1. Report Industry Investment Rating - No relevant content provided. 2. Core Views of the Report - China's real estate and construction industry is in a critical stage of transformation from scale expansion to quality and efficiency improvement, with AI and real - estate technology integration becoming a key driving force [12][17][24]. - The real - estate technology field is moving towards an AI - centered era, and data has become the core asset for the industry's digital transformation [31][110]. - The future of the real - estate industry will focus on asset activation, value reshaping, and ecological co - construction, with competition shifting from resource scale to algorithm density [24][76]. 3. Summary According to the Directory 3.1. KPMG China's "Future 50" Series of Industry Lists - KPMG China has launched the "Future 50" series of industry lists covering multiple sectors, aiming to guide enterprises in strategic choices and help the industry and capital select promising companies. The lists are characterized by professionalism, fairness, and platform - building [30]. 3.2. Overall Overview of the 2025 REBCAI New - Intelligence Practice Case Selection - Since 2021, KPMG China has been involved in real - estate technology selection and research. In 2025, with the development of AI, the industry has entered a new stage. The selection focuses on AI - driven new applications and practices [31]. - The selection process includes case collection and preliminary screening, material review and on - site visits, comprehensive evaluation by the review committee, and result announcement. The core evaluation dimensions are innovation, forward - looking, and growth [40][41][42]. 3.3. Real - Estate Technology Trends and Outlook 3.3.1. Current Situation - AI and large - model development are moving from technological breakthroughs to large - scale applications and ecological co - construction. The real - estate and construction industry has a clearer understanding of the need for AI, but faces challenges in innovation investment due to short - term return and long - term value trade - offs [47]. - Traditional real - estate technology, construction technology, and real - estate asset management technology all face difficulties in transformation, such as data isolation, lack of unified standards, and slow data governance [48][49][50]. 3.3.2. Solutions - Enterprises should focus on user - driven innovation, build a "credible data base", and create a "human - machine collaborative agile organization" [52][57]. - They should also use the method of "trial and error, small - step iteration" to explore effective paths [53]. 3.4. AI Base Capability Improvement from "Traditional Real Estate" to "Corporate Real Estate" and "Pan - Real Estate" 3.4.1. Industry Evolution - The real - estate industry is evolving from "traditional real estate" to "corporate real estate" and then to "pan - real estate", with a shift in value focus and investment logic [72]. 3.4.2. Trends - The AI base is evolving from "tool empowerment" to "ecological reconstruction", becoming a new "operating system" for the industry [73]. - Business value is shifting from "operational efficiency improvement" to "model innovation", with the emergence of new business models and evaluation dimensions [75]. - The core of competition is moving from "resource scale" to "algorithm density", with data quality and algorithm efficiency becoming key competitiveness indicators [76]. 3.5. Starting from First - Party Data 3.5.1. Industry Background - The real - estate industry is in a period of digital transformation, and first - party data has become the cornerstone of this transformation [110]. 3.5.2. Data Trends - Data collection is moving from isolated and fragmented to globally integrated, enabling comprehensive perception [111]. - Data analysis is shifting from descriptive statistics to predictive intelligence, enabling proactive decision - making [112]. - Data application is moving from general solutions to scenario - specific precision, addressing industry - specific problems [114]. - Data - driven goals are expanding from efficiency improvement to value creation, covering multiple dimensions such as asset value and user experience [115]. 3.6. How Today's CRM Moves towards the Future with Users 3.6.1. Challenges of Traditional CRM - Traditional CRM is difficult to meet the full - scenario and full - link service requirements of the real - estate industry, and has limitations in understanding customer needs and linking with other business processes [59][160][161]. 3.6.2. Future Trends - New - type CRM will integrate and analyze customer behavior data to build dynamic user profiles and better understand customers [161]. - It will enhance information collaboration across the entire chain, serving as a core hub for different business stages and providing more convenient services for customers [164]. - It will focus on emotional connection and long - term operation, enhancing customer trust through emotional computing [165]. 3.7. How Future "Good Houses" and "Good Communities" are Cultivated 3.7.1. Industry Trends - The housing construction industry is undergoing a transformation, and real - estate technology is showing three key trends: intelligent construction technology innovation, full - chain digital management, and intelligent operation and human - centered services [194][195][196]. 3.7.2. Leading Practices - In intelligent construction, technologies such as robots and laser scanning are used to improve construction efficiency and quality [201]. - In full - chain solutions, AI digital platforms are built to integrate various processes and promote industry transformation [201]. - In project management, data - driven systems are established to solve traditional management pain points [201]. 3.8. Asset Management Capability in the Era of Stock Assets Empowered by AI 3.8.1. Industry Background - The operation of stock assets has become more important, and the industry needs to balance risk management and asset value enhancement [252][253]. 3.8.2. Trends - Risk tracking management and early - warning granularity are the basis for risk bottom - line management, and data governance is crucial [254]. - KRI (Key Risk Indicator) sorting is a key to balancing the interests of all parties, and asset managers are using digital methods to improve risk control capabilities [255]. - The exploration of full - life - cycle asset value enhancement scenarios is necessary to balance short - term benefits and long - term capabilities, and AI can be used to drive asset management transformation [256]. 3.9. 2025 REBCAI New - Intelligence Practice Case List - The list includes the AI Breakthrough Award cases and AI Momentum Award cases, with details of the enterprises, case names, and corresponding pages [302][304]. 3.10. Annex - The annex includes information on the interview team, report - writing personnel, KPMG's real - estate technology industry insights, and contact information [309][310][317].