ESTIMATING THE VALUE OF LIGHT RAIL PROXIMITY IN SACRAMENTO
A Thesis
Presented to the faculty of the Department of Economics
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS in
Economics by
Clifton Bachmeier
SUMMER
2013

© 2013
Clifton Bachmeier
ALL RIGHTS RESERVED ii

ESTIMATING THE VALUE OF LIGHT RAIL PROXIMITY IN SACRAMENTO
A Thesis by
Clifton Bachmeier
Approved by:
__________________________________, Committee Chair
Dr. Katherine Chalmers
__________________________________, Second Reader
Dr. Craig Gallet
____________________________
Date iii

Student: Clifton Bachmeier
I certify that this student has met the requirements for format contained in the University format manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for the thesis.
__________________________, Graduate Coordinator ___________________
Dr. Kristin Kiesel Date
Department of Economics iv

Abstract of
ESTIMATING THE VALUE OF LIGHT RAIL PROXIMITY IN SACRAMENTO by
Clifton Bachmeier
Sacramento Regional Transit has many plans to expand its light rail system.
Understanding where the light rail is valued and where it is not valued should be beneficial in guiding future expansions. For this research, hedonic price models estimate how Sacramento County residents value their light rail system, how values for light rail may differ along the Blue and Gold lines, and how socio-economic and urban physical characteristics influence value for light rail, by measuring how rail proximity affects home values. Overall, immediate light rail proximity contributes to lower home-values in
Sacramento that become negligible with greater separation. Furthermore, light rail proximity contributes to higher home-values along the Gold Line and lower values along the Blue Line. Some socio-economic and urban physical characteristics affect how residents value rail proximity. Overall, light rail is not a highly valued amenity in
Sacramento, though some areas value it more than others do.
_______________________, Committee Chair
Dr. Katherine Chalmers
_______________________
Date v

ACKNOWLEDGEMENTS family.
I could not have done this without the love and support from my parents and
I would like to thank my advisor, Dr. Chalmers, for guiding me on the way to complete this thesis. I must also acknowledge Dr. Gallet for his extremely quick and helpful feedback on drafts of this thesis.
The last two years would not have been as enjoyable without the collaboration with my peers in the Masters program, or the most welcoming community at the Sac
State Catholic Newman Center.
I would like to thank Mr. Vice, my first economics teacher, who in high school reminded his students that “money makes the world go round” and to remember
“location, location, location.” Most of all his class got me interested in the subject of
Economics.
Last, but not least, I must thank Dr. Alejandra Cox Edwards at CSULB for encouraging me to continue my studies in economics beyond the undergraduate level. vi

TABLE OF CONTENTS
Page
Acknowledgements ..................................................................................................... vi
List of Tables .............................................................................................................. ix
List of Figures .............................................................................................................. xi
List of Maps .............................................................................................................. xiii
Chapter
1. INTRODUCTION ..........……………………………………………………….. 1
Problem Analysis and Background ................................................................... 1
Specific Relationship Examined ........................................................................3
Importance of Problem and Analysis ................................................................ 4
Summary of Results ..........................................................................................5
2. LITERATURE REVIEW ....................................................................................... 8
Review of Relevant Research ............................................................................8
Past Studies on Sacramento’s Light Rail ........................................................ 11
Hedonic Models on the Estimation of Light Rail Value................................. 12
Literature of Bid Rent Theory..........................................................................15
3. MODEL SET UP .................................................................................................. 17
4. DATA SUMMARY .............................................................................................. 22
Housing Data ...................................................................................................23
Locational Characteristics ................................................................................26 vii

Fixed Effect Variables .....................................................................................31
Kernel Variable ................................................................................................33
Data Summary Statistics ..................................................................................36
Graphical Description of Home-Values on Rail Proximity .............................42
5. EMPIRICAL ANALYSIS ......................................................................................49
Selection of Dependent Variable .....................................................................49
Specification of Independent Variables ...........................................................52
OLS vs. WLS ...................................................................................................65
Heteroskedasticity ............................................................................................70
Blue Line vs. Gold Line ...................................................................................75
Interaction Variables ........................................................................................77
Summary of Empirical Results ........................................................................80
6. CONCLUSION .......................................................................................................82
Appendix A Empirical Analysis Regressions ..............................................................86
References ................................................................................................................. 108 viii

LIST OF TABLES
Tables Page
4.1 List of Census Designated Places ................................................................... 32
4.2 List of neighborhoods ...................................................................................... 32
4.3
Description of variables ................................................................................... 34
4.4
Statistical summary of variables ...................................................................... 37
4.5
Median home-value per square foot by CDP ................................................... 38
4.6
Sample median home-values per SQFT vs. actual sales data for March 2012 39
4.7
Employment statistics by CDP in 2007 ........................................................... 41
4.8
Home-values per square foot on thresholds ..................................................... 47
5.1 OLS vs. WLS regression results ...................................................................... 67
5.2 Moments of residuals ....................................................................................... 73
5.3 Regressions comparing capitalization of Blue Line vs. Gold Line ................. 76
5.4 Regressions estimating impacts of physical and socio-economic variables on value for rail accessibility ................................................................................ 79
A.1 Regressions on various forms of SQFT ........................................................... 86
A.2 Regressions on various forms of LOTSIZE ..................................................... 88
A.3 Regressions on various forms of YRBLT ........................................................ 90
A.4 Regressions on various forms of HHINC and MEDINC ................................. 92
A.5 Regressions on various forms of POPDEN ..................................................... 94
A.6 Regressions on various forms of PCTRENT ................................................... 96 ix

A.7 Regressions on various forms of percent race variables .................................. 98
A.8 Regressions on various forms of DSTCBD ................................................... 100
A.9 Regressions on various forms of DSTHWY .................................................. 102
A.10 OLS and WLS with fixed effects ................................................................... 104 x

LIST OF FIGURES
Figures Page
4.1 Median scatter plot line of home-values per square foot on proximity to rail stations ............................................................................................................. 42
4.2 Predicted home-value per square foot on proximity to closest rail ................. 44
4.3 Predicted and median scatter plot home-values on proximity to Gold Line .... 44
4.4 Predicted and median scatter plot home-values on proximity to Blue Line .... 45
4.5 Predicted and median scatter plot home-values on proximity to closest rail ... 45
4.6 Median home-values per square foot by distance from light rail .................... 48
5.1 Scatter plot of log of home-values on SQFT with trend line ........................... 55
5.2 Scatter plot of log of home-values on lot size with trend line ......................... 56
5.3 Scatter plot of log of home-values on YRBLT with trend line ........................ 57
5.4 Scatter plot of log of home-values on HHINC with trend line ........................ 58
5.5 Scatter plot of log of home-values on MEDINC with trend line ..................... 59
5.6 Scatter plot of log of home-values on POPDEN with trend line ..................... 60
5.7 Scatter plot of log of home-values on PCTRENT with trend line ................... 61
5.8 Scatter plot of log of home-values on PCTBLACK with trend line ................ 62
5.9 Scatter plot of log of home-values on PCTHISP with trend line ..................... 62
5.10 Scatter plot of log of home-values on PCTASIAN with trend line ................. 62
5.11 Scatter plot of log of home-values on PCTOTHER with trend line ................ 62
5.12 Scatter plot of log of home-values on DSTCBD with trend line ..................... 63 xi

5.13 Scatter plot of log of home-values on DSTHWY with trend line .................... 64
5.14 Scatter plot of log of home-values on EMPGRAV with trend line ................. 65
5.15 OLS squared residuals on predicted values ..................................................... 71
5.16 WLS squared residuals on predicted values .................................................... 71
5.17 Distribution of OLS residuals against normal distribution .............................. 72
5.18 Distribution of WLS residuals against normal distribution ............................. 72 xii

LIST OF MAPS
Maps Page
4.1 Sacramento County Census Tracts, CDPs, Light Rail Lines and Stations ...... 23
4.2 Generating CDP variables................................................................................ 31 xiii

Chapter 1
INTRODUCTION
Problem Analysis and Background
California saw a significant increase in light rail systems in the 1980s with the
1 development of commuter rail in Sacramento, San Diego, and San Jose, with each starting construction in 1986 (Landis, Guhathakurta, & Zhang, 1994). Urban rail advocates across the west coast of the United States, including Sacramento, California have argued their case with New Urbanism ideals (Landis et al. 1994). New Urbanism argues that cities should be more densely populated, which is encouraged through the creation of urban rail transit infrastructure and housing that is more compact. New
Urbanism promotes communities that are automobile-independent where transportation is dependent on mass transit systems, including urban passenger rail systems, and other forms of transportation by foot and by bicycle. Two benefits of this land-use development are less pollution and shorter commutes. One problem for New Urbanism in Sacramento is that many residents prefer to live on their own lot of land in less compact cities.
Still, New Urbanism advocates pushed for light rail in Sacramento, though the suggested minimum threshold of population density levels to support a viable rail transit system is still lacking in the area. The Washington Natural Resources Defense Council suggests that population densities should be at least 9,472 residents per square mile to have a valuable rail transit system (Holtzclaw, 1994). Sacramento meets this threshold for only 19 out of 317 Census Tracts. Certainly not everyone in the county is an advocate for
New Urbanism. The discrepancy in preferences for how Sacramento should develop and

in what kind of neighborhoods Sacramento residents prefer to live, is a reason to believe
2 that the values residents put in the light rail may differ greatly. New Urbanism supporters may value it highly, while those who prefer to live in lower density areas may not care much for it and put no value or a discount value for the light rail. This study will estimate whether the light rail system is a desirable amenity in Sacramento County and how different exogenous characteristics may affect the value residents put in the rail system by measuring how much more or less homeowners are willing to spend for light rail accessibility by assessing home-value estimates for all of Sacramento County.
Sacramento’s Regional Transit Light Rail System started operating in 1987, connecting outer-city neighborhoods with Downtown Sacramento by rail in an effort to become less dependent on automobiles and to encourage New Urbanism like development. The light rail system connects northeastern and eastern suburbs of the region with Downtown Sacramento and south Sacramento. The Gold Line serves the eastern suburbs and the Blue Line serves the northeastern suburbs and south Sacramento.
The Gold Line added two new stations in 1994 to neighborhoods that originally were against the light rail (Gibson, 1994). The Gold Line was expanded east in 1998 with one station in Rancho Cordova and again in 2004 and 2005 with seven new stations reaching eastward from Rancho Cordova to Folsom. The Blue Line expanded to south Sacramento in 2003 with seven new stations to reach Meadowview Road. In 2006, the light rail expanded again to connect with Amtrak. Most recently, the new Green Line opened in
2012 to serve the River District in the northern Downtown Sacramento area.

3
Plans for future development of the light rail system include the expansion of the
Green Line to the Sacramento International Airport from Downtown and the expansion of the Blue Line to Cosumnes River College. The plan to connect the rail system to the
Sacramento International Airport has no timetable and faces budget problems. It is uncertain when that project would begin (Lillis, 2012). The immediate development plan is the Blue Line extension south to Cosumnes River College that requires 4.2 miles of additional rail. Construction of this development begins the summer of 2013 and should be operating in September 2015 (Blue Line to Cosumnes River College, n.d.).
The American Public Transportation Association reports that Sacramento’s light rail system serves 46,900 passengers per weekday, which is the 11 th
most for American light rail systems (Dickens, 2013). Overall, the Sacramento Light Rail system serves
Sacramento County with 37.5 miles of track and 48 stations in which 12 have parking lots with 7,482 parking spaces (Sacramento Regional Transit, n.d.).
Specific Relationship Examined
One way, to measure the attractiveness of a light rail system, is to produce hedonic estimates of the effect light rail proximity has on home-values. If the light rail reduces commute times and residents see it as a desirable amenity then home-values should be higher with light rail proximity, thereby reflecting larger demand for this amenity. If light rail is neither desired nor unwanted, then we expect that rail proximity will have no significant effect on home-values. If light rail proximity is a disamenity because it increases unwanted externalities such as unwanted traffic and noise in an area, all of which outweigh the summation of benefits, then a discount for rail proximity is

expected. Higher home-values related to rail proximity in a fully specified hedonic regression signify positive valuations of the light rail by its residents. This study estimates this relationship for homes along Sacramento’s light rail system as a whole. A comparison of the Gold Line and Blue Line helps determine if there are differences in
4 how people value these two lines, which serve different populations and have distinct routes. Additionally, exogenous physical and socio-economic characteristics of neighborhoods help to measure if they have significant effects on home-values when interacted with rail proximity. Exogenous variables from quantitative (Taylor, Miller,
Iseki, & Fink, 2009; Bowes & Ihlanfeldt, 2001) and qualitative (Mackett & Sutcliffe
2003) studies that are suggested to be important in rail success are used. In particular, these exogenous characteristics are population density, median household income, and distance from Downtown Sacramento. Each of these variables is interacted with rail proximity variables to measure if the exogenous variables significantly influence whether residents close to the light rail put additional or lesser value in their home because of light rail proximity. For example, an interaction variable of distance from the Central Business
District and rail proximity can measure if people who live farther from downtown consider paying more for rail proximity than someone who lives closer to downtown.
Importance of Problem and Analysis
This study aims at understanding which areas and neighborhoods most value light rail accessibility. City, regional, and transportation planners would be interested in this study because it provides an assessment of how much Sacramento residents value the light rail system. It also provides suggestions of where Sacramento’s light rail is

successful and where it is unsuccessful. There are assessments of which kinds of destinations may improve a light rail’s value. For example, this study gives evidence that
5 connections to employment centers are a possible driver in how residents value the light rail, consistent with other literature on public transit systems (Kain & Liu, 1999).
Knowledge of these trends could improve planning patterns for the city to produce infrastructure that will be valued. The results are relevant for planners outside
Sacramento County who are interested in better understanding what factors help predict rail success. Valuations of rail transit systems among metropolitan areas, as seen in previous research, are not homogenous. Finding which urban features lead to more desirable rail systems is valuable to planners both inside and outside Sacramento.
Previous studies show that residents in different urban areas give varying values for their commuter light rail system. As examples, light rail proximity had no positive effects on home-values for Miami (Gatzlaff & Smith, 1993), San Jose (Landis et al.,
1994), and New Jersey’s River Line (Chatman et al., 2011) where homebuyers tend to spend less or no significant premium for a home with closer rail proximity. However rail transit in Portland, Oregon (Al-Mosaud et al., 1993), Eastern Massachusetts (Armstrong
& Rodriguez, 2006), and Philadelphia (Voith, 1991) are seen more favorably by their residents who are willing to pay more for a home with rail proximity.
Summary of Results
The results of this study suggest that Sacramento’s light rail system has a discount for homes within 0.5 miles of the closest light rail station. After 0.5 miles the effect of home-values from rail proximity wavers positively and negatively around zero,

suggesting limited value in the light rail overall. However, there is a consistent and significant premium for rail proximity along the Gold Line and a discount for homes
6 along the Blue Line. The values along the Gold and Blue lines may offset one another to produce the estimates for the light rail system as a whole. Along the whole light rail, areas with higher median household incomes tended to value light rail accessibility more than poorer neighborhoods. Residents who lived farther from downtown Sacramento were willing to pay more for light rail proximity, similar to the results in Atlanta, (Bowes
& Ihlanfeldt, 2001). Population density had a very small economic and typically negative impact on home-values for homes between 0.5 and 3 miles from the light rail station.
Home-values did fair a little bit better with higher population densities when the home was in closest proximity to a light rail station. The results suggest that the development plans may boost the value of the light rail by connecting residents to centers of employment like the Sacramento International Airport and Cosumnes River College. The
Blue Line might be valued more highly if it reached Elk Grove because it could connect people to major sources of employment in the county and it would connect people farther from the Central Business District to the city where many Elk Grove residents may be employed. The higher income level of Elk Grove is another indicator that the Blue Line may be an amenity for Elk Grove residents. According to the dataset used in this research the median household income for Elk Grove was $78,000 compared to $65,000 for all of
Sacramento County. This study, however, is not a substitute for a thorough cost benefit analysis, which needed before making such extensions. This study only estimates the

perceived values homeowners put in the light rail as an indicator whether certain locations are ideal for light rail.
The next chapter reviews the literature relevant for the research. The model setup is described in chapter 3. A description of the data used in the empirical analysis follows
7 the model setup in chapter 4. Finally, the empirical analysis and results are presented in chapter 5, succeeded by the conclusion in chapter 6.

Chapter 2
Review of Relevant Research
LITERATURE REVIEW
The relevant literature for this research are previous studies that have estimated the impacts of light rail proximity on home-values, and both qualitative and quantitative
8 studies on the drivers of demand for urban rail systems.
Studies on the capitalization of rail transit proximity in real estate values have produced mixed results depending on the metropolitan area. Not all cities or metropolitan areas and the people who live in them are the same. Thus, light rail would not have the same impact on all households for all metropolitans with rail transit. Gatzlaff and Smith
(1993) found that the Miami Metrorail produced negligible to very weak price increases in nearby home-values. A study of the transit rail system in Portland, Oregon by Al-
Mosaind et al. (1993), however, estimated that home-values within 500 meters of the rail saw a 10% increase in prices. Voith (1991) also estimated results in Philadelphia that were more similar to Portland with a 10% increase in home-values closer to the rail.
Nelson (1992) found mixed results for Atlanta, depending on the median income level of the neighborhood. Landis et al. (1994) generated a unique study by analyzing five transit rail systems in California. In only two of the systems, BART and San Diego Trolley did living closer to the rail system increase single-family home-values. CalTrains, and the light rail systems in San Jose and Sacramento failed to improve single family homevalues. The San Jose light rail had a discount for rail proximity, while the Sacramento light rail suggested a slight premium for rail proximity though the coefficient was

statistically insignificant. Greater focus on the findings by Landis, et al. (1994) about
Sacramento’s light rail is given later. Armstrong and Rodriguez (2006) use a hedonic
9 spatial price model to estimate that in Eastern Massachusetts home prices within one-half mile of a rail station are 10.1% higher, and with each additional minute of driving time from the station home prices fall by 1.6%. Though most results have suggested positive effects in house prices near rail transit stations, the valuations vary by system.
Estimating which characteristics of urban areas relate to premiums or discounts for light rail proximity can help city and regional planners make predictions on whether a rail transit system will benefit a city, on where it should be placed, and on what it should connect. This study focuses on four studies whose efforts help determine what drives residents’ value for public transit systems. These studies provide insights on what urban characteristics may drive residents to value rail accessibility.
Two studies, one study by Mackett and Sutcliffe (2003) and another by Taylor, et al. (2009), find that exogenous socio-economic and physical characteristics of an urban area have the strongest impact on the success of a light rail system. Important socioeconomic and physical characteristics emphasized in the studies include population density, land-use patterns, and the household income levels of the region. Taylor et al.
(2009) measure success quantitatively as ridership. For Mackett and Sutcliffe (2003), success is based qualitatively on five criteria – high patronage, cost-effectiveness in operations and capital, increased public transportation usage, reduced traffic congestion and environmental problems, and improved urban growth patterns. In another study, Kain and Liu (1999) analyze San Diego and Houston transit systems to find “secrets of

success” for transit systems. Kain and Liu (1999) emphasize the location of jobs, which
10 could also fall into the exogenous socio-economic characteristics of a region. Kain and
Liu (1999) found that public transportation improved as employment grew in San Diego and Houston.
Bowes and Ihlandfeldt (2001) produced a unique study by estimating how metropolitan characteristics like median household income and distance from the CBD
(central business district) can lead to changes in home premiums for rail proximity by using interaction variables in their hedonic regression models. Bowes and Ihlanfeldt’s
(2001) estimated quantitatively that higher income neighborhoods saw a greater benefit from rail proximity in regards to higher home premiums, though not for immediate proximity. Chatman et al. (2011) found that lower income households with rail proximity saw their home-values increase with rail proximity while higher income households saw a decline in their home-values that were farther from rail transit, in what they refer to as a potential transfer in who benefited from the new rail transit system. Bowes and Ihlanfeldt
(2001) also observed that homeowners in Atlanta would pay more for a home that has closer access to the rail transit station when the home is farther from the central business district. This means that people who lived in the outer city or suburbs of Atlanta typically paid more for easier access to the rail transit system, perhaps to have easier commutes.
These insights could be valuable for regional planners.
One of the unique aspects of studying Sacramento’s light rail is that it consists of two separate routes, the Gold Line from west to east and the Blue Line from north to south that only overlap in some parts of Downtown Sacramento in the western part of

11
Sacramento County. This allows for comparisons of the two lines and the ability to assess how certain physical and socio-economic characteristics of different urban areas influence demand for light rail access. The importance of the location of jobs emphasized by Kain and Liu (1999) suggest that the Gold Line may be more highly valued because it connects residents to places with higher employment than the Blue Line, as seen in the coming data description section.
Past Studies on Sacramento’s Light Rail
There are few peer-reviewed articles on the value of Sacramento’s Light Rail system. Landis et al. (1994) and Mackett and Sutcliffe (2003) make up two studies that have included some focus on Sacramento’s light rail. Landis et al. (1994) found there to be a positive, but no significant impact of rail proximity on home-values in Sacramento, based on 1990 home sales. Landis et al. (1994) found that living space of the home and neighborhood income levels are the biggest drivers of home-values in Sacramento based on his 1990 data. Landis et al. (1994) suggested that freeway congestion was not bad enough in Sacramento to produce a problem as there was in San Diego. According to
Mackett and Sutcliffe (2003), Sacramento’s light rail is a low performing light rail system. It succeeds only at minimizing capital costs per passenger, not taking customers from using buses, and for seeing patronage increases for the system. Sacramento’s light rail performs seventh best out of nine observed rail systems. The physical and socioeconomic characteristics that Sacramento performs well in are having a viable CBD, having local popular support, and offering personal security in the system. Sacramento suffers from being the only system that is not located along a developed corridor, which

hurts its ability to be a success based on Mackett and Sutcliffe’s (2003) criteria.
According to Taylor et al. (2009), its ridership numbers would suffer because of the
12 undeveloped corridors around the rail that leads to lower population densities.
Furthermore, Holtzclaw (1994), in a study by the Washington Natural Resources Defense
Council claims there is a minimum population density threshold for the success of a light rail transit system that is 9,472 people per square mile by. Sacramento meets this threshold for only 19 out of 317 Census Tracts.
Hedonic Models on the Estimation of Light Rail Value
The most common econometric model to estimate the capitalization of light rail accessibility is the hedonic price model. Rosen (1974) developed the hedonic price model framework, saying the summation of the good’s characteristics determines the price of a heterogeneous good. The hedonic model provides estimations of the values for each specified characteristic of the good. Hedonic models work well when there is heterogeneity across the goods in the market examined. Homes vary largely in characteristics such as physical features, location characteristics, and other environmental aspects. Given that there is strong heterogeneity in housing that can determine a home’s value, the hedonic model has dominated this topic and is very popular for real estate economics in general.
Hedonic models do present certain issues, in particular, issues involving uncertainties in the estimated coefficients from omitted variable bias, choice of functional form, and spatial autocorrelation (Armstrong & Rodriguez, 2006). Spatial autocorrelation refers to how home-values for one area affect homes in a neighboring area. This is similar

13 to how in time series models serial autocorrelation persists when previous observations of the dependent variable predict current observations of the dependent variable. Spatial autocorrelation is trickier to deal with because it involves two dimensions, not just one dimension in time series models. To correct spatial autocorrelation Armstrong and
Rodriguez (2006) use a spatial log model with spatial weights to produce consistent estimates on the Eastern Massachusetts region. According to Armstrong and Rodriguez
(2006) spatial dependence or spatial autocorrelation can skew results where estimates could become inefficient in ordinary least squares (OLS) or inconsistent and biased in the estimates of implicit price. Therefore, Armstrong and Rodriguez (2006) use maximum likelihood methods. In contrast, Redfearn (2009) argues for the use of locally weighted regressions (LWR), which is a form of a weighted least squares (WLS) model to fix problems of average-effects persistent in OLS and for spatial heterogeneity. Redfearn
(2009) argues that this locally weighted regression method will produce more stable coefficients than the average-effects results from an OLS hedonic regression because the locally weighted regression allows estimators to be flexible across space. This research will develop an ordinary least squares model and compare it to a weighted least squares model that is similar to a locally weighted regression. Locally weighted regressions are not used because they require one unique location for each observation. This research uses Census Tract centroids for location of a parcel, where one centroid may have hundreds or thousands of observations. For this reason, spatial autocorrelation lags are also not included. Many of the spatial autocorrelations and spatial heterogeneity should be captured by the neighborhood and CDP (Census Designated Place) fixed effects

introduced later in this paper, though potentially this could lead to bias in the models if
14 spatial autocorrelation is not resolved.
Functional form consideration is also important to account for any potential of a nonlinear relationship between rail proximity and home-values. Studies have found nonlinear relationships between rail proximity and home-values where estimates with direct proximity of the light rail may see lower values from disamenities such as increased noise and traffic. Without a nonlinear form of this relationship, the researcher assumes there is no tradeoff between disamenities and amenities of rail proximity, which may be incorrect. Bowes and Ihlanfeldt (2001) and Chatman et al. (2011) use proximity thresholds to deal with any nonlinearity relationships with proximity to the light rail.
Bowes and Ihlanfeldt (2011) find that homes within a quarter of a mile to the rail station sold for 19% discount compared to homes more than 3 miles away from the rail station, and properties that were between one and three miles from a rail station had a premium of
3.5% compared to homes more than 3 miles away. Results from models that do not take the tradeoff between positive and negative externalities of rail proximity into consideration can lead to false conclusions. This research uses the threshold proximity variables to the closest light rail station as were used in these two studies. Landis et al.
(1994) only assume a linear relationship between rail proximity and home-values in their study.
Fixed effects are often used in these hedonic studies to account for differences in municipalities or counties that are unobservable but can affect demand for housing in that city or county. Chatman et al. (2011) use fixed county effects and fixed municipality

effects in their study of the New Jersey rail system. Bowes and Ihlandfeldt (2001) and
15
Landis et al. (1994) used municipality fixed effects in their models. This research will explore using CDP fixed effects and neighborhood fixed effects. There are distinct differences among Census Designated Places and among neighborhoods in Sacramento
County. Folsom CDP is very different from Florin CDP and the Tahoe Park neighborhood is very different from the Central Oak Park neighborhood. These fixed effects variables help limit omitted variable biases, and help measure spatial heterogeneity.
Past research has included various functional forms of the dependent and independent variables. Landis et al. (1994) use a linear form of home-values and independent variables. Bowes and Ihlanfeldt (2001) use a semi-log with a logged home-values variable regressed on the linear independent variables. Armstrong and Rodriguez (2006) compare a linear model, a semi-log model, and a double log model. This research uses a semi-log form of the hedonic model suggested by results from Box Cox transformation tests.
Literature of Bid Rent Theory
The theory behind home premiums where commuting to work is easier comes from the bid-rent model of a mono-centric city. The longer and more costly the commute, the less someone is willing to pay for housing because, in this model, all households spend the same total amount between housing and commute costs. Those who bear higher commute costs spend less for housing while those with cheaper commute costs, typically closer to the central city, spend more for housing (O’Sullivan, 2012). Wilson and Frew

16
(2007) find this to be the case in Portland, Oregon. They measured that rent is highest at the city center at $700 and that rent decreases as distance from the city increases, then increases next to the beltway, which forms a local maximum of rent values 14 miles from the city center, then resumes its decline to $300 for an apartment 20 miles from the city center. The study by Wilson and Frew (2007) demonstrates the importance of accessibility to viable transportation infrastructure that reduces commute times. This relationship should be the same for home-values with proximity to the light rail if it indeed saves time and some of the costs of commuting.
This research will add to the literature by updating the previous hedonic analysis of the Sacramento Light Rail performed by Landis et al. (1994) and by measuring the effects that determinants of ridership like median household income, population density, and proximity to the central business district have on homes that are close to the light rail.
The insight from this research provides evidence for the characteristics of neighborhoods that may see the most benefit from light rail transit.
The next chapter develops the models used in the empirical analysis of the paper.
Hedonic ordinary least squares and weighted least squares regressions are developed.
One model measures the value Sacramento homeowners pay for rail proximity and another model includes interaction variables to measure whether exogenous variables influence whether those near the light rail value this service. More discussion on the use of weighted least squares models is provided as well as the presentation of a weighted least squares model.

17
Chapter 3
MODEL SET UP
Multiple hedonic price models estimate the impact light rail proximity has on home-values in Sacramento County. Rosen (1974) developed the hedonic price model framework. Rosen (1974) argued that the summation of a heterogeneous good’s characteristics determines the price of the good. Each characteristic has an implicit, or hedonic, price that adds up to the final observed price. These models have become popular in real estate economics where there are many characteristics of a home that the homebuyer purchases. In this case, the proximity of a home to a light rail station is the main variable or characteristic of interest. The hedonic models assess whether rail proximity has a positive or negative implicit price to judge whether the light rail provides a valued service to the region and its economy.
The first model in this analysis estimates whether proximity to any Sacramento light rail station affects home prices. Comparisons from this model can then be made to previous studies, in particular Landis et al. (1994). This first ordinary least squares model will be compared to a weighted least squares model to see if there are enhancements for using a locational weight similar, but not exactly the same, to what Redfearn (2009) suggests.
Another hedonic price model estimates whether there are differences in homevalues based on proximity to the Gold Line compared to the Blue Line. A final set of regressions will include interactions between rail proximity and other distance and household characteristics such as the median household income of the Census tract, the

distance the home is from downtown Sacramento (central business district), and the
18 population density of the area. These interactions should provide insights into which homes with certain socio-economic and physical characteristics are valued more for rail proximity. For example, homes farther from the central business district may see higher values for light rail proximity, assuming that the light rail reduces long commute times
(similar to what Wilson and Frew, 2007, saw in Portland where there was an increase in home-values farther from the city center closer to an efficient transportation system).
This effect is observed through the interaction terms estimated as done in Bowes and
Ihlanfeldt (2001).
Equation 3.1, below, is the standard ordinary least squares hedonic price model used in the first two models of the empirical research.
V i
= β
0
+ β
1
H
I
+ β
2
D i
+ β
3
T i
+ β
4
P i
+ ε i
(Equation 3.1)
The model’s first regression estimates whether Sacramento County residents pay a premium for rail proximity to the Sacramento Light Rail overall. The model’s second regression estimates whether homebuyers value light rail station proximity with respect to the Blue and Gold Lines. In each model, V i
represents home-values, in particular the natural log of home-values in Sacramento County. H i
is a vector of home property characteristics such as the living-area of the home in square feet, the size of the lot in square feet, whether the home has a pool, and the year the home was built. D i
is a vector of distance variables such as distance to the closest light rail station in miles, distance to the closest highway in miles, and distance to the Central Business District (Downtown
Sacramento in this case) in miles. T i
is a set of Census Tract characteristics that could

19 affect home prices, such as the tract’s population density in people per square miles, the median household income of the tract in dollars, and the percentage of renter occupied homes. P i
represents the set of the Census Designated Places and neighborhoods in
Sacramento County, which will capture unobservable characteristics of the places that affect housing prices, such as quality of the environment, amount of green space, the number and quality of parks, etc. to help reduce omitted variable bias. And finally,
ε i is the error term.
A second model presented below estimates the impacts urban physical and socioeconomic characteristics may have in determining whether residents will value the light rail as shown in Equation 3.2
V i
= β
0
+ β
1
H
I
+ β
2
D i
+ β
3
T i
+ β
4
P i
+ β
5
D i
*T i
+ ε i
(Equation 3.2) where the only difference is the interacting variable D i
*T i
. This interaction variable is a set of interaction terms of proximity to light rail thresholds on Census Tract characteristics. The Census Tract characteristics observed are the distance to downtown
Sacramento, population density, and median Census Tract income. These interaction variables will help answer questions pertaining to which kinds of neighborhoods benefit most from a light rail station. In addition to these ordinary least squares hedonic models, a weighted least squares model estimates home-values and is then judged to see if estimates improve compared to the OLS model.
Standard hedonic price models that measure the impact of an amenity like proximity to a light rail station on home-values can produce estimates that are inefficient because of spatial heterogeneity or spatially driven auto-correlation (Redfearn 2009;

Armstrong & Rodriguez, 2006). One way to deal with spatial autocorrelation when
20 relatively few independent variables are used and there is suspicion of many unobserved spatially distributed variables (Chalermpong, 2007), is to use locally weighted regressions (LWR) originated from Cleveland and Devlin (1988). Redfearn (2009) suggests using a kernel function as the weight for these models. Locally weighted regressions are similar to weighted least square regressions but for the LWR case there is one regression for each observation. The locally weighted regression does not work well for this data set because each home’s location is the coordinates of the Census Tract centroid to which it belongs. This produces hundreds of households or more with the same locational coordinates. However a weighted least squares regression will be examined with a weight related to physical distance to see if it improves the model using a modified tri-cubic kernel variable similar to the one emphasized by Redfearn (2009).
The tri-cubic weight used in the WLS regression, measures distances between
Census Tracts. Its formula is presented in Equation 3.3. The weight variable, W i
, is calculated by finding the average distance between tract i and all other tracts j (dist ij avg
).
This value is divided by the maximum distance between tracts i and any other tract j in all of Sacramento County (dist i max ). This quotient is cubed and subtracted from 1. Finally, this difference is cubed. Homes in areas with closer proximity to other Census Tracts will have higher weight than those who are farther from others. Observations receive less weight at an accelerating rate as distance from the regression point increases, which could improve estimators (Redfearn, 2009)
.
The formula for calculating this weight variable w i is as follows

21 𝑎𝑣𝑔 𝑑𝑖𝑠𝑡
W i
= (1 − ( 𝑑𝑖𝑠𝑡 𝑖𝑗 𝑚𝑎𝑥 𝑖𝑗
)
3
)
3
(Equation 3.3)
Equation 3.4 presents the weighted least squares regression:
𝑉 𝑖
∗ = 𝛽
0
𝑋 ∗
0
+ 𝛽
1
𝐻 𝑖
∗ + 𝛽
2
𝐷 𝑖
∗ + 𝛽
3
𝑇 𝑖
∗ + 𝛽
4
𝑃 𝑖
∗ + ε 𝑖
∗
(Equation 3.4)
The variables with asterisks are weighted by W i
. Therefore, 𝐻 𝑖
∗ =
𝐻 𝑖
𝑊 𝑖
.
Also 𝑋 ∗
0
is the intercept in the OLS or unweighted model. If the weighted least squares model outperforms the ordinary least squares model then we may assume that the weight reduces any spatial auto-correlation problems and should be preferred to the ordinary least squares model.
The next chapter describes the data used in these models to get empirical results on whether homeowners of Sacramento County pay a premium for rail proximity.

22
Chapter 4
DATA SUMMARY
The Sacramento Regional Transit Light Rail is mapped below in Map 4.1 to provide a visual of the Sacramento Light Rail system and the area of analysis. The Gold
Line spreads from Downtown Sacramento in the west to Folsom in the east. The Blue
Line spreads from North Highlands in the north to south Sacramento at Meadowview in the south. The yellow lines on the map represent both light rail routes. The round pinpoints locate the rail stations along the two lines. The two lines merge near Downtown
Sacramento as the Blue Line moves toward the south and north, and the Gold Line moves east. The dark blue shapes indicate Census Designated Places inside Sacramento County.
The Purple shapes represent the areas in Sacramento County that are not part of a Census
Designated Place and are referred to as “County” in regards to CDP identifications in this paper. The lighter outlined shapes represent Sacramento County Census Tracts. The light blue areas are Census Designated Places outside Sacramento County such as West
Sacramento, which is part of Yolo County and is located west of the light rail system.
The light blue shaded areas are not included in the analysis.
The data for this study come primarily from DataQuick, the Census, and manipulations of data using Google Maps, GIS software, and Excel. The housing data set comes from DataQuick. Census Designated Place economic data and Census Tract housing and demographic data come from the US Census, in particular the 2007
Economic Census and the American Community Survey 2011 estimates respectively.
Distance data were calculated through Google Maps and Excel.

23
Map 4.1.
Sacramento County Census Tracts, CDPs, Light Rail Lines and Stations
Housing Data
The original data set on home property characteristics came from DataQuick, through the Sacramento Municipal Utility District (SMUD), which has parcel information on Sacramento County and a few Census Tracts in Placer County that are serviced by SMUD. SMUD received the home property data in May 2012 that reflects the housing market around that time. This housing dataset originally included information on 437,181 dwellings in SMUD’s service area, including areas outside Sacramento
County. The dataset was reduced to 318,028 dwellings. Observations were dropped if they were missing home-values, the size of the living area, or the household income values. Others were dropped because they were outside Sacramento County. The last set of observations dropped had extreme lot sizes, such as homes on lot sizes smaller than 17 square feet or lot sizes that were greater than 160,000 square feet (the top 1% of lot sizes). The housing dataset has home-values estimated by DataQuick that uses both

county assessed values and algorithms to maintain updated estimations of home-values
24 for homes in Sacramento County. They keep active property values by using computer algorithms that include information of the most recent comparable sales prices and other data typically used by appraisers. See DataQuick (2012) for a discussion on how
DataQuick uses computer algorithms to calculate up to date home-values.
The housing data set had values for all Sacramento County 2010 Census Tracts except Census Tract
700, and 5402, which have only commercial buildings and Census Tract 9883, which is
Folsom Prison.
The housing data set includes nine variables. These variables were the DataQuick assessed value of the property (HMVALUE), a Census Tract variable indicating which of the 317 Sacramento County 2010 Census Tract the home belongs to (TRACT), the amount of total living area in the home in square feet (SQFT), and the lot size of the homes that is also measured in square feet (LOTSIZE). There is information on whether the home has a pool (POOL), the year the home was built (YRBLT), whether the parcel is owner occupied (OWNOCC), the estimated income of that particular household
(HHINC) and whether it is a single unit home (SGLUNIT). DataQuick estimated the household income variable, HHINC, mostly in ranges of $5,000. More precisely, in the original DataQuick dataset, if the parcel had the number “0” for household income, then the estimated household income for this parcel was less than $15,000. “1” indicated the household income was between $15,000 and $19,999, “2” was given for household income between $20,000 and $24,999, “3” indicated the household income was between
$25,000 and $29,999 and, so forth. For the dataset used in this research, the observation

25 listed is the upper bound value plus $1. Therefore, if DataQuick gave a household income value of “1”, then the household was given a value $20,000 (19,999+1); “2” was $25,000
(24,999+1), and so forth. The largest value is “over $250,000”, which is truncated at the given value $300,000.
There are three potentially significant missing variables in the home data set. The year of the last home-value assessment by a county assessor is one of the missing data variables on housing. This omitted variable is helped by the automation assessing performed by DataQuick that changes home-values based on market developments that occur frequently over time to give people who make loans to customers an idea of how much someone’s home costs in the current market environment (see DataQuick, 2012).
Another missing variable is the condition of the home. A home may have characteristics of an expensive home, but the actual condition of the home may be very poor, which drives its value to be far lower than what would be predicted. DataQuick uses county assessor’s actual assessments to help estimate this, but many times, it may not be accurate. The last missing variables are the coordinates of each property. Because these homes do not have their own coordinates, their locations are their Census Tracts’ centroids, therefore causing measurement errors in the distance variables. There is no way to compare the home-value of a house that is immediately next to the light rail compared to a home in the same Census Tract but farther from the light rail without the coordinates for each home. Not having coordinates for each home limits the ability to perform spatial lags to address potential spatial auto-correlation. SMUD has address

information for each parcel that could be geocoded to get locational coordinates of each
26 property. However, this data are not publicly available for privacy reasons.
Locational Characteristics
Nine locational variables estimate home values. They are the median household income of the neighborhood (MEDINC), the population density of the Census Tract in which the property is located (POPDEN), the prevalence of rented homes (PCTRENT), the percentage of racial backgrounds of residents in the Census Tract (PCTWHITE,
PCTBLACK, PCTHISP, PCTASIAN, PCTOTHER), and whether the home is located near a body of water (WATER). The median income of a neighborhood (MEDINC) will be compared to the variable of each home’s household income, HHINC. People may pay more to live closer to richer people, not so much because they themselves have a lot of income but because they feel better living closer to richer people than poorer people.
Population density may be an important factor to where people locate and are willing to spend more money for a home. Some may prefer to live in rural areas whereas others want to live in the middle of the city, potentially leading to peaks for the highest populated places and for less populated places. The percentage of rented homes variable reflects the argument that renters pay less to maintain a home than an owner-occupied home which can have spill-over effects on neighboring properties (O’Sullivan 2012). The variables that represent the racial buildup of the area are PCTWHITE, PCTBLACK,
PCTASIAN, PCTHISP, and PCTOTHER that signify the percentage of white people, black people, Asian people, Hispanic people, and “other” ethnicity in the Census Tract.
The “Other” race in this research is the summation of the prevalence of those who

27 identify themselves in Census surveys as “other”, “two or more races”, Native American, or Pacific Islander in a Census Tract. The water variable was created by looking at
Census Tract data that includes the area of the tract that is filled by water. If this value was greater than 0, then the homes in the Census Tract were given a 1 for the WATER, otherwise homes received a 0. Homes near the rivers in Sacramento may be more desirable, especially with easy access to pleasant outdoor bike paths and parks, though there may be disamenities of river proximity such as proximity to where homeless people may slumber and live. The next set of locational characteristics describes proximity to highway on-ramps, light rail train stations, Downtown Sacramento, and employment.
Distance to the nearest highway onramp (DSTHWY) was calculated using Google
Maps. Distances from each Census Tract centroid to the nearest highway onramp were found by entering the Census Tract centroid coordinates and a far-away place that requires highway travel, and then adding up the miles to reach the closest major interstate or highway on-ramp. Road miles are the measurement of this data variable, not as the bird flies. Road miles may be more accurate to how one perceives distances.
The other distance locational variables, such as distance to Downtown
Sacramento (DSTCBD), distance to the nearest Sacramento Light Rail station
(DSTRAIL), distance to the nearest blue line light rail station (DSTBLUE) and gold station (DSTGOLD) were measured using great circle distances using an Excel spreadsheet and the formulas developed by Pearson (2009). The great circle distance is the shortest distance between two points on a sphere (Pearson, 2009).

28
The process of deriving the great circle distance entails calculating the interior spherical angle between the two points ( ∆ỡ ) and multiplying that product by the radius of the Earth to measure the distance between two points on the planet. The formula to calculate the interior spherical angle is shown below in Equation 4.1,
∆ỡ = 2arcsin (√sin (
∆ф
2
) + 𝑐𝑜𝑠ф 𝑠
𝑐𝑜𝑠ф 𝑓 𝑠𝑖𝑛 2 (
∆𝜆
2
)) (Equation 4.1) where ∆ỡ is the interior spherical angle, ∆ф is the difference of the two locations’ latitudes (Latitude
1
– Latitude
2
), ф 𝑠 is the latitude of the first location’s coordinates
(Latitude
1
), and ф 𝑓 is the latitude of the second location’s coordinates (Latitude
2
), and ∆𝜆 is the difference of the longitudes between the two locations (Longitude
1
– Longitude
2
).
The great circle distance formula requires multiplying the interior spherical angle by the
Earth’s radius. This product is multiplied by 0.621371 to convert measurements to miles.
This great circle distance formula was used in Excel to estimate a large number of distances between Census Tract centroid and other places most efficiently. This method also allowed for a more objective style of finding the closest distance between a Census tract centroid and the closest light rail station instead of merely “eye-balling” which one is the closest. One problem with this method of measurement is that it measures the distance more in how a bird could get between the two points, but not for humans by foot or car. Measuring distances by the shortest distance between two points could lead to biases in the measurements because they are less realistic than measuring it by how someone can get to a place by foot or road.

29
Two matrices were created to estimate the distance between homes and the closest
Blue and Gold Line stations light rail. The great circle formula developed by Pearson
(2009), the Census Tract centroid coordinates, and the coordinates of each station’s coordinates, found through Google Maps were used to create these two matrices. The first matrix was used to find the shortest distances between each Blue Line station and
Census Tract centroid. The second matrix performed the same procedure but to each
Gold Line station. The shortest distance for each Census Tract centroid to a light rail station was recorded for each light rail line. If the distance to a Blue Line station
(DSTBLUE) was shorter than the Census Tract’s distance to the Gold Line station
(DSTGOLD), then the distance to the Blue Line station was used as the distance to the closest overall light rail station (DSTRAIL). If the opposite was found, then the distance to the Gold Line was used for DSTRAIL. If they were equal, where the closest station serves both the Blue Line and the Gold Line, then this distance was given for the closest light rail. This occurs downtown where the two lines merge.
The great circle formula was also used to measure an employment gravity variable (EMPGRAV). An employment gravity variable measures Census Tract access to jobs. This variable is especially helpful when not all employment is located in one central location. The 2007 Economic Census data on the number of jobs in each Census
Designated Place (CDP) was used to calculate this variable. The number of jobs in each
Census Tract was calculated by dividing by the number of jobs in each CDP by the number of Tracts in the CDP. A loose assumption is that jobs in the CDP were equally distributed within the CDP .This assumption was applied because the actual geographical

locations of the jobs are unknown with more detail than in which Census Designated
30
Place they reside. With the employment data, distances between Census Tracts using the great circle distances are combined to create the employment gravity variable. It is calculated by equation 4.2 as was used by Bowes and Ihlanfeldt (2001),
EMPGRAV i
= [∑ j
(EMP 𝑗
/d 𝑖𝑗
) ] + EMP i
(Equation 4.2) where EMP j
is number of jobs in all other Census Tracts other than i, EMP i is the number of jobs in Census Tract i, and d ij
is the distance between the centroids of tracts i and j.
This employment gravity variable measures if home-values change based on better access to employment.
Distance to the central business district was also measured using the great circle formula. The coordinates for the central business district were the coordinates for the
Capitol Building (38.575783,-121.495239).
There are other variables one could come up with to predict home-values because there are many factors one may take into consideration before making an offer on a home. One example includes architectural styles, of which data are not readily available.
Two important locational characteristics of a home that determine home-values that are missing so far are safety or crime data and the quality of schools. These two factors are very important in determining where someone lives and how much they will pay for a home. Though crime data are available online, the difficulty for this analysis is to convert it to Census tract level. One way to help minimize the effect of these missing variables is to include fixed effects variables for Census Designated Place and neighborhoods that can capture some of the differences in crime and education qualities.

Fixed Effect Variables
The fixed effect variables in this analysis were added using GIS software and
31 shape files from the Census and the City of Sacramento. Census Tracts and Census
Designated Places (CDP) have separate boundaries. GIS software was used to identify in which Census Place a Census Tract lay. Below is a map of the Census Designated Place shape file on top of the Census Tract shape file. Using a map generated as in Map 4.2 below, each Census Tract was designated a Census Place. This is helpful in reducing the potential of omitted variable bias because the fixed effects variable captures the unobservable characteristics of place such as crime rates and school quality. In addition, it helped to create the employment gravity variable mentioned above. Overall, there are
21 CDP variables as listed in Table 4.1.
Map 4.2
. Generating CDP variables
To better estimate how home-values change across space, a neighborhood indicator variable was added. SMUD employees matched a neighborhood shape file provided by the City of Sacramento to Census Tract values to create the neighborhood

variable. The method mirrors the generation of CDP variables. There are 119 neighborhoods included in the dataset and are used to help minimize omitted variable
32 bias. These neighborhoods are listed in Table 4.2. One potential problem is multicollinearity between these two fixed effects variables where each of the CDPs, besides
“Sacramento”, is also represented in some form by the neighborhood variables.
Table 4.1
List of Census Designated Places
Arden Arcade
Carmichael
Folsom
Parkway-South
Sacramento
Foothill Farms Rancho Cordova
Citrus Heights Galt
County Gold River
Elk Grove
Fair Oaks
La Riviera
Rio Linda
Rosemont
Sacramento
North Highlands Vineyard
Florin Orangevale Wilton
Table 4.2
List of neighborhoods
Alkali Flats
American River Parkway
American River
Parkway/Northgate
Antelope
Arden Arcade
Arden Fair
Arden Highlands
Army Depot/Florin
Fruitridge Industrial Park
Avondale
Ben Ali
East Sacramento
Elk Grove
Mather AFB
McClellan AFB
Elmhurst
Elverta
Executive Airport
Fair Oaks
Fleming
Heights/South Haven
Florin
Florintown/Scottsdal e Meadows
Meadowview
Med Center
Metro
Center/Gateway
Center
Midtown
Natomas Corporate
Center
Natomas
Crossing/Sport
Complex
Folsom
Natomas Park
Natomas
Park/Village 14
Rio Linda
River Park
Robla/Young Heights
Rosemont
Sacramento
International
Airport/Natomas Creek
Sierra Meadows
Sierra View Terrace
South City Farms
South Hagginwood
South Land Park

33
Table 4.2 Continued
List of Neighborhoods
Bradshaw Business Park
Bradshaw Woods/Lincoln
Village
Brentwood/Woodbine
Cal Expo/Point West
Campus Commons
Carleton Tract
Carmichael
Citrus Heights
Cloverdale
College Glen
College Oak Estates
Colonial Heights/Tahoe
Park South
Colonial Village
Columbia Rancho
Cordova Meadows
Creekside
CSUS
Curtis Park
Del Paso Park
Downtown
Foothill Estates
Freeway
Estates/Creek View
Estates
Fruitridge Manor
Galt
Gardenland
New Era Park
Newton Booth
North City Farms
North Fruitridge Park
North Highlands
Northpoint/Robla/Ral ey Industrial Park
South Natomas
Southern Pacific Rail
Yards/Dos Rios
Triangle
Southgate
Southgate/Orange Park
Cove
Southside Park
Strawberry Manor Gateway West
Glen Elder Oak Park
Glenwood Meadows Orangevale
Gold River Pacific Terrace
Sundance Lake
Sunridge Park Village
Tahoe Park
Golf Course Terrace Parkcrest Estates
Granite Regional
Park Parkway
Greenhaven
Hollywood Park
La Riviera
Laguna
Land Park
Lawrence Park
Lindale
Little Pocket
Mansion Flats
Tallac Village
Upper Land Park
Village Green/Parker
Homes Patrician Plaza
Pell/Main Industrial
Park/Village 5 Vineyard
Pocket Walnut Grove
Rancho Cordova Westlake
Rancho
Cordova/Carmichael Whitney Estates
Rancho
Cordova/Gold River Wilhaggin Oaks
Rancho
Murieta/Sloughhouse Willow Creek
RB Sports
Complex/Creekside Wilton
Richmond Grove
Kernel Variable
One last variable that is used in the research is a tri-cubic kernel variable, shown in the previous section with Equation 3.3. This variable is the weight used in the weighted least squares model (WLS). The weight is encouraged by Redfearn (2009) in his research estimating the effects of light rail proximity in Los Angeles on housing

34 prices, and is considered by Sunding and Swoboda (2010) in their analysis of the effect of housing regulation on home-values in southern California when they use a locally weighted regression (LWR). A locally weighted regression is like a weighted least squares model but in the LWR case each observation has its own regression. Because observations do not have their own unique location coordinates, a WLS approach is used to compare with the OLS approach, rather than a LWR. This variable puts more weight on homes that are closer together and likely to be impacted by light rail. It also allows coefficients to have some flexibility and not be fixed to improve results while keeping the model efficient.
Table 4.3 describes the variables described previously, and their sources.
Table 4.3
Description of variables
Variable Description Source
Dependent Variable
HMVALUE Assessed home-value of property
TRACT
SQFT
LOTSIZE
YRBLT
HHINC
SGLUNIT
POOL
OWNOCC
MEDINC
POPDEN
DataQuick
Housing Characteristics
Indicates in which Census Tract the home is located
Living area size of property in square feet
Lot size of property in square feet
Year property was built
DataQuick
DataQuick
DataQuick
DataQuick
Estimates of household income in parcel
Dummy variable indicates whether home is single unit property
DataQuick
DataQuick
Dummy variable indicates whether the home has a pool DataQuick
Dummy variable indicates whether home is owner occupied DataQuick
Location Characteristics
Median income of Census tract
Population density of Census tract home is located
5 yr ACS
2011
5 yr ACS
2011

Table 4.3 Continued
Description of Variables
Variable Description
Location Characteristics (continued)
PCTRENT Percentage of rented homes in Census Tract
PCTWHITE Percentage of white residents in Census Tract
PCTBLACK Percentage of black residents in Census Tract
PCTHISP Percentage of Hispanic residents in Census Tract
PCTASIAN Percentage of Asian residents in Census Tract
Percentage of residents in Census Tract who said their
PCTOTHER race is other, two or more, Native American, or Pacific
Islander
WATER
DSTCBD
Indicates of Census tract has body of water in its area
Distance in miles from Census tract centroid to State
Capitol
DSTHWY Distance in miles to closest highway on ramp
EMPGRAV
CDP
HOOD
KERNEL
Tract proximity to employment measured by gravity variable
DSTRAIL Distance in miles to closest light rail station
DSTGOLD Distance in miles to closest Gold Line rail station
DSTBLUE Distance in miles to closest Blue Line rail station
Fixed Effects
Census Designated Place or Census Economic Places
Neighborhood property is located
Local Weight for WLS
Locally weighted tri-cubic variable used in WLS
35
Source
Excel
Google
Maps
Econ
Census
2007*,
Excel
Excel
Excel
Excel
5 yr ACS
2011
5 yr ACS
2011
5 yr ACS
2011
5 yr ACS
2011
5 yr ACS
2011
5 yr ACS
2011
Census
2007
Economic
Census
DataQuick
Excel

36
Data Summary Statistics
Table 4.4 provides a summary statistics of the variables previously mentioned and used in this study. According to the empirical data the average home in Sacramento
County has a mean value of $195,818, is 1,728 square feet on a 9,594 square foot lot and was built in 1976. The median home-value is $168,622, which is similar to the median sale price of a home in May, which was $165,000 according to the Sacramento Bee
(2012). The most recent construction of an assessed home in this dataset is from the year
2011, and the earliest is 1850. The number of single unit homes in the used dataset is different from the actual number of single unit homes in the county according to Census numbers. This housing sample of Sacramento County is heavier on single unit homes where 97.9% of homes are single unit compared to 70.6%, according to the Census’s
Selected Housing Characteristics from the 2007-2011 American Community Survey 5-
Year Estimates. Owner occupancy is also higher at 84.1% in this sample compared to
58.6% according to Census estimates. The data cleaning must have dropped more renter and multiunit homes. The mean household income (HHINC), which is an upper-bound estimate, is $86,970 in Sacramento County compared to the mean of the Census median household income of $65,400. The difference can be large because the value in HHINC is an upper bound estimate and the dataset contains mostly those who live in single-family homes and are homeowners, who typically have higher incomes. The average population density is 5,286 people per square mile, which is much lower than the threshold suggested by Holtzclaw (1994) for a viable light rail system. Typically, white people make up a majority in most Census Tracts with an average of 52.33% of people in an

average Sacramento County Census Tract being white. Hispanics make up the second
37 largest race at 19.39% followed by Asian at 13.94%, black at 8.71% and “other” at 5.6%.
About a quarter of households are located near a body of water. About 15% of homes have a swimming pool. People tend to locate 10 miles from downtown Sacramento, 2.42 miles from the closest highway onramp, and 3.40 miles from the closest Sacramento
Light Rail station. Observations on average have a larger weight at 0.89 with the kernel variable.
Table 4.4
Statistical summary of variables (Observations = 318,028)
Variable Mean Std. Dev Min Max
Hmvalue ($) tract sqft
Dependent Variable
195,818 112,928 23,075 1,825,810
Housing Characteristics
7093 2296
1,728 676
100
216
9900
8,095 lotsize yrblt sglunit pool ownocc
Income ($1,000s)
9,594 12,035
1976 21.09
120
1850
159,430
2011
0.979
0.148
0.142
0.356
0
0
1
1
0.841
86.97
0.366
48.71
0
15
Locational Characteristics
65.40 23.37 17.97
1
300
165.26 Medinc ($1,000s)
Popdensity (1,000s/sq mi.) 5.286
Pctrent (%) 35.09
Pctwhite (%)
Pctblack (%)
52.33
8.71
2.498 0.009
17.19 2.20
22.32
7.17
5
0
17.164
100
100
43.5
Pcthisp (%)
Pctasian (%)
Pctother (%)
Water
Dstcbd (mi.)
Dsthwy (mi.)
Empgrav (10,000s)
19.39
13.94
5.63
0.25
9.95
2.42
7.95
11.02
11.35
0
0
2.94 0
0.43 0.00
5.07 0.28
2.04 0.10
2.52 2.33
63.4
49.5
22.6
1.00
27.04
17.10
17.11

Table 4.4 (continued)
Statistical summary of variables (Observations = 318,028)
Variable
Dstrail (mi.)
Dstblue (mi.)
Dstgold (mi.) kernel
Mean Std. Dev Min Max
3.40 2.87 0.08
5.09
4.97
0.89
3.70
3.60
0.12
0.08
0.17
0.32
21.27
21.95
21.36
0.97
The median home-values per square foot are listed for each Census Designated
Place in Table 4.5. Folsom has the highest home-values at $156.0/sqft, while Parkway
South Sacramento has the cheapest housing at $65.1/sqft. The median home-value per square foot for Sacramento County according to this dataset is $101.5 compared to
38
$102.8 for March 2012, around the time the DataQuick data was last updated (Zillow,
2013). The distribution of the number of homes for the Census Designated Places in this data set appears to match expectations, with the most occurring in Sacramento and fewest in Wilton.
Table 4.5
Median home-value per square foot by CDP
CDP
Arden Arcade
Median Min Max N
120 40.5 787 22,406
Zillow a
124
Carmichael
Citrus Heights
County b
Elk Grove
Fair Oaks
Florin
124.7 34.6 483.3 12,572
101.8 35.6 313.2 20,642
98.5 25.4 292.9 35,351
101.6 46.9 658.4 37,085
136.5 51.8 641.3 8,023
74.8 30.6 153.3 4,745
135
105
n/a
103
139
74
Folsom
Foothill Farms
Galt
Gold River
La Riviera
156
84.4
95
139.2
103.7
45.5
46.8
47.1
87.2
47.5
295.9
216.3
453
178.4
247
17,179
3,791
5,484
2,869
2,553
157
84
97 n/a
103

Table 4.5 (continued)
Median home-value per square foot by CDP
CDP
North Highlands
Median Min Max N
77 40.9 354.4 8,632
Zillow a
82
Orangevale
Parkway-South Sacramento
Rancho Cordova
128.6
65.1
95.7
52.5
29.3
43.3
874.2
193.4
240.2
7,280
6,506
13,515
129 n/a
101
Rio Linda
Rosemont
Sacramento
Vineyard
88.8
98
96.1
101
42.3
37.4
22
48.3
286.4
165.4
529
205.3
2,791
5,522
96,127
4,727
94
97
94
104
Wilton 120.7 86.6 205 228 155 a
Zillow March 2012 Median Value Estimates b County represents the homes in Sacramento that are not in these places http://www.zillow.com/local-info/CA-home-value/r_9/
39
Table 4.6
Sample median home-values per SQFT vs. actual sales data for March 2012
Neighborhood Dataset
Zillow Value March
12
Sale Price March
12
124 111.7
135
105
125.8
106.1
103
139
74
157
84
103.9
126.1
76.3
154.3 n/a
97
103
82
129
94
97
104
155
96.5 n/a
80.0
130.5
103.7
96.6
104.9 n/a

Table 4.6, above, presents median home-values per square feet for select neighborhood variables that are the have the same name as the CDP variables. The
“Dataset” column, representing the sample data used, fits well with home sales prices because their data changes based on very current market trends. This means that the
40 neighborhood variables match well with actual regions of Sacramento County and that the home-values used in this research are reasonably close to actual sales prices, if each home was sold at the time the home-values data were provided.
Table 4.7 presents employment information for the county of Sacramento sorted by employment in descending order. The Census place with the highest employment measured by the number of workers who work in that Census Designated Place is
Sacramento. It has almost half of the county’s employment. However, Sacramento
Census Designated Place covers more land than any other CDP. Only the “County” has more land area. The County “CDP” incorporates all the area outside of the given Census places. When employment is divided by land area, Arden Arcade has the largest number of workers per square mile. If location of employment and employment density are important drivers of public transportation success (Kain & Liu, 1999), then looking at
Table 7, it seems as if the Gold Line may be more valuable because it connects more employment centers of the county such as Rancho Cordova and Folsom, in addition to
Sacramento CDP. The Blue Line, however, does not reach far enough south to Elk Grove or north to Citrus Heights or Arden Arcade to have a major impact on reaching employment centers outside of Sacramento CDP.

41
In Table 4.7, Employment represents the number of jobs located in the CDP. The third column “% Region Emp” is the percentage of the county’s employment located in the CDP. “Aland (SqMi)” is the area of land the CDP covers in square miles. “Emp
Density (per SqMi)” is the employment density per square mile. “PayAnn ($1,000s)” is the annual payroll of workers who work in the CDP in thousands of dollars.
“Payroll/Emp ($1,000s)” is the average salary for a worker in the CDP in thousands of dollars.
Table 4.7
Employment statistics by CDP in 2007
CDP Employment
Sacramento 201,315
%
Region
Emp
Aland
(SqMi)
Emp
Density
(per SqMi)
49.49% 146.66 1,372.63
Arden Arcade
Rancho Cordova
Elk Grove
51,950
28,568
23,590
12.77%
7.02%
5.80%
18.71
22.12
15.19
2,776.28
1,291.55
1,553.22
Folsom
Citrus Heights
North Highlands
County
19,582
18,961
14,288
11,938
4.81%
4.33%
3.51%
2.93%
27.11
14.58
13.37
531.46
722.19
1,300.34
1,068.56
22.46
Carmichael
Fair Oaks
Florin
Orangevale
Parkway-South
Sacramento
Gold River
Rosemont
9,269
6,092
4,666
3,890
3,726
2.28%
1.50%
1.15%
0.96%
0.92%
10.76
9.89
4.70
10.07
6.15
861.30
616.14
993.27
386.20
605.93
Galt
La Riviera
Vineyard
Foothill Farms
Rio Linda
Total
2,899
2,170
1,315
1,271
572
472
262
406,796
0.71%
0.53%
0.32%
0.31%
0.14%
0.12%
0.06%
1.34
3.88
14.27
1.82
4.07
2.83
3.97
2,165.53
560.00
92.15
698.93
150.37
166.53
65.92
PayAnn
($1,000s)
118,092
54,271
26,663
39,426
12,749
10,374
4,868
15,717,688
8,582,378
2,205,079
1,343,448
670,374
650,107
460,178
425,997
353,133
242,425
180,337
151,556
96,915
89,318
Payroll/Emp
($1,000s)
40.74
25.01
20.28
31.02
22.29
21.98
18.58
42.63
42.45
47.03
28.42
33.20
24.27
29.82
29.58
26.15
29.60
32.48
24.91
23.97

Graphical Description of Home-values on Rail Proximity
42
Graphical representations of home-values based on rail proximity are provided in this section to present this relationship. Figure 4.1 displays the median scatter plot lines of home-values per square foot based on proximity to the systems overall rail stations,
Blue Line stations and Gold Line stations, where only homes within 10 miles of proximity to the closest rail station are observed. It is difficult to find strong trends with these median scatter plot lines. There appears to be a premium for homes with proximity to the Gold Line (yellow colored line), perhaps because of its connections to employment. There is a discount for homes with proximity to the Blue Line (the blue colored line). The whole Sacramento Light Rail (the black colored line) has valuations that appear to fall in-between these two.
Median Scatter Plot Line of Home Values Per Square Foot on Proximity to Rail Stations
0 2 4 6 8 10
Gold Line
Either Line
Blue Line
Figure 4.1
Median scatter plot line of home-values per square foot on proximity to rail stations.

43
Figure 4.2 shows a fractional polynomial line to create a smoother representation of home-values per square foot in relation to proximity to the light rail for the overall light rail system (black line), the Gold Line (the yellow line), and the Blue Line (the blue line). It appears more strongly in Figure 4.2 that the Gold Line provides a premium for rail proximity, but there seems to be less evidence of a discount for the Blue Line with these lines compared to the median scatter plot lines. Overall home-values along the Blue
Line and overall light rail appear to be relatively flat in relation to rail proximity. Figures
4.3, 4.4, and 4.5, chart the median scatter plot line and the fractional polynomial fitting line for the Gold Line, Blue Line, and overall Sacramento Light Rail system respectively.
Figure 4.3 supports the idea that there is a premium along the Gold Line. At immediate proximity to the light rail, home-values along the Gold Line are their highest compared to home-values farther from the Gold Line. Figure 4.4 suggests that there is a discount for living in close proximity to the Blue Line, especially by its median scatter plot line. For the whole Sacramento light rail system charted in Figure 4.5, there is inconclusive information on there being a premium for living close to the light rail perhaps because the
Blue Line and Gold Line may cancel out each other.

Home Value Per Square Foot on Proximity to Closest Light Rail Station
44
0 2 4
Gold Line
Either Line
6
Blue Line
8 10
Figure 4.2 Predicted home-value per square foot on proximity to closest rail
Home Values per Square Foot on Proximity to Gold Line
0 2 4
DstGold predicted valuepersqft
6 8
Median spline
10
Figure 4.3 Predicted and median scatter plot home-values on proximity to
Gold Line

Home Values Per Square Foot on Proximity to Blue Line
45
0 2 4
DstBlue predicted valuepersqft
6 8
Median spline
10
Figure 4.4 Predicted and median scatter plot home-values on proximity to Blue Line
Home Value Per Square Foot on Proximity to Light Rail
0 2 4
Closest Rail
6 predicted valuepersqft
8
Median spline
10
Figure 4.5 Predicted and median scatter plot home-values on proximity to closest rail
Table 4.8 displays the mean, median, and standard deviation of home-values per square foot and the count of home-values per square foot for homes in 6 rail proximity thresholds. Threshold “Q” represents homes within a 0.25 miles of the closest light rail station. “H” represents homes between 0.25 and 0.5 miles of the closest light rail station.

“1” represents homes that are between 0.5 and 1 mile from the closest light rail station.
46
Threshold “1H” represents homes between 1 and 1.5 miles from the light rail. Threshold
“2” represents homes between 1.5 and 2 miles of the closest light rail station and “3” represents homes that are between 2 and 3 miles of the closest light rail station. Using the thresholds is a way to account for the potential nonlinearity of home-values on rail proximity (Chatman, et al., 2011 and Bowes & Ihlanfeldt, 2001).
The pattern of home-values per square feet on rail proximity in Table 4.8 appears to be consistent with the previous graphics. Home-values per square foot are the cheapest with closest proximity to the light rail, but they increase for the overall light rail for homes between 0.25 and 0.5 miles, and peak again between 1 and 2 miles. The number of homes within a 0.25 of the Sacramento Light Rail station, however, is very small with
976 homes overall. If the light rail lies along less developed corridors then ridership and the value of the station would be lower according to Mackett and Sutcliffe (2003). Only about 19,000 homes lie within one-half mile of the station compared to 36,000 homes that are located between 0.5 and 1 mile of the station. The median home-values per square foot presented in Table 4.8 are presented graphically in Figure 4.6 with the median home-value per square foot for the whole county represented by the red horizontal line at
$101.5 per square foot.
Figure 4.6 suggests that homebuyers pay more to live in proximity to the Gold
Line. There appears to be a large discount in home-values for properties along the Blue
Line. The overall Sacramento Light Rail again appears to fall between the premium along the Gold Line and the discount along the Blue Line.

Table 4.8
Home-values per square foot on thresholds
Q
RAIL
MEAN MEDIAN SDEV N
87.2 79.3 27.3
H
1
1H
2
3
121.1
122
126.4
114.5
976
104.4 49.9 17,800
100.9 65.4 36,122
112.2 61 30,522
109.5 48.8 34,407
Q
103.4 93.8 53.6 47,067
GOLD
MEAN MEDIAN SDEV N
113.7 116.3 34.7 187
H
1
1H
2
3
118
142.2
150.6
138.6
113.1
87.2
109.6 32.6
137.9 62.2
9,675
125.5 62.2 24,235
19,826
136.3 56.2 21,804
104.8 52.5 29,237
BLUE
MEAN MEDIAN SDEV N
79.3 27.3 976 Q
H
1
1H
2
3
124.4
101.5
99.5
109
112.2
98.4
78
75.6
80.4
90.8
63.3
64.3
47.2
66.1
64.2
8,599
15,506
15,265
20,290
45,292
47

48
160
140
120
100
80
60
40
20 rail gold blue
Median
0
Q H 1 1H 2 3
Figure 4.6 Median home-values per square foot by distance from light rail
Regression analysis with the variables presented in this Data Summary is a better way to measure whether homeowners value rail station proximity. The graphical approach was presented to get a general feel of how home prices change with rail proximity in Sacramento County. However, many confounding factors are missing in the graphs above. Including these confounding factors in a regression analysis can lead to a more accurate estimation of how people value light rail proximity in Sacramento County.
In the next chapter, analysis with these data gives an answer to whether
Sacramento County residents tend to spend more for light rail proximity, to see whether one route is more highly desirable, and to describe how exogenous characteristics of the urban area can describe how home-values may change with light rail accessibility in
Sacramento County.

49
Chapter 5
EMPIRICAL ANALYSIS
In this chapter, the proper specifications of the dependent and independent variables are chosen. An ordinary least squares (OLS) regression and a weighted least squares (WLS) regression are developed and compared. Diagnoses were taken to choose the better performing model. Estimation on the hedonic value of rail proximity is made for the overall Sacramento Light Rail system, followed by a comparison of the Blue Line and Gold Line. Finally, interaction variables to measure the impact three exogenous socio-economic and physical characteristics of the region that affect the success of a rail transit system are examined. These exogenous characteristics are distance from the central business district, median household income in a Census Tract, and population density that are each interacted with proximity to rail station dummy variables.
Selection of Dependent Variable
To determine the appropriate form of the dependent variable for this study, a Box
Cox (1964) Transformation procedure is applied. Two forms of the Box Cox
Transformation are employed. First, the STATA boxcox command was utilized to determine theta, which recommends the form of the dependent variable as either linear if theta is equal to one, logged if theta is equal to zero, and the reciprocal if theta is equal to negative one. Additionally, theta is statistically tested if it equals one, zero, or negative one. A second variation of a Box Cox procedure helps to determine if the dependent variable should be linear or logged because theta is not statistically equal to either one or

50 zero, but falls between these two values. The process of the second Box Cox test follows the procedure presented by Dougherty (2011) Introduction to Econometrics .
The Box Cox transformation allows one to compare a linear model (Equation 5.1) and a log linear model (Equation 5.2), which is invalid without an appropriate transformation. Comparing these two models based on adjusted R
2 values is invalid because two different dependent variables are being regressed.
Y = β
1
+ β
2
X + u log Y = β
1
+ β
2
X + u
(Equation 5.1)
(Equation 5.2)
The Box Cox transformation, presented in Equation 5.3, allows one to compare indirectly between different forms of the dependent variable.
𝑌 𝜃
−1 𝜃
= β
1
+ β
2
𝑋 + 𝑢 (Equation 5.3)
The model is not linear so the maximum likelihood Box Cox model estimates θ. If θ = 1 then the dependent variable is linear (Y-1). If θ = 0 (or rather approaches zero) then the dependent variable is log(Y). Statistical tests determine if the values are statistically one or zero.
The theta coefficient (θ) estimated in the Box Cox test on home-values is 0.185.
This suggests that the dependent variable should likely be in natural log form. However, the test if theta is statistically equal to 0 is rejected. Therefore, the procedure presented by
Dougherty (2011) will help choose the linear or log form of home-values as the dependent variable.
The Box Cox procedure for determining between a log and linear dependent variable (Dougherty 2011) requires transforming the dependent variable by dividing Y by

the geometric mean of Y to generate an adjusted dependent variable labeled Y*. Two
51 ordinary least squares regressions are then run. One regression has the dependent variable
Y* (Equation 5.4) and the other is log(Y*) (Equation 5.5). They are both regressed on the same linear independent variables but will have different estimators than Equations 5.1 and 5.2, notated by the primes in Equations 5.4 and 5.5 over the parameters, β i
. The regression with the smallest residual sums of squares (RSS) is said to be the better model.
If the regression from Equation 5.4 has the smaller RSS then the Box Cox transformation would prefer the linear dependent variable specification. If Equation 5.5 has the smaller
RSS then the logarithmic specification of the dependent variable is preferred.
Y* = β′
1
+ β′
2
X + u
Log(Y*) = β′
1
+ β′
2
X + u
(Equation 5.4)
(Equation 5.5)
In applying Dougherty’s (2013) method to the data, the geometric mean of the variable HMVALUE was 170,083.8. This geometric mean is divided by the linear homevalues to create an adjusted dependent variable (Y*). The natural log of the adjusted home-value variable (log (Y*)) is also generated. These two variables are both regressed on the same independent variables. The residual sums of squares (RSS) of the two regressions are compared. The RSS for the linear specification is 40,213.7 and the RSS for the log specification is 24,977.7. A chi distribution test to see if these two values are statistically different was performed. The chi distribution test supports the log form of the dependent variable. Therefore, the dependent variable in this analysis will be the natural log of home-values (LOGHMVALUE).

Specification of Independent Variables
In this section, independent variables are specified with the appropriate linear or
52 nonlinear forms to reduce the possibility of miss-specification errors. The first set of variables examined is the fixed effect variables.
Tests on whether neighborhood and CDP fixed effects are significant were made.
The statistics on median home-value per square foot in Tables 4.5 and 4.6 suggest that these variables would be significant because the home-values differ greatly across neighborhoods. The addition of the CDP and neighborhood variables act like fixed effects where it controls for omitted variables across space, in this case across neighborhoods and CDPs. The variables are binary variables that give observations that land in both a CDP and neighborhood area a unique intercept. These CDP and neighborhood fixed effects help absorb influences of omitted variables that differ from one area to the next. The largest coefficients for the neighborhood variables in the final chosen regression displayed in Appendix A (Table A.10), were Curtis Park, East
Sacramento, Land Park, New Era Park, and Tahoe Park, which were each above 100% more than an Elk Grove home, when the Elk Grove was the chosen reference neighborhood variable to prevent perfect multicollinearity. This suggests that these neighborhoods have amenities that are not explained by the other variables that improve their home-values by about 100% or more than Elk Grove homes. The neighborhoods with the lowest coefficients included homes in Galt, Golf Course Terrace, and Strawberry
Manor. Their coefficients suggest that homes in these neighborhoods are discounted by at least 20% compared to an Elk Grove home that has the same characteristics. This

suggests that there are disamenities in these areas relative to Elk Grove that are not captured in the model that are driving their home prices down.
53
Each set of fixed effects variables improves the model’s fit. Joint significance F tests also support the addition of both sets of fixed effects. However, there are issues with multicollinearity with these two sets of fixed effects variables. For example, the CDPs and neighborhoods of Florin, Folsom, and Rosemont are perfectly multi-collinear and are therefore omitted from the regressions by STATA. Furthermore, each of the CDP’s is represented by a neighborhood. The addition of the CDP fixed effects is not very large compared to just including the neighborhood fixed effects. The adjusted R
2 improves by approximately 0.004 from 0.9072 to 0.9116. The potential problems from multicollinearity with these two fixed effects variables seem to outweigh the small improvement in goodness of fit. Therefore, the CDP fixed effects will be omitted from the regressions. The F-statistics for the joint significance of using only the neighborhood variables is 2,491.91, which suggests they are jointly significant, and they improve adjusted R
2 from 0.8095 without any fixed effects to 0.9072. When only CDP fixed effects are included the adjusted R
2 is 0.8364. In this study, only neighborhood fixed effects will be applied.
The rest of the independent variables are examined to assess whether nonlinear forms of the explanatory variables better explain home-values. Robust standard errors are used for each regression to better ensure t-statistics follow a t distribution. Economic theory and regression analysis help choose the optimal linear or nonlinear form of the independent variables. In general, four forms of the independent variables are examined –

linear, quadratic, cubic, and the logged form. Having the wrong specification of the
54 regressors can lead to omitted variable bias and inaccurate estimators, therefore, models can improve by using the proper specification. It is also important to fit theory with the form chosen for each variable.
The first variable examined is the living area in square feet variable (SQFT).
Figure 5.1 shows a scatterplot of the log of home-values and SQFT with a fractional polynomial fitted line. This graph strongly suggests some kind of nonlinear relationship between the two variables. This is consistent with the economic theory of diminishing marginal returns. Initially each additional square foot provides a greater return of utility for homebuyers. However, after some point, according to this graph perhaps at 1,500 sqft, each additional square foot provides less incentive to spend more money for more SQFT as the return of utility that the homebuyers receive from the extra square foot diminishes.
The graph seems to suggest a quadratic or log form for the SQFT variable. Outputs from running regressions with the different forms of the SQFT variable, shown in Appendix A,
Table A.1, suggested the natural log of the sqft fits the model best. Including the natural log form of the sqft variable increased the adjusted R
2 from 0.9072 to 0.9258.

55
Figure 5.1
Scatter plot of log of home-values on SQFT with trend line
The graph of the logged home-value variable on lot size in Figure 5.2 does not appear to have as distinct of a trend as the previous variable by looking at the scatter plot.
However based on the fractional polynomial fit line it appears that a similar phenomenon of diminishing utility from higher lot sizes is true for homeowners. Based on the regression output (Table A.2) the best fitting form of the lotsize variable was the natural log form, similar to the SQFT variable. The improvement in the fit of the model increased the adjusted R
2 from 0.9258 to 0.9303.

56
Figure 5.2
Scatter plot of log of home-values on lot size with trend line
The year built variable is difficult to predict and has produced different results both significant and insignificant, and both positive and negative, for different hedonic model studies on home-values (Coulson & Lahr, 2005; Redfearn, 2009). In this case, for
Sacramento County, there appears to be no strong trend in the scatter plot, but the fractional polynomial regression predicts an upward shifting quadratic shape where homes built around 1950 are the cheapest. The regressions preferred the cubic form based on the adjusted R
2
and the t-stats of the year built variables. However, the improvement was very small compared to the quadratic form. The quadratic shaped form makes more sense for Sacramento where people may be willing to spend more on old Victorian homes or new homes in recent subdivisions. Therefore, the quadratic form will be used for
YRBLT. The t stats for both year built variables are significant and an F-test suggests they are jointly statistically significant. These regressions are in Table A.3.

57
Figure 5.3
Scatter plot of log of home-values on YRBLT with trend line
The household income (HHINC) and median Census Tract household income
(MEDINC) variables were assessed to see which variable better predicted home-values.
Below, Figure 5.4 and Figure 5.55 display the graphs of these two variables on the logged home-values variable. The trends of the two are similar where home-values increase with household income but at a decreasing rate. After running the regressions, see Table A.4, the MEDINC variable performed better. The adjusted R
2 when HHINC was included was
0.9312 compared to 0.9334 when MEDINC was used instead of HHINC. Among the
MEDINC forms, the cubic regression performed the best in regards to the adjusted R 2 measurement. However, it improved the model’s fit by a very small margin compared to the quadratic form. The quadratic form makes more economic sense suggesting that home-values increase as household incomes in an area rise but at a decreasing rate as suggested in the graphics below. This quadratic form suggests a household will spend a

higher percentage of money on a home to go from a poor neighborhood to a middle-
58 income neighborhood than they would to move from a middle-income neighborhood to a higher-income neighborhood. The coefficient on the squared MEDINC variable is negative suggesting a decreasing return on living in higher-income neighborhoods. The tstats are significant as is the F-stat.
Figure 5.4
Scatter plot of log of home-values on HHINC with trend line

59
Figure 5.5
Scatter plot of log of home-values on MEDINC with trend line
The trend from the scatter plot of home-values on population densities is difficult to assess without the line of fit. The fractional polynomial fit line in Figure 5.6 suggests that in Sacramento County, smaller population densities see higher home-values, where home-values decline slightly as population density increases. Meanwhile, homes within the highest population densities have higher home-values than the ones with a slightly less population density. This may reflect the people of Sacramento County where some love having their own space without neighbors living on top of them, while a smaller group of households prefers to live in the densely populated city. The cubic form model fits the data best judging by the Adjusted R
2 measurement of fit in Table A.5. But it does not fit that much better with the quadratic form or with the theory of how Sacramento residents tend to prefer more space but there is a small group of residents who do like to live in more densely populated areas. The quadratic form will be used.

60
Figure 5.6
Scatter plot of log of home-values on POPDEN with trend line
The percentage of renters in a Census Tract may have a strong effect on homevalues in a neighborhood. Some believe that renters have less incentive to maintain a home like a homeowner would. Therefore, the renters’ homes will tend to have worse upkeep than non-renter homes. This lack of upkeep of homes may have a detrimental effect on other homes’ values (O’Sullivan, 2012). Figure 5.7 below seems to pick up that trend where areas with higher proportions of renters have lower home-values. This trend decreases at a decreasing rate. The linear form is best because there is no substantial improvement with the quadratic and cubic terms (Table A.6). However, there is a problem with the sign for this variable in all forms explored. The coefficient is positive which counters expectations. This may be the case because of imperfect multicollinearity.
The percentage of renters in a Census Tract is highly correlated (-0.740) with the median income of a Census Tract. As median income decreases, the percentage of renters in the

Census Tract increases. To deal with this multicollinearity, the PCTRENT variable will
61 be removed from the final model, as median income appears to have a stronger impact on home-values than the percentage of renters, as judged by the t-stats.
Figure 5.7
Scatter plot of log of home-values on PCTRENT with trend line
Figures 5.8 through 5.11 show the relationship between race and the log of homevalues. The trends for the percentage of Hispanics, Blacks, and “Other” are downward, while the Asian relationship with home-values is mostly flat. The variables on the percentage of Blacks, Asians and “Other” ethnicity appear to be linear. The relationship between the percentage of Hispanics and the log of home-values appears to be more nonlinear. By observing the regressions in Table A.7, the addition of the quadratic terms do not substantially enhance the model, therefore linear forms will be used, which is also similar to Landis et al. (1994), where comparisons can be made across studies.

62
Figure 5.8
Scatter plot of log of home-values on
PCTBLACK with trend line
Figure 5.9
Scatter plot of log of home-values on
PCTHISP with trend line
Figure 5.10
Scatter plot of log of home-values on
PCTASIAN with trend line
Figure 5.11
Scatter plot of log of home-values on
PCTOTHER with trend line
The relationship of proximity to the central business district and home-values is expected to follow the bid-rent curve where home prices should be the highest in the central business district where work commutes are the shortest and people will therefore have more money to spend on homes. People who live farther from the central business district would tend to spend less on housing but more on commuting. This theory is the bid-rent curve (O’Sullivan, 2012). The shape of this relationship for Sacramento in

Figure 5.12 is less evident. There appears to be a stronger upward trend in home-values
63 as distance from the CBD grows. This may suggest that Sacramento County residents prefer to live away from downtown and do not mind commuting to downtown for work if needed, or perhaps commutes are not long enough for there to be a desire to live so close to work if their work is in Downtown Sacramento. The value of the DSTCBD coefficients in the regressions (Table A.8) captures the lack of the bid rent curve for
Sacramento County and estimates a positive trend, which seems to be the trend after about five miles from downtown according to Figure 5.12. The quadratic form of the distance to the central business district performed the best suggesting home-values increase at a decreasing rate from Downtown Sacramento and that Sacramento does not satisfy the mono-centric urban economic bid-rent theory, ceteris paribus.
Figure 5.12
Scatter plot of log of home-values on DSTCBD with trend line

The relationship between a home’s value and proximity to a highway onramp is difficult to predict (Landis et al., 1994). Like proximity to a light rail station, on one
64 hand, being close to the highway can be an amenity, especially for those who must commute in a sprawled region. However, living near a highway can have its disamenities such as increased traffic and pollution. According to Figure 5.13, there is a wide range of home-values close to the highway. The line of fit predicts a sharp decline in home prices with immediate proximity and slower gradual increases in home-values farther from the highway after about three miles. The quadratic form was chosen from the regressions from Table A.9 because it better fits the polynomial fitted line in Figure 5.13.
Figure 5.13
Scatter plot of log of home-values on DSTHWY with trend line
Figure 5.14 displays the relationship between the employment gravity variable and the log of home-values. To be consistent with the literature (Bowes & Ihlanfeldt,
2001; Maennig & Brand, 2010) the employment gravity variable will remain in linear

form. Figure 5.14 suggests that EMPGRAV does not predict large changes in home-
65 values based on closer proximity to high levels employment.
Figure 5.14
Scatter plot of log of home-values on EMPGRAV with trend line
Now that the structural forms of the model for both the dependent variable and the independent variables have been chosen, an ordinary least squares will be run and compared to a weighted least squares using a weight derived by the distances between
Census Tracts.
OLS vs. WLS
The ordinary least squares (OLS) and weighted least squares models (WLS) are compared in measuring the value homeowners have for rail proximity in Sacramento
County. The hedonic OLS model has the specifications discussed in the previous section.
The second model will include a locational weight using a value that is similar to the tricubic weight advocated by Redfearn (2009) to allow flexibility in the coefficients. The

two regression outputs are presented below in Table 5.1 with the neighborhood effect
66 variables omitted from the presented output for brevity (The full model with fixed effects included is in the Appendix in Table A.10).
Both models perform very similarly to each other. Both models have resembling coefficients and adjusted R
2 values. The OLS performs slightly better than the WLS model based on the adjusted R
2 value at 0.9343 for the OLS model compared to 0.9339 for the WLS model. One of the main differences is that the coefficient for home-values between 0.5 and 1 mile from the light rail is statistically significant under the WLS model, while it is statistically insignificant under the OLS model. Both coefficients are positive, but neither displays strong economic significance. The WLS model suggests home-values between 0.5 and 1 mile from the rail have a premium of 0.6%. Otherwise, the relationship between rail proximity and home-values appears to be similar for both models. There is no statistically significant impact for direct rail proximity for both models up to a quarter mile from a light rail station, but there is a large discount for homes between 0.25 and 0.5 miles from the rail at about 6% for both models. This discount disappears for homes between one-half and one mile from the light rail. From 1 to 1.5 miles the two models estimate a 1% discount for rail proximity at this threshold, followed by a premium of about 3% for homes between 1.5 and 2 miles from the light rail, followed by another discount of 4% for properties between two and three miles from the light rail.

67
Table 5.1
OLS vs. WLS regression results
VARIABLES coef
OLS se coef
WLS se
LOGSQFT
LOGLOTSIZE
YRBLT
YRBLT2
SGLUNIT
POOL
OWNOCC
WATER
PCTBLACK
PCTHISP
PCTASIAN
PCTOTHER
MEDINC
MEDINC2
POPDEN
POPDEN2
DSTHWY
DSTHWY2
DSTCBD
DSTCBD2
EMPGRAV
DSTRAILQM
DSTRAILHM
DSTRAIL1M
DSTRAIL1HM
DSTRAIL2M
DSTRAIL3M
Constant
Observations
Adjusted R-squared
0.81287**
0.10457**
-0.19782**
(0.00096)
(0.00054)
(0.00232)
0.81214**
0.10661**
-0.19295**
(0.00097)
(0.00055)
(0.00235)
0.00005** (0.00000) 0.00005** (0.00000)
0.17195** (0.00189) 0.17470** (0.00186)
0.03551** (0.00073) 0.03659** (0.00075)
0.04018** (0.00068) 0.04148** (0.00068)
0.00716** (0.00101) 0.00929** (0.00104)
-0.00231** (0.00007) -0.00262** (0.00007)
-0.00161** (0.00006) -0.00213** (0.00006)
-0.00095** (0.00005) -0.00061** (0.00005)
-0.00197** (0.00013) -0.00238** (0.00013)
0.00762** (0.00009) 0.00728** (0.00009)
-0.00003** (0.00000) -0.00003** (0.00000)
-0.02241** (0.00060) -0.02196** (0.00062)
0.00129** (0.00004) 0.00135** (0.00005)
-0.00910** (0.00055) -0.00897** (0.00058)
0.00063** (0.00006) 0.00056** (0.00006)
0.04793** (0.00095) 0.04797** (0.00100)
-0.00087** (0.00004) -0.00089** (0.00004)
0.02613** (0.00049) 0.02613** (0.00049)
0.00884 (0.01103) 0.00602 (0.01087)
-0.06174** (0.00244) -0.06436** (0.00242)
0.00328 (0.00170) 0.00604** (0.00170)
-0.01442** (0.00180) -0.01354** (0.00180)
0.02895** (0.00138) 0.03186** (0.00138)
-0.04294** (0.00123) -0.03889** (0.00124)
197.33770** (2.28357) 192.60078** (2.30947)
318,028
0.93426
318,028
0.93387
Standard errors in parentheses
** p<0.01, * p<0.05
Neighborhood Fixed Effects were included in model but omitted from table
The regression results from both models suggest that keeping all else constant there is a discount for close proximity up to one-half mile from the light rail station of 6%

that could be capturing possible disamenities of direct rail proximity. Beyond this threshold, there is no distinct trend for a premium or discount for rail proximity, as the
68 trend oscillates between premium and discount with relatively small coefficients. This may suggest that Sacramento residents do not strongly value access nor do they disvalue access to the light rail station, similar to the results of Landis et al. (1994) whose regression estimated an insignificant premium or discount for rail proximity.
The coefficients of the other variables look appropriate. A 1% increase in the living area of the home leads to a 0.81% increase in home-values. Like in Landis et al.
(1994), SQFT is most significant in determining home-values in Sacramento County. A
1% increase in lot size leads to a 0.10% increase in home-values. Home-values decrease for homes built up to 1978 compared to homes built before then, keeping all else constant. However, after 1978 home-values begin to increase at an increasing rate. A single unit home is valued 17% more than is a multi-unit home. A home with a pool is valued about 3.6% higher than a similar home without a pool, whereas an owner occupied home is valued about 4.0% greater than a renter occupied home is. Living in a
Census Tract nearby a body of water provides very little value to a home, if any at about
0.7% to 0.9% increase in the home-value. Race has a very small impact on the value of homes. Landis et al. (1994) found significant relationships between race and home-values for Hispanics. There is generally a negative relationship for each of the race variables and the log of home-values, similar to the estimated trends in the regressions run by Landis et al. (1994). The largest race parameters in magnitude are for areas with more blacks and
“Other” ethnicity, perhaps capturing differences in education levels that affect incomes

and the ability of people to live in more expensive homes in general. A 10-percentage
69 point increase in the percentage of African Americans in a Census Tract is associated with a 2.3% decrease in home-values.
Median income of an area is also one of the most significant drivers of homevalues, similar to study by Landis et al. (1994). The coefficients for this quadratic term suggest that homebuyers will spend more on a home for each additional thousand dollars of median household income in a neighborhood at a decreasing rate up until $127,000, where then homebuyers are then willing to pay less to live by wealthier people. The population density variables suggest that home-values decline with increasing population density up to 8,686 people per square mile. Population densities greater than 8,686 on average see to increasing home-values for each additional person per square mile. Homevalues from highway access decrease up to about 7.2 miles from the closest onramp until they then begin to increase according to the OLS model. The value of a home farther away from downtown rise but at a decreasing rate as distance from downtown increases.
Finally, homes that are in Census Tracts with more employment nearby tend to see higher home-values. The model suggests that a 10,000-unit increase in the employment gravity is associated with a 2.6% increase in home-values. This value is similar to Maennig and
Brandt (2010) who received an estimate rounded at 2% for every 10,000-unit increase in the employment variable. The employment gravity variable in Bowes and Ihlanfeldt
(2001) was statistically insignificant but suggested a 0.4% increase in home-values for an additional 10,000-unit increase in employment gravity.

70
More diagnostics of the two models is taken next. The first set of diagnostics look at the models’ residuals for heteroskedasticity, assess whether the residuals are normal, and then test for omitted variables.
Heteroskedasticity
Heteroskedasticity occurs when the variances of the error term is dependent on the independent variables. To obtain efficient coefficients, residuals must be homoskedastic in that errors must vary at a constant variance across observations. One informal way to diagnose heteroskedasticity is to plot the squared residuals on the predicted values of the dependent variable. Figure 5.15
shows that the errors for the OLS model exhibit some heteroskedasticity, where there is a slight trend of larger errors when the predicted value of the home increases, and vice versa, where there are smaller errors with smaller predicted home-values. More formal, Breusch-Pagan / Cook-Weisberg, tests for heteroskedasticity were performed. The test on the OLS model assuming normal distribution of errors fails to reject heteroskedasticity. Its Chi-squared test statistic is
835.48. The heteroskedasticity test assuming non-normal residuals while using an F-test rejects that there is heteroskedasticity because the test statistic is 102.46. One way to deal with the heteroskedasticity is to use robust standard errors developed by White
(1980).
The plot of the WLS squared residuals in Figure 5.16 almost replicates the same plot as the OLS model. The more formal tests for heteroskedasticity produce a chisquared statistic of 1,101.39 with the assumption of normality, and 134.27 with the normality assumption dropped. This suggests that there is heteroskedasticity in the

residuals that the weight was not able to fix. The WLS model actually performed worse
71 than the OLS model for testing for heteroskedasticity.
Figure 5.15
OLS squared residuals on predicted values
Figure 5.16
WLS squared residuals on predicted values

Additional analyses of the residuals of the two models to measure whether the residuals follow a normal distribution were performed. The distribution of the OLS and
72
WLS residuals are displayed below in Figure 5.17 and Figure 5.18 respectively. Both models’ residuals follow similar distributions that fall in line with a normal distribution.
Figure 5.17
Distribution of OLS residuals against normal distribution
Figure 5.18
Distribution of WLS residuals against normal distribution

73
Descriptive summary statistics in Table 5.2 provide the four moments to judge for normality. Compared to the WLS residuals, the OLS residuals have a mean closer to zero with 11 zeros after the decimal place. The residuals for the WLS model are also small but with three zeros after the decimal place with a mean of 0.00014. The difference between the models’ mean residuals and 0 is a round off error. The variances of the two residuals are similar at about 0.0182 for each model. The OLS residuals are less skewed with a skewness of 0.02843 compared to 0.03836 for WLS residuals. Both residuals, however, have very fat tails with kurtosis at about 17.664 for each model.
Table 5.2
Moments of residuals
OLS
Mean
Variance
WLS
-2.84E-12 0.00014
0.01823 0.01825
Skewness
Kurtosis
0.02843 0.03836
17.664 17.664
The Ramsey regression specifications error test (RESET) created by Ramsey
(1969) suggests that both regressions suffer from omitted variables. The Ramsey RESET
F-statistics is 131.83 for the WLS model and 138.36 for the OLS regression. The addition of the weight actually reduces the F-statistic for the RESET omitted variable test. The possible omitted variables include confounding variables that help determine homevalues such as the crime level or safety level of the neighborhood, the condition of the home, the quality of schools near the home, among other possible variables that could impact home-values. The neighborhood fixed effect variables can pick up some of the omitted variables on crime and quality of education in an area. Yet including them

explicitly should improve the models and potentially reduce any omitted variable bias.
74
The information on crime is more concerning because it is possible that there is correlation between rail proximity and crime that could lead to omitted variable bias
(Bowes and Ihlanfeldt, 2001). Data on crime are available but not at the Census Tract level or at a level that could help in this analysis for the Sacramento County area without significant GIS skills. Overall, however, the models perform well at predicting homevalues as seen by the distribution of the residuals.
For this analysis, adding weights to the model did not improve the OLS model.
The locally weighted regression model is better suited for models with few independent variables and many unobserved spatially distributed variables (Chatman et al. 2011). The inclusion of the employment gravity variable and neighborhood fixed effects variables along with the highway and distance to the central business district variables may have made the WLS approach less suitable compared to the OLS model. The locally weighted regressions may work better when the space analyzed is very large, like the whole southern California area analyzed in Redfearn (2009) and Sunding and Swoboda, (2010), compared to this analysis of just Sacramento County, which is much smaller and could be explained by neighborhood fixed effects without compromising too many degrees of freedom. Therefore, the rest of the analysis will use the OLS model with robust standard errors starting with a comparison between the capitalization of light rail proximity for homes along the Blue Line and Gold Line of the Sacramento light rail system.

Blue Line vs. Gold Line
The regressions in Table 5.3 give evidence that homes along the Gold Line
75 receive greater value for light rail proximity than homes along the Blue Line. The closest threshold for these models is within 0.5 miles from the closest light rail station instead of
0.25 miles because the sample sizes of homes within 0.25 miles of a light rail for the two routes were too small to get valid results. The values of homes along the Gold Line see a premium that increase up to 2 miles from the light rail. Residents along the Gold Line prefer to live with proximity of the light rail, within 2 miles, but prefer to maintain some distance from the light rail station. The Blue Line, however, sees major discounts for rail proximity. Households disvalue the light rail along the Blue Line with a discount of
17.9% for immediate proximity, which decreases to about 11.4% for homes that lie between 2 to 3 miles from light rail access. These regressions provide evidence that
Sacramento households value the Gold Line significantly more than the Blue Line. It appears that the estimated coefficients for Sacramento Light rail as a whole (see Table
5.1) may fall between the positive benefits of the Gold Line and the discounts of the Blue
Line. The estimates for both routes in regression (3) of Table 5.3 appear to be more accurate with smaller robust standard errors. Regressions (1) and (2) of Table 5.3 provide an upper bound on premium and discount values respectively and regression (3) provides a lower bound in magnitude.

Table 5.3
Regressions comparing capitalization of Blue Line vs. Gold Line
VARIABLES
LOGSQFT
LOGLOTSIZE
YRBLT
YRBLT2 coef
Gold (1) se coef
Blue (2) se
Both (3) coef se
0.81227** (0.00143) 0.81371** (0.00143) 0.81249** (0.00142)
0.10378** (0.00097) 0.10480** (0.00097) 0.10425** (0.00097)
-0.19542** (0.00403) -0.19261** (0.00401) -0.19238** (0.00403)
0.00005** (0.00000) 0.00005** (0.00000) 0.00005** (0.00000)
SGLUNIT
POOL
OWNOCC
WATER
PCTBLACK
PCTHISP
PCTASIAN
PCTOTHER
MEDINC
MEDINC2
POPDEN
POPDEN2
DSTHWY
DSTHWY2
DSTCBD
0.17378**
0.03619**
0.04045**
-0.00130
-0.00178**
-0.00094**
-0.00081**
(0.00524)
(0.00067)
(0.00082)
(0.00110)
(0.00007)
(0.00006)
(0.00004)
0.17447**
0.03577**
0.03997**
-0.00544**
-0.00174**
-0.00105**
-0.00116**
(0.00525)
(0.00067)
(0.00082)
(0.00106)
(0.00007)
(0.00006)
(0.00004)
0.17565**
0.03572**
0.03998**
-0.01099**
-0.00157**
-0.00066**
-0.00086**
(0.00525)
(0.00066)
(0.00082)
(0.00109)
(0.00007)
(0.00006)
(0.00004)
-0.00147** (0.00013) -0.00221** (0.00013) -0.00112** (0.00013)
0.00697** (0.00009) 0.00717** (0.00009) 0.00666** (0.00009)
-0.00003** (0.00000) -0.00003** (0.00000) -0.00003** (0.00000)
-0.01415** (0.00061) -0.02999** (0.00060) -0.02608** (0.00060)
0.00071** (0.00005) 0.00184** (0.00004) 0.00154** (0.00004)
-0.01530** (0.00054) -0.01140** (0.00053) -0.01475** (0.00053)
0.00130** (0.00006) 0.00065** (0.00006) 0.00098** (0.00006)
0.04229** (0.00117) 0.01885** (0.00129) 0.01825** (0.00128)
DSTCBD2
EMPGRAV
DSTGOLDHM
DSTGOLD1M
-0.00069** (0.00004) -0.00011* (0.00004) 0.00003 (0.00005)
0.01614** (0.00057) 0.01675** (0.00054) 0.01528** (0.00054)
0.02599** (0.00357)
0.09382** (0.00228)
0.01753**
0.07785**
(0.00346)
(0.00222)
DSTGOLD1HM 0.11033** (0.00274)
DSTGOLD2M 0.12810** (0.00168)
DSTGOLD3M
DSTBLUEHM
0.06049** (0.00163)
-0.21580** (0.00587)
0.08948**
0.10595**
0.03010**
-0.17906**
(0.00263)
(0.00159)
(0.00163)
(0.00580)
DSTBLUE1M
DSTBLUE1HM
DSTBLUE2M
DSTBLUE3M
Constant
Observations
Adjusted R 2
-0.17134**
-0.17515**
-0.14542**
-0.13070**
0.93553
(0.00427)
(0.00311)
(0.00271)
(0.00149)
-0.15157**
-0.15456**
-0.12674**
-0.11448**
318,028
0.93666
(0.00420)
(0.00302)
(0.00259)
(0.00144)
195.01111** (3.97493) 192.59884** (3.96065) 192.34591** (3.97532)
318,028
0.93510
318,028
Robust standard errors in parentheses
** p<0.01, * p<0.05
76

Interaction Variables
Next, interaction variables are included to the models (Table 5.4) for the overall
77 rail system (Regression (3) of Table 5.4), the Blue Line (Regression 2), and the Gold
Line (Regression 1). The interaction variables estimate the effects exogenous urban area characteristics impact the value of households with proximity to the Sacramento light rail system.
The Sacramento light rail follows a similar trend as MARTA in Atlanta, (Bowes
& Ihlanfeldt, 2001) where people who live farther from the central business district will pay more for a home that is closer to a light rail station, perhaps because it makes for a more convenient commute. The value of a home within 0.5 miles of the light rail station increases by an additional 3.3% for each additional mile from the central business district that the home lays. The coefficient becomes negative for homes between 1.5 miles and 2 miles from the nearest light rail station. This suggests that living farther away from downtown Sacramento and a light rail is less desirable than living farther from downtown but closer to the light rail. In other words, downtown residents do not have a particular desire to live near the light rail station, but those farther from Downtown Sacramento have higher demand for light rail proximity.
A similar pattern exists along the Gold Line but only for homes with immediate proximity to the Gold Line. These homes are valued more as distance from downtown increases. Homes that lie farther from the central business district and are beyond 0.5 miles of the light rail are discounted, suggesting that only immediate proximity to the light rail is valued along the Gold Line as distance from the central business district

increases. Along the Blue Line, for immediate rail proximity there is a discount that
78 increases as distance from the CBD increases. This might suggest that the Blue Line serves less desirable areas away from the CBD. Homes within 1.5 miles of the light rail are correlated with cheaper homes as the distance from Downtown Sacramento increases.
If one lives along the Blue Line, it is better to be located closer to Downtown
Sacramento. However, for homes between 1.5 and 3 miles from the rail station, homevalues face a premium for being farther from downtown Sacramento. This suggests that one is better off with less proximity to the Blue Line as distance from Downtown
Sacramento increases.
Neighborhoods with higher median household incomes value rail proximity more than poorer neighborhoods for the whole Sacramento light rail. A $10,000 increase in median household incomes within 0.5 miles of the light rail is associated with an additional 1.6% increase in home-values. Median household income combined with proximity to the light rail is suggested to increase home-values at an increasing rate. This suggests that the Sacramento Light Rail is more valuable to higher income households whose workers may be more likely to use the rail for commuting to work. Along the Blue
Line there is generally greater value for the light rail among richer neighborhoods beyond
0.5 miles from the closest light rail station. Along the Gold Line there is no positive effect on home-values for higher income neighborhoods along the rail until after 1.5 miles. Within 1.5 miles from the Gold Line, lower income neighborhoods value the light rail more. Beyond one-and-one-half mile from the Gold Line, there are more benefits for higher income neighborhoods. In Atlanta (Bowes and Ihlanfeldt, 2001) there was a

discount for higher income households with immediate proximity but a premium for
79 homes outside one-half mile, similar to the Blue Line.
Population density appears to help increase values of homes along the Sacramento
Light Rail because the coefficient for immediate proximity is positive. However, none of the coefficients appears to be economically significant. This may reflect the lack of construction around the routes of the light rail as expressed in Mackett and Sutcliffe
(2003), where this rail transit system was the only rail, of the nine studied, not to run through developed corridors. A 1,000 person per square mile increase in population density is correlated with an additional 0.004% increase in home-values for homes within
0.5 miles from the closest light rail station. Beyond 0.5 miles, there is no benefit for higher population densities leading to higher home premiums. This suggests that there may be somewhat more benefits of higher population densities around the light rail, especially the Gold Line, but the expected impact may be very small on home-values.
Table 5.4
Regressions estimating impacts of physical and socio-economic variables on value for rail accessibility
(1)
Gold
(2)
Blue
VARIABLES coef se coef se coef
(3)
Sac Light Rail se
LOGSQFT
LOGLOTSIZE
YRBLT
YRBLT2
SGLUNIT
POOL
OWNOCC
WATER
PCTBLACK
PCTHISP
PCTASIAN
PCTOTHER
MEDINC
0.80855**
0.10649**
-0.18393**
0.00005**
0.17385**
(0.00141)
(0.00097)
(0.00405)
(0.00000)
(0.00521)
0.81226**
0.10626**
-0.19600**
0.00005**
0.17459**
(0.00142)
(0.00096)
(0.00409)
(0.00000)
(0.00523)
0.81123**
0.10685**
-0.20145**
0.00005**
0.17037**
(0.00142)
(0.00097)
(0.00407)
(0.00000)
(0.00523)
0.03469** (0.00064) 0.03583** (0.00066) 0.03495** (0.00066)
0.04006** (0.00081) 0.04011** (0.00082) 0.03976** (0.00081)
-0.01128** (0.00111) 0.01048** (0.00106) 0.00339** (0.00110)
-0.00132** (0.00007) -0.00101** (0.00008) -0.00081** (0.00007)
0.00010 (0.00006) -0.00050** (0.00006) -0.00039** (0.00006)
-0.00075** (0.00004) -0.00108** (0.00004) -0.00136** (0.00004)
0.00028* (0.00013) -0.00274** (0.00012) -0.00087** (0.00013)
0.00721** (0.00009) 0.00642** (0.00009) 0.00732** (0.00009)

Table 5.4 Continued
Regressions estimating impacts of physical and socio-economic variables on value for rail accessibility
VARIABLES
Gold Line coef
(1) se
(2)
Blue Line coef se Coef
(3)
Sac Light Rail se
MEDINC2
POPDEN
POPDEN2
DSTHWY
DSTHWY2
DSTCBD
DSTCBD2
EMPGRAV
DSTRAILHM
DSTRAIL1M
-0.00003**
-0.01104**
(0.00000)
(0.00059)
-0.00002**
-0.02145**
(0.00000)
(0.00061)
-0.00003**
-0.02106**
(0.00000)
(0.00061)
0.00044** (0.00004) 0.00121** (0.00005) 0.00138** (0.00005)
-0.01291** (0.00054) -0.01377** (0.00054) -0.01350** (0.00055)
0.00098** (0.00006) 0.00091** (0.00006) 0.00096** (0.00006)
0.06921** (0.00123) 0.03301** (0.00132) 0.03262** (0.00147)
-0.00139** (0.00005) -0.00033** (0.00004) 0.00024** (0.00006)
0.09412** (0.00446) 0.18177** (0.00495) 0.30420** (0.00512)
-0.29859** (0.03998) 0.54502** (0.13849) -0.79719** (0.03389)
0.73959** (0.01413) -0.09583 (0.06087) -0.05828** (0.01226)
DSTRAIL1HM
DSTRAIL2M
DSTRAIL3M
DSTCBDRAILHM
1.05292** (0.02353) 0.19812** (0.01982) 0.06263** (0.01334)
0.46707** (0.01439) -0.16394** (0.01911) -0.14977** (0.00950)
0.26023** (0.00729) -0.21192** (0.01031) -0.13660** (0.00543)
0.01032** (0.00143) -0.04161** (0.00705) 0.03335** (0.00116)
DSTCBDRAIL1M -0.03016** (0.00050) -0.02662** (0.00400) 0.00422** (0.00048)
DSTCBDRAIL1HM -0.04092** (0.00084) -0.05850** (0.00187) -0.00921** (0.00064)
DSTCBDRAIL2M -0.02751** (0.00039) 0.00318* (0.00131) 0.00008 (0.00035)
DSTCBDRAIL3M
MEDINCRAILHM
-0.01189** (0.00045) 0.00331** (0.00080) 0.00819** (0.00039)
-0.00024 (0.00022) -0.00326** (0.00083) 0.00159** (0.00019)
MEDINCRAIL1M -0.00161** (0.00011) 0.00341** (0.00063) 0.00054** (0.00011)
MEDINCRAIL1HM -0.00139** (0.00020) 0.00162** (0.00014) 0.00194** (0.00011)
MEDINCRAIL2M
MEDINCRAIL3M
POPDENRAILHM
POPDENRAIL1M
0.00230**
0.00094**
0.00006**
-0.00002**
(0.00008)
(0.00010)
(0.00000)
(0.00000)
0.00024
0.00101**
-0.00003**
-0.00001
(0.00021)
(0.00010)
(0.00001)
(0.00001)
0.00291**
0.00067**
0.00004**
-0.00001**
(0.00007)
(0.00007)
(0.00000)
(0.00000)
POPDENRAIL1HM -0.00007** (0.00000) -0.00002** (0.00000) -0.00004** (0.00000)
POPDENRAIL2M -0.00003** (0.00000) -0.00000* (0.00000) -0.00001** (0.00000)
POPDENRAIL3M
Constant
-0.00003**
183.420**
(0.00000)
(4.00051)
0.00000*
195.215**
(0.00000)
(4.03590)
-0.00001**
200.837**
(0.00000)
(4.01296)
Observations
Adjusted R 2
318,028
0.93890
318,028
0.93638
318,028
0.93696
Robust standard errors in parentheses
** p<0.01, * p<0.05
Summary of Empirical Results
The empirical analysis suggests that for homes closest to Sacramento’s light rail
80 there is a discount of about 6% for proximity within 0.5 miles of the closest station.

81
Beyond 0.5 miles, there is a no substantial trend of either a discount or premium for light rail proximity. However, along the Gold Line, households are valued more for rail proximity, where homes within 0.5 miles face a 2% premium for proximity to the Gold
Line. The premium for proximity to the Gold Line increases for homes up to 2 miles away, reaching roughly 11%. Homes along the Blue Line are valued less for rail proximity. Within 0.5 miles of the closest Blue Line station, homes are discounted on average at about 18% because of light rail proximity. This discount falls to 11% for homes between 2 and 3 miles from the Blue Line. One of the major differences between the two lines that could be driving the difference in home-values is that the Gold Line connects more employment centers in Sacramento County than does the Blue Line.
Homeowners on average may be better able to enjoy the option value of living in proximity to the light rail along the Gold Line than the Blue Line because the Gold Line connects more areas of employment. Along the Sacramento Light Rail as a whole, areas with higher median household income tend to value light rail accessibility more than poorer neighborhoods. Those who live farther from downtown also are willing to pay more for light rail proximity. Population density has a positive impact on home-values for homes closest to the light rail. This benefit on home-values disappears for homes farther than 0.5 miles from the closest light rail.

82
Chapter 6
CONCLUSION
Sacramento Regional Transit, that operates the light rail system, has multiple plans to expand its light rail system. This research answers the question of whether
Sacramento County residents value their light rail system as a way to understand if the rail provides economic benefit to the region. This research can also provide hints as to whether the new developments will be successful through some of the deeper analyses.
The deeper analysis in this study answered questions on whether one route is more valued than the other route. Estimations of how exogenous socio-economic and physical characteristics of the area contribute to Sacramento residents’ value of the light rail were made. These exogenous variables were encouraged by literature on what characteristics determine light rail success.
Answers to these issues were reached through hedonic regressions. OLS and WLS models were created. The OLS model slightly outperformed the WLS model. These first models estimated the value Sacramento homeowners put in rail proximity for the whole
Sacramento light rail, and again with a focus on distinguishing possible differences in values between the Gold and Blue Lines. A final set of OLS hedonic regressions was performed that included the interaction variables of rail proximity tied with median household income, population density, and distance from the CBD.
This research contributes to the literature by analyzing two separate routes in one urban area to provide important insights in what drives value for rail transit by residents.
The interaction variables provide more clues on where light rail succeeds and where it

may not perform as well. This research can be useful for Sacramento regional planners
83 who have many on-hold plans for the light rail system to guide in answering the question of whether expansions will be of value to the county.
This study found that Sacramento homeowners typically do not pay a premium for light rail proximity. For immediate proximity, there is a discount in home-values of
6%. Beyond 0.5 miles, there is no consistent or economically significant premium or discount for rail proximity for the overall light rail. These results are similar to what
Landis et al. (1994) discovered. There is, however, a large difference between how
Sacramento residents view the Gold Line compared to the Blue Line. Proximity to the
Gold Line, which connects more employment centers, correlates with a premium in home-values. Immediate proximity to the Gold Line is associated with a 2% premium for home-values. The premium for Gold Line proximity reaches 11% for homes between 1 and 2 miles away. The Blue Line, which fails to connect significant employment centers, correlates with a discount in home-values for rail proximity, with homes within 0.5 miles facing discount of 18% that declines to 11% for homes between 2 and 3 miles from the
Blue Line.
Proximity to the overall light rail in Sacramento is correlated with higher homevalues as distance from the CBD increases, as median household incomes rise, and with higher population densities for homes closest to the light rail. Proximity to the Gold Line is correlated with higher home-values only for immediate proximity when distances from the CBD increases, when incomes increase for homes between 1.5 and 3 miles from the light rail, and when population densities increase for homes within 0.5 miles from the

Gold Line. Proximity to the Blue Line is correlated with higher home-values for homes
84 beyond 1.5 miles from the Blue Line when distance from the CBD increases, for homes beyond 0.5 miles from the Blue Line when median household incomes increase, and provide no economically significant premium when population density increases.
These findings can help guide planners of Sacramento who attempt to place the light rail where it can have the most benefit to its residents. It may also be a guide for planners in urban areas similar to Sacramento. The lessons from this study include the importance of connecting the light rail to areas of employment. For example, the Gold
Line succeeds better than the Blue Line at this, which may be reflected in the premium home buyers put in the Gold Line compared to the Blue Line. Routes built to suburbs should connect closely with home developments because homes farther from the central city value rail transit access more. Connecting places of higher income levels may improve the value of the rail because it would better connect job commuters who would place more value on the system.
Adding crime and education quality variables could improve the models. Though these characteristics could be captured indirectly in the fixed effects variables, the model could improve if they are directly included. Without the crime variable, there could be omitted variable bias for rail proximity because crime is likely to be correlated with both rail proximity and the error term. Future collaborative efforts with someone skilled in geocoding who can add crime data to the model could help improve estimates. Future studies can also attempt to use quasi-experimental research methods to see how homevalues adjust based on the expansion of the Green and Blue Lines. The research would

measure how home prices change in an area before there is a rail station in proximity
85 compared to after the station is built (see Chatman, et al., 2011). Using a quasiexperiment like this requires more of a time series or panel study approach with time fixed effects. The time dimension is very important in this kind of a study. Another issue is choosing exactly how much before and how much after the expansion is made to assess its effects. Would the amenity be capitalized once there is news of the new station is going to be built, or right before it opens? How far after its construction, would its full effect be capitalized? To better assess if light rail in Sacramento has helped shape New
Urbanism development patterns in Sacramento, a time series or panel study would be necessary. Future research can focus more on the renter demographic of Sacramento
County. This research used home-values as the variable of focus, which favored homeowners over renters. It is possible that renters have different priorities and could pay different rent rates for proximity to the light rail that may reflect different priorities that renters tend to have than homeowners. Future research on renters could get a more complete picture of how Sacramento residents value their light rail. Estimates could also improve by obtaining locations for each parcel that would produce more accurate distance measurements for light rail proximity. Though the home-values estimated by
DataQuick appear to be in line with home sales trends, future research could focus on using home sales data to get the actual prices homeowners pay for homes, rather than an estimate of its current market value.

Appendix A
EMPIRICAL ANALYSIS REGRESSIONS
Table A.1
Regressions on various forms of SQFT
(1)
VARIABLES sqft sqft2 sqft3 logsqft lotsize
(2) (3) (4) loghmvalue loghmvalue loghmvalue Loghmvalue
0.00043** 0.00088** 0.00119**
(0.00000) (0.00000) (0.00002)
-0.00000** -0.00000**
(0.00000) (0.00000)
0.00000**
(0.00000)
0.86381**
(0.00137)
0.00000** 0.00000** 0.00000** 0.00000** yrblt sglunit pool ownocc
(0.00000) (0.00000) (0.00000) (0.00000)
0.00213** 0.00133** 0.00119** 0.00114**
(0.00003) (0.00003) (0.00003) (0.00003)
0.24202** 0.21658** 0.20402** 0.19900**
(0.00590) (0.00549) (0.00546) (0.00542)
0.04499** 0.03751** 0.03799** 0.03930**
(0.00080) (0.00071) (0.00071) (0.00071)
0.04593** 0.04028** 0.03919** 0.03861** water income popdensity pctrent pctblack pcthisp pctasian pctother
(0.00094) (0.00088) (0.00087) (0.00087)
0.02439** 0.02379** 0.02423** 0.02516**
(0.00119) (0.00108) (0.00108) (0.00108)
0.00059** 0.00052** 0.00052** 0.00054**
(0.00001) (0.00001) (0.00001) (0.00001)
-0.01015** -0.00953** -0.00942** -0.00951**
(0.00021) (0.00019) (0.00019) (0.00019)
-0.00096** -0.00100** -0.00102** -0.00101**
(0.00003) (0.00003) (0.00003) (0.00003)
-0.00447** -0.00398** -0.00392** -0.00397**
(0.00009) (0.00008) (0.00008) (0.00008)
-0.00381** -0.00340** -0.00328** -0.00326**
(0.00007)
-0.00012*
(0.00006)
-0.00000
(0.00006) (0.00006)
0.00026** 0.00042**
(0.00005) (0.00005) (0.00005) (0.00005)
-0.00281** -0.00250** -0.00252** -0.00261**
(0.00015) (0.00013) (0.00013) (0.00013)
86

dstcbd dsthwy
0.03419** 0.03536** 0.03624** 0.03648**
(0.00045) (0.00042) (0.00041) (0.00041)
-0.00440** -0.00383** -0.00354** -0.00340**
(0.00027) (0.00024) (0.00024) (0.00024) empgrav5
DstRailQm
0.02503** 0.02460** 0.02384** 0.02325**
(0.00069) (0.00064) (0.00063) (0.00063)
0.03057
(0.02715)
0.01704
(0.02574)
0.01243
(0.02538)
0.00856
(0.02506)
DstRailHm
DstRail1m
-0.08716** -0.08307** -0.08171** -0.08073**
(0.00317) (0.00297) (0.00296) (0.00295)
0.00138
(0.00225)
0.00247
(0.00211)
0.00385
(0.00209)
0.00480*
(0.00208)
DstRail1Hm
DstRail2m
-0.01632** -0.01408** -0.01237** -0.01200**
(0.00225) (0.00206) (0.00205) (0.00205)
0.03143** 0.04179** 0.04484** 0.04525**
(0.00170) (0.00153) (0.00151) (0.00152)
DstRail3m
Constant
-0.05718** -0.05006** -0.04883** -0.04970**
(0.00135) (0.00123) (0.00123) (0.00122)
6.96633** 8.05282** 8.12796** 3.24190**
(0.08082) (0.07705) (0.07633) (0.07462)
Observations
Adjusted R-squared
318,028
0.90716
318,028
0.92395
318,028
0.92541
318,028
0.92577
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
87

Table A.2
Regressions on various forms of LOTSIZE
(1) (2)
VARIABLES logsqft lotsize
(0.00137)
0.00000**
(0.00140)
0.00001**
(3)
(0.00141)
0.00001**
(4) loghmvalue loghmvalue loghmvalue loghmvalue
0.86381** 0.84414** 0.83119** 0.82106**
(0.00144) lotsize2
(0.00000) (0.00000)
-0.00000**
(0.00000)
-0.00000** lotsize3
(0.00000) (0.00000)
0.00000** loglotsize
(0.00000)
0.10099** yrblt 0.00114** 0.00150** 0.00175**
(0.00096)
0.00200** sglunit pool
(0.00003)
0.19900**
(0.00542)
0.03930**
(0.00003)
0.19394**
(0.00533)
0.03297**
(0.00003)
0.18401**
(0.00532)
0.02950**
(0.00003)
0.16889**
(0.00532)
0.02872** water ownocc income popdensity
(0.00071)
0.02516**
(0.00108)
0.03861**
(0.00070)
0.02388**
(0.00107)
0.03732**
(0.00070)
0.02457**
(0.00106)
0.03652**
(0.00070)
0.02756**
(0.00106)
0.03654**
(0.00087)
0.00054**
(0.00086)
0.00052**
(0.00085)
0.00050**
(0.00084)
0.00049**
(0.00001) (0.00001) (0.00001) (0.00001)
-0.00951** -0.00881** -0.00869** -0.00823** pctblack pcthisp pctasian pctother pctrent dstcbd
(0.00019) (0.00019) (0.00019) (0.00019)
-0.00397** -0.00383** -0.00377** -0.00376**
(0.00008) (0.00008) (0.00008) (0.00008)
-0.00326** -0.00321** -0.00320** -0.00335**
(0.00006)
0.00042**
(0.00006)
0.00047**
(0.00006)
0.00053**
(0.00006)
0.00046**
(0.00005) (0.00005) (0.00005) (0.00005)
-0.00261** -0.00243** -0.00245** -0.00229**
(0.00013) (0.00013) (0.00013) (0.00013)
-0.00101** -0.00102** -0.00100** -0.00091**
(0.00003)
0.03648**
(0.00041)
(0.00003)
0.03528**
(0.00041)
(0.00003)
0.03456**
(0.00040)
(0.00003)
0.03405**
(0.00040)
88

dsthwy empgrav5
-0.00340** -0.00352** -0.00340** -0.00358**
(0.00024) (0.00024) (0.00023) (0.00023)
0.02325**
(0.00063)
0.02264**
(0.00063)
0.02211**
(0.00062)
0.02151**
(0.00061)
DstRailQm
DstRailHm
0.00856
(0.02506)
0.00049
(0.02523)
-0.00365
(0.02530)
0.00669
(0.02533)
-0.08073** -0.08163** -0.08240** -0.08074**
(0.00295) (0.00294) (0.00294) (0.00291)
DstRail1m
DstRail1Hm
0.00480*
(0.00208)
0.00187
(0.00204)
-0.00073
(0.00204)
0.00110
(0.00204)
-0.01200** -0.01368** -0.01578** -0.01400**
(0.00205) (0.00204) (0.00204) (0.00202)
DstRail2m
DstRail3m
0.04525**
(0.00152)
0.03824**
(0.00151)
0.03357**
(0.00150)
0.03648**
(0.00148)
-0.04970** -0.05393** -0.05512** -0.05397**
(0.00122) (0.00121) (0.00120) (0.00118)
Constant 3.24190**
(0.07462)
2.68939**
(0.07414)
2.31035**
(0.07444)
1.13253**
(0.07497)
Observations
Adjusted Rsquared
318,028 318,028 318,028 318,028
0.92577 0.92769 0.92858 0.93030
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
89

Table A.3
Regressions on various forms of YRBLT
(1)
VARIABLES logsqft
(2) loghmvalue loghmvalue
0.82106** 0.80741** loglotsize
Yrblt
(0.00144)
0.10099**
(0.00096)
0.00200**
(0.00003)
(0.00146)
0.10508**
(0.00096)
-0.20818** yrblt2
(0.00403)
0.00005**
(0.00000) yrblt3 logyrblt sglunit
Pool
0.16889**
(0.00532)
0.02872**
0.16893**
(0.00520)
0.03320** ownocc
Water income popdensity
(0.00070)
0.03654**
(0.00084)
0.02756**
(0.00106)
0.00049**
(0.00001)
-0.00823**
(0.00070)
0.03581**
(0.00083)
0.02830**
(0.00104)
0.00047**
(0.00001)
-0.00776** pctrent pctblack pcthisp pctasian pctother dstcbd
(0.00019)
-0.00091**
(0.00003)
-0.00376**
(0.00008)
-0.00335**
(0.00006)
0.00046**
(0.00005)
-0.00229**
(0.00013)
0.03405**
(0.00040)
(0.00018)
-0.00098**
(0.00003)
-0.00398**
(0.00007)
-0.00334**
(0.00006)
-0.00047**
(0.00005)
-0.00273**
(0.00013)
0.03195**
(0.00039)
(4) loghmvalue
0.82150**
(0.00144)
0.10080**
(0.00096)
3.88811**
(0.06481)
0.16900**
(0.00532)
0.02866**
(0.00070)
0.03655**
(0.00084)
0.02757**
(0.00106)
0.00050**
(0.00001)
-0.00823**
(0.00019)
-0.00091**
(0.00003)
-0.00375**
(0.00008)
-0.00335**
(0.00006)
0.00048**
(0.00005)
-0.00228**
(0.00013)
0.03409**
(0.00040)
0.16787**
(0.00519)
0.03455**
(0.00070)
0.03552**
(0.00083)
0.02944**
(0.00104)
0.00047**
(0.00001)
-0.00741**
(0.00018)
-0.00096**
(0.00003)
-0.00406**
(0.00007)
-0.00348**
(0.00006)
-0.00071**
(0.00005)
-0.00298**
(0.00013)
0.03116**
(0.00039)
(3) loghmvalue
0.80802**
(0.00145)
0.10423**
(0.00095)
4.99072**
(0.28521)
-0.00260**
(0.00015)
0.00000**
(0.00000)
90

dsthwy empgrav5
-0.00358**
(0.00023)
0.02151**
(0.00061)
-0.00384**
(0.00022)
0.02530**
(0.00061)
-0.00398**
(0.00022)
0.02603**
(0.00061)
-0.00357**
(0.00023)
0.02145**
(0.00061)
DstRailQm
DstRailHm
0.00669
(0.02533)
-0.08074**
(0.00291)
0.02535
(0.02517)
-0.08505**
(0.00285)
0.03414
(0.02506)
-0.08612**
(0.00283)
0.00659
(0.02533)
-0.08085**
(0.00291)
DstRail1m
DstRail1Hm
0.00110
(0.00204)
-0.01400**
(0.00202)
0.00060
(0.00201)
-0.01140**
(0.00199)
0.00012
(0.00200)
-0.01261**
(0.00199)
0.00104
(0.00204)
-0.01407**
(0.00202)
DstRail2m
DstRail3m
0.03648**
(0.00148)
-0.05397**
(0.00118)
0.03589**
(0.00145)
-0.05272**
(0.00117)
0.03477**
(0.00145)
-0.05318**
(0.00117)
0.03647**
(0.00148)
-0.05401**
(0.00118)
Constant 1.13253** 207.70709** -3,188.35007** -24.43110**
(0.07497) (3.97939) (186.82203) (0.49010)
Observations
Adjusted R-squared
318,028
0.93030
318,028
0.93199
318,028
0.93219
318,028
0.93026
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
91

Table A.4
Regressions on various forms of HHINC and MEDINC
(1) (2)
VARIABLES
Logsqft
Loglotsize loghmvalue
0.80741**
(0.00146)
0.10508** loghmvalue
0.81183**
(0.00144)
0.10408**
Yrblt yrblt2
Sglunit
Pool
(0.00096)
-0.20818**
(0.00403)
0.00005**
(0.00000)
0.16893**
(0.00520)
0.03320**
(0.00098)
-0.19266**
(0.00403)
0.00005**
(0.00000)
0.17608**
(0.00522)
0.03672**
Ownocc
Water
Income
(0.00070)
0.03581**
(0.00083)
0.02830**
(0.00104)
0.00047**
(0.00001)
(0.00069)
0.04096**
(0.00083)
0.02435**
(0.00104)
Medinc 0.00305**
(0.00003) medinc2 medinc3
Logmedinc popdensity
Pctrent
Pctblack
Pcthisp
Pctasian
-0.00776**
(0.00018)
-0.00098**
(0.00003)
-0.00398**
(0.00007)
-0.00334**
(0.00006)
-0.00047**
(0.00005)
-0.00608**
(0.00018)
0.00112**
(0.00004)
-0.00364**
(0.00007)
-0.00317**
(0.00006)
-0.00120**
(0.00005)
(3) loghmvalue
0.81124**
(0.00143)
0.10546**
(0.00097)
-0.18598**
(0.00401)
0.00005**
(0.00000)
0.17204**
(0.00525)
0.03606**
(0.00068)
0.04061**
(0.00082)
0.01151**
(0.00103)
0.00999**
(0.00009)
-0.00004**
(0.00000)
-0.00743**
(0.00018)
0.00198**
(0.00004)
-0.00304**
(0.00007)
-0.00211**
(0.00006)
-0.00114**
(0.00004)
92
(4) (5) loghmvalue loghmvalue
0.81188** 0.81189**
(0.00143)
0.10535**
(0.00097)
-0.18537**
(0.00143)
0.10447**
(0.00097)
-0.18596**
(0.00401)
0.00005**
(0.00000)
0.17129**
(0.00526)
0.03612**
(0.00068)
0.04061**
(0.00082)
0.01195**
(0.00103)
(0.00401)
0.00005**
(0.00000)
0.17172**
(0.00524)
0.03637**
(0.00068)
0.04068**
(0.00082)
0.01624**
(0.00104)
0.01257**
(0.00026)
-0.00007**
(0.00000)
0.00000**
(0.00000)
-0.00739**
(0.00018)
0.00202**
(0.00004)
-0.00301**
(0.00007)
-0.00205**
(0.00006)
-0.00110**
(0.00004)
0.27358**
(0.00223)
-0.00682**
(0.00018)
0.00188**
(0.00004)
-0.00326**
(0.00007)
-0.00235**
(0.00006)
-0.00116**
(0.00004)

93
Pctother
Dstcbd
Dsthwy empgrav5
DstRailQm
DstRailHm
DstRail1m
DstRail1Hm
-0.00273**
(0.00013)
0.03195**
(0.00039)
-0.00384**
(0.00022)
0.02530**
(0.00061)
0.02535
(0.02517)
-0.08505**
(0.00285)
0.00060
(0.00201)
-0.01140**
(0.00199)
-0.00422**
(0.00013)
0.02450**
(0.00039)
-0.00155**
(0.00024)
0.02525**
(0.00059)
0.03125
(0.02552)
-0.05866**
(0.00291)
0.00439*
(0.00199)
0.00424*
(0.00197)
-0.00297**
(0.00013)
0.02461**
(0.00038)
-0.00091**
(0.00025)
0.02718**
(0.00058)
0.03387
(0.02586)
-0.05946**
(0.00294)
-0.00506*
(0.00197)
-0.00972**
(0.00194)
-0.00309**
(0.00013)
0.02462**
(0.00038)
-0.00084**
(0.00025)
0.02698**
(0.00058)
0.03788
(0.02596)
-0.05753**
(0.00296)
-0.00336
(0.00198)
-0.00801**
(0.00195)
DstRail2m
DstRail3m
0.03589**
(0.00145)
-0.05272**
(0.00117)
0.03016**
(0.00139)
-0.03643**
(0.00115)
0.02482**
(0.00138)
-0.03442**
(0.00113)
0.02584**
(0.00138)
-0.03419**
(0.00113)
0.02858**
(0.00138)
-0.03359**
(0.00114)
Constant 207.70709** 192.22041** 185.40197** 184.74609** 184.73053**
(3.97939) (3.97360) (3.95644) (3.95592) (3.95775)
Observations
Adjusted R-squared
318,028
0.93199
318,028
0.93335
318,028
0.93458
318,028
0.93460
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
318,028
0.93430
-0.00384**
(0.00012)
0.02405**
(0.00038)
-0.00096**
(0.00024)
0.02615**
(0.00058)
0.04557
(0.02602)
-0.05280**
(0.00292)
0.00255
(0.00197)
-0.00129
(0.00194)

Table A.5
Regressions on various forms of POPDEN
(1)
VARIABLES logsqft loghmvalue
0.81124**
(2) loghmvalue
0.81205** loglotsize yrblt
(0.00143)
0.10546**
(0.00097)
-0.18598**
(0.00144)
0.10453**
(0.00097)
-0.18574** yrblt2 sglunit pool ownocc
(0.00401)
0.00005**
(0.00000)
0.17204**
(0.00525)
0.03606**
(0.00068)
0.04061**
(0.00401)
0.00005**
(0.00000)
0.17324**
(0.00525)
0.03607**
(0.00068)
0.04055** water medinc medinc2 popdensity popden2
(0.00082)
0.01151**
(0.00103)
0.00999**
(0.00009)
-0.00004**
(0.00000)
-0.00743**
(0.00018)
(0.00082)
0.00502**
(0.00112)
0.01010**
(0.00009)
-0.00004**
(0.00000)
-0.01904**
(0.00058)
0.00094**
(0.00004) popden3 logpopden pctrent pctblack pcthisp pctasian
0.00198**
(0.00004)
-0.00304**
(0.00007)
-0.00211**
(0.00006)
-0.00114**
(0.00004)
0.00186**
(0.00004)
-0.00317**
(0.00007)
-0.00223**
(0.00006)
-0.00115**
(0.00004)
(0.00082)
0.00876**
(0.00114)
0.00989**
(0.00009)
-0.00004**
(0.00000)
0.00903**
(0.00128)
-0.00432**
(0.00021)
0.00028**
(0.00001)
(3) loghmvalue
0.81130**
(0.00144)
0.10536**
(0.00097)
-0.18769**
(0.00402)
0.00005**
(0.00000)
0.17491**
(0.00523)
0.03553**
(0.00068)
0.04047**
0.00193**
(0.00004)
-0.00331**
(0.00007)
-0.00215**
(0.00006)
-0.00107**
(0.00005)
-0.02389**
(0.00071)
0.00187**
(0.00004)
-0.00321**
(0.00007)
-0.00256**
(0.00006)
-0.00107**
(0.00004)
(4) loghmvalue
0.81222**
(0.00144)
0.10510**
(0.00097)
-0.18438**
(0.00402)
0.00005**
(0.00000)
0.17268**
(0.00526)
0.03640**
(0.00068)
0.04063**
(0.00082)
0.00927**
(0.00113)
0.01004**
(0.00009)
-0.00004**
(0.00000)
94

pctother dstcbd
-0.00297**
(0.00013)
0.02461**
(0.00038)
-0.00273**
(0.00013)
0.02431**
(0.00038)
-0.00244**
(0.00013)
0.02419**
(0.00039)
-0.00321**
(0.00013)
0.02403**
(0.00038) dsthwy empgrav5
-0.00091**
(0.00025)
0.02718**
(0.00058)
-0.00176**
(0.00025)
0.02757**
(0.00058)
-0.00123**
(0.00024)
0.02674**
(0.00058)
-0.00154**
(0.00025)
0.02718**
(0.00058)
DstRailQm
DstRailHm
0.03387
(0.02586)
-0.05946**
(0.00294)
0.00613
(0.02597)
-0.06083**
(0.00293)
-0.03354
(0.02621)
-0.05818**
(0.00291)
0.01867
(0.02595)
-0.06428**
(0.00294)
DstRail1m
DstRail1Hm
-0.00506*
(0.00197)
-0.00972**
(0.00194)
-0.00568**
(0.00196)
-0.01433**
(0.00193)
-0.00628**
(0.00196)
-0.01534**
(0.00193)
-0.00478*
(0.00197)
-0.01048**
(0.00194)
DstRail2m
DstRail3m
0.02482**
(0.00138)
-0.03442**
(0.00113)
0.02203**
(0.00140)
-0.03928**
(0.00113)
0.01983**
(0.00139)
-0.03954**
(0.00113)
0.02521**
(0.00139)
-0.03480**
(0.00113)
Constant 185.40197** 185.27266** 187.18064** 183.88810**
(3.95644) (3.95912) (3.96728) (3.96402)
Observations
Adjusted R-squared
318,028
0.93458
318,028
0.93467
318,028
0.93482
318,028
0.93454
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
95

Table A.6
Regressions on various forms of PCTRENT
(1)
VARIABLES logsqft loghmvalue
0.81205**
(2) loghmvalue
0.81179** loglotsize yrblt
(0.00144)
0.10453**
(0.00097)
-0.18574**
(0.00144)
0.10469**
(0.00098)
-0.18520** yrblt2 sglunit pool ownocc
(0.00401)
0.00005**
(0.00000)
0.17324**
(0.00525)
0.03607**
(0.00068)
0.04055**
(0.00401)
0.00005**
(0.00000)
0.17358**
(0.00525)
0.03596**
(0.00068)
0.04054** water medinc medinc2 popdensity popden2 pctrent
(0.00082)
0.00502**
(0.00112)
0.01010**
(0.00009)
-0.00004**
(0.00000)
-0.01904**
(0.00058)
0.00094**
(0.00004)
0.00186**
(0.00004) pctrent2
(0.00082)
0.00655**
(0.00112)
0.01034**
(0.00010)
-0.00004**
(0.00000)
-0.01902**
(0.00058)
0.00093**
(0.00004)
0.00113**
(0.00009)
0.00001**
(0.00000) pctrent3 pctrentlog pctblack pcthisp
-0.00317**
(0.00007)
-0.00223**
(0.00006)
-0.00321**
(0.00007)
-0.00222**
(0.00006)
(0.00082)
0.00653**
(0.00112)
0.01032**
(0.00010)
-0.00004**
(0.00000)
-0.01901**
(0.00058)
0.00093**
(0.00004)
0.00122**
(0.00021)
0.00001
(0.00001)
0.00000
(0.00000)
(3) loghmvalue
0.81180**
(0.00144)
0.10471**
(0.00098)
-0.18520**
(0.00401)
0.00005**
(0.00000)
0.17360**
(0.00525)
0.03596**
(0.00068)
0.04054**
-0.00321**
(0.00007)
-0.00222**
(0.00006)
(4) loghmvalue
0.81315**
(0.00144)
0.10461**
(0.00097)
-0.19003**
(0.00402)
0.00005**
(0.00000)
0.17246**
(0.00526)
0.03623**
(0.00068)
0.04048**
(0.00082)
0.00224*
(0.00113)
0.00837**
(0.00008)
-0.00003**
(0.00000)
-0.01987**
(0.00060)
0.00103**
(0.00005)
0.03860**
(0.00080)
-0.00286**
(0.00007)
-0.00220**
(0.00006)
96

pctasian pctother
-0.00115**
(0.00004)
-0.00273**
(0.00013)
-0.00111**
(0.00005)
-0.00265**
(0.00013)
-0.00111**
(0.00004)
-0.00265**
(0.00013)
-0.00118**
(0.00004)
-0.00288**
(0.00013) dstcbd dsthwy
0.02431**
(0.00038)
-0.00176**
(0.00025)
0.02402**
(0.00039)
-0.00199**
(0.00025)
0.02403**
(0.00039)
-0.00198**
(0.00025)
0.02587**
(0.00038)
-0.00183**
(0.00025) empgrav5
DstRailQm
0.02757**
(0.00058)
0.00613
(0.02597)
0.02761**
(0.00058)
0.00178
(0.02600)
0.02761**
(0.00058)
0.00113
(0.02608)
0.02774**
(0.00059)
0.00483
(0.02587)
DstRailHm
DstRail1m
-0.06083**
(0.00293)
-0.00568**
(0.00196)
-0.06224**
(0.00293)
-0.00564**
(0.00196)
-0.06220**
(0.00293)
-0.00563**
(0.00196)
-0.05882**
(0.00295)
-0.00265
(0.00198)
DstRail1Hm
DstRail2m
-0.01433**
(0.00193)
0.02203**
(0.00140)
-0.01456**
(0.00193)
0.02202**
(0.00140)
-0.01460**
(0.00193)
0.02199**
(0.00139)
-0.01671**
(0.00194)
0.02284**
(0.00139)
DstRail3m
Constant
-0.03928**
(0.00113)
-0.03975**
(0.00114)
-0.03976**
(0.00114)
-0.04077**
(0.00114)
185.27266** 184.71843** 184.71662** 189.49254**
(3.95912) (3.95544) (3.95537) (3.96625)
Observations
Adjusted R-squared
318,028
0.93467
318,028
0.93468
318,028
0.93468
318,028
0.93448
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
97

Table A.7 egressions on various forms of percent race variables
(1) (2)
VARIABLES logsqft loglotsize yrblt yrblt2 loghmvalue
0.81350**
(0.00144)
0.10441**
(0.00097)
-0.19575**
(0.00402)
0.00005** loghmvalue
0.81392**
(0.00144)
0.10443**
(0.00097)
-0.19593**
(0.00402)
0.00005** sglunit pool ownocc water pctblack
(0.00000)
0.17143**
(0.00524)
0.03550**
(0.00068)
0.04025**
(0.00082)
0.00889**
(0.00112)
-0.00235**
(0.00007) pctblack2 pcthisp -0.00186**
(0.00006)
(0.00000)
0.17194**
(0.00524)
0.03551**
(0.00068)
0.04030**
(0.00082)
0.00907**
(0.00112)
-0.00381**
(0.00016)
0.00005**
(0.00000)
-0.00176**
(0.00006) pcthisp2 pctasian -0.00093**
(0.00004)
-0.00088**
(0.00005) pctasian2 pctother -0.00202**
(0.00013)
-0.00194**
(0.00013) pctother2 medinc medinc2
0.00790**
(0.00008)
-0.00003**
(0.00000)
0.00786**
(0.00008)
-0.00003**
(0.00000)
0.00262**
(0.00013)
-0.00008**
(0.00000)
-0.00225**
(0.00013)
0.00800**
(0.00008)
-0.00003**
(0.00000)
(4) loghmvalue
0.81513**
(0.00144)
0.10427**
(0.00097)
-0.19468**
(0.00403)
0.00005**
(0.00000)
0.17086**
(0.00525)
0.03524**
(0.00068)
0.04019**
(0.00082)
0.00767**
(0.00111)
-0.00229**
(0.00007)
-0.00215**
(0.00006)
(3) loghmvalue
0.81323**
(0.00144)
0.10436**
(0.00097)
-0.19624**
(0.00402)
0.00005**
(0.00000)
0.17101**
(0.00524)
0.03546**
(0.00068)
0.04027**
(0.00082)
0.00935**
(0.00112)
-0.00219**
(0.00007)
-0.00313**
(0.00012)
0.00003**
(0.00000)
-0.00097**
(0.00005)
-0.00198**
(0.00013)
0.00794**
(0.00008)
-0.00003**
(0.00000)
-0.00106**
(0.00005)
0.00322**
(0.00036)
-0.00037**
(0.00002)
0.00802**
(0.00008)
-0.00003**
(0.00000)
(5) loghmvalue
0.81334**
(0.00144)
0.10499**
(0.00097)
-0.19519**
(0.00402)
0.00005**
(0.00000)
0.17218**
(0.00524)
0.03542**
(0.00068)
0.04024**
(0.00082)
0.00851**
(0.00111)
-0.00216**
(0.00007)
-0.00186**
(0.00006)
98

99 popdensity popden2 dsthwy dstcbd empgrav5
DstRailQm
DstRailHm
DstRail1m
DstRail1Hm
DstRail2m
DstRail3m
Constant
-0.02277**
(0.00060)
0.00129**
(0.00005)
-0.00424**
(0.00024)
0.02639**
(0.00039)
0.02744**
(0.00059)
-0.00315
(0.02588)
-0.05843**
(0.00296)
0.00705**
(0.00196)
-0.02294**
(0.00060)
0.00130**
(0.00005)
-0.00453**
(0.00025)
0.02630**
(0.00039)
0.02717**
(0.00059)
-0.00065
(0.02588)
-0.06094**
(0.00294)
0.00472*
(0.00195)
-0.02126**
(0.00061)
0.00119**
(0.00005)
-0.00421**
(0.00024)
0.02623**
(0.00038)
0.02669**
(0.00058)
-0.00172
(0.02590)
-0.05844**
(0.00297)
0.00464*
(0.00199)
-0.02169**
(0.00059)
0.00131**
(0.00005)
-0.00312**
(0.00024)
0.02747**
(0.00039)
0.02651**
(0.00059)
-0.00776
(0.02597)
-0.05104**
(0.00302)
0.01152**
(0.00196)
-0.01649**
(0.00194)
0.02837**
(0.00139)
-0.01789**
(0.00192)
0.02707**
(0.00139)
-0.01598**
(0.00194)
0.02659**
(0.00140)
-0.01476**
(0.00194)
0.03126**
(0.00140)
-0.01138**
(0.00198)
0.02809**
(0.00139)
-0.04506**
(0.00115)
-0.04663**
(0.00116)
-0.04478**
(0.00115)
-0.04368**
(0.00114)
-0.04675**
(0.00114)
195.27979** 195.46589** 195.76845** 194.24331** 194.72800**
(3.96798) (3.96989) (3.96753) (3.97170) (3.96384)
-0.02371**
(0.00060)
0.00136**
(0.00004)
-0.00448**
(0.00024)
0.02631**
(0.00038)
0.02736**
(0.00059)
-0.00915
(0.02591)
-0.05944**
(0.00297)
0.00288
(0.00197)
Observations
Adjusted R-squared
318,028
0.93411
318,028
0.93414
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
318,028
0.93414
318,028
0.93425
318,028
0.93418

Table A.8
Regressions on various forms of DSTCBD
(1) (2)
VARIABLES loghmvalue logsqft 0.81350** loglotsize
(0.00144)
0.10441** loghmvalue
0.81297**
(0.00144)
0.10444** yrblt yrblt2 sglunit pool
(0.00097)
-0.19575**
(0.00402)
0.00005**
(0.00000)
0.17143**
(0.00524)
0.03550**
(0.00098)
-0.19785**
(0.00403)
0.00005**
(0.00000)
0.17210**
(0.00525)
0.03557** ownocc water pctblack pcthisp pctasian pctother medinc medinc2 popdensity popden2 dsthwy dstcbd
(0.00068)
0.04025**
(0.00082)
0.00889**
(0.00112)
-0.00235**
(0.00007)
-0.00186**
(0.00006)
-0.00093**
(0.00004)
-0.00202**
(0.00013)
0.00790**
(0.00008)
-0.00003**
(0.00000)
-0.02277**
(0.00060)
0.00129**
(0.00005)
-0.00424**
(0.00024)
0.02639**
(0.00039)
(0.00000)
-0.02323**
(0.00060)
0.00132**
(0.00005)
-0.00391**
(0.00024)
0.04761**
(0.00117)
(0.00068)
0.04022**
(0.00082)
0.00678**
(0.00112)
-0.00224**
(0.00007)
-0.00163**
(0.00006)
-0.00093**
(0.00004)
-0.00188**
(0.00013)
0.00750**
(0.00009)
-0.00003**
(0.00082)
0.00679**
(0.00112)
-0.00223**
(0.00007)
-0.00164**
(0.00006)
-0.00093**
(0.00005)
-0.00186**
(0.00013)
0.00749**
(0.00009)
-0.00003**
(0.00000)
-0.02317**
(3) loghmvalue
0.81295**
(0.00144)
0.10443**
(0.00098)
-0.19754**
(0.00406)
0.00005**
(0.00000)
0.17210**
(0.00525)
0.03558**
(0.00068)
0.04022**
(0.00060)
0.00132**
(0.00005)
-0.00393**
(0.00024)
0.04524**
(0.00295)
(0.00082)
0.00878**
(0.00113)
-0.00255**
(0.00007)
-0.00178**
(0.00006)
-0.00111**
(0.00004)
-0.00243**
(0.00013)
0.00762**
(0.00008)
-0.00003**
(0.00000)
-0.02355**
(0.00060)
0.00140**
(0.00005)
-0.00331**
(0.00024)
(4) loghmvalue
0.81353**
(0.00144)
0.10474**
(0.00098)
-0.20911**
(0.00407)
0.00005**
(0.00000)
0.17165**
(0.00524)
0.03538**
(0.00068)
0.04027**
100

dstcbd2 dstcbd3 dstcbdlog empgrav5
DstRailQm
DstRailHm
DstRail1m
DstRail1Hm
DstRail2m
DstRail3m
Constant
0.02744**
(0.00059)
-0.00315
(0.02588)
-0.05843**
(0.00296)
0.00705**
(0.00196)
-0.01649**
(0.00194)
0.02837**
(0.00139)
-0.04506**
(0.00115)
-0.00085**
(0.00004)
0.02653**
(0.00059)
0.00307
(0.02583)
-0.06181**
(0.00293)
0.00285
(0.00198)
-0.01478**
(0.00195)
0.02795**
(0.00139)
-0.04486**
(0.00114)
-0.00065**
(0.00021)
-0.00000
(0.00000)
0.02670**
(0.00060)
0.00110
(0.02595)
-0.06263**
(0.00307)
0.00232
(0.00211)
-0.01524**
(0.00201)
0.02761**
(0.00144)
-0.04493**
(0.00114)
0.22333**
(0.00408)
0.02099**
(0.00056)
0.16486**
(0.02616)
-0.04666**
(0.00290)
0.00294
(0.00196)
-0.01121**
(0.00198)
0.02810**
(0.00139)
-0.04655**
(0.00116)
195.27979** 197.36743** 197.06501** 208.44816**
(3.96798) (3.97650) (4.00301) (4.01104)
Observations
Adjusted Rsquared
318,028 318,028 318,028 318,028
0.93411 0.93423 0.93423 0.93399
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
101

Table A.9
Regressions on various forms of DSTHWY
(1)
VARIABLES logsqft loglotsize loghmvalue
0.81297**
(0.00144)
0.10444** yrblt yrblt2 sglunit pool
(0.00098)
-0.19785**
(0.00403)
0.00005**
(0.00000)
0.17210**
(0.00525)
0.03557** ownocc water pctblack pcthisp pctasian pctother medinc medinc2 popdensity popden2 dsthwy
(0.00068)
0.04022**
(0.00082)
0.00678**
(0.00112)
-0.00224**
(0.00007)
-0.00163**
(0.00006)
-0.00093**
(0.00004)
-0.00188**
(0.00013)
0.00750**
(0.00009)
-0.00003**
(0.00000)
-0.02323**
(0.00060)
0.00132**
(0.00005)
-0.00391**
(0.00024) dsthwy2
(0.00082)
0.00716**
(0.00112)
-0.00231**
(0.00007)
-0.00161**
(0.00006)
-0.00095**
(0.00004)
-0.00197**
(0.00013)
0.00762**
(0.00009)
-0.00003**
(0.00000)
-0.02241**
(2) loghmvalue
0.81287**
(0.00144)
0.10457**
(0.00098)
-0.19782**
(0.00403)
0.00005**
(0.00000)
0.17195**
(0.00525)
0.03551**
(0.00068)
0.04018**
(0.00061)
0.00129**
(0.00005)
-0.00910**
(0.00055)
0.00063**
(0.00006)
(0.00082)
0.01039**
(0.00114)
-0.00215**
(0.00007)
-0.00145**
(0.00006)
-0.00104**
(0.00004)
-0.00186**
(0.00013)
0.00766**
(0.00009)
-0.00003**
(0.00000)
-0.02529**
(3) loghmvalue
0.81327**
(0.00144)
0.10502**
(0.00098)
-0.19722**
(0.00403)
0.00005**
(0.00000)
0.17204**
(0.00525)
0.03570**
(0.00068)
0.04016**
(0.00062)
0.00144**
(0.00005)
0.02202**
(0.00118)
-0.00755**
(0.00029)
(0.00082)
0.00752**
(0.00112)
-0.00218**
(0.00007)
-0.00167**
(0.00006)
-0.00089**
(0.00005)
-0.00197**
(0.00013)
0.00747**
(0.00009)
-0.00003**
(0.00000)
-0.02171**
(0.00060)
0.00124**
(0.00005)
(4) loghmvalue
0.81263**
(0.00144)
0.10423**
(0.00098)
-0.19777**
(0.00403)
0.00005**
(0.00000)
0.17194**
(0.00525)
0.03556**
(0.00068)
0.04027**
102

dsthwy3 0.00050**
(0.00002) logdsthwy dstcbd dstcbd2 empgrav5
DstRailQm
DstRailHm
DstRail1m
DstRail1Hm
DstRail2m
DstRail3m
Constant
0.04761**
(0.00117)
-0.00085**
(0.00004)
0.02653**
(0.00059)
0.00307
(0.02583)
-0.06181**
(0.00293)
0.00285
(0.00198)
-0.01478**
(0.00195)
0.02795**
(0.00139)
-0.04486**
(0.00114)
0.04793**
(0.00117)
-0.00087**
(0.00004)
0.02613**
(0.00059)
0.00884
(0.02586)
-0.06174**
(0.00294)
0.00328
(0.00199)
-0.01442**
(0.00195)
0.02895**
(0.00139)
-0.04294**
(0.00116)
0.04807**
(0.00117)
-0.00084**
(0.00004)
0.02672**
(0.00059)
-0.01845
(0.02587)
-0.06634**
(0.00291)
-0.00346
(0.00201)
-0.02248**
(0.00197)
0.02309**
(0.00140)
-0.04887**
(0.00115)
-0.00244**
(0.00049)
0.04763**
(0.00117)
-0.00087**
(0.00004)
0.02620**
(0.00059)
0.00454
(0.02588)
-0.06044**
(0.00294)
0.00413*
(0.00201)
-0.01204**
(0.00194)
0.02897**
(0.00139)
-0.04380**
(0.00116)
197.36743** 197.33770** 196.75058** 197.28589**
(3.97650) (3.97747) (3.97542) (3.97584)
Observations
Adjusted R-squared
318,028
0.93423
318,028
0.93426
318,028
0.93445
318,028
0.93419
Robust standard errors in parentheses
** p<0.01, * p<0.05
Fixed effects variables were in models but are omitted from display for brevity
103

Table A.10
OLS and WLS with fixed effects
(1) loghmvalue
VARIABLES logsqft loglotsize yrblt yrblt2 sglunit pool ownocc coef
0.81287**
0.10457**
-0.19782**
0.00005**
0.17195**
0.03551**
0.04018**
(2) (3) loghmvalue
(4) se coef se
(0.00144) 0.81214** (0.00148)
(0.00098) 0.10661** (0.00099)
(0.00403)
(0.00000)
(0.00525)
(0.00068)
(0.00082)
-0.19295**
0.00005**
0.17470**
0.03659**
0.04148**
(0.00395)
(0.00000)
(0.00518)
(0.00070)
(0.00085) water pctblack pcthisp pctasian pctother medinc medinc2 popdensity popden2 dsthwy dsthwy2 dstcbd dstcbd2 empgrav5
DstRailQm
DstRailHm
DstRail1m
DstRail1Hm
DstRail2m
DstRail3m
Alkali Flats
American River Parkway
American River
Parkway/Northgate
Antelope
Arden Arcade
Arden Fair
Arden Highlands
Army Depot/Florin
Fruitridge Industrial Park
Avondale
Ben Ali
Bradshaw Business Park
Bradshaw Woods/Lincoln
Village
Brentwood/Woodbine
0.00716**
0.00762**
0.00129**
0.00063**
0.04793**
(0.00112) 0.00929** (0.00116)
-0.00231** (0.00007) -0.00262** (0.00007)
-0.00161** (0.00006) -0.00213** (0.00006)
-0.00095** (0.00004) -0.00061** (0.00005)
-0.00197** (0.00013) -0.00238** (0.00013)
(0.00009)
-0.00003** (0.00000) -0.00003** (0.00000)
-0.02241** (0.00061) -0.02196** (0.00064)
(0.00005) 0.00135** (0.00005)
-0.00910** (0.00055) -0.00897** (0.00056)
(0.00006)
(0.00117)
0.00728**
0.00056**
0.04797**
(0.00009)
(0.00006)
(0.00125)
-0.00087** (0.00004) -0.00089** (0.00004)
0.02613** (0.00059) 0.02613** (0.00060)
0.00884 (0.02586) 0.00602 (0.02587)
-0.06174** (0.00294) -0.06436** (0.00294)
0.00328
-0.01442**
0.02895**
-0.04294**
0.63040**
0.32576**
0.14638**
(0.00199)
(0.00195)
(0.00139)
(0.00116)
(0.05079)
(0.00901)
0.00604**
-0.01354**
0.03186**
-0.03889**
0.63350**
0.33157**
(0.00201)
(0.00196)
(0.00139)
(0.00117)
(0.05064)
(0.00917)
-0.01090** (0.00172) -0.01257** (0.00176)
0.31769**
0.21485**
0.19963**
0.00491
-0.00472
0.00122
-0.18359**
-0.22157**
0.14752**
(0.00662) 0.15544** (0.00690)
(0.00409)
(0.00540)
(0.02010)
(0.01420)
(0.00442)
(0.00619)
(0.00388)
(0.00530)
(0.00728)
0.30886**
0.22371**
0.19948**
0.00545
-0.01956**
0.01056
-0.18084**
-0.22319**
0.14568**
(0.00428)
(0.00555)
(0.02015)
(0.01424)
(0.00452)
(0.00628)
(0.00398)
(0.00536)
(0.00734)
104

Campus Commons
Carleton Tract
Carmichael
Citrus Heights
Cloverdale
College Glen
College Oak Estates
Colonial Heights/Tahoe
Park South
Colonial Village
Columbia Rancho
Cordova Meadows
Creekside
Curtis Park
Del Paso Park
Downtown
East Sacramento
Elmhurst
Elverta
Executive Airport
Fair Oaks
Fleming Heights/South
Haven
Florin
Florintown/Scottsdale
Meadows
Folsom
Foothill Estates
Freeway Estates/Creek
View Estates
Fruitridge Manor
Galt
Gardenland
Gateway West
Glen Elder
Glenwood Meadows
Gold River
Golf Course Terrace
Granite Regional Park
Greenhaven
Hollywood Park
La Riviera
Laguna
Land Park
Lawrence Park
Lindale
Little Pocket
Mansion Flats
0.62997**
0.74811**
(0.00641) 0.61854** (0.00668)
(0.00894) 0.74511** (0.00910)
0.24338** (0.00403) 0.23464** (0.00413)
-0.05336** (0.00213) -0.05213** (0.00216)
-0.07423** (0.00539) -0.07731** (0.00550)
0.43536** (0.00474) 0.43804** (0.00487)
0.00639 (0.00715) 0.00389 (0.00723)
0.26859**
0.07840**
0.49060**
0.03927**
0.12559**
1.00129**
(0.00682) 0.26901** (0.00693)
(0.00451) 0.08914** (0.00462)
(0.00631) 0.47464** (0.00641)
(0.00905) 0.04583** (0.00906)
(0.00291) 0.11895** (0.00301)
(0.00939) 1.00079** (0.00961)
-0.06257*
0.67566**
1.10896**
(0.02633) -0.05908* (0.02634)
(0.03972) 0.67249** (0.03987)
(0.00673) 1.09992** (0.00706)
0.72966** (0.00797) 0.72878** (0.00817)
-0.11457** (0.00515) -0.11256** (0.00522)
-0.11247** (0.00481) -0.11335** (0.00497)
0.13974** (0.00287) 0.13707** (0.00291)
-0.03698** (0.00409) -0.03852** (0.00414)
-0.07179** (0.00428) -0.06924** (0.00434)
-0.04167** (0.00411) -0.03401** (0.00419)
0.19352** (0.00383) 0.19178** (0.00394)
0.07179** (0.00371) 0.06612** (0.00378)
0.12397**
0.11524**
(0.00723) 0.12687** (0.00724)
(0.00474) 0.10285** (0.00488)
-0.24426** (0.00588) -0.21875** (0.00599)
0.15015** (0.00755) 0.16534** (0.00769)
0.16534**
0.02790**
(0.00458)
(0.00376)
0.16794**
0.01141**
(0.00475)
(0.00389)
0.03692**
0.07380**
(0.00402) 0.03745** (0.00417)
(0.00369) 0.06529** (0.00370)
-0.20558** (0.00339) -0.20908** (0.00347)
0.56897** (0.01060) 0.56522** (0.01074)
0.58805**
0.50208**
0.22493**
0.06247**
1.08881**
0.04787**
0.13344**
0.64614**
0.66567**
(0.00464) 0.57876** (0.00484)
(0.00698) 0.49788** (0.00715)
(0.00471) 0.22228** (0.00480)
(0.00124) 0.05968** (0.00128)
(0.00809) 1.07831** (0.00845)
(0.00748) 0.04779** (0.00760)
(0.00375) 0.12985** (0.00383)
(0.00681) 0.63636** (0.00703)
(0.01891) 0.66791** (0.01898)
105

Mather AFB
McClellan AFB
-0.19075** (0.00347) -0.18304** (0.00351)
-0.15127** (0.00458) -0.13977** (0.00466)
-0.04770** (0.00303) -0.04746** (0.00312) Meadowview
Med Center
Metro Center/Gateway
Center
Midtown
Natomas Corporate Center
0.57572**
0.44689**
0.70010**
0.30805**
(0.00905) 0.57683** (0.00925)
(0.00683) 0.44704** (0.00712)
(0.01507) 0.69516** (0.01522)
(0.00540) 0.31330** (0.00569)
Natomas Crossing/Sport
Complex
Natomas Park
Natomas Park/Village 14
New Era Park
Newton Booth
0.21974**
0.18544**
0.09251**
1.01090**
(0.00441)
(0.00331)
(0.00370)
(0.01344)
0.21360**
0.17895**
0.08751**
1.00241**
(0.00458)
(0.00343)
(0.00383)
(0.01362)
North City Farms
North Fruitridge Park
North Highlands
Northpoint/Robla/Raley
Industrial Park
Oak Park
0.88686**
0.27785**
0.07467**
-0.06392**
(0.01458)
(0.00836)
(0.00866)
(0.00190)
0.89202**
0.29172**
0.07880**
-0.06427**
(0.01473)
(0.00851)
(0.00876)
(0.00194)
-0.10571** (0.00430) -0.10912** (0.00444)
0.11647** (0.00669) 0.11811** (0.00688)
Orangevale
Pacific Terrace
South Natomas
0.04332** (0.00260) 0.04068** (0.00261)
-0.05650** (0.00541) -0.04993** (0.00553)
Parkcrest Estates
Parkway
Patrician Plaza
Pell/Main Industrial
Park/Village 5
Pocket
Sierra View Terrace
South City Farms
0.09410**
0.29837**
0.48265**
Rancho Cordova
Rancho
Cordova/Carmichael
(0.00489) -0.14035** (0.00495)
Rancho Cordova/Gold River 0.19824**
Rancho
Murieta/Sloughhouse
RB Sports
Complex/Creekside
0.80541**
-0.06756**
(0.00361)
(0.01525)
(0.00404)
0.19763**
0.78537**
-0.06689**
(0.00374)
(0.01545)
(0.00413)
Richmond Grove
Rio Linda
River Park
Robla/Young Heights
Rosemont
Sacramento International
Airport/Natomas Creek
Sierra Meadows
-0.13907**
-0.09657** (0.01051) -0.07345** (0.01084)
0.87166**
-0.08152**
0.09921**
0.02909
0.33435**
0.18812**
0.04463**
(0.00785) 0.85837** (0.00810)
(0.00427)
(0.00305)
(0.05083)
(0.00841)
(0.00623)
(0.01008)
-0.07956**
0.09773**
-0.00190
0.32425**
0.17957**
0.05157**
(0.00439)
(0.00315)
(0.05076)
(0.00855)
(0.00622)
(0.01015)
-0.05415**
0.58325**
(0.01001)
(0.00724) 0.30746** (0.00740)
(0.00336) 0.47180** (0.00352)
(0.00673)
(0.00481)
0.07303**
-0.05760**
0.57659**
(0.01015)
(0.00684)
(0.00503)
South Hagginwood
South Land Park
-0.02560** (0.00169) -0.02622** (0.00174)
0.02338** (0.00317) 0.02437** (0.00326)
0.19284**
0.65938**
(0.00500)
(0.03526)
0.19583**
0.67039**
(0.00529)
(0.03547)
0.01035 (0.00585) 0.02087** (0.00593)
106

Southern Pacific Rail
Yards/Dos Rios Triangle
Southgate
Southgate/Orange Park
Cove
Southside Park
Strawberry Manor
Sundance Lake
Sunridge Park Village
Tahoe Park
Tallac Village
Upper Land Park
Village Green/Parker
Homes
Vineyard
Walnut Grove
Westlake
Whitney Estates
Wilhaggin Oaks
Willow Creek
Wilton
Constant
Observations
Adjusted R-squared
-0.01445*
0.69655**
196.70731**
(0.00577)
(0.01834)
(3.97984)
-0.01752**
0.68869**
191.96728**
318,028
0.93426
Robust standard errors in parentheses
** p<0.01, * p<0.05
318,028
0.93387
(0.00586)
(0.01847)
-0.04380** (0.00470) -0.03768** (0.00487)
0.21759** (0.00453) 0.21764** (0.00464)
-0.23743** (0.00304) -0.23720** (0.00305)
0.57670** (0.00617) 0.57499** (0.00638)
-0.06128** (0.00550) -0.05365** (0.00564)
1.29449** (0.00872) 1.29254** (0.00905)
-0.13429** (0.00461) -0.13046** (0.00471)
0.02076** (0.00194) 0.01792** (0.00199)
-0.12908** (0.02603) -0.10435** (0.02615)
0.15047** (0.00384) 0.15237** (0.00389)
0.18539**
0.73172**
(0.00364)
(0.00632)
0.18273**
0.72464**
(0.00372)
(0.00644)
0.28877** (0.00620) 0.28256** (0.00650)
-0.19102** (0.00836) -0.17684** (0.00853)
-0.16086** (0.00582) -0.16063** (0.00587)
-0.13106** (0.00575) -0.13433** (0.00578)
(3.89427)
107

108
References
Al-Mosaind M. A., Dueker K. J., & Strathman J. G. (1993). Light rail transit stations and property values: A hedonic price approach.
Portland, Oregon Center for Urban
Studies. Retrieved from http://www.pdx.edu/cus/home
Armstrong, R. J. (1995) Impacts of commuter rail service as reflected in single family residential property values.
Transportation Research record¸1466,
88-98.
Retrieved from http://www.trb.org/Main/Home.aspx
Armstrong, R. J. & Rodriguez D. A. (2006). An evaluation of the accessibility benefits of commuter rail in Eastern Massachusetts using spatial hedonic price functions.
Transportation , 33 (1), 21-43. DOI: 10.1007/s11116-005-0949-x
Blue Line to Cosumnes River College (n.d.). Project schedule. Retrieved from http://blueline2crc.com/project-schedule/ .
Bowes D. R. & Ihlanfeldt K. R. (2001). Identifying the impacts of rail transit stations on residential property values. Journal of Urban Economics 50, 1-25.
doi:10.1006_juec.2001.2214
Box, G. E. P. & D. R. Cox (1964). An analysis of transformations. Journal of the Royal
Statistical Society . Series B (Methodological), 26(2), 211-252.
Cervero, R. & Duncan, M. (2002) Benefits of proximity to rail on housing markets: experiences in Santa Clara County. Journal of Public Transportation , 1-18.
Retrieved from http://www.nctr.usf.edu/journal/articles-3/

Chalermpong, S. (2007) Rail transit and residential land use in developing countries:
109 hedonic study of residential property prices in Bangkok, Thailand. Transportation
Research Record , 2038, 111–119. DOI: 10.3141/2038-15
Chatman, D. G., Tulach N. K., & Kim K. (2011). Evaluating the economic impacts of light rail by measuring home appreciation: a first look at New Jersey’s River Line.
Urban Studies , 49 (3), 467-487. DOI:10.1177/0042098011404933
Cleveland, W.S., & Devlin, S.J., (1988). Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical
Association 83, 596–610.
DOI:10.1080/01621459.1988.10478639
Coulson, N.E., & Lahr, M.L., 2005. Gracing the land of Elvis and Beale Street: historic designation and property values in Memphis. Real Estate Economics 33, 487–
508. DOI: 10.1111/j.1540-6229.2005.00127.x
DataQuick (2012). Appraisal quality control. http://www.dataquick.com/wpcontent/uploads/2012/11/AppraisalQ-White-Paper.pdf.
Dickens, M. (2013, March 1). Public transportation ridership report fourth quarter 2012
[Data set]. American Public Transportation Association . Retrieved from http://www.apta.com/resources/statistics/Documents/Ridership/2012-q4ridership-APTA.pdf
.
Dougherty, C. (2011). Online resource centres, Chapter 4. Introduction to econometrics
(4 th
edition). Oxford University Press. Retrieved from http://global.oup.com/uk/orc/busecon/economics/dougherty4e/01student/ppts/ch0
4/

110
Gatzlaff, D. H, & Smith, M. T. (1993). The impact of the Miami Metrorail on the value of residences near station locations. Land Economics 69(1), 54-66. DOI:
10.2307/3146278
Gibson, S. (1994, July 15). Two light rail stations open". The Sacramento Bee, p. B4.
Holtzclaw, J. (1994). Using residential patterns and transit to decrease auto dependence and costs. Washington Natural Resources Defense Council .
Hudson, S. (July 19, 2012). Sacramento region home sales show market improvement.
Sacramento Bee. http://www.sacbee.com/2012/07/19/4641329/sacramentoregion-home-sales-show.html
.
Kain, J. F., & Liu, Z. (1999). Secrets of success: Assessing the large increases in transit ridership achieved by Houston and San Diego transit providers. Transportation
Research: Part A: Policy and Practice , 33 (1999), 601-624. doi:10.1016/S0965-
8564(99)00009-9
Landis, J, Guhathakurta, S. & Zhang, M. (1994). Capitalization of transit investments into single-family home prices: A comparative analysis of five California rail transit systems. University of California Transportation Center No. 246 . from http://uctc.net/papers/246
Lillis, R. (2012, June 16). Sacramento light rail now extends to River District. The
Sacramento Bee . Retrieved from http://www.sacbee.com/.
Mackett, R. L. & Sutcliffe, E. B. (2003). New urban rail systems: a policy-based technique to make them more successful. Journal of Transport Geography 11,
151-164. doi:10.1016/S0966-6923(03)00003-6

111
Maenning, W. & Brandt, S. (2010). Perceived externalities of cell phone base stations – the case of property prices in Hamburg, Germany. Hamburg Contemporary
Economic Discussions . (39). Retrieved from http://econpapers.repec.org/paper/hcewpaper/039.htm
Nelson, A. C. (1992). Effects of elevated heavy-rail transit stations on house prices with respect to neighborhood income, Transportation Research Record 1359. 127-132.
O’Sullivan, A. (2012). Urban economics (8 th ed.). New York. McGraw-Hill Irwin.
Pearson, C (2009). Latitude, Longitude, And Great Circles. Pearson Software Consulting,
LLC . http://www.cpearson.com/Excel/LatLong.aspx
.
Ramsey, J.B. (1969). Tests for specification errors in classical linear least squares regression analysis, Journal of the Royal Statistical Society, Series B.
, 31(2), 350–
371.
Redfearn, C. L. (2009). How informative are average effects? Hedonic regression and amenity capitalization in complex urban housing markets. Regional Science and
Urban Economics .
39, 297-306. doi:10.1016/j.regsciurbeco.2008.11.001
Rosen (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy. 82, 34-55. doi: 10.1086/260169
Sacramento Regional Transit (n.d.). Light rail facts. Retrieved from http://www.sacrt.com/lightrail.stm#Future Extensions .
Skaburskis, A. (1982). Externalities and property prices: a test of the Schall hypothesis,
Journal of Regional Science , 22(2), 213–223. doi: 10.1111/j.1467-
9787.1982.tb00746.x

Sunding, D. L. & Swoboda, A. M. (2010). Hedonic analysis with locally weighted
112 regression: An application to the shadow cost of housing regulation in Southern
California.
Regional Science and Urban Economics, 40, 550–573. doi:
10.1016/j.regsciurbeco.2010.07.002
Taylor, B. D., Miller, D., Iseki, H., & Fink, C. (2009). Nature and/or nurture? Analyzing the determinants of transit ridership across US urbanized areas. Transportation
Research: Part A: Policy And Practice, 43 (1), 60-77. doi:
10.1016/j.tra.2008.06.007
Voith, R. (1991). Transportation, sorting and house values. American Real Estate and
Urban Economics Association, 19, 117-137. doi: 10.1111/1540-6229.00545
White, H. 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica (48), 817–838. doi: 10.2307/1912934
Wilson, B., & Frew, J. (2007). Apartment rents and locations in Portland, Oregon: 1992-
2002. Journal of Real Estate Research , 29(2), 201-217.
Zillow (2013). Sacramento County home prices and home-values. http://www.zillow.com/local-info/CA-Sacramento-County-home-value/r_3017/ ).