Screen investments · Use case

How do I find properties priced below the area's market value?

To find properties priced below an area's market value, benchmark the area's asking price per m² with Area Insights (ma_get_time_series_data, ma_get_price_distribution). Then search Property Sourcing with POST /api/v2/properties/search and price_per_sqm_to under that benchmark. Confirm each candidate with comps_get-comparables by comparing its asking price with estimated_prices.fair_market_price. The biggest spread marks the strongest candidate.

Products: Area Insights, References, Property Sourcing, Comparables & Valuation

#Short answer

Work as a three-step funnel:

1. Benchmark. Get the area's average asking price per m² from Area Insights.
2. Search. Look in the property graph for properties asking less than that per m², with the type, bedrooms and condition you want.
3. Confirm. Run Comparables & Valuation on each candidate. Compare its asking price with estimated_prices.fair_market_price from comps_get-comparables, and check whether it is flagged is_outlier.

The graph keeps one record per property. A property advertised by several agencies and portals counts once in the benchmark and appears once in the search. See The property graph.

#Steps

#Benchmark: ma_get_time_series_data with avg_price_psqm (latest period) and ma_get_price_distribution

1. Resolve the place name with ma_get_location_typeahead. The location_name must be in English, for example Lisbon. When several places share a name, pick the right location_id from breadcrumbs and administrative_level.

2. Call ma_get_time_series_data with these request_data fields:

   • type_group: apartment or house
   • business_type: sale
   • alias: a name for the segment
   • custom_location_boundary: location_ids (1 to 10 ids) or a circle (distance in meters, 50 to 50000)
   • data_point: avg_price_psqm
   • date_interval: for example month
   • date_range.min

   Add bedrooms_range and conditions to match the segment you will search. The value of the latest date_start is your benchmark.

3. Call ma_get_price_distribution with the same boundary and filters. Each bucket returns lower_bound, upper_bound, properties_count and is_mean. Together they show how total prices spread and which bucket holds the mean, so you can tell whether the cheap end of the area is thin or crowded.

#Search: POST /api/v2/properties/search with location_ids and price_per_sqm_to below the benchmark, plus type, bedrooms and condition

4. Call POST /api/v2/properties/search with:

   • search_operations: sale
   • location_ids: documented as the ids returned by POST /api/v1/references/locations, so resolve them there
   • price_per_sqm_to: below the benchmark, for example benchmark × 0.85 for 15% under
   • types
   • bedrooms_from and bedrooms_to
   • conditions

   Leave ruin out of conditions so run-down properties do not look like bargains. Other useful filters are total_area_from/total_area_to, days_on_market_from/days_on_market_to and private. The response schema is on the operation page.

#Confirm: comps_get-comparables on each candidate with exclude_outliers false; compare asking price with fair_market_price and look at is_outlier

5. For each candidate, call comps_get-comparables with:
   • location_boundary.circle.target_point: the candidate's coordinates or address
   • distance: in kilometers. The tool suggests 0.5 to 1 km in urban areas and 2 to 5 km in rural ones.
   • operation: sale
   • comparables_types: types from one type group only
   • comparables_count: 1 to 50
   • the candidate's bedrooms, condition, floors and total_area
   • exclude_outliers: false
6. Read estimated_prices.fair_market_price, fair_market_price_per_sqm, fast_sell_price and out_of_market_price. Then find the candidate in results by property_id and read its is_outlier and similarity_score.

#Sort by the spread

7. For each candidate, compute spread = (fair_market_price − asking price) / fair_market_price, and sort from largest to smallest.
   • Take the asking price from the search response, or from sale_price if the candidate appears in the comparables results.
   • Keep casafari_link (the Comparative Market Analysis page) and valuation_id (persistent, expires in a year) for your shortlist.

#Example: apartments in Lisbon under the area price per m²

A person asks their assistant: "Find 2 to 3-bedroom apartments in Lisbon asking at least 15% under the area price per m², and leave out ruins."

The assistant calls list_servers, then ma_get_location_typeahead with Lisbon. It then calls ma_get_time_series_data with type_group apartment, business_type sale, data_point avg_price_psqm, date_interval month and bedrooms_range 2 to 3. Call the latest value B. The threshold is T = 0.85 × B. ma_get_price_distribution on the same boundary shows how many properties sit in each price bucket.

The search step runs over REST, from your own integration with a Casafari client account:

{
  "search_operations": ["sale"],
  "location_ids": ["<LOCATION_ID>"],
  "types": ["apartment"],
  "price_per_sqm_to": "<T>",
  "bedrooms_from": 2,
  "bedrooms_to": 3,
  "conditions": ["used", "very-good", "new"]
}

Replace the placeholders with numbers, and check value formats on the operation page. For each candidate, the assistant then calls comps_get-comparables:

{
  "comparables_count": 20,
  "location_boundary": {"circle": {"target_point": {"address": "Avenida da Liberdade, Lisbon"}, "distance": 1}},
  "operation": "sale",
  "comparables_types": ["apartment"],
  "bedrooms": 2,
  "exclude_outliers": false
}

If a candidate asks P and fair_market_price is F, its spread is (F − P) / F. The assistant ranks candidates by that spread and reports casafari_link for each. All figures come from the responses; this page gives no market numbers.

#How to read the result

#Outliers are excluded by default: turn exclude_outliers off on purpose

exclude_outliers defaults to true in comps_get-comparables and in the Area Insights tools. It removes properties that are significantly underpriced or overpriced compared to similar ones, which are exactly the properties you are hunting.

• Benchmark: keep it on, so extreme values do not pull the average.
• Comparables call: turn it off, so the candidate stays visible.

A candidate with is_outlier true is priced far from similar properties. That makes it either a real bargain or a case with a reason behind the price, so verify it before acting.

#A low price may reflect condition, floor or ruin status: read the fields

Before calling a property underpriced, read these fields:

• condition (ruin, used, very-good, new, other)
• floor_number and features.floor (no_floor, ground, middle, top)
• features.orientation
• energy_rating
• total_area against living_area
• features.characteristics: values such as rented_out, life_annuity or no_elevator can explain a lower asking price that is not a discount to market value

total_sale_price_change and last_sale_price_reduction show whether the price got there through cuts.

#Limits

#The search step needs a Casafari client account

What each step needs:

• MCP access: the Area Insights tools and comps_get-comparables are available to Casafari clients with MCP access, using their Casafari account.
• Casafari client accounts: POST /api/v2/properties/search is a REST operation for Casafari client accounts. It is not offered over Casafari MCP (details).
• Self-serve access for new users is coming soon, for these tools and for the search.

Without it, start from a property you already know (an address, coordinates or, in Spain only, a cadastral reference) and use comparables (example).

Coverage limits:

• The API's permissions list 16 markets.
• The cadastral-reference input of comps_get-comparables is Spain only.
• ma_get_location_typeahead expects English place names.
• The Area Insights tools take type_group apartment or house only.
• Other tools name no market.

The sources give no figures for pricing, quotas, rate limits or data refresh frequency; ask Casafari.

#The spread is computed by you from the returned fields

No tool returns a discount or spread field: you compute it from the asking price and estimated_prices. The estimates are calculated from the comparables found, so they depend on the boundary and filters you set.

The data cannot tell you a property's interior state beyond condition, its legal situation, or why the seller asks what they ask. Inspect before you buy.

#Questions

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