Is high rent bad for your health?
What a national survey of 14,110 renter households says about rent bills and mental health. The short answer: the connection is real, but it works differently than the headline version suggests.
This page comes in two parts: the story in plain language first, then technical notes with the data construction, model specification, and full estimates.
One in four renters rates their mental health as fair or poor
Is that connected to how much of their income the rent eats? It sounds obvious that it would be. But "sounds obvious" is exactly the kind of claim worth testing, because if the connection is real, it tells governments that housing help is also health help.
The data comes from the Canadian Housing Survey, a large government survey where Statistics Canada asks thousands of households across the country about their housing and their lives. The answers are anonymous, and a public version is free for anyone to download, which is the version I used. Among many other things, the survey records how much of a household's income goes to rent and utilities, and asks people to rate their own mental health.
I sorted 14,110 renter households into four groups, from those spending less than 30% of their income on housing (the usual definition of "affordable") up to those spending more than everything they earn, which happens when people live off savings, loans, or family help.
How to read the first chart: each row is one of those four groups. The bar shows how many renters out of 100 in that group rate their own mental health as fair or poor. A survey can't ask everyone in the country, so there is always some wiggle room around each number; the thin whisker under each bar marks the range the true number very likely falls in.
Fair-or-poor mental health climbs with the share of income going to rent
Out of every 100 renters in each group, how many rate their own mental health fair or poor – whiskers mark the range the true number very likely falls in – Canada, 2022
Canadian Housing Survey 2022 · 14,110 renter households · weighted to represent all renters
Compare renters with the same income, and the picture changes
The first chart mixes two things together. People spending most of their income on rent usually earn less to begin with, and lower income is itself linked to worse health. So is it the rent bill, or the income behind it?
To separate the two, I used a statistical model that compares like with like: renters with the same income, age, gender, education, and household type, who differ mainly in how big their housing bill is. I also followed Statistics Canada's own recommended method for calculating how certain we can be about each estimate.
How to read the second chart: each row asks "compared with the baseline group, how much do the odds of fair-or-poor mental health change?" A value of 1× means no difference. A value of 2× means the odds are doubled. The whisker again shows the range we can be fairly confident in; if it crosses 1×, the difference could plausibly be zero.
Once income is taken into account, the rent-share groups all land close to 1×: spending half your income on rent, by itself, no longer predicts worse mental health. What stands out instead is the top row. Renters who actually missed or delayed a housing payment in the past year, about 9 in 100, have more than double the odds of rating their mental health poorly, and that holds at every income level. That last phrase is a testable claim rather than a figure of speech, and the technical notes test it.
Compare renters with the same income, and what matters is missing a payment
How the odds of fair-or-poor mental health compare with the baseline group – 1× means no difference – after accounting for income, age, gender, education and household type
Canadian Housing Survey 2022 · whiskers mark 95% confidence ranges
The same answer, in plain units. Odds ratios take practice to read, so the third chart asks the question again in the units the first chart used: people out of every 100. Each group appears twice. The light dot is the raw share from the first chart. The dark dot is the model's like-for-like estimate: what that group's share would be if all four groups contained the same mix of people by income, age, gender, education, and household type.
Compared like for like, the staircase flattens to about 26, 26, and 28 out of 100 across the first three groups. The raw climb mostly reflected who sits in each group: renters with heavy housing costs tend to have lower incomes, and low income travels with poorer mental health for reasons of its own.
Hold income constant and the staircase flattens
raw share from the first chart · the model's answer for the same group, in the same units, holding income, age, gender, education and household type constant – whisker = 95% range on the adjusted share
Canadian Housing Survey 2022 · adjusted shares are model-based averages (g-computation), bootstrap 95% ranges
Why does the highest-burden group drop below everyone else? Paying more than 100% of income on shelter is rare (269 households here), and it usually means the household's measured income was unusually low that year: a student living on savings, someone between jobs, one bad year for a business. Incomes that low predict poor mental health all by themselves, and this group's raw 37 in 100 is roughly what such incomes produce. Once income gets that credit, the ratio itself stops adding risk, and the like-for-like estimate even dips below the other groups. The dip is too uncertain to lean on: with so few households, its plausible range runs from about 11 to 26 in 100.
In plain words: the survey suggests the harm shows up less in the size of the bill and more in the moment the bill stops being payable.
Did the pandemic change this?
The same survey ran in 2018, before the pandemic. Rebuilding the identical definitions in that cycle, 22,906 renter households strong, turns one snapshot into a comparison of two worlds.
How to read this chart: each row compares renters in 2018 (light) with renters in 2022 (dark). Only one row moves. The share of renters rating their mental health fair or poor rose from 16% to 26%, a ten-point deterioration in four years. Meanwhile the housing side stood remarkably still: roughly one renter in eleven missed a payment in both cycles, and almost exactly one third spent past the 30% affordability line in both cycles. Whatever drove the mental-health decline, it was not a wave of new housing hardship.
Between the two surveys, poor mental health rose ten points; the housing side barely moved
2018 · 2022 – weighted shares of renter households, harmonized across cycles (22,906 and 14,230 households)
Canadian Housing Survey 2018 + 2022 PUMFs · same definitions in both cycles
And the penalty attached to hardship held steady too: renters who missed a payment carried about double the odds of poor mental health in 2018 (2.1 times) just as in 2022 (2.3 times), and the difference between those two numbers is well within survey noise. The technical notes treat it as no change. Put together, the two cycles say the hardship-health link is structural rather than a pandemic artifact: the pandemic raised the floor of distress for everyone, and missing a rent payment cost roughly the same, large amount of wellbeing on top, in both worlds.
The honest fine print
This survey is a snapshot of one moment. It can show that missed payments and poor mental health go together; it cannot show which causes which. Struggling with rent can wear a person down, and poor mental health can also make it harder to keep up with bills. Both stories fit this data, and the write-up says so plainly.
A detail I caught while building this: the survey question I first picked as the measure of "housing hardship" turned out, on a careful read of the questionnaire, to measure general money trouble instead. I switched to the housing-specific measure before running anything. Reading the documentation is part of the job.
Technical terms, translated
- Odds
- A way of expressing how likely something is. "Double the odds" means the chance is meaningfully higher, though not literally twice the percentage.
- 95% confidence range (the whiskers)
- Surveys ask a sample, never everyone. The whisker marks the range where the true number for all renters very likely sits.
- "After accounting for income, age, ..."
- The model compares people who are similar on those traits, so the comparison isolates the housing bill instead of everything at once.
- Survey weights
- Some kinds of households are harder to reach than others. Weights scale each answer so the results represent all Canadian renters, not just the people who picked up the phone.
Technical notes
Everything above, restated with the detail a methods reviewer needs: data construction, model specification, full estimates, and the checks behind the claims.
Data and sample
Canadian Housing Survey 2022 public-use microdata file (PUMF), downloaded
directly from Statistics Canada. The file holds 38,657 households, of which
21,719 rent; the analytic sample is 14,110 renter households after complete-case
restriction on all model variables (the missed-payment model, which does not
condition on the ratio, retains 14,192). Outcome: self-rated mental health
(GH_10), dichotomized as fair or poor (4, 5) versus good to
excellent (1–3); weighted prevalence among renters, 25.9%. Primary
exposure: grouped shelter-cost-to-income ratio (PSTIR_GR): under
30%, 30–50%, 50–100%, over 100%. Secondary exposure: missed or
delayed a rent or mortgage payment in the past year (EHA_25),
weighted prevalence 9.1%. A hardship item (EHA_10) was considered
and rejected as the exposure after a questionnaire read showed it measures
general financial hardship; it serves only as a mechanism check.
Design and estimation
Survey-weighted logistic regression (binomial GLM), with design-consistent variance from the survey's 1,000 bootstrap replicate weights (the survey bootstrap of Rao, Wu and Yue, 1992): the model is refitted under every replicate and the variance taken as the mean squared deviation from the full-sample estimate. Covariates: age group (reference 30–44), gender, log household income, education (reference bachelor's or higher), household type (reference one-person), region (reference Ontario), household employment, immigrant status, and visible-minority status. Average marginal effects come from g-computation (Robins, 1986): set every household to each ratio level, predict, and take the weighted mean. The full bootstrap run takes about seven minutes.
where pi = Pr(fair-or-poor mental health), k runs over the 30–50%, 50–100% and over-100% burden groups (reference: under 30%), and Xi collects age band, gender, log household income, education, household type, region, employment, immigrant and visible-minority status. Weighted maximum likelihood (PFWEIGHT); variance from 1,000 bootstrap replicates. The secondary model replaces the STIR terms with MissedPayi; the homogeneity check adds δ·MissedPayi × log Inci.
| Group | % fair or poor | 95% CI | Unweighted n |
|---|---|---|---|
| Under 30% of income | 23.4% | 21.6 to 25.1 | 9,525 |
| 30–50% | 28.2% | 24.8 to 31.7 | 3,301 |
| 50–100% | 34.4% | 28.1 to 40.7 | 1,015 |
| Over 100% | 37.0% | 27.8 to 46.2 | 269 |
| Term | OR | 95% CI | Model n |
|---|---|---|---|
| Ratio 30–50% (vs under 30%) | 1.01 | 0.81 to 1.25 | 14,110 |
| Ratio 50–100% | 1.10 | 0.79 to 1.54 | 14,110 |
| Ratio over 100% | 0.62 | 0.36 to 1.06 | 14,110 |
| Missed or delayed a housing payment | 2.33 | 1.73 to 3.14 | 14,192 |
Both models adjust for age group, gender, log household income, education, household type, region, household employment, immigrant and visible-minority status; CIs from 1,000 bootstrap replicate weights. Average marginal effects on the probability scale are essentially flat across ratio groups: 26.0, 26.1, 27.7, 18.5 percentage points.
| Exposure group | Poor general health OR | 95% CI | Low life satisfaction OR | 95% CI |
|---|---|---|---|---|
| Ratio 30–50% | 0.90 | 0.74 to 1.10 | 0.91 | 0.74 to 1.12 |
| Ratio 50–100% | 0.78 | 0.59 to 1.03 | 0.89 | 0.63 to 1.25 |
| Ratio over 100% | 0.30 | 0.18 to 0.50 | 0.40 | 0.24 to 0.68 |
n = 14,082 (general health) and 14,029 (life satisfaction). The far-below-1 estimates in the over-100% group are a caution about the ratio's denominator at the extreme, discussed below.
Interpretation notes
Three things worth flagging. The adjusted ratio coefficients sit near 1 while the descriptive gradient is steep, which is the signature of confounding by income. The missed-payment association is large, precise, and stable across specifications. And the far-below-1 estimates for the over-100% group on the secondary outcomes are a caution light about the ratio's denominator: households reporting shelter costs above their income include students, people living on savings, and people with volatile incomes, so extreme ratio values partly measure income mismeasurement rather than true burden.
Homogeneity of regression
The plain-language section says the missed-payment association "holds at every income level." That phrase assumes the exposure does not interact with income, so the assumption is tested rather than left implicit: adding a missed-payment × log-income interaction to the model (income continuous, never categorized), the interaction is a precise null. The missed-payment odds ratio changes by a factor of 1.04 per doubling of income (95% CI 0.80 to 1.33, p = .79, bootstrap SE from all 1,000 replicates). The constant-effect claim is earned, and the same check is the reason no income-specific claims appear anywhere on this page.
Limitations
Cross-sectional design, so no causal ordering; a single-item self-rated outcome; complete-case analysis, which assumes missingness is unrelated to the outcome given the covariates. All estimates are associations.
Change over cycles: 2018 vs 2022
The 2018 PUMF is harmonized to the 2022 definitions with three documented adjustments: the 2018 file carries no immigrant-status variable, so that covariate is dropped from both cycles in the comparison model (the primary 2022 model above keeps it and is unchanged); 2018 records respondent age in years, collapsed into the 2022 bands; and the 2018 PUMF ships no bootstrap replicate weights, so 2018 uses weighted-GLM HC1 standard errors, which understate design effects. That understatement is quantified rather than waved at: on the identical 2022 model, the bootstrap standard error is 2.36 times the HC1 one, and the difference test is run twice, once as-is and once with the 2018 error inflated by that ratio.
| Cycle | n (model) | Fair/poor mental health | Missed a payment | Missed-payment OR |
|---|---|---|---|---|
| 2018 | 22,906 | 16.1% | 9.6% | 2.09 (1.87–2.33, HC1) |
| 2022 | 14,230 | 25.9% | 9.1% | 2.34 (2.06–2.66 HC1; 1.73–3.17 bootstrap) |
The cross-cycle ratio of odds ratios is 1.12, with p = .18 under
HC1 errors and p = .57 under the design-inflated sensitivity:
treated as no change, both ways. The burden distribution is likewise stable
(33.5% versus 33.4% of renters above the 30% line), which rules out a
composition story for the ten-point mental-health rise. Full estimates in
output/change_2018_2022.json, produced by
run_change.py.
Reproducibility
Analysis scripts (run_analysis.py, make_charts.py)
on pandas and statsmodels, run against the PUMF files. Results are serialized
to results.json, which is also the direct source of every number,
chart, and table on this page. Code and write-up available on request.
References
- Statistics Canada. (2022). Canadian Housing Survey, 2022: public use microdata file. Ottawa: Statistics Canada. Used under the Statistics Canada Open Licence; downloaded July 2026.
- Rao, J. N. K., Wu, C. F. J., & Yue, K. (1992). Some recent work on resampling methods for complex surveys. Survey Methodology, 18(2), 209–217.
- Robins, J. (1986). A new approach to causal inference in mortality studies with a sustained exposure period. Mathematical Modelling, 7, 1393–1512.