Residential Registry Markups

For an explanation of the Markups logic, please see General Description

Update the residential registry data

The code below gives an example of how you would use the client to fetch all markups.

import mysql.connector 
import valueguard

vgClient = valueguard.Client()
vgClient.authenticate("<username>", "<password>")

mydb = mysql.connector.connect(
   host="<db_host>",
   user="<db_username>",
   passwd="<db_password>",
   database="<db_name>"
)

mycursor = mydb.cursor()

update_at_min = "2018-02-11"

limit = 20000
i = 0
while True:
    data = vgClient.residential_registry_markups(offset=i, limit=limit,
                                                 search_criteria={"updated_at_min": update_at_min})
    for markup_data in data['residences']:
        markup_data_id = markup_data['id']
        del markup_data['id']
        sql_set_part = ""
        val = []
        for field_to_update in markup_data:
            val.append(markup_data[field_to_update])
            if sql_set_part == "":
                sql_set_part = "SET " + field_to_update + " = %s"
            else:
                sql_set_part += ", " + field_to_update + " = %s"
        sql = "UPDATE residential_registry " + sql_set_part + " WHERE id =%s;"
        val.append(markup_data_id)
        mycursor.execute(sql, val)
        mydb.commit()
    if data['meta_data']['total_nr_records'] <= data['meta_data']['offset'] + data['meta_data']['response_rows']:
        break
    i += data['meta_data']['response_rows']

Import all markups from date

The code below gives an example of how would you use the client to fetch all markups.

import mysql.connector 
import valueguard

vgClient = valueguard.Client()
vgClient.authenticate("<username>", "<password>")

mydb = mysql.connector.connect(
   host="<db_host>",
   user="<db_username>",
   passwd="<db_password>",
   database="<db_name>"
)

mycursor = mydb.cursor()

update_at_min = "2018-02-11"


limit = 20000
i = 0
while True:
    data = vgClient.residential_registry_markups(offset=i, limit=limit,
                                                     search_criteria={"updated_at_min": update_at_min})
    for markup_data in data['residences']:
        sql_fields_list = ""
        sql_val_prep = ""
        val = []
        for field_to_update in markup_data:
            val.append(markup_data[field_to_update])
            if sql_fields_list == "":
                sql_fields_list = "(" + field_to_update
                sql_val_prep = "(%s"
            else:
                sql_fields_list += "," + field_to_update
                sql_val_prep += ",%s"
        sql_fields_list += ")"
        sql_val_prep += ")"
        sql = "insert into <database_table> " + sql_fields_list + " VALUES " + sql_val_prep + ";"
        mycursor.execute(sql, val)
        mydb.commit()
    if data['meta_data']['total_nr_records'] <= data['meta_data']['offset'] + data['meta_data']['response_rows']:
        break
    i += data['meta_data']['response_rows']

Search in markups

Example:

import valueguard

vgClient = valueguard.Client()
vgClient.authenticate("<username>", "<password>")

limit=1

print (vgClient.residential_registry_markups(offset=0, limit=limit, search_criteria={
    "id":"324312"
}))

Field list

OBS! You will only get update with fields that you have access to.

Field name	Type	Example	Description
id	varchar(255)	481515	unique id (serial number) for all residences source: Valueguard
property_key	int(8)	170053749	unique id for all properties source: Fastighetsregistret LMV
address	varchar(60)	1:a villagatan 1, 65341	address of the residence (street, nr, letter, postal code, postal area) source: Fastighetsregistret LMV
street	varchar(60)	1:a villagatan	street name in address
number	varchar(10)	1	number of the street in address
letter	char(5)	A	letter of the street in address
entrance	varchar(5)	U1	entance of the address
is_meter_address	tinyint(1)	1	1 = the address is a meter address (i.e. the number of the address is of type "302-12") 0 = the address is not a meter address
postal_code	varchar(7)	65341	postal code
postal_area	varchar(40)	Karlstad	postal area
house_nr	mediumint(5)	1	unique number on every house belonging to the same property
apartment_nr	smallint(4)	1101	unique number on every apartment at the same address
living_space	double	137	area of residence in sqm (only for houses) source: Lägenhetsregistret washed with Fastighetsregistret LMV
apartment_area	smallint(4)	137	area of residence in sqm (all home types) source: Lägenhetsregistret LMV
rooms	double	5	number of rooms for areas above 7sqm, excluding kitchen and bathroom (not available for houses)
floor	double	2	the floor of the residence
floors	double	2	the highest floor on the address
apartment_category	tinyint(2)	1	1 = ordinary apartment 2 = special apartment customized for elderly or disabled 3 = student residence 4 = other special residence 9 = missing
kitchen_type	tinyint(2)	1	1 = kitchen 2 = kitchenette 3 = kitchen cabinet 4 = no kitchen equipment 9 = unknown
building_purpose	tinyint(2)	1	1 = home 2 = industry 3 = society 4 = organization 5 = business building 6 = complement buildning 7 = other buildning
sweref99x	int(7)	6583373	X-coordinate (longitude) from SWEREF a Swedish positioning system by LMV used from 2007
sweref99y	int(7)	413689	Y-coordinate (latitude) from SWEREF a Swedish positioning system by LMV used from 2007
rt90x	double	6586700	X-coordinate (longitude) from RT90 a Swedish positioning system by LMV used before 2007
rt90y	double	1367905	Y-coordinate (latitude) from RT90 a Swedish positioning system by LMV used before 2007
wgs84lat	double	59.3801951334688	Y-coordinate (latitude) from WGS84 a global positioning system
wgs84lon	double	13.4808053113366	X-coordinate (longitude) from WGS84 a global positioning system
construction_year	smallint(4)	1920	building year The construction_year = 1909 for houses and 1929 for flats/apartments are in some cases used by Lantmäteriet when the true year of construction is unknown. For more info see detailed explanation
owner_id	char(11)	769613-3375	organizational number
building_type	tinyint(2)	33	-1 = missing 1 = bathhouse 2 = fire station 3 = bus station 4 = distribution building 5 = veterinary 6 = army 7 = health care center 8 = university 9 = ice rink 10 = railway station 11 = municipality building 12 = correctional institution 13 = cultural building 14 = police station 15 = treatment plant 16 = riding area 17 = community 18 = hospital 19 = school 20 = sports area 21 = university 22 = water treatment plant 24 = multi arena 30 = detached house 31 = terraced house 32 = townhouse 33 = block of apartments 35 = house with several apartments 40 = other production industry 41 = gas turbine facility 42 = hotel for industries 43 = chemical industry 44 = condensing plant 45 = nuclear plant 46 = food industry 47 = metal- or machine industry 48 = textile industry 49 = wood industry 50 = hydroelectric plant 51 = windmills 52 = heating plant 53 = other industry building 99 = unspecified
type_code	smallint(3)	320	113 = farm unit (building value < 50000 SEK) 120 = farm unit (built-up) 213 = house (building value < 50000 SEK) 220 = house (built-up) 221* = house (cottege) 222* = house (3 or more residence buildnings) 320 = rental (residences) 321 = rental (residences & local places) 325 = rental (local places) 823 = special unit. * = type code ended in 2015 but is in the register. These are the codes for number of residences of 10000 or more. Complete list can be found at the Swedish Tax Agency.
home_type	varchar(20)	lägenhet	the different types of residence are divided into: radhus (townhouse) lägenhet (apartment) kedjehus (terraced house) villa (house) lantbruk (farm) småhus övriga (other houses) lantbruk övriga (other farms) okänd (unknown)
ownership_type	varchar(30)	bostadsrätt	the different types of ownership are divided into: äganderätt (property rights) bostadsrätt (co-operative apartment) hyresrätt (rented apartment) tomträtt (site leasehold) okänd (unknown), vast majority rentals
living_area	smallint(5)	130	area of residence in sqm based on apartment_area but using other sources where apartment_area has missing values source: Fastighetsregistret LMV
ancillary_area	smallint(5)	40	ancillary area in sqm, not part of the primary building area
standard_points	tinyint(2)	32	a number between 0-59 based on assessed value measuring the condition of the residence, higher number represents better condition (not available for co-operative apartments)
plot_area	int(9)	896	area of the plot belonging to the residence in sqm
error_code	tinyint(1)	0	1 = suspected error in data 0 = no suspected error in data Variable explanation
deleted	tinyint(1)	0	0 = not deleted 1 = deleted 2 = maybe deleted (see probablity_deleted) Variable explanation
probability_deleted	decimal(3,2)	0.00	percentage that it no longer exists
area_id	int(9)	7850	id for area_name
area_name	varchar(60)	Karlstad_Romstad/Strand	Valueguard's own neighbourhood definitions
updated_at	varchar(10)	2016-06-19	latest date of update from the sources. ads: ad date commercial_site: update date with bank-id private_user: date for register in system public_data: date for delivery from LM sales: contract date
updated_from	varchar(60)	public_data	designates the source of an update. List of sources
county_code	varchar(2)	05	County code
municipality_code	varchar(2)	13	Municipality code
parish_code	varchar(2)	02	Parish code
grade	tinyint(2)	1	From 1 to 3, where 1 is the most reliable
vacation_like_home	tinyiny(1)	1	3 = Estimated to be a vacation home 2 = Estimated to be a vaction home but there is someone registered at the address 1 = Estimated to not be a vacation home but no one is registered at the address 0 = Estimated to not be a vacation home
person_registered	tinyiny(1)	0	1 = Someone is registered at the address 0 = No one is registered at the address, or we have not been able to match the address.
person_registered_date	varchar(10)	2016-06-19	Update date for person_registered

Updated_from

name	Description
public_data	official update from Lantmäteriet
private_user	update from someone who manually writes us to inform us of an error (usually resident)
ads	update originating in a broker ad for this object
sales	update originating in sales data
commercial_site	update where a user at some commercial site sends in corrections through that website
valueguard_internal	Updates of internal variables, for example vacation_like_home

Explanation for deleted and error_code

• These fields exist to inform the user that an object either doesn't exist or that there's something weird with it
• deleted = 1 means that a home has been removed from the official registries. The most frequent reason for this is that it has been demolished, the second most frequent reason is that it was wrongly registered and the third most common reason is that it was merged with another home
• error_code = 1 means that there's something wrong with this object and that it should be used with caution or not at all. The most common reason for a home to get error_code = 1 is that the number of homes grouped together differs depending on definition in ways that it shouldn't, for example that number of duplicates on FNR and coordinates differ. The second most frequent reason is that the coordinates are outside of Sweden. In both cases, something is off and we suspect the data is wrong.

Explanation for null values in construction_year

• construction_year = null could mean that the register lacks a construction year for the building. For newer buidlings this is most likely caused by the taxation register, from which the build year is collected for newer buildings, being updated every third year. This in combination with that we on our end only update our Residential Registry (bostadsregistret) with changes to the apartment information and not changes on the information about the buildings. Thus if the taxation register lacks a build year when we first receive the information about new apartments, we are not able to retrieve the information at a later point in time when the taxation register is updated with the information about the build year.

Overrepresentation of years 1909 and 1929 in construction_year

• The construction_year = 1909 for houses and 1929 for flats/apartments are in some cases used by Lantmäteriet when the true year of construction is unknown. The true year of construction is probably older than 1909/1929.
• The cause of the problem is probably this:
  A new Real Property Register (fastighetsregister) was created in 1910, which most likely is the cause of why in-reality-older buildings have a registered build year of 1909 (i.e. many buildings built prior to 1909 have a false build year of 1909 in the register). In the register there are also build years older than 1909 and these may be due to owners having corrected the information about their properties in the register.
• Above applies for houses but flats/apartments have the corresponding issue with build year 1929 due to different but similar reasons.

How to see if an object is deleted

• The field "deleted" is a boolean that is 1 when a residence no longer exists
• The field "probability_deleted" is the probability that this residence no longer exists when we are unsure. For example, a piece of property which previously had 5 homes may have been rearranged in a way that 4 of the previous homes no longer exists but we do not know which home still exists. In such a case, all five homes will have 0.80 (4/5 deleted) in this field.

How to use updated_at to get only updates

We recommend that you set update_at_min to the latest updated_at date you currently have for data where updated_from is 'public_data'. That way you will get all changes since then. Do not put the date when you get a notification from Valueguard that new updates are available since this update may include changes entered between this update and the last one.

How to interpret NULL

When you get NULL for a field in this table, that means that it has changed from it's previous state to NULL. It does NOT mean that there is no new value for this field.

Search Criteria fields

Field name	Example	Description
id	1234	The id of the object
updated_at_max	2019-10-01	Maximum update date
updated_at_min	1995-03-19	Minimum update date
updated_from	public_data	Designates the source of an update. List of sources

Raw request

curl https://api.valueguard.se/v1/residential/registry/markups?access_token={ACCESS_TOKEN}&offset={OFFSET}&limit={LIMIT}&{SEARCH_CRITERA AS VALUE-KEY PAIR &id="22133"...}

Normal use case in Jupyter notebook

• Residential_registry_markups (Update from public data)