This statistic represents answers to a survey about the book purchasing process in France as of 2021. It reveals that 23 percent of book buyers exclusively chose the book they were willing to purchase before going to the point of sale.

Seventy percent of Polish respondents in 2019 claimed they bought books based solely on their taste. Over half could be swayed by a discount price, whereas only 9 percent took into consideration literary awards.

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Dataset

This dataset was created by Joy Chakraborty

Released under Database: Open Database, Contents: Database Contents

Contents

Official statistics on the number of sales of dwellings under the Right to Buy scheme. These statistics relate only to sales by local authorities under the Right to Buy scheme, excluding sales by registered providers under preserved Right to Buy.

During a September 2024 survey among adults in the United States, approximately 62 percent of respondents somewhat or strongly agreed that they tended to buy brands that reflected their personal values. Around one-third of the interviewees disagreed, while six percent did not know.

The data in this data set was provided by HM Treasury and details mortgage completions on properties supported by Help to Buy: mortgage guarantee completions, by local authority, England. The data set covers the period 8 October 2013 to 30 June 2014.

United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data was reported at 4.500 % in Apr 2025. This records a decrease from the previous number of 5.600 % for Mar 2025. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data is updated monthly, averaging 3.600 % from Feb 1967 (Median) to Apr 2025, with 637 observations. The data reached an all-time high of 7.700 % in Jul 2020 and a record low of 1.700 % in Dec 2009. United States CCI: Plans to Buy Within 6 Mos: sa: Home: Yes data remains active status in CEIC and is reported by The Conference Board. The data is categorized under Global Database’s United States – Table US.H054: Consumer Confidence Index: Buying Plans & Intended Vacations. [COVID-19-IMPACT]

The recommendations to buy hotel real estate of different types in the United States in 2025 were significantly higher than to sell. Nearly ** percent of industry experts surveyed recommended buying upscale hotels in 2024, while ** percent recommended selling and ** percent recommended holding.

Help to Buy - Wales is a £170 million shared equity loan scheme designed to support home ownership, stimulate building activity and provide a boost to the housing sector and wider economy. Under the scheme, loans are available to buyers wishing to purchase a new-build property worth up to £300,000. Help to Buy Wales support is available to all home buyers (not just first time buyers) who wish to purchase a new home, but may be constrained in doing so – for example, as a result of deposit requirements – but who could otherwise be expected to repay a mortgage. A shared equity loan of up to a maximum of 20% of the property purchase price is available. In addition, buyers are required to provide a 5% and will then need to secure a mortgage to cover the remaining balance. All builders (of all sizes) are able to register with the scheme and sell properties through the initiative.

Netherlands Consumer Confidence Indicator: Willingness to Buy data was reported at 4.000 % in Nov 2018. This records an increase from the previous number of 3.000 % for Oct 2018. Netherlands Consumer Confidence Indicator: Willingness to Buy data is updated monthly, averaging 10.000 % from Jan 2017 (Median) to Nov 2018, with 23 observations. The data reached an all-time high of 13.000 % in Jan 2018 and a record low of 3.000 % in Oct 2018. Netherlands Consumer Confidence Indicator: Willingness to Buy data remains active status in CEIC and is reported by Statistics Netherlands. The data is categorized under Global Database’s Netherlands – Table NL.H008: Consumer Confidence Indicator .

Where and how to buy tickets for TEC : https://www.letec.be/#/View/Where_can_you_buy_your_travel_document/296

As of August 2022, the main reason why Mexicans bought fashion online was that they could have their orders delivered directly to their home, with nearly ********** (** percent) of respondents stating this as their reason for purchase. Meanwhile, ** percent of respondents cited deals and discounts as motivation for buying fashion items online. An equivalent proportion said they appreciated the comfort and practicality of online shopping in this category.

United States CSI: Home Buying Conditions: Bad Time: Prices are High data was reported at 22.000 % in May 2018. This records an increase from the previous number of 18.000 % for Apr 2018. United States CSI: Home Buying Conditions: Bad Time: Prices are High data is updated monthly, averaging 10.000 % from Feb 1978 (Median) to May 2018, with 467 observations. The data reached an all-time high of 34.000 % in Aug 1978 and a record low of 2.000 % in Sep 2012. United States CSI: Home Buying Conditions: Bad Time: Prices are High data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to buy a house? Responses to the query 'Why do you say so?'

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.

Is LON:LIKE Stock Buy or Sell? (Stock Forecast)

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