .000 115.067 214.636 .759 .332 .184 .282 3.547
Pet
-169.347 33.278 -.154 -5.089.000 -234.802 -103.891 -.398 -.265 .879 1.138
.144
Clubhouse 147.628 25.249 .195 5.847 .000 97.965 197.292 .379 .301 .166 .725 1.380
OneMile 322.996 41.948 .231 7.700.000 240.488 405.503 .133 .384 .218 .889 1.125
Bed 136.815 20.084 .351 6.812 .000 97.312 176.317 .715 .345 .193 .301 3.317
Age -4.716 1.215 -.129 -3.880.000 -7.106 -2.325 -.338 -.205 .724 1.382
.110
7 (Constant) 456.979 49.495 9.233 .000 359.626 554.332
Bath 156.311 24.951 .329 6.265 .000 107.235 205.388 .759 .321 .174 .280 3.577
Pet
-144.442 33.340 -.131 -4.332.000 -210.021 -78.864 -.398 -.228 .844 1.185
.120
Clubhouse 132.856 25.099 .175 5.293 .000 83.488 182.224 .379 .275 .147 .707 1.415
OneMile 327.751 41.197 .235 7.956 .000 246.719 408.783 .133 .395 .221 .888 1.126
Bed 137.056 19.715 .352 6.952 .000 98.279 175.834 .715 .352 .193 .301 3.317
Age -4.605 1.193 -.126 -3.859.000 -6.952 -2.258 -.338 -.204 .723 1.383

Furnished91.758 24.563.112 3.736.000 43.445 140.072 .364 .198.10 .853 1.
Furnished 91.758 24.563 .112 3.736.000 43.445 140.072 .364 .198 .104 .853 1.172










Table B-7 Continued.


8 (Constant)
Bath
Pet

Clubhouse
OneMile
Bed
Age

Furnished
FourMile
9 (Constant)
Bath
Pet

Clubhouse
OneMile
Bed
Age

Furnished
FourMile
Fireplace


424.370
156.907

-134.666

129.587
342.021
139.383

-4.346

93.320
75.299
421.945
149.872

-138.781

123.529
329.435
145.407

-4.566

97.566
76.682
67.851


50.398
24.710

33.204

24.884
41.119
19.542

1.185

24.331
27.079
50.043
24.699

33.006

24.827
41.143
19.555

1.180

24.217
26.889
27.709


.331

-.122

.171
.245
.358

-.119

.114
.079


.316

-.126

.163
.236
.374

-.125

.120
.080
.069


8.420 .000
6.350 .000

-4.056 .000

5.208 .000
8.318 .000
7.133 .000

-3.666 .000

3.835 .000
2.781 .006
8.432 .000
6.068 .000

-4.205 .000

4.976 .000
8.007 .000
7.436 .000

-3.869 .000

4.029 .000
2.852 .005
2.449 .015


325.240
108.304

-199.976

80.642
261.142
100.946

-6.677

45.462
22.037
323.513
101.291

-203.702

74.695
248.508
106.942

-6.888

49.932
23.794
13.348


523.500
205.510

-69.356

178.531
422.899
177.820

-2.014

141.178
128.562
520.377
198.453

-73.859

172.362
410.363
183.872

-2.245

145.200
129.571
122.355


.759

-.398

.379
.133
.715

-.338

.364
.078


.759

-.398

.379
.133
.715

-.338

.364
.078
.049


.325

-.215

.271
.411
.360

-.195

.203
.149


.313

-.222

.261
.398
.374

-.205

.213
.153
.132


3.577

1.199

1.418
1.144
3.323

1.391

1.172
1.055


3.626

1.202

1.432
1.162
3.377

1.400

1.179
1.055
1.057










Table B-7 Continued.
10 (Constant) 405.039 50.523 8.017 .000 305.662 504.417
Bath 157.118 24.851 .331 6.323 .000 108.237 205.999 .759 .325 .172 .270 3.704
Pet
-126.794 33.394 -.115 -3.797.000 -192.480 -61.109 -.398 -.202 .805 1.242
.103
Clubhouse 129.351 24.885 .170 5.198 .000 80.403 178.299 .379 .272 .141 .689 1.452
OneMile 340.970 41.359 .244 8.244.000 259.617 422.322 .133 .409 .224 .844 1.184
Bed 143.387 19.494 .368 7.355 .000 105.042 181.731 .715 .371 .200 .295 3.386
Age -4.255 1.185 -.117 -3.591 .000 -6.587 -1.924 -.338 -.191 .702 1.424
.098
Furnished 102.976 24.258 .126 4.245 .000 55.260 150.692 .364 .225 .116 .838 1.193
FourMile 66.091 27.281 .069 2.423 .016 12.430 119.752 .078 .130 .066 .912 1.096
Fireplace 67.066 27.589 .068 2.431 .016 12.800 121.333 .049 .131 .066 .946 1.057
TownHouse
TonHouse -58.985 29.305 -.059 -2.013.045 -116.627 -1.343 .054 -.109 .849 1.178
.055
Dependent Variable: Price









Table B-8 Excluded variables.
Collinearity Statistics
Partial Minimum
Model Beta In t Sig. Correlation Tolerance VIF Tolerance

1 Studio -.060a -1.679 .094 -.090 .964 1.037 .964
Bed .272a 4.441 .000 .232 .308 3.252 .308
TownHouse -.072a -2.037 .042 -.109 .974 1.027 .974
PatioBalcony .107a 3.091 .002 .164 .995 1.005 .995
Pet -.200a -5.748 .000 -.295 .921 1.086 .921
Furnished .175a 5.009 .000 .260 .930 1.076 .930
Dishwasher .090a 2.508 .013 .133 .931 1.074 .931
LaundryRoom -.035a -.987 .324 -.053 .965 1.036 .965
WasherDryer .046a 1.257 .210 .067 .892 1.122 .892
Fireplace .048a 1.388 .166 .074 1.000 1.000 1.000
Pool .017a .489 .625 .026 .969 1.032 .969
Tennis .099a 2.795 .005 .148 .943 1.060 .943
Clubhouse .191a 5.485 .000 .282 .929 1.076 .929
Age -.171a -4.935 .000 -.256 .946 1.057 .946
OneMile .153a 4.494 .000 .235 .999 1.001 .999
TwoMile -.070a -2.022 .044 -.108 .994 1.006 .994
ThreeMile -.057a -1.649 .100 -.088 1.000 1.000 1.000
FourMile .090a 2.588 .010 .138 1.000 1.000 1.000
FourPlusMiles -.057a -1.617 .107 -.086 .992 1.008 .992









Table B-8 Continued.
2 Studio -.074b -2.191 .029 -.117 .959 1.043 .883
Bed .286b 4.896 .000 .255 .307 3.256 .296
TownHouse -.039b -1.133 .258 -.061 .944 1.060 .882
PatioBalcony .099b 2.975 .003 .158 .993 1.007 .918
Furnished .140b 4.041 .000 .212 .890 1.124 .881
Dishwasher .111b 3.227 .001 .171 .922 1.084 .849
LaundryRoom -.027b -.796 .426 -.043 .963 1.038 .894
WasherDryer .032b .900 .369 .048 .887 1.128 .838
Fireplace .060b 1.800 .073 .096 .996 1.004 .917
Pool .057b 1.659 .098 .089 .933 1.072 .874
Tennis .107b 3.152 .002 .167 .942 1.062 .868
Clubhouse .183b 5.510 .000 .284 .928 1.078 .865
Age -.144b -4.250 .000 -.223 .923 1.083 .889
OneMile .133b 4.060 .000 .213 .988 1.013 .910
TwoMile -.050b -1.481 .139 -.079 .982 1.018 .910
ThreeMile -.048b -1.434 .152 -.077 .997 1.003 .918
FourMile .070b 2.091 .037 .112 .988 1.012 .910
FourPlusMiles 052b -1 542 124 -083 991 1 009 912









Table B-8 Continued.
3 Studio -.046c -1.402 .162 -.075 .934 1.070 .843
Bed .296c 5.317 .000 .275 .307 3.260 .288
TownHouse -.044c -1.337 .182 -.072 .943 1.060 .832
PatioBalcony .054c 1.617 .107 .087 .918 1.089 .858
Furnished .107c 3.131 .002 .166 .855 1.170 .844
Dishwasher .069c 2.019 .044 .108 .865 1.156 .822
LaundryRoom -.018C -.545 .586 -.029 .961 1.041 .845
WasherDryer -.009C -.270 .788 -.015 .845 1.184 .811
Fireplace .055c 1.717 .087 .092 .996 1.004 .865
Pool -.007c -.210 .834 -.011 .819 1.220 .815
Tennis .027c .722 .471 .039 .731 1.367 .720
Age -.078c -2.120 .035 -.113 .744 1.344 .744
OneMile .192c 6.035 .000 .309 .922 1.084 .865
TwoMile -.037c -1.130 .259 -.061 .977 1.024 .864
ThreeMile -.045c -1.416 .158 -.076 .997 1.003 .865
FourMile .049c 1.510 .132 .081 .974 1.027 .860
FourPlusMiles -.077c -2.385 .018 -.127 .973 1.028 .862









Table B-8 Continued.
4 Studio -.087d -2.723 .007 -.145 .899 1.113 .841
Bed .337d 6.416 .000 .327 .303 3.299 .285
TownHouse -.081d -2.557 .011 -.137 .912 1.096 .832
PatioBalcony .060d 1.894 .059 .102 .917 1.090 .808
Furnished .114d 3.516 .000 .186 .854 1.171 .838
Dishwasher .072d 2.199 .029 .118 .865 1.156 .817
LaundryRoom -.008d -.251 .802 -.014 .958 1.044 .845
WasherDryer -.009d -.278 .781 -.015 .845 1.184 .811
Fireplace .027d .866 .387 .047 .971 1.030 .862
Pool .015 d .446 .656 .024 .810 1.235 .780
Tennis .049d 1.377 .169 .074 .724 1.381 .696
Age -.113d -3.188 .002 -.169 .728 1.374 .725
TwoMile -.010d -.322 .747 -.017 .957 1.045 .856
ThreeMile -.020d -.647 .518 -.035 .978 1.023 .864
FourMile .075 d 2.422 .016 .129 .957 1.045 .860
FourPlusMiles -.040d -1.252 .212 -.067 .930 1.075 .861









Table B-8 Continued.
5 Studio -.002e -.059 .953 -.003 .728 1.374 .245
TownHouse -.075e -2.504 .013 -.134 .911 1.097 .280
PatioBalcony .058e 1.927 .055 .103 .917 1.090 .285
Furnished .115e 3.757 .000 .199 .854 1.171 .283
Dishwasher .052e 1.684 .093 .091 .856 1.168 .285
LaundryRoom -.004e -.123 .902 -.007 .958 1.044 .284
WasherDryer .005e .143 .886 .008 .841 1.189 .274
Fireplace .050e 1.693 .091 .091 .958 1.044 .282
Pool -.012e -.361 .718 -.019 .796 1.256 .285
Tennis .042e 1.226 .221 .066 .723 1.383 .285
Age -.129e -3.880 .000 -.205 .724 1.382 .282
TwoMile -.010e -.334 .738 -.018 .957 1.045 .285
ThreeMile -.041e -1.395 .164 -.075 .966 1.035 .283
FourMile .084e 2.885 .004 .154 .954 1.048 .285
FourPlusMiles -.033e -1.108 .269 -.060 .929 1.076 .284









Table B-8 Continued.
6 Studio .008' .251 .802 .014 .723 1.383 .243
TownHouse -.060f -2.024 .044 -.109 .894 1.119 .275
PatioBalcony .066f 2.225 .027 .119 .913 1.095 .282
Furnished .112' 3.736 .000 .198 .853 1.172 .280
Dishwasher .024' .774 .440 .042 .804 1.244 .282
LaundryRoom .033' 1.073 .284 .058 .873 1.145 .282
WasherDryer -.026f -.826 .409 -.045 .791 1.265 .273
Fireplace .059f 2.049 .041 .110 .952 1.051 .279
Pool .010o .303 .762 .016 .772 1.295 .282
Tennis .065' 1.941 .053 .104 .702 1.424 .281
TwoMile -.006' -.219 .827 -.012 .956 1.046 .282
ThreeMile -.018' -.619 .537 -.033 .923 1.083 .281
FourMile .076f 2.641 .009 .141 .949 1.054 .282
FourPlusMiles -.049f -1.669 .096 -.090 .912 1.096 .281









Table B-8 Continued.
7 Studio .007g .205 .838 .011 .723 1.383 .243
TownHouse -.074g -2.516 .012 -.135 .882 1.134 .274
PatioBalcony .072g 2.503 .013 .134 .910 1.099 .280
Dishwasher .020g .657 .512 .036 .803 1.246 .279
LaundryRoom .016g .520 .603 .028 .853 1.173 .279
WasherDryer -.023g -.728 .467 -.039 .790 1.266 .270
Fireplace .067g 2.365 .019 .127 .947 1.056 .276
Pool .001g .022 .982 .001 .768 1.302 .280
Tennis .057g 1.720 .086 .093 .699 1.431 .278
TwoMile .000g .002 .998 .000 .952 1.050 .280
ThreeMile -.024g -.827 .409 -.045 .921 1.086 .278
FourMile .079g 2.781 .006 .149 .948 1.055 .280
FourPlusMiles -.055g -1.884 .060 -.101 .910 1.098 .279
S 8 Studio .006h .187 .852 .010 .723 1.383 .243
TownHouse -.060h -2.033 .043 -.110 .849 1.177 .274
PatioBalcony .069h 2.392 .017 .129 .908 1.102 .280
Dishwasher .020h .658 .511 .036 .803 1.246 .279
LaundryRoom .012h .414 .679 .022 .851 1.175 .279
WasherDryer -.017h -.558 .577 -.030 .787 1.271 .270
Fireplace .069h 2.449 .015 .132 .946 1.057 .276
Pool .019h .595 .552 .032 .737 1.358 .280
Tennis .064h 1.950 .052 .105 .695 1.439 .278
TwoMile .018h .632 .528 .034 .906 1.104 .280
ThreeMile -.006h -.201 .841 -.011 .872 1.147 .278
FourPlusMiles -.023hl -.726 .469 -.039 .728 1.374 .279









Table B-8 Continued.
9 Studio .006' .183 .855 .010 .723 1.383 .240
TownHouse -.059' -2.013 .045 -.109 .849 1.178 .270
PatioBalcony .058' 2.012 .045 .109 .881 1.135 .276
Dishwasher .010' .336 .737 .018 .788 1.269 .276
LaundryRoom .0111 .356 .722 .019 .851 1.176 .275
WasherDryer -.0081 -.272 .786 -.015 .775 1.290 .265
Pool .013' .420 .675 .023 .733 1.365 .276
Tennis .055' 1.672 .095 .090 .685 1.461 .275
TwoMile .024' .832 .406 .045 .900 1.111 .276
ThreeMile -.004' -.137 .891 -.007 .871 1.148 .274
FourPlusMiles -.029' -.912 .362 -.049 .724 1.382 .275
10 Studio .0051 .153 .879 .008 .723 1.384 .239
PatioBalcony .055O 1.913 .057 .103 .879 1.138 .270
Dishwasher .019' .627 .531 .034 .772 1.295 .270
LaundryRoom .013i .432 .666 .023 .849 1.177 .269
WasherDryer .002 .076 .940 .004 .752 1.329 .262
Pool .014O .428 .669 .023 .733 1.365 .270
Tennis .051 1.566 .118 .085 .682 1.465 .269
TwoMile .032i 1.101 .271 .060 .886 1.129 .270
ThreeMile .004j .120 .904 .007 .857 1.167 .268
FourPlusMiles -.045 -1.375 .170 -.075 .691 1.446 .269
a. Predictors in the Model: (Constant), Bath
b. Predictors in the Model: (Constant), Bath, Pet
c. Predictors in the Model: (Constant), Bath, Pet, Clubhouse
d. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile
e. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed










Table B-8 Continued.
f. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age
g. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished
h. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile
i. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile, Fireplace
j. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile, Fireplace,
TownHouse
k. Dependent Variable: Price








00
C










Table B-9. Coefficient correlations
Model Bath Pet Clubhouse OneMile Bed Age Furnished FourMile Fireplace TownHouse
1 Correlations Bath 1.000
Covariances Bath 274.083
2 Correlations Bath 1.000 .281
Pet .281 1.000
Covariances Bath 272.577 178.081
Pet 178.081 1468.342
3 Correlations Bath 1.000 .263 -.247
Pet .263 1.000 .038
Clubhouse -.247 .038 1.000
Covariances Bath 267.590 158.513 -101.909
Pet 158.513 1355.776 35.538
Clubhouse -101.909 35.538 638.063
4 Correlations Bath 1.000 .260 -.241 -.010
Pet .260 1.000 .068 .122
Clubhouse -.241 .068 1.000 .257
OneMile -.010 .122 .257 1.000
Covariances Bath 242.767 143.102 -93.475 -7.132
Pet 143.102 1248.432 59.817 191.235
Clubhouse -93.475 59.817 619.810 284.275
OneMile -7.132 191.235 284.275 1971.217










Table B-9. Continued


5 Correlations


Covariances


6 Correlations


Covariances


Bath
Pet
Clubhouse
OneMile
Bed
Bath
Pet
Clubhouse
OneMile
Bed
Bath
Pet
Clubhouse
OneMile
Bed
Age
Bath
Pet
Clubhouse
OneMile
Bed
Age


1.000
.168
-.187
-.096
-.819
659.082
144.496
-113.353
-103.669
-429.443
1.000
.146
-.127
-.111
-.820
.108
640.643
123.268
-81.327
-117.422
-416.587
3.314


.168
1.000
.066
.119
-.023
144.496
1118.839
52.484
167.697
-15.868
.146
1.000
-.009
.142
-.010
-.171
123.268
1107.449
-7.255
198.393
-6.869
-6.932


-.187
.066
1.000
.262
.060
-113.353
52.484
557.159
261.155
28.809
-.127
-.009
1.000
.174
.025
.400
-81.327
-7.255
637.534
184.814
12.829
12.289


-.096
.119
.262
1.000
.110
-103.669
167.697
261.155
1787.072
94.596
-.111
.142
.174
1.000
.119
-.156
-117.422
198.393
184.814
1759.624
100.480
-7.943


-.819
-.023
.060
.110
1.000
-429.443
-15.868
28.809
94.596
417.590
-.820
-.010
.025
.119
1.000
-.073
-416.587
-6.869
12.829
100.480
403.351
-1.785


.108
-.171
.400
-.156
-.073
1.000
3.314
-6.932
12.289
-7.943
-1.785
1.477


-










Table B-9. Continued


7 Correlations


Covariances


8 Correlations


Bath
Pet
Clubhouse
OneMile
Bed
Age
Furnished


Bath
Pet
Clubhouse
OneMile
Bed
Age
Furnished
Bath
Pet
Clubhouse
OneMile
Bed
Age
Furnished
FourMile


Covariances Bath
Pet
Clubhouse
OneMile
Bed
Age
Furnished
FourMile


1.000
.124
-.111
-.113
-.816
.105
-.092


622.553
103.540
-69.327
-116.059
-401.573
3.126
-56.154
1.000
.125
-.111
-.111
-.815
.105
-.091
.009
610.580
102.294
-68.240
-112.720
-393.642
3.085
-54.949
5.798


.124
1.000
-.040
.145
-.009
-.163
.200


-.111
-.040
1.000
.167
.024
.391
-.158


-.113
.145
.167
1.000
.119
-.155
.031


-.816
-.009
.024
.119
1.000
-.073
.003


.105
-.163
.391
-.155
-.073
1.000
.025


-.092
.200
-.158
.031
.003
.025
1.000


t t + t *


103.540
1111.588
-33.354
199.659
-6.187
-6.482
163.754
.125
1.000
-.045
.157
-.005
-.153
.201
.106
102.294
1102.490
-36.844
213.846
-3.126
-6.029
162.567
95.206


-69.327
-33.354
629.968
173.054
12.106
11.724
-97.132
-.111
-.045
1.000
.160
.022
.386
-.158
-.047
-68.240
-36.844
619.189
163.680
10.889
11.388
-95.917
-31.838


-116.059
199.659
173.054
1697.203
96.905
-7.616
31.269
-.111
.157
.160
1.000
.124
-.143
.034
.125
-112.720
213.846
163.680
1690.770
99.328
-6.990
33.547
138.955


-401.573
-6.187
12.106
96.905
388.675
-1.718
1.588
-.815
-.005
.022
.124
1.000
-.069
.004
.043
-393.642
-3.126
10.889
99.328
381.872
-1.607
2.028
22.656


3.126
-6.482
11.724
-7.616
-1.718
1.424
.728
.105
-.153
.386
-.143
-.069
1.000
.027
.079
3.085
-6.029
11.388
-6.990
-1.607
1.405
.766
2.526


-56.154
163.754
-97.132
31.269
1.588
.728
603.337
-.091
.201
-.158
.034
.004
.027
1.000
.023
-54.949
162.567
-95.917
33.547
2.028
.766
592.005
15.207


.009
.106
-.047
.125
.043
.079
.023
1.000
5.798
95.206
-31.838
138.955
22.656
2.526
15.207
733.264


-










Table B-9. Continued


9 Correlations


Covariances


Bath
Pet
Clubhouse
OneMile
Bed
Age
Furnished
FourMile
Fireplace


Bath
Pet
Clubhouse
OneMile
Bed
Age
Furnished
FourMile
Fireplace


10 Correlations Bath
Pet
Clubhouse
OneMile
Bed
Age
Furnished
FourMile
Fireplace
TownHouse


1.000
.130
-.098
-.095
-.818
.113
-.099
.006
-.116


.130
1.000
-.039
.162
-.011
-.149
.197
.105
-.051


-.098
-.039
1.000
.170
.010
.391
-.164
-.049
-.100


-.095
.162
.170
1.000
.106
-.132
.024
.121
-.125


-.818
-.011
.010
.106
1.000
-.078
.013
.045
.126


.113
-.149
.391
-.132
-.078
1.000
.021
.077
-.076


-.099
.197
-.164
.024
.013
.021
1.000
.025
.072


.006
.105
-.049
.121
.045
.077
.025
1.000
.021


-.116
-.051
-.100
-.125
.126
-.076
.072
.021
1.000


.5 4 4 4 *I 4 4


610.017
105.645
-60.147
-96.327
-395.026
3.300
-59.138
4.091
-79.605
1.000
.152
-.080
-.073
-.816
.130
-.081
-.022
-.117
-.145


105.645
1089.394
-32.154
219.396
-7.216
-5.790
157.305
92.882
-46.566
.152
1.000
-.018
.182
-.020
-.122
.212
.067
-.053
-.178


-60.147
-32.154
616.369
174.032
4.645
11.447
-98.823
-32.776
-68.553
-.080
-.018
1.000
.184
.004
.400
-.149
-.070
-.101
-.116


-96.327
219.396
174.032
1692.773
85.248
-6.426
24.149
134.045
-142.417
-.073
.182
.184
1.000
.098
-.112
.039
.091
-.126
-.139


-395.026
-7.216
4.645
85.248
382.410
-1.805
6.265
23.719
68.172
-.816
-.020
.004
.098
1.000
-.084
.007
.054
.126
.051


3.300
-5.790
11.447
-6.426
-1.805
1.393
.599
2.439
-2.499
.130
-.122
.400
-.112
-.084
1.000
.035
.050
-.078
-.130


-59.138
157.305
-98.823
24.149
6.265
.599
586.464
15.968
48.055
-.081
.212
-.149
.039
.007
.035
1.000
.003
.070
-.111


4.091
92.882
-32.776
134.045
23.719
2.439
15.968
722.996
15.654
-.022
.067
-.070
.091
.054
.050
.003
1.000
.023
.193


-79.605
-46.566
-68.553
-142.417
68.172
-2.499
48.055
15.654
767.815
-.117
-.053
-.101
-.126
.126
-.078
.070
.023
1.000
.014


-.145
-.178
-.116
-.139
.051
-.130
-.111
.193
.014
1.000














Table B-9. Continued
Covariances Bath 617.550 126.143 -49.200 -74.841 -395.125 3.827 -48.937 -14.887 -80.301 -105.492
Pet 126.143 1115.167 -14.644 251.570 -13.129 -4.818 171.911 60.720 -48.475 -174.511
Clubhouse -49.200 -14.644 619.253 189.059 1.701 11.792 -90.170 -47.705 -69.071 -84.764
OneMile -74.841 251.570 189.059 1710.555 78.738 -5.482 39.336 102.699 -143.386 -167.934
Bed -395.125 -13.129 1.701 78.738 380.016 -1.944 3.512 28.789 67.957 29.413
Age 3.827 -4.818 11.792 -5.482 -1.944 1.405 1.009 1.603 -2.537 -4.530
Furnished -48.937 171.911 -90.170 39.336 3.512 1.009 588.471 1.683 46.580 -78.761
FourMile -14.887 60.720 -47.705 102.699 28.789 1.603 1.683 744.254 17.567 154.202
Fireplace -80.301 -48.475 -69.071 -143.386 67.957 -2.537 46.580 17.567 761.138 11.431
TownHouse -105.492 -174.511 -84.764 -167.934 29.413 -4.530 -78.761 154.202 11.431 858.769
a. Dependent Variable: Price
00
(l-










Table B-10 Collinearity diagnostics
Variance Proportions
Condition One
Model Dimension Eigenvalue Index (Constant) Bath Pet Clubhouse Mile Bed Age Furnished FourMile Fireplace TownHouse

1 1 1.914 1.000 .04 .04
2 .086 4.723 .96 .96
2 1 2.768 1.000 .01 .02 .01
2 .194 3.782 .00 .42 .30
3 .039 8.453 .99 .57 .68
3 1 3.391 1.000 .00 .01 .01 .03
2 .395 2.929 .01 .00 .07 .80
3 .175 4.396 .00 .48 .24 .17
4 .039 9.373 .99 .50 .69 .00
4 1 3.460 1.000 .00 .01 .01 .02 .01
2 .975 1.883 .00 .00 .00 .04 .79
3 .357 3.114 .01 .00 .09 .71 .14
4 .171 4.499 .00 .51 .21 .21 .03
5 .037 9.617 .99 .48 .69 .01 .03
5 1 4.343 1.000 .00 .00 .01 .01 .00 .00
2 .977 2.108 .00 .00 .00 .03 .78 .00
3 .359 3.477 .01 .00 .07 .78 .14 .00
4 .252 4.151 .01 .04 .19 .12 .01 .06
5 .041 10.231 .85 .01 .64 .04 .06 .19
6 .027 12.654 .14 .95 .10 .01 .00 .75













Table B-10 Continued
6 1 4.973 1.000 .00 .00 .00 .01 .00 .00 .01
2 1.028 2.199 .00 .00 .00 .05 .61 .00 .02
3 .527 3.072 .00 .00 .02 .22 .31 .00 .16
4 .264 4.338 .00 .04 .06 .37 .02 .06 .03
5 .143 5.907 .01 .00 .38 .26 .01 .00 .68
6 .039 11.294 .75 .03 .42 .10 .04 .26 .07
7 .026 13.762 .23 .93 .11 .00 .00 .67 .04
7 1 5.346 1.000 .00 .00 .00 .01 .00 .00 .01 .01
2 1.074 2.231 .00 .00 .00 .04 .50 .00 .02 .05
3 .687 2.789 .00 .00 .02 .01 .29 .00 .04 .42
4 .432 3.519 .00 .00 .00 .30 .14 .00 .12 .41
5 .257 4.556 .00 .05 .05 .29 .01 .07 .05 .05
6 .140 6.187 .01 .00 .38 .28 .01 .00 .65 .03
7 .038 11.895 .75 .04 .45 .07 .04 .25 .07 .03
8 .026 14.268 .23 .91 .11 .00 .00 .67 .04 .00
8 1 5.549 1.000 .00 .00 .00 .01 .00 .00 .00 .01 .01
2 1.132 2.214 .00 .00 .00 .03 .42 .00 .02 .02 .10
3 .764 2.695 .00 .00 .00 .02 .01 .00 .01 .20 .64
4 .668 2.882 .00 .00 .02 .00 .38 .00 .04 .25 .19
5 .428 3.599 .00 .00 .00 .31 .12 .00 .11 .41 .02
6 .257 4.648 .00 .05 .05 .30 .01 .07 .05 .04 .00
7 .140 6.306 .01 .00 .38 .27 .01 .00 .65 .03 .00
8 .036 12.401 .72 .05 .43 .06 .05 .29 .07 .03 .05
9 .026 14.571 .26 .90 .12 .00 .00 .63 .05 .00 .01












Table B-10 Continued
9 1 5.752 1.000 .00 .00 .00 .01 .00 .00 .00 .01 .01 .01
2 1.190 2.198 .00 .00 .00 .03 .33 .00 .02 .03 .08 .08
3 .791 2.697 .00 .00 .00 .00 .05 .00 .00 .16 .33 .34
4 .733 2.800 .00 .00 .00 .02 .16 .00 .01 .02 .38 .40
5 .659 2.955 .00 .00 .02 .01 .26 .00 .05 .24 .12 .10
6 .421 3.696 .00 .00 .00 .28 .13 .00 .10 .44 .02 .04
7 .253 4.769 .00 .04 .05 .32 .02 .07 .05 .03 .00 .02
8 .140 6.421 .01 .00 .37 .27 .01 .00 .65 .03 .00 .00
9 .036 12.651 .74 .04 .43 .06 .04 .28 .07 .04 .05 .00
10 .026 14.929 .24 .91 .12 .00 .00 .65 .05 .00 .00 .01
10 1 5.985 1.000 .00 .00 .00 .01 .00 .00 .00 .01 .00 .01 .01
00 2 1.245 2.192 .00 .00 .00 .02 .26 .00 .01 .02 .13 .04 .06
00
3 .926 2.543 .00 .00 .00 .01 .04 .00 .01 .09 .19 .16 .24
4 .744 2.836 .00 .00 .00 .02 .25 .00 .01 .01 .09 .61 .01
5 .663 3.004 .00 .00 .02 .02 .24 .00 .05 .32 .02 .10 .02
6 .566 3.253 .00 .00 .00 .00 .00 .00 .01 .04 .50 .01 .60
7 .420 3.775 .00 .00 .00 .27 .13 .00 .10 .41 .03 .04 .01
8 .252 4.877 .00 .04 .05 .32 .02 .06 .05 .04 .01 .02 .01
9 .139 6.550 .01 .00 .36 .27 .01 .00 .64 .03 .00 .00 .00
10 .035 13.003 .68 .07 .40 .06 .05 .33 .07 .04 .03 .00 .01
11 1.025 15.454 .30 .88 .16 .00 .00 .60 .06 .00 .00 .01 .03
a. Dependent Variable: Price















Table B-11 Residuals statistics
Minimum Maximum Mean Std. Deviation N
Predicted Value $420.60 $1,968.29 $950.89 $328.290 350
Std. Predicted Value -1.615 3.099 .000 1.000 350
Standard Error of Predicted
StandardErrorofPredicted 18.617 65.783 33.165 8.335 350
Value
Adjusted Predicted Value $419.26 $1,964.89 $950.93 $327.244 350
Residual $-492.065 $1,010.729 $1.754E-13 $190.095 350
Std. Residual -2.551 5.240 .000 .986 350
Stud. Residual -2.602 5.325 .000 1.005 350
Deleted Residual $-511.720 $1,043.648 $-.035 $197.584 350
Stud. Deleted Residual -2.624 5.554 .002 1.017 350
Mahal. Distance 2.254 39.599 9.971 5.742 350
Cook's Distance .000 .102 .004 .011 350
Centered Leverage Value .006 .113 .029 .016 350
a. Dependent Variable: Price










Charts


Histogram



Dependent Variable: Price


Mean =8.81E-16
Std. Dev. =0.986
N =350


Regression Standardized Residual


Figure B-1. Histogram
















Normal P-P Plot of Regression Standardized Residual





Dependent Variable: Price


1 .




0.

0
EQ
CL
4.
E 0.
3



0.
C0
X

0.


0.0 0.2 0.4 0.6

Observed Cum Prob

Figure B-2. Normal P-P plot of regression standardized residual.















Scatterplot




Dependent Variable: Price


Regression Standardized Predicted Value


Figure B-3. Scatter plot


6-

a'

41 4
S 4-2-




c e
"o'-




Vi -
o4-

a -2-
L0


-4-


00


o 0

o o

S0 o 0 0

00 oo oo o8



o8 o 0 oo 0
'" W w 0 3D 0 0ffl o









APPENDIX C
AFTERWORD

After the completion of this project, many new apartment and condo projects were started

within steps of where the recommendations in this paper were maid. The following is

photographic evidence of the benefits for following the algorithm for locating a building. The

fact that there are new developments going in, that are in close proximity to my

recommendations lends credence to the algorithm.


Figure C-1. 13th Street and NW 7th Avenue location.






























Figure C-2. 13th Street and NW 7th Avenue. The image above is of the same complex from the
back side, this complex is very large, stretching from 7th Street to 8th street, making it
almost a full city block. It is assumed that it would be an entire city block, except the
trophy shop refused to sale


Figure C-3. Trophy shop surrounded by new complex at NW 7th Avenue. The trophy shop is
now completely surrounded by apartments






























Figure C-4. SW 2nd Avenue and 6n Street


Figure C-5. Alternate shot of SW 2nd Avenue and 6th Street. This is a shot of the same complex,
both pictures for this complex show the extent of how large these buildings are,
depending on its floor plan, this project has a very good chance at being successful
based on its location. Both of the sites in the pictures above, fit with the rules set up in
the algorithm, which leads me to believe that the algorithm is correct in its
assumptions.









LIST OF REFERENCES


"Apartment Finder," Network Communications, Inc., December 13, Issue 1.
www. apartmentfinder. com

Applebaum, William, "Methods for determining Store Trade Areas, Market Penetration, and
Potential Sales." Journal of Marketing Research, Vol. 3, No. 2 (May, 1966), pp.
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Asabere, Paul K., Forrest E. Huffman, "Thoroughfares and Apartment Values," The Journal of
Real Estate Research, Volume 12, Number 1, 1996.

Benjaimn, John D., G. Stacy Sirmans and C.F. Sirmans "Determining Apartment Rent: The
Value of Amenities, Services and External Factors" The Journal of Real Estate Research,
Volume 4, Number 2, 1989.

Benjamin, John D., G. Stacy Sirmans "Mass Transportation, Apartment Rent andProperty
Values" The Journal of Real Estate Research, Volume 1, 1996.

Bible, Douglas S., Cheng-Ho Hsieh, "Applications of Geographic Information Systems for the
Analysis of Apartment Rents," The Journal of Real Estate Research, Volume 12,
Number 1, 1996.

Bussa, Robert G., Austin J. Jaffe, "Using a Simple Model to Estimate Market Rents: A case
Study," The Appraisal Journal, Vol. 45, 1977

Cervero, Robert, Michael Duncan, "Walking, Bicycling, and Urban Landscapes: Evidence from
the San Francisco Bay Area" American Journal of Public Health, Vol. 93, no. # 9,
September, 2003

"Community Tapestry Handbook," ESRI, Geographic Information Systems, 2007.
www.esri.com/library/brochures/pdfs/community-tapestry-handbook.pdf

Crapo, Ed. "Alachua County Land Parcel Database," Alachua County Property Appraiser, 2006.
www. acpafl. org/search.html

Darden, William R., Fred D. Reynolds, "Intermarket Patronage: A Psychographic Study of
Consumer Outshoppers." Journal of Marketing, Vol. 36, No.4. (Oct., 1972), pp. 50-54.

Dutta-Bergman, Mohann J., "Beyond Demographic Variables: Using Psychographic Research
to Narrate the Story of Internet Users." Studies In Media & Information Literacy
Education (Simile) August 2002, Vol.2, Issue 3.

"Enrollment by Major: Fall 2007," Table I-l.a, The office of institutional Research at the
University of Florida, www.ir.ufl.edu/factbook/i-01.ahist.xls

Frew, James R., G. Donald Jud, and Daniel T. Winkler, "Atypicalities and Apartment Rent
Concessions" The Journal of Real Estate Research, Volume 5, Number 2, 1990.









Frew, James, Beth Wilson, "Estimating the connection between location and Property value,"
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"Gainesville, Florida," Sperling's Best Places, 2005, www.bestplaces.net/city/GainesvilleFl-
51225175010.aspx

"Gainesville's Premier Apartment Guide," Paradigm Properties, January-December, 2007.
www. apartment4gators. com

Guntermann, Karl L., Stefan Norrbin, "Explaining the Variability of Apartment Rents"
AREUEA Journal, Winter 1987; 15,4; ABI/INFORM Global

Haydam, Norbert, Nancy Nuntsu, and Dimitri Tassiopoulos,"Wine Tourists in South Africa: A
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No.1, pp.51-63.

"Historical Enrollment: Fall Terms 1905 to 2007," Table 1-5.a, The office of institutional
Research at the University of Florida, www.ir.ufl.edu/factbook/i-05.ahist.xls.

Kain, John F., John M. Quigley, "Measuring the Value of Housing Quality." Journal of the
American Statistical Association, Vol. 65, No. 330. (June., 1970), pp. 532 548.

Knapp, Garrit-Jan, Yang Song, "New urbanism and housing values: a disaggregate Assessment"
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Lewis, Jim. Telephone Interview. Office of Institutional Research, Santa Fe Community College,
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Malpezzi, Stephen, "Hedonic Pricing Models: A Selective and Applied Review." The Center for
Urban Land Economics, The University of Wisconsin, April, 2002.

Ogur, Jonathan D., "Higher Education and Housing: The Impact of Colleges and Universities
on local Rental Housing Markets" American Journal of Economics and Sociology, Vol.
32, No. 4. 1973

Pagliari, Joseph L. Jr,. James R. Webb "On Setting Apartment Rental Rates: A Regression
Based Approach." The Journal Of Real Estate Research, Volume 12, Number 1, 1996.

Perry, Marc J. "State to State Migration Flows: 2000 to 2005," August, 2003, US Census
Bureau, www.census.gov/prod/2003pubs/censr-8.pdf

Rogerson, Peter A., "Statistical Methods for Geography," Sage Publications, London, 2004.

Schenker, Pamela, "Florida Demographic Study," Florida Legislature, Office of Economic and
Demographic Research. November, 2007.









Schwartz, Arthur L. Jr., Greg T. Smersh, and Marc T. Smith, "Factors Affecting Residential
Property Development Patterns." The Journal of Real Estate Research, Jan-Mar 2003,
Vol. 25, Issue 1, pg 61.

SPSS for Windows, Rel. 16.0.2007.Chicago: SPSS Inc.

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"The Gainesville Apartment & Condominium Guide," Quest Publications, Issue 74, Fall, 2007.
www.gainsville-rent.com

Thrall, Grant Ian, "Business Geography and New Real Estate Market Analysis," Oxford
University Press. New York, New York, 2002.

Weathers, Robert, Lin Lin, Donna Johnson, Chanam Lee, Chez Garvin, Allen Cheadle, et al.
"Operational Definitions of Walkable Neighborhoods: Theoretical and Empirical
Insights." Journal of Physical Activity and Health, 3, 2006.









BIOGRAPHICAL SKETCH

Gabriel Bolden was born in San Diego, California, February 19th, 1981. He was raised

there by his single Mother and Grandparents. An active child, Gabriel played many sports

growing up. In high school, he attended University City High School, where he was the captain

of the soccer team and a member of the golf team. In high school, Gabriel was not focused on

academics, it was not because he did not have the aptitude, rather it was because he felt the

education system he was a part of was developed for some one else.

Although following graduation, he realized that without a good education he would be just

another stereotype from Clairemont (his neighborhood in San Diego). With little other options,

Gabriel attended the local community college called Mesa. Gabriel made a promise to himself

that he would graduate in two years and transfer to a good university. Reinvigorated, Gabriel

kept that promise he made to himself, finishing Mesa Community College in exactly two years,

he was then accepted to the University of Los Angeles, California.

At UCLA, Gabriel majored in Geography, where he met faculty who would change his life

forever. Gabriel worked as the librarian of the geography library, and finished up his course

work in two years. So in four years he went from a high school graduate with little prospects to

a graduate from one of the best public universities in the United States. Following graduation

from UCLA, and on the recommendation of Dr. Tom Gillespie and Dr. Judith Carney, he

traveled through central America for three months. It was in Guatemala that he heard of a local

plant called "tres puntas," which was sort of cure all herbal remedy used by the Garifuna people

as well as the natives of central America. This peaked his interest in studying the Garifuna

people and their plant in graduate school.

Upon returning to the United States, he consulted professors Gillespie and Carney at

UCLA about going to grad school outside of California in order to broaden his horizons, he had









one stipulation though, where ever he went it had to be warm. Both professors recommended

that he apply to the University of Florida because of the faculty and his underlying wish to be

warm. Gabriel applied to the University of Florida and was accepted. He had never seen the

campus or even ever been to Florida but that was not going to stop him. He got on a train and

five days later he was in Florida ready to start studying the Garifuna and central America through

cultural geography. Although, not everything goes as planned, his first semester was horrible, he

wasn't quite ready to a grad student and it showed in his behavior. Gabriel found out that there

was not any faculty members working with the garifuna and central America, which meant there

was no one to work with, that compiled with his horrible semester and pathetic GPA made him

contemplate quitting graduate school.

Going home over the break and having to explain himself to his family reinforced the

belief that quitting was not an option. So instead of pursuing academia for a career, he decided

that he would end his education after he attained a master's degree. He decided that economic

geography was the way to go after checking the titles of upcoming classes. It is at this point in

which he took Dr. Grant Ian Thrall's introductory economic geography course, immediately

taking to the material presented in the course, he was satisfied with the choice he made. Being in

the economic geography discipline reinvigorated him which in turn reinvigorated his GPA.

While working on his course work and thesis, Gabriel worked for SeaGrant, as well as

helped Dr. Thrall with research for his consulting business. Throughout this process Dr. Thrall

became his graduate thesis chair, and even though he took his time, he eventually wrote a thesis

on apartment location in Gainesville, Florida. The thesis was unique as it was the first to give a

specific set of steps to locate an apartment building; he was able to use different techniques, like

the use of GIS, psychographics, and statistics to come up with the best location for a new









apartment building in Gainesville, Florida. The steps taken in the thesis can be applied to

locating an apartment or any other type of building anywhere in the United States, because most

of the software used is available to the public, this information can help professionals make

better real estate decisions, therefore simplifying the decision making process.





PAGE 1

1 GEOSPATIAL PROCEDURES FOR IDENTIFYING A PROSPECTIVE DEVELOPMENT LOCATION: APARTMEN T ORIGIN WALKABLE DESTINATION SCENARIO By GABRIEL KENNETH BOLDEN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2008

PAGE 2

2 2008 Gabriel Kenneth Bolden

PAGE 3

3 To my Mom, Colin, Grandma and Grandpa, Aunt Kathy, Great Grandma and the rest of my family.

PAGE 4

4 ACKNOWLEDGMENTS I thank Dr. Thrall for his patience and guidance. I would like to thank m y family for all of their support. I would also like to th ank Richard Sheffler for all of his statistical recommendations. Finally, I would like to thank all of my friends here in Florida, without whom I never would have made it.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS...............................................................................................................4 LIST OF TABLES................................................................................................................. ..........7 LIST OF FIGURES.........................................................................................................................8 LIST OF ABBREVIATIONS........................................................................................................ 10 ABSTRACT...................................................................................................................................11 CHAP TER 1 INTRODUCTION..................................................................................................................12 2 LITERATURE REVIEW.......................................................................................................14 Psychographics.......................................................................................................................14 Apartment Studies: Hedonic Pricing Models......................................................................... 15 3 APPLICATION OF ALGORITHM....................................................................................... 20 Procedural Issues....................................................................................................................20 Heuristic Algorithm............................................................................................................ ....20 Demand Generator............................................................................................................... ...21 Population Identification...................................................................................................... ..25 Iterative Procedure for Calculati ng T rade Area and Apartment Demand.............................. 26 Geospatial Characteristics of UF s L1-High Society Students ............................................... 27 Apartment Supply............................................................................................................... ....29 Development of a Hedonic Model.......................................................................................... 31 Data.........................................................................................................................................32 Hedonic Model Test Result.................................................................................................... 35 Pipeline...................................................................................................................................39 Qualitative Analysis........................................................................................................... .....42 4 ALGORITHM SUMMARY AND SITE SELECTION......................................................... 44 5 CONCLUSION..................................................................................................................... ..52 APPENDIX A PSYCHOGRAPHIC SUBMARKETS................................................................................... 53 B REGRESSION TABLES........................................................................................................57 C AFTERWORD...................................................................................................................... .93

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6 LIST OF REFERENCES...............................................................................................................96 BIOGRAPHICAL SKETCH.........................................................................................................99

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7 LIST OF TABLES Table page 3-1 2004-2005 Change in ESRI tapestry lifem ode groups for University of Florida students ....................................................................................................................... .......26 3-2 Variable table............................................................................................................. ........36 4-1 Score card: comparison and ranki ng of three prospective sites ......................................... 48 4-2 Score card: comparison and ranking of three prospective f inalist sites ......................... 51 B-1 Notes...................................................................................................................... ............57 B-2 Descriptive statistics..................................................................................................... .....59 B-3 Correlations............................................................................................................... .........60 B-4 Variable entered and removed........................................................................................... 63 B-5 Model summary.............................................................................................................. ...65 B-6 ANOVA table................................................................................................................ ....66 B-7 Coefficient table.......................................................................................................... .......68 B-9 Coefficient correlations................................................................................................... ...81 B-10 Collinearity diagnostics.................................................................................................. ...86 B-11 Residuals statisticsa............................................................................................................89

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8 LIST OF FIGURES Figure page 3-1 Student population by distance from campus.................................................................... 24 3-2 Location of UF students with LifeMode L1 ......................................................................28 3-3 Primary trade area for university of Flor ida student cam pus addresses, and locations of apartments with that trade area...................................................................................... 30 3-4 Apartment rent per bedroom by location, Gainesville FL................................................. 34 3-5 Map of apartments and condominiums in the pipeline for 2005 and 2006....................... 41 3-6 Map of apartments and condominiums in the pipeline for 2007....................................... 41 3-7 Map of grocery stores in Gainesville, Florida...................................................................42 4-1 Three prospective apartment development locations......................................................... 45 4-2 Site A -University Avenue and 6th Street........................................................................... 46 4-3 Site B Main Street and Williston Road........................................................................... 46 4-4 Site C 13th Street and 7th Avenue.....................................................................................47 4-5 Site B Replacement of Site B with 1216 SW 2nd Avenue..............................................49 4-6 Photo of replacement site B at 1216 SW 2nd Avenue........................................................50 A-1 Map of L1 students............................................................................................................53 A-2 Map of L2 students............................................................................................................53 A-3 Map of L3 students............................................................................................................54 A-4 Map of L4 students............................................................................................................54 A-5 Map of L5 students............................................................................................................55 A-6 Map of L6 students............................................................................................................55 B-1 Histogram.................................................................................................................. .........90 B-2 Normal P-P plot of regression standardized residual. ........................................................ 91 B-3 Scatter plot.........................................................................................................................92

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9 C-1 13th Street and NW 7th Avenue location............................................................................ 93 C-2 13th Street and NW 7th Avenue.......................................................................................... 94 C-3 Trophy shop surrounded by new complex at NW 7th Avenue...........................................94 C-4 SW 2nd Avenue and 6Th Street............................................................................................95 C-5 Alternate shot of SW 2nd Avenue and 6th Street................................................................95

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10 LIST OF ABBREVIATIONS DG De mand Generator GIS Geographical Information System LSP Life Style Segmentation Profile SFCC Santa Fe Community College UF University of Florida

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11 Abstract of Thesis Presen ted to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science GEOSPATIAL PROCEDURES FOR IDENTIFYING A PROSPECTIVE DEVELOPMENT LOCATION: APARTMEN T ORIGIN WALKABLE DESTINATION SCENARIO By Gabriel Kenneth Bolden August 2008 Chair: Grant Ian Thrall Major: Geography We set forth a set of procedures for determ ining the best location for a new apartment complex in Gainesville, Florida. An apartments location is central to its economic performance, thus it is important to find a location that woul d be optimal for a new apartment complex. We reviewed literature on apartment complexes and se t forth a set of procedures for locating an apartment complex. It adds to the existing lite rature on apartments, and apartment markets, and can help to improve the decision making pr ocess for real estate professionals. Different methods were used to determine th e proper site of a new apartment complex. Psychographics were used to determine the demand and trade areas of apartments in Gainesville. Hedonic modeling was used to determine the character istics that go into re nt in the Gainesville market. Once everything was analyzed, an algo rithm was developed to determine the best site for a new apartment complex. The algorithm has se ven steps for identifying a potential location for development of an apartment complex, these steps are composed by analyzing the Demand Generator, Population Identific ation, Hedonic Pricing Model, Pi peline Construction, Locational Amenities, Locational Necessities, and finally Site Av ailability. This paper is the first to set out a specific list of rules for locating a new building.

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12 CHAPTER 1 INTRODUCTION The location of a new apartment development can mean the difference between successful business investments, versus investments that ma y not even break even. These are the steps that an analyst can follow to identify and evaluate the best prospective locations for new apartment developments. Data for Gainesville, Florida, were used to illustrate the steps. The procedures introduced here can be replicated for any loca tion if the appropriate data is available. Among the data required for the study is the location and amount of competitive supply of apartments, and historic trends of apartment construction, property values, a nd rental rates. In addition to current supply, apartment developm ent scheduled for construction and therefore adding to future supply must be considered; th is is known as the supply pipeline which is assembled from building permits. This information is compiled to predict future values and rental rates by location, a necessary component fo r making business decisions that will lead to a higher likelihood of generating a profitable return. Often ignored in apartment studies, is the human component of a successful apartment development. People will be living at the location. The location and the amenities of the apartment development must be amenable with the people making the choice to live there. A contribution of this study is the use of psychographic measurements which will identify the type of person most likely to find the amenities and locati on suitable. Conversel y, with the target psychographic group identified, the appropriate location and amen ity bundle can be selected by the developer. Psychographic measurements are also known as Life-style Segmentation Profiles (LSPs). Here, ESRIs Tapestry LSPs are used which breaks the population into twelve Life Mode Groups. Tapestry also offers a more granul ar set of 65 LSP groups. The more granular the segmentation, the more narrow the demographic profile. The benefits of narrowing the

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13 demographic profile are that the selection of amenities and ther efore costs of development can more closely fit the target population. A disa dvantage of a highly granular segmentation approach, however, is that the developer might a ppeal to only a very small demographic market leading to a thin demand and ther efore higher risk. Balancing these tradeoffs, this study therefore uses the boarder 12 Tapestry segments. Hedonic modeling is used to determine the relationship between rent, and apartment location and their amenities. An algorithm asse mbling the above information is introduced to evaluate the geographic trend of apartment re nts and thereby prospective rents for the new apartment development. Following Grant Thra lls hierarchical categories of geographic reasoning, the penultimate stage of geospatial reasoning is judgment (Thrall, 2002). Judgment is the so what stage of analysis. Im proving the judgmental decision is the ultimate objective of business geography. Therefore, a reco mmendation will be made for a new apartment complex. The primary data used for the analys is was compiled through 2 years of research beginning in 2005. Development in the pipeline and development that has actually occurred since the termination date of the data collection is used to document if this study actually anticipated future development trends. Thesis Questions: Where are the locations that can be st take advantage of the increasing student market demand for housing? What are the characteristics of the UF student market; in other words, what can we learn from the students psychographic LSP profile that can inform the developer of the composition of amenities that are best included in a new apartment complex targeted to this market?

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14 CHAPTER 2 LITERATURE REVIEW Psychographics Psychographics provide a com plete desc ription of consumer behavior based on demographics but also components of consumer behavior not captured by demographics alone. Reynolds and Darden (1972) used psychographics to determine the trend of women shoppers in Dublin, Georgia. Through traditional methods and the use of psychographics they were able to determine which type of women is more likely to shop outside Dubli n. These researchers revealed that women who are more likely to s hop greater distances outside the local area tended to have more wealth, be more highly educated, and tended to be middle aged in comparison to women that shopped predominantly in the local area (1972). This pione ering study corroborates the above finding and helps explain why UFs young student population is tig htly clustered near the core campus area. Tassiopoulos, Nuntsu and Haydam (2004) used psychographics to determine the makeup of the average wine tourist in South Africa. Their research showed that the average wine tourist for that region is a young professional woma n without children. The current generation that fits that profile are referre d to in the popular pre ss as a Generation Xer (Tassiopoulos, Nuntsu, Haydam, 2004). Psychographics have been used to identify the charac teristics of people that visit specific areas. Dutta-Bergman (2002) was able to extrapol ate the picture of the av erage internet user. Dutta-Bergman discovered that the average internet user is a young person who exercises, is a crafty consumer, and is innovative (2002). This research has allowed web-masters to design internet pages better more specific and relevant to the needs of the average web site viewer. Psychographics are an important area of study because it allows an individual to explore

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15 variables not covered by conventional demogra phics and thereby get a better understanding of demand and prospective changing demand. Apartment Studies: Hedonic Pricing Models Hedonic pricing m odels have been used on all ar eas of the real estate market to evaluate property value or rental ra tes, it involves running a regression to capture tre nds in the real estate market of interest. Stephen Malpezzi states th at, The method of hedonic equations is one way expenditures on housing can be decom posed into measurable prices and quantities, so that rents for different dwellings or for identical dwelli ngs in different places can be predicted and compared. At its simplest, a hedonic equation is a regression of expenditures (rents or values) on housing characteristics (Malpezzi, 2002). Hedoni c modeling has been used to calculate the impact that quality of lif e has on rental rates. John Ka in, and John Quigley (1970), used hedonic modeling to determine how the quality of housing affects value; th eir findings were that the quality of a dwelling had as much imp act on property value as does other standard components of a hedonic model, such as square fo otage, age, number of bathrooms, fireplace, and bedrooms (Kain and Quigley, 1970). Determinants of market housing rent have focused on how various amenities affect valuation. Hedonic modeling is used to distingui sh the impact of how the various amenities affect rent. Pagliari and We bb (1996) successfully modeled m onthly rent as a function of occupancy rates and rental incentives. They were also able to set the appropriate rental rate through the results of their regre ssion. Benjamin, Sirmans, and Si rmans (1989) studied the effect of amenities, services and external factors on apartment rental rates. They used 188 observations from 92 apartment complexes in Lafayette, Louisi ana. They demonstrated that size of the apartment, size of the complex, and age of the co mplex were the primary factors that affected rent. Amenities such as covered parking, all util ities paid and a modern kitchen were highly

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16 prized in their sample, as were swimming pools. Amenities internal to the apartment complex affected rent the most. Only one external fact or affected rent in th eir sample: the degree of congestion was the main external determinant a ffecting rent. Bible and Hsiehs (1996) study of apartments in the Shreveport-Bossier, Louisiana, used GIS to explore the spatial relationships of the local market. They found that the age of an apartment building and the size of the apartments have a negative impact on apartment rent, whil e the presence of swimmi ng pools and fireplaces result in higher apartment rents. They also f ound that the distance to a co llege or university has a negative impact on rent. The conclusion of these st udies is that location is an amenity, and that the amenity of location matters. The impact of absolute location versus rela tive location has been examined by Frew and Wilson (1990). Their hedonic model calculated a re nt gradient for apartments in Portland, Oregon. They included amenities of the apartment complex as well as dummy variables to represent distance relative to th e downtown. They found that location was crucial in determining land values and rent. The result s clearly show that rent values drop substantially for the first ten miles outside the city center, i ndicating that the downt own area is the central urban hub (Frew and Wilson, 1990, pp. 22). Their results also reveal ed that a rent gradient ridge occurs around freeway corridors; they hypothesized the ridge was attributable to acc essibility to tr ansportation. The highest apartment rents do not necessarily concentrate around the center of the city; local maximums can occur in other areas because of neighborhood (location) effects, and amenities of the apartment complex itself. Nevertheless, the ge neral trend is for rent to decline away from a central node, while rising with proximity to ot her albeit comparatively minor nodes. Twelve years following their publication on apartment rent s, Frew and Wilson revisited their earlier study and confirmed that the highest rents are not solely concentr ated in the downtown area, but

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17 that different areas command different rents ba sed on their access to tr ansportation corridors. Their model showed that rent was highly correl ated to access to a highway and highway intersection (Frew and Wilson, 200 2). Frew and Wilson confirmed that apartment rent varies with location to different nodes and access to transportation. Therefore, minimizing distance to a central node and good access to tr ansportation is important when choosing a location for a new apartment complex.1 Asabere and Huffman (1996) evaluate the actu al value of the apar tment building rather than apartment rents. Their anal ysis concludes that proximity to freeways, and thoroughfares in Philadelphia correlates with higher property values, as does proximity to the central business district. Benjamin and Sirmans too looked at the impact of proximity to transportation on apartment rent. Their study incorporated 250 apar tments in the Washington D.C area that were near Metrorail Stations. They found that rent decreases as di stance from a Metrorail station increases. Internalizing Benjamin and Sirman s conclusions into framework for improving apartment location decisions would suggest that developers give higher priority to locations near to transportation hubs. If locating near the desired central node is not possible, then a second best solution would be to locate near a trans portation hub that accesses that specific node.2 Song and Garrit-Jan (2003) in their study of willingness to pay for new urbanist features found that people are willing to pay more for access to transportation, and be tter walking access to commercial uses; however, these same people consider disamenities to include higher density, nearby multifamily housing, and proxim ity of commercial real estate. 1 Access to transportation or transportation corridors is important for people commuting to work on a daily basis. Most people would rather live closer to their occupation but when that is not an option those same people prefer to live where it is easiest to get to work. Researchers ha ve used hedonic modeling to assess the distance of transportation hubs to apartments and the resultant affect on their rental rates. 2 This result was proven in Thrall (1988, ch. 7). Thrall dem onstrates that as transportation cost and time decrease, access to a transportatio n hub, or the central node accessed via the hub becomes equivalent.

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18 The literature indicates that th ere are a wide variety of des tinations that people want to access (Cervero and Duncan, 2003). Generally, pl aces of work and shopping rank as the most important destinations. Destinations affecting apartment rents include hospitals, downtown city centers, shopping locations, and unive rsities. Each destination require s unique analysis to capture the characteristics of the population placing high priority on accessing the specific destination. The importance of transportation access cannot be underestimated. Is the destination a Walkable (Cheadle, Garvin, Johnson, Lee, Lin, Moudon, Schmid, Weathers, 2006) place, with public transit access, or is the destination a car en vironment accessed only by private transportation? The type of destination and the mode of travel linking apartment origin and destin ation must be considered. The example of this thesis is the walkable environment of University of Florida which has limited automobile parking; therefor e, the transportation linkage between proposed apartment development and the UF destination is critical to the success of the development. A universitys effect on rental rates has been studied by O gur (1973). His study evaluated the rental housing market in comparison to the population of student s enrolled in school versus employment in the manufacturing sector. The study consisted of information gathered from 62 counties in New York State in 1960. His hedonic regression documen ted that a university has a significant impact on nearby rental housing. His results further re vealed that there was no clear distinction between rents that non-whites versus whites pay. Instead, it was the presence of a university that incremented rents to a higher level (Ogur, 1973). Distance to a university is important to the students attending the university, and consequently may be willing to pay more th an others for premium university access. Guntermann and Norbinn (1987) demonstrated that variation in rents in Mesa and Tempe Arizona depended on access to the university. Th eir primary data base of 104 apartments was

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19 created via personal contact with property managers. Their hedonic model revealed that university students rank as high pr iority both access to the universi ty and the condition of the apartment. Condition of the apartment was not depending upon age; rather, condition as measured by Guntermann and Norbinn (1987) wa s dependent upon the characteristics of the amenities of the apartment and apartment comple x. Therefore, good access to University of Florida is hypothesized to be the most important factor when selecting th e best locations for new apartment complexes targeted to UF students. UF is the primary destination for the target rental population. Walking and bicycling proximity to UF is one measure of access; availability of public transportation is a second best locati on. The bundle of amenities offered is hypothesized to also important to the UF student when choosing an apartment.

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20 CHAPTER 3 APPLICATION OF ALGORITHM Procedural Issues The procedu re followed here for the large part follows the general framework introduced by Thrall (2002); namely, first ca lculate trade area; second calc ulate demand; third, calculate competitive supply; and fourth, integrate these fi ndings to make a recommendation. However, to calculate the relevant trade area, the target student population will need to be derived, and this implicitly calculates demand. So the order of Thralls (2002) procedure will need to be rearranged, and indeed even iterated between steps. Heuristic Algorithm The Heuristics algorithm allows for a standard ized set of procedures for locating an apartment complex, for our purposes the algorithm addr esses the needs that must be met to locate a prospective apartment site. Se ven heuristic steps or rules are introduced to identify prospective sites for a new apartment complex. 1. Demand Generator (DG) identify a demand generator and the area surrounding the node that prospective renters are wi lling and able to commute between the demand generator and a prospective apartment complex location. The distance will vary by demand generator, population characteristics, a nd transportation access. 2. Population Identification identify the psychographic LSP group to target. Confirm the current demand of the target LSP accessing the DG. Identification of the target LSP group gives rise to necessary amenities and afford ability requirements of the new housing. Project the increase in target LSP population accessing the DG. If there is no increase in target LSP population, then the new development must compete solely on the basis of price, higher level amenities, and superior location. The developer must be so forewarned and then calculate if the proposed development with the necessary characteristics and price is financially viable. 3. Hedonic Pricing ModelConstruct a database of rents and amenities by location (address). Thematically map the current rents adjusted by bedrooms and other amenities. Derive the in apartment surface, including distance decay of rents from the DG. 4. Pipeline AnalysisConstruct a database of competitive pipeline supply. Identify locations of clusters of competitive supply. Avoid the locatio n if the competitive su pply will require the new development to compete on the basis of pr ice in order to achieve occupancy over 94%

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21 (Thrall, 2002). Hot spot development areas can also serve as gravitat ional attractants, as the place to reside. 5. Locational Amenitiesidentify second order locational amenities that will piggy back onto the primary DG. Secondary locational amenities include good proximity to restaurants, nightlife, and so on. 6. Locational NecessitiesA successful development will also have available those necessities that prospective renters expect to obtain on a daily or weekly basis. If the apartment complex and the DG are walking environments, then the n earby necessities must also be available via waking or public transportation. If the target population group is likely to have children, then the quality of schools must be assessed. The crime rate must be considered. While all desire a low crime rate, the rate is more important when making a location decision for some LSP groups than others. 7. Site Availability identify sites that can be acquire d to support the development, and estimate their necessary cost of acquisition. With out adequate prospective development sites, there can be no new development. Demand Generator Florida is the third fastest gr owing state in the nation. Even during the recent downturn in the econom y and migration to Florida, despite a recent slowing in the rate of growth in new residents, Florida remains on of the fastest growi ng states in the nation (Schenker, 2007). As our economy slows down Florida re mains an affordable place to live: which makes it an attractive destination. During the 1990s, Floridas population increased by 3 million. Only California and Texas experienced population increases equal to or exceeding Florida during the decade (Schenker, 2007). Floridas population incr ease is attributable to in-migration from other states and other nations; natural increase, the difference between birth and death rates, contributed less in population in crease. Most of the populatio n increase has been in the southeastern region of Florida however, this population increase has implications for north central Florida, particularly Gainesville Florida. As the states population increases, the increase translates into greater enrollments at University of Florida and Santa Fe Community College. In 1997, UFs student population equaled 42,053, and SFCC equaled 12,486. UFs student numbers

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22 in 2008 exceed 51,000 (http://www.ir.ufl.edu/f actbook/i-05.a_hist.xls) and SFCC exceeds 16,000 (Jim Lewis, Office of Institutional Resear ch, SFCC). The surge in increase in population results in an increase in th e demand for student housing. Gainesvilles economic base remains concentrated in education and government services, most of which are located in the downtown to UF campus corridor, an exception being the SFCC main campus in NW Gainesville1. Gainesvilles metropolitan area hosts a population projected at 251,332 people (www.ams.usda.gov), while 108,655 people reside within the city limits (www.bestplaces.net). Gainesvilles spatial popul ation trend has been westward, away from the historic central downtown and toward the Inters tate and its concentration of retail shopping opportunities. (see for example, Smersh, Smith and Schwartz, 2004)2. In 2004, the University of Florida admitted 46,441, and in 2005, the university admitted 49,780, resulting in a net increase of student enrollment at UF of 3,339 students (http://www.ir.ufl.edu/factbook/i-05. a_hist.xls). Assuming that students were adequately housed in 2004, and that the market over time had adjusted so that there was not an oversupply of student housing, then an incr ease of 3,339 students represents a significant increase in demand for additional student housing. To accommodate a nd take advantage of the growth in student population, where should new student housing be located? Answ ering that question is the central focus of this thesis. Appl ying a gamut of geospatial proce dures, that question is answered here. 1 According to ESRIs business analyst, 19% of Alachua Countys working population is engaged in education based upon NAICS employment data. 2 It is conventional in Real Estate Ma rket Analysis to provide an overall de scription of trade area that places the population in context.

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23 The registrar at University of Florida allo wed Dr. Grant Thrall to have access to the student records. The confidentiality and sensit ivity of accessing individual student records necessitated that Dr. Thrall perform the basic geospatial analysis, the results of which were provided to me for subsequent analysis and synthesis. Among the information in the student database is permanent address and address on campus, as well as personally identifying demographic characteristics such as age, ge nder, year in school, and other educational curriculum information. Dr. Thralls and my analysis determined that 80% of the students at UF have campus addresses within 3.65 miles of the core UF campus as defined by the intersection of University Avenue and 13th Street, this area makes up the Univ ersity of Floridas primary trade area3. The University of Floridas trade area is the entire state of Florida and to a lesser extent the entire nation, and even globally; however th e primary trade area is concentrated around the university4. Grant Thrall explains states that, The pr imary trade area is the geographic core from which a real estate project will get the majo rity of its business. Applebaum (1996) used supermarket data for metropolitan areas to rev eal that the core trade area accounted for 60-70 percent of the real estate projec ts customers. Analysts today of ten define the primary trade area as having 80 percent of the customers (2002), th is rule still remains relevant for defining the University of Floridas primary trade area5. The University Avenue and 13th Street location is adjacent to UFs College of Business and near to the College of Liberal Arts and Sciences. Both colleges account for over 40% of total student enrollment at UF (www .ir.ufl.edu/factbook/i01.a_hist.xls). The intersection of University Avenue and 13th Street was chosen because the 6 The highest density of students is at the North East co rner of the campus. The North East corner was selected because that is the destination for th e majority of the student population. 4 Using the primary trade area allows th e largest and most dominant group to be identified, and this revealed group is then used to establish the value platform of the apartment complex including its location. 5 The whole issue of calculating trade areas is very debatable. Using any locus will enter bias into the analysis. The NE corner as a locus will capture the general market trend.

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24 University is so expansive that if we were to use the entire circumference of the University you would get apartments that were within in a mile of the university but outside a walkable distance to the core. University Avenue and 13th Street represents a focal point as to base walking distance. The 3.65 mile UF primary trade area, as shown in Figure 3-1, provides a spatial delineation of what distance a student targeted apartment complex should not exceed from UF6. Figure 3-1. Student populati on by distance from campus Taking the overall population of Gainesville (108,655) and the student population of the university (51,599) reveals that about 48% of the Gainesvilles population is made up of university students. Given the historic incr eases in student population, and large student population base, it is reasonable for this demonstr ation to restrict the example to decisions on prospective locations for new apartment complexe s that are targeted to UF students. Thrall (2002) states that: Apartments are targeted to a specific target population niche. Competing apartments can be identified in a vari ety of ways, includi ng geocoding databases of apartment addresses, visualizing th e resulting mapped data, and calculating the lifestyle segmentation profiles (LSPs) [Life Mode Group] of apartments that are derived using addresses of the apartments. The LSPs (Life Mode Group) can be used as surrogate measures of apartmen t type and revealed target demographic niche (2002). 6 This figure is automatically generated by ESRIs Commun ity Coder when geocoding the data set. It allows the input of only one locus for the market core The graphical output has no user options.

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25 Population Identification The derivation of UF students psychographics is prelim inary to calcu lation of trade area and demand. Psychographics allows researchers to evaluate trends in human behavior not accounted for by demographics alone. UF student information was used to calcu late the students ps ychographic profile by submarket within UFs larger 3.65 mile primary campus trade area. This is accomplished by first calculating each students LSP based upon their pe rmanent address. That derived characteristic was then mapped using each students campus address. ESRIs Tapestry twelve category Life Mode LSP was used for the study. A life mode group is a combination of the more granular 65 category ESRI Tapestry LSP database. A st udents permanent addres s is generally their parents addresses. This allows calculation of their pr opensity to consume. Without their parents addresses, commercial LSP databases like Tape stry merely list the student as an unknown student consumption profile. Students at UF ha ve different propensity to consume than SFCC, and other colleges and universities. Subsequent to analysis of th e students LSP profiles, it is possible to determine which LSP group comprise s the largest increase in student population, thereby revealing a granular insight into what type of housing is demanded, and where. Life mode groups arise via geo-statistical procedures that allow myriad descriptive measurements to be combined into a small set of commonalities that explain the variation among the population subgroups (Thrall, 2 002). Life mode groups range from the elite at the top of the society to the poverty stricken at the bottom of society. Once it is determined which Life Mode Group represents the largest increa se in students, that group can be mapped out to discover any trends associated with that Li fe Mode Groups housing habits.

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26 Iterative Procedure for Calculating Trade Area and Apartment Demand ESRIs Business Analyst (www.esri.com ) was used to calculate the number of UF students by their Life Mode Groups, and the change in their numbers between years. This is shown in Table 3-1. Table 3-1: 2004-2005 Change in ESRI tapestry lifemode groups for University of Florida students Lifemode (www.esribis.com ) psyc hographics of UF student s for 2004 and 2005 are calculated, and the difference by year derived for c ount of students by Lifemode segment. The largest change in Life Mode Group between 2004 and 2005 was the L1: High Society life mode group with 44% of the total incoming stude nt population. Second ranked was the L9 Family Portrait group with 15%, the thir d ranked was the L5 Seni or Styles life mode group with 11%. Because L1 life mode group re presents the largest segment of incoming UF students, that group will be the focus for deriving prospective apartment demand7. 7 It is assumed that investment would have lower risk by targeting the largest market, which the North East corner of campus and the L1 segment group.

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27 Geospatial Characteristics of UFs L1-High Society Students People in th e L1 category tend to enjoy fine dining, the arts, social activities and travel. These people place importance on family and leisure time, they are also considered active investors and savvy financial planners8. L1s are at the top of the fi nancial ladder. L1s have a median household income of $185,415 dollars a year. L1s own at least one home, the average median home value of people in the L1 life mode group is $1,078,501 dollars. This life mode group will not just live anywhere; in stead, the apartment in which they reside must have certain amenities or the L1 population will not be attracted to that apartment complex. L1s are used to a certain lifestyle that they will not want to part with once they get to college9. Neighborhood is also important to the L1 student. The L1 nei ghborhood must provide good access to UF, and also be clean, prestigious, and safe (http://www.esri .com/library/brochures/pdfs/community-tapestryhandbook.pdf). Following the derivation of the Life Mode segment for each student based upon their permanent address, the campus address locations of students with L1 Life Mode segments was mapped to reveal which neighborhoods L1 stude nts choose. It was f ound that L1 students primarily resided to the north and west of the university. This is doc umented in Figure 2-1. Each dot in Figure 3-2 represents the loca tion of one ore more students with the L1 characteristic. The map that results from the application of this procedure identifies the campus locations of students with the L1 characteristic, and therefore the prim ary trade area of L1 students attending UF. 8 There is an implicit assumption made and am aware of it, but I am trying to capture the general market trend. 9 The methodology employed follows standard business geography procedures. People can be segmented into segments (LSPs, psychographics, demographics), parents have behavioral traits that are passed down to their children, however, they may not always be the same.

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28 Figure 3-2. Location of UF student s with LifeMode L1. Note: Because many students reside in apartment complexes, and the geocoding pr ocedure assigns a geographic coordinate based upon address, and also since each apartment complex is assigned only one address, therefore many students may be positioned at the same geographic coordinate and represented by the same dot in the map. The trade area is visualized in Figure 3-2. The L1 trade area is within the 80% primary trade area for all UF students; namely, within 3.65 radial m iles from the NE corner of campus. Now that it

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29 has been determined where L1 students live, it is important to find a suitable location for an apartment marketed toward L1 students10. Apartment Supply The next step in conducting geospatial m arket analysis for a new development (Thrall, 2002) is to calculate competitive supply. The existi ng apartments in the city of Gainesville need to be enumerated, along with their locations. Als o, the known future supply of apartments needs to be considered when calculating competitive supply11. Existing apartment supply was compiled from information gathered from three local apartment guides; Apartment Finder (2007), Gainesville Apartment and Condominium Guide (2007), and Gainesvilles Premier Apartment Guide (2007) All three publica tions are marketed both to university students and the general popu lation. These guides were used instead of the property assessment data files because they cont ained more information relevant to this study than is available in the assessment property records.12 The database comprised 109 apartments. 10 A question was raised as to that segment of L1 students whose parents believe they should make it on their own. To this I respond by stating that we do make certain assumptions, however, in all likelihood things will continue to stay the same. L1 dominance is assumed to continue. The assumption is not heroic that they will conform to behavior associated with the L1 life mode group. 11 A question was raised as to why single family homes were not included in this study. The reason single family dwellings were not included in the st udy is because we are not competing w ith single family housing. We are looking at multi-family housing. Also, it does not make sense to try and develop affordable housing around UF because of high property costs. The ob jective was to have a location close enough to be able to walk, bike or take a short bus ride to the Demand Generator. Few Houses are walkable to the core of our primary trade area. 12 To view the property assessment data records for any property in Alachua County, see http://www.acpafl.org/search.html

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30 The map in Figure 3-3 reveals that the major ity of apartments listed in the apartment guides are located within th e 3.65 mile primary trade area13. Figure 3-3 provides the analyst the opportunity to visualize the location of relevant existi ng supply of apartments. Figure 3-3. Primary trade area for university of Florid a student campus addresses, and locations of apartments with that trade area To calculate the proportionate shar e of apartments in Gainesville that fall within UFs 80% trade area for resident students, the count of apartments within the trade area were divided by the total number of apartments in the Gainesville. In Gainesville 72% of the apartments are located 13 Note: The trade area calculated here is 3.65 radial miles. The map projection used in Business Analyst distorts the circular trade area to instead appear egg shaped.

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31 within the University of Floridas 3.65 mile primary trade area14. Developers, therefore, consider proximity to the university to be a key aspect of apartment location in Gainesville. Most apartment market analysis studies are proprietary to the client and the analyst, including studies specific to ma rket analysis of apartments ta rgeted to university students.15 For that reason, with the exception of Thrall (2002), few articles have been published specifically on campus area apartment market analysis. Generally, there is a lack of re search into the housing side of the University experien ce. The publications readily avai lable to those researching real estate have been concerned with the presence of Universities on property values (See Jaffe and Bussa, 1977, for example). These publications re ly upon hedonic modeling to extrapolate the relationship between rent and amenities. Development of a Hedonic Model According to Rogerson the regression equation used in SPSS is: The dependent variable is monthly rent a nd the independent vari ables are made up of variables based on different amenities as well as dummy variables. 14 The fact that 72% of UF apartments are within the 3. 65 mile marker, which makes it the place for straight line analysis, is confirmatory. 15 For example, PricewaterhouseCoope rs Financial Advisory Services Real Estate division produces market analyses of student apartments for th eir clients. PwCs procedure is very different from that which is introduced here. PwC relies on survey questionnaires ad ministered to prospective tenants.

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32 Data An experiment is conducted to docum ent the effect of distance from UF upon rental rates, and to document the effect of amenities on rental rates. A primary data base of 109 apartments was created. Three locally pub lished and readily available apartment guides were used to establish the addresses, names, rental rates and amenity bundles of each apartment. Different sized apartments were maintained in the data base as separate obser vations, resulting in 350 observations for 109 apartments. The database included number of bedrooms number of bathrooms whether it was a studio or not, whether the apartment complex had a clubhouse or not, and if the apartment unit was a townhouse Number of bedrooms was used for an approximation of size because squa re footage was not available fo r all of the records in the database. Dummy variables were created identifying the characteristics of amenities hypothesized to be relevant, including Pets Allowed Furnished, Patio, Balcony Dishwasher, apartment complex Laundry Washer and Dryer in the unit, apartment complex Pool apartment complex Tennis Court, apartment unit Fireplace The apartment complexs age was included in the database as a measure of condition or architectural style. The actual driving distance from the apartmen t to the Universitys Admissions Office was taken and then divided into five concentr ic zones in order to create dummy variables representing which mile marker the apartment complex is located in.16 The Admissions administrative office was chosen to represent the campus location because that is a location that all students must periodically access, and that lo cation is nestled in between the main classrooms of the College of Business and the College of Li beral Arts and Sciences; together representing 16 Each one mile increment is expected to be correlated with a different mode of transportation: walking, bicycling, bus, automobile, and beyond that locations are competing with different markets. The objective is to capture the general market trend.

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33 40% of UF students major colleges (www.ir.ufl. edu). Subsequent to documenting that distance is significant, and then dummy variables repres enting concentric circles of a mile each around the university will be used to determine differences by distance from the university, if any. Segmenting the market by concentric circles arises from the work of William Applebaum and his Customer Spotting Technique. Applebaum used concentric circ les around grocery stores to capture potential drawing power (1966). The concentric circle method allows for the determination of which distance has the greatest impact on rent17. The dummy variables were created by designating 1 for the apar tment if it fell with in the co rresponding mile range, and a 0 if not. The 1 mile variable included every apartment that was less than or equal to one mile, this process was followed for the 2 mile, 3 mile, 4mile, until the 4plus mile variable which consisted of all of the other apartments within the Gainesv ille. Prior to executing the analysis it was hypothesized that monthly rents decline with increasing distance from the university; the experiment is designed to allow for this hypothesis to be tested. Figure 3-4 shows a map of apartment location an d amount rent per bedroom. Rent is the rate, while bedroom is an approximation for the size of the apartment. While the resulting map displayed in Figure 3-4 is desc riptive, it does convey that there is a relationship between availability of apartment supply and proximity to UF. Also, the higher rent al rates are close to UF. It is expected that each of the variables collected in the primary database will affect monthly rent. However, it is not known how each characteristic affects apartment rent in the 17 The rational for choosing the NE corner as an origin fo r the concentric circles surrounding the campus is because I am not addressing housing for people working at Shands Hospital but rather I am addressing housing that is targeted to students.

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34 Gainesville market. Regression analysis is used to calibrate the hedonic model and thereby reveal how each variable affects monthly rent. Figure 3-4. Apartment rent per bedroom by loca tion, Gainesville FL. Data updated as of 21 August 2007 It is expected that number of bedrooms w ill have a significant positive impact on rent because the more bedrooms the larger the apartment, hence higher rent. The relationship between bathrooms and rent is less clear, however it is expected to be positive because all apartments contain at least one bathroom, people probably would not live in an apartment

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35 without a bathroom. The relationship between cl ubhouses, townhouses, and rent is not clear. It is expected that a pool will have a positive impact on rent and thus be significant, as well as the presence of a washer and dryer in the unit. The proximity to UF variable is expected to have a significant positive relationship with the price of monthly rent. Moreover it is expected that the distances within the three mile ra nge of the university will also ha ve a significant positive impact on rent, while anything over four miles will have less. Hedonic Model Test Result The Hedonic Model Test modifies the distance vari able so that it is partitioned into five zones of decreasing proximity to UFs core NE campus. With an F-Statistic of 110.894 the regression equation can be accep ted as greater than 99.99% c onfidence level as generating significant explanatory power. The multiple correlation coefficient, R is .864, demonstrating a strong relationship between rent an d the hypothesized variables model18. The coefficient of determination, r2, is equal to .746, which means that about of the variation in price is explained by the model. The histogram and P-P plot show that the model fits the assumptions of normality. There is a high co-linear ity with bed, and price, as we ll as, bathroom and price based on VIF score of 3.377 and 3.626 respectively. The VIF is the variance inflation factor, a score greater than 5 for the VIF indicates problems with multi-colinearity, the VIF is also the reciprocal of the tolerance (Rogerson, 2004). Th is is expected as bedrooms and bathrooms are the two most dominant features in apartments. The Step Wise regression returned ten models, although the ninth was the one selected to use. The ninth model returned from the model was used because the condition index for Townhouses was greater than 15, which indicates a possible problem with co-linearity. In model 9 the highest condition index returned was 14.929, which 18 Thereby confirming the hypothesized reasonableness of assuming five rings.

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36 being less than 15 was acceptable for this study. The tables and charts for this regression are in Appendix B. Table 3-2 Variable table Mean Std. Deviation N Price $950.89 $379.355 350 Studio .03 .182 350 Bed 2.04 .975 350 Bath 1.80 .800 350 TownHouse .18 .382 350 PatioBalcony .89 .315 350 Pet .86 .344 350 Furnished .31 .465 350 Dishwasher .91 .289 350 LaundryRoom .80 .401 350 WasherDryer .45 .498 350 Fireplace .18 .385 350 Pool .89 .308 350 Tennis .34 .473 350 Clubhouse .53 .500 350 Age 14.73 10.395 350 OneMile .08 .272 350 TwoMile .16 .364 350 ThreeMile .17 .380 350 FourMile .19 .396 350 FourPlusMiles .39 .489 350 The first variable19 that is returned in the regression report is the bath rooms variable. The bathroom variable has an expected value of 149.872 (with a confidence interval between 100.97 and 198.78 at the 95% confidence level). A dding an extra bathroom will command $149.87 more dollars to the monthly rent on average. 19 In forward stepwise regression, the first variable returned is the most significant in explaining the variation in the dependent model.

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37 The next variable, as in the first model, was Pet ; it was negative as well. The model returned an expected value of negative 138.781, (with a confidence interv al between -73.43 to 204.13 at the 95% confidence level). This is interp reted as apartments that allow pets are less costly per month than places that do not allow pets. The presence of a club house returned an expected value of 123.529, (with a confidence interval between 74.37 and 172.69 at the 95% confidence level). Apartment complexes that have clubhouses (basic ally common areas, different clubhouses have different amenities associ ated with them but this was not factored in here, only whether or not it had one), command an extra $138.78 dollars on average. The next variable returned in the model wa s the dummy variable representing less than or equal to 1 mile. The expected value for the 1 mile variable was 329.435, which was the highest expected value in the model (with a conf idence interval of 247.37 to 410.90 at the 95% confidence level). Apartments within the one mile radius fetch $329.43 dollars on average than those outside that range. The bedroom variable came up next in the model, the addition of an additional bedroom to an apartment unit will increa se the monthly rent by $145.41. The expected value of the model was 145.407 with a confidence interval of 106.69 to 184.13 at the 95% confidence level. The age variable followed bedroom and this has nega tive significance associated with it. The expected value for the age variable was -4.566 with a confiden ce interval between -2.23 to -6.9 at the 95% confidence level. The furnished variable returned an expected value of 97.566 meaning that on average, apartments that are furnished cost $97.57 more a month than nonfurnished apartments. The furnished variable had a confidence interval between 49.62 and 145.52 at the 95% confidence level.

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38 The 4 mile variable was next. This is interesting because it suggests that there is a rent gradient ridge in Gainesville. The expected value for the 4 mile variable is 76.682 (with a confidence interval at the 95th percentile at 23.44 to 129.92). Apartments at the four mile zone command $76.68 more dollars a month in rent than apartments in the othe r zones except for the one mile zone. This reinforces previous lite rature on rent gradient ridges by Frew and Wilson who found that rent increases around freeway corridors (1990). The four mile zone corresponds to the I-75 freeway and shopping at Gainesvilles two highest order retail centers (Butler Plaza, Oaks Mall). Access to transportation and other important destinations increase rent20. The final variable in the model was the fireplace variable. The expected value for this variable was 67.851 (with a confidence interval of 12.99 to 122.71 at the 95% confidence level). An apartment with a fireplace can command $67.85 more dollars a month than apartments that do not have a fireplace. The fi replace variable was not expected to significant however, it makes sense that a fireplace would cont ribute greatly to rent because it is considered a luxury in apartment living. Using the predicted values and residuals at th e 95% level will reveal what the predicted rent of an apartment should be. For a two bedroo m, two bathroom apartment, that does not have a townhouse or allow pets, that is furnished with a patio or balcony, a dishwasher and a communal laundry room, that is a within a mile of the school and five years old, returns a predicted monthly rent at the 95% conf idence level between $1207.06 and $1444.43 dollars a month. Predicted values can reveal where pr operties are properly priced. Comparing the 21 For documentation on the theory and standard model, see Thrall, Grant I., 1998, Land Use and Urban Form: The Consumption Theory of Land Rents; Rutledge/Methuen: London and New York.

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39 predicted monthly rent to the actual monthly rent will reveal which apartments in Gainesville are priced properly. This regression al lows for the formation of a rental platform that can be used to calculate the proper amount of rent to charge fo r a new apartment complex in Gainesville. The variables returned in the model show what vari ables or rather amenities are important to rent valuation and that the va riables selected should be used in the development of a new apartment building. Pipeline To get an id ea of what was being planned on being built in Gainesville projects in the development pipeline were considered. Thrall (2002) states that the pipelin e is real estate at various stages of construction from conceptu alization to the awardi ng of a certificate of occupancy. What is in the pipeline can be used as a measure of the prospective increase in the supply of real estate. Both condominiums and apartments are considered the competitive supply. Single family dwellings while part of housing supply are not competitive in most circumstances with condominiums and apartment. Visual display of the locations of L1 stude nts revealed (see Figure 1) the existing L1 submarket. The question arises is the supply in the pipeline focused on L1 students in their revealed trade area? The pipeline data will al so give insight to whet her the L1 apartment market will soon be saturated. To obtain the pipeline information in Gaines ville, a visit to the Alachua County Building Department First Step Program was required. Developers applying for building permits must begin the permitting process at First Step. The developments recorded at First Step are either in the planning phase, construction phase, or are finished but yet to r eceive a cer tificate of occupancy (CO). First Step records are held in a folder containing forms of completed building permit applications. Regrettably, the perm itting office prior to 2007 neither required that

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40 the forms be filled out in thei r entirety, nor required information be updated as plans became more specific. The First Step Program has not ye t gone digital so the person assembling the data must hand transcribe the records, and then later convert them to digital format. Because the proposed construction is in the planning phase when First Step begins its process, information is often missing, including proposed number of apartment units, nu mber of floors, and square footage. As plans solidify, the First Step forms are not updated. Accurate and complete information is not publically available until af ter the project has been completed and a CO awarded. The pipeline supply is important for th e big spatial picture of future competition by location, and which sub markets are receiving developers attention; however, the pipeline supply data for Alachua County is not suffici ently accurate for statistical analysis. While 2005 and 2006 pipeline data was of a quality that restricted its use to being purely qualitative and visual, pipeline for 2007 was much improved as compared to recent previous years. Pipeline supply for 2007 is shown in Figure 3-6. The pipeline supply collected for 2007 more complete, allowing for density plots of planned apartments and condominiums by the number of units planned. Considering the information in the pipeline supply allows the an alyst to guide the deve loper away from small submarkets that might in the near future have an over supply of competing units, and at the same time provide guidance as to the near future lo cations of high levels of development before construction is readily apparent on the ground. The benefits of agglomeration might guide the development toward others in the pipeline; avoi dance of high levels of competitive supply might guide the development away from concentra tions of new development in the pipeline.

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41 Figure 3-5. Map of apartments and condominium s in the pipeline for 2005 and 2006. Pipeline data obtained from Alachua County Buildi ng Permits office First Step. Data accurate as of 24 August 2006. Figure 3-6.Map of apartments and condominium s in the pipeline for 2007. Pipeline data obtained from Alachua County Building Permits office First Step. Data accurate as of 24 August 2007.

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42 Qualitative Analysis The objective of business geogr aphy is for geospatial analysis to im prove the business decision (Thrall, 2002). Not all real estate development deci sions can be based solely on quantitative analysis. Qualitativ e analysis based upon reliable data is equally if not more important than quantitative analysis. When locat ing an apartment complex qualitative analysis, which includes a large measure of what is ofte n referred to as common sense, should be drawn upon to account for goods and services that poten tial renters will need on a regular basis. Without large pantries for storage, the apartment dweller will need either to go frequently to the grocery store or regularly to n earby restaurants. Renters value good proximity of grocery stores and pharmacies, as well as good proximity to reta il shops offering goods and services consumed on a daily or weekly basis. Figure 3-7. Map of grocery stor es in Gainesville, Florida.

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43 This map allows for the visualization of grocer y stores across the city of Gainesville. The map shows a lack of grocery stor es concentrated in the downtown and eastside of Gainesville. This area may be in need of a new grocery store to meet the needs of a larger population. Publix, grocery store, is the franchise that has the most stores in Gainesville, th e distribution of Publix stores seem dispersed enough to cover every se ction of Gainesville, except the eastside. Also, the size of the lot that you are tryi ng to build on should also be considered. Although not impossible, getting parcels rezoned is a hurdle that does not n eed to be encountered during the process of locating an apartment, choosing the proper parc el size for avoid going through the hassle of rezoning. It is difficult to get permission to build a large apartment building where it is zoned for a single family development and adjacent to existing single family homes.

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44 CHAPTER 4 ALGORITHM SUMMARY AND SI TE SELECTION The above seven step algorithm is followed for identification of a set of pr ospective sites for new apartment development. Demand generator : In Gainesville the most important DG is the University of Florida. The target population for the new develo pment is students attending UF. Population identification : Given the UF student population as the target renters of the new development, the L1 LSP segment has the greatest base at UF, and is expected to increase more than other LSP groups in the near future. Therefore, the target group is the L1 students. Hedonic pricing model: The regression model revealed that a. pets should not be allowed b. a clubhouse designed for student use should be included in the development c. location should be within a mile of UF Pipeline analysis: The pipeline for Gainesville revealed that potential market saturation is occurring along 13th Street north and south of Univers ity Avenue, as well as SW Archer Road east and west of the interstate, and along SW 24th Avenue between SW 34th Street and the interstate. Locational amenities: L1 students are financially able to enjoy nightlife and fine restaurants. The student oriented nightlife in Gainesville is clustered near the downtown and midtown areas. Therefore, proximity to both UF and downtown must be considered as a primary locational asset. Locational necessities: A map of grocery stores in Gain esville reveals that downtown and the east side of UF are not served by grocer y stores. This is both an opportunity for new grocery store development, and a prerequisite for a successful new apartment development. Site availability: the analyst is at times required to perform muddy boots geography and qualitatively assess sites visually, as well as with in a layer of parcels displayed within a GIS. Three sites were identified and will be compared. Three Sites:Three prospective sites for the new apartment development were identified by viewing the Alachua County Land Parcel Database ( http://www.acpafl.org/search.html ) within a GIS, and by personal inspection, based on the visualiza tion of the sites, on a map, and in real life. The three sites were chosen based upon their close location to UF, pr oximity to other L1

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45 students, proximity to downtown, proximity to shopping, and size of the parcel. These three sites are then ranked based upon the above discussed criteria. The locations of the three sites are displayed in Figure 4-1. Images of the three sites are displayed in Figures 4-2, 4-3 and Figure 44. Figure 4-1. Three prospective apar tment development locations. Selection completed September 14, 2005. Initially, site selection was ba sed on the availability of land, the size of the parcel and the location of the parcel. This type of strategy is commonly applied in real estate market analysis. Site A is currently occupied by Central Flor ida Office Supply. This two acre parcel lies within UFs 3.65 mile trade area, and within a mile of the downtown district. Zoned as a redevelopment district by the City of Gainesvi lle, this site has the potential to hold up to 300 units.

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46 Figure 4-2. Site A -University Avenue and 6th Street. Photograph taken February 3, 2008. This site was chosen for two reasons. First is its proximity to campus and the downtown area. Though not within the established L1 student trade area, L1 stude nts are expected to find the location attractive because of its access to both UF and downtown. It is on a public transportation route, which UF students can ride at no additional charge beyond their required student fees. Second is to avoid the saturated submar kets of new apartment complexes. Figure 4-3. Site B Main Street and Williston Road. Photograph taken February 3, 2008.

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47 Site B is a two acre assembled lot that provides great access to major roads, such as: SR 20, SR 24, US 441, and SR 301. Site B also lies within the 3.65 mile trade area of UF, and is 1.5 miles from downtown. This location was chosen because a new mixed use development (Thrall, 2002) combining housing and retail on this site c ould provide both the site and situation needs required by the target renters. Figure 4-4. Site C 13th Street and 7th Avenue The site C location is within the Universitys trade area. It is located near the future mixed use development of University Corners ( www.universitycorners.com ) which is under construc tion and will contain over 150 square foot of retail within the one-million square foot development. This site is located relatively cl ose to housing in the pipe line, which can provide agglomeration benefits as well as competitive s upply. Therefore, the devel oper must include site amenities within the apartment complex to insure its competitive position. Site C is presently occupied by a scuba dive shop. The area has pot ential to be developed into a complex that would be suitable for L1s.

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48 Table 4-1. Score card: comparison and ranking of three prospective sites Site A Site B Site C Best Proximity to UF 1 3 2 Site A Proximity to L1s 2 3 1 Site C Land parcel size 1 1 2 Site A & B Proximity to downtown 1 2 3 Site A Proximity to Mid-Town 2 3 1 Site C Proximity to shopping 2 3 1 Site C The three sites are compared and ranked from 1 to 3 according to which was qualitatively judged to be the best for the specific criterion. There are six criterion listed in Table 3 which were also discussed above. Site A and C tied for the best development sites. Both sites A and C are located within one mile of UF. Site B greatest di sadvantage is that it is outside the desired one mile distance from UF, and not close to existing shopping or tran sportation to compensate. A fourth site was then chosen to substitute for site B, and to conf irm more closely to the de sired criteria. The site chosen was the present location of College Manor Apartments, which is directly across the street from UF, on SW 13th Street. The apartment complex is older and suffering from lack of recent renovation. The site is suitable for demolition and rebuilding to a higher then current density.

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49 Figure 4-5. Site B Replacement of Site B with 1216 SW 2nd Avenue The new site to replace site B is located at the corner of Southwest Second Avenue, and Southwest 12th Street. This site was selected because of its relative proximity to the university, its relative proximity to other L1 students, as well as its relative proximity to Downtown, as well as good proximity to everyday shopping needs with the development completion of University Corners. The site is the second oldest apartment complex in Gainesville; the Age of a building has a negative affect on rent. The age of the current apartment building is 37 years old. Because there is already an apartment complex at this location, the predictive model from the regression can be used to evaluate whethe r the property is being utilized properly. The predicted monthly rent interval for this apartment complex is between $378 and $1352 dollars a month at the 95% confidence level, the actual price per month for this apartment complex is $580 dollars a month. The actual monthly rent falls on the smaller si de of the prediction interval. Plugging the

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50 variables for this locatio n into the regression equation gives you a monthly rent of $739 dollars a month for a one bedroom, one bath apartment. Plugging in variables for a new apartment Figure 4-6. Photo of replacement site B at 1216 SW 2nd Avenue. Photograph taken February 3, 2008. complex at this location gives a predicted m onthly rent of $1165 dollars, this results from changing the age of 37 to one, signifying a new development, also a clubhouse was included, and pets not allowed, as the regression results showed that the age of a building as well as the allowance of pets are variables that affect re nt negatively. A new apartment complex at this location can command rent much greater than it currently receives. A fi nancial analysis would need to be performed, working backwards from th e target (the predicted monthly rent interval) $378 and $1352 dollars a month, cost of ac quisition, demolition and construction.

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51 Table 4-2. Score card: comparison and ranking of three prospective finalist sites Site A Replacement Site B Site C Best Proximity to UF 3 1 2 Site B Proximity to L1s 3 2 1 Site C Land Parcel Size 2 1 3 Site B Proximity to Downtown 1 2 3 Site A Proximity to Mid-Town 3 2 1 Site C Proximity to shopping 2 3 1 Site C After substituting the new Site B for the previous Site B, the results of Table 4 indicate that Site C should be considered by developers as a prospective location for a ne w apartment complex. Site C is located at 13th Street and Northwest 7th Avenue. It is within the L1 student trade area, and offers L1 type amenities nearby. It is within the premium one mile zone distant from UF. The site is large enough to support the development of an apartment complex and to provide adequate onsite parking. The property would need to be rezoned from reta il to either mixed use or high density housing. The disa dvantage of Site C is that it is least proximate to downtown than the two other competitive lo cations. The pipeline analysis s hows that other new apartments or condos are planned to be constructed in th e area. The competitive supply might be an agglomerative benefit since the site is at the periphery of the L1 trad e area. It can be stated, that this is the best location for a new apartment building. (A discussion of developments in the market that took place after this study wa s concluded can be found in Appendix C).

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52 CHAPTER 5 CONCLUSION This analysis introduces a seven step proc edure for identifying prospective sites for development. The seven steps are: 1. Demand Generator identification 2. Population Identification 3. Hedonic Pricing Model 4. Pipeline Analysis 5. Locational Amenities 6. Locational Necessities 7. Site Availability The seven steps were applied to select a locat ion for a new apartment complex development. The analysis of the seven steps is used to justif y the selection of the fina list site. Step 3 was a hedonic model is used to calculate potential rent s for a site. This is valuable because the geospatial analyst is responsible for data creatio n that is input to the financial analysis. The difference between potential and realized rents is the opportunity cost of not developing or redeveloping a site. Psychographi c lifestyle segmentation profiles (LSP) were used to obtain the characteristics of current and future demand; among the characteristics are the type of amenities that need to be included in the new devel opment, and establishing constraints on location. Pipeline analysis reveals potenti al overdevelopment, or possibl e locations that might benefit from agglomeration. These seven steps will assi st real estate profe ssionals in their ranking properties for construction or acquisition. The analysis suppo rts qualitative judgment with geospatial procedures. The seven steps will re duce the risk of making a bad judgment, and to increase the likelihood making a successful business investment.

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53 APPENDIX A PSYCHOGRAPHIC SUBMARKETS Mapping the location of students in Gainesville by Life Mode Group allows for the visualization of subm arkets demarcated by their psychographic profile. Figure A-1. Map of L1 students. The L1 population groups predominately to the North and West of the University. Figure A-2. Map of L2 students.

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54 L2 students do not have the same numbers as the L1 students but one can see a pattern emerge as to where the L2 students live. Figure A-3. Map of L3 students L3 Students group to the North and North East of the University. Figure A-4. Map of L4 students.

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55 L4 Students seem to cluster around Main Street and around the I-75 interchange. Figure A-5. Map of L5 students. L5 Students are concentrated to the North of the University like the L1 students, however, L5 students have a greater concentration while having less of a concentration in the Northwest. Figure A-6. Map of L6 students.

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56 L6 Students are concentrated along 13th Avenue as well as scattered across the University itself. Looking at these maps allows one to see the diffe rent psychographic groups and where they live in Gainesville. These maps reinforce the belief that similar people will group together, there is overlap in every psychographic market, howev er, each psychographic group has its own unique pattern of residential behavior. Some groups are willing to live in areas other groups are not, reinforcing the idea that individu als that are similar in psychographic make-up make decisions that are indistinguishable from one another.

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57 APPENDIX B REGRESSION TABLES Table B-1. Notes Output Created 27-Jan-2008 19:07:57 Comments Data C:\Documents and Settings\Gabriel Bolden\Desktop\aptList.sav Active Dataset DataSet1 Filter Weight Split File Input N of Rows in Working Data File 350 Definition of Missing User-defined missing values are treated as missing. Missing Value Handling Cases Used Statistics are based on cases with no missing values for any variable used. Syntax REGRESSION /DESCRIPTIVES MEAN STDDEV CORR SIG N /MISSING LISTWISE /STATISTICS COEFF OUTS CI BCOV R ANOVA COLLIN TOL ZPP /CRITERIA=PIN(.05) POUT(.10) CIN(95) /NOORIGIN /DEPENDENT Price /METHOD=STEPWISE Studio Bed Bath TownHouse PatioBalcony Pet Furnished Dishwasher LaundryRoom WasherDryer Fireplace Pool Tennis Clubhouse Age OneMile TwoMile ThreeMile FourMile FourPlusMiles /SCATTERPLOT=(*SDRESID ,*ZPRED) /RESIDUALS HIST(ZRESID) NORM(ZRESID) /SAVE ZPRED COOK LEVER MCIN ICIN ZRESID.

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58 Table B-1 Continued. Processor Time 00:00:02.016 Elapsed Time 00:00:01.579 Memory Required 14076 bytes Resources Additional Memory Required for Residual Plots 760 bytes ZPR_4 Standardized Predicted Value ZRE4 Standardized Residual COO4 Cook's Distance LEV4 Centered Levera g e Value LMCI_4 95% Mean Confidence Interval Lower Bound for Price UMCI_4 95% Mean Confidence Interval Upper Bound for Price LICI_4 95% Individual Confidence Interval Lower Bound for Price Variables Created or Modified UICI_4 95% Individual Confidence Interval Upper Bound for Price

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59 Table B-2. Descriptive statistics. Mean Std. Deviation N Price $950.89 $379.355 350 Studio .03 .182 350 Bed 2.04 .975 350 Bath 1.80 .800 350 TownHouse .18 .382 350 PatioBalcony .89 .315 350 Pet .86 .344 350 Furnished .31 .465 350 Dishwasher .91 .289 350 LaundryRoom .80 .401 350 WasherDryer .45 .498 350 Fireplace .18 .385 350 Pool .89 .308 350 Tennis .34 .473 350 Clubhouse .53 .500 350 Age 14.73 10.395 350 OneMile .08 .272 350 TwoMile .16 .364 350 ThreeMile .17 .380 350 FourMile .19 .396 350 FourPlusMiles .39 .489 350

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60Table B-3. Correlations. Price Studi o Bed Bath TownHous e PatioBalcon y Pet Furnishe d Dishwashe r LaundryRoo m WasherDry er Fireplac e Pool Tenni s Clubhous e Age One Mile Two Mile Thre e Mile Four Mile FourPlu s Miles Price 1.00 0 -.201 .715 .759 .054 .160 -.398 .364 .283 -.176 .291 .049 .151 .275 .379 -.338 .133 -.126 -.067 .078 .013 Studio -.201 1.000 -.395 -.189 -.005 -.083 -.016 -.060 -.267 .094 -.107 .075 -.191 -.134 -.200 .156 .234 -.038 -.045 -.013 -.088 Bed .715 -.395 1.00 0 .832 .112 .061 -.214 .213 .268 -.163 .234 -.073 .215 .213 .204 -.156 -.077 -.050 .059 -.035 .069 Bath .759 -.189 .832 1.00 0 .163 .071 -.281 .265 .262 -.187 .329 .000 .176 .238 .266 -.232 -.025 -.074 -.013 -.015 .091 TownHouse .054 -.005 .112 .163 1.000 -.050 .120 .105 .147 .045 .168 .036 .111 .033 .063 .110 .139 .129 .162 -.228 -.114 PatioBalcon y .160 -.083 .061 .071 -.050 1.000 -.062 .025 .329 .073 .027 .166 -.063 .195 .284 -.090 -.096 .153 -.077 .082 -.067 Pet -.398 -.016 -.214 -.281 .120 -.062 1.00 0 -.267 .018 .091 -.158 .057 .133 -.032 -.110 .210 -.097 .126 .052 -.098 -.001 Furnished .364 -.060 .213 .265 .105 .025 -.267 1.000 .129 .062 .120 -.077 .133 .194 .257 -.195 -.064 -.106 .030 .025 .096 Dishwashe r .283 -.267 .268 .262 .147 .329 .018 .129 1.000 -.159 .167 .097 .149 .226 .296 -.339 -.089 -.135 .067 .030 .073 LaundryRoo m -.176 .094 -.163 -.187 .045 .073 .091 .062 -.159 1.000 -.511 .030 .037 .054 -.100 .310 -.037 .020 .154 .011 -.152 WasherDr y e r .291 -.107 .234 .329 .168 .027 -.158 .120 .167 -.511 1.000 -.109 -.064 .171 .288 -.368 -.054 -.105 -.111 -.007 .213 Fire p lace .049 .075 -.073 .000 .036 .166 .057 -.077 .097 .030 -.109 1.000 .064 .106 .025 .076 .136 -.080 -.039 -.042 .033 Pool .151 -.191 .215 .176 .111 -.063 .133 .133 .149 .037 -.064 .064 1.00 0 .245 .364 -.030 -.207 .123 .060 -.160 .106 Tennis .275 -.134 .213 .238 .033 .195 -.032 .194 .226 .054 .171 .106 .245 1.000 .504 -.131 -.210 -.175 .166 -.014 .154 Clubhouse .379 -.200 .204 .266 .063 .284 -.110 .257 .296 -.100 .288 .025 .364 .504 1.000 -.475 -.249 -.095 -.019 .117 .153 Age -.338 .156 -.156 -.232 .110 -.090 .210 -.195 -.339 .310 -.368 .076 -.030 -.131 -.475 1.00 0 .221 .073 .181 -.149 -.213 OneMile .133 .234 -.077 -.025 .139 -.096 -.097 -.064 -.089 -.037 -.054 .136 -.207 -.210 -.249 .221 1.00 0 -.127 -.135 -.145 -.238 TwoMile -.126 -.038 -.050 -.074 .129 .153 .126 -.106 -.135 .020 -.105 -.080 .123 -.175 -.095 .073 -.127 1.00 0 -.198 -.212 -.348 ThreeMile -.067 -.045 .059 -.013 .162 -.077 .052 .030 .067 .154 -.111 -.039 .060 .166 -.019 .181 -.135 -.198 1.00 0 -.226 -.371 FourMile .078 -.013 -.035 -.015 -.228 .082 -.098 .025 .030 .011 -.007 -.042 -.160 -.014 .117 -.149 -.145 -.212 -.226 1.00 0 -.367 Pearson Correlatio n FourPlusMil es .013 -.088 .069 .091 -.114 -.067 -.001 .096 .073 -.152 .213 .033 .106 .154 .153 -.213 -.238 -.348 -.371 -.367 1.000

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61Table B-3. Continued Price Studio Bed Bath TownHouse PatioBalcony Pet Furnished Dishwasher LaundryRoom WasherDryer Fireplace Pool Tennis Clubhouse Age One Mile Two Mile Three Mile Four Mile FourPlus Miles Price .000 .000 .000 .159 .001 .000 .000 .000 .000 .000 .182 .002 .000 .000 .000 .006 .009 .105 .072 .402 Studio .000 .000 .000 .462 .061 .382 .132 .000 .039 .023 .080 .000 .006 .000 .002 .000 .238 .200 .403 .051 Bed .000 .000 .000 .018 .127 .000 .000 .000 .001 .000 .087 .000 .000 .000 .002 .075 .175 .137 .257 .099 Bath .000 .000 .000 .001 .093 .000 .000 .000 .000 .000 .497 .000 .000 .000 .000 .318 .083 .406 .390 .044 TownHouse .159 .462 .018 .001 .177 .013 .025 .003 .201 .001 .252 .019 .268 .118 .020 .005 .008 .001 .000 .016 PatioBalcon y .001 .061 .127 .093 .177 .124 .323 .000 .088 .305 .001 .121 .000 .000 .047 .036 .002 .076 .063 .105 Pe t .000 .382 .000 .000 .013 .124 .000 .371 .044 .002 .143 .006 .276 .020 .000 .035 .009 .167 .033 .491 Furnished .000 .132 .000 .000 .025 .323 .000 .008 .125 .013 .076 .006 .000 .000 .000 .118 .023 .290 .318 .036 Dishwashe r .000 .000 .000 .000 .003 .000 .371 .008 .001 .001 .035 .003 .000 .000 .000 .048 .006 .104 .285 .085 Laundr y Roo m .000 .039 .001 .000 .201 .088 .044 .125 .001 .000 .290 .244 .155 .031 .000 .246 .357 .002 .420 .002 WasherDr y e r .000 .023 .000 .000 .001 .305 .002 .013 .001 .000 .021 .118 .001 .000 .000 .156 .025 .019 .446 .000 Fire p lace .182 .080 .087 .497 .252 .001 .143 .076 .035 .290 .021 .115 .023 .318 .078 .005 .068 .235 .216 .270 Pool .002 .000 .000 .000 .019 .121 .006 .006 .003 .244 .118 .115 .000 .000 .291 .000 .011 .132 .001 .023 Tennis .000 .006 .000 .000 .268 .000 .276 .000 .000 .155 .001 .023 .000 .000 .007 .000 .001 .001 .396 .002 Clubhouse .000 .000 .000 .000 .118 .000 .020 .000 .000 .031 .000 .318 .000 .000 .000 .000 .037 .363 .015 .002 A g e .000 .002 .002 .000 .020 .047 .000 .000 .000 .000 .000 .078 .291 .007 .000 .000 .086 .000 .003 .000 OneMile .006 .000 .075 .318 .005 .036 .035 .118 .048 .246 .156 .005 .000 .000 .000 .000 .009 .006 .003 .000 TwoMile .009 .238 .175 .083 .008 .002 .009 .023 .006 .357 .025 .068 .011 .001 .037 .086 .009 .000 .000 .000 ThreeMile .105 .200 .137 .406 .001 .076 .167 .290 .104 .002 .019 .235 .132 .001 .363 .000 .006 .000 .000 .000 FourMile .072 .403 .257 .390 .000 .063 .033 .318 .285 .420 .446 .216 .001 .396 .015 .003 .003 .000 .000 .000 Sig. (1tailed) FourPlusMiles .402 .051 .099 .044 .016 .105 .491 .036 .085 .002 .000 .270 .023 .002 .002 .000 .000 .000 .000 .000

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62Table B-3. Continued Price Studio Bed Bath TownHouse PatioBalcony Pet Furnished Dishwasher LaundryRoom WasherDryer Fireplace Pool Tennis Clubhouse Age One Mile Two Mile Three Mile Four Mile FourPlus Miles Price 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Studio 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Bed 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Bath 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 TownHouse 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 PatioBalcon y 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Pe t 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Furnished 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Dishwashe r 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Laundr y Roo m 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 WasherDr y e r 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Fire p lace 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Pool 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Tennis 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 Clubhouse 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 A g e 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 OneMile 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 TwoMile 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 ThreeMile 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 FourMile 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 N FourPlusMiles 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350 350

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63 Table B-4. Variable entered and removed. Model Variables Entered Variables Removed Method 1 Bath Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 2 Pet Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 3 Clubhouse Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 4 OneMile Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 5 Bed Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100).

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64 Table B-4 Continued. 6 Age Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 7 Furnished Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 8 FourMile Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 9 Fireplace Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). 10 TownHouse Stepwise (Criteria: Probability-ofF-to-enter <= .050, Probability-ofF-to-remove >= .100). a. Dependent Variable: Price

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65 Table B-5. Model summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .759a .576 .575 $247.287 2 .783b .613 .611 $236.634 3 .803c .644 .641 $227.217 4 .824d .678 .675 $216.411 5 .844e .713 .709 $204.815 6 .851f .725 .720 $200.755 7 .858g .736 .730 $197.067 8 .861h .741 .735 $195.156 9 .864i .746 .739 $193.742 10 .865j .749 .741 $192.878 a. Predictors: (Constant), Bath b. Predictors: (Constant), Bath, Pet c. Predictors: (Constant), Bath, Pet, Clubhouse d. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile e. Predictors: (Constant), Bat h, Pet, Clubhouse, OneMile, Bed f. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age g. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished h. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile i. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile, Fireplace j. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile, Fireplace, TownHouse k. Dependent Variable: Price

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66 Table B-6. ANOVA table. Model Sum of Squares df Mean Square F Sig. Regression 2.894E7 1 2.894E7 473.328 .000a Residual 2.128E7 348 61150.620 1 Total 5.022E7 349 Regression 3.079E7 2 1.540E7 274.968 .000b Residual 1.943E7 347 55995.868 2 Total 5.022E7 349 Regression 3.236E7 3 1.079E7 208.942 .000c Residual 1.786E7 346 51627.625 3 Total 5.022E7 349 Regression 3.407E7 4 8516782.277 181.852 .000d Residual 1.616E7 345 46833.527 4 Total 5.022E7 349 Regression 3.579E7 5 7158823.445 170.654 .000e Residual 1.443E7 344 41949.357 5 Total 5.022E7 349 Regression 3.640E7 6 6066829.415 150.533 .000 f Residual 1.382E7 343 40302.389 6 Total 5.022E7 349 Regression 3.694E7 7 5277560.853 135.895 .000g Residual 1.328E7 342 38835.585 7 Total 5.022E7 349 Regression 3.724E7 8 4654678.070 122.215 .000h Residual 1.299E7 341 38085.840 8 Total 5.022E7 349 Regression 3.746E7 9 4162498.765 110.894 .000i Residual 1.276E7 340 37535.903 9 Total 5.022E7 349

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67 Table B-6 Continued. Regression 3.761E7 10 3761320.932 101.105 .000j Residual 1.261E7 339 37202.025 10 Total 5.022E7 349 a. Predictors: (Constant), Bath b. Predictors: (Constant), Bath, Pet c. Predictors: (Constant), Bath, Pet, Clubhouse d. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile e. Predictors: (Constant), Bat h, Pet, Clubhouse, OneMile, Bed f. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age g. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished h. Predictors: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile i. Predictors: (Constant), Bath, Pet, Clubhous e, OneMile, Bed, Age, Furnished, FourMile, Fireplace j. Predictors: (Constant), Bath, Pet, Clubhous e, OneMile, Bed, Age, Furnished, FourMile, Fireplace, TownHouse k. Dependent Variable: Price

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68 Table B-7. Coefficient table. Unstandardized Coefficients Standardized Coefficients 95% Confidence Interval for B Correlations Collinearity Statistics Model B Std. Error Beta t Sig. Lower Bound Upper Bound Zeroorder Partial Part Tolerance VIF (Constant) 302.255 32.613 9.268 .000 238.112 366.398 1 Bath 360.182 16.555 .759 21.756 .000 327.621 392.743 .759 .759 .759 1.000 1.000 (Constant) 540.397 51.871 10.418 .000 438.376 642.419 Bath 333.471 16.510 .703 20.198 .000 300.999 365.943 .759 .735 .674 .921 1.086 2 Pet -220.244 38.319 -.200 -5.748 .000 -295.611 -144.877 -.398 -.295 .192 .921 1.086 (Constant) 500.173 50.339 9.936 .000 401.164 599.182 Bath 311.241 16.358 .656 19.027 .000 279.067 343.415 .759 .715 .610 .865 1.156 Pet -212.492 36.821 -.193 -5.771 .000 -284.913 -140.071 -.398 -.296 .185 .919 1.088 3 Clubhouse 139.182 25.260 .183 5.510 .000 89.499 188.864 .379 .284 .177 .928 1.078 (Constant) 437.632 49.052 8.922 .000 341.153 534.111 Bath 310.272 15.581 .654 19.913 .000 279.626 340.918 .759 .731 .608 .865 1.157 Pet -186.499 35.333 -.169 -5.278 .000 -255.994 -117.003 -.398 -.273 .161 .906 1.104 Clubhouse 177.821 24.896 .234 7.143 .000 128.854 226.788 .379 .359 .218 .866 1.154 4 OneMile 267.933 44.398 .192 6.035 .000 180.607 355.259 .133 .309 .184 .922 1.084 (Constant) 410.120 46.622 8.797 .000 318.421 501.820 Bath 175.434 25.673 .370 6.834 .000 124.939 225.929 .759 .346 .197 .285 3.505 Pet -191.481 33.449 -.174 -5.725 .000 -257.271 -125.691 -.398 -.295 .165 .905 1.105 Clubhouse 186.867 23.604 .246 7.917 .000 140.440 233.293 .379 .393 .229 .863 1.158 OneMile 297.635 42.274 .213 7.041 .000 214.487 380.782 .133 .355 .203 .911 1.097 5 Bed 131.116 20.435 .337 6.416 .000 90.923 171.310 .715 .327 .185 .303 3.299

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69Table B-7 Continued. (Constant) 486.623 49.769 9.778 .000 388.733 584.513 Bath 164.851 25.311 .347 6.513 .000 115.067 214.636 .759 .332 .184 .282 3.547 Pet -169.347 33.278 -.154 -5.089 .000 -234.802 -103.891 -.398 -.265 .144 .879 1.138 Clubhouse 147.628 25.249 .195 5.847 .000 97.965 197.292 .379 .301 .166 .725 1.380 OneMile 322.996 41.948 .231 7.700 .000 240.488 405.503 .133 .384 .218 .889 1.125 Bed 136.815 20.084 .351 6.812 .000 97.312 176.317 .715 .345 .193 .301 3.317 6 Age -4.716 1.215 -.129 -3.880 .000 -7.106 -2.325 -.338 -.205 .110 .724 1.382 (Constant) 456.979 49.495 9.233 .000 359.626 554.332 Bath 156.311 24.951 .329 6.265 .000 107.235 205.388 .759 .321 .174 .280 3.577 Pet -144.442 33.340 -.131 -4.332 .000 -210.021 -78.864 -.398 -.228 .120 .844 1.185 Clubhouse 132.856 25.099 .175 5.293 .000 83.488 182.224 .379 .275 .147 .707 1.415 OneMile 327.751 41.197 .235 7.956 .000 246.719 408.783 .133 .395 .221 .888 1.126 Bed 137.056 19.715 .352 6.952 .000 98.279 175.834 .715 .352 .193 .301 3.317 Age -4.605 1.193 -.126 -3.859 .000 -6.952 -2.258 -.338 -.204 .107 .723 1.383 7 Furnished 91.758 24.563 .112 3.736 .000 43.445 140.072 .364 .198 .104 .853 1.172

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70Table B-7 Continued. (Constant) 424.370 50.398 8.420 .000 325.240 523.500 Bath 156.907 24.710 .331 6.350 .000 108.304 205.510 .759 .325 .175 .280 3.577 Pet -134.666 33.204 -.122 -4.056 .000 -199.976 -69.356 -.398 -.215 .112 .834 1.199 Clubhouse 129.587 24.884 .171 5.208 .000 80.642 178.531 .379 .271 .143 .705 1.418 OneMile 342.021 41.119 .245 8.318 .000 261.142 422.899 .133 .411 .229 .874 1.144 Bed 139.383 19.542 .358 7.133 .000 100.946 177.820 .715 .360 .196 .301 3.323 Age -4.346 1.185 -.119 -3.666 .000 -6.677 -2.014 -.338 -.195 .101 .719 1.391 Furnished 93.320 24.331 .114 3.835 .000 45.462 141.178 .364 .203 .106 .853 1.172 8 FourMile 75.299 27.079 .079 2.781 .006 22.037 128.562 .078 .149 .077 .948 1.055 (Constant) 421.945 50.043 8.432 .000 323.513 520.377 Bath 149.872 24.699 .316 6.068 .000 101.291 198.453 .759 .313 .166 .276 3.626 Pet -138.781 33.006 -.126 -4.205 .000 -203.702 -73.859 -.398 -.222 .115 .832 1.202 Clubhouse 123.529 24.827 .163 4.976 .000 74.695 172.362 .379 .261 .136 .698 1.432 OneMile 329.435 41.143 .236 8.007 .000 248.508 410.363 .133 .398 .219 .861 1.162 Bed 145.407 19.555 .374 7.436 .000 106.942 183.872 .715 .374 .203 .296 3.377 Age -4.566 1.180 -.125 -3.869 .000 -6.888 -2.245 -.338 -.205 .106 .715 1.400 Furnished 97.566 24.217 .120 4.029 .000 49.932 145.200 .364 .213 .110 .849 1.179 FourMile 76.682 26.889 .080 2.852 .005 23.794 129.571 .078 .153 .078 .948 1.055 9 Fireplace 67.851 27.709 .069 2.449 .015 13.348 122.355 .049 .132 .067 .946 1.057

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71Table B-7 Continued. (Constant) 405.039 50.523 8.017 .000 305.662 504.417 Bath 157.118 24.851 .331 6.323 .000 108.237 205.999 .759 .325 .172 .270 3.704 Pet -126.794 33.394 -.115 -3.797 .000 -192.480 -61.109 -.398 -.202 .103 .805 1.242 Clubhouse 129.351 24.885 .170 5.198 .000 80.403 178.299 .379 .272 .141 .689 1.452 OneMile 340.970 41.359 .244 8.244 .000 259.617 422.322 .133 .409 .224 .844 1.184 Bed 143.387 19.494 .368 7.355 .000 105.042 181.731 .715 .371 .200 .295 3.386 Age -4.255 1.185 -.117 -3.591 .000 -6.587 -1.924 -.338 -.191 .098 .702 1.424 Furnished 102.976 24.258 .126 4.245 .000 55.260 150.692 .364 .225 .116 .838 1.193 FourMile 66.091 27.281 .069 2.423 .016 12.430 119.752 .078 .130 .066 .912 1.096 Fire p lace 67.066 27.589 .068 2.431 .016 12.800 121.333 .049 .131 .066 .946 1.057 10 TownHouse -58.985 29.305 -.059 -2.013 .045 -116.627 -1.343 .054 -.109 .055 .849 1.178 Dependent Variable: Price

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72 Table B-8 Excluded variables. Collinearity Statistics Model Beta In t Sig. Partial Correlation Tolerance VIF Minimum Tolerance Studio -.060a -1.679 .094 -.090 .964 1.037 .964 Be d .272a 4.441 .000 .232 .308 3.252 .308 TownHouse -.072a -2.037 .042 -.109 .974 1.027 .974 PatioBalcon y .107a 3.091 .002 .164 .995 1.005 .995 Pet -.200a -5.748 .000 -.295 .921 1.086 .921 Furnishe d .175a 5.009 .000 .260 .930 1.076 .930 Dishwashe r .090a 2.508 .013 .133 .931 1.074 .931 Laundr y Room -.035a -.987 .324 -.053 .965 1.036 .965 WasherDr y e r .046a 1.257 .210 .067 .892 1.122 .892 Fire p lace .048a 1.388 .166 .074 1.000 1.000 1.000 Pool .017a .489 .625 .026 .969 1.032 .969 Tennis .099a 2.795 .005 .148 .943 1.060 .943 Clubhouse .191a 5.485 .000 .282 .929 1.076 .929 A g e -.171a -4.935 .000 -.256 .946 1.057 .946 OneMile .153a 4.494 .000 .235 .999 1.001 .999 TwoMile -.070a -2.022 .044 -.108 .994 1.006 .994 ThreeMile -.057a -1.649 .100 -.088 1.000 1.000 1.000 FourMile .090a 2.588 .010 .138 1.000 1.000 1.000 1 FourPlusMiles -.057a -1.617 .107 -.086 .992 1.008 .992

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73Table B-8 Continued. Studio -.074b -2.191 .029 -.117 .959 1.043 .883 Be d .286 b 4.896 .000 .255 .307 3.256 .296 TownHouse -.039 b -1.133 .258 -.061 .944 1.060 .882 PatioBalcon y .099 b 2.975 .003 .158 .993 1.007 .918 Furnishe d .140 b 4.041 .000 .212 .890 1.124 .881 Dishwashe r .111 b 3.227 .001 .171 .922 1.084 .849 Laundr y Room -.027 b -.796 .426 -.043 .963 1.038 .894 WasherDr y e r .032 b .900 .369 .048 .887 1.128 .838 Fire p lace .060 b 1.800 .073 .096 .996 1.004 .917 Pool .057 b 1.659 .098 .089 .933 1.072 .874 Tennis .107 b 3.152 .002 .167 .942 1.062 .868 Clubhouse .183 b 5.510 .000 .284 .928 1.078 .865 A g e -.144 b -4.250 .000 -.223 .923 1.083 .889 OneMile .133 b 4.060 .000 .213 .988 1.013 .910 TwoMile -.050 b -1.481 .139 -.079 .982 1.018 .910 ThreeMile -.048 b -1.434 .152 -.077 .997 1.003 .918 FourMile .070 b 2.091 .037 .112 .988 1.012 .910 2 FourPlusMiles -.052b -1.542 .124 -.083 .991 1.009 .912

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74Table B-8 Continued. Studio -.046c -1.402 .162 -.075 .934 1.070 .843 Be d .296c 5.317 .000 .275 .307 3.260 .288 TownHouse -.044c -1.337 .182 -.072 .943 1.060 .832 PatioBalcon y .054c 1.617 .107 .087 .918 1.089 .858 Furnishe d .107c 3.131 .002 .166 .855 1.170 .844 Dishwashe r .069c 2.019 .044 .108 .865 1.156 .822 Laundr y Room -.018c -.545 .586 -.029 .961 1.041 .845 WasherDr y e r -.009c -.270 .788 -.015 .845 1.184 .811 Fire p lace .055c 1.717 .087 .092 .996 1.004 .865 Pool -.007c -.210 .834 -.011 .819 1.220 .815 Tennis .027c .722 .471 .039 .731 1.367 .720 A g e -.078c -2.120 .035 -.113 .744 1.344 .744 OneMile .192c 6.035 .000 .309 .922 1.084 .865 TwoMile -.037c -1.130 .259 -.061 .977 1.024 .864 ThreeMile -.045c -1.416 .158 -.076 .997 1.003 .865 FourMile .049c 1.510 .132 .081 .974 1.027 .860 3 FourPlusMiles -.077c -2.385 .018 -.127 .973 1.028 .862

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75Table B-8 Continued. Studio -.087d -2.723 .007 -.145 .899 1.113 .841 Be d .337 d 6.416 .000 .327 .303 3.299 .285 TownHouse -.081 d -2.557 .011 -.137 .912 1.096 .832 PatioBalcon y .060 d 1.894 .059 .102 .917 1.090 .808 Furnishe d .114 d 3.516 .000 .186 .854 1.171 .838 Dishwashe r .072 d 2.199 .029 .118 .865 1.156 .817 Laundr y Room -.008 d -.251 .802 -.014 .958 1.044 .845 WasherDr y e r -.009 d -.278 .781 -.015 .845 1.184 .811 Fire p lace .027 d .866 .387 .047 .971 1.030 .862 Pool .015 d .446 .656 .024 .810 1.235 .780 Tennis .049 d 1.377 .169 .074 .724 1.381 .696 A g e -.113 d -3.188 .002 -.169 .728 1.374 .725 TwoMile -.010 d -.322 .747 -.017 .957 1.045 .856 ThreeMile -.020 d -.647 .518 -.035 .978 1.023 .864 FourMile .075 d 2.422 .016 .129 .957 1.045 .860 4 FourPlusMiles -.040d -1.252 .212 -.067 .930 1.075 .861

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76Table B-8 Continued. Studio -.002e -.059 .953 -.003 .728 1.374 .245 TownHouse -.075e -2.504 .013 -.134 .911 1.097 .280 PatioBalcon y .058e 1.927 .055 .103 .917 1.090 .285 Furnishe d .115e 3.757 .000 .199 .854 1.171 .283 Dishwashe r .052e 1.684 .093 .091 .856 1.168 .285 Laundr y Room -.004e -.123 .902 -.007 .958 1.044 .284 WasherDr y e r .005e .143 .886 .008 .841 1.189 .274 Fire p lace .050e 1.693 .091 .091 .958 1.044 .282 Pool -.012e -.361 .718 -.019 .796 1.256 .285 Tennis .042e 1.226 .221 .066 .723 1.383 .285 A g e -.129e -3.880 .000 -.205 .724 1.382 .282 TwoMile -.010e -.334 .738 -.018 .957 1.045 .285 ThreeMile -.041e -1.395 .164 -.075 .966 1.035 .283 FourMile .084e 2.885 .004 .154 .954 1.048 .285 5 FourPlusMiles -.033e -1.108 .269 -.060 .929 1.076 .284

PAGE 77

77Table B-8 Continued. Studio .008f .251 .802 .014 .723 1.383 .243 TownHouse -.060 f -2.024 .044 -.109 .894 1.119 .275 PatioBalcon y .066 f 2.225 .027 .119 .913 1.095 .282 Furnishe d .112 f 3.736 .000 .198 .853 1.172 .280 Dishwashe r .024 f .774 .440 .042 .804 1.244 .282 Laundr y Room .033 f 1.073 .284 .058 .873 1.145 .282 WasherDr y e r -.026 f -.826 .409 -.045 .791 1.265 .273 Fire p lace .059 f 2.049 .041 .110 .952 1.051 .279 Pool .010 f .303 .762 .016 .772 1.295 .282 Tennis .065 f 1.941 .053 .104 .702 1.424 .281 TwoMile -.006 f -.219 .827 -.012 .956 1.046 .282 ThreeMile -.018 f -.619 .537 -.033 .923 1.083 .281 FourMile .076 f 2.641 .009 .141 .949 1.054 .282 6 FourPlusMiles -.049f -1.669 .096 -.090 .912 1.096 .281

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78Table B-8 Continued. Studio .007g .205 .838 .011 .723 1.383 .243 TownHouse -.074g -2.516 .012 -.135 .882 1.134 .274 PatioBalcon y .072g 2.503 .013 .134 .910 1.099 .280 Dishwashe r .020g .657 .512 .036 .803 1.246 .279 Laundr y Room .016g .520 .603 .028 .853 1.173 .279 WasherDr y e r -.023g -.728 .467 -.039 .790 1.266 .270 Fire p lace .067g 2.365 .019 .127 .947 1.056 .276 Pool .001g .022 .982 .001 .768 1.302 .280 Tennis .057g 1.720 .086 .093 .699 1.431 .278 TwoMile .000g .002 .998 .000 .952 1.050 .280 ThreeMile -.024g -.827 .409 -.045 .921 1.086 .278 FourMile .079g 2.781 .006 .149 .948 1.055 .280 7 FourPlusMiles -.055g -1.884 .060 -.101 .910 1.098 .279 Studio .006h .187 .852 .010 .723 1.383 .243 TownHouse -.060 h -2.033 .043 -.110 .849 1.177 .274 PatioBalcon y .069 h 2.392 .017 .129 .908 1.102 .280 Dishwashe r .020 h .658 .511 .036 .803 1.246 .279 Laundr y Room .012 h .414 .679 .022 .851 1.175 .279 WasherDr y e r -.017 h -.558 .577 -.030 .787 1.271 .270 Fire p lace .069 h 2.449 .015 .132 .946 1.057 .276 Pool .019 h .595 .552 .032 .737 1.358 .280 Tennis .064 h 1.950 .052 .105 .695 1.439 .278 TwoMile .018 h .632 .528 .034 .906 1.104 .280 ThreeMile -.006 h -.201 .841 -.011 .872 1.147 .278 8 FourPlusMiles -.023h -.726 .469 -.039 .728 1.374 .279

PAGE 79

79Table B-8 Continued. Studio .006i .183 .855 .010 .723 1.383 .240 TownHouse -.059 i -2.013 .045 -.109 .849 1.178 .270 PatioBalcon y .058 i 2.012 .045 .109 .881 1.135 .276 Dishwashe r .010 i .336 .737 .018 .788 1.269 .276 Laundr y Room .011 i .356 .722 .019 .851 1.176 .275 WasherDr y e r -.008 i -.272 .786 -.015 .775 1.290 .265 Pool .013 i .420 .675 .023 .733 1.365 .276 Tennis .055 i 1.672 .095 .090 .685 1.461 .275 TwoMile .024 i .832 .406 .045 .900 1.111 .276 ThreeMile -.004 i -.137 .891 -.007 .871 1.148 .274 9 FourPlusMiles -.029i -.912 .362 -.049 .724 1.382 .275 Studio .005j .153 .879 .008 .723 1.384 .239 PatioBalcon y .055 j 1.913 .057 .103 .879 1.138 .270 Dishwashe r .019 j .627 .531 .034 .772 1.295 .270 Laundr y Room .013 j .432 .666 .023 .849 1.177 .269 WasherDr y e r .002 j .076 .940 .004 .752 1.329 .262 Pool .014 j .428 .669 .023 .733 1.365 .270 Tennis .051 j 1.566 .118 .085 .682 1.465 .269 TwoMile .032 j 1.101 .271 .060 .886 1.129 .270 ThreeMile .004 j .120 .904 .007 .857 1.167 .268 10 FourPlusMiles -.045j -1.375 .170 -.075 .691 1.446 .269 a. Predictors in the Model: (Constant), Bath b. Predictors in the Model: (Constant), Bath, Pet c. Predictors in the Model: (Constant), Bath, Pet, Clubhouse d. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile e. Predictors in the Model: (Constant ), Bath, Pet, Clubhouse, OneMile, Bed

PAGE 80

80Table B-8 Continued. f. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age g. Predictors in the Model: (Constant), Bat h, Pet, Clubhouse, OneMile, Bed, Age, Furnished h. Predictors in the Model: (Constant), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile i. Predictors in the Model: (Consta nt), Bath, Pet, Clubhouse, OneMile, Be d, Age, Furnished, FourMile, Fireplace j. Predictors in the Model: (Constant ), Bath, Pet, Clubhouse, OneMile, Bed, Age, Furnished, FourMile, Fireplace, TownHouse k. Dependent Variable: Price

PAGE 81

81Table B-9. Coefficient correlations Model Bath Pet Clubhouse OneMile Bed Age Furnished FourMile Fireplace TownHouse Correlations Bath 1.000 1 Covariances Bath 274.083 Bath 1.000 .281 Correlations Pet .281 1.000 Bath 272.577 178.081 2 Covariances Pet 178.081 1468.342 Bath 1.000 .263 -.247 Pe t .263 1.000 .038 Correlations Clubhouse -.247 .038 1.000 Bath 267.590 158.513 -101.909 Pe t 158.513 1355.776 35.538 3 Covariances Clubhouse -101.909 35.538 638.063 Bath 1.000 .260 -.241 -.010 Pe t .260 1.000 .068 .122 Clubhouse -.241 .068 1.000 .257 Correlations OneMile -.010 .122 .257 1.000 Bath 242.767 143.102 -93.475 -7.132 Pe t 143.102 1248.432 59.817 191.235 Clubhouse -93.475 59.817 619.810 284.275 4 Covariances OneMile -7.132 191.235 284.275 1971.217

PAGE 82

82Table B-9. Continued Bath 1.000 .168 -.187 -.096 -.819 Pe t .168 1.000 .066 .119 -.023 Clubhouse -.187 .066 1.000 .262 .060 OneMile -.096 .119 .262 1.000 .110 Correlations Bed -.819 -.023 .060 .110 1.000 Bath 659.082 144.496 -113.353 -103.669 -429.443 Pe t 144.496 1118.839 52.484 167.697 -15.868 Clubhouse -113.353 52.484 557.159 261.155 28.809 OneMile -103.669 167.697 261.155 1787.072 94.596 5 Covariances Bed -429.443 -15.868 28.809 94.596 417.590 Bath 1.000 .146 -.127 -.111 -.820 .108 Pe t .146 1.000 -.009 .142 -.010 -.171 Clubhouse -.127 -.009 1.000 .174 .025 .400 OneMile -.111 .142 .174 1.000 .119 -.156 Bed -.820 -.010 .025 .119 1.000 -.073 Correlations Age .108 -.171 .400 -.156 -.073 1.000 Bath 640.643 123.268 -81.327 -117.422 -416.587 3.314 Pe t 123.268 1107.449 -7.255 198.393 -6.869 -6.932 Clubhouse -81.327 -7.255 637.534 184.814 12.829 12.289 OneMile -117.422 198.393 184.814 1759.624 100.480 -7.943 Bed -416.587 -6.869 12.829 100.480 403.351 -1.785 6 Covariances Age 3.314 -6.932 12.289 -7.943 -1.785 1.477

PAGE 83

83Table B-9. Continued Bath 1.000 .124 -.111 -.113 -.816 .105 -.092 Pe t .124 1.000 -.040 .145 -.009 -.163 .200 Clubhouse -.111 -.040 1.000 .167 .024 .391 -.158 OneMile -.113 .145 .167 1.000 .119 -.155 .031 Bed -.816 -.009 .024 .119 1.000 -.073 .003 A g e .105 -.163 .391 -.155 -.073 1.000 .025 Correlations Furnished -.092 .200 -.158 .031 .003 .025 1.000 Bath 622.553 103.540 -69.327 -116.059 -401.573 3.126 -56.154 Pe t 103.540 1111.588 -33.354 199.659 -6.187 -6.482 163.754 Clubhouse -69.327 -33.354 629.968 173.054 12.106 11.724 -97.132 OneMile -116.059 199.659 173.054 1697.203 96.905 -7.616 31.269 Bed -401.573 -6.187 12.106 96.905 388.675 -1.718 1.588 A g e 3.126 -6.482 11.724 -7.616 -1.718 1.424 .728 7 Covariances Furnished -56.154 163.754 -97.132 31.269 1.588 .728 603.337 Bath 1.000 .125 -.111 -.111 -.815 .105 -.091 .009 Pe t .125 1.000 -.045 .157 -.005 -.153 .201 .106 Clubhouse -.111 -.045 1.000 .160 .022 .386 -.158 -.047 OneMile -.111 .157 .160 1.000 .124 -.143 .034 .125 Bed -.815 -.005 .022 .124 1.000 -.069 .004 .043 A g e .105 -.153 .386 -.143 -.069 1.000 .027 .079 Furnished -.091 .201 -.158 .034 .004 .027 1.000 .023 Correlations FourMile .009 .106 -.047 .125 .043 .079 .023 1.000 Bath 610.580 102.294 -68.240 -112.720 -393.642 3.085 -54.949 5.798 Pe t 102.294 1102.490 -36.844 213.846 -3.126 -6.029 162.567 95.206 Clubhouse -68.240 -36.844 619.189 163.680 10.889 11.388 -95.917 -31.838 OneMile -112.720 213.846 163.680 1690.770 99.328 -6.990 33.547 138.955 Bed -393.642 -3.126 10.889 99.328 381.872 -1.607 2.028 22.656 A g e 3.085 -6.029 11.388 -6.990 -1.607 1.405 .766 2.526 Furnished -54.949 162.567 -95.917 33.547 2.028 .766 592.005 15.207 8 Covariances FourMile 5.798 95.206 -31.838 138.955 22.656 2.526 15.207 733.264

PAGE 84

84Table B-9. Continued Bath 1.000 .130 -.098 -.095 -.818 .113 -.099 .006 -.116 Pe t .130 1.000 -.039 .162 -.011 -.149 .197 .105 -.051 Clubhouse -.098 -.039 1.000 .170 .010 .391 -.164 -.049 -.100 OneMile -.095 .162 .170 1.000 .106 -.132 .024 .121 -.125 Bed -.818 -.011 .010 .106 1.000 -.078 .013 .045 .126 A g e .113 -.149 .391 -.132 -.078 1.000 .021 .077 -.076 Furnished -.099 .197 -.164 .024 .013 .021 1.000 .025 .072 FourMile .006 .105 -.049 .121 .045 .077 .025 1.000 .021 Correlations Fireplace -.116 -.051 -.100 -.125 .126 -.076 .072 .021 1.000 Bath 610.017 105.645 -60.147 -96.327 -395.026 3.300 -59.138 4.091 -79.605 Pe t 105.645 1089.394 -32.154 219.396 -7.216 -5.790 157.305 92.882 -46.566 Clubhouse -60.147 -32.154 616.369 174.032 4.645 11.447 -98.823 -32.776 -68.553 OneMile -96.327 219.396 174.032 1692.773 85.248 -6.426 24.149 134.045 -142.417 Bed -395.026 -7.216 4.645 85.248 382.410 -1.805 6.265 23.719 68.172 A g e 3.300 -5.790 11.447 -6.426 -1.805 1.393 .599 2.439 -2.499 Furnished -59.138 157.305 -98.823 24.149 6.265 .599 586.464 15.968 48.055 FourMile 4.091 92.882 -32.776 134.045 23.719 2.439 15.968 722.996 15.654 9 Covariances Fireplace -79.605 -46.566 -68.553 -142.417 68.172 -2.499 48.055 15.654 767.815 Bath 1.000 .152 -.080 -.073 -.816 .130 -.081 -.022 -.117 -.145 Pe t .152 1.000 -.018 .182 -.020 -.122 .212 .067 -.053 -.178 Clubhouse -.080 -.018 1.000 .184 .004 .400 -.149 -.070 -.101 -.116 OneMile -.073 .182 .184 1.000 .098 -.112 .039 .091 -.126 -.139 Bed -.816 -.020 .004 .098 1.000 -.084 .007 .054 .126 .051 A g e .130 -.122 .400 -.112 -.084 1.000 .035 .050 -.078 -.130 Furnished -.081 .212 -.149 .039 .007 .035 1.000 .003 .070 -.111 FourMile -.022 .067 -.070 .091 .054 .050 .003 1.000 .023 .193 Fire p lace -.117 -.053 -.101 -.126 .126 -.078 .070 .023 1.000 .014 10 Correlations TownHouse -.145 -.178 -.116 -.139 .051 -.130 -.111 .193 .014 1.000

PAGE 85

85 Table B-9. Continued Bath 617.550 126.143 -49.200 -74.841 -395.125 3.827 -48.937 -14.887 -80.301 -105.492 Pe t 126.143 1115.167 -14.644 251.570 -13.129 -4.818 171.911 60.720 -48.475 -174.511 Clubhouse -49.200 -14.644 619.253 189.059 1.701 11.792 -90.170 -47.705 -69.071 -84.764 OneMile -74.841 251.570 189.059 1710.555 78.738 -5.482 39.336 102.699 -143.386 -167.934 Bed -395.125 -13.129 1.701 78.738 380.016 -1.944 3.512 28.789 67.957 29.413 A g e 3.827 -4.818 11.792 -5.482 -1.944 1.405 1.009 1.603 -2.537 -4.530 Furnished -48.937 171.911 -90.170 39.336 3.512 1.009 588.471 1.683 46.580 -78.761 FourMile -14.887 60.720 -47.705 102.699 28.789 1.603 1.683 744.254 17.567 154.202 Fire p lace -80.301 -48.475 -69.071 -143.386 67.957 -2.537 46.580 17.567 761.138 11.431 Covariances TownHouse -105.492 -174.511 -84.764 -167.934 29.413 -4.530 -78.761 154.202 11.431 858.769 a. Dependent Variable: Price

PAGE 86

86 Table B-10 Collinearity diagnostics Variance Proportions Model Dimension Eigenvalue Condition Index (Constant) Bath Pet Clubhouse One Mile Bed Age Furnished FourMile Fireplace TownHouse 1 1.914 1.000 .04 .04 1 2 .086 4.723 .96 .96 1 2.768 1.000 .01 .02 .01 2 .194 3.782 .00 .42 .30 2 3 .039 8.453 .99 .57 .68 1 3.391 1.000 .00 .01 .01 .03 2 .395 2.929 .01 .00 .07 .80 3 .175 4.396 .00 .48 .24 .17 3 4 .039 9.373 .99 .50 .69 .00 1 3.460 1.000 .00 .01 .01 .02 .01 2 .975 1.883 .00 .00 .00 .04 .79 3 .357 3.114 .01 .00 .09 .71 .14 4 .171 4.499 .00 .51 .21 .21 .03 4 5 .037 9.617 .99 .48 .69 .01 .03 1 4.343 1.000 .00 .00 .01 .01 .00 .00 2 .977 2.108 .00 .00 .00 .03 .78 .00 3 .359 3.477 .01 .00 .07 .78 .14 .00 4 .252 4.151 .01 .04 .19 .12 .01 .06 5 .041 10.231 .85 .01 .64 .04 .06 .19 5 6 .027 12.654 .14 .95 .10 .01 .00 .75

PAGE 87

87 Table B-10 Continued 1 4.973 1.000 .00 .00 .00 .01 .00 .00 .01 2 1.028 2.199 .00 .00 .00 .05 .61 .00 .02 3 .527 3.072 .00 .00 .02 .22 .31 .00 .16 4 .264 4.338 .00 .04 .06 .37 .02 .06 .03 5 .143 5.907 .01 .00 .38 .26 .01 .00 .68 6 .039 11.294 .75 .03 .42 .10 .04 .26 .07 6 7 .026 13.762 .23 .93 .11 .00 .00 .67 .04 1 5.346 1.000 .00 .00 .00 .01 .00 .00 .01 .01 2 1.074 2.231 .00 .00 .00 .04 .50 .00 .02 .05 3 .687 2.789 .00 .00 .02 .01 .29 .00 .04 .42 4 .432 3.519 .00 .00 .00 .30 .14 .00 .12 .41 5 .257 4.556 .00 .05 .05 .29 .01 .07 .05 .05 6 .140 6.187 .01 .00 .38 .28 .01 .00 .65 .03 7 .038 11.895 .75 .04 .45 .07 .04 .25 .07 .03 7 8 .026 14.268 .23 .91 .11 .00 .00 .67 .04 .00 1 5.549 1.000 .00 .00 .00 .01 .00 .00 .00 .01 .01 2 1.132 2.214 .00 .00 .00 .03 .42 .00 .02 .02 .10 3 .764 2.695 .00 .00 .00 .02 .01 .00 .01 .20 .64 4 .668 2.882 .00 .00 .02 .00 .38 .00 .04 .25 .19 5 .428 3.599 .00 .00 .00 .31 .12 .00 .11 .41 .02 6 .257 4.648 .00 .05 .05 .30 .01 .07 .05 .04 .00 7 .140 6.306 .01 .00 .38 .27 .01 .00 .65 .03 .00 8 .036 12.401 .72 .05 .43 .06 .05 .29 .07 .03 .05 8 9 .026 14.571 .26 .90 .12 .00 .00 .63 .05 .00 .01

PAGE 88

88 Table B-10 Continued 1 5.752 1.000 .00 .00 .00 .01 .00 .00 .00 .01 .01 .01 2 1.190 2.198 .00 .00 .00 .03 .33 .00 .02 .03 .08 .08 3 .791 2.697 .00 .00 .00 .00 .05 .00 .00 .16 .33 .34 4 .733 2.800 .00 .00 .00 .02 .16 .00 .01 .02 .38 .40 5 .659 2.955 .00 .00 .02 .01 .26 .00 .05 .24 .12 .10 6 .421 3.696 .00 .00 .00 .28 .13 .00 .10 .44 .02 .04 7 .253 4.769 .00 .04 .05 .32 .02 .07 .05 .03 .00 .02 8 .140 6.421 .01 .00 .37 .27 .01 .00 .65 .03 .00 .00 9 .036 12.651 .74 .04 .43 .06 .04 .28 .07 .04 .05 .00 9 10 .026 14.929 .24 .91 .12 .00 .00 .65 .05 .00 .00 .01 1 5.985 1.000 .00 .00 .00 .01 .00 .00 .00 .01 .00 .01 .01 2 1.245 2.192 .00 .00 .00 .02 .26 .00 .01 .02 .13 .04 .06 3 .926 2.543 .00 .00 .00 .01 .04 .00 .01 .09 .19 .16 .24 4 .744 2.836 .00 .00 .00 .02 .25 .00 .01 .01 .09 .61 .01 5 .663 3.004 .00 .00 .02 .02 .24 .00 .05 .32 .02 .10 .02 6 .566 3.253 .00 .00 .00 .00 .00 .00 .01 .04 .50 .01 .60 7 .420 3.775 .00 .00 .00 .27 .13 .00 .10 .41 .03 .04 .01 8 .252 4.877 .00 .04 .05 .32 .02 .06 .05 .04 .01 .02 .01 9 .139 6.550 .01 .00 .36 .27 .01 .00 .64 .03 .00 .00 .00 10 .035 13.003 .68 .07 .40 .06 .05 .33 .07 .04 .03 .00 .01 10 11 .025 15.454 .30 .88 .16 .00 .00 .60 .06 .00 .00 .01 .03 a. Dependent Variable: Price

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89 Table B-11 Residuals statisticsa Minimum Maximum Mean Std. Deviation N Predicted Value $420.60 $1,968.29 $950.89 $328.290 350 Std. Predicted Value -1.615 3.099 .000 1.000 350 Standard Error of Predicted Value 18.617 65.783 33.165 8.335 350 Adjusted Predicted Value $419.26 $1,964.89 $950.93 $327.244 350 Residual $-492.065 $1,010.729 $1.754E-13 $190.095 350 Std. Residual -2.551 5.240 .000 .986 350 Stud. Residual -2.602 5.325 .000 1.005 350 Deleted Residual $-511.720 $1,043.648 $-.035 $197.584 350 Stud. Deleted Residual -2.624 5.554 .002 1.017 350 Mahal. Distance 2.254 39.599 9.971 5.742 350 Cook's Distance .000 .102 .004 .011 350 Centered Leverage Value .006 .113 .029 .016 350 a. Dependent Variable: Price

PAGE 90

90 Charts Figure B-1. Histogram

PAGE 91

91 Figure B-2. Normal P-P plot of regression standardized residual.

PAGE 92

92 Figure B-3. Scatter plot

PAGE 93

93 APPENDIX C AFTERWORD After the completion of this project, m any new apartment and condo projects were started within steps of where the recommendations in this paper were maid. The following is photographic evidence of the benefits for follo wing the algorithm for lo cating a building. The fact that there are new developments going in, that are in close proximity to my recommendations lends credence to the algorithm. Figure C-1. 13th Street and NW 7th Avenue location.

PAGE 94

94 Figure C-2. 13th Street and NW 7th Avenue. The image above is of the same complex from the back side, this complex is very large, stretching from 7th Street to 8th street, making it almost a full city block. It is assumed that it would be an entire city block, except the trophy shop refused to sale Figure C-3. Trophy shop surrounde d by new complex at NW 7th Avenue. The trophy shop is now completely surrounded by apartments

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95 Figure C-4. SW 2nd Avenue and 6Th Street Figure C-5. Alternate shot of SW 2nd Avenue and 6th Street. This is a shot of the same complex, both pictures for this complex show the extent of how large these buildings are, depending on its floor plan, this project has a very good chance at being successful based on its location. Both of the sites in the pictures above, fit with the rules set up in the algorithm, which leads me to believe that the algorithm is correct in its assumptions.

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96 LIST OF REFERENCES Apartm ent Finder, Network Communications, Inc., December 13, Issue 1. www.apartmentfinder.com Applebaum William, Methods for determinin g Store Trade Areas, Market Penetration, and Potential Sales. Journal of Marketin g Research, Vol. 3, No. 2 (May, 1966), pp. 127141. Asabere, Paul K., Forrest E. Huffman, Thoroughf ares and Apartment Values, The Journal of Real Estate Research, Volume 12, Number 1, 1996. Benjaimn, John D., G. Stacy Sirmans and C.F. Sirmans Determining Apartment Rent: The Value of Amenities, Services and External Factors The Journal of Real Estate Research, Volume 4, Number 2, 1989. Benjamin, John D., G. Stacy Sirmans Mass Transportation, Apartment Rent andProperty Values The Journal of Real Es tate Research, Volume 1, 1996. Bible, Douglas S., Cheng-Ho Hsieh, Applications of Geographic Information Systems for the Analysis of Apartment Rents, The Jour nal of Real Estate Research, Volume 12, Number 1, 1996. Bussa, Robert G., Austin J. Jaffe, Using a Si mple Model to Estimate Market Rents: A case Study, The Appraisal Journal, Vol. 45, 1977 Cervero, Robert, Michael Duncan, Walking, Bi cycling, and Urban Landscapes: Evidence from the San Francisco Bay Area American Jour nal of Public Health, Vol. 93, no. # 9, September, 2003 Community Tapestry Handbook, ESRI, Geographic Information Systems, 2007. www.esri.com/library/brochures/ pdfs/community-tapestry-handbook.pdf Crapo, Ed. Alachua County Land Parcel Databa se, Alachua County Property Appraiser, 2006. www.acpafl.org/search.html Darden, William R., Fred D. Reynolds, Inter market Patronage: A Psychographic Study of Consumer Outshoppers. Journal of Mark eting, Vol. 36, No.4. (Oct., 1972), pp. 50-54. Dutta-Bergman, Mohann J., Beyond Demographi c Variables: Using Psychographic Research to Narrate the Story of Inte rnet Users. Studies In Me dia & Information Literacy Education (Simile) August 2002, Vol.2, Issue 3. Enrollment by Major: Fall 2007, Table I-1.a, Th e office of instituti onal Research at the University of Florida, www.ir.ufl.edu/factbook/i-01.a_hist.xls Frew, Jam es R., G. Donald Jud, and Daniel T. Winkler, Atypicalities and Apartment Rent Concessions The Journal of Real Es tate Research, Volume 5, Number 2, 1990.

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97 Frew, James, Beth Wilson, Estimating the c onnection between locati on and Property value, Journal of Real Estate Practice and educa tion; 2002; 5, 1: ABI/INFORM Global pg. 17. Gainesville, Florida, Sp erlings Best Places, 2005, www.bestplaces.net/city /Gainesville_Fl51225175010.aspx Gainesvilles Premier Apartment Guide, Paradigm Properties, January-December, 2007. www.apartment4gators.com Gunterm ann, Karl L., Stefan Norrbin, Expl aining the Variability of Apartment Rents AREUEA Journal, Winter 1987; 15,4; ABI/INFORM Global Haydam, Norbert, Nancy Nuntsu, and Dimitri Ta ssiopoulos,Wine Tourists in South Africa: A Demographic and Psychographic Study, Journal of Wine Research, 2004, Vol. 15, No.1, pp.51-63. Historical Enrollment: Fall Terms 1905 to 2007, Table 1-5.a, The office of institutional Research at the University of Florida, www.ir.ufl.edu/factbook/i-05.a_hist.xls. Kain, John F., John M. Quigley, Measuring the Value of Housing Quality. Journal of the American Statistical Associati on, Vol. 65, No. 330. (June., 1970), pp. 532 548. Knapp, Garrit-Jan, Yang Song, New urbanism a nd housing values: a disaggregate Assessment Journal of Urban Economics 54 (2003). Lewis, Jim. Telephone Interview. Office of Inst itutional Research, Santa Fe Community College, 2008. Malpezzi, Stephen, Hedonic Pricing Models: A Selective and Applied Review. The Center for Urban Land Economics, The University of Wisconsin, April, 2002. Ogur, Jonathan D., Higher Education and Housi ng: The Impact of Colleges and Universities on local Rental Housing Markets American Journal of Economics and Sociology, Vol. 32, No. 4. 1973 Pagliari, Joseph L. Jr,. James R. Webb On Setting Apartment Rental Rates: A Regression Based Approach. The Journal Of Real Estate Research, Volume 12, Number 1, 1996. Perry, Marc J. State to State Migration Flows: 2000 to 2005, August, 2003, US Census Bureau, www.census.gov/prod/2003pubs/censr-8.pdf Rogerson, Peter A., Statistical Methods for Geography, Sage Publications, London, 2004. Schenker, P amela, Florida Demographic Study, Florida Legislature, Office of Economic and Demographic Research. November, 2007.

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98 Schwartz, Arthur L. Jr., Greg T. Smersh, and Marc T. Smith, Factors Affecting Residential Property Development Patterns. The Journa l of Real Estate Re search, Jan-Mar 2003, Vol. 25, Issue 1, pg 61. SPSS for Windows, Rel.16.0.2007.Chicago: SPSS Inc. State Summaries, Gainesville, FL, United St ates Department of Agriculture, Agriculture Marketing Service, www.ams.usda.gov/statesummari es/Fl/MSA/MSA.pdf/gainesville.pdf The Gainesville Apartm ent & Condominium Guide, Quest P ublications, Issue 74, Fall, 2007. www.gainsville-rent.com Thrall, Grant Ian, Business Geography and Ne w Real Estate Market Analysis, Oxford University Press. New York, New York, 2002. Weathers, Robert, Lin Lin, Donna Johnson, Chanam Lee, Chez Garvin, Allen Cheadle, et al. Operational Definitions of Walkable Neighborhoods: Theoretical and Empirical Insights. Journal of Physic al Activity and Health, 3, 2006.

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99 BIOGRAPHICAL SKETCH Gabriel Bolden was born in San Diego, California, February 19th, 1981. He was raised there by his single Mother and Grandparents. An active child, Gabriel played many sports growing up. In high school, he attended Univers ity City High School, where he was the captain of the soccer team and a member of the golf t eam. In high school, Gabriel was not focused on academics, it was not because he did not have the aptitude, rather it was because he felt the education system he was a part of was developed for some one else. Although following graduation, he re alized that without a good education he would be just another stereotype from Clairem ont (his neighborhood in San Diego). With little other options, Gabriel attended the local community college calle d Mesa. Gabriel made a promise to himself that he would graduate in two years and transf er to a good university. Reinvigorated, Gabriel kept that promise he made to himself, finishi ng Mesa Community College in exactly two years, he was then accepted to the University of Los Angeles, California. At UCLA, Gabriel majored in Geography, where he met faculty who would change his life forever. Gabriel worked as the librarian of the geography library, and finished up his course work in two years. So in four years he went fr om a high school graduate w ith little prospects to a graduate from one of the best public universities in the Unit ed States. Following graduation from UCLA, and on the recommendation of Dr. Tom Gillespie and Dr. Judith Carney, he traveled through central America fo r three months. It was in Guatemala that he heard of a local plant called tres puntas, which was sort of cure all herbal re medy used by the Garifuna people as well as the natives of central America. This peaked his interest in studying the Garifuna people and their plant in graduate school. Upon returning to the United States, he cons ulted professors Gillespie and Carney at UCLA about going to grad school outside of Califo rnia in order to broaden his horizons, he had

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100 one stipulation though, where ever he went it ha d to be warm. Both professors recommended that he apply to the University of Florida because of the faculty and his underlying wish to be warm. Gabriel applied to the University of Florida and was accepted. He had never seen the campus or even ever been to Florida but that was not going to stop him. He got on a train and five days later he was in Flor ida ready to start studying the Ga rifuna and central America through cultural geography. Although, not everything goes as planned, his first semester was horrible, he wasnt quite ready to a grad student and it showed in his behavior. Gabriel found out that there was not any faculty members working with the garifuna and central America, which meant there was no one to work with, that compiled with hi s horrible semester and pathetic GPA made him contemplate quitting graduate school. Going home over the break and having to expl ain himself to his family reinforced the belief that quitting was not an option. So instea d of pursuing academia for a career, he decided that he would end his education after he attained a masters degree. He decided that economic geography was the way to go after ch ecking the titles of upcoming classes. It is at this point in which he took Dr. Grant Ian Thralls introdu ctory economic geography course, immediately taking to the material presented in the course, he was satisfied with the choice he made. Being in the economic geography discipline reinvigorated hi m which in turn reinvigorated his GPA. While working on his course work and thesis, Gabriel worked for SeaGrant, as well as helped Dr. Thrall with research for his consul ting business. Throughout this process Dr. Thrall became his graduate thesis chair, and even though he took his time, he eventually wrote a thesis on apartment location in Gainesville, Florida. The thesis was unique as it wa s the first to give a specific set of steps to locate an apartment building; he was able to use di fferent techniques, like the use of GIS, psychographics, and statistics to come up with the best location for a new

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101 apartment building in Gainesville, Florida. Th e steps taken in the thesis can be applied to locating an apartment or any other type of building anyw here in the United States, because most of the software used is available to the public this information can help professionals make better real estate decisions, therefore simplifying the decision making process.


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