1

MEMORANDUM

SUBJECT: Demographic and Social Patterns in Housing Units Near Large Highways and 

other Transportation Sources

FROM: Chad Bailey, Office of Transportation and Air Quality/Assessment and Standards 

Division

TO: Docket # EPA-HQ-OAR-2011-0135

I. Executive Summary

I. A. Background

The purpose of this memo is to examine the issue of environmental justice as it relates to housing 

near roads. EPA defines environmental justice as the fair treatment and meaningful involvement 

of all people regardless of race, color, national origin, or income with respect to the 

development, implementation, and enforcement of environmental laws, regulations, and policies. 

EPA seeks to provide the same degree of protection from environmental health hazards for all 

people. 

Concentrations of many air pollutants are elevated near high-traffic roadways. If minority 

populations and low-income populations disproportionately live near such roads, then an issue of 

environmental justice may be present. 

This memo explores demographic and socioeconomic differences that exist on a national level 

between populations near roads and populations that are not near roads. Characteristics of 

housing units near roads are compared to those not near roads in order to determine if there is a 

statistically significant difference in demographic or socioeconomic traits between the two 

groups. 

I. B. Method

Data employed in this analysis are from the American Housing Survey (AHS). Every two years, 

the U.S. Census Bureau conducts the AHS on a national level. The AHS is sponsored by the 

United States Department of Housing and Urban Development and gathers data about housing

units in the United States. In this repeated survey, the same set of housing units is surveyed every

odd-numbered year in order to track the changes in households and their housing. Basic 

information about the unit, resident characteristics, housing and neighborhood quality, size of 

living space, and financial information are among categories of data gathered in the AHS.1The 

2009 AHS used in this analysis contained responses from 73,222 households. The Census 

Bureau makes the data available in several different groups called modules. These modules 

group the data by type, with similar variables put together into one module. Data from two 

modules are used in this analysis: “newhouse”

2

, which contains general information about the 

households and housing units surveyed; and “mortg”, which contains mortgage information for 

different households. 

Most statistical analyses were performed using SPSS 10.1.
2

The variable that will be focused on in the analysis is coded as etrans and contains each 

household’s response to the question “How about any railroads, airports or highways with at 

least 4 lanes – any of these within a half block of this building [your housing unit]?” or 300 feet3

The Census Bureau reports the presence of these features within 300 feet (about 91 meters) of 

each housing unit.

4 Concentrations of many pollutants are elevated with reduced distance to 

roads, with the steepest declines often occurring within about 100 meters. Select pollutants, 

including NOx and CO, have been found to drop to below 50% of at-road concentration outside 

100 meters (m).

5 As seen in Figure 1, for numerous pollutants, the highest concentrations and 

largest gradients occur within 100 m of roads. As a result, the half block (300 feet) specified in 

the variable etrans will represent a difference in pollutants from on-road emissions. All the 

statistical analysis detailed in this memo is done with respect to the binary variable etrans. The 

other variables used in the analyses are detailed at the outset of each section.

Figure 1 – Regression of Normalized Pollutant Concentration against Distance from Source

Source: Karner, A. A., Eisinger, D. S., & Niemeier, D. A. (2010). Near-roadway air quality: Synthesizing the 

findings from real-world data. Environmental Science & Technology, 44(14), 5334-5344.

Within SPSS, we weighted the sample data from the American Housing Survey according to the

specifications of the Census Bureau.

6 As a result of the weighting, the data shows a 

representation of the 130,111,607 households in the United States in 2009. The weighting 

accounts for survey design and non-responses, ensuring all housing units nationally are properly

represented in summary statistics.

A

The statistical tests used are two-sample t-tests and logistic 

regression. 

Of the 130,111,607 households represented in the 2009 AHS, the sampling data characterizes 

households near four-lane highways, railroads, and airports in the following way:

- 22,075,098 households were within a half-block (17.0%)

 

A While these analyses factor in survey weights recommended by the Census Bureau, the information required to 

incorporate the survey design into the estimate of variances was unavailable in the AHS dataset. We contacted the 

preparers of the codebook and dataset about this issue, but received no responses. Without the ability to perform a 

redistribution sample on the data, there is no way to recalibrate the variance for the population. The variance is thus 

lower than it should be to properly represent the national housing data. Consequently, summary statistics may 

underestimate standard errors and underestimate heterogeneity in national housing stock.
3

- 104,936,084 were not (80.7%)

- 284,953 were represented by samples that didn’t know (0.2%)

- 391,903 were represented by samples that refused to answer (0.3%)

- 2,423,569 were represented by samples that were not reported (1.9%)

The sum of the weight for each category was used to determine the population for each value of 

the variable.

I. C. Key Findings

The statistical analyses described below indicate significant differences between average 

characteristics of households residing within a half block of railroads, airports, or four-lane 

highways and characteristics of those residing outside that range. Specifically, minority

householders, householders with lower educational attainment, and lower-income households are

all more likely to be within a half block of railroads, airports, or four-or-more-lane (4+ lane) 

highways than households not fitting those characteristics. 

II. T-tests For Significance

II. A. Introduction

We used two-sided Student’s t-tests assuming independent samples to determine the significance 

of pairwise contrasts in various demographic and economic characteristics between households 

within or outside 300 feet from railroads, airports, or 4+lane highways (the etrans response).

7

The raw results of the t-tests performed in this section are in Appendix A.

II. B. Variables

The variables which will be examined using a t-test are ammort, ammrt2, lot, unitsf, cars, value, 

lmed, zinc2, and zinc. These coded names represent the following variables:

 ammort – amount of first mortgage when acquired

 ammrt2 – amount of second mortgage when acquired

 lot – area of lot in square feet

 unitsf – area of the unit in square feet

 cars – number of cars kept for use of members of the household

 value – current market value of the unit

 lmed – average area median family income

 zinc2 – household income

 zinc – family income

We examined these variables to separately control for differences in housing unit characteristics 

(unisf, value), site characteristics (lot), metropolitan statistical area economic patterns (lmed), 

family and household income characteristics (zinc2, zinc), and household financing (ammort, 

ammrt2).
4

II. C. Results

Detailed statistics and t-test results for each variable are presented in Appendix A.

Overall, households with a positive etrans response, that is, those located within a half block of a 

railway, airport, or four-lane highway, had worse economic situations. Households with positive 

etrans responses, in univariate comparisons, have lower average household income, family 

income, smaller homes and lots. If they are own their homes, they have smaller first and second 

mortgages. Lastly, housing units with a positive etrans response live in metropolitan areas 

(MSAs) with slightly higher average median incomes.

The results suggest that personal economic situations and regional economic trends affect the 

odds that a housing unit is located within a half block of a railway, airport, or four-lane highway

(etrans response). 

Reflecting personal economic situations, households that reside away from such sources have, on 

average, higher incomes, more durable assets (e.g., cars), larger homes, larger lots, and, if they

own their homes, have greater mortgage debt. This suggests that people living near railways, 

airports, and 4+ lane highways have lower socioeconomic status (SES).

Regional economic trends also appear to influence the likelihood of living near a transportation 

sources, with housing units within half a block having greater average median income at the 

MSA level. These differences will be examined further in the following sections.

I. A. Summary

II. Univariate Logistic Regression

II. A. Introduction

In order to investigate the significance of binary variables in relation to the variable etrans, the 

focus of this analysis, we have applied logistic regression to examine whether various qualitative 

factors change the odds of living within a block of a railway, airport, or 4+ lane highway. These 

results are expressed in terms of how each variable affects the “odds ratio” (OR) of an 

affirmative etrans response. An odds ratio reflects how the chance that a given outcome happens 

depends an independent variable (e.g., race, income). B

II. B. Variables

 

B An odds ratio (OR) is a value which indicates how much more likely the success of a binary response variable is 

when the value of a certain variable increases by one. For the purposes of this analysis, a “success” in the variable 

etrans is when a household is located within a half block of an airport, railway, or four-lane highway. This is a 

convenient method to use with binary explanatory variables; as the only possible values are 1 and 0, an increase of 

one represents the presence of that characteristic. That is, in this setting, the odds ratio will relate how much more 

likely a success in the variable etrans is when a given variable characteristic is present. The odds ratio will indicate 

how the corresponding characteristic affects the odds of a household being within a half block of a four-lane 

highway, railway, or airport by multiplying the original odds when that characteristic is present. Thus, if the odds 

ratio is greater than one, greater odds are produced, and vice versa. The raw SPSS output for this section can be 

located in Appendix B.
5

The binary variables examined using this method are described below:

 Householder race and ethnicity

o hhspan – whether or not the householder is Hispanic

o white – whether or not the householder is white

 Household socioeconomic status

o hhhsgrad – whether or not the householder graduated from high school

o unigrad – whether or not the householder graduated from a four-year university

o hhwlineq – whether or not the householder worked in the last week

o qfs1 – whether or not the household received foods stamps in the past year

o qwelf – whether or not the household received welfare in the past year

o qdiv – whether or not the household received stock dividends in the past year

 Housing unit characteristics

o proj – whether or not the unit is owned by a public housing authority

o garage – whether or not a garage or carport is included with the unit

For the most part, these variables represent demographic and economic characteristics of the 

householder, or the house itself. 

II. C. Results

The detailed tables for each variable examined are presented in Appendix B of this 

memorandum, along with SPSS output tables with logistic regression results.

Overall, every variable described in Section II.B had a statistically-significant OR (OR), 

suggesting that the demographic and economic characteristics analyzed affect whether a 

household lives near a major transportation source. 

Figure II-1 summarizes the ORs for univariate regressions, ranking them by magnitude.

C

 As 

shown, nearly every EJ-related variable results in a substantial increase in the likelihood that a 

housing unit will be located in a location where traffic-related air quality is a concern. The only 

SES-related variable that results in little change in the odds of a positive etrans response is 

hhwlineq, which indicates whether the householder worked in the past week. This pattern 

suggests that living in a location with greater potential traffic-related air pollution is associated 

more with individual factors (e.g., race and ethnicity) and economic factors (e.g., educational 

attainment, housing unit features) that reflect longer-term issues.

Figure II-1

 

C

In Figure 1, odds ratios are displayed on the basis of an affirmative response to EJ-related questions (e.g., race, 

ethnicity, and SES). As such, a variable with an odds ratio >1 indicates potentially greater traffic-related air quality 

problems for those responders.
6

III.Multivariate Logistic Regression Model

III. A. Introduction

Multivariate logistic regression is a method of finding a relationship between an OR (OR) of a 

binary variable resulting from a number of explanatory variables. To examine the effect of any 

particular variable, logistic regression allows for the control of other variables, aiding in 

interpretation. 

This section presents the results of four logistic regression models: one model employing a small 

number of EJ-related variables, one including a moderate number of variables, one including 

many variables that relate to both demographic and socioeconomic characteristics, and the last 

one readjusted to remove cases of multicollinearilty from the large model. The raw SPSS output 

for this section can be located in Appendix C.

III. B. Minimal EJ Logistic Regression Model

Out of the 73,222 households surveyed, 52,217 indicated an answer to etrans. Executive Order 

12898 stipulates that all federal agencies consider the impacts of their actions affecting “minority

populations and low-income populations.”

8

Thus, adhering strictly to the characteristics 

1.65

1.52 1.51

1.49

1.45

1.40

1.33

1.30

1.20

1.00

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

HH No 

Garage/Carport

HH No 

Dividends

HH Nonwhite Welfare in Past 

Year

Public Housing Food Stamps in 

Past Year

HH Hispanic HH Not 

Graduated 

from 4-year 

University

HH Not High 

School 

Graduate

HH Not 

Worked in the 

Past Week

Odds Ratios for Affirmative etrans Associated with Race/SES Variables
7

specifically enumerated in the E.O., this model only includes the variables hhspan (householder 

Hispanic status); white (householder white/nonwhite); and zinc2 (household income).

D

In performing the regression, SPSS identifies which cases are valid to use. Due to the use of 

survey weights to make the data representative of the general population, cases with a missing or 

zero weight value were removed. Of the 73,222 households surveyed, 53,354 have a useable 

weight value. 9,385 of those cases were removed for having missing values in at least one of the 

explanatory variables or the response variable, leaving 43,969 cases to be weighted and analyzed 

as part of the regression.

According to the model, the three explanatory variables included all had significant effect on 

whether or not a living unit is within a half block of a four-lane highway, a railway, or an airport. 

We assessed fit using the Hosmer and Lemeshow test. However, a known limitation of this test 

is that for large sample sizes, most of these tests result in a significant “bad fit.” As the analysis 

contain in this section has a total weighted population of 130,111,607 households, this large 

sample size may contribute to the significance found in the Hosmer-Lemeshow tests.9

Table III-1 shows correlation coefficients between the variables. For correlation between two 

binary variables, hhspan and white, a phi coefficient was used. For correlation between zinc2 and 

each of the two binary variables, a point-biserial correlation coefficient was used.

Table III-1

Correlation Household Income White Householder

Hispanic Householder -0.074 0.103

White Householder 0.092

The largest correlation was between the variables white householder and Hispanic householder, 

but all correlations were small and unlikely to affect the model’s outputs due to their low 

multicollinearity. 

The results of the logistic regression, that is, the variables and their corresponding ORs, are 

shown in Table III-2.

Table III-2

Variable OR 95% Confidence Interval

Constant 0.283 -

Hispanic Householder 1.332 1.331-1.334

 

D

zinc2 was included in the model in a modified form. First, as zinc2 is a variable expressed in dollars, coefficients 

produced for this variable would represent the odds ratio for a change in income of $1. In order to have observable 

differences, the values of the variable were divided by 10,000 so that the variable was expressed in terms of 

$10,000. Also, to make the constant more representative, the variable was centered at the average value of $66,200 

by subtracting 6.62 from each value in the variable. The value of the constant, which represents the odds of a 

success if each variable is 0, then represents the odds of success in the case of the family income being $66,200 

instead of $0.
8

White Householder 0.678 0.677-0.679

Household Income (10,000s) 0.962 0.961-0.962

As described above, the income-based OR is based on how each household’s income differs 

from $66,200, the average level reported in the AHS. The “constant” represents odds of a 

household with a non-Hispanic, nonwhite householder with a average household income living 

within a half block of a four-lane highway, a railway, or an airport (i.e., an affirmative etrans

response). These odds are 0.283, which equates to roughly a 22% chance of an affirmative etrans

response. If the householder is white, then the odds decrease to 0.192, with probability of an 

affirmative etrans response of about 16%. If the householder is Hispanic and nonwhite, the odds 

increase to 0.377, with a probability of etrans of about 27%. For housing units with Hispanic 

householders, the odds of living within a half block of an affirmative etrans response are about 

twice that of a non-Hispanic white householder. Also, a change of $10,000 in household income 

results in a proportional change in odds of 4%.

Compared with the univariate analysis above, the OR for housing units with Hispanic and 

nonwhite householders are nearly identical, suggesting that they are confounded by neither one 

another nor by income.

The results of this model suggest that race/ethnicity and income status both have a significant 

effect on the odds of a household living within a half block of a four-lane highway, a railway, or 

an airport.

III. C. Medium-level Logistic Regression Model

To address the potential for confounding by other factors and to determine which factors 

contribute to the probability of living near railway, airport, or 4+lane highway, we developed a 

regression model including more variables. In addition to the variables included in the above 

model, the following variables were used: whether the household had received welfare in the 

past year, whether the household had received stock dividends in the past year, number of 

bedrooms in the housing unit (difference from 3), and indicator variables representing the type of 

unit that the household lives in (nunit2). The Hosmer and Lemeshow test produced a chi-square 

statistic of 1598.2 for this model, indicating significance in the test. 

Table III-3 shows correlation coefficients between the variables. For correlation between two 

binary variables, a phi coefficient was used. For correlation between quantitative variables and 

binary variables, a point-biserial correlation coefficient was used. The correlation between the 

binary variables and the nominal variable nunit2, reports Cramer’s V as the measure. The 

significance of contrasts in household income (zinc2) among unit types in the nominal housing 

unit type variable nunit2 is computed using the Scheffe method as a part of post-hoc analysis, 

and is reported below the table.

Table III-3

Correlation Unit 

Type

Received Stock 

Dividends in Past 

Year

Received 

Welfare in 

Past Year

Household 

Income

White 

Householder
9

Hispanic 

Householder 0.111 0.086 0.038 -0.074 0.103

White Householder 0.175 0.085 0.072 0.092

Household Income * -0.080 0.190

Received Welfare 

in Past Year 0.085 0.038

Received Stock 

Dividends in Past 

Year

0.105

*Scheffe post-hoc test indicates significant differences for each contrast between unit types.

The largest correlation was between the variables household income and Welfare in Past Year. 

However, 0.190 is a very low correlation coefficient. None of the variables in this model are very

correlated with the others.

The variables and their corresponding ORs are as shown in Table 36.

Table III-4

Variable OR 95% Confidence Interval

Constant 0.200 -

Hispanic Householder 1.166 1.164-1.168

White Householder 0.783 0.782-0.784

Household Income (10,000s) 0.983 0.983-0.983

Received Welfare in Past Year 1.022 1.019-1.026

Received Stock Dividends in Past Year 0.864 0.862-0.865

Bedrooms 0.865 0.865-0.866

Unit Type

One-unit building, attached 1.364 1.361-1.367

Building with 2+ apartments 2.028 2.025-2.031

One-unit Mobile Home 1.158 1.156-1.161

The value of the constant represents the odds of living within a half block of a four-lane 

highway, a railway, or an airport for a household with a non-Hispanic, nonwhite householder 

with a household income of $66,200 that hasn’t received welfare or stock dividends in the past 

year, in a one-unit, detached building with three bedrooms. These odds are 0.198, which equates 

to roughly a 16.5% probability of an affirmative etrans response. The odds change depending on 

the differing characteristics present and their corresponding ORs, above.

III. D. Summary of Logistic Models

Comparisons between the univariate ORs, the “minimal” multivariate model, and the larger 

model containing more variables, there are several notable commonalities. First, the most 

directly EJ-related variables, race, Hispanic status, and income, maintain statistically significant 

ORs. Second, between the univariate and “minimal” multivariate models, the ORs (and 

associated confidence intervals) for race and ethnicity change very little in magnitude, 
10

suggesting that they are confounded neither by one another, nor by income. Third, comparing 

the expanded model, including housing unit characteristics and added economic indicators, with 

the “minimal” and univariate models, the ORs for Hispanic status and race are attenuated to 

some extent, though they remain significant. Fourth, comparing the “minimal” model with the 

expanded model, the OR for household income remains very similar, despite the inclusion of 

indicators of high-income and low-income status (e.g., receiving dividend income and welfare).

The above observations suggest that income may be a non-linear effect on the probability of 

residence near an airport, railway, or 4+ lane highway, with larger effects at the “tails” of the 

income distribution. Though nonlinear income terms were not examined in this analysis, we 

plan to examine them in forthcoming analyses.

Furthermore, it appears that accounting for very high and very low income indicators, in addition 

to housing unit characteristics (e.g., bedroom number and unit type), account for the attenuation 

in race and ethnicity indicators. This phenomenon will be examined in greater detail in 

subsequent analyses.

IV. Discussion

The above analyses generally support a conclusion that households near airports, railways, and 

4+ lane highways are more likely to be of a racial or ethnic minority and lower in income. 

People on welfare are also more likely to live near such large transportation sources. Housing 

units near facilities named in etrans are also smaller and more often attached structures (e.g., 

multi-unit dwellings).

In subsequent analyses, we plan to address other issues that affect how demographics and SES 

affect responses to etrans.

.

 

1 U.S. Census Bureau, Housing and Household Economic Statistics Division. (2005, February 18). Overview. In 

American Housing Survey (AHS). Retrieved from http://www.census.gov/hhes/www/housing/ahs/overview.html 

2

It should be noted that the name “newhouse” refers to a new grouping of housing variables, not data on newly 

constructed or newly included housing units in the survey.

3 Etrans. (2011). In Codebook for the American Housing Survey, public use file: 1997 and later (p. 576). 

4 U.S. Department of Housing and Urban Development, & U.S. Census Bureau. (n.d.). Age of other residential 

buildings within 300 feet. In American Housing Survey for the United States: 2009 (pp. A-1). Retrieved from 

http://www.census.gov/hhes/www/housing/ahs/ahs01/appendixa.pdf 

5 Karner, A. A., Eisinger, D. S., & Niemeier, D. A. (2010). Near-roadway air quality: Synthesizing the findings from 

real-world data. Environmental Science & Technology, 44(14), 5334-5344. 

6 Appendix I: AHS data users FAQs. (2011). In Codebook for the American Housing Survey, public use file: 1997 

and later (p. 1255).

It should be noted that this instruction only included readjustment due to weights, and did not include a system for 

replication of the survey. This could result in lower variances than is accurate and thus findings may be for 

significant than they are in reality.

7 When there is an indication that one sample may have a higher mean value for a certain variable than another 

sample, the t-test is preformed looking for that difference in particular. This indication is typically tied into the 

hypothesis, and can result in statistical significance being easier to achieve.
11

 

8 Exec. Order. No. 12898, 59 Fed. Reg. 32 (1994, February 16), http://www.archives.gov/federal-register/executiveorders/pdf/12898.pdf.

9 Garcon, G. D. (2011, April 27). Significance tests. In Logistic Regression. Retrieved July 25, 2011, from 

http://faculty.chass.ncsu.edu/garson/PA765/logistic.htm 
12

Appendix	A:	T-test	Data	and	Results

A.1 Data	Summary

Mortgage

t-tests were run to determine if there were significant differences in the values of mortgages for 

households located within/outside of a half block of a railway, airport, or four-lane highway. 

One t-test examined the initial value of the first mortgage (variable ammort), and another t-test 

examined the initial value of the second mortgage (variable ammrt2). As not every household 

assumes one or more mortgage, there are missing values for each mortgage measurement. 

Households without mortgages may be renters or own their homes outright. Out of a total of 

130,111,607 households represented in the AHS, 47,995,888 households have values for the first 

mortgage with 82,115,719 missing, and 5,517,534 values for the second mortgage with 

124,594,073 values missing. Additional cases are left out due to missing values in the variable 

etrans. The summary statistics of these groups are shown in Tables A-1 and A-2 below:

Table A-1

First Mortgage Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

2488 6,339,776 $147,719.59 $135,168.162

Outside 1⁄2 block 

of 

airport/railway/ 

highway

15,788 40,410,963 $168,015.23 $154,991.759

Difference 

(within-outside)

(t-value)

-$20,295.63

(t=-344.23)

Table A-2

Second 

Mortgage

Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

286 741,548 $52,521.57 $70,785.283
13

Outside 1⁄2 block 

of 

airport/railway/ 

highway

1874 4,733,338 $57,022.88 $72,479.515

Difference 

(within-outside)

-$4,501.30

(t=-50.75)

Households that are not within a half block of an airport, railroad, or four-lane highway assume a 

larger first and second mortgage, on average. Table A-1 shows the difference in first mortgages 

between the two groups (95% CI: -$20,180 - -$20,411). Table A-2 shows the difference in 

second mortgage values (95% CI: $4327-$4675).

Lot	and	Unit	Area

The variable measuring total lot area (lot) has 95,876,348 valid households analyzed as part of 

the mean of either near road households or non-near road households. Values of this variable less 

than 200 ft2 were listed as 200 ft2, and values over 999,997 ft2 were recorded as 999,997 ft2.

E,10

Unit area for each household (unitsf), which gives a measure of total interior livable area, had 

119,404,448 valid responses and 10,707,159 missing. Values under 99 ft2 were coded at 99 ft2, 

and values at over 24,870 ft2 were recorded at 24,870 ft2.

The summary statistics of the groups of the two different types of area are shown below in 

Tables A-3 and A-4:

Table A-3

Lot Area Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

4983 12,532,783 42,437.64 ft2130,819.283 ft2

Outside 1⁄2 block 

of 

airport/railway/ 

highway

31,890 81,021,724 76,202.05 ft2191,377.678 ft2

Difference 

(within-outside)

-33,764 ft2

(t=-791.98)

 

E

Setting lower and upper limits for a variable’s value is known as bottom-coding and topcoding, respectively. It is done to protect the anonymity of the households taking the survey. 

Outliers could potentially be identified using location data also available as part of the AHS, and 

thus the confidentiality of their survey responses would be compromised.
14

Table A-4

Unit Area Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

8378 20,345,461 1548.24 ft22072.338 ft2

Outside 1⁄2 block 

of 

airport/railway/ 

highway

38,609 96,458,011 1906.16 ft22240.453 ft2

Difference 

(within-outside)

-357.92 ft2

(t=-697.77)

t-tests were run again with the same hypothesis and variables, only with the top-coded and 

bottom-coded values removed. Qualitatively, results were similar and statistically significant, 

with mean lot size difference of -18,389 ft2and unit area difference of 335.13 ft2. 

Vehicle	Ownership

A t-test was performed to determine whether there was a significant difference in number of cars 

(cars variable) for households with positive and negative etrans responses. 

Of the 130,111,607 households in the United States in 2009, there were 18,250,711 households 

represented by the response “Not Applicable” and thus were left out of the analysis. The means 

were calculated from a split sample of a total of 111,860,896 households. The variable cars is 

top-coded at 5, indicating that 5 or more cars are available for household use.

Table A-5 shows summary statistics by proximity to transportation sources.

Table A-5

Cars Original Sample 

Size

Weighted 

Sample Size Weighted Mean Weighted Standard 
15

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

8004 19,621,627 1.123 0.872

Outside 1⁄2 block 

of 

airport/railway/ 

highway

35,965 89,422,881 1.265 0.930

Difference 

(within-outside)

0.142

(t=-645.31)

Housing	Unit	Value

A t-test was performed to determine whether there was a significant difference in value, the 

variable indicating the unit value for households with different etrans responses. 

The variable value contains the estimated market value of the unit at the time of the survey. This 

variable only includes housing units which are owner-occupied or is otherwise non-rental. 

87,944,404 households are represented by the responses in the survey. Table A-6 presents the 

summary statistics:

Table A-6

Market Value of 

the Unit

Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

4784 11,844,689 $201,147.67 $224,696.916

Outside 1⁄2 block 

of 

airport/railway/ 

highway

30,082 76,099,715 $243,271.65 $293,497.539

Difference 

(within-outside)

-$42,123.98

(t=573.53)

Income

Separate tests were performed to examine differences in responder-level income (family and 

household income) and metropolitan statistical area-level income. These tests reflect economic 

issues at different geographic scales. 

The summary statistics and t-test results are displayed in Table A-7, Table A-8, and Table A-9

for each of the three types of income (household, family, and median MSA):

Table A-7
16

Household 

Income

Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

8004 19,621,627 $53,236.85 $56,373.439

Outside 1⁄2 block 

of 

airport/railway/ 

highway

35,965 89,422,881 $67,882.66 $68,911.201

Difference 

(within-outside)

-$14,645.81

(t=-998.68)

Table A-8

Family Income

Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

9305 22,075,098 $44,896.56 $54,558.41

Outside 1⁄2 block 

of 

airport/railway/ 

highway

42,912 104,936,084 $55,792.51 $67,048.362

Difference

(within-outside)

-10,895.95

(t=-817.42)

Table A-9

Average Area 

Median Income

Original Sample 

Size

Weighted 

Sample Size Weighted Mean

Weighted 

Standard 

Deviation

Within 1⁄2 block 

of 

airport/railway/ 

highway

9305 22,075,098 $65,765.70 $11,537.298

Outside 1⁄2 block 

of 

airport/railway/ 

highway

42,912 104,936,084 $64,801.81 $11,712.817

Difference 

(within-outside)

$963.89

(t=355.85)
17

The difference in the average incomes of individual households (family or household income) 

within a half block of a railway, airport, or 4+ lane highway from those outside half a block 

suggests that personal economic situations affect the likelihood of living near transportation 

sources.

As indicated in Table A-9, MSA-level median incomes tend to be higher for housing units within 

half a block of these transportation sources. This finding suggests that regional economic 

patterns influence the likelihood of homes being built near these transportation sources.

A.2 Summary	Statistics	and	T-Tests

Table A-12

For Table A-2 and Table A-3, “Levene’s Test for Equality of Variances” refers to a significance 

test performed to determine whether it can be assumed that the variances for each subgroup (the 

explanatory variable divided based on etrans) are equal. The null hypothesis being tested is that 

they are equal. As each test found a significant difference in the values of the variances, the 

second row (“Equal variances not assumed”) in each variable should be the one referenced. 

Group Statistics

6339776 147719.59 135168.162 53.683

40410963 168015.23 154991.759 24.381

741548 52521.57 70785.283 82.200

4733338 57022.88 72479.515 33.314

12532783 42437.64 130819.283 36.953

81021724 76202.05 191377.678 21.261

20345461 1548.24 2072.338 .459

96458011 1906.16 2240.453 .228

12368995 31235.08 80157.897 22.792

78703093 49623.61 111339.848 12.550

20200023 1404.81 961.483 .214

95657432 1739.93 1081.621 .111

19621627 1.12 .872 .000

89422881 1.27 .930 .000

11844689 201147.67 224696.916 65.288

76099715 243271.65 293497.539 33.644

19621627 53236.85 56373.439 12.726

89422881 67882.66 68911.201 7.287

22075098 65765.70 11537.298 2.456

1.0E+08 64801.81 11712.817 1.143

22075098 44896.56 54558.410 11.612

1.0E+08 55792.51 67048.362 6.545

Within 1/2 Block

of Transport

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

Yes

No

mort1

mort2

Lot Area

Unit Area

Lot Area Recode

Unit Area Recode

Cars

Market Value

Household Income

Area Median Income

Family Income

N Mean Std. Deviation

Std. Error

Mean
18
19

T

able

A-2
20

T

able

A-3
21

Appendix	B:	Univariate	Logistic	Survey	Data	and	Regression	Results

This appendix presents the survey data from the 2009 AHS and the SPSS outputs from the 

logistic regression procedure. Section B.1 presents the survey data and ORs for each included 

variable. Section B.2 presents the logistic regression results.

B.1 Survey	Data

B.1.1 Householder	Race	and	Ethnicity

hhspan is a binary variable that measures whether the householder was Hispanic. Of the 73,222 

households surveyed, there were 43,969 responses to both hhspan and etrans. Table B-1

describes the survey responses for these variables.

Table B-1

Original Sample Weighted Values

Households Householder 

Hispanic

Householder 

Not Hispanic

Householder 

Hispanic

Householder 

Not Hispanic

Within 1⁄2 block of 

airport/railway/ 

highway

1115 6889 2,743,936 16,877,692

Outside 1⁄2 block of 

airport/railway/ 

highway

4062 31,903 9,748,796 79,674,084

22.0% of Hispanic householders and 17.5% of non-Hispanic householders respond affirmatively 

to etrans. The OR for living “nearby” corresponding to a householder being Hispanic is 1.329 

(95% CI 1.327-1.330), meaning that the a Hispanic householder is about 33% more likely to live 

near 

B.1.2 Householder	Race

Of the 73,222 households surveyed, 43,969 both answered whether the household was within a 

half block of a four-lane highway, railway, or airport and indicated the race of the householder. 

For this analysis, we coded a variable white, indicating whether the householder was white or 

nonwhite. Table B-2 describes the survey responses for these variables.

Table B-2

Original Sample Weighted Values

Households Householder 

White

Householder 

Not White

Householder 

White

Householder 

Not White

Within 1⁄2 block of 

airport/railway/ 

highway

6,137 1,867 14,937,801 4,683,826

Outside 1⁄2 block of 

airport/railway/ 29,807 6,158 74,064,319 15,358,562
22

highway

16.8% of housing units with white householders and 23.4% of those with nonwhite householders 

responded affirmatively to etrans. The OR corresponding to a householder being white is 0.661 

(95% CI .661-.662). The odds of a household being within a half-block of a four-lane highway, 

railway, or airport decrease by 34% if the householder is white.

B.1.3 Householder	High	School	Graduation

The variable hhhsgrad indicates whether the householder graduated high school. Of the 73,222 

households surveyed, 43,969 responded to both hhhsgrad and etrans. Table B-3 describes the 

survey responses for these variables.

Table B-3

Original Sample Weighted Values

Households Householder 

Graduated

Householder 

Not Graduated

Householder 

Graduated

Householder 

Not 

Graduated

Within 1⁄2 block of 

airport/railway/ 

highway

6,746 1,258 16,592,399 3,029,228

Outside 1⁄2 block of 

airport/railway/ 

highway

31,259 4706 77,593,559 11,829,321

17.6% of housing units with a high school graduate as householder responded affirmatively to 

etrans, as did 20.4% of those with householders who were not graduates. The OR corresponding 

to a householder having graduated from high school is 0.835 (95% CI 0.834-0.836). The odds of 

a household being within a half-block of a four-lane highway, railway, or airport decrease by 

16.5% if the householder graduated high school.

B.1.4 Householder	University	Graduation

The second variable examined in relation to education is unigrad, the binary variable of whether 

the householder graduated from a university. Of the 73,222 households surveyed, 43,969 

answered whether the household was within a half block of a four-lane highway, railway, or 

airport and indicated the householder’s level of education. Table B-4 describes the survey

responses for these variables.

Table B-4

Original Sample Weighted Values

Households Householder 

Graduated

Householder 

Not Graduated

Householder 

Graduated

Householder 

Not 

Graduated

Within 1⁄2 block of 

airport/railway/ 2,112 5,892 5,004,499 14,617,128
23

highway

Outside 1⁄2 block of 

airport/railway/ 

highway

11,340 24,625 27,586,625 61,836,256

15.3% of units whose householders graduated from university and 19.1% of other units 

responded affirmatively to etrans. The OR corresponding to a householder having graduated 

from a university is 0.768 (95% CI 0.767-0.769), meaning that a college degree reduces one’s 

chance of being in a “nearby” housing unit by 23.2%.

B.1.5 Current	Householder	Employment

hhwlineq designates whether the householder worked in the past week. Of the 73,222 households 

surveyed, 43,412 responded to both etrans and hhwlineq. Table B-5 describes the survey

responses for these variables.

Table B-5

Original Sample Weighted Values

Households Householder 

Worked

Householder 

Did Not Work

Householder 

Worked

Householder 

Did Not 

Work

Within 1⁄2 block of 

airport/railway/ 

highway

4,664 3,259 11,830,420 7,592,285

Outside 1⁄2 block of 

airport/railway/ 

highway

21,089 14,400 53,717,301 34,528,774

18.05% of housing units with householders working in the past week and 18.02% of those with 

householders that did not work in the last week responded to etrans affirmatively. The OR

corresponding to a householder having worked in the past week is 1.002 (95% CI 1.001-1.003). 

Relative to the ORs of longer-term economic indicators, this short-term does not address most of 

the etrans responses.

IV. A. 1. Public Housing

proj is the binary variable indicating whether the household resided within a building owned by a 

public housing authority. Of the 73,222 households surveyed, 4721 both answered both proj and 

etrans. Table B-6 describes the survey responses for these variables.

Table B-6

Original Sample Weighted Values

Households Public Housing Not Public 

Housing Public Housing Not Public Housing

Within 1⁄2 block of 243 958 559,723 1,693,293
24

airport/railway/ 

highway

Outside 1⁄2 block of 

airport/railway/ 

highway

536 2984 1,192,188 5,231,271

Households in units owned by public housing authorities had a 32.0% chance of living within a 

half block of an airport, railroad, or 4+ lane highway (i.e., affirmative etrans response). 

Households living in units not owned by public housing authorities had a 24.5% chance of an 

affirmative etrans response. The OR corresponding to a household having resided within a 

building owned by a public housing authority is 1.450 (95% CI 1.445-1.456). 

IV. A. 2. Food Stamps

Another socioeconomic variable examined is qfs1, the binary variable of whether the household 

received food stamps in the past year. Of the 73,222 households surveyed, 12,286 both answered 

whether the household was within a half block of a four-lane highway, railway, or airport and 

whether the household had received food stamps in the past year. Table B-8 describes the survey

responses for these variables.

Table B-8

Original Sample Weighted Values

Households Receive Food 

Stamps

Do Not Receive 

Food Stamps

Receive Food 

Stamps

Do Not 

Receive Food 

Stamps

Within 1⁄2 block of 

airport/railway/ 

highway

727 2102 1,815,348 5,115,085

Outside 1⁄2 block of 

airport/railway/ 

highway

1917 7540 4,766,553 18,844,023

27.5% of households receiving food stamps lived in areas within a half block of an airport, 

railroad, or 4+ lane highway (i.e., affirmative etrans response). 19.8% of households not 

receiving food stampes had affirmative etrans responses. The OR corresponding to a household 

having received food stamps in the past year is 1.403 (95% CI 1.400-1.406).

IV. A. 3. Welfare

qwelf indicates whether the household received welfare in the past year. Of the 73,222 

households surveyed, 43,969 both answered whether the household was within a half block of a 

four-lane highway, railway, or airport (etrans) and whether the household had received welfare 

in the past year (qwelf). Table B-9 describes the survey responses for these variables.

Table B-9
25

Original Sample Weighted Values

Households Received 

Welfare

Not Received 

Welfare

Received 

Welfare

Not Received 

Welfare

Within 1⁄2 block of 

airport/railway/ 

highway

199 7805 482,598 19,139,029

Outside 1⁄2 block of 

airport/railway/ 

highway

601 35,364 1,492,707 87,930,173

17.9% of households not receiving welfare in the past year responded affirmatively to etrans. 

24.5% of households receiving welfare in that time frame responded affirmatively. The OR

corresponding to a household having received welfare in the past year is 1.485 (95% CI 1.480-

1.490), meaning that the odds of living near an airport, railroadd, or 4+lane highway were 48.5% 

greater for households receiving welfare in the past year.

IV. A. 4. Stock Dividends

qdiv reports whether the household received stock dividends in the past year. Of the 73,222 

households surveyed, 43,969 both answered qdiv and etrans. Table B-10 describes the survey

responses for these variables.

Table B-10

Original Sample Weighted Values

Households Stock Dividends No Stock 

Dividends Stock Dividends No Stock Dividends

Within 1⁄2 block of 

airport/railway/ 

highway

550 7454 1,269,987 18,351,641

Outside 1⁄2 block of 

airport/railway/ 

highway

3643 32,322 8,492,672 80,930,208

Among households not receiving dividends, 18.5% responded affirmatively to etrans; 13.0% of 

those receiving dividends did as well. The OR corresponding to a household having received 

stock dividends in the past year is 0.660 (95% CI 0.659-0.661), meaning that the odds of a 

household being within a half-block of a four-lane highway, railway, or airport decrease by 34% 

if the household received stock dividends in the past year.

IV. A. 5. Garage

Of the 73,222 households surveyed, 52,177 both answered etrans and whether the household had 

had a garage or carport available for their use. Table B-11 describes the survey responses for 

these variables.

Table B-11
26

Original Sample Weighted Values

Households Garage No Garage Garage No Garage

Within 1⁄2 block of 

airport/railway/

highway

4,846 4,455 11,827,620 10,240,417

Outside 1⁄2 block of 

airport/railway/ 

highway

27,753 15,123 68,740,753 36,115,500

22.1% of households with no garage or carport available for their use responded affirmatively to 

etrans. 14.7% of households with access to a garage or carport responded affirmatively. The 

OR for a garage or carport is 0.607 (95% CI 0.606-0.607), meaning odds of a household with a 

garage or carport was 39% lower than if the household had no garage or carport available for 

their use.

B.2 Logistic	Regression	Results

Table B-12

Table B-13

Table B-14

Table B-15

Variables in the Equation

.284 .001 149803.8 1 .000 1.329 1.327 1.330

-1.552 .000 3.4E+07 1 .000 .212

HHSPAN

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: HHSPAN.

Variables in the Equation

-.413 .001 475827.4 1 .000 .661 .661 .662

-1.188 .001 5061853 1 .000 .305

WHITE

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: WHITE.

Variables in the Equation

-.180 .001 66561.327 1 .000 .835 .834 .836

-1.362 .001 4475443 1 .000 .256

HHHSGRAD

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: HHHSGRAD.
27

Table B-16

Table B-17

Table B-18

Table B-19

Table B-20

Variables in the Equation

-.264 .001 218089.6 1 .000 .768 .767 .769

-1.442 .000 2.5E+07 1 .000 .236

UNIGRAD

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: UNIGRAD.

Variables in the Equation

.002 .001 9.686 1 .002 1.002 1.001 1.003

-1.515 .000 1.4E+07 1 .000 .220

HHWLINEQ

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: HHWLINEQ.

Variables in the Equation

.372 .002 40588.801 1 .000 1.450 1.445 1.456

-1.128 .001 1627601 1 .000 .324

PROJ

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: PROJ.

Variables in the Equation

.339 .001 113631.9 1 .000 1.403 1.400 1.406

-1.304 .000 6840804 1 .000 .271

QFS1

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: QFS1.

Variables in the Equation

.396 .002 55771.053 1 .000 1.485 1.480 1.490

-1.525 .000 3.7E+07 1 .000 .218

QWELF

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: QWELF.
28

Table B-21

 

10 Appendix I: AHS data users FAQs. (2011). In Codebook for the American Housing Survey, public use file: 1997 

and later (p. 1260).

Variables in the Equation

-.415 .001 177605.5 1 .000 .660 .659 .661

-1.484 .000 3.3E+07 1 .000 .227

QDIV

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: QDIV.

Variables in the Equation

-.500 .000 1111785 1 .000 .607 .606 .607

-1.260 .000 1.3E+07 1 .000 .284

GARAGE

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: GARAGE.
29

Appendix C – Multivariate Logistic Regression Models

For the strict model, shown first, there are three tables shown. The first two tables shown are the 

results of the Hosmer and Lemeshow test, and the third table shows the results of the logistic 

regression. In the other three models, there are four tables displayed for each. Before the table 

showing the results of the logistic regression, there is a table indicating the representations of the 

categorical variables.

Strict Model

Table C-1

Table C-2

Table C-3

Contingency Table for Hosmer and Lemeshow Test

3348 3314.129 0 34.317 3348

4405 4348.431 0 56.984 4405

4770 4696.664 0 73.207 4770

4841 4762.109 0 79.012 4841

3098 3046.254 0 52.215 3098

3151 3097.380 0 53.820 3151

2678 2631.885 0 45.989 2678

19403 19068.592 0 333.946 19403

640 4770.590 4226 94.599 4865

87927883 8.8E+07 19297919 1.9E+07 1.1E+08

1

2

3

4

5

6

7

8

9

10

Step

1

Observed Expected

Within 1/2 Block of

Transport = No

Observed Expected

Within 1/2 Block of

Transport = Yes

Total

Hosmer and Lemeshow Test

184717.609 8 .000

Step

1

Chi-square df Sig.

Variables in the Equation

.287 .001 145819.2 1 .000 1.332 1.331 1.334

-.388 .001 395413.7 1 .000 .678 .677 .679

-.039 .000 622793.0 1 .000 .962 .961 .962

-1.264 .001 5343423 1 .000 .283

HHSPAN

WHITE

XKINC266

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: HHSPAN, WHITE, XKINC266.
30

Medium-level Model

Table C-4

Table C-5

Table C-6

Contingency Table for Hosmer and Lemeshow Test

4448 4329.759 0 118.444 4448

5083 4917.812 0 165.124 5083

4770 4602.723 0 167.628 4770

3959 3807.268 0 151.572 3959

4644 4461.908 0 181.701 4644

4231 4065.139 0 166.055 4231

5269 5062.106 0 207.209 5269

5036 4833.102 0 203.232 5036

1685 4481.945 2997 200.332 4682

87935091 8.8E+07 19299147 1.9E+07 1.1E+08

1

2

3

4

5

6

7

8

9

10

Step

1

Observed Expected

Within 1/2 Block of

Transport = No

Observed Expected

Within 1/2 Block of

Transport = Yes

Total

Hosmer and Lemeshow Test

42200.882 8 .000

Step

1

Chi-square df Sig.

Categorical Variables Codings

27856 .000 .000 .000

2725 1.000 .000 .000

10707 .000 1.000 .000

1975 .000 .000 1.000

One-unit building,

detached

One-unit building,

attached

Building with 2+

apartments

One-unit mobile home

Unit

Type

Frequency (1) (2) (3)

Parameter coding
31

Table C-7

Wide-scope Model

Table C-8

Table C-9

Variables in the Equation

.154 .001 39470.829 1 .000 1.166 1.164 1.168

-.245 .001 145425.4 1 .000 .783 .782 .784

-.017 .000 116767.6 1 .000 .983 .983 .983

.022 .002 163.553 1 .000 1.022 1.019 1.026

-.147 .001 20444.050 1 .000 .864 .862 .865

-.145 .000 194146.9 1 .000 .865 .865 .866

938428.3 3 .000

.311 .001 75098.801 1 .000 1.364 1.361 1.367

.707 .001 927208.1 1 .000 2.028 2.025 2.031

.147 .001 17562.546 1 .000 1.158 1.156 1.161

-1.611 .001 6368956 1 .000 .200

HHSPAN

WHITE

XKINC266

QWELF

QDIV

BEDRMS3

NUNIT2

NUNIT2(1)

NUNIT2(2)

NUNIT2(3)

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

a. Variable(s) entered on step 1: HHSPAN, WHITE, XKINC266, QWELF, QDIV, BEDRMS3, NUNIT2.

Contingency Table for Hosmer and Lemeshow Test

2523 2497.508 0 25.665 2523

3681 3634.666 0 46.099 3681

4233 4168.201 0 64.482 4233

5362 5260.583 0 101.719 5362

4575 4482.677 0 92.244 4575

5247 5135.374 0 111.417 5247

5507 5382.064 0 125.389 5507

4651 4542.498 0 108.932 4651

4411 4305.582 0 105.336 4411

87934027 8.8E+07 19302144 1.9E+07 1.1E+08

1

2

3

4

5

6

7

8

9

10

Step

1

Observed Expected

Within 1/2 Block of

Transport = No

Observed Expected

Within 1/2 Block of

Transport = Yes

Total

Hosmer and Lemeshow Test

797.555 8 .000

Step

1

Chi-square df Sig.
32 Table C-10
33

Table C-11

Variables in the Equation

-.066 .000 26959.074 1 .000 .936 .936 .937

-.228 .001 184547.9 1 .000 .796 .795 .797

-.064 .001 12739.193 1 .000 .938 .937 .939

.009 .000 1259.345 1 .000 1.009 1.009 1.010

-.011 .000 602.005 1 .000 .989 .988 .990

.010 .000 961.278 1 .000 1.011 1.010 1.011

-.015 .002 74.443 1 .000 .985 .982 .988

-.087 .001 6859.794 1 .000 .917 .915 .919

.004 .000 442.547 1 .000 1.004 1.004 1.005

-.012 .000 3374.339 1 .000 .988 .987 .988

-.016 .000 3095.654 1 .000 .984 .983 .985

.353 .003 19590.919 1 .000 1.424 1.416 1.431

.022 .005 20.643 1 .000 1.022 1.013 1.032

.161 .001 45041.265 1 .000 1.174 1.172 1.176

-.182 .001 15084.193 1 .000 .834 .831 .836

.071 .002 1045.210 1 .000 1.074 1.069 1.079

906816.6 4 .000

.085 .001 17643.041 1 .000 1.088 1.087 1.090

-.567 .001 326667.4 1 .000 .567 .566 .568

-.265 .001 58919.413 1 .000 .767 .765 .769

-.780 .001 520990.2 1 .000 .458 .457 .459

39634.697 2 .000

.153 .001 39580.637 1 .000 1.165 1.163 1.167

.045 .002 429.220 1 .000 1.046 1.041 1.050

92387.138 16 .000

.148 .005 864.812 1 .000 1.159 1.148 1.171

.084 .004 562.031 1 .000 1.088 1.080 1.095

-.182 .003 3284.076 1 .000 .834 .829 .839

-.206 .003 4749.734 1 .000 .814 .809 .819

.070 .003 542.384 1 .000 1.072 1.066 1.079

.118 .003 1701.780 1 .000 1.125 1.118 1.131

-.032 .003 132.085 1 .000 .968 .963 .973

.071 .003 599.939 1 .000 1.074 1.068 1.080

.026 .002 112.172 1 .000 1.026 1.021 1.031

.018 .002 56.648 1 .000 1.018 1.014 1.023

.095 .003 1205.377 1 .000 1.099 1.094 1.105

.082 .003 940.950 1 .000 1.085 1.079 1.091

.021 .003 62.320 1 .000 1.021 1.016 1.027

-.038 .002 253.949 1 .000 .962 .958 .967

-.225 .003 7863.146 1 .000 .798 .794 .802

-.115 .003 1282.646 1 .000 .891 .886 .897

BEDRMS3

BATHS2

HALFB1

PER3

ZADULT2

CARS1

QWELF

QDIV

XKINC266

XKZINC63

XKLMED65

NATIVE

PACIFIC

BLACK

ASIAN

MIXED

METRO3

METRO3(1)

METRO3(2)

METRO3(3)

METRO3(4)

TENURE

TENURE(1)

TENURE(2)

HHGRAD

HHGRAD(1)

HHGRAD(2)

HHGRAD(3)

HHGRAD(4)

HHGRAD(5)

HHGRAD(6)

HHGRAD(7)

HHGRAD(8)

HHGRAD(9)

HHGRAD(10)

HHGRAD(11)

HHGRAD(12)

HHGRAD(13)

HHGRAD(14)

HHGRAD(15)

HHGRAD(16)

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)
34

Wide-scope Model, Adjusted

Table C-12

Table C-13

445695.2 3 .000

.405 .001 214676.7 1 .000 1.499 1.496 1.502

.565 .001 438900.2 1 .000 1.759 1.756 1.762

.329 .001 138231.5 1 .000 1.389 1.387 1.391

456433.1 3 .000

.260 .001 49050.331 1 .000 1.297 1.294 1.300

.560 .001 408526.8 1 .000 1.751 1.748 1.754

.367 .001 98622.114 1 .000 1.444 1.440 1.447

-2.172 .003 737715.0 1 .000 .114

REGION

REGION(1)

REGION(2)

REGION(3)

NUNIT2

NUNIT2(1)

NUNIT2(2)

NUNIT2(3)

Constant

Variable(s) entered on step 1: BEDRMS3, BATHS2, HALFB1, PER3, ZADULT2, CARS1, QWELF, QDIV, XKINC266,

XKZINC63, XKLMED65, NATIVE, PACIFIC, BLACK, ASIAN, MIXED, METRO3, TENURE, HHGRAD, REGION, NUNIT2

a. 

Contingency Table for Hosmer and Lemeshow Test

3681 3638.708 0 42.058 3681

4334 4281.212 0 53.100 4334

4812 4731.725 0 80.539 4812

4258 4176.162 0 81.556 4258

4881 4782.565 0 98.258 4881

5076 4968.036 0 107.730 5076

4909 4798.606 0 110.465 4909

4062 3963.712 0 98.137 4062

5062 4936.645 0 124.952 5062

89381807 8.9E+07 19621627 2.0E+07 1.1E+08

1

2

3

4

5

6

7

8

9

10

Step

1

Observed Expected

Within 1/2 Block of

Transport = No

Observed Expected

Within 1/2 Block of

Transport = Yes

Total

Hosmer and Lemeshow Test

813.427 8 .000

Step

1

Chi-square df Sig.
35

T

able

C-14
36

Table C-15

Variables in the Equation

-.080 .001 21371.635 1 .000 .923 .922 .924

-.008 .000 540.080 1 .000 .992 .991 .993

.008 .000 586.319 1 .000 1.008 1.007 1.009

-.006 .002 10.161 1 .001 .994 .991 .998

-.084 .001 6410.036 1 .000 .920 .918 .922

-.018 .000 4043.759 1 .000 .982 .981 .982

.346 .002 19353.645 1 .000 1.414 1.407 1.421

.014 .005 7.869 1 .005 1.014 1.004 1.023

.139 .001 35135.869 1 .000 1.149 1.148 1.151

-.169 .001 13593.654 1 .000 .844 .842 .847

.053 .002 601.454 1 .000 1.055 1.050 1.059

931190.3 4 .000

.088 .001 19390.393 1 .000 1.092 1.091 1.093

-.567 .001 329048.7 1 .000 .567 .566 .569

-.270 .001 62122.771 1 .000 .763 .762 .765

-.788 .001 537678.8 1 .000 .455 .454 .456

50729.353 2 .000

.168 .001 50054.124 1 .000 1.183 1.181 1.184

.014 .002 41.252 1 .000 1.014 1.010 1.018

93735.059 16 .000

.135 .005 746.954 1 .000 1.145 1.134 1.156

.044 .004 158.808 1 .000 1.045 1.038 1.053

-.193 .003 3753.909 1 .000 .824 .819 .829

-.222 .003 5578.574 1 .000 .801 .797 .806

.066 .003 492.578 1 .000 1.068 1.062 1.074

.099 .003 1229.211 1 .000 1.104 1.098 1.110

-.058 .003 423.700 1 .000 .944 .939 .949

.058 .003 398.902 1 .000 1.059 1.053 1.065

.012 .002 26.945 1 .000 1.013 1.008 1.017

.017 .002 51.683 1 .000 1.017 1.013 1.022

.092 .003 1153.131 1 .000 1.097 1.091 1.103

.077 .003 847.596 1 .000 1.080 1.075 1.086

.019 .003 52.879 1 .000 1.019 1.014 1.025

-.040 .002 278.069 1 .000 .961 .956 .965

-.230 .003 8316.292 1 .000 .794 .790 .798

-.120 .003 1400.240 1 .000 .887 .882 .893

456316.4 3 .000

.408 .001 221992.7 1 .000 1.504 1.501 1.506

.566 .001 449908.5 1 .000 1.761 1.758 1.764

.333 .001 144754.5 1 .000 1.395 1.392 1.397

649952.1 3 .000

.281 .001 59844.057 1 .000 1.325 1.322 1.328

.612 .001 586148.5 1 .000 1.843 1.840 1.846

.394 .001 116177.4 1 .000 1.482 1.479 1.486

-.264 .000 305066.6 1 .000 .768 .767 .769

-.008 .000 17752.795 1 .000 .993 .992 .993

-2.203 .003 773867.8 1 .000 .110

HALFB1

ZADULT2

CARS1

QWELF

QDIV

XKLMED65

NATIVE

PACIFIC

BLACK

ASIAN

MIXED

METRO3

METRO3(1)

METRO3(2)

METRO3(3)

METRO3(4)

TENURE

TENURE(1)

TENURE(2)

HHGRAD

HHGRAD(1)

HHGRAD(2)

HHGRAD(3)

HHGRAD(4)

HHGRAD(5)

HHGRAD(6)

HHGRAD(7)

HHGRAD(8)

HHGRAD(9)

HHGRAD(10)

HHGRAD(11)

HHGRAD(12)

HHGRAD(13)

HHGRAD(14)

HHGRAD(15)

HHGRAD(16)

REGION

REGION(1)

REGION(2)

REGION(3)

NUNIT2

NUNIT2(1)

NUNIT2(2)

NUNIT2(3)

BATHS2

XKZINC63

Constant

Step

1

a

B S.E. Wald df Sig. Exp(B) Lower Upper

95.0% C.I.for EXP(B)

Variable(s) entered on step 1: HALFB1, ZADULT2, CARS1, QWELF, QDIV, XKLMED65, NATIVE, PACIFIC, BLACK, ASIAN,

MIXED, METRO3, TENURE, HHGRAD, REGION, NUNIT2, BATHS2, XKZINC63.

a.