Add market direction to a property dashboard without a wall of charts

A practical workflow for PropTech SaaS teams

A property dashboard can show a useful current snapshot and still leave the user with the hardest question: what direction is this market moving? Adding more charts often transfers the interpretation work to the customer.

A compact market pulse can answer that question more clearly. Keep one familiar headline from the market summary, then add one supply indicator and one demand indicator. Show the long-term and short-term regression slope for each as four separate signals. The result adds context without turning historical direction into a forecast or a made-up score.

For Sydney NSW 2000 units, the HtAG snapshot for the period ending 31 August 2026 reports a Typical Price of $1,755,173 with High data confidence. The directional layer uses stock-on-market percentage for supply and days on market for demand. Every market response uses the same suburb identifier, property type and source period.

Turn four slopes into one readable pulse

The card answers two small questions: does recent supply direction agree with the longer trend, and does recent demand direction agree with its longer trend? The reviewed evidence is:

• Stock on market percentage, long term: ls_som_perc is -0.01 percentage points per regression time step. The falling direction is a tighter-supply signal.
• Stock on market percentage, short term: ss_som_perc is a genuine 0.00 percentage points per regression time step across the 13-month window, so the fitted direction is flat at returned precision.
• Days on market, long term: ls_dom is 3.48 days per regression time step. The rising direction is a softer buyer-demand signal.
• Days on market, short term: ss_dom is -3.45 days per regression time step across the 13-month window. The falling direction is a stronger buyer-demand signal.

What the horizons show: the supply pair is mixed because the long-term SoM% slope is falling while the short-term slope is flat at returned precision. The demand pair is also mixed because the long-term DOM slope is rising while the short-term slope is falling. These are separate observations, not an overall market score.

Make disagreement useful

The two supply horizons do not point in the same direction: the full-history stock-on-market percentage slope is falling, while the 13-month slope is flat at returned precision. The two demand horizons also differ: the full-history days-on-market slope is rising, while the 13-month slope is falling.

That difference is the product insight. A dashboard that displays only the recent signal can hide the longer pattern. A dashboard that displays only the long-term signal can miss a change in recent direction. Showing both horizons lets a user see the tension immediately, then open the explanation for the exact field, value, window and source period.

The interface keeps the four observations separate. A slope is the signed result of a regression over a metric’s time series, expressed in that metric’s unit per regression time step. It is not percentage growth. Stock-on-market percentage slopes are converted once from decimal-fraction change to percentage-point change; days-on-market slopes remain in days. Their magnitudes should not be compared with each other.

Give the user evidence behind every label

Each signal should carry the suburb, property cohort, source period, exact endpoint field and regression window. Long-term slopes use the metric’s full available history. The selected short-term stock-on-market percentage and days-on-market slopes use 13 months. The current contract does not name the duration of one regression time step, so the interface keeps the accurate label per regression time step rather than inventing a monthly or annual rate.

A positive slope is not automatically favourable. For stock on market percentage and days on market, falling directions generally align with tighter supply or stronger buyer demand. The interpretation belongs to the named metric, while the raw sign and value remain available for review.

What this captured example supports

Observation: in the 31 August 2026 Sydney unit snapshot, the long-term supply slope is falling and the short-term supply slope is flat at returned precision. The long-term demand slope is rising and the short-term demand slope is falling.

Interpretation: recent direction differs from the full-history pattern on both selected indicators. A product can surface that difference as useful context while keeping the underlying values visible.

Suggested action: add the four-signal pulse beside the existing market summary, default to the current source period, and let the user switch signals to see the exact evidence before opening a deeper research view.

The pulse does not predict a particular unit’s outcome. It helps a product explain the returned suburb-level historical signals clearly, using the same reviewed evidence everywhere that Sydney unit context appears.

Reuse one reviewed tile across the product

This production example used 6 distinct operations, 6 requests and 8 returned rows: 3 locality reference rows and 1 accepted row from each of the five market operations. Those workload figures do not establish a charge. Teams should check current HtAG pricing for their planned request and row volume.

Cache the accepted locality identity, cohort and dated snapshot according to the product’s freshness policy. Reuse the same reviewed tile wherever that locality and cohort appear. The article demonstration embeds the reviewed snapshot, so changing tabs makes zero HtAG API calls in the reader’s browser.

Implementation appendix

1. GET /reference/locality — resolve the locality and accept it only after the name, state and postcode agree with the intended place. The accepted loc_pid becomes the suburb area_id.
2. GET /markets/summary — request the accepted area_id at level=suburb, property_type=unit and bedrooms=All. Keep the returned identity, period and confidence with the headline metric.
3. GET /markets/supply/ls and GET /markets/supply/ss — retrieve ls_som_perc and ss_som_perc for the same suburb and property type. Convert decimal-fraction slopes once to percentage-point change for display.
4. GET /markets/demand/ls and GET /markets/demand/ss — retrieve ls_dom and ss_dom for the same suburb and property type, retaining days per regression time step.

Reconcile area_id, property_type and period_end across all five market responses before rendering the tile. Preserve the all-bedroom summary setting because the slope routes do not expose a bedroom filter. Save the validated snapshot for the demo, with no credentials or live HtAG requests in the reader’s browser. See the HtAG documentation for current request and response details.