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 explore s 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 statis tically

significant difference in demographic or socioeconomic traits between the two groups. **I. B. Method**  Data emplo yed in this analysis are from the American Housing Survey (AHS) . Every two years, the U.S. Census Bureau conduct s the AHS on a national level . The AHS is sponsored b y 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 ye ar in order to track the change s in households and their housing . B asic 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. 1 The 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 b y type, with similar variables put together into one module. Data from two mod ules are used in this anal ys is: “ 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 analys is is coded as etrans and contains each household’s response to the question “ How about any railroads, airports or hig hways with at least 4 lanes

– any of these within a half block of this building [ your housing unit] ? ” or 300 feet 3 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 of ten 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 oth er variables used in the analys e s are detailed at the outset of each section . Figure 1

– Regression of Normalized Pollutant Concentration against Distance from S ource

Source: Karner, A. A., Eisinger, D. S., & Niemeier, D. A. (2010). Near
-roadway air quality: Synthesizing the findings from real
- wo rld data. Environmental Science & Technology, 44(14), 5334
- 5344. Within SPSS, we weighted the sample data from the Ame rican 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 account s 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 analyse s 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 nat ional 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 b y 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 anal ys e s des cribed below indicate significant difference s 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 househol ders, householders with lower education al 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 - test s 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 . Th ese 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 separatel y 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. I f they are own their homes, they have smaller first and second mortgages. Lastl y, 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
- lan e 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 uni ts 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 anal ys is, 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 househol d 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 househol d 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  Househol d socioeconomic status o hhhsgrad

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

– whether or not the householder graduated from a four
- ye ar 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 anal yz ed affect whether a household lives near a major transportation source. Figure II - 1 summarizes the OR s for univariate regressions, ranking them by magnitude . C As shown , nearl y 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 onl y SES - related variable that results in little change in the odds of a positive e trans 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 tra ffic
-related air quality problems for those responders.6

**III. Multivariate**  **Logistic Regression Model III. A.**  **Introduction** Multivariate l ogistic regression is a method of finding a relationship b etween 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 stri ctly 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 Gar age /Carport HH No

Divide nds HH Nonwhite Welfare in Pa st Ye ar Public H ousing Food Stamps in Past Ye ar HH Hispanic HH Not Gr aduate d from 4 - yea r Un ivers ity HH Not High

School Gr ad uate HH Not

Worked in th e Past We ek 

### Odds **R at**  **ios for**  **Affirmat iv** **e et**  **ra ns**  **Asso ciated**  **with Race/SES**  **Va riables** 7

specificall y 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 surveye d, 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 anal yz ed 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 analys is 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 OR s , 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 wa s 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 roughl y a 22% chance of an affirmative etrans response . If the householder is white, then the odds decrease to 0.192, with probability of an aff irmative etrans response of about 16%. If the householder is Hispanic and nonwhite, the odds increase to 0.37 7 , with a probability of etrans of about 27%. For housing units with Hispanic householders, the odds of living within a half block of an affirmativ e 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 anal ys is above, the OR for housing units with Hispanic and nonwhite householders are nearl y identical, suggesting that they are confounded by neither one another nor b y 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 b y other factors and to determine which factors contribute to the probability of living near railw ay , 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 ty

pes in the nominal housing unit ty pe variable nunit2 is computed using the Scheffe method as a part of post - hoc anal ys is, and is reported below the table. Table III - 3 Correlation Unit

Ty pe Received Stock Dividends in Past Year Received

Welfare in Past Year Household

In come White

Householder9

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 ty pes . The largest correlation was between the variables household income and Welfare in Past Year. However, 0.190 is a very low correlation coeffi cient. None of the variables in this model are very correlated with the others. The variables and their c orresponding OR s 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 $6 6 , 2 00 that hasn’t received welfare or stock dividends in the past ye ar, in a one
-unit, detached building with three bedrooms . These odds are 0.198, which equates to roughl y a 16.5% probability of an affirmative etrans response . The odds change depending on the differing characteristics present and their corresponding OR s, 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 b y 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 s ignificant. 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 anal ys es. 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 ty pe) , account for the attenuati on in race and ethnicity indicators. This phenomenon will be examined in greater detail in subsequent anal ys es. **IV. Discussion**  The above anal ys es 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 w elfare 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). Overvie w. 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 t he 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: 2 009 (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
- wo rld data. Environmental Science & Technology, 44 (14), 5334
- 5344. 6 Appendix I: AHS d ata u sers 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 i n statistical significance being easier to achieve.11 8 Exec. Order. No. 12898, 59 Fed. Reg. 32 (1994, February 1 6), http://www.archives.gov/ federal
-register/executive
orders/pdf/12898.pdf. 9 Garcon, G. D. (2011, April 27). Significance tests. In Logistic Re gression . Retrieved July 25, 2011, from http://faculty.chass.ncsu.edu/garson/PA765/logistic.htm12



### 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 m ortgage 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 $70,785.28313

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 ty pes 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 ft2 130,819.283 ft2 Outside 1⁄2 block of airport/railway

/ highway 31,890 81,021,724 76,202.05 ft2 191,3 77.678 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 top
coding, respectively . It is done to protect the anony mity of the households taking the survey . Outliers could potentially be identified using location data also available as par t 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 ft2 2072.338 ft2 Outside 1⁄2 block of airport/railway

/ highway 38,609 96,458,011 1906.16 ft2 2240.453 ft2 Difference (within
- outside) - 357.92 ft2 (t=
- 697.77) t - tests were run again with the same hypothesis and variables, onl y with the top
- coded and bottom
- coded values removed. Qualitativel y, results were similar and statistically significant, with mean lot size difference of
- 18,389 ft2 and unit area difference of 33 5.13 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 b y the response “Not Applicable” and thus were left out of the anal ys is. 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 Standard15 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 onl y includes housing units which are owner
- occupied or is otherwise non
- rental. 87,944,404 households are represented b y 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
- 716

Household In come 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)1 7

The difference in the average incomes of individual households (famil y 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 - 1 2

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 hy pothesis 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 Wi thin 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 Mean1819TableA-220TableA-321



### 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 OR s 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 affirmativel y to etrans . The OR for living “nearb y”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 likel y 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 anal ys is, 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 15,358,56222

highway 16.8% of housing units with white householders and 23.4% of those with nonwhite householders responded affirmativel y t o 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% CI0.834 - 0.836). The odds of a household being within a half
-block of a four
- lane highway, railway, or airport decrease b y 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. Tabl e 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 14,617,12823

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 affirmativel y t o 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 re duces 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 . Tabl e 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% CI1.001 - 1.003). Relative to the OR s 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 th ese 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 1,693,29324

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 b y 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 ye ar. 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 correspo nding 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 - 925 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 i s 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
- 1 0 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 us e. Table B - 1 1 describes the survey responses for these variables . Table B
- 1 126 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 affirmativel y. The OR for a garage or carport is 0.607 (95% CI0.606 - 0.607), meaning odds of a household with a garage or carport was 39% lower than if the household had no garage or carp ort available for their use. 

### B.2 **Logistic	Regression	Results** Table B - 1 2

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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: HHSPAN. a.
**Variables in the Equation**
-.413 .001 475827.4 1 .000 .661 .661 .662 -1.188 .001 5061853 1 .000 .305 WH ITE Constant Step 1 a B S.E. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: W HITE. a.
**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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: HHHSGRAD. a.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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: UNIGRAD. a.
**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 HHW LINEQ Constant Step 1 a B S.E. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: HHW LINEQ. a.
**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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: PROJ. a.
**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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: QFS1. a.
**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 QW ELF Constant Step 1 a B S.E. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: QW ELF. a.28

Table B - 21

10 Appendix I: AHS d ata u sers 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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: QDIV. a.
**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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)
Variable(s) entered on step 1: GARAGE. a.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

Wi thin 1/2 Block of Transport = No Observed Expected Wi thin 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 WH ITE XKINC266 Constant Step 1 a B S.E. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)

Variable(s) entered on step 1: HHSPAN, W HITE, XKINC266. a.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

Wi thin 1/2 Block of Transport = No Observed Expected Wi thin 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 coding31

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 WH ITE XKINC266 QW ELF QDIV BEDRMS3 NUNIT2 NUNIT2(1) NUNIT2(2) NUNIT2(3) Constant Step 1 a B S.E. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)

Variable(s) entered on step 1: HHSPAN, W HITE, XKINC266, QW ELF, QDIV, BEDRMS3, NUNIT2. a.

**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

Wi thin 1/2 Block of Transport = No Observed Expected Wi thin 1/2 Block of Transport = Yes Total

**Hosmer and Lemeshow Test**  797.555 8 .000 Step 1 Chi-square df Sig.32TableC-1033

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 QW ELF 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. Wa ld 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, QW ELF, 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

Wi thin 1/2 Block of Transport = No Observed Expected Wi thin 1/2 Block of Transport = Yes Total

**Hosmer and Lemeshow Test**  813.427 8 .000 Step 1 Chi-square df Sig.35TableC-1436

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 QW ELF 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. Wa ld df Sig. Exp(B) Lower Upper 95.0% C.I.for EXP(B)

Variable(s) entered on step 1: HALFB1, ZADULT2, CARS1, QW ELF, QDIV, XKLMED65, NATIVE, PACIFIC, BLACK, ASIAN, MIXED, METRO3, TENURE, HHGRAD, REGION, NUNIT2, BATHS2, XKZINC63. a.