17 datasets found
  1. Robustness tests of rent-buy policies on house prices.

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 3, 2025
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    Fang Liu; Chen Liang (2025). Robustness tests of rent-buy policies on house prices. [Dataset]. http://doi.org/10.1371/journal.pone.0325274.t004
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Fang Liu; Chen Liang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Robustness tests of rent-buy policies on house prices.

  2. f

    Variables description.

    • figshare.com
    xls
    Updated Jun 3, 2025
    + more versions
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    Fang Liu; Chen Liang (2025). Variables description. [Dataset]. http://doi.org/10.1371/journal.pone.0325274.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Fang Liu; Chen Liang
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Considering the notable influence of traditional Confucian culture on China’s housing market, this study introduces an innovative index to quantify the magnitude of the real estate bubble within China, employing a familial generational iterative model. Utilizing rent-buy policy as a conceptual framework, our research constructs a difference-in-differences model to investigate the impact of macroeconomic policies on the housing bubble phenomenon. Empirical observations from 2022 reveal pronounced bubble dynamics in first and second-tier cities, while housing prices in third and fourth-tier cities, alongside select fifth-tier cities, exhibit a declining trend. On a national scale, apart from minor affordability observed during 2005–2007, no significant affordability was identified in other years, with the housing price bubble index demonstrating a downward trajectory from 2020 to 2022. Furthermore, the implementation of the rent-buy policy that equality the rights of renter and owner has directly influenced the housing market, notably mitigating the overall escalation of housing prices. Additional analysis indicates that the rent-and-buy policy has been more successful in curbing price hikes in newly constructed and smaller-sized housing units compared to second-hand and larger-scale properties.

  3. Car Price Prediction

    • kaggle.com
    zip
    Updated Sep 21, 2024
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    Zafar (2024). Car Price Prediction [Dataset]. https://www.kaggle.com/datasets/zafarali27/car-price-prediction/code
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    zip(46557 bytes)Available download formats
    Dataset updated
    Sep 21, 2024
    Authors
    Zafar
    License

    ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
    License information was derived automatically

    Description

    1. Understanding the Features

    Brand and Model: Analyze how different brands and models influence car prices. Are luxury brands significantly more expensive than economy brands? Year of Manufacture: Discuss the depreciation of car prices over time. How does the year affect pricing, and are there notable trends for specific brands? Engine Size: Explore the relationship between engine size and price. Does a larger engine correlate with a higher price, and how does this vary across different fuel types? Fuel Type: Evaluate how fuel types (Petrol, Diesel, Electric, Hybrid) impact pricing. Are electric vehicles priced higher due to their technology, or do they vary based on other factors? Transmission: Discuss if manual or automatic transmissions affect car pricing, especially in different markets or demographics.

    2. Price Predictions and Modeling

    Machine Learning Models: Explore which models (e.g., linear regression, decision trees, or ensemble methods) are best suited for predicting car prices using this dataset. Feature Importance: Discuss the importance of different features in predicting price. Which features contribute most to the price prediction accuracy, and how can feature selection improve the model?

    3. Market Trends and Insights

    Price Distribution: Analyze the distribution of car prices. Are there a lot of high-priced luxury cars, or is the dataset skewed towards more affordable options? Mileage vs. Price: Investigate the correlation between mileage and price. How does higher mileage affect pricing, and is there a threshold where price reduction becomes significant? Condition Impact: Discuss how the condition of the car (New, Used, Like New) influences the price. Are there significant price drops for used cars compared to new ones?

    4. Regional Analysis

    Location Impact: If geographic location is included, discuss how prices vary by region. Are there markets where certain brands/models are more popular and thus command higher prices? Economic Factors: Consider how broader economic factors (like inflation, fuel prices, and consumer preferences) might influence car prices in different regions.

    5. Future Developments and Trends

    Electric Vehicle Market: With the rise of electric vehicles, discuss how this dataset reflects the growing demand and pricing trends for EVs compared to traditional fuel cars. Impact of Technology: Consider how advancements in technology, safety features, and autonomous driving capabilities might influence future pricing.

    6. Limitations of the Dataset

    Data Completeness: Discuss any potential limitations in the dataset, such as missing values or biases in the data collection process. Generalization: Reflect on the ability to generalize the findings from this dataset to broader car markets or regions. Are there potential confounding factors that should be considered?

    7. Potential Applications

    Pricing Strategies: How can dealerships or private sellers utilize insights from this dataset to set competitive pricing? Consumer Decision-Making: Discuss how consumers can leverage this dataset to make informed purchasing decisions based on price predictions and feature evaluations.

    These discussion points can help guide deeper analysis and insights into the Car Price Prediction dataset, making it a valuable resource for both academic and practical applications. If you have specific areas you want to focus on, let me know!

  4. Monthly headline consumer price index 2019-2024, by region

    • abripper.com
    • statista.com
    Updated May 30, 2025
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    Jose Sanchez (2025). Monthly headline consumer price index 2019-2024, by region [Dataset]. https://abripper.com/lander/abripper.com/index.php?_=%2Ftopics%2F8378%2Finflation-worldwide%2F%2341%2FknbtSbwPrE1UM4SH%2BbuJY5IzmCy9B
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    Dataset updated
    May 30, 2025
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Jose Sanchez
    Description

    In August 2024, the global consumer price index, excluding the United States, stood at 187.7, compared to 160.1 for the U.S. The data for the world and emerging economies are distorted by hyperinflation in Venezuela and may not accurately reflect the inflation rate of other countries. However, Russia's war in Ukraine caused a surge in prices globally through 2022 and 2023. The headline consumer price index tracks the changes in the price level of a basket of goods and services purchased by households. Economic challenges in Argentina While CPI increases have been significant globally, certain economies have experienced more dramatic increases than others. Argentina is a notable case of these increases, as the CPI has increased more than 100 percent between 2020 and 2023. Currently, most of the Argentinian public considers inflation and low wages to be the biggest challenges facing the country. Consumer responses to price increases Globally, consumers are coping with price increases in many ways. In a May 2023 survey, 68 percent respondents from over 14 countries indicated they were more conscious about prices than previously. In another survey from earlier that year, over 40 percent of respondents indicated they were most concerned about inflation and had changed their consumption habits as a result.

  5. Wind Speed vs Spanish Power Prices

    • kaggle.com
    zip
    Updated Jun 6, 2024
    + more versions
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    Afroz (2024). Wind Speed vs Spanish Power Prices [Dataset]. https://www.kaggle.com/datasets/pythonafroz/wind-speed-vs-spanish-power-prices/code
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    zip(630902 bytes)Available download formats
    Dataset updated
    Jun 6, 2024
    Authors
    Afroz
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset examines how wind speeds affect electricity prices in the short-term Spanish market, OMIE. It includes daily minimum, average, and maximum power prices (euros per megawatt hour) alongside wind speed and gust data (kilometers per hour) from observation points. By studying the link between weather and energy markets, this data offers valuable insights for:

    Energy stakeholders: Improve forecasting and price management. Scientists, weather agencies, and environmental regulators: Understand the impact of changing wind patterns on short-term pricing. Educators: Provide a clear example of how external factors influence electricity costs.

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8127972%2Fbb6b49d4323030fce46b4977b1f6a943%2Fpark-wind-farm-3820819_1920-1.jpg?generation=1713751430959640&alt=media" alt="">Average, min and max daily OMIE power prices (Spanish market) with corresponding wind average speed and maximum speed for each day. Units: €/MWh (Power Price), km/h (wind speed).

    This dataset is like a treasure map, helping us understand how wind speed observations affect electricity prices in Spain's OMIE market. By following this map, stakeholders can develop winning strategies for:

    Forecasting electricity prices: Knowing how wind speed affects prices allows us to predict costs more accurately, avoiding surprises and making better financial decisions. Managing energy production: If we can predict wind power generation based on wind speed, we can adjust production from other sources (like gas or solar) to balance supply and demand, keeping the grid stable and efficient. Optimizing energy consumption: Understanding how wind affects prices can help consumers shift their usage to cheaper times, saving money on electricity bills. To unlock these benefits, let's dive into the data! We can start by visualizing the daily price (€/MWh) alongside wind speed readings (km/h) from different locations. This will give us a feel for the typical patterns and any seasonal trends.

    Here are some additional ideas for exploring the data:

    Compare maximum prices with peak wind speeds: This might reveal how much wind power can actually offset high demand periods. Look for relationships between price fluctuations and temperature: Hot or cold weather can increase demand for electricity, potentially impacting prices. Go beyond averages: Consider hourly or daily data to capture the dynamic nature of both wind power generation and electricity usage. Once we have a good grasp of the individual pieces, we can use more advanced techniques like linear regression. This can help us quantify the exact influence of factors like temperature on price fluctuations. With this knowledge, we can not only analyze current data but also make informed predictions about the future. This can involve considering market trends, weather forecasts, and other external factors that might affect supply and demand.

    By combining data analysis with a bit of detective work (correlation studies, causality analysis), this dataset can be a powerful tool for navigating the ever-changing world of energy!

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8127972%2Fa922850aa4c9502ca44db394c224e971%2FPrice.JPG?generation=1714463972160946&alt=media" alt="">https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8127972%2F13939d7f99a74402f281b36143cc72b5%2Fwind.JPG?generation=1714463979605196&alt=media" alt="">

  6. f

    S1 Data -

    • plos.figshare.com
    bin
    Updated Aug 11, 2023
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    Shiting Ding; Qintian Pan; Yanming Zhang; Jingru Zhang; Qiong Yang; Jingdong Luan (2023). S1 Data - [Dataset]. http://doi.org/10.1371/journal.pone.0290079.s001
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    binAvailable download formats
    Dataset updated
    Aug 11, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Shiting Ding; Qintian Pan; Yanming Zhang; Jingru Zhang; Qiong Yang; Jingdong Luan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The Chinese economy has undergone a long-term transition reform, but there is still a planned economy characteristic in the financial sector, which is financial repression. Due to the existence of financial repression, China’s actual interest rate level should be lower than the Consumer Price Index (CPI). However, based on official China’s interest rates and CPI, over half of the years China’s actual interest rate remained higher than CPI by our calculation from 1999 to 2022. This is inconsistent with the financial repression that exists in China, and the main reason is the calculation methods of China’s CPI. China’s CPI measurement system originated from the planned economy era, which did not fully consider the rise in housing purchase prices, so the current CPI measurement system can be more realistically presented by taking the rise in housing prices into consider. The core idea of this study is to mining relevant official statistical data and calculate the proportion of Chinese residents’ expenditure on purchasing houses to their total expenditure. By taking the proportion of house purchases as the weight of house price factor, and taking the proportion of other consumption as the weight of official CPI, the Generalized CPI (GCPI) is formulated. The GCPI is then compared with market interest rates to determine the actual interest rate situation in China over the past 20 years. This study has found that if GCPI is used as a measure, China’s real interest rates have been negative for most years since 1999. Chinese residents have suffered the negative effects of financial repression over the past 20 years, and their property income cannot keep up with the actual losses caused by inflation. Therefore, it is believed that China’s CPI calculation method should be adjusted to take into account the rise in housing prices, so China’s actual inflation level could be more accurately reflected. In view of the above, deepening interest rate marketization reform and expand channels for financial investment are the future development goals of China’s financial system.

  7. Comparison among related studies.

    • plos.figshare.com
    xls
    Updated May 2, 2025
    + more versions
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    Yan Wen; Yan Wei; Xiyuan Yu (2025). Comparison among related studies. [Dataset]. http://doi.org/10.1371/journal.pone.0322143.t001
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    xlsAvailable download formats
    Dataset updated
    May 2, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Yan Wen; Yan Wei; Xiyuan Yu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In the context of the reality that pharmaceutical manufacturers and retailers are simultaneously opening online sales channels, three different multi-channel pharmaceutical supply chain game models were constructed based on game theory, considering channel power, triple price competition among channels, and health insurance reimbursement policies, among others, comparing and analyzing the effects of each influencing factor on pharmaceutical pricing and profits in the pharmaceutical supply chain. It was found that inter-channel price competition did not always reduce the prices of a retailer’s pharmaceuticals; price competition between physical retail channels and online retail channels could lead to higher pharmaceutical prices, but direct sales channels effectively reduced pharmaceutical prices. Furthermore, as the channel power of pharmaceutical retailers strengthens, retail prices will increase, and retailers’ profits will rise. However, as the channel power of pharmaceutical manufacturers increases, their profits will grow, but retail prices will correspondingly decrease. Appropriate increasing in the health insurance reimbursement rate could improve pharmaceutical pricing in each channel and the total profits of the pharmaceutical supply chains.

  8. U.S. projected annual inflation rate 2010-2029

    • statista.com
    Updated Nov 19, 2025
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    Statista (2025). U.S. projected annual inflation rate 2010-2029 [Dataset]. https://www.statista.com/statistics/244983/projected-inflation-rate-in-the-united-states/
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    Dataset updated
    Nov 19, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The inflation rate in the United States is expected to decrease to 2.1 percent by 2029. 2022 saw a year of exceptionally high inflation, reaching eight percent for the year. The data represents U.S. city averages. The base period was 1982-84. In economics, the inflation rate is a measurement of inflation, the rate of increase of a price index (in this case: consumer price index). It is the percentage rate of change in prices level over time. The rate of decrease in the purchasing power of money is approximately equal. According to the forecast, prices will increase by 2.9 percent in 2024. The annual inflation rate for previous years can be found here and the consumer price index for all urban consumers here. The monthly inflation rate for the United States can also be accessed here. Inflation in the U.S.Inflation is a term used to describe a general rise in the price of goods and services in an economy over a given period of time. Inflation in the United States is calculated using the consumer price index (CPI). The consumer price index is a measure of change in the price level of a preselected market basket of consumer goods and services purchased by households. This forecast of U.S. inflation was prepared by the International Monetary Fund. They project that inflation will stay higher than average throughout 2023, followed by a decrease to around roughly two percent annual rise in the general level of prices until 2028. Considering the annual inflation rate in the United States in 2021, a two percent inflation rate is a very moderate projection. The 2022 spike in inflation in the United States and worldwide is due to a variety of factors that have put constraints on various aspects of the economy. These factors include COVID-19 pandemic spending and supply-chain constraints, disruptions due to the war in Ukraine, and pandemic related changes in the labor force. Although the moderate inflation of prices between two and three percent is considered normal in a modern economy, countries’ central banks try to prevent severe inflation and deflation to keep the growth of prices to a minimum. Severe inflation is considered dangerous to a country’s economy because it can rapidly diminish the population’s purchasing power and thus damage the GDP .

  9. Pet owners switching brands due to price increases 2024

    • statista.com
    Updated Nov 25, 2025
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    Statista (2025). Pet owners switching brands due to price increases 2024 [Dataset]. https://www.statista.com/statistics/1466046/pet-owners-switching-brands-due-to-inflation/
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    Dataset updated
    Nov 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United Kingdom, France, Worldwide, United States, Canada
    Description

    In 2024, pet owners made a move to switching brands. ** percent of United Kingdom consumers considered switching brands due to rising prices that year.

  10. U.S. projected Consumer Price Index 2010-2029

    • statista.com
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    Statista, U.S. projected Consumer Price Index 2010-2029 [Dataset]. https://www.statista.com/statistics/244993/projected-consumer-price-index-in-the-united-states/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, the U.S. Consumer Price Index was 309.42, and is projected to increase to 352.27 by 2029. The base period was 1982-84. The monthly CPI for all urban consumers in the U.S. can be accessed here. After a time of high inflation, the U.S. inflation rateis projected fall to two percent by 2027. United States Consumer Price Index ForecastIt is projected that the CPI will continue to rise year over year, reaching 325.6 in 2027. The Consumer Price Index of all urban consumers in previous years was lower, and has risen every year since 1992, except in 2009, when the CPI went from 215.30 in 2008 to 214.54 in 2009. The monthly unadjusted Consumer Price Index was 296.17 for the month of August in 2022. The U.S. CPI measures changes in the price of consumer goods and services purchased by households and is thought to reflect inflation in the U.S. as well as the health of the economy. The U.S. Bureau of Labor Statistics calculates the CPI and defines it as, "a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services." The BLS records the price of thousands of goods and services month by month. They consider goods and services within eight main categories: food and beverage, housing, apparel, transportation, medical care, recreation, education, and other goods and services. They aggregate the data collected in order to compare how much it would cost a consumer to buy the same market basket of goods and services within one month or one year compared with the previous month or year. Given that the CPI is used to calculate U.S. inflation, the CPI influences the annual adjustments of many financial institutions in the United States, both private and public. Wages, social security payments, and pensions are all affected by the CPI.

  11. Public opinion on the impact of containments on price increases in France...

    • statista.com
    Updated May 5, 2025
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    Statista (2025). Public opinion on the impact of containments on price increases in France 2021 [Dataset]. https://www.statista.com/statistics/1242421/public-opinion-impact-containments-price-increases-france/
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    Dataset updated
    May 5, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 31, 2021 - Apr 1, 2021
    Area covered
    France
    Description

    In April 2021, nearly nine out of ten French people believed that the successive containments put in place in the country to fight the COVID-19 pandemic had resulted in an acceleration of price increases. While the rise in prices may be considered a good thing from the point of view of economists, the French do not all share this enthusiasm: nearly a third of them find their purchasing power rather low, and nearly half say they have seen it decrease. Moreover, the French do not seem to be particularly optimistic about the country's economic future.

  12. Price change of music festival tickets in the U.S. 2024

    • statista.com
    Updated Oct 16, 2020
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    Statista (2020). Price change of music festival tickets in the U.S. 2024 [Dataset]. https://www.statista.com/statistics/1536500/music-festival-tickets-price-change-us/
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    Dataset updated
    Oct 16, 2020
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    United States
    Description

    A 2024 analysis looked at how the ticket prices of selected music festivals in the United States changed compared to 2014. Electric Forest, held in Michigan, reported the highest price increase among the events in the ranking, with its admission cost growing by ** percent. Overall, the price change of all the selected festivals was higher than the U.S. national inflation rate over the period considered.

  13. Brent crude oil price annually 1976-2025

    • statista.com
    Updated Oct 2, 2025
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    Statista (2025). Brent crude oil price annually 1976-2025 [Dataset]. https://www.statista.com/statistics/262860/uk-brent-crude-oil-price-changes-since-1976/
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    Dataset updated
    Oct 2, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    As of August 2025, the average annual price of Brent crude oil stood at 71.3 U.S. dollars per barrel. This is over nine U.S. dollars lower than the 2024 average. Brent is the world's leading price benchmark for Atlantic basin crude oils. Crude oil is one of the most closely observed commodity prices as it influences costs across all stages of the production process and consequently alters the price of consumer goods as well. What determines crude oil benchmarks? In the past decade, crude oil prices have been especially volatile. Their inherent inelasticity regarding short-term changes in demand and supply means that oil prices are erratic by nature. However, since the 2009 financial crisis, many commercial developments have greatly contributed to price volatility, such as economic growth by BRIC countries like China and India, and the advent of hydraulic fracturing and horizontal drilling in the U.S. The outbreak of the coronavirus pandemic and the Russia-Ukraine war are examples of geopolitical events dictating prices. Light crude oils - Brent and WTI Brent Crude is considered a classification of sweet light crude oil and acts as a benchmark price for oil around the world. It is considered a sweet light crude oil due to its low sulfur content and low density and may be easily refined into gasoline. This oil originates in the North Sea and comprises several different oil blends, including Brent Blend and Ekofisk crude. Often, this crude oil is refined in Northwest Europe. Another sweet light oil often referenced alongside UK Brent is West Texas Intermediate (WTI). WTI oil prices amounted to 76.55 U.S. dollars per barrel in 2024.

  14. Estimated y-o-y online price change of food products in North America...

    • statista.com
    Updated Sep 28, 2023
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    Statista (2023). Estimated y-o-y online price change of food products in North America 2019-2025 [Dataset]. https://www.statista.com/statistics/1416064/food-online-inflation-north-america/
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    Dataset updated
    Sep 28, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    North America
    Description

    Online inflation of food products followed the trend of physical stores and showed a significant peak in 2022. In North America, online food prices went up by **** percent that year, before decreasing to a **** year-over-year percentage change in 2023. By 2025, online prices of food products might increase by **** percent in the considered region.

  15. Price change on annual basis of 32 different building materials in the U.S....

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). Price change on annual basis of 32 different building materials in the U.S. 2014-2025 [Dataset]. https://www.statista.com/statistics/1046602/inflation-construction-materials-us/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Mar 2014 - Jun 2025
    Area covered
    United States
    Description

    Building materials made of steel, copper and other metals had some of the highest price growth rates in the U.S. in the first half of 2025 in comparison to the previous year. The growth rate of the cost of several construction materials was slightly lower than in late 2024. It is important to note, though, that the figures provided are Producer Price Indices, which cover production within the United States, but do not include imports or tariffs. This might matter for lumber, as Canada's wood production is normally large enough that the U.S. can import it from its neighboring country. Construction material prices in the United Kingdom Similarly to these trends in the U.S., at that time the price growth rate of construction materials in the UK were generally lower 2024 than in 2023. Nevertheless, the cost of some construction materials in the UK still rose that year, with several of those items reaching price growth rates of over **** percent. Considering that those materials make up a very big share of the costs incurred for a construction project, those developments may also have affected the average construction output price in the UK. Construction material shortages during the COVID-19 pandemic During the first years of the COVID-19 pandemic, there often were supply problems and material shortages, which created instability in the construction market. According to a survey among construction contractors, the construction materials most affected by shortages in the U.S. during most of 2021 were steel and lumber. This was also a problem on the other side of the Atlantic: The share of building construction companies experiencing shortages in Germany soared between March and June 2021, staying at high levels for over a year. Meanwhile, the shortage of material or equipment was one of the main factors limiting the building activity in France in June 2022.

  16. Change in gold price from 1900 to 2024

    • statista.com
    Updated Mar 15, 2025
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    Statista (2025). Change in gold price from 1900 to 2024 [Dataset]. https://www.statista.com/statistics/268027/change-in-gold-price-since-1990/
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    Dataset updated
    Mar 15, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    In 2024, one troy ounce of gold had an annual average price of ******** U.S. dollars. Gold pricing determinants Gold is a metal that is considered malleable, ductile, and is known for its bright lustrous yellow color. This transition metal is highly valued as a precious metal for its use in coins, jewelry, and in investments. Gold was also once used as a standard for monetary policies between different countries. The price of gold is determined by daily fixings where participants agree to buy or sell at a set price or to maintain the price through supply and demand control. For gold, companies like Barclays Capital, Scotia-Mocatta, Sociétè Générale, HSBC, and Deutsche Bank are members in gold fixing at the London Bullion Market Association.

  17. Quarterly house price to income ratio New Zealand 2019-2025

    • statista.com
    Updated Nov 29, 2025
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    Statista (2025). Quarterly house price to income ratio New Zealand 2019-2025 [Dataset]. https://www.statista.com/statistics/1026956/house-price-to-income-ratio-new-zealand/
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    Dataset updated
    Nov 29, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    New Zealand
    Description

    New Zealand has one of the highest house price-to-income ratios in the world; nonetheless, since the first quarter of 2022, the country's house price-to-income ratio started to trend downward. In the first quarter of 2025, the ratio was *****, a decrease from the same quarter of the previous year. This ratio was calculated by dividing nominal house prices by nominal disposable income per head, and is considered a measure of affordability. Homeownership dream New Zealand has been in what is widely considered a housing bubble. The disproportionately large increases in residential house prices have placed the dream of owning their own home out of reach for many in the country. In 2025, around ** percent of residential properties were sold for over a million New Zealand dollars. The majority of mortgage lending in the country went to owner-occupiers where the property was not their first home, with first-home buyers often struggling to secure a loan. In general, only New Zealand residents and citizens can buy homes in the country to live in, with new regulations tightening investment activity in that market. Rent affordability Due to New Zealand's high property prices, many individuals and families are stuck renting for prolonged periods. However, with rent prices increasing across the country and the share of monthly income spent on rent trending upwards in tandem with a highly competitive rental market, renting is becoming a less appealing prospect for many. The Auckland and Bay of Plenty regions had the highest weekly rent prices across the country as of December 2024, with the Southland region recording the lowest rent prices per week.

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Fang Liu; Chen Liang (2025). Robustness tests of rent-buy policies on house prices. [Dataset]. http://doi.org/10.1371/journal.pone.0325274.t004
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Robustness tests of rent-buy policies on house prices.

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Dataset updated
Jun 3, 2025
Dataset provided by
PLOShttp://plos.org/
Authors
Fang Liu; Chen Liang
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

Robustness tests of rent-buy policies on house prices.

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