AI-Based Residual Value Prediction Market - Strategic Insights and Forecasts (2026-2031)

The AI-Based Residual Value Prediction Market is forecast to grow at a CAGR of 12.2%, reaching USD 11,683.8 million in 2031 from USD 6,580.8 million in 2026.

AI-Based Residual Value Prediction Markets consist of software solutions that use artificial intelligence to estimate the future resale value of vehicles and other assets by analysing large datasets, market trends, historical records, and asset conditions. The growing role of the artificial intelligence industry has strengthened the ability of these systems to improve predictions related to depreciation, insurance pricing, and fleet management. Government transportation authorities and statistical agencies are increasingly exploring AI-driven valuation models to produce more reliable residual value estimates for passenger and commercial vehicles, supporting informed policymaking, taxation frameworks, and sustainable mobility initiatives. As data availability expands and regulatory requirements promote transparent valuation practices, AI-powered residual value prediction tools are becoming essential for financial forecasting, leasing strategies, and the evolving automotive secondary market.

AI-Based Residual Value Prediction Market Analysis

Growth Drivers

• National AI Strategy

The UK Office for Artificial Intelligence (OAI) and the European Commission are creating a framework for implementing and accelerating AI in the Transport sector, through developing and implementing legislative/policy initiatives to support the uptake and to facilitate the use of AI in the transport sector. They also support Trustworthy AI, Interoperable AI Technologies, and the ability to use them for the creation of Residual Value Estimates; therefore, this will promote the greater adoption of AI Technologies throughout the Automotive and Fleet Management sectors.

• Transport Authority's Data Sharing Efforts

Certain Public Transport Authorities are sharing standardised Data on Vehicle Registration, Vehicle Ownership and Vehicle Emissions to assist in the development of AI Solutions using large vast amounts of rich data to provide better Residual Value Estimates.

• Regulators Need to Work with AI Forecasting

There is growing reliance from many Government Regulators on AI Forecasting for developing their Economic Policies (including Taxation, Incentives and Compliance). By predicting residual value using AI, Government Regulators can simulate the long-term effects of Electric Vehicles and Alternative Fuels on Economic Trends.

• Digital Fleet Management

An increase in the use of AI-based Analytics for Depreciation Cycle Optimisation, Budget Forecasting, and Replacement Planning for Public Sector Fleets and Government Funded Transport Services, is creating a growing demand for Residual Value Prediction Tools (RVPTs) to support Financial Forecasting for these organisations.

Challenges and Opportunities

• The quality and access of data create a significant challenge with AI-based prediction of residual values. The majority of government vehicle data/ownership datasets are broken down by jurisdiction, creating difficulties when attempting to develop uniform models and representative of multiple regions. Furthermore, as the trustworthy AI standards continue to evolve, government agencies will require AI models to meet their criteria for fairness, accountability, and transparency when they use AI algorithms to make decisions. Nevertheless, there are growing opportunities through initiatives such as national AI strategies and open data initiatives that are increasing access to vehicle emissions and sales data. In addition, the transport sector's official frameworks (provided by the transport agency as well as all AI regulatory offices) will be able to help develop sound, explainable prediction tools so that regulators, fleet managers, and financial institutions can make better forecasting decisions with respect to vehicle depreciation.

Key Development

• June 2026: MakoLab introduced an advanced predictive Total Cost of Ownership framework using machine learning algorithms to forecast software-defined residual values based on over-the-air updates and battery health.

• January 2026: Black Book introduced its 2026 Market Outlook framework, utilizing advanced machine learning algorithms to precisely model complex value fragmentation, electric vehicle returns, and secondary-market depreciation patterns.

Market Segmentation

The market is segmented by component, deployment mode, application and geography.

By Component: AI Software Solutions

The AI software solutions used in the predictions of residual values has become foundational to the inherent value of the system in predicting future resale values of vehicles as all AI software solutions utilize historical data and machine-learning based predictive analytics to provide forecasts for resale values. New government transportation databases, emission information, and registration database records are also being included into these software solutions to improve the accuracy of predictive value models. AI frameworks from public agencies are more focused on explainability, data security, and the fairness of the systems they partner with, which influence the ways in which the software solutions are built. As more public agencies focus on digital governance and transparency of their operations, AI software solutions will continue to be key products for all regulatory bodies, fleet operators, leasing companies, and insurers that require standardized and verifiable/residual value predictions.

By Deployment Model: Cloud-Based

With cloud deployment being the dominant model in this segment, the use of cloud has enabled real-time processing of information by providing instant access to the cloud as well as also providing the necessary scalable computational power for businesses to grow. The implementation of cloud services by governments and the transport industry globally, has made integration of cloud-based solutions an important part of the strategies used to create AI predictive value systems. The vast capabilities of the cloud provide businesses with the ability to update predictive value models on a regular basis, quickly analyze data and share their research results with each other in a secure way. The policies related to public-sector cloud-based solutions support interoperability and reduce costs of deploying cloud-based predictive value solutions for all governmental, financial institutions, and OEMs (OEM = Original Equipment Manufacturers).

By Application: Fleet Management

In fleet management, AI-based residual value prediction helps public and private operators estimate depreciation, optimise replacement cycles, and improve long-term budgeting. Government transport agencies and municipal fleets use these forecasts to plan vehicle transitions, especially for electric and low-emission vehicles. AI tools support lifecycle cost analysis, regulatory reporting, and investment planning. As national transport digitisation programs expand, fleet managers increasingly rely on AI-driven valuation systems to ensure transparency, cost control, and alignment with sustainability targets.

Regional Analysis

North America Market Analysis

Transport and economic forecasting positively impact society through the enhancement of the AI and Data Analytics segments within the transportation and vehicle marketplaces; therefore, the Federal and State Governments in Canada, as well as the U.S. Department of Transportation (USDOT) encourage the use of AI and Data Analytics within the respective Governments' Vehicle marketplaces. In addition to the Federal and State Open Data Initiatives, there are numerous vehicles' registrational and emissions-level databases available for both vehicles and fleets, which allow the strengthening of AI prediction models for residual value calculations. Similar to Canada, the U.S. Government also promotes the use of established Transport and Statistical Agencies that provide publicly available, standardised vehicle and fleet-related datasets that can be used for residual value forecasting; thus, improving the overall reliability of Residual Value Prediction Methods for fleet managers, regulators, and Insurers.

South America Market Analysis

In South America, there is a concerted effort by both national transportation agencies and statistical agencies to establish digital vehicle registries and provide access to Open Mobile Data Platforms to facilitate Government digital initiatives. Brazil and Chile are among several South American nations that are creating a public data infrastructure based on their respective national open-data policies to provide AI Models with Access to Richer Datasets for the purpose of generating Residual Values; therefore, the quality of the calculations made using AI Models is significantly improved. In addition to their creation of sustainable Digital Data Collectives, many nations are now working to develop shared Data Standards to improve the scalability of AI Prediction Tools by facilitating the communication of data between various nations and organisations.

Europe Market Analysis

Europe is advancing the AI-based residual value prediction market through strong policy support for AI, open data, and vehicle emissions standards. The European Commission’s AI strategy and data-sharing regulations (including the Data Governance Act) promote access to vehicle telematics, emissions, and lifecycle data that enhance forecasting models. EU sustainability policies like Fit for 55 also elevate the importance of accurate residual value forecasts for electric and low-emission vehicles. National transport ministries and statistical offices in Germany, France, and the Netherlands publish standardised datasets that AI developers use for training and validation, enabling regulators, OEMs, and fleet managers to adopt compliant, explainable prediction tools.

Middle East and Africa Market Analysis

Investment in digital transformation and smart mobility initiatives is one of many ways governments in this Region are working toward economic diversification and sustainability. In the UAE and Saudi Arabia, this translates into initiatives focused on developing smart transportation data platforms and providing funding for research led by private companies using artificial intelligence (AI). Although the full picture of vehicle datasets has yet to fully materialise, national open data policies, as well as ongoing connected vehicle pilots provide new opportunities to apply AI to forecast residual vehicle values, among other things. In addition to this work, the African Union is exploring opportunities related to data development and sharing through cooperative efforts in the areas of data infrastructure and technical capabilities. Developing countries on this continent are creating infrastructure that will support the use of AI-based platforms to estimate future vehicle residual values in their respective countries based on market conditions and regulatory systems.

Asia Pacific Market Analysis

Asia-Pacific nations are actively building the data infrastructure and regulatory frameworks needed for AI-driven forecasting markets. Governments in Japan, South Korea, China, and Australia are investing in smart transport systems and vehicle data platforms that publish registration, emissions, and usage data. National AI strategies emphasise explainable and trustworthy AI, ensuring that prediction tools used for vehicle residual values align with regulatory expectations. Public sector digitisation initiatives standardise datasets across jurisdictions, promoting model interoperability. These policies help insurers, OEMs, and financial institutions adopt AI-based residual value prediction tools that support leasing, certification, and economic planning.

List of Companies

• Autovista Group

• ALG (J.D. Power)

• Cox Automotive

• Cap HPI

• Black Book

• Residual Value Intelligence (RVI)

• AlgoDriven

• Irasus Technologies

• Dataforce

• Berylls Strategy Advisors

The industry is in the process of consolidation as players target the provision of " AI-Based Residual Value Prediction Market" toolchains.

Autovista Group

Autovista Group is a premier source of vehicle valuation, residual value prediction, and market analysis services throughout Europe and beyond. The company's data platforms provide access to historical pricing, mileage, registration, and market trend data to enable OEMs, financial institutions, and leasing companies to estimate depreciation and assess risk. Autovista is progressively integrating enhanced analytical capabilities and predictive models similar to AI-based forecasting technologies, creating more accurate pricing estimates. In addition to producing vehicle valuations for regulatory reporting and fleet management purposes, the company collaborates with national transportation authorities from around the world to promote transparency in vehicle pricing.

ALG

Part of J.D. Power, ALG specialises in residual value forecasting and provides automotive analytics to both OEMs (original equipment manufacturers), insurers, and other financial institutions. A portion of ALG's estimates is used for vehicle financing, leasing, and risk assessment; these estimates also serve as the basis for many OEMs', insurers’, and financial institutions' official benchmarks and indices. The factors that influence ALG's estimates have generally been based on a combination of historical patterns, current market conditions, and indicators of demand; however, ALG recently implemented machine learning techniques to further enhance the predictive power of its residual value forecasts. As such, ALG has established itself as a trusted name in the industry regarding the forecasting of vehicle residual values.

Cox Automotive

Cox Automotive offers a comprehensive suite of automotive data, analytics, and valuation tools used widely by OEMs, dealers, and fleet operators. Its vAuto, Black Book, and related platforms provide dynamic pricing and market trend data. Cox Automotive leverages digital data from auctions, registrations, and market transactions to fuel predictive analytics, and increasingly AI-augmented forecasting models for residual values. These insights support asset management, remarketing strategies, and financial planning across vehicle lifecycles. The company also partners with transportation authorities and service providers to improve data quality and forecasting transparency.

AI-Based Residual Value Prediction Market Scope