REdistribute modernizes real estate data accessibility by providing access to fresh, reliable listings from trusted MLS sources.

For Valuation and Investment, this comprehensive bulk data set empowers: - Accurate comparables for valuation models and AVMs - Improved portfolio risk assessment and stress testing - Data-backed investment strategies for acquisition and disposition

Key features: • Flexible Delivery: Available via a bulk data API or directly through Snowflake • Residential or Multi-Class: Choose a residential-only dataset or full MLS coverage across all property types, including residential, multi-family, land, commercial, rentals, farm and more • Comprehensive Field Access: Explore 800+ fields providing a complete view of both residential and non-residential property data • Fast & Fresh: Stay current with daily updates sourced directly from trusted MLSs partners

The sample data covers one listing in JSON format. For access to a broader set of sample listings (10,000+), reach out to the REdistribute sales contact.

ABOUT REDISTRIBUTE

REdistribute aims to modernize real estate data accessibility, fostering innovation and transparency through direct access to the most reliable MLS data. Our commitment to data integrity and direct MLS involvement guarantees the freshest, most accurate insights, empowering businesses across industries to drive innovation and make informed decisions.

Home Valuation Analytics Market Outlook

According to our latest research, the global home valuation analytics market size reached USD 4.12 billion in 2024, driven by the increasing adoption of data-driven valuation solutions across the real estate sector. The market is registering a robust CAGR of 12.6% and is forecasted to reach USD 12.12 billion by 2033. This growth is primarily attributed to the rising demand for accurate, real-time property valuation, digital transformation within the real estate industry, and heightened regulatory requirements for transparency and risk management.

One of the primary growth factors fueling the expansion of the home valuation analytics market is the rapid digitalization of the real estate sector. As real estate professionals, financial institutions, and government agencies increasingly rely on digital tools for property assessment, the adoption of advanced analytics platforms has surged. The integration of artificial intelligence and machine learning into automated valuation models (AVMs) has significantly improved the accuracy and speed of property appraisals. This digital shift is further supported by the proliferation of big data, which allows for more granular insights into property trends, market fluctuations, and risk factors. As a result, stakeholders across the value chain are increasingly turning to home valuation analytics to gain a competitive edge, reduce operational costs, and enhance decision-making processes.

Another significant driver is the growing regulatory emphasis on transparency and risk mitigation in property transactions. Governments and regulatory bodies worldwide are mandating more robust and standardized valuation processes to protect consumers and ensure fair lending practices. This has led to the widespread adoption of automated and data-driven valuation solutions, particularly among mortgage lenders and appraisers. The need for compliance with international accounting standards and anti-money laundering regulations has further accelerated the shift towards analytics-driven property valuation. As these regulatory frameworks continue to evolve, the demand for scalable, auditable, and highly accurate valuation tools is expected to remain strong, providing sustained momentum for market growth.

Additionally, the emergence of new business models and the increasing sophistication of end-users are contributing to the market's expansion. Real estate agencies, mortgage lenders, and institutional investors are leveraging home valuation analytics to streamline workflows, improve client engagement, and optimize portfolio management. The growing popularity of online real estate platforms and proptech startups is also driving innovation in valuation methodologies, including the use of geospatial analytics, predictive modeling, and visualization tools. These advancements are enabling stakeholders to respond more effectively to market volatility, identify investment opportunities, and deliver personalized services to clients. As competition intensifies, the ability to harness actionable insights from complex data sets will be a key differentiator for market participants.

The integration of Property Data Analytics for Mortgage has become increasingly vital in the home valuation analytics market. With the growing complexity of mortgage lending processes, the ability to harness comprehensive property data analytics is transforming how mortgage lenders assess collateral risk and make informed lending decisions. By leveraging advanced analytics, lenders can gain deeper insights into property values, market trends, and borrower profiles, enabling them to enhance risk management and improve loan origination efficiency. This approach not only supports compliance with stringent regulatory requirements but also fosters more transparent and fair lending practices, ultimately benefiting both lenders and borrowers in the long run.

Regionally, North America continues to dominate the home valuation analytics market, accounting for the largest revenue share in 2024. This leadership position is underpinned by the region's advanced real estate infrastructure, high penetration of digital technologies, and a well-established regulatory environment. However, significant growth is also being observed in the Asia Pacific and European markets, where rapid urbanization, in

The TIC Form SLT collects monthly data on the market value of long-term cross-border securities holdings by country, type of foreign holder (official or private), and type of security. We estimate transactions as well as valuation change that is, the monthly change in the market value of the securities arising from price or exchange rate changes. Since the valuation change estimates are based on the country of issuer, the price indexes used for U.S. securities are the same for all holder countries. Over the ten years that TIC SLT data have been collected, this method has yielded estimated transactions more consistent with positions reported in the TIC SLT, with the findings of the annual security-level survey data, and with our expectations based on other information, such as market commentary or patterns observed across time.This dataset includes position, estimated transaction, and estimated valuation change data for counterparty countries that (1) have published TIC SLT position data and (2) have significant reported positions. This set of countries accounts for 95 to 99 percent of all long-term cross-border securities holdings.

With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.

With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.

Release Date: 2023-03-23.Release Schedule:.The data in this file come from the 2021 Annual Survey of Manufactures data files released in March 2023. For more information about the Annual Survey of Manufactures data, see About: Annual Survey of Manufactures...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records: ..Total inventories, end of year ($1,000) .Relative standard error for estimate of total inventories, end of year (%) .Last-in, first-out (LIFO) inventory costing, end of year ($1,000) .Relative standard error for estimate of Last-in, first-out (LIFO) inventory costing, end of year (%) .LIFO reserve, end of year ($1,000) .Relative standard error for estimate of LIFO reserve, end of year (%) .Total inventories by valuation method (non-LIFO methods), end of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (non-LIFO methods), end of year (%) .Total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), end of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), end of year (%) .First-in, first-out (FIFO) inventory costing, end of year ($1,000) .Relative standard error for estimate of First-in, first-out (FIFO) inventory costing, end of year (%) .Average cost inventory valuation, end of year ($1,000) .Relative standard error for estimate of average cost inventory valuation, end of year (%) .Standard cost inventory valuation, end of year ($1,000) .Relative standard error for estimate of standard cost inventory valuation, end of year (%) .Other non-LIFO inventory, end of year ($1,000) .Relative standard error for estimate of other non-LIFO inventory, end of year (%) .Total inventories, beginning of year ($1,000) .Relative standard error for estimate of total inventories, beginning of year (%) .Last-in, first-out (LIFO) inventory costing, beginning of year ($1,000) .Relative standard error for estimate of last-in, first-out (LIFO) inventory costing, beginning of year (%) .LIFO reserve, beginning of year ($1,000) .Relative standard error for estimate of LIFO reserve, beginning of year (%) .Total inventories by valuation method (non-LIFO methods), beginning of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (non-LIFO methods), beginning of year (%) .Total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), beginning of year ($1,000) .Relative standard error for estimate of total inventories by valuation method (LIFO gross amount plus amount not subject to LIFO), beginning of year (%) .First-in, first-out (FIFO) inventory costing, beginning of year ($1,000) .Relative standard error for estimate of first-in, first-out (FIFO) inventory costing, beginning of year (%) .Average cost inventory valuation, beginning of year ($1,000) .Relative standard error for estimate of average cost inventory valuation, beginning of year (%) .Standard cost inventory valuation, beginning of year ($1,000) .Relative standard error for estimate of standard cost inventory valuation, beginning of year (%) .Other non-LIFO inventory, beginning of year ($1,000) .Relative standard error for estimate of other non-LIFO inventory, beginning of year (%) ..Geography Coverage:.The data are shown for employer establishments and firms for the U.S. level that vary by industry..For information about 2021 Annual Survey of Manufactures, see About: Annual Survey of Manufactures...Industry Coverage:.The data are shown at the 2- through 6-digit 2017 NAICS code levels for the U.S. For information about NAICS, see Annual Survey of Manufactures (ASM): Technical Documentation: ASM Product Class Codes and Descriptions...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/asm/data/2021/AM1831IVAL.zip..API Information:.Annual Survey of Manufactures API data are housed in the Census Bureau API. For more information, see ASM API..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only..To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, co...

Number and value of loans by category

The timing flexibility of investments in oil and gas assets can potentially add value. In this article, we examine the value of waiting in exploration projects and propose a real option–based valuation method using least-squares Monte Carlo simulation. We show that the dynamics of the oil and gas prices have a large impact on the value of the option to wait, especially for projects with long lead times and durations. The uncertainty in the forward price curve is modeled using a two-factor stochastic price process. The article also presents the valuation method in the form of MATLAB functions and routines that can be used as an efficient test and analysis platform using the industry-standard input formats.

Local authorities must publish details of the value of social housing stock that is held in their Housing Revenue Account. The following social housing stock data must be published: • valuation data to be listed at postal sector level34 (e.g. PO1 1**), without indicating individual dwelling values, and ensuring that data is not capable of being made disclosive of individual properties, in line with disclosure protocols set out in paragraphs 15 to 18 • valuation data for the dwellings using both Existing Use Value for Social Housing and market value (valued in accordance with guidance35) as at 1 April. This should be based on the authority’s most up to date valuation data at the time of the publication of the information The valuation data and information must be published in the following format: • for each postal sector level, the valuation data should be classified within set bands of value. Authorities must set their valuation bands within the general parameters set out in the table below, in light of the local characteristics of the housing market in their area, in order to ensure that valuation data published by all authorities is consistent and clear to understand: • From £50,000 or less to £99,999 : 6 Bands of £10,000 • From £100,000 to £299,999 : 10 Bands of £20,000 • From £300,000 to £499,999 : 4 Bands of £50,000 • From £500,000 to £999,999 : 5 Bands of £100,000 • From £1,000,000 to £2,999,999 or higher : 5 Bands of £500,000

Turkey Foreign Direct Investment Income: Outward: USD: Total: Private Real Estate Activities data was reported at 0.000 USD mn in 2023. This stayed constant from the previous number of 0.000 USD mn for 2022. Turkey Foreign Direct Investment Income: Outward: USD: Total: Private Real Estate Activities data is updated yearly, averaging 0.000 USD mn from Dec 2020 (Median) to 2023, with 4 observations. The data reached an all-time high of 0.000 USD mn in 2023 and a record low of 0.000 USD mn in 2023. Turkey Foreign Direct Investment Income: Outward: USD: Total: Private Real Estate Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Turkey – Table TR.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Treatment of debt FDI transactions and positions between fellow enterprises: asset/liability basis Resident Special Purpose Entities (SPEs) are not significant. Valuation method used for listed inward equity positions: Market value. Valuation method used for listed outward equity positions: Own funds at book value. Valuation method used for unlisted inward equity positions: Market capitalisation method. Valuation method used for unlisted outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Nominal value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. Direct investment relationships are identified according to the immediate counterpart method. Debt between fellow enterprises are completely covered except in outward FDI positions. Collective investment institutions are covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Total-Yield-That-Is-Dividend-Plus-Net-Buyback-Yield Time Series for Altus Group Limited. Altus Group Limited provides asset and funds intelligence solutions for commercial real estate (CRE) in Canada, the United States, the United Kingdom, France, Europe, the Middle East, Africa, Australia, and the Asia Pacific. It operates through Analytics; and Appraisals and Development Advisory segments. The Analytics segment portfolio includes software, data analytics, market data, valuation management solutions, and technology consulting services; ARGUS-branded and finance active-branded debt management software solutions; technology consulting services, such as strategic advisory for front-to-back-office strategies, processes, and technology; and software services related to education, training, and implementation. Its Appraisals and Development Advisory segments include services in the field of commercial property valuation appraisals, such as valuation appraisals of properties for transactional purposes, due diligence, and litigation support; and commercial development advisory services in the areas of construction feasibility studies, budgeting, cost and loan monitoring, and construction project management. The company also provides ARGUS Intelligence, a portfolio performance solution; ARGUS Enterprise, a commercial property valuation and cash flow forecasting software; ARGUS EstateMaster, a property development feasibility and management software; ARGUS Developer, a real estate development software; ARGUS ValueInsight, a valuation management platform; ARGUS Taliance, a real estate fund management software; Altus Data Studio, a data analytics platform; Fairways Debt, a debt management software; Forbury, a commercial real estate valuation, appraisal, and investment analysis software; and Reonomy, which provides access to property owner information, property records, and company data. In addition, the company offers valuation advisory services. Altus Group Limited was incorporated in 2005 and is headquartered in Toronto, Canada.

Norway Foreign Direct Investment Financial Flows: Inward: USD: Total: Mali data was reported at 0.000 USD mn in 2023. This stayed constant from the previous number of 0.000 USD mn for 2022. Norway Foreign Direct Investment Financial Flows: Inward: USD: Total: Mali data is updated yearly, averaging 0.000 USD mn from Dec 2021 (Median) to 2023, with 3 observations. The data reached an all-time high of 0.000 USD mn in 2023 and a record low of 0.000 USD mn in 2023. Norway Foreign Direct Investment Financial Flows: Inward: USD: Total: Mali data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.FDI: Foreign Direct Investment Financial Flows: USD: by Region and Country: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) is treated as portfolio investment. A survey is underway to evaluate the importance of reverse investment in equity. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series excluding resident SPEs only. Valuation method used for listed inward equity positions: Own funds at book value. Valuation method used for listed outward equity positions: Own funds at book value, Book values. Valuation method used for unlisted inward equity positions: Own funds at book value. Valuation method used for unlisted outward equity positions: Own funds at book value, Book value. Valuation method used for inward and outward debt positions: Book value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are partially covered. Collective investment institutions are partially covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

ABSTRACT This paper contributes by encouraging discussions about the public policy of setting tariffs for public services based on the value of the investment made by the providers of these services. The purpose of this study was, in an unprecedented way and by combining theories of equity valuation and finance, to identify the asset valuation method that can lead to a fair value and balance between an affordable price for the consumer and an adequate return on investment for the concessionaires. The value assigned to these assets affects the tariff in two ways: (i) via depreciation/amortization, which affects the cost of service; (ii) via the return on investment, which is the portion that corresponds to the investor’s profit. We analyzed the Brazilian electricity sector, in which the rates set by the Brazilian Electricity Regulatory Agency (ANEEL) currently use the new replacement value (NRV) approach. We carried out empirical tests using data available on the ANEEL website from the second cycle periodic tariff review and information obtained in financial statements from 1995 onwards. The analysis included the NVR and restated historical cost (RHC) methods, the latter being updated by the extended consumer price index (IPCA). After the descriptive and statistical analyses, we used the test of means to verify the differences between the variables in terms of NRV vs. RHC. The first conclusion was the absence of a significant difference between the NRV and RHC methods; that is, on average, the replacement price showed no significant difference to what would be the pure and simple restatement of assets. But this was found to hide something relevant, the fact that this average is derived from two main groups: that of the consumers who are paying more for energy services than they should, which constitutes a visible benefit to investors and loss for these consumers, and that of the consumers who are paying less than they should, which benefits them but harms investors.

In most tropical countries, coral reef ecosystems provide coastal populations with a number of goods and services. However, a variety of anthropogenic practices threatens reef health and therefore jeopardizes the benefits flowing from these goods and services. These threats range from local pollution, sedimentation, destructive fishing practices and coral mining, to global issues such as coral bleaching. By “getting some of the numbers on the table”, economic valuation can help shed light on the importance of the goods and services and show the costs of inaction in the face of threats. Creating markets for sustainable resource use can highlight the value of these goods and services to local populations Available online Call Number: [EL] Physical Description: 27 p.

Lithuania LT: Foreign Direct Investment Financial Flows: Inward: USD: Total: Private Real Estate Activities data was reported at 146.107 USD mn in 2023. This records a decrease from the previous number of 180.641 USD mn for 2022. Lithuania LT: Foreign Direct Investment Financial Flows: Inward: USD: Total: Private Real Estate Activities data is updated yearly, averaging 47.905 USD mn from Dec 2005 (Median) to 2023, with 19 observations. The data reached an all-time high of 180.641 USD mn in 2022 and a record low of -4.710 USD mn in 2009. Lithuania LT: Foreign Direct Investment Financial Flows: Inward: USD: Total: Private Real Estate Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.FDI: Foreign Direct Investment Financial Flows: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market and Nominal values. .; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Inward FDI positions according to the ultimate counterparty (the ultimate investing country) are also available and publishable. In the dataset 'FDI statistics by parner country and by industry - Summary', inward FDI positions are showed according to the UIC. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI transactions and positions. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the non resident direct investment enterprise. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

The VSAC is a repository and authoring tool for public value sets created by external programs. Value sets are lists of codes and corresponding terms, from NLM-hosted standard clinical vocabularies (such as SNOMED CT®, RxNorm, LOINC® and others), that define clinical concepts to support effective and interoperable health information exchange. The VSAC does not create value set content. The VSAC also provides downloadable access to all official versions of value sets specified by the Centers for Medicare & Medicaid Services (CMS) electronic Clinical Quality Measures (eCQMs). For information on CMS eCQMs, visit the eCQI Resource Center. The VSAC is provided by the National Library of Medicine (NLM), in collaboration with the Office of the National Coordinator for Health Information Technology (ONC) and CMS.

The energy transformation is changing the structure of the energy sector in Europe and Germany. In this paper the current structure of the energy sector is analysed both empirically as well as theoretically. Therefore, the authors have developed the business model framework for the energy transformation (BMFE). The framework is a synthesis of classical business model designs. An exhaustive survey of existing business models based on primary data collection and a literature review leads to 638 business models. Finally, 69 prototypical business models of the energy sector are the result of the classification of these business models. The information of the business models within the BMFE is applied to show the growing importance of value creation networks in energy industry. The work represents the current status of the business models in the energy sector.

There is no consensus on which statistical model estimates school value-added (VA) most accurately. To date, the two most common statistical models used for the calculation of VA scores are two classical methods: linear regression and multilevel models. These models have the advantage of being relatively transparent and thus understandable for most researchers and practitioners. However, these statistical models are bound to certain assumptions (e.g., linearity) that might limit their prediction accuracy. Machine learning methods, which have yielded spectacular results in numerous fields, may be a valuable alternative to these classical models. Although big data is not new in general, it is relatively new in the realm of social sciences and education. New types of data require new data analytical approaches. Such techniques have already evolved in fields with a long tradition in crunching big data (e.g., gene technology). The objective of the present paper is to competently apply these “imported” techniques to education data, more precisely VA scores, and assess when and how they can extend or replace the classical psychometrics toolbox. The different models include linear and non-linear methods and extend classical models with the most commonly used machine learning methods (i.e., random forest, neural networks, support vector machines, and boosting). We used representative data of 3,026 students in 153 schools who took part in the standardized achievement tests of the Luxembourg School Monitoring Program in grades 1 and 3. Multilevel models outperformed classical linear and polynomial regressions, as well as different machine learning models. However, it could be observed that across all schools, school VA scores from different model types correlated highly. Yet, the percentage of disagreements as compared to multilevel models was not trivial and real-life implications for individual schools may still be dramatic depending on the model type used. Implications of these results and possible ethical concerns regarding the use of machine learning methods for decision-making in education are discussed.

Norway Foreign Direct Investment Financial Flows: Inward: USD: Total: Gambling and Betting Activities: Sporting and Other Recreational Activities data was reported at -10.417 USD mn in 2023. This records a decrease from the previous number of 66.694 USD mn for 2021. Norway Foreign Direct Investment Financial Flows: Inward: USD: Total: Gambling and Betting Activities: Sporting and Other Recreational Activities data is updated yearly, averaging 28.139 USD mn from Dec 2021 (Median) to 2023, with 2 observations. The data reached an all-time high of 66.694 USD mn in 2021 and a record low of -10.417 USD mn in 2023. Norway Foreign Direct Investment Financial Flows: Inward: USD: Total: Gambling and Betting Activities: Sporting and Other Recreational Activities data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Norway – Table NO.OECD.FDI: Foreign Direct Investment Financial Flows: USD: by Industry: OECD Member: Annual. Reverse investment:Reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) is treated as portfolio investment. A survey is underway to evaluate the importance of reverse investment in equity. Netting of reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle). FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series excluding resident SPEs only. Valuation method used for listed inward equity positions: Own funds at book value. Valuation method used for listed outward equity positions: Own funds at book value, Book values. Valuation method used for unlisted inward equity positions: Own funds at book value. Valuation method used for unlisted outward equity positions: Own funds at book value, Book value. Valuation method used for inward and outward debt positions: Book value.; FDI statistics are available by geographic allocation, vis-à-vis single partner countries worldwide and geographical and economic zones aggregates. Partner country allocation can be subject to confidentiality restrictions. Geographic allocation of inward and outward FDI transactions and positions is according to the immediate counterparty. Intercompany debt between related financial intermediaries, including permanent debt, are excluded from FDI positions. Direct investment relationships are identified according to the criteria of the Framework for Direct Investment Relationships (FDIR) method. Debt between fellow enterprises are partially covered. Collective investment institutions are partially covered as direct investment enterprises. Non-profit institutions serving households are covered as direct investors. FDI statistics are available by industry sectors according to ISIC4 classification. Industry sector allocation can be subject to confidentiality restrictions. Inward FDI transactions and positions are allocated to the activity of the resident direct investment enterprise. Outward FDI transactions are allocated according to the activity of the resident direct investor. Outward FDI positions are allocated according to the activity of the non resident direct investment enterprise. Statistical unit: Enterprise.

Proposed attributes.