Prepared by the Center for Advancing Research to Practice 

at ServeMinnesota

Peter Nelson, Ph.D. and Patrick Kaiser, M.Ed. 

An Evaluation of the Twin Cities 

Habitat for Humanity AmeriCorps 

Program

Impact Report October 

2020
1 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

Table of Contents Introduction................................................................................................... 2

Twin Cities Habitat for Humanity Overview .......................................................... 2

About the Study ........................................................................................... 2

Impact Assessment........................................................................................... 4

Matching and Participants ............................................................................... 4

Measures.................................................................................................... 6

Analytic Plan............................................................................................... 7

Results ...................................................................................................... 7

Conclusion and Discussion .................................................................................. 9

References ................................................................................................... 11

About the Authors........................................................................................... 12

Appendix A: Habitat for Humanity Family Survey...................................................... 13
2 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

Introduction 

Twin Cities Habitat for Humanity 

Overview 

The mission of Twin Cities Habitat for 

Humanity (TCHFH) is to bring people 

together to create, preserve, and promote 

affordable homeownership and advance 

racial equity in housing. Since its 

establishment in 1985, TCHFH has helped 

over 1,500 people secure affordable 

housing and has completed more than 

2,000 critical home repairs across the 

seven-county metro area. The current 

program builds 35 new homes and 

rehabilitates 15 homes each year, then 

sells those homes to low-income clients at 

an affordable rate. Construction and 

renovation practices are aligned to 

promote safety, health, and affordability. 

Partner families are prepared for success 

through eight hours of homeownership 

training and one-on-one financial coaching 

from TCHFH. Partner families contribute 

250 credit hours of "sweat equity" by 

volunteering to build or rehab Habitat 

homes. Clients are also able to purchase a 

home with a realtor through TCHFH’s Open 

Market program. Every Habitat family 

receives a mortgage that is no more than 

30% of their income with an affordable, 

fixed interest rate. 

The majority of the labor to build and 

repair homes is completed by volunteers. 

TCHFH AmeriCorps members are integral to 

program implementation. Members support 

the program by recruiting, training, and 

supervising community volunteers which 

significantly reduces program costs while 

boosting program capacity to meet local 

need. 

The Twin Cities’ housing needs will grow 

dramatically in the decade ahead. The Met 

Council estimates by 2030, 37,900 new 

affordable housing units will be needed to 

keep up with population growth. Affordable 

homeownership is a multi-generational 

game changer for a family. It contributes 

to better outcomes in education, health, 

and economic stability. Today, 3 in 4 White 

families in the Twin Cities own homes 

compared to 1 in 4 Black families—

approximately 85% of Habitat clients are 

people of color.

About the Study

The primary objective and outcome of 

interest for Twin Cities Habitat for 

Humanity is to increase access to 

affordable and safe housing for families 

with an income between 30% and 80% of 

the median income. Thus, the primary 

outcome of interest for TCHFH is the 

number of families who experience more 

favorable living conditions as a result of 

TCHFH services. 

Previous research on TCHFH provides some 

descriptive evidence that families served 

by the program experience a variety of 

benefits (Mattessich & Hanson, 2015), but 

this research lacked a relevant comparison 

group. Recent changes to program 

selection criteria introduced the 

opportunity to make more meaningful 

comparisons between families matched 

with a home and those who are not yet 

matched. Thus, a quasi-experimental 

design (QED) study comparing families 

matched with a home through TCHFH and 

similar families that have qualified for a 

home match but have not yet closed on a 

home is now feasible. The purpose of the 

present evaluation was to evaluate TCHFH 

impact on overall housing quality and 

satisfaction. 
3 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

The primary research questions guiding the 

evaluation were: 

1. To what extent do families matched 

with a TCHFH home have a lower 

Persons-per-Bedroom (PPB) rate 

relative to similar families who have 

not been matched with a home? 

2. To what extent does the probability 

of meeting federal guidelines for 

PPB differ between families 

matched with a TCHFH home and 

those still awaiting a match? 

3. Relative to a matched comparison 

group, to what extent do families 

matched with a TCHFH home report 

higher feelings of overall home 

satisfaction measured by general 

satisfaction, safety, and housing 

issues?
4 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

Impact Assessment 

As described previously, the current 

evaluation adopted a QED approach in 

which families acquiring a home through 

TCHFH were matched with similar families 

that qualified for TCHFH support but had 

not yet closed on a home. 

Once matched, the 

primary outcomes of 

interest included the 

Person per Bedroom ratio 

(PPB), the probability of 

having a PPB ratio less 

than or equal to 2, and 

family ratings on a survey 

of home satisfaction, safety, and housing 

issues. Below, we describe the matching 

process, analytic sample and procedures, 

and observed results.

Matching and Participants

Matching. The procedures for matching in 

the current evaluation leveraged TCHFH 

procedures for family selection and home 

placement. To be entered in the TCHFH 

system, families must qualify for support 

and become mortgage ready. Once a family 

is mortgage ready, they may be matched 

with a TCHFH home. This infrastructure 

essentially creates two groups in the 

TCHFH data system—those that have 

qualified for a new home and those that 

have qualified and been matched to a new 

home. For the purposes of the current 

study, families matched to a home 

compose the treatment group and families 

that have qualified for support compose 

the comparison group. Upon entry into the 

TCHFH data system, families provide 

information on income, household 

members, bedrooms, race, and age. 

Families also complete the aforementioned 

survey at entry. Finally, after families 

close on a home, they provide updated 

information on PPB and complete the 

survey a second time. 

The goal of the matching procedures in the 

current study was to identify families 

entering the system 

at approximately the 

same time as families 

who had recently 

been matched to a 

home. In these cases, 

the bedroom and 

householder member 

data on the intake 

survey for comparison families can be 

compared to the post-placement data for 

families matched with a home. At the end 

of the evaluation program year, there were 

a total of 147 families listed in the TCHFH 

system. Of those families, 49 had been 

matched with a home, leaving an 

additional 98 families who had qualified for 

the matching process but had not yet been 

matched to a home. Propensity scores were 

used to create a matched comparison 

group. Propensity matching is largely 

advocated as a valid and useful quasiexperimental method for evaluating group 

differences, as it is designed to balance 

groups across a number of pre-existing 

factors that could otherwise account for 

differences in the outcome measures 

(Smith, 1997; Stuart & Rubin, 2008). In the 

current evaluation we used the MatchIt 

package in R to create a matched sample 

(Ho, Imai, King, & Stuart, 2011). 

The matching process included two analytic 

steps. In the first step, logistic regression 

was used to calculate each family’s 

propensity (or likelihood) for home 

placement based on a variety of 

information known about the family. In the 

The evaluation adopted a QED 

approach in which 49 families 

acquiring a home through TCHFH 

were matched with 49 similar families 

that qualified for TCHFH support but 

had not yet closed on a home. 
5 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

case of the present evaluation, families 

were matched based on their (1) annual 

gross income, (2) household members, (3) 

weeks from the beginning of the program 

year, (4) race, and (5) applicant age. The 

“weeks” variable was included to ensure 

that the time at which household data and 

survey data were obtained were similar for 

a given matched pair. 

The second analytic step involved matching 

cases according to their propensity scores, 

where families in the treatment group were 

matched with a family that had not closed 

on a home but had a similar propensity for 

placement. In the present analysis we used 

nearest neighbor matching without 

replacement to pair cases based on their 

likelihood of home placement (Rubin, 

1973). Comparison cases not matched to a 

treatment case were excluded from further 

analysis. Thus, the final sample consisted 

of 98 families (49 in treatment and 49 

comparison).

Participants. Descriptive data for the 

analytic sample separated by group are 

included in Table 1. The variables of most 

interest for the matching procedures and 

baseline equivalence include household 

member data, annual income, whether or 

not a household was designated as a 

household of color, primary applicant age, 

and weeks elapsed from the start of the 

program year. From Table 1, it follows that 

families in the two groups were generally 

quite similar across those variables. On 

average, families in the treatment group 

had slightly fewer family members (M = 

4.14; SD = 1.78) compared to those in the 

comparison group (M = 4.47; SD = 2.06). A 

majority of families in both groups were 

designated as households of color; 

however, treatment families tended to 

have a slightly larger proportion of White 

families (29%) relative to comparison 

families (14%). 

Comparison and treatment families tended 

to have very similar household income and 

age data. Data for comparison families was 

obtained, on average, one week later than 

treatment families using July 1, 2019 as a 

the start date. To evaluate the degree to 

which participant families differed 

between the two groups of interest, a 

series of chi-square and t-tests were 

conducted revealing no statistically 

significant differences between groups at 

baseline and providing evidence for the 

integrity of the matching procedures.
6 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

Table 1. Descriptive Data across Groups

Treatment

(n = 49)

Comparison

(n = 49)

M SD M SD

Demographics

Weeks from Start 18.06 12.17 19.67 10.75

Annual Income (Gross) 58,918 10,346 59,832 15,203

Household of Color 71% - 86% - 

Primary Applicant Age 39.51 8.99 39.69 8.77

Housing Data

Household Members 4.14 1.78 4.47 2.06

Bedrooms 3.41 0.91 2.24 0.86

Person Per Bedroom (PPB) 1.28 0.82 2.24 0.85

Met PPB Benchmark (<= 2) 96% - 67% - 

Survey Scores

Full Score 3.52 0.44 2.89 0.48

Satisfaction 3.36 0.49 2.52 0.48

Safety 3.57 0.60 2.95 0.72

Housing Issues 3.59 0.57 3.07 0.56

Note: All survey items scaled from 1-4. M = Mean; SD = Standard Deviation.

Measures

Housing and Income Data. Information on 

the number of household members, current 

bedrooms, and annual income of families 

was obtained and verified as part of the 

intake process for TCHFH and stored in 

their secure database.

Household Survey Data. Prior to the 2019-

2020 program year, the evaluation team 

worked closely with the TCHFH team to 

create a survey to be included in the intake 

for all families and as a follow-up for 

families matched with a home. The survey 

was created to assess a variety of housingrelated information that is consistent with 

the goals of the TCHFH program. In 

selecting items for the survey, the 

evaluation team used the existing research 

literature on housing to identify exemplar 

items or sets of items (e.g., Buron et al., 

2002; Dunn & Hayes, 1999; Sandel & 

Wright, 2006; Smith et al., 1993). 

In total, there were 19 items on the final 

survey that were further partitioned into 

three subscales of interest: Housing 

Satisfaction (6 items), Safety (2 items), and 

Housing Issues (11 items). All items were 

scored on a scale of 1 to 4. The anchors for 

the satisfaction items ranged from 

“Strongly Agree” to Strongly Disagree.” The 

anchors for the safety items ranged from 

“Very Safe” to “Very Unsafe.” The anchors 

for housing issues ranged from “Very True” 

to “Very Untrue.” Items were reverse 

scored when necessary. The full survey 

average score as well as subscale averages 

were used for analysis and interpretation. 

See Appendix A for a full list of the survey 

items.
7 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

Analytic Plan

To evaluate group differences in regard to 

housing data and survey data, five linear 

regression models were fit to the data 

(PPB; full survey score; satisfaction score; 

safety score; issues score). In each case the 

outcome of interest was regressed on the 

primary predictor of interest (treatment) 

as well as five covariates: annual income 

(mean centered), weeks from start of 

program year, number of household 

members, whether a household was coded 

as a household of color, and applicant age. 

Those covariates were included in the 

model despite the fact that no baseline 

differences were observed to increase 

model precision. 

In addition to the linear models, a logistic 

regression model was fit to the data to 

evaluate any group differences in the 

probability of having a PPB at or below the 

industry standard (Blake et al., 2007). That 

model also included the aforementioned 

covariates. 

Results

PPB Differences. The first research 

question of interest in regard to the ratio 

of household members to bedrooms 

evaluated the PPB ratio as a continuous 

variable. In this case, lower PPB values are 

preferred as this is indicative of increased 

alignment between household members and 

available bedrooms. Results from that 

model are included in Table 2. In regard to 

the covariates included in the model, the 

only statistically significant effect was 

observed for household members—each one 

unit increase in household members was 

associated with a 0.30 increase in the PPB 

ratio (p < .001). A statistically significant 

and negative association was observed 

between treatment (i.e., being matched to 

a TCHFH home) and PPB ratio. More 

specifically, being matched to a TCHFH 

home was associated with a 0.68 decrease 

in the PPB ratio. To capture the 

standardized effect of home placement on 

PPB, marginal means were used to 

calculate the associated Cohen’s d effect 

size, which was equal to 0.76 or 

approximately three fourths of a standard 

deviation.

The results from the linear regression 

model help articulate the impact of TCHFH 

home placement on the overall PPB ratio 

for families. However, it is equally relevant 

to consider the proportion of families in 

both groups that met common guidelines 

for the PPB ratio. In the case of the 

present study, we used guidelines 

described by the U.S. Department of 

Housing and Urban Development, which 

suggest that families should have a PPB 

ratio of 2 or less. To evaluate the impact of 

home placement on the probability of 

meeting that criterion, a dichotomous 

variable was created in which families 

were coded as 1 if their PPB was at or 

below 2 and 0 if their PPB was above 2. In 

addition to treatment (i.e., home 

placement), information on household 

members and income were included in the 

final model. 

Results from the logistic regression model 

provide evidence for a statistically 

significant increase in the log-odds of a 

family meeting the PPB benchmark if they 

were matched to a TCHFH home. The 

observed effect of TCHFH home placement 

on the log-odds of a family meeting the 

PPB benchmark (<=2) was translated into a 

probability increase to ease in 

interpretation. For a family with an annual 

income and household membership equal to 

the sample mean, the probability of 

meeting the PPB benchmark after being 

matched to a TCHFH home was 99%, or 
8 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

nearly 1. That same probability among 

families who had not yet been matched to 

home was equal to 78%. That is, TCHFH 

home placement was associated with a 21 

percentage point increase in the 

probability of meeting the PPB ratio 

benchmark. 

Survey Differences. Similar to the PPB 

linear model, a series of four models were 

fit to the data to assess program impact on 

the full survey score, satisfaction score, 

safety score, and 

housing issues score. 

In all cases, higher 

values were 

associated with 

greater satisfaction 

and/or fewer 

housing issues. 

Across all four outcomes, a statistically 

significant and positive effect on survey 

responses was observed for treatment (see 

Table 2). The largest effects were observed 

for the full survey score (B = 0.63; d = 1.03) 

and Home Satisfaction (B = 0.86; d = 1.36). 

In those cases, TCHFH home placement was 

associated with more than a full standard 

deviation increase the average survey 

score. Smaller effects were observed for 

the Safety (B = 0.62; d = 0.83) and Housing 

Issues (B = 0.44; d = 0.71) subscales; 

however, both effects were also quite 

large. A statistically significant and 

negative effect was observed for the 

number of household members when 

examining the full survey score (B = -0.06) 

and Housing Issues score (B = -0.09). 

Finally, families selfidentifying as a 

household of color 

tended to have a 

higher PPB and lower 

survey ratings across 

all subscales. 

However, the only 

association demonstrating statistical 

significance was Home Satisfaction. More 

specifically, there was a 0.25 expected 

decrease in home satisfaction among 

households of color (d = 0.40).

Table 2. Regression Results of TCHFH Impact across Persons per

Bedroom and Survey Responses

PPB Full Survey 

Score

Home 

Satisfaction Safety Housing Issues

Covariates B B B B B

Annual Income -0.04 0.01 .001 0.07* 0.05

Household Members 0.30*** -0.06* -0.01 -0.002 -0.09**

Household of Color -0.12 -0.20 -0.25* 0.07 -0.23

Age -0.002 -0.01* -0.01* -0.02* -0.01

Weeks 0.004 -0.01* -0.01* -0.002 -0.01

Primary Predictor B D B D B D B D B D

Treatment -0.68*** 0.76 0.57*** 1.03 0.86*** 1.36 0.62*** 0.83 0.44*** 0.71

Note: Annual income mean centered; effects associated with a $5,000 increase in gross annual 

household income. B = unstandardized coefficient; D = Cohen’s D standardized effect size.

TCHFH home placement was associated 

with a 0.68 decrease in the Persons-perBedroom ratio and a 21 percentage point 

increase in the probability of meeting the 

Persons-per-Bedroom ratio benchmark. 
9 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

Conclusion and Discussion 

In the current evaluation, we sought to 

examine the degree to which support from 

TCHFH—made possible through the use 

AmeriCorps resources—improves the quality 

of housing available to families in need. 

Improvements to housing in this case were 

defined as a lower PPB ratio and 

improvements to elements of housing 

satisfaction. 

The observed results provide consistent and 

strong evidence that TCHFH helps to 

decrease PPB ratios and increase housing 

satisfaction. In the context of PPB, families 

matched to a home had, on average, a PPB 

ratio that was 0.68 lower than similar 

families not matched to home—an effect 

that was nearly three quarters of a 

standard deviation when standardized. 

Likewise, nearly all families matched with 

a home had a PPB ratio at or below federal 

guidelines, compared to only 78% of 

families not matched with a home. The 

effects for PPB ratio observed in the 

current study exceed the performance 

measure goal set by the program (based on 

a goal of 85% of families reported a 

reduced PPB). The effects on PPB are 

particularly significant given its welldocumented relationship with a host of 

quality of life metrics (Blakely et al., 2003; 

Office of the Deputy Prime Minister, 2004), 

and the population served by TCHFH. For 

example, 22% of families not matched to a 

home in the present study fell above the 

PPB benchmark. These families represent 

the typical distribution of families not 

matched to a home on any given year and 

stand in stark contrast to national rates of 

attaining a PPB ratio of 2 or less, which are 

less than 3% (Blake et al., 2007). In other 

words, it is likely that TCHFH has a direct 

and meaningful impact on a measure of life 

quality (PPB) among families that need that 

impact most.

The effects observed for PPB ratio offer a 

compelling look at the direct value of the 

program as well as a variety of benefits 

that might be assumed given their close 

relationship with overcrowding. Data from 

the TCHFH survey contribute to this 

argument as the survey asked families to 

report on a variety of characteristics likely 

to be associated with a lower PPB ratio. 

Indeed, the correlation between PPB and 

the total survey score across both groups in 

the current evaluation was equal to -0.43, 

providing some evidence that higher PPB 

ratios were associated with lower survey 

scores. When examining between-group 

data on the survey, families matched to a 

house through TCHFH had total survey 

scores that were over a standard deviation 

higher than those observed among similar 

families that had not been matched to 

home. That is, families matched to a home 

uniformly reported more favorable views of 

overall home satisfaction, feelings of 

safety, and housing issues.

Below, and consistent with the broader 

push for using evaluation to guide program 

improvement, we describe the most 

relevant contextual variables for the 

current evaluation and offer insight on 

current changes adopted by TNAC intended 

to address those variables moving forward.

Future Programming and Evaluation. As 

the TCHFH program begins planning for the 

next grant cycle, there are a number of 

learnings from the evaluation worth 

consideration. First, despite limitations 

associated with the current design (e.g., 

lack of random assignment to treatment 
10 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

and control), the evidence associated with 

the program may be sufficiently strong to 

examine components of program 

implementation that may lead to greater 

efficiency, effectiveness, and/or equity. 

For example, it may be useful to examine 

benefits across relevant demographic and 

geographic variables. The presence of 

variability across families, cost of building, 

and home satisfaction might facilitate 

discussion regarding which families benefit 

most from support as well as which 

neighborhoods and/or builds are 

particularly desirable. Demographic data 

may also be useful to examine the impact 

of TCHFH activities on reducing racial or 

ethnic disparities in housing. Across nearly 

all outcomes, families of color were more 

likely to have higher PPB ratios and report 

lower levels of housing satisfaction. 

Although only one outcome was statistically 

significant (housing satisfaction scores), 

the consistent negative association 

between being a household of color and the 

outcomes of interest may prompt the 

program to consider the ways in which they 

are partnering with households of color. 

Finally, future evaluations might consider 

capturing the value-add of members in 

terms of building or renovation efficiency. 

Such analyses might help articulate the 

specific value of AmeriCorps support within 

the model. 
11 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

References

Blakely, T., Atkinson, J., Kiro, C., Blaiklock, A., & D’Souza, A. (2003). Child mortality, 

socioeconomic position, and one-parent families: independent associations and 

variation by age and cause of death. International journal of epidemiology, 32(3), 410-

418.

Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2011). MatchIt: nonparametric preprocessing for 

parametric causal inference. Journal of Statistical Software, 42, 1-28.

Mattessich P. & Hansen M. (2015). Impact of habitat for humanity homeownership. Wilder 

Research. 

https://cdn2.hubspot.net/hubfs/92650/Impact_2020/Full%20Wilder%20Report.pdf. 

Rubin, D. (1973). Matching to remove bias in observational studies. Biometrics 29, 159–183.

Smith, H. L. (1997). 6. Matching with Multiple Controls to Estimate Treatment Effects in 

Observational Studies. Sociological methodology, 27(1), 325-353.

Stuart, E. A., & Rubin, D. B. (2008). Best practices in quasi-experimental designs. Best 

practices in quantitative methods, 155-176.

The United Kingdom Office of the Deputy Prime Minister. “The Impact of Overcrowding on 

Health & Education: A Review of Evidence and Literature.” Office of the Deputy Prime 

Minister Publications, 2004.
12 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

About the Center for Advancing Research to Practice 

National service has the potential to close the research-to-practice gap. Each year, individuals 

commit billions of hours toward improving their communities. This is a massive resource that can be aligned 

with best practices from research to make a real, profound impact. 

ServeMinnesota’s Center for Advancing Research to Practice is dedicated to closing the gap — using research

to identify what works best in communities and applying those findings in practice through national service

programs. 

The Center for Advancing Research to Practice is uniquely positioned at the nexus of research and service. 

Its mission is to ensure national service is an effective solution to complex social problems.

About the Authors 

Peter Nelson, Ph.D.

Director of Research and Innovation, ServeMinnesota

Peter Nelson is the Director of Research and Innovation at ServeMinnesota where he engages with 

ServeMinnesota pilot programs and existing programs to ensure key principles of effective implementation 

and evidence-building occur. Peter has more than 10 years of experience in research and evaluation 

methodology with a particular focus on the educational context. Before joining ServeMinnesota, Peter was an 

Assistant Professor of School Psychology at Penn State University. In addition to his direct work with 

AmeriCorps programs, Peter has published over 35 peer-reviewed articles in academic journals and helped 

secure over 2 million dollars in funding to facilitate new program development and research related to databased decision making and academic intervention in the school setting. Peter holds a B.A. in Psychology from 

St. Olaf College, an M.A. in Education from the University of Mississippi, and an M.Ed and Ph.D. from the 

University of Minnesota in School Psychology. Peter serves on the editorial board for School Psychology

Review, School Psychology Forum and Assessment for Effective Intervention. 

Patrick Kaiser, M.Ed.

Senior Evaluation Manager, ServeMinnesota

Patrick Kaiser serves as the Senior Evaluation Manager at ServeMinnesota where he manages evaluation and 

reporting for ServeMinnesota’s programs with a focus on the strategic initiatives of Minnesota Reading Corps 

and Minnesota Math Corps. He also supports the evaluation needs of partner organizations replicating these 

programs across the country. Patrick previously taught fifth grade in Savannah, GA as an AmeriCorps member 

and worked in education policy advocacy in Atlanta, GA. Patrick attended the University of Notre Dame 

where he received a B.A. in Political Science and Economics and a M.Ed. in Elementary Education.
13 | An Evaluation of the Twin Cities Habitat for Humanity AmeriCorps Program

Appendix A: Habitat for Humanity Family Survey 

Items based on needs of the Habitat for Humanity program and existing research on housing-related issues. 1234

Occupants and Bedrooms

How many people live in your house or apartment?

How many bedrooms are in your house or apartment?

Overall Satisfaction: Rate the degree to which you agree with the following statements about your current 

house/apartment and neighborhood. 

Strongly 

Agree

Somewhat 

Agree

Somewhat 

Disagree

Strongly 

Disagree

My house or apartment is affordable. 4 3 2 1 

My current housing situation is a source of financial stress. 1 2 3 4 

Overall, I am proud of my house or apartment. 4 3 2 1 

Overall, I am satisfied with the house or apartment I live in right now. 4 3 2 1 

Overall, I am satisfied with the neighborhood I live in right now. 4 3 2 1 

Overall, I am satisfied with the condition of the house or apartment I live in right now. 4 3 2 1 

Overall Safety: Rate the degree to which you feel safe in your current home/apartment and neighborhood. Very 

Safe

Somewhat 

Safe

Somewhat

Unsafe

Very 

Unsafe

Overall, how safe do you feel in your current house or apartment? 4 3 2 1 

Overall, how safe do you feel in your current neighborhood? 4 3 2 1 

Specific Issues: Rate the degree to which the housing-related factors below are true for your current living 

situation

Very

True

Somewhat

True

Somewhat 

Untrue

Very 

Untrue

Lack of living space 1 2 3 4 

Walls with peeling paint or broken plaster 1 2 3 4 

Plumbing that does not work properly 1 2 3 4 

Unsafe wiring or other electrical issues 1 2 3 4 

Rats or mice in the house or apartment 1 2 3 4 

Cockroaches or other bugs in the house or apartment 1 2 3 4 

Broken locks or no locks on the door to unit or house 1 2 3 4 

Heat not working 1 2 3 4 

Fear of eviction 1 2 3 4 

Desire to, but unable to move 1 2 3 4 

Excessive noise in home 1 2 3 4 

1 Sandel, M., & Wright, R. J. (2006). When home is where the stress is: expanding the dimensions of housing that influence asthma morbidity. Archives of Disease in Childhood, 91(11), 942-948. 2 Smith, C. A., Smith, C. J., Kearns, R. A., & Abbott, M. W. (1993). Housing stressors, social support and psychological distress. Social Science & Medicine, 37(5), 603-612. 3 Buron et al. (2002) The HOPE VI Resident Tracking Study. United States Department of Housing and Urban Development.

4 Dunn, J. R., & Hayes, M. V. (1999). Identifying social pathways for health inequalities: The role of housing. Annals of the New York Academy of Sciences, 896(1), 399-402.