House Price Index YoY in the United States decreased to 3 percent in April from 3.90 percent in March of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

30 Year Mortgage Rate in the United States increased to 6.75 percent in July 17 from 6.72 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Key information about House Prices Growth

• US house prices grew 5.2% YoY in Dec 2024, following an increase of 5.4% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 1992 to Dec 2024, with an average growth rate of 5.4%.
• House price data reached an all-time high of 17.7% in Sep 2021 and a record low of -12.4% in Dec 2008.

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in March 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.

Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This paper examines the effects of real estate transfer taxes (RETT) on property prices using a rich micro dataset of roughly 17 million German properties for the period from 2005 to 2019. Our empirical analysis exploits variation in RETT rate hikes across German states and over time. Our monthly event study estimates indicate a price response that strongly exceeds the change in the tax burden for single transactions. Twelve months after a reform, a one percentage point increase in the tax rate reduces property prices by on average 3%. Price effects are larger for apartments (-4%) than for single-family houses (-2%). Exploring potential mechanisms, we provide evidence that different holding periods are the main driver of the differential price effect between property types. Please note that the main data that we use is proprietary to the firm FuB IGES. The online replication package includes our do-files, a codebook of our main data, and the resulting log files, tables and figures. For the purpose of replication, the data, along with all code, can be accessed at the Economics and Business Data Center (EBDC) of the ifo Institute and the University of Munich. The EBDC offers researchers to use its facilities and access the data stored there at no costs. Further information about the EBDC can be found here: https://www.ifo.de/en/EBDC.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Understanding the Dynamics and Implications of a Housing Market Recession

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Fixed 30-year mortgage rates in the United States averaged 6.84 percent in the week ending July 18 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Index in Hong Kong decreased to 136.44 points in July 13 from 136.68 points in the previous week. This dataset provides - Hong Kong House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The residential vacancy rate is the percentage of residential units that are unoccupied, or vacant, in a given year. The U.S. Census Bureau defines occupied housing units as “owner-occupied” or “renter-occupied.” Vacant housing units are not classified by tenure in this way, as they are not occupied by an owner or renter.

The residential vacancy rate serves as an indicator of the condition of the area’s housing market. Low residential vacancy rates indicate that demand for housing is high compared to the housing supply. However, the aggregate residential vacancy rate is lacking in granularity. For example, the housing market for rental units in the area and the market for buying a unit in the same area may be very different, and the aggregate rate will not show those distinct conditions. Furthermore, the vacancy rate may be high, or low, for a variety of reasons. A high vacancy rate may result from a falling population, but it may also result from a recent construction spree that added many units to the total stock.

The residential vacancy rate in Champaign County appears to have fluctuated between 8% and 14% from 2005 through 2022, reaching a peak near 14% in 2019. In 2023, this rate dropped to about 7%, its lowest value since 2005. However, this rate was calculated using the American Community Survey’s (ACS) estimated number of vacant houses per year, which has year-to-year fluctuations that are largely not statistically significant. Thus, we cannot establish a trend for this data.

The residential vacancy rate data shown here was calculated using the estimated total housing units and estimated vacant housing units from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Occupancy Status.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (4 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table SB25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Urban housing location and locational amenities play an important role in median house price distribution and growth among the suburbs of many metropolitan cities in developed countries, such as Australia. In particular, distance from the central business district (CBD) and access to the transport network plays a vital role in house price distribution and growth over various suburbs in a city. However, Australian metropolitan cities have experienced increases in housing prices by up to 120% over the last 20 years, and the growth pattern was different across all suburbs in a city, such as in Melbourne. Therefore, this study examines the impacts of locational amenities on house price changes across various suburbs in Melbourne over the three census periods of 2006, 2011, and 2016, and suggests some strategic guidelines to improve the availability and accessibility of locational amenities in the suburbs with less concentrated amenities.

This study chose three Local Government Areas (LGAs) of Maribyrnong, Brimbank and Wyndham in Melbourne. Each LGA has been selected as a case study because many low-income people live in these LGAs’ areas. Further, some suburbs of these LGAs have maintained similar housing prices for an extended time, while some have not.

The study applied a quantitative spatial methodology to examine the housing price distribution and growth patterns by evaluating the concentration and accessibility of locational urban amenities using GIS-based techniques and a spatial data set. The spatial data analyses were performed by spatial statistics methods to measure central tendency, Local Moran’s I of LISA clustering, Kernel Density Estimation (KDE), Kernel Density Smoothing (KDS). These tests were used to find the patterns of house price distribution and growth. The study also identified the accessibility of amenities in relation to median house price distribution and growth. Spatial Autoregressive Regression (SAR), Spatial Lag, and Spatial Errors models were used to identify the spatial dependencies to test the statistical significance between the median house price and the concentration and access of local urban amenities over the three census years.

This study found three median house price distribution and growth patterns among the suburbs in the three selected LGAs. There are growth differences in the median house price for different census years between 2006 and 2011, 2011 and 2016, and 2006 and 2016. The Low-High (LH) median house price distribution clusters between 2006 and 2011 became High-High (HH) clusters between the census years 2011 and 2016, and 2006 and 2016. The median house price growth rate increased significantly in the census years between 2006 and 2011. Most of the HH median house price distribution and growth clusters’ tendencies were closer to the Melbourne CBD. On the other hand, the Low-Low (LL) distribution and growth clusters were closer to Melbourne’s periphery. The suburbs located further away had low access to amenities. The HH median house price clusters are located closer to stations and educational institutes. Better access to locational amenities led to more significant HH median house price clusters, as the median house price increased at an increasing rate between 2011 and 2016. The HH median house price clusters recorded more growth between 2006 and 2016. The suburbs with train stations had better access to most other locational amenities. Almost all HH median house price clusters had train stations with higher access to amenities.

There was a consistent relationship between median house price distribution, growth patterns, and locational urban amenities. The spatial lag and spatial error model tests showed that between 2006 and 2011, and 2006 and 2016, there were differences in the amenities. Still, these did not affect the outcomes in observations, and were related only to immeasurable factors for some reason. Therefore, the higher house price in the neighbouring suburb could increase the price in that suburb. The research also found from the regression analysis that highly significant amenities confirming travel time to the CBD by bus, and distance to the CBD, were negatively related in all three previous census years. This negative relationship estimates that the house price growth is lower when the distance is longer. Due to this travel to the CBD by bus is not a popular option for households. The train stations are essential for high house price growth. The house price growth is low when homes are further away from train stations and workplaces.

This thesis has three contributions. Firstly, it uses the Rational Choice Theory (RCT), providing a theoretical basis for analysing households’ mutually interdependent preferences of urban amenities that are found to regulate house price growth clusters. Secondly, the methodological contribution uses the GIS-defined cluster mapping and spatial statistics in queries and reasoning, measurements, transformations, descriptive summaries, optimisation, and hypothesis testing models between house price distribution and growth, and access to urban locational amenities. Thirdly, this research contributes to designing practical guidelines to identify local urban amenities for planning local area development.

Overall, this thesis demonstrates that the median house price distribution and growth patterns are highly correlated with the concentration and accessibility of locational urban amenities among the suburbs in three selected LGAs in Melbourne over the three census years (i.e., 2006, 2011, and 2016). The findings bring to the fore the need for research at the local and state levels to identify specific amenities relevant to the middle-class house distribution strategy, which can be helpful for investors, estate agents, town planners, and builders as partners for effective local development. The future study might use social, psychological, and macroeconomic variables not considered or used in this research.

Housing Index in the United Kingdom decreased to 511.60 points in June from 511.80 points in May of 2025. This dataset provides - United Kingdom House Price Index - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The average resale house price in Canada was forecast to reach nearly ******* Canadian dollars in 2026, according to a January forecast. In 2024, house prices increased after falling for the first time since 2019. One of the reasons for the price correction was the notable drop in transaction activity. Housing transactions picked up in 2024 and are expected to continue to grow until 2026. British Columbia, which is the most expensive province for housing, is projected to see the average house price reach *** million Canadian dollars in 2026. Affordability in Vancouver Vancouver is the most populous city in British Columbia and is also infamously expensive for housing. In 2023, the city topped the ranking for least affordable housing market in Canada, with the average homeownership cost outweighing the average household income. There are a multitude of reasons for this, but most residents believe that foreigners investing in the market cause the high housing prices. Victoria housing market The capital of British Columbia is Victoria, where housing prices are also very high. The price of a single family home in Victoria's most expensive suburb, Oak Bay was *** million Canadian dollars in 2024.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Housing Starts in the United States increased to 1321 Thousand units in June from 1263 Thousand units in May of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Norway was last recorded at 4.25 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Mexico was last recorded at 8 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Germany was last recorded at 4.50 percent. This dataset provides - Germany Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.