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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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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.
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Key information about House Prices Growth
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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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Graph and download economic data for Residential Property Prices for Denmark (QDKN628BIS) from Q1 1970 to Q2 2025 about Denmark, residential, HPI, housing, price index, indexes, and price.
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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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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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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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TwitterGeneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.
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Denmark Dismantled House Price: Flat: Capital Region: Copenhagen City (CC) data was reported at 45,172.000 DKK/sq m in Jun 2018. This records an increase from the previous number of 44,538.000 DKK/sq m for May 2018. Denmark Dismantled House Price: Flat: Capital Region: Copenhagen City (CC) data is updated monthly, averaging 31,366.500 DKK/sq m from Jan 2004 (Median) to Jun 2018, with 174 observations. The data reached an all-time high of 45,172.000 DKK/sq m in Jun 2018 and a record low of 19,056.000 DKK/sq m in Jan 2004. Denmark Dismantled House Price: Flat: 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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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.
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TwitterIn the first quarter of 2025, Stockholm was the Nordic city with the most expensive apartments. The average square meter price of apartments was around ***** euros per square meter. The capital cities of Denmark and Norway followed in the ranking, with apartments costing respectively ***** euros and ***** euros per square meter.
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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.
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Denmark Dismantled House Price: DR: Capital Region: Copenhagen Suburban data was reported at 33,324.000 DKK/sq m in Jun 2018. This records a decrease from the previous number of 33,479.000 DKK/sq m for May 2018. Denmark Dismantled House Price: DR: Capital Region: Copenhagen Suburban data is updated monthly, averaging 27,023.500 DKK/sq m from Jan 2004 (Median) to Jun 2018, with 174 observations. The data reached an all-time high of 33,479.000 DKK/sq m in May 2018 and a record low of 17,396.000 DKK/sq m in Jan 2004. Denmark Dismantled House Price: DR: Capital Region: Copenhagen Suburban 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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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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The Scandinavian commercial property market, encompassing Denmark, Norway, and Sweden, presents a dynamic investment landscape characterized by a robust 7.41% CAGR (2019-2033). Key drivers include strong economic performance in the Nordic region, increasing urbanization leading to higher demand for office, retail, and residential spaces, and a growing logistics sector fueled by e-commerce expansion. Significant investment in sustainable and technologically advanced buildings further contributes to market growth. While the market enjoys considerable strength, potential restraints include fluctuations in global economic conditions, increasing construction costs, and potential regulatory changes affecting property development. The market is segmented by property type (offices, retail, industrial, logistics, multi-family, hospitality) and geography (Denmark, Norway, Sweden, with key cities like Oslo, Stockholm, and Copenhagen exhibiting high activity). Major players include developers like Vasakronan AB, Jeudan A/S, Citycon, and NREP (Logicenters), alongside significant real estate agencies such as CBRE, Europages, and Colliers International. The presence of smaller, innovative companies and startups also adds dynamism to the sector. The regional breakdown reveals that while the Nordics are the core market, international investment continues to play a role. The high CAGR suggests that the market will continue its upward trajectory, although potential economic downturns could moderate growth in specific years. Analysis of individual cities within each country is crucial for a granular understanding of market opportunities and risks. For example, Oslo's burgeoning tech scene might drive higher office demand, while Stockholm’s strong retail sector could impact shopping center valuations. Investors should carefully assess the specific sub-markets within the broader Scandinavian commercial property landscape to identify the most promising investment opportunities and effectively manage associated risks. A focus on sustainability and technological integration will likely be critical for success in this evolving market. Notable trends are: Increase in Transaction Volume in the Office Market of Scandinavian Countries.
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TwitterThe average transaction price of new residential housing in Denmark increased by *** percent in Copenhagen from 2020 to 2021. In contrast, the new residential property prices increased by only *** percent in the Danish city of Odense during the same period.
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Denmark Dismantled House Price: DR: Capital Region: CC: ow Copenhagen data was reported at 46,031.000 DKK/sq m in Jun 2018. This records a decrease from the previous number of 47,225.000 DKK/sq m for May 2018. Denmark Dismantled House Price: DR: Capital Region: CC: ow Copenhagen data is updated monthly, averaging 30,864.000 DKK/sq m from Jan 2004 (Median) to Jun 2018, with 174 observations. The data reached an all-time high of 47,830.000 DKK/sq m in Mar 2018 and a record low of 18,689.000 DKK/sq m in Jan 2004. Denmark Dismantled House Price: DR: Capital Region: CC: ow Copenhagen 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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Discover the booming Scandinavian commercial property market! This in-depth analysis reveals a 7.41% CAGR, driven by urbanization and e-commerce. Explore key trends, investment opportunities, and top players in Oslo, Stockholm, Copenhagen, and beyond. Learn about market segmentation, growth forecasts (2025-2033), and key challenges. Key drivers for this market are: Overall economic growth driving the market, The growth of business and industries driving the market. Potential restraints include: Fluctuating economic conditions hindering the growth of the market, Difficulty in landownership and leasing rights affecting the market. Notable trends are: Increase in Transaction Volume in the Office Market of Scandinavian Countries.
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Price to Rent Ratio in Denmark increased to 129.42 in the second quarter of 2025 from 129.09 in the first quarter of 2025. This dataset includes a chart with historical data for Denmark Price to Rent Ratio.
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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… :-)