Tenant Survey (housing satisfaction)

The full housing-satisfaction survey for cooperatives and housing providers: the home, the neighbourhood, service and community – analysable right down to the individual estate.

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Tenant Survey (housing satisfaction) – questionnaire preview

A tenant survey rarely fails on the questionnaire and almost always on the analysis: an overall score of 3.8 helps nobody, because it irons flat the one estate with the maintenance backlog where the dissatisfaction actually sits. This template is therefore built so every block yields its own analysis dimension – home, neighbourhood, service, community – and so the structural questions at the end (length of tenancy, household size, age group) work as filters. Append a parameter per estate to the survey link and the portfolio becomes a comparison dimension in the dashboard too, without you having to ask for it.

When should you use this template?

This template is a great fit for:

  • As the major housing-satisfaction survey on a two- to three-year cycle
  • As a baseline before a neighbourhood or modernisation programme
  • Via QR code on the cover letter, in the stairwell or in the tenant portal
  • As the basis for reporting to the supervisory board and members' meeting

Every question in this template

  1. 1

    Overall, how satisfied are you with your housing situation? *

    Rating
  2. 2

    How would you rate your home on the following points?

    Matrix
    Very poorPoorAverageGoodVery good
    Size and layout
    Structural condition and maintenance
    Bathroom and sanitary fittings
    Heating and hot water
    Sound insulation
  3. 3

    And how would you rate your neighbourhood?

    Matrix
    Very poorPoorAverageGoodVery good
    Cleanliness of building and stairwell
    Outdoor areas and green spaces
    Play and leisure areas
    Parking situation
    Bin store and waste disposal
  4. 4

    How satisfied are you with our service?

    Matrix
    Very dissatisfiedDissatisfiedNeitherSatisfiedVery satisfied
    Reachability of your contact person
    Friendliness and helpfulness
    Time taken to handle requests
    Quality of contractor work
    Clarity of letters and statements
  5. 5

    How do you rate your rent and service charges against what you get for them?

    Rating
  6. 6

    How do you experience community life in your neighbourhood?

    Single choice
    • Very good – people help each other
    • Good – friendly but distant
    • Neutral – we barely know each other
    • Rather difficult – conflicts happen regularly
    • Poor – there are persistent problems
  7. 7

    How likely are you to recommend us as a landlord? *

    NPS (0–10)
  8. 8

    Where should we invest first, in your view? (Please choose no more than three)

    Multiple choice
    • Modernising the flats
    • Energy retrofit and heating
    • Stairwell and building cleaning
    • Outdoor areas, greenery and playgrounds
    • Car and bicycle parking
    • Faster handling of repairs
  9. 9

    What should we improve first on your estate?

    Please write freely – the more concrete, the better.

    Long text
  10. 10

    How long have you been living with us?

    Single choice
    • Less than 2 years
    • 2 to 5 years
    • 6 to 15 years
    • 16 to 30 years
    • More than 30 years
  11. 11

    How many people live in your household?

    Single choice
    • 1 person
    • 2 people
    • 3 to 4 people
    • 5 or more people
  12. 12

    Which age group do you belong to?

    Single choice
    • Under 30
    • 30 to 44
    • 45 to 59
    • 60 to 74
    • 75 and over
    • Prefer not to say
  13. 13

    How would you prefer us to keep you informed?

    Single choice
    • Letter or notice board
    • Email
    • Tenant portal or app
    • Telephone
    • In person at the office

From questionnaire to dashboard

The questionnaire produces a dashboard the housing sector can actually steer with:

  • Estates compared: The crosstab of rating aspects against estate as a heatmap: red cells show immediately which estate slips on which topic – instead of an overall mean that triggers nothing.
  • Diverging profile of the rating blocks: The matrix questions on home, neighbourhood and service as split bars: satisfied shares to the right, dissatisfied to the left – the fastest read on the ranking of problem areas.
  • Recommendation (NPS question): The 0–10 distribution separates committed members from critics; filtered by length of tenancy it shows whether loyalty grows or erodes over the years.
  • Open answers by topic and sentiment: AI coding turns free text into two new variables – topic and sentiment. That turns "nobody reads this anyway" into an ordinary analysis, cross-tabulatable by estate and length of tenancy.

Related survey templates

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Frequently asked questions

How do we analyse results per estate without giving up anonymity?
Append a URL parameter per estate to the survey link – for example ?estate=northroad. The value is stored as its own variable with every response and is available in the dashboard as a filter and comparison dimension. The response itself stays anonymous: DataLion stores neither IP address nor contact details. So nobody can infer individuals from small estates, you can additionally set a minimum case count below which figures are suppressed.
Many of our members prefer to answer on paper. Is that possible?
The questionnaire is designed as an online survey – DataLion cannot print and scan paper forms. The usual route is a mixed one: a QR code and short link on the cover letter for everyone answering online, with paper returns entered into the same dataset via Excel import. You then analyse both channels together.
How do we make sure each household answers only once?
You can generate one-time links for the survey and download them as CSV (up to 1,000 per request). Each link works exactly once and contains no personal data whatsoever – you put one of them on each household's cover letter as a QR code. Whether you keep a record of which link went to which address is your decision; for an anonymous survey you deliberately do not.
What do we do with all the open-text answers?
Experience shows the open question at the end is the most productive – and the one most often left unread. DataLion codes open answers by topic and sentiment using AI and writes both back into the dataset as new variables. The topic can then be cross-tabulated like any other: which theme do long-term tenants raise, which the recent arrivals, which one particular estate? You can also give the AI your own list of topics so the coding stays comparable across waves. Note that coding requires a dataset collected in DataLion — paper returns imported afterwards cannot be coded this way.

Start with this template

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