Demand IntelligenceStudent housing demand analytics
Data as of 28 Oct 2026Method

Tracking Roadmap

Nothing on this page is a finding. It lists what the current exports cannot answer and the exact data needed to unlock it — regenerated from each upload, so a repaired tracking defect disappears here automatically.

Configurator selections
8,219

All filter selections in the current configurator upload.

Observedn = 8,219
Usable for analysis
94%

534 selections carry no analysable value and are discarded.

Observedn = 8,219
Blocking data gaps
3

Gaps that make a whole analysis family impossible today, not just less precise.

Observedn = 9
Leads with unusable budget
62

Of 2,496 leads — form validation would remove this loss.

Observedn = 2,496

What the configurator export delivers today

Per filter type, counted from the current upload.

Observedn = 8,219
3 rows
Filter typeSelectionsDistinct valuesUsedReason
Size4,68360Yes60 distinct option values, mapped and used in the analysis
Occupancy3,0022Yes2 distinct option values, mapped and used in the analysis
Term5341NoEvery selection exports without a value — tracking defect

Priority 1 — blocks whole analysis families

2 items.

2 rows
Analysis unlockedRequired data / exportGrainStatus in the current upload
Configurations, features, FF&E ranking, feature co-occurrence, true demand clustersConfigurator event export with one row per selected option, including feature groups (bed, desk, storage, kitchen, bathroom, seating, add-ons)one row per session × optionToday the export delivers 3 filter types (Size, Occupancy, Term) and no feature-level data.
Price × size × product × timing on user level, and the search → configurator → lead funnelA shared session / visitor id in all three exportsone id per visitor sessionNo shared key exists today: searches and configurator rows carry no id, 2,496 of 2,496 leads carry a hashed contact key only. All cross-source links stay at location level (Indication).

Priority 2 — materially improves precision

4 items.

4 rows
Analysis unlockedRequired data / exportGrainStatus in the current upload
Configurator demand over time, seasonality of product preference, period filtering of product KPIsTimestamp on every configurator selectionone timestamp per selectionThe configurator export has no dates, so product pages ignore the period filter.
Term as an analysable dimension (534 selections currently discarded)Repair the Term tracking so the selected value is written to the exportone value per selectionEvery selection exports without a value — tracking defect
Realised willingness to pay, conversion per price band, ERV validationBooking / contract export: property, room type, size, contract rent, contract start, term, booking dateone row per contractNo booking data in the app — every price figure is stated willingness to pay, never a realised rent.
Clean budgets without manual plausibility filteringForm validation on the exit-intent budget field (200–3000 EUR per month)—62 of 2,496 leads carry an implausible or empty budget and are excluded.

Priority 3 — adds new dimensions

3 items.

3 rows
Analysis unlockedRequired data / exportGrainStatus in the current upload
Search-term intelligence: amenities, university, transport, furnished, private bathroom, shared livingSite-search keyword export: term, timestamp, location, result countone row per queryOnly date-range searches are exported today; free-text queries are not captured.
Origin × price × room type × stayCountry of origin or university field in the exit-intent form (or aggregate IP country)one attribute per leadNo origin attribute in the lead export.
Room-type demand (studio / en-suite / shared) and hard unit-mix evidenceRoom-type attribute on configurator sessions and leadsone attribute per session / leadOccupancy (single vs double) is the only product signal available today.

How to commission this

Each row above is written so it can be handed to the website or CRM team as a specification.

  • The single highest-value change is one shared session id across searches, configurator and exit-intent form — it turns every location-level indication into a user-level fact.
  • The second is a booking export, which converts stated willingness to pay into realised rent and validates the ERV assumptions (Estimated Rental Value — the rent a room is expected to achieve).
  • Everything else adds dimensions (features, room type, origin, keywords) rather than changing the reliability of what is already here.