This table shows the average purchase price that has been paid in the reporting period for existing own homes purchased by a private individual. The average purchase price of existing own homes may differ from the price index of existing own homes. The average purchase price is no indicator for price developments of owner-occupied residential property. The average purchase price reflects the average price of dwellings sold in a particular period. The fact that de dwellings sold differs from one period to another is not taken into account. The following instance explains which problems are entailed by the continually changing of the quality of the dwellings sold. Suppose in February of a particular year mainly big houses with extensive gardens beautifully situated alongside canals are sold, whereas in March many small terraced houses are sold. In that case the average purchase price in February will be higher than in March but this does not mean that house prices are increased. See note 3 for a link to the article 'Why the average purchase price is not an indicator'. Data available from: 1995 Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions that are registered every month by the Dutch Land Registry Office (Kadaster). A revision of the figures is exceptional and occurs specifically if an error significantly exceeds the acceptable statistical margins. The average purchasing prices of existing owner-occupied sold homes can be calculated by Kadaster at a later date. These figures are usually the same as the publication on Statline, but in some periods they differ. Kadaster calculates the average purchasing prices based on the most recent data. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the revision policy described above. Changes as of 17 February 2025: Added average purchase prices of the municipalities for the year 2024. When will new figures be published? New figures are published approximately one to three months after the period under review.

The Hedonic Price Model, used in existing house price modeling, may not address the relationship between house prices and streetscapes perceived at the human eye level. Therefore, in this study, we analyzed the relationship between streetscapes perceived at eye level and single-family home prices in Seoul, Korea, using computer vision technology and machine learning algorithms. We used transaction data for 13,776 single-family housing sales between 2017 and 2019. To measure visually perceived streetscapes, this study used the Deeplab V3+ deep-learning model with 233,106 Google Street View panoramic images. Then, the best machine-learning model was selected by comparing the explanatory powers of the hedonic price model and all alternative machine-learning models. According to the results, the Gradient Boost model, a representative ensemble machine learning model, performed better than XGBoost, Random Forest, and Linear Regression models in predicting single-family house prices. In addition, this study used an interpretable machine learning model of the SHAP method to identify key features that affect single-family home price prediction. This solves the "black box" problem of machine learning models. Finally, by analyzing the nonlinear relationship and interaction effects between perceived streetscape characteristics and house prices, we easily and quickly identified the relationship between variables the hedonic price model partially considers.

Context

Once there lived an atrocious King with the finest sword a man could bear at that time. Alzar, the record keeper, lost papers that had prices for houses in the kingdom. As he trembled with mortal fear, he went to Elric the sorcerer seeking for help. "King is very specific and rather precise with numbers!" exclaimed Elric seeing the records.

Fortunately, some records were still present, but they were too scattered! King has commanded Alzar to present to him the complete record with price (in golden grains) of each house against its unique ID. Now Elric invites you through time travel to help poor Alzar lest he should lose his life to sword. Alzar will present to you the information that he has. 1) Each paper is specific to one builder family with details of houses that they built. 2) Alzar has sorted for you the house details with builder family name and ,,Not Known" where builder's information was lost. "But certainly, there are only ten builder families" he remarks.

"Careful! Black Magic has scraped off some more data from the records" says Elric as you begin to think upon...

Inspiration

• Analyse the data and find out what factors affects the housing prices?
• Create machine learning model to estimate housing price?

Additional data from the HPSSAs on the number and percentage of property sales of £1 million or more in England and Wales, by country, English region and local authorities

Graph and download economic data for Housing Inventory: Median Listing Price per Square Feet in the United States (MEDLISPRIPERSQUFEEUS) from Jul 2016 to May 2025 about square feet, listing, median, price, and USA.

S&P/Case-Shiller home price index and 12 demographic and macroeconomic factors in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco (SF) data were collected from the Federal Reserve Bank, FBI, and Freddie Mac. https://fred.stlouisfed.org; http://www.freddiemac.com/pmms/; https://www.philadelphiafed.org/surveys-and-data/community-development-data/consumer-credit-explorer; https://ucr.fbi.gov/crime-in-the-u.s/2005;

This dataset includes the listing prices for the sale of properties (mostly houses) in Ontario. They are obtained for a short period of time in July 2016 and include the following fields: - Price in dollars - Address of the property - Latitude and Longitude of the address obtained by using Google Geocoding service - Area Name of the property obtained by using Google Geocoding service

This dataset will provide a good starting point for analyzing the inflated housing market in Canada although it does not include time related information. Initially, it is intended to draw an enhanced interactive heatmap of the house prices for different neighborhoods (areas)

However, if there is enough interest, there will be more information added as newer versions to this dataset. Some of those information will include more details on the property as well as time related information on the price (changes).

This is a somehow related articles about the real estate prices in Ontario: http://www.canadianbusiness.com/blogs-and-comment/check-out-this-heat-map-of-toronto-real-estate-prices/

I am also inspired by this dataset which was provided for King County https://www.kaggle.com/harlfoxem/housesalesprediction

Abstract: The present article consists of the analysis of the relevant attributes in the formation of the prices of residential properties for sale in Conselheiro Lafaiete, MG, in 2016. A hedonic model was estimated from a multiple linear regression that allowed to associate the real estate price with the properties’ characteristics and its surroundings. The results suggest that the variables were relevant to explain the variability in real estate prices, and reflected the reality of the real estate market of Conselheiro Lafaiete.

Graph and download economic data for Zillow Home Value Index (ZHVI) for All Homes Including Single-Family Residences, Condos, and CO-OPs in the United States of America (USAUCSFRCONDOSMSAMID) from Jan 2000 to May 2025 about 1-unit structures, family, residential, housing, indexes, and USA.

This dataset is used for predicting house prices from both images and textual information. It is composed of 535 sample houses from California, USA.

This table shows the changes of the sale prices of existing own homes. Besides the price indices, also the numbers sold, the average purchase price of these dwellings and the total sum of the puchase prices of these dwellings are published. The House Price Index of existing own homes is based on a complete registration of sales of dwellings by the Dutch Land Registry Office (Kadaster) and Value Immovable Property (in Dutch: WOZ) of all dwellings in The Netherlands. Indices can fluctuate, for example when the number of dwellings sold of a certain type of dwelling in a region is limited. In that case it is recommended to use the long term change of the index. The average purchase price of existing own homes may differ from the price index of existing own homes. The change in the average purchase price is, however, not an indicator for price developments of existing own homes. For more information on this subject, see the article at chapter 3 "Why the average purchase price is not an indicator". Data available from: January 1995 Status of the figures. The figures are definitive. When are new figures published? This table is stopped as from 3-8-2013 and will be continued as House Price Index by region; existing own homes, 2010 = 100 and House Price Index by type of dwelling; existing own homes; 2010 = 100. See paragraph 3.

Problem Statement

👉 Download the case studies here

Investors and buyers in the real estate market faced challenges in accurately assessing property values and market trends. Traditional valuation methods were time-consuming and lacked precision, making it difficult to make informed investment decisions. A real estate firm sought a predictive analytics solution to provide accurate property price forecasts and market insights.

Challenge

Developing a real estate price prediction system involved addressing the following challenges:

Collecting and processing vast amounts of data, including historical property prices, economic indicators, and location-specific factors.

Accounting for diverse variables such as neighborhood quality, proximity to amenities, and market demand.

Ensuring the model’s adaptability to changing market conditions and economic fluctuations.

Solution Provided

A real estate price prediction system was developed using machine learning regression models and big data analytics. The solution was designed to:

Analyze historical and real-time data to predict property prices accurately.

Provide actionable insights on market trends, enabling better investment strategies.

Identify undervalued properties and potential growth areas for investors.

Development Steps

Data Collection

Collected extensive datasets, including property listings, sales records, demographic data, and economic indicators.

Preprocessing

Cleaned and structured data, removing inconsistencies and normalizing variables such as location, property type, and size.

Model Development

Built regression models using techniques such as linear regression, decision trees, and gradient boosting to predict property prices. Integrated feature engineering to account for location-specific factors, amenities, and market trends.

Validation

Tested the models using historical data and cross-validation to ensure high prediction accuracy and robustness.

Deployment

Implemented the prediction system as a web-based platform, allowing users to input property details and receive price estimates and market insights.

Continuous Monitoring & Improvement

Established a feedback loop to update models with new data and refine predictions as market conditions evolved.

Results

Increased Prediction Accuracy

The system delivered highly accurate property price forecasts, improving investor confidence and decision-making.

Informed Investment Decisions

Investors and buyers gained valuable insights into market trends and property values, enabling better strategies and reduced risks.

Enhanced Market Insights

The platform provided detailed analytics on neighborhood trends, demand patterns, and growth potential, helping users identify opportunities.

Scalable Solution

The system scaled seamlessly to include new locations, property types, and market dynamics.

Improved User Experience

The intuitive platform design made it easy for users to access predictions and insights, boosting engagement and satisfaction.

This table provides an overview of the average sales price paid in the reporting period for existing owner-occupied homes purchased by a private individual. The average sales price may show a different development than the Price Index for Existing Owner-occupied Homes (PBK). The average purchase price is not an indicator for the price development of owner-occupied homes. The average purchase price reflects the average price of homes sold in a specific period. Because different homes are sold every period, any different characteristics of the sold homes are not taken into account. An example to explain the problems this entails: Suppose in February of a year mainly large canal houses with a garden were sold, while in March many small townhouses changed hands. As a result, the average purchase price in February will be higher than in March, but this does not mean that house prices have risen. See Section 3 for a link to the article 'Why the average purchase price is not a house price indicator'. Data available from: 1995 Status of the figures: The figures in this table are immediately definitive. The calculation of these figures is based on the number of notary transactions established each month by the Kadaster. A revision of the figures only takes place in exceptional cases, namely only if there is a significant error outside the usual statistical margins. The average sales prices of existing owner-occupied homes can also be calculated by the Kadaster at a later date. Usually these figures are the same as the publication on Statline, but they differ from each other in some periods. In these cases, Kadaster uses the most up-to-date figures. These may have changed since the first publication. Statistics Netherlands uses figures from the first publication in accordance with the above revision policy. Changes as of February 23, 2022 Correction: The average sales prices of the municipalities of Bloemendaal and Blaricum in 2021 were incorrectly displayed. The last digit (7th position) was missing for these municipalities. These figures have been added. Changes as of February 15, 2023 Figures average sales prices of the municipalities year 2022 added. When will new numbers come out? The new figures will be published approximately one to three months after the year under review.

Overview: This dataset was collected and curated to support research on predicting real estate prices using machine learning algorithms, specifically Support Vector Regression (SVR) and Gradient Boosting Machine (GBM). The dataset includes comprehensive information on residential properties, enabling the development and evaluation of predictive models for accurate and transparent real estate appraisals.Data Source: The data was sourced from Department of Lands and Survey real estate listings.Features: The dataset contains the following key attributes for each property:Area (in square meters): The total living area of the property.Floor Number: The floor on which the property is located.Location: Geographic coordinates or city/region where the property is situated.Type of Apartment: The classification of the property, such as studio, one-bedroom, two-bedroom, etc.Number of Bathrooms: The total number of bathrooms in the property.Number of Bedrooms: The total number of bedrooms in the property.Property Age (in years): The number of years since the property was constructed.Property Condition: A categorical variable indicating the condition of the property (e.g., new, good, fair, needs renovation).Proximity to Amenities: The distance to nearby amenities such as schools, hospitals, shopping centers, and public transportation.Market Price (target variable): The actual sale price or listed price of the property.Data Preprocessing:Normalization: Numeric features such as area and proximity to amenities were normalized to ensure consistency and improve model performance.Categorical Encoding: Categorical features like property condition and type of apartment were encoded using one-hot encoding or label encoding, depending on the specific model requirements.Missing Values: Missing data points were handled using appropriate imputation techniques or by excluding records with significant missing information.Usage: This dataset was utilized to train and test machine learning models, aiming to predict the market price of residential properties based on the provided attributes. The models developed using this dataset demonstrated improved accuracy and transparency over traditional appraisal methods.Dataset Availability: The dataset is available for public use under the [CC BY 4.0]. Users are encouraged to cite the related publication when using the data in their research or applications.Citation: If you use this dataset in your research, please cite the following publication:[Real Estate Decision-Making: Precision in Price Prediction through Advanced Machine Learning Algorithms].

This table shows the changes of the sale prices of existing own homes by COROP and 25 biggest municipalities (more then 100.000 inhabitants on 01-01-2005). Besides the price indices, also the numbers sold, the average purchase price of these dwellings and the total sum of the puchase prices of these dwellings are published. The House Price Index of existing own homes is based on a complete registration of sales of dwellings by the Dutch Land Registry Office (Kadaster) and Value Immovable Property (in Dutch: WOZ) of all dwellings in The Netherlands. Indices can fluctuate, for example when the number of dwellings sold in a region is limited. In that case it is recommended to use the long term change of the index. The average purchase price of existing own homes may differ from the price index of existing own homes. The change in the average purchase price is, however, not an indicator for price developments of existing own homes. For more information on this subject, see the article at chapter 3 "Why the average purchase price is not an indicator". Data available from: January 1995 - December 2012. Status of the figures: The figures are definitive. When are new figures published? This table is stopped as from 3-8-2013 and will be continued as House Price Index by region; existing own homes, 2010 = 100. See paragraph 3.

This project comprises two studies that examine the relationship between investor attention and house prices in the Australian housing market. The first study investigates the correlation between investor attention, measured by the Google Search Volume Index, and house prices in Australia. It uncovers a strong positive correlation, indicating that fluctuations in investor attention closely align with changes in house prices. The study also highlights the predictive potential of investor attention in forecasting housing market trends, supported by behavioural finance principles that emphasise the impact of investor sentiment on asset pricing, particularly in real estate. The second study explores the bidirectional relationship between house prices and investor attention using OLS regression, VAR modeling, Granger causality tests, impulse response functions, and forecast error variance decomposition. The findings confirm that investor attention significantly influences housing prices, and past house prices can also impact current investor attention. In addition, short-term shocks in house prices cause fluctuations in investor attention, although these effects are transient. This study underscores the importance of integrating investor attention with traditional economic factors to better understand and predict housing market dynamics. These empirical studies contribute significantly to the literature on investor attention and housing market dynamics, representing some of the earliest empirical inquiries into the relation between housing market fluctuations and investor attention. By bridging these two critical domains, the research provides valuable insights for policymakers, real estate investors, and market analysts. The findings also lay a foundation for scholars and practitioners to enhance housing market analysis and prediction, offering substantial implications for market forecasting and intervention strategies.

This table shows the changes of the sale prices of existing own homes by COROP and 25 biggest municipalities (more then 100.000 inhabitants on 01-01-2005). Besides the price indices, also the numbers sold, the average purchase price of these dwellings and the total sum of the puchase prices of these dwellings are published. The House Price Index of existing own homes is based on a complete registration of sales of dwellings by the Dutch Land Registry Office (Kadaster) and Value Immovable Property (in Dutch: WOZ) of all dwellings in The Netherlands. Indices can fluctuate, for example when the number of dwellings sold in a region is limited. In that case it is recommended to use the long term change of the index. The average purchase price of existing own homes may differ from the price index of existing own homes. The change in the average purchase price is, however, not an indicator for price developments of existing own homes. For more information on this subject, see the article at chapter 3 "Why the average purchase price is not an indicator".

Data available from: January 1995 - December 2012.

Status of the figures: The figures are definitive.

When are new figures published? This table is stopped as from 3-8-2013 and will be continued as House Price Index by region; existing own homes, 2010 = 100. See paragraph 3.

please cite our article: Do street-level scene perceptions affect housing prices in Chinese megacities? An analysisi using open access datasets and deep learning

Graph and download economic data for Housing Inventory: Median Listing Price in Los Angeles County, CA (MEDLISPRI6037) from Jul 2016 to Dec 2024 about Los Angeles County, CA; Los Angeles; CA; listing; median; price; and USA.