Transparency
How SpendVerdict works
Every verdict is built on publicly available data from national statistics agencies. No scraping, no self-reported crowdsourcing without validation, no invented benchmarks. Here is exactly what we use and how.
How the verdict is calculated
SpendVerdict computes your rent-to-income ratio: your monthly rent divided by your gross monthly income (annual salary ÷ 12). This ratio is then compared against two things:
Universal thresholds
Four tiers — Comfortable, Manageable, Stretch, Risky — based on established housing affordability research (see below).
City-specific norms
Where your ratio sits in the actual distribution of renters in your city. We show your percentile: what share of renters in that city pay more or less than you as a proportion of income.
All ratios use gross income, not net. This is the standard used by housing researchers and mortgage lenders globally. Your actual take-home is lower — keep that in mind when interpreting your result.
Affordability tiers
The 30% rule (spend no more than 30% of gross income on rent) dates to US housing policy from the 1980s. We use a four-tier system that reflects a more complete picture of financial pressure:
Comfortable
Under 25%
You have meaningful financial headroom. Room for savings, emergencies, and lifestyle spending without housing pressure. This is the threshold most financial planners consider genuinely sustainable long-term.
Manageable
25–35%
Within the commonly cited 30% rule. Housing is a significant expense but not yet dominating your budget. Common for median earners in most major cities.
Stretch
35–45%
Above the 30% guideline. Savings capacity is reduced and you have less buffer for unexpected costs. Not unusual in high-cost cities, but worth monitoring.
Risky
45%+
Housing is consuming an unsustainable share of income. High risk of financial stress. Even moderate unexpected costs could cause difficulty. Common in Dublin, New York, and London for median earners.
Rent distribution model
For each city we define three points of the rent distribution for a 1-bedroom apartment (~40–60m²) in the new-let market:
P10
Cheapest 10%
Budget areas, outer districts
P50
Median
Most common rent level
P90
Top 10%
Premium / central locations
We fit a log-normal distribution to these three points (empirical rent data is right-skewed, making log-normal a better fit than normal distribution). This lets us compute any percentile — e.g. "what share of renters pay less than your rent."
Note: all figures represent the new-let free market. Existing long-term or regulated tenancies are typically lower. Prices in the note on each city reflect asking rents someone searching today would find.
Data sources by city
Each source is rated High, Medium, or Low confidence. This rating is shown directly on your result page.
ONS — Office for National Statistics (UK)
HighUsed for: London
Private Rental Market Survey and English Housing Survey. Published annually by the UK's national statistics authority. Used for London rent distribution and rent-to-income ratios.
Destatis — German Federal Statistics Office
HighUsed for: Berlin
Housing cost ratio data from the Einkommens- und Verbrauchsstichprobe (EVS) and IWU/DIW housing studies. Used for Berlin rent benchmarks and income distribution.
INE — Instituto Nacional de Estadística (Spain)
HighUsed for: Barcelona, Madrid
Encuesta de Presupuestos Familiares (household budget survey) and Índice de Precios de Alquiler. Supplemented with Generalitat de Catalunya rental index and Banco de España housing report.
Statistics Canada
HighUsed for: Toronto, Vancouver
Census housing data and the Canadian Housing Survey. Used for Canadian city rent distributions and household income benchmarks.
OECD Income Distribution Database (IDD)
HighUsed for: All cities
Cross-country income distribution data used to derive city-level P25/P50/P75 income bands. Applied via country-level Gini coefficients to scale city median incomes into a full distribution.
ABS — Australian Bureau of Statistics
MediumUsed for: Sydney, Melbourne
Census of Population and Housing and Survey of Income and Housing. Used for Australian city rent and income benchmarks.
Numbeo (adjusted)
Medium / LowUsed for: Cities without full official data
Crowdsourced rental and cost-of-living data. Used only where official statistics are unavailable or incomplete. Adjusted downward to correct for Numbeo's known over-indexing toward city-centre, higher-income users. Cities using adjusted Numbeo data receive a Medium or Low confidence rating.
Income distribution methodology
To show where you sit relative to other renters in your city, we need to model the full income distribution — not just the median.
Median income
Sourced from national statistics for each city. Represents gross annual income for full-time workers in or near the city.
P25 / P75 income bands
Derived from country-level OECD IDD Gini coefficients. We apply the country's P25/median and P75/median ratios to the city median. Urban distributions differ from national averages — this is an approximation.
Rent-to-income norms
The P25/P50/P75 of the rent-to-income ratio for renters in each city — i.e. what real renters actually spend. These are estimated from the combination of the rent distribution and income distribution, and validated against available survey data where published.
Limitations and caveats
Figures are for a 1-bedroom apartment in the new-let market. Studio, 2-bed, or co-living costs differ significantly.
City averages mask large neighbourhood-level variation. A €1,400 rent in outer Paris is very different from €1,400 in the Marais.
Data vintages vary by city (2022–2024). Rapidly changing markets (e.g. Dublin, Singapore post-2022) may lag real-time conditions.
We use gross income throughout. Net take-home depends on your specific tax situation, which varies by filing status, deductions, and country.
The OECD income distribution data is national, not city-specific. Cities with extreme high-earner concentrations (San Francisco, Zurich) may have higher effective income distributions than national data suggests.