Time Series

POSThttps://api.casafari.com/market-analytics-api/time-series

Bearer token49 parameters

Send Authorization: Bearer $CASAFARI_TOKEN; see how to get a token.

Over MCP: ma_get_time_series_data (equivalent). MCP and REST compared.

Description

Returns time series data for the real estate market.

Each time series unit includes the interval start date based on the provided date_interval and a value corresponding to the selected data_point.

Request body

Content type application/json. Required. Type: object.

typesstring[]required

The list of property types by type groups: apartment: ["apartment", "studio", "duplex", "penthouse", "dachgeschosswohnung", "etagenwohnung", "erdgeschosswohnung"] house: ["country_house", "house", "palace", "townhouse", "villa", "country_estate", "chalet", "bungalow", "family_house", "reihenhaus", "reihenendhaus", "reihenmittelhaus", "einfamilienhaus", "zweifamilienhaus", "landwirtschaftliche_betriebe"] room: ["room"] building: ["apartment_building", "office_building", "mix_use_building"] investment: ["retail", "office", "industrial", "warehouse", "hotel", "building", "other_commercial", "restaurant", "werkstatt"] development: ["apartment_development", "house_development"] plot: ["urban_plot", "rural_plot"] other: ["parking", "garage", "other"] Note that you can select multiple types only from one property type group. The list of property types specific to certain countries: apartment: ○ dachgeschosswohnung: Germany ○ etagenwohnung: Germany ○ erdgeschosswohnung: Germany house: ○ family_house: Germany ○ reihenhaus: Germany ○ reihenendhaus: Germany ○ reihenmittelhaus: Germany ○ einfamilienhaus: Germany ○ zweifamilienhaus: Germany ○ landwirtschaftliche_betriebe: Germany investment: ○ restaurant: Germany ○ werkstatt: Germany plot: ○ urban_plot: Italy, Germany, Portugal, Spain, France ○ rural_plot: Italy, Germany, Portugal, Spain, France

42 allowed values

apartment studio duplex penthouse dachgeschosswohnung etagenwohnung erdgeschosswohnung country_house house palace townhouse villa country_estate chalet bungalow family_house reihenhaus reihenendhaus reihenmittelhaus einfamilienhaus zweifamilienhaus landwirtschaftliche_betriebe retail office industrial warehouse hotel building other_commercial restaurant werkstatt urban_plot rural_plot room parking garage other apartment_building office_building mix_use_building apartment_development house_development

at least 1 item

business_typestringrequired

sale rent

price_rangeobjectnullable

2 properties

minintegernullable

1–2147483647

maxintegernullable

1–2147483647

price_per_sqm_rangeobjectnullable

2 properties

minintegernullable

1–2147483647

maxintegernullable

1–2147483647

rooms_rangeobjectnullable

2 properties

minintegernullable

1–15000

maxintegernullable

1–15000

bedrooms_rangeobjectnullable

2 properties

minintegernullable

0–15000

maxintegernullable

0–15000

bathrooms_rangeobjectnullable

2 properties

minintegernullable

1–15000

maxintegernullable

1–15000

total_area_rangeobjectnullable

2 properties

minintegernullable

1–1000000

maxintegernullable

1–1000000

plot_area_rangeobjectnullable

2 properties

minintegernullable

1–1000000

maxintegernullable

1–1000000

construction_year_rangeobjectnullable

2 properties

minintegernullable

1–3000

maxintegernullable

1–3000

characteristicsobjectnullable

2 properties

must_havestring[]

Include only properties that have all these characteristics.

balcony elevator no_elevator garage garden parking storage swimming_pool terrace rental_license furniture rented_out life_annuity

excludestring[]

Exclude properties that contain any of these characteristics.

balcony elevator no_elevator garage garden parking storage swimming_pool terrace rental_license furniture rented_out life_annuity

conditionsstring[]

Property conditions, as returned by the GET /api/v1/references/conditions endpoint.

used ruin very-good new other

at least 1 item

privateboolean

Whether the property is listed by a private individual, as opposed to an agent or a professional.

bankboolean

Whether the property is owned by the bank.

auctionboolean

Whether the property is the subject of an auction.

default false

exclude_outliersboolean

Exclude properties that are significantly underpriced or overpriced compared to similar properties.

default false

custom_location_boundaryobjectrequired

3 properties

location_idsinteger[]

List of location IDs.

1–10 items1–2147483647

polygonobject[]

List of geo-points to search within. Polygon must contain at least 4 points. First and last points must match.

at least 4 items

2 properties

latitudenumberrequired

-90–90

longitudenumberrequired

-180–180

circleobject

Circle boundary to search within.

2 properties

distanceintegerrequired

Maximum distance in meters from the requested target_point to the properties.

50–50000

target_pointobjectrequired

Target point coordinates to search around.

2 properties

latitudenumberrequired

-90–90

longitudenumberrequired

-180–180

data_pointstringrequired

Specifies the type of real estate data to retrieve for a given period.

avg_price avg_price_psqm available_on_market new sold_or_rented price_up price_down

date_intervalstringrequired

Defines the time interval for aggregating data points. Determines the frequency at which data is reported in the response.

week month quarter year

date_rangeobjectrequired

2 properties

minstring (date)required

Start date in the format YYYY-MM-DD. If the specified date is not Monday - the closest previous Monday will be selected.

maxstring (date)nullable

End date in the format YYYY-MM-DD. If the specified date is not Sunday - the closest previous Sunday will be selected.

Example request

Placeholders only: replace the token and the values with your own.

curl

curl -X POST "https://api.casafari.com/market-analytics-api/time-series" \
  -H "Authorization: Bearer $CASAFARI_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "types": [
    "apartment"
  ],
  "business_type": "sale",
  "custom_location_boundary": {
    "location_ids": [
      1
    ]
  },
  "data_point": "avg_price",
  "date_interval": "week",
  "date_range": {
    "min": "2025-01-01"
  }
}'

Responses

200 Successful Response

Type: object[].

Show the response shape (2 fields)

date_startstring (date)required

The starting date of the period associated with the passed date_interval field value. The format follows YYYY-MM-DD.

valuenumberrequired

The numerical value corresponding to the passed data_point field value for the given date_start.

204 No Content

No body.

400 Bad Request

Type: object.

Show the response shape (3 fields)

messagestringrequired

errorsstring[]nullable

detailsstringnullable

422 Validation Error

Type: object.

Show the response shape (6 fields)

detailobject[]

5 properties

loc(string | integer)[]required

msgstringrequired

typestringrequired

inputany

ctxobject

Questions about this

How do I analyse how prices in an area have moved over time?

Time Series

#Description

#Request body

#Example request

#Responses

#200 Successful Response

#204 No Content

#400 Bad Request

#422 Validation Error

#Questions about this

Description

Request body

Example request

Responses

200 Successful Response

204 No Content

400 Bad Request

422 Validation Error

Questions about this