Rent burden and the Great Recession in the USAexiting rent burden became more difficult after the...

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Urban Studies1–18� Urban Studies Journal Limited 2016Reprints and permissions:sagepub.co.uk/journalsPermissions.navDOI: 10.1177/0042098016665953usj.sagepub.com

Rent burden and the Great Recessionin the USA

Gregg ColburnUniversity of Minnesota, USA

Ryan AllenUniversity of Minnesota, USA

AbstractIn the aftermath of the recent recession, the percentage of households facing rent burden in theUSA reached historically high levels, while cost burden for owners has shrunk. This study usestwo panels from the Survey of Income and Program Participation (SIPP) to compare the preva-lence, distribution and household responses to the phenomenon of rent burden in the USA inthe years immediately before and after the Great Recession. Results suggest that rent burden hasbecome more prevalent after the recession and that income, household composition and locationare major drivers of this phenomenon, both before and after the recession. Results also indicatethat exiting rent burden was more difficult in the years after the recession and that an increasinglycommon coping mechanism for rent burdened households is to increase their household sizes.These results indicate that renters have experienced increased financial stress related to theirhousing. This finding is notable given the lack of policy responses that address hardship amongrenter households in contrast to the privileged status enjoyed by homeowners in the policydomain.

Keywordshousing market, housing policy, recession, rent burden, rental market

Received February 2016; accepted July 2016

Introduction

While the US government response to sup-port homeowners during the recent housingcrisis was anaemic (Immergluck, 2009), pol-icy responses to aid renter households havebeen virtually non-existent. The lack of atten-tion to the plight of renters continues a pol-icy legacy that has rewarded homeownersrather than renters (Krueckeberg, 1999) and

has helped to deepen the class cleavage thatexists in the USA (Shlay, 2015). One exampleof uneven policy support by housing tenureis the ability for homeowners to deduct

Corresponding author:

Gregg Colburn, Humphrey School of Public Affairs,

University of Minnesota, 301 19th Avenue South,

Minneapolis, MN 55455, USA.

Email: [email protected]

at University of Minnesota Libraries on September 28, 2016usj.sagepub.comDownloaded from

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mortgage interest and property tax paymentsfrom their incomes when calculating incometaxes, a benefit not typically provided to ren-ters. Lack of support for low-income renterhouseholds is also evident in other policydomains as there has been a decline in othermeans-tested social programmes in the USAthat scholars describe as selective welfareretrenchment (Mettler, 2007).

The lack of a policy response to aid renterhouseholds has grown more conspicuous inrecent years as housing cost burden, a condi-tion in which a household pays over 30% ofits income on rent and utilities, has becomemore pronounced for renters and less pro-nounced for homeowners. Nearly 50% of allrenters in the USA were housing cost bur-dened in 2014 compared with about 47% in2005 (Fernald, 2015a, 2015b). The rate ofhousing cost burden among homeownershas moved in the opposite direction. In2014 about 25% of homeowners were in acost burdened state, compared with almost29% in 2005 (Fernald, 2015b). AmericanCommunity Survey (ACS) data from 2014indicate that the number of housing costburdened renter households exceeds that ofhousing cost burdened homeowner house-holds (about 21.7 million compared with18.9 million). As a result, renter householdsare more likely than homeowner householdsto experience threats to their viability andhealth that are associated with living in ahousing cost burdened state. For example,housing cost burdened households spend farless on essential expenditures such as foodand healthcare (Fernald, 2015b), and maycompel household members to work addi-tional hours, change their residence or altertheir household composition. The afford-ability of rental housing takes on increasingimportance as the share of US householdsthat rent has reached the highest level sincethe 1960s (Fernald, 2015a).

The purpose of this study is to comparethe phenomenon of housing cost burden for

renter households in the USA in the yearsimmediately before and after the GreatRecession. Specifically, we focus on threerelated research questions. First, how did theprevalence and distribution of housing costburden for renter households change in theyears before and after the Great Recession?Second, what factors are associated withhousing cost burden at the household-levelfor renters and how does the magnitude ofthese factors compare before and after theGreat Recession? Third, what renter house-hold behavioural responses are associatedwith experiencing housing cost burden andhow have these responses changed since theGreat Recession? In answering each ques-tion, we focus our analysis on a period priorto the Great Recession (2004–2005), as wellas the period immediately following the offi-cial end of the recession (2009–2011). Werely on data from two distinct nationallyrepresentative panels from the US CensusBureau’s Survey of Income and ProgramParticipation (SIPP) to provide a detailedpicture of housing cost burden for renters,which we refer to as rent burden hereafter.By using panel data that cover pre- andpost-recessionary periods, this studyimproves upon the findings of other researchthat use cross-sectional data from singleyears.

Our findings provide a deeper under-standing of rent burden and how it changedin the aftermath of the Great Recession.Although descriptive statistics suggest thatexperiencing rent burden is unequally dis-tributed along racial, national origin andgender lines, regression analysis demon-strates that rent burden is associated withincome, household composition and locationfar more than with other demographic fac-tors. In keeping with other recent researchon this topic, our analysis of pre- and post-recession periods shows that rent burden hasbecome more prevalent after the recessionand that higher-income households are at

2 Urban Studies



greater risk of rent burden in the aftermathof the crisis. Our results also indicate thatexiting rent burden became more difficultafter the recession. Finally, the analyses sug-gest that increasing the number of adults ina household is an important mechanism bywhich rent burdened households exit thatstate.

Dynamics of the US housingmarket and rent burden

In the years following the Great Recession,housing in the USA became a tale of twovery different markets. It is not uncommonfor the owner-occupancy and rental marketsto diverge, and this split was evident in theaftermath of the financial crisis (Kroll,2013). The tepid demand for owner-occupancyhousing stood in stark contrast to the sus-tained growth in the number of renters and theprices they paid for housing (Fernald, 2015b;Kroll, 2013). These housing trends produced apost-recession rental market with decreasingvacancy rates and higher rents (Fernald,2015a).

Higher demand for, or lower supply of,rental housing has the potential to pushmarket rents higher. In the years immedi-ately after the recession, the strength of therental market was largely attributed toincreased demand (Collinson, 2011). Supplyfactors also played a role as Collinsonfound that while overall supply may havebeen sufficient, the stock of affordable unas-sisted rental housing was inadequate.According to Collinson and Winter (2010),from 2001 to 2007 the affordable unassistedrental stock fell 6.3% while the supply ofhigh-end rental housing increased by94.3%. The under-development of afford-able unassisted rental housing continuedafter the Great Recession as mortgage mar-ket dislocations constrained the constructionof multi-family units (DiPasquale, 2011).For extremely low-income households (those

earning 30% of area median income or less),the housing choices were even more limited.In 2013, 11.2 million extremely low-incomehouseholds competed for only 7.3 millionaffordable units, implying a significant sup-ply and demand imbalance (Fernald, 2015b).Although rental housing market dynamicspaint a grim picture for renters, an analysisof rent burden is not complete without con-sidering income dynamics. The long-termtrend of income levels for renter householdssuggests that household incomes are notkeeping pace with the rise in rents. Accordingto Collinson (2011), real incomes for rentersfell from 1990 to 2009, and, when combinedwith higher rents, produced rent burden lev-els in 2009 that were 16.6% higher than in1990 (Collinson, 2011).

The challenging market conditions in theyears after the Great Recession producedan unprecedented amount of rent-burdenedhouseholds, nearly 50% according to manystudies (Bean, 2012; Collinson, 2011; Kroll,2013). This phenomenon is particularlyacute among the low-income segment ofrenters where 75% of households facerent burden (Bean, 2012). While low-income households are disproportionatelyburdened, moderate income householdsalso faced increasing rates of burden in theyears following the recession (Fernald,2015a).

Some studies have focused on beha-vioural responses to economic or housinghardship, such as rent burden. Susin (2007)found that many rent-burdened householdswere successful at exiting rent burden after ashort spell in that state, by finding housingwith lower rent, increasing earnings, secur-ing housing assistance or moving intoanother household (doubling up). Multiplestudies have investigated householdresponses to hardship stemming from theGreat Recession. These studies have foundthat in response to the recession, housingmobility fell, but among movers there was a

Colburn and Allen 3



marked increase in local moves (Stoll, 2013);household formation fell sharply (Lee andPainter, 2013); more complex householdswere formed (Mykyta and Macartney,2011); and household spending fell (Hurdand Rohwedder, 2010).

Data and methods

In this study, we use data from the USCensus Bureau’s 2004 and 2008 Survey ofIncome Program Participation (SIPP). TheSIPP provides panel data on approximately40,000 households in the USA over thecourse of three years. By design, the SIPPoversamples low-income households and,therefore, we apply household weights in ouranalyses to make the sample more nationallyrepresentative. Respondents provide house-hold attribute, demographic, income andprogramme participation data during inter-views conducted every four months.Housing and utility cost data are collectedon an annual basis in each year of the panel.The longitudinal nature of SIPP data pro-vides an opportunity to assess changes in theprevalence of rent burden over time, as wellas determine how households react to rentburden. Based on the timing of housing andutility cost data collection in SIPP, the 2004panel provides two data points (2004 and2005) and the 2008 panel generates threeobservations (2009, 2010 and 2011). Noobservations exist for the recession years of2007 and 2008.

In constructing the data set used in thisanalysis, we imposed various restrictions onthe SIPP data to create two samples ofhouseholds that were renters in the privatehousing market for the duration of the yearsin each panel. Households that entered lateor left early from a panel or received govern-ment assistance for housing at any time inthe panel were dropped from the analysis. Inaddition, households that changed tenurestatus during the panel (i.e. moved from

renter to homeowner, or vice versa) werealso excluded from the analysis. Followingprevious research, households that reportedzero income or zero rent costs were droppedbecause of concerns over data validity(Steffen et al., 2015; Susin, 2007). We estab-lish a householder for each household unitbased on the reference person designation inthe SIPP. In this construction, each house-hold assumes the race, gender and age of thehouseholder.

In a study on rent burden that is compara-ble in some ways with our own study, Susin(2007) used the 2001 SIPP panel and adjustedreported data on utility costs, which hebelieved to be inflated, by a factor generatedfrom reported utility data from the AmericanHousing Survey. Despite some potential con-cern that SIPP respondents may inflate theirutility costs, we do not alter reported data onutility costs in SIPP. Instead, we use reportedutility costs to construct a gross rent for eachhousehold. We conduct subsequent sensitivityanalyses where we exclude utility costs fromour construction of our rent burden measureto ensure that our results are not unduly influ-enced by potential systematic errors in thereporting of utility costs. We use a ratio of thegross rent and reported household income toconstruct a dichotomous rent burden variablefor each household in each year of the datathat is equal to 1 if the ratio of gross rent tohousehold income is equal to or greater than0.30 and 0 otherwise. In Table 1, we also pro-vide descriptive statistics for the prevalence ofsevere rent burden in which gross rentaccounts for at least 50% of householdincome. Given that the patterns and disparitiesfor rent burden are very similar to the patternsevident for severe rent burden, we restrict oursubsequent analyses to rent burden.

Table 2 provides a summary of the dataused in this study. Although there are someslight differences in the samples within theyears of each panel, we only present datafrom 2004 and 2009 in the interest of brevity.

4 Urban Studies



After data cleaning, the 2004 panel and 2008panel include 6238 and 4498 households,respectively. The smaller sample in 2008 islargely due to the fact that more observa-tions were excluded because of sample attri-tion, changes in tenure status or receivinghousing assistance in the three years of thispanel in comparison to the two years in the2004 panel. Comparing the two samples

reveals some minor differences. Specifically,the proportion of the sample that was non-white increased between 2004 and 2009, withan increased proportion of Hispanics in 2009driving this change. Foreign-born house-holds also had a larger representation in the2009 sample compared with the 2004 sample.Each of these differences is consistent withincreasing ethnic diversity and the

Table 1. Rate of rent burden (30%) and severe rent burden (50%) among renter households in SIPP 2004and 2009 panels.

2004 2005 2009 2010 2011

30% 50% 30% 50% 30% 50% 30% 50% 30% 50%

Total rent burden 50.4 23.5 48.9 22.8 55.1 27.3 53.7 26.5 53.0 23.2Race

White (ref) 47.3 22.0 45.4 21.2 52.2 24.3 50.9 24.7 49.2 23.2Black 56.6 26.5 56.5 26.2 57.1 29.4 55.6 26.3 57.8 25.1Hispanic 53.9 23.9 51.0 24.0 63.1 34.1 59.9 30.6 59.5 33.9Asian 48.1 23.5 50.6 24.0 50.6 28.0 51.2 32.0 49.7 25.2Other 53.8 32.6 54.7 24.7 44.0 18.7 51.7 22.5 53.1 28.4

NativityNative-born (ref) 49.3 23.4 47.7 22.3 53.2 25.1 51.9 24.6 50.9 23.9Foreign-born 54.5 23.7 53.2 24.6 61.3 34.3 59.3 32.5 59.8 33.4

GenderMale (ref) 45.1 19.7 43.0 18.5 50.0 24.8 49.2 25.5 48.5 24.3Female 55.2 26.9 54.2 26.7 59.8 31.2 57.8 28.0 57.2 29.1

Child statusNo children (ref) 48.4 23.0 46.4 21.7 50.9 22.2 51.3 23.2 50.2 23.1With children 53.7 24.3 52.9 24.5 61.7 32.0 57.4 29.6 57.5 28.9Doubled-up status

Not doubled (ref) 53.4 25.6 51.1 24.7 56.5 28.6 54.9 27.9 54.3 27.4Doubled 36.4 13.4 39.7 15.1 48.7 21.6 48.8 20.8 47.1 20.4

Monthly household income (2011 dollars)Less than US$1500 (ref) 92.9 76.3 85.0 68.8 91.9 76.8 85.2 70.3 83.4 69.3US$1500 to US$3000 73.0 25.8 71.1 27.0 76.9 32.0 74.1 33.3 72.9 29.0US$3000 to US$4500 36.2 4.8 37.5 7.2 40.2 8.0 42.1 7.0 43.4 8.5Greater than US$4500 10.3 1.3 13.3 2.2 16.2 1.3 16.5 2.8 15.7 3.6

Metro statusMetro (ref) 51.0 23.9 49.2 22.7 55.9 28.5 54.7 27.3 53.7 27.0Non-metro 45.8 20.9 44.8 22.8 49.9 19.1 48.9 22.5 48.4 21.7Not identified 52.1 21.1 54.3 24.8 52.6 26.2 46.5 22.3 52.0 21.5

RegionSouth (ref) 50.7 24.3 51.2 24.0 56.0 28.2 55.4 27.3 53.2 23.5Northeast 49.9 24.2 46.7 22.8 52.2 26.5 51.9 26.8 50.1 27.6Midwest 48.5 23.3 46.5 21.8 52.1 23.3 48.4 24.8 49.8 23.5West 51.8 22.0 49.5 22.1 58.1 29.4 56.3 26.5 56.8 29.9

N 6238 6238 4498 4498 4498

Note: Results are weighted and within category tests for statistical significance are performed with findings relative to the

reference group (with Bonferroni adjustment); results with p \ 0.05 in bold.

Colburn and Allen 5



proportion of the foreign-born in the USpopulation over time.

In addition to the descriptive analysisdescribed below, we use logistic regressionmodels to assess the association of certaintraits with a household being rent burdened.The logistic regression model we use has thefollowing general specification:

Logit Rent Burdenð Þ=b0

+b1 Demographicsð Þ+b2 Household attributesð Þ+b3(Geography)

ð1Þ

where demographic characteristics includerace, nativity, gender and age; householdattributes include marital status, householdincome, household size, presence of childrenand household composition; and geographicfactors include metro status and region ofthe USA. To facilitate comparisons acrosstime, income categories for each year arebased on 2011 real incomes. We incorporaterobust standard errors to guard againstpotential misspecification in the model. Toease interpretation of results of these modelswe also calculate average marginal effectsfor each predictor and report these alongside

Table 2. Sample characteristics for 2004 and 2009, by rent burden status.

2004 2009

Total Rent burdened Total Rent burdened

RaceWhite 57.5 54.0 54.8 51.9Black 18.1 20.3 16.3 16.9Hispanic 17.6 18.8 21.8 25.0Asian 3.7 3.5 4.3 4.0Other 3.1 3.3 2.7 2.1

NativityForeign-born 20.7 22.4 23.5 26.2

Gender of householderFemale 52.5 57.5 51.8 56.3

Monthly household income(2011 dollars)

Less than US$1500 18.6 34.2 20.0 33.4US$1500 to US$3000 30.4 44.0 31.0 43.2US$3000 to US$4500 22.1 15.9 20.7 15.1Greater than US$4500 29.0 5.9 28.4 8.4

Household compositionHousehold size (mean) 2.4 2.4 2.5 2.5Houses with children 37.5 39.9 38.7 43.3Doubled-up households 17.6 12.7 18.6 16.4

Metro statusMetro 84.1 85.1 84.3 85.6Non-metro 11.8 10.7 11.9 10.8Not identified 4.1 4.2 3.8 3.6

RegionSouth 32.3 32.5 33.3 33.8Northeast 21.7 21.5 20.1 19.1Midwest 19.4 18.6 18.4 17.4West 26.7 27.4 28.2 29.7

N 6238 3192 4498 2446

Note: Household weights used to establish proportions; all reported data are proportions unless otherwise specified.

6 Urban Studies



odds-ratios. We selectively calculate pre-dicted probabilities for experiencing rentburden for representative households andpresent the results of this analysis in a sepa-rate table.

Results

Prevalence and disparities in rent burden

The descriptive statistics shown in Table 1demonstrate how rent burden (30% ofincome) and severe rent burden (50% ofincome) changed before and after the GreatRecession. Across the population, and inmost demographic categories, rent burdenincreased meaningfully between 2005 and2009. The prevalence of rent burden wasgreatest in 2009 while the effects of the reces-sion were most acute. In 2010 and 2011, theprevalence of rent burden moderatedslightly, but still remained at elevated levelswhen compared with the pre-recessionperiod. By 2011, over half of all householdswere rent burdened. Rates of rent burden inthis study are slightly higher than other stud-ies that use ACS data have found (Bean,2012; Fernald, 2015b; Kroll, 2013). Onepotential explanation for the higher rates ofrent burden is the rules that we employ tocreate our longitudinal data set. We con-strain our sample only to households thatwere renters in each year of the panel, whichnecessarily excludes households that enteredthe rental market after being homeowners orexited the rental market in favour of home-ownership during the course of the panel.We also exclude households that obtainedgovernment-subsidised housing during thepanel, resulting in a reduction of roughly20% of the rental sample. This relativelyhigh figure is likely due to the oversamplingof welfare programme participants in theSIPP.

The data presented in Table 1 also high-light the disparities of rent burden and severerent burden based on a variety of demo-graphic characteristics. First, black andHispanic households had consistently higherrates of rent burden and severe rent burdenfor each of the years in the study than didwhite households. The proportion ofHispanic households experiencing rent bur-den and extreme rent burden increased dra-matically after the recession, makingHispanics the ethnic group most heavilyimpacted by rent burden in the USA in thepost-recession era. Second, in all periods, USnative-born households had lower rates ofburden than did foreign-born households,and foreign-born households were dispro-portionately affected in the post-recessionperiod. Third, female-headed householdshave a persistently higher rate of rent burdenand severe rent burden than do male-headedhouseholds. Finally, households that includeat least one child have significantly higherrates of rent burden and severe rent burdenthan do households without children.

An analysis of the distribution of rentburden is not complete without consideringincome disparities among renter households.As Table 1 indicates, lower income renterhouseholds are far more susceptible to rentburden and severe rent burden than arehigher income renter households. The vastmajority of households at the lowest level ofmonthly household income experiencedrent burden and severe rent burden in everyyear included in this analysis, clearly distin-guishing them from wealthier households inthe analysis. Among wealthier renter house-holds, rent burden became more prevalentafter the recession. This increase in rent bur-den among higher income households standsin contrast to the steady or slightly decliningrate of rent burden among households withlower incomes.

Colburn and Allen 7



Predicting rent burden

The descriptive statistics suggest that certaindemographic groups are at higher risk forbeing rent burdened. In Table 3, we uselogistic regression models to identify thosetraits and characteristics that predict rentburden after controlling for a variety of cov-ariates. A number of results from the modelsummarised in Table 3 deserve mentionbecause they do not match the results fromthe descriptive statistics presented inTable 1. First, the results in Table 3 indicatelimited explanatory power of race and nativ-ity status in predicting rent burden in boththe pre- and post-recessionary periods.These results suggest that the racial andnativity disparities observed in Table 1 arelikely a product of other factors, such asincome disparities, rather than a function ofrace or nativity, per se. One exception is thenegative relationship with experiencing rentburden for Hispanic households relative towhite households in 2004 and 2005. Thisrelationship is no longer statistically signifi-cant in the years after the recession. Thisresult may be explained by how the house-hold incomes of Hispanics map onto theincome categories we control for in the mod-els. For example, in 2004 the averagemonthly household income for Hispanicscategorised in the lowest monthly householdincome category (less than US$1500 permonth) was US$866 compared with aboutUS$800 for white and black households.Similar income differences existed in 2005,but not for the post-recession years weinclude in our analysis. Because of the coarseway that we control for monthly householdincome in our models, we are not able toaccount for the fact that in the pre-recessionyears Hispanic households included in thelowest household income category werewealthier than white households in the sameincome category and therefore were some-what less likely to experience rent burden.Second, while female-headed households are

disproportionately rent burdened, regressionmodels suggest that gender is not a consistentpredictor of rent burden. Odds ratios showthat female-headed households are at a higherrisk of being rent burdened, but the coefficientsare not statistically significant in a majority ofthe years. Therefore, like the finding related torace and nativity, it is likely that other attri-butes associated with female-headed house-holds, such as the presence of children andlower incomes, might be the driver of thedisparity that is evident in the descriptivestatistics.

While household characteristics such asrace, nativity, gender and marital status hadlimited explanatory power in these models,household size and composition were impor-tant predictors of rent burden. Householdsize was a persistent predictor of rent burdenwith each additional person in a householdassociated with an increase in the probabilityof the household experiencing rent burdenby as much as 2% in some years. The pres-ence of at least one child in the house wasalso associated with a statistically significantincrease in the probability of a householdfacing rent burden in most years consideredin the study. These household attributes mayproduce higher rates of rent burden in twoprimary ways. First, rent costs may increaseas households with more people requirelarger, more expensive housing. Second, thepresence of a child may constrain the earn-ing potential of the adults in that household.

Location also has predictive powerregarding the likelihood that a renter house-hold will experience rent burden. Across allyears included in this analysis, renter house-holds living in metropolitan areas of theUSA faced a significantly higher likelihoodof experiencing rent burden than those livingin non-metropolitan areas. This differencewas relatively consistent across years, withnon-metropolitan households 10–13% lesslikely to experience rent burden than metro-politan households. In addition to the

8 Urban Studies



Tab

le3.

Logi

stic

regr

essi

on

pre

dic

ting

rent

burd

enst

atus

for

rente

rs,2

004–2005

and

2009–2011.

2004

2005

2009

2010

2011

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Const

ant

8.2

68

***

4.0

14

***

7.3

01

***

5.6

82

***

5.2

26

***

Rac

e White

(Ref

.)Bla

ck1.0

73

0.0

10

1.1

95

0.0

29

0.8

23

20.0

29

0.8

53

20.0

27

1.0

73

0.0

12

His

pan

ic0.7

17

20.0

47

*0.7

42

20.0

47

*0.8

76

20.0

20

0.8

72

20.0

23

0.9

04

20.0

17

Asi

an1.0

68

0.0

09

1.3

52

0.0

49

0.9

99

20.0

00

1.0

48

0.0

08

1.0

78

0.0

13

Oth

er1.0

61

0.0

08

1.2

28

0.0

33

0.5

46

20.0

91

**

0.8

34

20.0

31

0.9

68

20.0

06

Fore

ign-b

orn

1.1

39

0.0

19

1.1

00

0.0

15

1.1

41

0.0

20

1.0

32

0.0

05

1.1

70

0.0

27

Fem

ale-

hea

ded

house

hold

1.1

61

0.0

21

1.3

34

0.0

47

***

1.1

79

0.0

25

1.0

86

0.0

14

1.1

30

0.0

21

Mar

itals

tatu

sM

arri

ed(R

ef.)

Wid

ow

ed1.2

66

0.0

34

0.9

91

20.0

01

1.0

44

0.0

06

1.0

25

0.0

04

0.9

99

20.0

00

Separ

ated

/div

orc

ed1.0

43

0.0

06

1.0

10

0.0

02

0.9

76

20.0

04

0.8

43

20.0

29

0.9

06

20.0

17

Nev

erm

arri

ed0.8

97

20.0

16

0.8

13

20.0

34

1.1

07

0.0

15

0.8

95

20.0

19

0.9

52

20.0

08

Age

1.0

01

0.0

00

1.0

00

0.0

00

1.0

03

0.0

00

1.0

00

20.0

00

0.9

94

20.0

01

Mon

thly

hous

ehol

din

com

e(2

011

$)

\U

S$1500

(Ref

.)U

S$1500–U

S$3000

0.1

89

20.1

96

***

0.4

05

20.1

40

***

0.2

67

20.1

49

***

0.4

61

20.1

13

***

0.4

82

20.1

15

***

US$

3000–U

S$4500

0.0

34

20.5

73

***

0.0

86

20.4

86

***

0.0

46

20.5

23

***

0.1

00

20.4

52

***

0.1

26

20.4

17

***

.U

S$4500

0.0

06

20.8

37

***

0.0

19

20.7

39

***

0.0

11

20.7

73

***

0.0

24

20.7

15

***

0.0

27

20.7

03

***

House

hold

size

1.1

21

0.0

16

**

1.1

17

0.0

18

*1.0

85

0.0

12

1.1

55

0.0

24

**

1.1

03

0.0

17

*C

hild

inhouse

hold

1.4

46

0.0

53

**

1.2

78

0.0

40

*1.9

73

0.1

01

***

1.0

52

0.0

09

1.1

87

0.0

29

Double

d-u

p0.7

74

20.0

37

*1.0

59

0.0

09

0.9

69

20.0

05

1.0

80

0.0

13

1.0

77

0.0

13

Met

rost

atus

Met

roar

ea(R

ef.)

Non-m

etro

area

0.4

45

20.1

15

***

0.5

15

20.1

08

***

0.4

23

20.1

28

***

0.5

05

20.1

17

***

0.5

65

20.0

99

***

Not

iden

tifie

d1.3

83

0.0

46

1.9

92

0.1

09

***

0.9

45

20.0

08

0.6

75

20.0

67

*0.9

66

20.0

06

Reg

ion

South

(Ref

.)

(con

tinue

d)

Colburn and Allen 9



metropolitan versus non-metropolitan split,certain regions of the country were associ-ated with higher levels of rent burden. Inmany years, renter households living in theNortheast and West of the USA were morelikely to experience rent burden than renterhouseholds living in the South or Midwest.

These regional differences in rent burdenare likely due to the different housing andeconomic dynamics found in metropolitanand non-metropolitan areas and differentregions of the country. For example, earn-ings for households in non-metropolitanareas are lower than the earnings of house-holds in metropolitan areas, but rental hous-ing costs are typically substantially lower innon-metropolitan areas than in metropolitanareas. On a regional basis, data from the USCensus Bureau indicate that householdincomes were higher in the West and theNortheast in comparison with the Midwestand the South. At the same time, rentalvacancy rates were considerably lower in theNortheast and West (around 7% in 2005)compared with the Midwest and the South(around 12% in 2005).

Of all the predictors included in the logis-tic models, household income was mostimportant in predicting rent burden. Notsurprisingly, renter households with highermonthly incomes were less likely to experi-ence rent burden than were those renterhouseholds with lower monthly incomes.Across the years included in this analysishouseholds earning between US$3000 andUS$4500 per month were 43–57% less likelyto experience rent burden than were house-holds with less than US$1500 of income permonth.

As noted above, we rely on utility expen-diture and rent data from the SIPP in orderto calculate gross rent expenses for house-holds. Given potential concerns over thevalidity of the utility data in SIPP, we con-ducted analyses of rent burden without util-ity costs in order to see if there areT

ab

le3.C

ontinued

2004

2005

2009

2010

2011

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Odds

-rat

iody/

dx

Sig.

Nort

hea

st1.5

82

0.0

65

***

1.0

56

0.0

09

1.1

91

0.0

26

1.1

45

0.0

23

1.1

27

0.0

21

Mid

wes

t0.9

53

20.0

07

0.8

43

20.0

28

0.7

33

20.0

46

**

0.6

11

20.0

84

***

0.8

05

20.0

38

*W

est

1.8

57

0.0

87

***

1.4

39

0.0

59

***

1.5

24

0.0

62

***

1.1

90

0.0

29

1.3

64

0.0

53

**

Chi-Sq

uar

e1391.0

***

1232.2

***

1090.0

***

854.2

***

870.5

***

N6238

6238

4498

4498

4498

Not

e:R

esults

ofw

eigh

ted

logi

stic

regr

essi

on

model

s:*p

\0.0

5,**p

\0.0

1,***p

\0.0

01.

10 Urban Studies



systematic differences in the results based onthe two different specifications of rent bur-den. The logistic regression models producesubstantively similar results, with the onlyexception being that non-native born house-holds have a statistically significant and pos-itive association with rent burden in themodel in which utility costs are excluded.Given that nativity is the only difference inthe two models, we feel comfortable that theutility cost data in SIPP did not distort thekey findings of this study.

The results of the logistic regression mod-els described above help to explain the rela-tionship between various covariates and rentburden, but do not assess the likelihood of aspecific household actually experiencing rentburden. In addition, the non-linear form ofthe logistic regression model makes it diffi-cult to easily assess the likelihood that ahousehold will experience rent burden. InTable 4, we present a set of predicted prob-abilities of experiencing rent burden for twohypothetical households at various incomelevels. We use the results from models pre-sented in Table 3 to calculate the predictedprobabilities for rent burden. When

calculating the predicted probabilities wespecify categorical predictors at pre-determined values, such that we model pre-dicted rent burden probabilities across arange of household incomes for a native-born Hispanic, married, female-headed,non-doubled-up, four-person renter house-hold with at least one child living in a metro-politan area of the West region of the USA.

The results in Table 4 indicate that house-hold income is an important factor in pre-dicting rent burden. For households that fallinto the lowest income category consideredin this analysis (less than US$1500 permonth) the predicted probability of experi-encing rent burden was above 90%. Higherincome categories were associated with pro-gressively lower predicted probabilities ofrent burden. In all income categories therewas a significantly higher predicted prob-ability of experiencing rent burden in 2009compared with 2005. When compared with2005, differences in the predicted probabil-ities of experiencing rent burden in 2010 and2011 were no longer statistically significant,indicating that the effect of the recession onrent burden may have waned over time. To

Table 4. Predicted probabilities for experiencing rent burden for a Hispanic, married, female-headedhousehold in the West.

Rent burden Rent burden

Householdmonthly income

2004 2005 (ref.) 2009 2010 2011 Pre-recession(ref.)

Post-recession

Less than US$1500 0.969** 0.921 0.973*** 0.923 0.906 0.947 0.937US$1500 to US$3000 0.854 0.824 0.907** 0.846 0.826 0.839 0.860US$3000 to US$4500 0.512 0.500 0.622* 0.544 0.557 0.506 0.572Greater than US$4500 0.155 0.179 0.287* 0.221 0.214 0.169 0.238**

Note: Data reported are the predicted probabilities of a renter household experiencing rent burden in a given year or

given time period. When calculating the predicted probabilities we use results from models reported in Table 3.

Categorical explanatory variables are set such that we predict the probability of rent burden for a native-born Hispanic,

married female-headed non-doubled-up, four-person renter household with at least one child in a metropolitan area of

the West region of the USA. Predicted probabilities are compared across years with 2005 and pre-recession serving as

the reference category. *p \ 0.05, **p \ 0.01, ***p \ 0.001.

Colburn and Allen 11



increase the statistical power of the models,we collapsed the data from 2004–2005 and2009–2011 into pre and post-recession mod-els, respectively. When comparing the pre-dicted probabilities from the pre-recessionperiod (2004–2005) to the post-recessionperiod (2009–2011), there was a statisticallysignificant increase in the probability ofrent burden among the highest incomehouseholds.

Behavioural responses to rent burden

In order to understand how exiting from rentburden changed after the Great Recession,we examined the rate of exit from rent bur-dened status both before and after the reces-sion. Exits can only be calculated for periodswhere two consecutive years of data areavailable for the same households, so in thisanalysis we calculate the rate of exit fromrent burden for 2005, 2010 and 2011. Forexample, a 2005 exit occurred if a householdwas rent burdened in 2004 and no longerrent burdened in 2005. Results suggest thathouseholds were less likely to exit rent bur-den in the post-recession period when com-pared with the pre-recession period. Forexample, 26.8% of households that were rentburdened in 2004 exited rent burden in 2005.After the recession, exit rates fell to 24.8% in2010 and 23.5% in 2011 (the difference inexit rates between 2005 and 2010, and 2005and 2011 were statistically significant at leastat the 95% confidence level). These resultsindicate that the difficult economic andrental market conditions that prevailed afterthe recession may have constrained the abil-ity of households to escape a rent burdenedstate.

When faced with rent burden, householdsmay respond in a variety of ways to mitigatethe financial stress. Two potential householdresponses include moving, presumably to acheaper residence, and increasing the num-ber of rent-paying adults living in the

household. After constraining the sample tohouseholds that were rent burdened in agiven year, we estimate a series of logisticregression models to predict whether movingor changes in household composition areassociated with exits from rent burden in thefollowing year. Our regression modelincludes dummy variables for three beha-vioural responses: moves, increases in thenumber of adults in the household anddecreases in the number of adults in a house-hold. In addition to these dummy variables,we also include a series of covariates thatcontrol for demographic characteristics,household attributes and location. In con-trast to previous models where we controlfor household income using dummy vari-ables for household income levels, in thesemodels we control for household income peradult. We make this change because includ-ing changes in household size as a variablein the model has implications for aggregatehousehold income and potentially intro-duces collinearity in a model that also con-trols for total household income.

As summarised in Table 5, results fromthese models suggest that higher per adulthousehold income and increases in house-hold size have positive relationships withexiting rent burden. This is true both beforeand after the recession, but the magnitude ofboth effects wanes in the years after therecession. Moving has a positive relationshipwith exiting rent burden in 2005, but has noeffect on exiting rent burden in the post-recession years, perhaps reflecting risingrents and stagnant household incomes afterthe recession. Gender had an effect on exit-ing rent burden prior to the recession, whenfemale-headed households were about 5%less likely to exit rent burden than male-headed households, but gender did not havea statistically significant relationship withexiting rent burden after the recession. In allyears married households had an advantagein exiting rent burden in comparison with

12 Urban Studies



Tab

le5.

Pre

dic

tors

for

exitin

gre

nt

burd

en.

2005

2010

2011

Odds

ratio

dy/

dx

Sig.

Odds

ratio

dy/

dx

Sig.

Odds

ratio

dy/

dx

Sig.

Move

1.4

90.0

66

**

1.2

60.0

38

1.0

10.0

01

Mai

ntai

nho

useh

old

size

(ref

.)In

crea

sehouse

hold

size

3.1

40.1

88

***

2.9

90.1

80

***

2.0

00.1

11

**

Dec

reas

ehouse

hold

size

0.6

52

0.0

70

0.7

52

0.0

47

0.8

42

0.0

29

Rac

e White

(ref

.)Bla

ck1.1

10.0

17

1.1

40.0

22

0.8

12

0.0

32

His

pan

ic1.3

40.0

50

1.4

10.0

57

*1.2

50.0

37

Asi

an0.7

92

.0.0

36

1.0

60.0

09

1.1

30.0

20

Oth

er1.1

10.0

16

1.1

60.0

23

0.9

92

0.0

02

Fore

ign-b

orn

1.0

30.0

04

0.9

22

0.0

14

0.8

62

0.0

23

Fem

ale

0.7

42

0.0

50

**

0.9

92

0.0

02

1.0

50.0

08

Mar

itals

tatu

sM

arri

ed(R

ef.)

Wid

ow

ed0.6

02

0.0

87

*0.5

92

0.0

85

*0.5

22

0.1

08

**

Separ

ated

/div

orc

ed0.5

52

0.1

00

***

0.6

82

0.0

63

*0.5

82

0.0

90

**

Nev

erm

arri

ed0.6

22

0.0

81

**

0.7

72

0.0

46

0.5

52

0.0

99

***

Inco

me

per

adult

(per

thousa

nd)

2.4

40.1

47

***

1.9

50.1

09

***

1.8

30.0

97

***

Child

inhouse

hold

0.8

82

0.0

22

1.1

10.0

16

0.9

92

0.0

02

Met

rost

atus

Met

roar

ea(r

ef.)

Non-m

etro

area

1.5

30.0

75

**

1.7

40.0

98

***

1.6

50.0

87

**

Not

iden

tifie

d0.7

52

0.0

43

1.9

80.1

24

**

0.8

32

0.0

27

Reg

ion

South

(ref

.)N

ort

hea

st1.0

40.0

07

0.8

32

0.0

30

1.0

10.0

01

Mid

wes

t1.2

50.0

38

1.2

20.0

35

1.0

30.0

05

Wes

t0.9

82

0.0

04

0.8

02

0.0

35

0.8

32

0.0

29

Chi-sq

uar

e243.5

***

164.1

***

97.1

***

N3192

2446

2399

Not

e:R

esults

ofw

eigh

ted

logi

stic

regr

essi

on

model

s:*p

\0.0

5,**p

\0.0

1,***p

\0.0

01.




the different types of non-married house-holds. This analysis also demonstrates thathouseholds in non-metropolitan areas weremore likely to exit rent burden than weresimilarly situated households in metropoli-tan areas, which is potentially explained bythe lower rents typically found in non-metropolitan areas compared with metro-politan areas.

To help interpret the results from themodels presented in Table 5 we calculatedpredicted probabilities for exit from rentburden in 2010 for a household thatincreased the number of adults comparedwith one that maintained the same numberof adults living in the household. When cal-culating the predicted probabilities we spe-cify categorical predictors at pre-determinedvalues, such that we model predicted prob-abilities of exiting rent burden across a rangeof household incomes for a native-bornblack, married, male-headed, non-doubled-

up renter household with at least one childliving in a metropolitan area of the Westregion of the USA. Figure 1 demonstratesthat increasing household size improved theprobability that a rent-burdened householdwould exit that state. Households that chan-ged their household size by adding one ormore adults increased their householdincomes by approximately 50% and weremuch more likely to exit rent burden thancomparable households that did not increasethe number of adults in the household.

The effectiveness of increasing householdsize as a strategy for exiting rent burdendecreased over time. Figure 2 illustrates thepredicted probability of exiting rent burdenfor households that increased the number ofadults living in the household for 2005, 2010and 2011. In Figure 2, we model predictedprobabilities of exiting rent burden across arange of incomes for the same hypotheticalhousehold used in Figure 1. The predicted

Figure 1. Predicted probabilities for exiting rent burden for households with an increase and no change inthe number of adults in the household (2010).Note: Predicted probabilities are based on results from the model of rent burden exit for 2010 in Table 5. The predicted

probabilities assume a range of household incomes for a native-born black, married, male-headed, non-doubled-up renter

household with at least one child living in a metropolitan area of the West region of the USA.

14 Urban Studies



probability curves indicate that increasingthe household size for a household withUS$1500 household income per adultresulted in a predicted probability of exitingrent burden of about 57% in 2005, 45% in2010 and only 32% in 2011. Thus, the strat-egy of increasing household size as a meansof exiting rent burden was significantly lesseffective in the post-recession years.

Sensitivity analysis

To assess the robustness of the findingsdescribed above, we conducted two series ofsensitivity analyses. First, rather than mak-ing rent burden a dichotomous variable inwhich a household is either burdened or not,we created a continuous rent burden vari-able equal to the ratio of gross householdexpenses to income. Both the descriptive sta-tistics in Table 1 and the results of the logis-tic regression in Table 3 were very similarwhen predicting a continuous rent burden

dependent variable. Specifically, similar dis-parities exist in the descriptive statistics andin the regression model, while the same inde-pendent variables tend to be significant whenthe dependent variable is a continuous mea-sure of rent burden.

The second sensitivity analysis addressespotential concerns about the different com-position of the samples in the pre- and post-recession periods. Although the samples arenationally representative, there are differ-ences in the composition of the rental house-holds used in each period. Therefore, inorder to isolate the role of the recession onrent burden outcomes, we employ a propen-sity score matching approach in which sam-ple households are matched based onobservable characteristics. Using the reces-sion as the treatment effect, the analysesshow a statistically significant average treat-ment effect on the treated. The interpreta-tion of this result is that the probability ofrent burden among the treatment group

Figure 2. Predicted probabilities for exiting rent burden for households with an increase in the number ofadults in the household (2005, 2010 and 2011).Note: Predicted probabilities are based on results from the models of rent burden exit for 2005, 2010 and 2011 in Table

5. The predicted probabilities assume a range of household incomes for a native-born black, married, male-headed, non-

doubled-up renter household with at least one child living in a metropolitan area of the West region of the USA.




(post-recession) was significantly higher thanthe probability of rent burden among thecontrol group (pre-recession). The matchingresults suggest that the differences found inthis study between the pre- and post-recession periods are most likely not a func-tion of changes in the composition of thesamples.

Discussion

In this article, we present results from aseries of predictive models that help toexplain the characteristics associated withrisk for rent burden for renter households.Although descriptive statistics suggest signif-icant racial- and nativity-based disparities inexperiencing rent burden during these timeperiods, those disparities disappeared aftercontrolling for income and other householdattributes. Instead, logistic regression modelssuggest that households with lower incomes,larger households, households with childrenand households in metropolitan areas arehighly associated with increased risk of rentburden.

The comparison of periods before andafter the Great Recession produces interest-ing findings. First, rent burden has unam-biguously increased post-recession and thisfinding is consistent with findings from pre-vious studies. Second, exits from rent burdenappear to be more challenging after therecession, as tight rental market conditionsand stagnating earnings may have frustratedefforts by households to exit a rent burdenedstate. Finally, after the recession, rent bur-den has become more pervasive for higherincome households. Given the challengingrental housing market conditions in theUSA, there is a risk that rent burden willcontinue to persist at historically high levels,and affect an increasingly affluent set ofhouseholds.

The findings of this study are relevant foradvocates and policymakers concerned

about the affordability of housing in theUSA. One strategy that policymakers couldpursue to reduce rent burden in the USA isto offer more government-subsidised hous-ing, but in our assessment, the appetite forthis kind of policy change is waning. A dif-ferent approach might be to follow the leadof some states that offer renter households,perhaps below a given household incomethreshold, the ability to deduct some portionof their rent payments from their incomesfor tax purposes. Such a policy would mimicthe well-known and politically popular pol-icy that allows homeowners to deduct mort-gage interest and property tax paymentsfrom their incomes when calculating theirtax burden. Other scholars have suggestedmodifications to the Earned Income TaxCredit (EITC) program in the USA thatwould offer additional money to recipientsto defray housing expenses (Stegman et al.,2003). A more market-based solution is toreduce restrictions on residential density indevelopments in exchange for the inclusionof subsidised housing (Mukhija et al., 2010).Such a policy would increase the supply ofhousing and reduce the cost to build hous-ing, while producing housing that is specifi-cally affordable to lower income households.While these policy suggestions hold somepromise of reducing rent burden, it is impor-tant to keep in mind that households withextremely low incomes require deeper hous-ing subsidies than market-based solutionsfor affordable housing are typically designedto offer.

While the problem of rent burden hascrept up the income ladder in the years sincethe Great Recession, it is important to notlose sight of the fact that rent burden is aproblem that is disproportionately experi-enced by renter households with low andmoderate household incomes. Thus, ourresearch findings provide some nuance to theidea that housing tenure is an importantcomponent of class distinctions in the USA

16 Urban Studies



(Shlay, 2015). While we often acknowledgesocial, political and economic power differ-entials between homeowners and renters in away that reflexively confers higher status tohomeowners over renters (Shlay, 2015), it isalso true that renters as a group are stratifiedby the same characteristics. As rents increaseand incomes stagnate, larger proportions ofhousehold income devoted to housing canput a strain on a greater number of house-holds in the USA, but particularly those ren-ters at the lower end of the income spectrum.For these renter households, housing costsmay crowd out expenditures on food, cloth-ing and other basic necessities, much lessinvestments in human capital that play animportant role in increased socioeconomicmobility. As rent burden increases, house-holds are more prone to housing insecurityand the negative consequences, includingincreased unemployment, associated withevictions and other types of forced displace-ment (Desmond and Gershenson, 2016).Thus it is possible to see how renting housingin the USA subjects households to additionalfinancial stresses related solely to status as arenter without any of the tax benefits thataccrue to homeowners.

Solutions to rent burden pursued byhouseholds, such as moving or increasingthe number of adults in the household, mayhelp to resolve the rent burden problem butalso could carry substantial costs that afflictrenters disproportionately. For example,moving to a cheaper apartment may resolverent burden, but disrupt place-based, sup-portive social networks that are importantfor psychological and material wellbeing.How long such disruptions last and whatlonger-term effects they might have onhousehold members is contested (Pettit,2004). Though the negative effects oftenattributed to overcrowded residential situa-tions are disputed (Myers et al., 1996), it isreasonable to believe that at some point add-ing an additional adult to a household to

help pay for rent can also increase social ten-sion as more people share a fixed amount ofliving space (Skobba and Goetz, 2015).Once again, because lower income renterhouseholds disproportionately experiencerent burden, it is lower income renter house-holds that are more likely to face the poten-tial costs associated with residential movesand increased household sizes. Thus, theresearch findings from this article supportthe idea that housing costs for renters are animportant, and often overlooked, compo-nent of the larger story of rising inequalityin American society.

Funding

This research received no specific grant from anyfunding agency in the public, commercial, or not-for-profit sectors.

References

Bean JA (2012) Renters more often burdened by

housing costs after recession: Nearly half of all

renters spent over 30 percent of income on hous-

ing by 2010. The Carsey Institute at the Scho-

lars’ Repository Paper 169.Collinson R (2011) Rental housing affordability

dynamics, 1990–2009. Cityscape: A Journal of

Policy Development and Research 13(2):

71–104.Collinson R andWinter B (2010) U.S. rental hous-

ing characteristics: Supply, vacancy, and afford-

ability. HUD PD&RWorking Paper 10–01.Desmond M and Gershenson C (2016) Housing

and employment insecurity among the work-

ing poor. Social Problems 63: 46–67.DiPasquale D (2011) Rental housing: Current

market conditions and the role of federal pol-

icy. Cityscape: A Journal of Policy Develop-

ment and Research 13(2): 57–70.Fernald M (2015a) America’s Rental Housing:

Expanding Options for Diverse and Growing

Demand. Cambridge, MA: Joint Center for

Housing Policy of Harvard University.Fernald M (2015b) The State of the Nation’s

Housing. Cambridge, MA: Joint Center for

Housing Policy of Harvard University.




Hurd MD and Rohwedder S (2010) Effects of thefinancial crisis and Great Recession on Ameri-

can households. National Bureau of EconomicResearch Working Paper Series No. 16407.

Immergluck D (2009) The foreclosure crisis, fore-closed properties, and federal policy: Someimplications for housing and communitydevelopment planning. Journal of the Ameri-

can Planning Association 75(4): 406–423.Kroll CA (2013) The Great Recession and Housing

Affordability. Fisher Center Working Papers,Berkeley, CA: UC Berkeley.

Krueckeberg DA (1999) The grapes of rent: A his-tory of renting in a country of owners.Housing

Policy Debate 10(1): 9–30.Lee KO and Painter G (2013) What happens to

household formation in a recession? Journal ofUrban Economics 76: 93–109.

Mettler S (2007) The transformed welfare stateand the redistribution of political voice. In:Pierson P and Skocpol T (eds) The Transfor-mation of American Politics: Activist Gov-

ernment and the Rise of Conservatism.Princeton, NJ: Princeton University Press,pp. 191–222.

Mukhija V, Regus L, Slovin S, et al. (2010) Caninclusionary zoning be an effective and effi-cient housing policy? Evidence from LosAngeles and Orange counties. Journal of

Urban Affairs 32(2): 229–252.Myers D, Baer WC and Choi S-Y (1996) The

changing problem of overcrowded housing.

Journal of the American Planning Association

62(1): 66–84.Mykyta L and Macartney S (2011) The effects of

recession on household consumption: ‘Doubling

up’ and economic well-being. SEHSD WorkingPaper Number 2011–4, Washington, DC.

Pettit B (2004) Moving and children’s social con-nections: Neighborhood context and the con-sequences of moving for low-income families.Sociological Forum 19(2): 285–311.

Shlay AB (2015) Life and liberty in the pursuit ofhousing: Rethinking renting and owning in

post-crisis America. Housing Studies 30(4):560–579.

Skobba K and Goetz EG (2015) Doubling up andthe erosion of social capital among very lowincome households. International Journal of

Housing Policy 15(2): 127–147.Steffen BL, Carter GR, Martin M, et al. (2015)

Worst Case Housing Needs – 2015 Report to

Congress. Washington, DC.Stegman M, Davis W and Quercia R (2003) Tax

Policy as Housing Policy: The EITC’s Poten-

tial to Make Housing More Affordable for

Working Families. Washington, DC: Brook-ings Institute.

Stoll MA (2013) Great Recession Spurs a Shift to

Local Moves. US2010 Project. New York: TheRussell Sage Foundation.

Susin S (2007) Duration of rent burden as a mea-sure of need. Cityscape: A Journal of Policy

Development and Research 9(1): 157–174.

18 Urban Studies



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