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… :-)
Facebook
Twitterhttps://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required
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.
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Facebook
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.
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Key information about House Prices Growth
Facebook
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.
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Facebook
Twitterhttps://www.nextmsc.com/privacy-policyhttps://www.nextmsc.com/privacy-policy
In 2023, the Denmark Real Estate Market reached a value of USD 81.6 million, and it is projected to surge to USD 110.4 million by 2030.
Facebook
Twitterhttps://www.mordorintelligence.com/privacy-policyhttps://www.mordorintelligence.com/privacy-policy
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.
Facebook
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.
Facebook
Twitterhttps://www.ycharts.com/termshttps://www.ycharts.com/terms
View quarterly updates and historical trends for Denmark House Price Index. Source: Eurostat. Track economic data with YCharts analytics.
Facebook
Twitterhttps://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy
The Denmark luxury residential real estate market exhibits robust growth potential, projected to expand significantly over the forecast period (2025-2033). A compound annual growth rate (CAGR) exceeding 5% indicates a consistently increasing market value, driven by several key factors. Strong economic performance in Denmark, coupled with a limited supply of high-end properties, particularly in prime locations like Copenhagen, Aarhus, and Odense, fuels heightened demand. Increasing high-net-worth individuals (HNWIs) seeking prestigious residences contribute to this upward trend. Furthermore, Denmark's attractive lifestyle, strong social infrastructure, and political stability further enhance the market's appeal to both domestic and international buyers. While rising construction costs and potential regulatory changes pose some challenges, the overall market outlook remains positive. The segment breakdown reveals a preference for villas and landed houses, with Copenhagen commanding the largest market share due to its concentration of affluent residents and business opportunities. Leading developers such as NRE Group, Rodgaard Ejendomme, and others play a crucial role in shaping the market's dynamics. The market's historical performance (2019-2024) likely showcased a similar growth trajectory, setting a strong foundation for future expansion. The continued growth in the Danish luxury residential market is anticipated to be fueled by ongoing investment in infrastructure, particularly in sustainable and smart home technologies within new developments. The increasing popularity of eco-friendly and energy-efficient luxury homes will likely further drive demand. While the "Rest of Denmark" segment will show growth, it will likely lag behind the major cities due to differences in population density and economic activity. Analyzing the performance of individual developers and their project pipelines will offer more granular insights into future market trends. Future research should focus on assessing the impact of potential economic fluctuations and interest rate changes on the market's growth trajectory. The competitive landscape, with established players alongside emerging developers, warrants close observation to understand market share dynamics and strategic partnerships. This comprehensive report provides an in-depth analysis of the Denmark luxury residential real estate market, covering the period from 2019 to 2033. With a focus on high-value properties in key cities like Copenhagen, Aarhus, Odense, and Aalborg, this report offers invaluable insights for investors, developers, and industry professionals seeking to navigate this dynamic market. The study period spans from 2019-2033, with 2025 serving as the base and estimated year, and a forecast period from 2025-2033. Historical data from 2019-2024 provides a robust foundation for future projections. Key Search Terms: Denmark luxury real estate, Copenhagen luxury apartments, Aarhus luxury villas, Danish real estate market, luxury property investment Denmark, residential real estate Denmark, Danish luxury homes, high-end real estate Denmark. Recent developments include: November 2022: The AkademikerPension expands real estate allocation. Whereas the portfolio currently consists primarily of offices in Copenhagen, the distribution in 2026 should be 50% residential, 30% offices, and various construction projects. Most investments will be made in Copenhagen and Aarhus, but approximately 25% of the real estate investments will be made in smaller Danish cities., June 2022: Orange Capital Partners, a European real estate investment firm, has purchased a portfolio of seven residential blocks in Denmark from NREP for an undisclosed sum. NREP stated that its Nordic Strategies Fund II held the 110,000 sqm residential portfolio. The portfolio comprises 1,220 modern rental apartments spread across seven residential buildings in Copenhagen and Aarhus. Teglgrdshusene, Green Square Garden, Nordhuset, Restad Have, Resource Rows, Risskov Brynet, and Lisbjerg are among the properties.. Key drivers for this market are: 4., Increasing manufacturing sites4.; The increasing middle-income group and access to mortgage finance. Potential restraints include: 4., Rising cost of construction materials.. Notable trends are: Increasing demand for luxury residences driving the market.
Facebook
Twitterhttps://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy
The Denmark Luxury Residential Real Estate Market is poised for substantial growth, projected to reach approximately USD 18,500 million by 2025, and is expected to expand at a Compound Annual Growth Rate (CAGR) exceeding 5.00% from 2019 to 2033. This robust expansion is fueled by a confluence of factors, including increasing disposable incomes among high-net-worth individuals, a persistent demand for premium living spaces, and the enduring appeal of Denmark's high quality of life, picturesque landscapes, and stable economic environment. The market is characterized by a strong preference for both meticulously designed villas and landed houses offering privacy and space, as well as contemporary condominiums and apartments in urban centers, catering to diverse lifestyle choices. Emerging trends point towards a growing emphasis on sustainable luxury, with eco-friendly materials and energy-efficient designs becoming increasingly sought after by discerning buyers. Furthermore, the integration of smart home technology and bespoke amenities is shaping the future of high-end residential offerings, enhancing both convenience and exclusivity. Despite the promising outlook, the market faces certain restraints that warrant strategic consideration. Rising interest rates and a potential tightening of credit availability could temper buyer sentiment, particularly for those relying on financing. Additionally, the inherent cost of land and construction in prime Danish locations, coupled with stringent building regulations, contributes to elevated property prices, potentially limiting affordability for a segment of the luxury buyer pool. However, the underlying demand, driven by global wealth accumulation and a desire for secure, high-value assets, is expected to largely counteract these challenges. Key urban hubs like Copenhagen and Aarhus continue to lead the market, attracting significant investment and commanding premium prices, while other regions like Odense and Aalborg are witnessing steady development and offer attractive entry points. The competitive landscape features a dynamic array of established and emerging real estate developers and agencies, all vying to capture a share of this lucrative market. This report provides a comprehensive analysis of the Denmark Luxury Residential Real Estate Market, offering in-depth insights into its structure, dynamics, and future trajectory. We delve into key segments, regional trends, driving forces, challenges, and emerging opportunities, supported by an extensive list of leading players and significant industry developments. This report is an essential resource for investors, developers, and stakeholders seeking to understand and capitalize on the high-end property landscape in Denmark. Key drivers for this market are: 4., Increasing manufacturing sites4.; The increasing middle-income group and access to mortgage finance. Potential restraints include: 4., Rising cost of construction materials.. Notable trends are: Increasing demand for luxury residences driving the market.
Facebook
TwitterThe average sales price of all properties in Denmark decreased to *** million Danish Kroner compared to the previous year. Nevertheless, the last two years in this industry recorded a significantly higher average price than the preceding years.Find more statistics on all properties in Denmark with key insights such as Average purchasing price for single-family houses and Average purchasing price for holiday houses.
Facebook
Twitterhttps://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy
The Scandinavian real estate market, encompassing countries like Sweden, Norway, Denmark, Finland, and Iceland, exhibits robust growth potential, fueled by a combination of factors. A consistently strong CAGR exceeding 5% indicates a healthy and expanding market. Key drivers include increasing urbanization, a growing population, particularly in major cities like Oslo, Stockholm, and Copenhagen, and a sustained demand for both residential and commercial properties. Furthermore, government policies supporting sustainable development and infrastructure projects contribute to the market's positive trajectory. The market is segmented into villas and landed houses, which often command higher prices due to limited supply and desirable locations, and apartments and condominiums, catering to a broader range of buyers and representing a larger portion of the market. The dominance of established players like Riksbyggen, OBOS BBL, and Balder highlights the market's maturity, yet the presence of smaller, more agile companies signifies ongoing competition and innovation. While data on exact market size is unavailable, a conservative estimation placing the 2025 market value at approximately €150 Billion ( based on general European real estate market values and applying the provided CAGR) seems plausible. Further growth is expected, driven by continued economic stability and ongoing investment in the region's infrastructure. Looking forward, the Scandinavian real estate market is expected to face some challenges, including rising interest rates impacting affordability, and potential fluctuations in the global economy. However, the strong underlying fundamentals of population growth, limited land availability in desirable urban areas, and continued investment in infrastructure suggest resilience and continued expansion. The market's diversity, with a mix of large established companies and smaller players, ensures a competitive landscape and capacity for adaptation. Trends toward sustainable construction and smart homes will likely play an increasingly significant role in shaping the future of the market, with companies prioritizing environmentally friendly practices and technologically advanced properties. Segmentation within the market will continue to be relevant, with the demand for specific property types varying across regions and based on changing demographic needs. Key drivers for this market are: 4., Increasing manufacturing sites4.; The increasing middle-income group and access to mortgage finance. Potential restraints include: 4., Rising cost of construction materials.. Notable trends are: Growing Housing Market in Norway to Drive the Market.
Facebook
TwitterAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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
TwitterMost Danes with a share of ** percent of respondents did not have second-hand furniture in their home in 2018. The most frequent reason to have second-hand furniture in the home was because they were inherited, reaching ** percent of respondents. The most infrequent reasons were a better quality and concerns for the environment with roughly *** percent of the respondents choosing these reasons.
Facebook
Twitterhttps://fred.stlouisfed.org/legal/#copyright-citation-requiredhttps://fred.stlouisfed.org/legal/#copyright-citation-required
Graph and download economic data for Harmonized Index of Consumer Prices: Actual Rentals for Housing for Denmark (CP0410DKM086NEST) from Jan 1996 to Oct 2025 about Denmark, rent, harmonized, CPI, housing, price index, indexes, and price.
Facebook
Twitterhttps://www.ycharts.com/termshttps://www.ycharts.com/terms
View quarterly updates and historical trends for Denmark House Price Index. Source: Federal Reserve Bank of Dallas. Track economic data with YCharts analy…
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… :-)