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TwitterAbout the dataset (cleaned data)
The dataset (parquet file) contains approximately 1,5 million residential household sales from Denmark during the periode from 1992 to 2024. All cleaned data is merged into one parquet file here on Kaggle. Note some cleaning might still be nessesary, see notebook under code.
Also, added a random sample (100k) of the dataset as a csv file.
Done in Python version: 2.6.3.
Raw data
Raw data and more info is avaible on Github repositary: https://github.com/MartinSamFred/Danish-residential-housingPrices-1992-2024.git
The dataset has been scraped and cleaned (to some extent). Cleaned files are located in: \Housing_data_cleaned \ named DKHousingprices_1 and 2. Saved in parquet format (and saved as two files due to size).
Cleaning from raw files to above cleaned files is outlined in BoligsalgConcatCleanigGit.ipynb. (done in Python version: 2.6.3)
Webscraping script: Webscrape_script.ipynb (done in Python version: 2.6.3)
Provided you want to clean raw files from scratch yourself:
Uncleaned scraped files (81 in total) are located in \Housing_data_raw \ Housing_data_batch1 and 2. Saved in .csv format and compressed as 7-zip files.
Additional files added/appended to the Cleaned files are located in \Addtional_data and named DK_inflation_rates, DK_interest_rates, DK_morgage_rates and DK_regions_zip_codes. Saved in .xlsx format.
Content
Each row in the dataset contains a residential household sale during the period 1992 - 2024.
“Cleaned files” columns:
0 'date': is the transaction date
1 'quarter': is the quarter based on a standard calendar year
2 'house_id': unique house id (could be dropped)
3 'house_type': can be 'Villa', 'Farm', 'Summerhouse', 'Apartment', 'Townhouse'
4 'sales_type': can be 'regular_sale', 'family_sale', 'other_sale', 'auction', '-' (“-“ could be dropped)
5 'year_build': range 1000 to 2024 (could be narrowed more)
6 'purchase_price': is purchase price in DKK
7 '%_change_between_offer_and_purchase': could differ negatively, be zero or positive
8 'no_rooms': number of rooms
9 'sqm': number of square meters
10 'sqm_price': 'purchase_price' divided by 'sqm_price'
11 'address': is the address
12 'zip_code': is the zip code
13 'city': is the city
14 'area': 'East & mid jutland', 'North jutland', 'Other islands', 'Capital, Copenhagen', 'South jutland', 'North Zealand', 'Fyn & islands', 'Bornholm'
15 'region': 'Jutland', 'Zealand', 'Fyn & islands', 'Bornholm'
16 'nom_interest_rate%': Danish nominal interest rate show pr. quarter however actual rate is not converted from annualized to quarterly
17 'dk_ann_infl_rate%': Danish annual inflation rate show pr. quarter however actual rate is not converted from annualized to quarterly
18 'yield_on_mortgage_credit_bonds%': 30 year mortgage bond rate (without spread)
Uses
Various (statistical) analysis, visualisation and I assume machine learning as well.
Practice exercises etc.
Uncleaned scraped files are great to practice cleaning, especially string cleaning. I’m not an expect as seen in the coding ;-).
Disclaimer
The data and information in the data set provided here are intended to be used primarily for educational purposes only. I do not own any data, and all rights are reserved to the respective owners as outlined in “Acknowledgements/sources”. The accuracy of the dataset is not guaranteed accordingly any analysis and/or conclusions is solely at the user's own responsibly and accountability.
Acknowledgements/sources
All data is publicly available on:
Boliga: https://www.boliga.dk/
Finans Danmark: https://finansdanmark.dk/
Danmarks Statistik: https://www.dst.dk/da
Statistikbanken: https://statistikbanken.dk/statbank5a/default.asp?w=2560
Macrotrends: https://www.macrotrends.net/
PostNord: https://www.postnord.dk/
World Data: https://www.worlddata.info/
Dataset picture / cover photo: Nick Karvounis (https://unsplash.com/)
Have fun… :-)
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Graph and download economic data for Real Residential Property Prices for Denmark (QDKR628BIS) from Q1 1970 to Q2 2025 about Denmark, residential, HPI, housing, real, price index, indexes, and price.
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Housing Index in Denmark increased to 151.21 points in the second quarter of 2025 from 148.65 points in the first quarter of 2025. This dataset provides - Denmark House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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TwitterThe average house price in Denmark increased sharply in 2021, but growth slowed down to approximately *** percent in 2022. According to the forecast, 2023 is going to see house prices fall by almost **** percent. In 2024, house prices are expected to decrease further by about *** percent. As of 2021, the average sales price of single family homes in Denmark amounted to over *** Danish kroner.
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Key information about House Prices Growth
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Denmark - House price index was 7.30% in June of 2025, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Denmark - House price index - last updated from the EUROSTAT on December of 2025. Historically, Denmark - House price index reached a record high of 30.40% in June of 2006 and a record low of -15.60% in March of 2009.
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TwitterThe house price index in Denmark increased between 2015 and 2023, with prices peaking in 2022. The index tracks the price development for residential real estate, with 2015 chosen as a baseline year. In the fourth quarter of 2023, the index for existing dwellings amounted to *** index points, suggesting a price increase of ** percent since 2015.
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Residential Property Prices in Denmark increased 7.31 percent in June of 2025 over the same month in the previous year. This dataset includes a chart with historical data for Denmark Residential Property Prices.
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View quarterly updates and historical trends for Denmark House Price Index. Source: Eurostat. Track economic data with YCharts analytics.
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Forecast: Housing Cost Overburden in Denmark 2024 - 2028 Discover more data with ReportLinker!
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TwitterNew residential housing in Copenhagen cost, on average, *** percent of the national average in Denmark in 2023 and represented the largest difference in the average transaction price of new residential properties in the country that year. The corresponding figure for Aarhus was ***** percent, and **** percent for Odense during the evaluated period.
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Denmark Property Price Index: One Family Houses data was reported at 283.100 1995=100 in Sep 2007. This records a decrease from the previous number of 283.900 1995=100 for Jun 2007. Denmark Property Price Index: One Family Houses data is updated quarterly, averaging 145.900 1995=100 from Mar 1992 (Median) to Sep 2007, with 63 observations. The data reached an all-time high of 283.900 1995=100 in Jun 2007 and a record low of 79.700 1995=100 in Jun 1993. Denmark Property Price Index: One Family Houses data remains active status in CEIC and is reported by Statistics Denmark. The data is categorized under Global Database’s Denmark – Table DK.EB004: Property Price Index: 1995=100.
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TwitterThe house price to rent ratio in Denmark stood at ****** points in the first quarter of 2025. This is higher than the observation from the first quarter one year earlier, when the ratio had been ****** points.
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Denmark Dismantled House Price: DR: Capital Region: Copenhagen City (CC) data was reported at 41,355.000 DKK/sq m in Nov 2018. This records a decrease from the previous number of 43,589.000 DKK/sq m for Oct 2018. Denmark Dismantled House Price: DR: Capital Region: Copenhagen City (CC) data is updated monthly, averaging 29,946.000 DKK/sq m from Jan 2004 (Median) to Nov 2018, with 179 observations. The data reached an all-time high of 43,589.000 DKK/sq m in Oct 2018 and a record low of 18,469.000 DKK/sq m in Feb 2004. Denmark Dismantled House Price: DR: Capital Region: Copenhagen City (CC) data remains active status in CEIC and is reported by Association of Danish Mortgage Banks. The data is categorized under Global Database’s Denmark – Table DK.EB005: Housing Supply Statistics.
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Key information about Denmark Real Residential Property Price Index Growth
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Denmark - Housing cost overburden rate: Owner, with mortgage or loan was 5.50% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Denmark - Housing cost overburden rate: Owner, with mortgage or loan - last updated from the EUROSTAT on November of 2025. Historically, Denmark - Housing cost overburden rate: Owner, with mortgage or loan reached a record high of 23.00% in December of 2009 and a record low of 3.40% in December of 2023.
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Denmark Property Price: DR: Zealand Region: WZ: Lolland data was reported at 4,406.000 DKK/sq m in Mar 2018. This records an increase from the previous number of 4,042.000 DKK/sq m for Dec 2017. Denmark Property Price: DR: Zealand Region: WZ: Lolland data is updated quarterly, averaging 3,723.000 DKK/sq m from Mar 1992 (Median) to Mar 2018, with 105 observations. The data reached an all-time high of 6,498.000 DKK/sq m in Sep 2008 and a record low of 2,364.000 DKK/sq m in Mar 1993. Denmark Property Price: DR: Zealand Region: WZ: Lolland data remains active status in CEIC and is reported by Association of Danish Mortgage Banks. The data is categorized under Global Database’s Denmark – Table DK.P002: Property Price: by Region.
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The Denmark Luxury Residential Real Estate Market Report is Segmented by Type (Apartments and Condominiums, Villas, and Landed Houses) and by Cities (Copenhagen, Aarhus, Odense, Aalborg, and the Rest of Denmark). The Report Offers Market Size and Forecasts for the Denmark Luxury Homes Market in Value (USD Billion) for all the Above Segments.
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TwitterThe average sales price of one-family houses in Denmark increased by *** thousand Danish Kroner (+**** percent) in 2023. In total, the average price amounted to *** million Danish Kroner in 2023. Find more statistics on one-family houses in Denmark with key insights such as Average purchasing price for condominiums and Average purchasing price for holiday houses.
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Denmark - Housing cost overburden rate: Tenant, rent at market price was 28.70% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Denmark - Housing cost overburden rate: Tenant, rent at market price - last updated from the EUROSTAT on November of 2025. Historically, Denmark - Housing cost overburden rate: Tenant, rent at market price reached a record high of 37.10% in December of 2013 and a record low of 25.20% in December of 2020.
Facebook
TwitterAbout the dataset (cleaned data)
The dataset (parquet file) contains approximately 1,5 million residential household sales from Denmark during the periode from 1992 to 2024. All cleaned data is merged into one parquet file here on Kaggle. Note some cleaning might still be nessesary, see notebook under code.
Also, added a random sample (100k) of the dataset as a csv file.
Done in Python version: 2.6.3.
Raw data
Raw data and more info is avaible on Github repositary: https://github.com/MartinSamFred/Danish-residential-housingPrices-1992-2024.git
The dataset has been scraped and cleaned (to some extent). Cleaned files are located in: \Housing_data_cleaned \ named DKHousingprices_1 and 2. Saved in parquet format (and saved as two files due to size).
Cleaning from raw files to above cleaned files is outlined in BoligsalgConcatCleanigGit.ipynb. (done in Python version: 2.6.3)
Webscraping script: Webscrape_script.ipynb (done in Python version: 2.6.3)
Provided you want to clean raw files from scratch yourself:
Uncleaned scraped files (81 in total) are located in \Housing_data_raw \ Housing_data_batch1 and 2. Saved in .csv format and compressed as 7-zip files.
Additional files added/appended to the Cleaned files are located in \Addtional_data and named DK_inflation_rates, DK_interest_rates, DK_morgage_rates and DK_regions_zip_codes. Saved in .xlsx format.
Content
Each row in the dataset contains a residential household sale during the period 1992 - 2024.
“Cleaned files” columns:
0 'date': is the transaction date
1 'quarter': is the quarter based on a standard calendar year
2 'house_id': unique house id (could be dropped)
3 'house_type': can be 'Villa', 'Farm', 'Summerhouse', 'Apartment', 'Townhouse'
4 'sales_type': can be 'regular_sale', 'family_sale', 'other_sale', 'auction', '-' (“-“ could be dropped)
5 'year_build': range 1000 to 2024 (could be narrowed more)
6 'purchase_price': is purchase price in DKK
7 '%_change_between_offer_and_purchase': could differ negatively, be zero or positive
8 'no_rooms': number of rooms
9 'sqm': number of square meters
10 'sqm_price': 'purchase_price' divided by 'sqm_price'
11 'address': is the address
12 'zip_code': is the zip code
13 'city': is the city
14 'area': 'East & mid jutland', 'North jutland', 'Other islands', 'Capital, Copenhagen', 'South jutland', 'North Zealand', 'Fyn & islands', 'Bornholm'
15 'region': 'Jutland', 'Zealand', 'Fyn & islands', 'Bornholm'
16 'nom_interest_rate%': Danish nominal interest rate show pr. quarter however actual rate is not converted from annualized to quarterly
17 'dk_ann_infl_rate%': Danish annual inflation rate show pr. quarter however actual rate is not converted from annualized to quarterly
18 'yield_on_mortgage_credit_bonds%': 30 year mortgage bond rate (without spread)
Uses
Various (statistical) analysis, visualisation and I assume machine learning as well.
Practice exercises etc.
Uncleaned scraped files are great to practice cleaning, especially string cleaning. I’m not an expect as seen in the coding ;-).
Disclaimer
The data and information in the data set provided here are intended to be used primarily for educational purposes only. I do not own any data, and all rights are reserved to the respective owners as outlined in “Acknowledgements/sources”. The accuracy of the dataset is not guaranteed accordingly any analysis and/or conclusions is solely at the user's own responsibly and accountability.
Acknowledgements/sources
All data is publicly available on:
Boliga: https://www.boliga.dk/
Finans Danmark: https://finansdanmark.dk/
Danmarks Statistik: https://www.dst.dk/da
Statistikbanken: https://statistikbanken.dk/statbank5a/default.asp?w=2560
Macrotrends: https://www.macrotrends.net/
PostNord: https://www.postnord.dk/
World Data: https://www.worlddata.info/
Dataset picture / cover photo: Nick Karvounis (https://unsplash.com/)
Have fun… :-)