Just as in many other countries, the housing market in the UK grew substantially during the coronavirus pandemic, fueled by robust demand and low borrowing costs. Nevertheless, high inflation and the increase in mortgage rates has led to house price growth slowing down. According to the forecast, 2024 is expected to see house prices decrease by ***** percent. Between 2024 and 2028, the average house price growth is projected at *** percent. A contraction after a period of continuous growth In June 2022, the UK's house price index exceeded *** index points, meaning that since 2015 which was the base year for the index, house prices had increased by ** percent. In just two years, between 2020 and 2022, the index surged by ** index points. As the market stood in December 2023, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next years. Growth is forecast to be stronger in 2024 and slow down in the period between 2025 and 2028. The rental market in London is expected to follow a similar trend, with Central London slightly outperforming Greater London.
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Single Family Home Prices in the United States increased to 422800 USD in May from 414000 USD in April of 2025. This dataset provides - United States Existing Single Family Home Prices- actual values, historical data, forecast, chart, statistics, economic calendar and news.
House prices in Spain are forecast to fall in 2024, after increasing by 1.2 percent in 2023. Nevertheless, prices are expected to pick up in 2025, with an increase of one percent. The Portuguese housing market, on the other hand, grew by 5.5 percent in 2023, but was forecast to contract in the next two years.
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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].
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Key information about House Prices Growth
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Housing Index in Germany increased to 218.58 points in May from 217.43 points in April of 2025. This dataset provides the latest reported value for - Germany House Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
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The US residential real estate market, a cornerstone of the American economy, is projected to experience steady growth over the next decade. While the provided CAGR of 2.04% is a modest figure, it reflects a market maturing after a period of significant expansion. This sustained growth is driven by several key factors. Firstly, population growth and urbanization continue to fuel demand for housing, particularly in densely populated areas and emerging suburban markets. Secondly, low interest rates (historically, though this can fluctuate) have made mortgages more accessible, stimulating buyer activity. Thirdly, a robust construction sector, though facing challenges in material costs and labor shortages, is gradually increasing the housing supply, mitigating some of the upward pressure on prices. However, challenges remain. Rising inflation and potential interest rate hikes pose a risk to affordability, potentially dampening demand. Furthermore, the ongoing evolution of remote work is reshaping residential preferences, with a shift toward larger homes in suburban or exurban locations. This trend impacts the relative demand for various property types, potentially increasing the appeal of landed houses and villas compared to apartments and condominiums in certain regions. The segmentation of the market into apartments/condominiums and landed houses/villas provides crucial insights into consumer preferences and investment strategies. High-density urban areas will continue to see strong demand for apartments and condos, while suburban and rural areas are likely to experience a greater increase in landed property sales. Major players like Simon Property Group, Mill Creek Residential, and others are strategically adapting to these trends, focusing on both development and management across various property types and geographic locations. Analyzing regional data within the US (e.g., comparing growth in the Northeast versus the Southwest) will highlight market nuances and potential investment opportunities. While the global data provided is valuable for understanding broader market forces, focusing the analysis on the US market allows for a more granular understanding of the specific drivers, trends, and challenges within this significant segment of the real estate sector. The forecast period (2025-2033) suggests continued, albeit measured, expansion. Recent developments include: May 2022: Resource REIT Inc. completed the sale of all of its outstanding shares of common stock to Blackstone Real Estate Income Trust Inc. for USD 14.75 per share in an all-cash deal valued at USD 3.7 billion, including the assumption of the REIT's debt., February 2022: The largest owner of commercial real estate in the world and private equity company Blackstone is growing its portfolio of residential rentals and commercial properties in the United States. The company revealed that it would shell out about USD 6 billion to buy Preferred Apartment Communities, an Atlanta-based real estate investment trust that owns 44 multifamily communities and roughly 12,000 homes in the Southeast, mostly in Atlanta, Nashville, Charlotte, North Carolina, and the Florida cities of Jacksonville, Orlando, and Tampa.. Key drivers for this market are: Investment Plan Towards Urban Rail Development. Potential restraints include: Italy’s Fragmented Approach to Tenders. Notable trends are: Existing Home Sales Witnessing Strong Growth.
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Housing Index in Belgium increased to 142.24 points in the fourth quarter of 2024 from 141.16 points in the third quarter of 2024. This dataset provides - Belgium Housing Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Housing Index in Italy increased to 113.60 points in the fourth quarter of 2024 from 112.80 points in the third quarter of 2024. This dataset provides - Italy House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
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Housing Index in Portugal increased to 247.05 points in the first quarter of 2025 from 235.68 points in the fourth quarter of 2024. This dataset provides - Portugal House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.
According to the forecast, the North East and Wales are the regions in the United Kingdom estimated to see the highest overall growth in house prices over the five-year period between 2024 and 2028. Just behind are North West, Yorkshire & the Humber, and Scotland, which are forecast to see house prices increase by **** percent over the five-year period. In London, house prices are expected to rise by **** percent.
House prices in Norway fell by 1.4 percent and, according to the forecast, are expected to continue to fall until 2024. In 2023, properties were forecast to experience a decline in prices of 12 percent. In 2025, growth is projected to recover, rising to five percent.
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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.
The quarterly pulse monitor expects the Dutch house prices to fall by five percent in 2023 due to the decline in purchasing power, higher cost of borrowing and worsening economic conditions. The price of Dutch residential property in 2022 was approximately 489,000 euros. These developments came on top of other issues that were already prevalent in the Dutch housing market, such as the discussion about nitrogen and its effect on housing construction. The effects of nitrogen on the price of a house At the end of 2019, months before the coronavirus, there was already a lot of uncertainty whether their predictions would hold true. This had to do with the so-called “nitrogen decision” (in Dutch: stikstofbesluit) in May 2019. Simply put, a Dutch advisory body found that the domestic policy for nitrogen emission (formally known as Programmatische Aanpak Stikstof or Programmatic Approach Nitrogen) went against European rules. As of August 2019, a sizable share of the Dutch population was not familiar with this nitrogen policy. However, the advisory body’s decision led to an immediate stop to all construction in the country (amongst other things). By the end of 2019, this stop was still in place. For 2020, newly to be constructed houses have to comply to new rules regarding nitrogen emission. This puts new pressure on a housing market that already had to keep with increasing demand. How about the housing market in Amsterdam? In the year 2022, Amsterdam ranked as the most expensive city in the Netherlands to acquire an apartment, with an average price per square meter that was 2,000 euros more expensive than in Utrecht. Amsterdam was also well above the average rents found in other cities. A house in Amsterdam had a rent of approximately 26 euros per square meter in 2023, whereas rents in Rotterdam cost roughly 18 euros per square meter. It should be noted, however, that rent changes in the Dutch capital are significantly lower than those found in Rotterdam and especially Utrecht.
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NZT Forecast: House Prices: YoY data was reported at 4.400 % in 2023. This records a decrease from the previous number of 5.000 % for 2022. NZT Forecast: House Prices: YoY data is updated yearly, averaging 6.500 % from Jun 2013 (Median) to 2023, with 11 observations. The data reached an all-time high of 9.300 % in 2017 and a record low of 3.700 % in 2020. NZT Forecast: House Prices: YoY data remains active status in CEIC and is reported by New Zealand Treasury. The data is categorized under Global Database’s New Zealand – Table NZ.EB003: House Price: Forecast: New Zealand Treasury.
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This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.
Column Name | Description |
---|---|
Country | The country where the housing market data is recorded 🌍 |
Year | The year of observation 📅 |
Average House Price ($) | The average price of houses in USD 💰 |
Median Rental Price ($) | The median monthly rent for properties in USD 🏠 |
Mortgage Interest Rate (%) | The average mortgage interest rate percentage 📉 |
Household Income ($) | The average annual household income in USD 🏡 |
Population Growth (%) | The percentage increase in population over the year 👥 |
Urbanization Rate (%) | Percentage of the population living in urban areas 🏙️ |
Homeownership Rate (%) | The percentage of people who own their homes 🔑 |
GDP Growth Rate (%) | The annual GDP growth percentage 📈 |
Unemployment Rate (%) | The percentage of unemployed individuals in the labor force 💼 |
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Key information about House Prices Growth
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While the traditional genetic algorithms are capable of forecasting house prices, they often suffer from premature convergence, which adversely affects the reliability of the forecasts. To address this issue, the research employs a genetic-particle swarm optimization (GA-PSO) algorithm and develops a GA-PSO-BP neural network model through the integration of the BP neural network. Building upon this foundation, the study considers several pivotal factors affecting housing prices and employs a dataset comprising 1,824 transactions of second-hand homes from 2023 to 2024, gathered from Lianjia.com, to forecast housing prices in China. This work shows that the GA-PSO-BP neural network model demonstrates exceptional forecasting performance when dealing with complex and high-dimensional data, significantly minimizing forecasting errors. The test set achieved an RMSE of 0.786 and a MAPE of 8.9%. Its effectiveness in forecasting prices of second-hand houses notably surpasses that of a BP neural network model optimized by a single algorithm. This research provides more accurate forecasts of second-hand house prices in rapidly growing urban areas such as Guangzhou, thus providing essential insights for investors contemplating real estate investment.
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SSB Forecast: House Prices: YoY data was reported at 3.100 % in 2020. This records an increase from the previous number of -1.600 % for 2019. SSB Forecast: House Prices: YoY data is updated yearly, averaging 3.100 % from Dec 2014 (Median) to 2020, with 7 observations. The data reached an all-time high of 7.300 % in 2016 and a record low of -5.000 % in 2018. SSB Forecast: House Prices: YoY data remains active status in CEIC and is reported by Statistics Norway. The data is categorized under Global Database’s Norway – Table NO.P002: House Price: Year on Year Growth: Forecast: Statistics Norway.
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Case Shiller Home Price Index in the United States increased to 341.48 points in April from 338.39 points in March of 2025. This dataset provides the latest reported value for - United States S&P Case-Shiller Home Price Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.
Just as in many other countries, the housing market in the UK grew substantially during the coronavirus pandemic, fueled by robust demand and low borrowing costs. Nevertheless, high inflation and the increase in mortgage rates has led to house price growth slowing down. According to the forecast, 2024 is expected to see house prices decrease by ***** percent. Between 2024 and 2028, the average house price growth is projected at *** percent. A contraction after a period of continuous growth In June 2022, the UK's house price index exceeded *** index points, meaning that since 2015 which was the base year for the index, house prices had increased by ** percent. In just two years, between 2020 and 2022, the index surged by ** index points. As the market stood in December 2023, the average price for a home stood at approximately ******* British pounds. Rents are expected to continue to grow According to another forecast, the prime residential market is also expected to see rental prices grow in the next years. Growth is forecast to be stronger in 2024 and slow down in the period between 2025 and 2028. The rental market in London is expected to follow a similar trend, with Central London slightly outperforming Greater London.