Advertisement
| data science and real estate: Real Estate Analysis in the Information Age Kimberly Winson-Geideman, Andy Krause, Clifford A. Lipscomb, Nick Evangelopoulos, 2017-11-09 The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate. |
| data science and real estate: Getting Started with Data Science Murtaza Haider, 2015-12-14 Master Data Analytics Hands-On by Solving Fascinating Problems You’ll Actually Enjoy! Harvard Business Review recently called data science “The Sexiest Job of the 21st Century.” It’s not just sexy: For millions of managers, analysts, and students who need to solve real business problems, it’s indispensable. Unfortunately, there’s been nothing easy about learning data science–until now. Getting Started with Data Science takes its inspiration from worldwide best-sellers like Freakonomics and Malcolm Gladwell’s Outliers: It teaches through a powerful narrative packed with unforgettable stories. Murtaza Haider offers informative, jargon-free coverage of basic theory and technique, backed with plenty of vivid examples and hands-on practice opportunities. Everything’s software and platform agnostic, so you can learn data science whether you work with R, Stata, SPSS, or SAS. Best of all, Haider teaches a crucial skillset most data science books ignore: how to tell powerful stories using graphics and tables. Every chapter is built around real research challenges, so you’ll always know why you’re doing what you’re doing. You’ll master data science by answering fascinating questions, such as: • Are religious individuals more or less likely to have extramarital affairs? • Do attractive professors get better teaching evaluations? • Does the higher price of cigarettes deter smoking? • What determines housing prices more: lot size or the number of bedrooms? • How do teenagers and older people differ in the way they use social media? • Who is more likely to use online dating services? • Why do some purchase iPhones and others Blackberry devices? • Does the presence of children influence a family’s spending on alcohol? For each problem, you’ll walk through defining your question and the answers you’ll need; exploring how others have approached similar challenges; selecting your data and methods; generating your statistics; organizing your report; and telling your story. Throughout, the focus is squarely on what matters most: transforming data into insights that are clear, accurate, and can be acted upon. |
| data science and real estate: Real Estate Analysis in the Information Age Kimberly Winson-Geideman, Andy Krause, Clifford A. Lipscomb, Nick Evangelopoulos, 2017-11-09 The creation, accumulation, and use of copious amounts of data are driving rapid change across a wide variety of industries and academic disciplines. This ‘Big Data’ phenomenon is the result of recent developments in computational technology and improved data gathering techniques that have led to substantial innovation in the collection, storage, management, and analysis of data. Real Estate Analysis in the Information Age: Techniques for Big Data and Statistical Modeling focuses on the real estate discipline, guiding researchers and practitioners alike on the use of data-centric methods and analysis from applied and theoretical perspectives. In it, the authors detail the integration of Big Data into conventional real estate research and analysis. The book is process-oriented, not only describing Big Data and associated methods, but also showing the reader how to use these methods through case studies supported by supplemental online material. The running theme is the construction of efficient, transparent, and reproducible research through the systematic organization and application of data, both traditional and 'big'. The final chapters investigate legal issues, particularly related to those data that are publicly available, and conclude by speculating on the future of Big Data in real estate. |
| data science and real estate: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science and real estate: Real Estate Modelling and Forecasting Chris Brooks, Sotiris Tsolacos, 2010-04-15 As real estate forms a significant part of the asset portfolios of most investors and lenders, it is crucial that analysts and institutions employ sound techniques for modelling and forecasting the performance of real estate assets. Assuming no prior knowledge of econometrics, this book introduces and explains a broad range of quantitative techniques that are relevant for the analysis of real estate data. It includes numerous detailed examples, giving readers the confidence they need to estimate and interpret their own models. Throughout, the book emphasises how various statistical techniques may be used for forecasting and shows how forecasts can be evaluated. Written by a highly experienced teacher of econometrics and a senior real estate professional, both of whom are widely known for their research, Real Estate Modelling and Forecasting is the first book to provide a practical introduction to the econometric analysis of real estate for students and practitioners. |
| data science and real estate: The Beginner’s Guide to Data Science Jason Brownlee, Vinod Chugani, 2024-03-27 In today’s data-driven world, businesses and industries constantly seek insights to drive innovation, enhance decision-making, and stay ahead of the curve. Data science is not just a skill but a superpower that empowers you to extract meaningful patterns and knowledge from raw data, unlocking limitless opportunities. The theme of data science is to tell a story from data. There are many tools to help you build a narrative, but you should be focused on something other than the tool since the end is more important than the means. If you are a beginner, how should you embark on data science? You can learn many models, read many examples, and eventually gain the right mindset to handle a data science project. You can also learn the data science mindset first and then learn models that fit the picture when needed. The Beginner’s Guide to Data Science is your gateway to learn the data science mindset from examples. This ebook is written in the engaging and approachable style you are familiar with from Machine Learning Mastery. Discover exactly how to start and what the thought process is in dealing with a data science project. |
| data science and real estate: An Introduction to Real Estate Finance Edward Glickman, 2013-10-14 An Introduction to Real Estate Finance serves as the core of knowledge for a single-semester first course in real estate finance. Unlike other real estate finance textbooks, with their encyclopedic but often stale details, it combines a short traditional text with a living website. The book gives students and professors highly applied information, and its regularly updated online features makes it especially useful for this practitioner-oriented audience. It covers fundamental topics such as accounting and tax, mortgages, capital markets, REITs and more. It also addresses the 2008 financial crisis and its impact on the real estate profession. This text is a valuable companion for students of real estate finance as well as financial analysts, portfolio managers, investors and other professionals in the field. - Offers a concise, efficient, finance-centric alternative to traditional real estate finance texts - Website gives readers the tools to find current information about their own areas of specialization—a unique approach not found in other real estate finance textbooks - Gives students and professors the material to examine every subject in broad and highly detailed terms |
| data science and real estate: Data Science and Predictive Analytics Ivo D. Dinov, 2023-02-16 This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings. Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book’s fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials. |
| data science and real estate: R for Health Data Science Ewen Harrison, Riinu Pius, 2020-12-31 In this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms. |
| data science and real estate: Real Estate Market Valuation and Analysis Joshua Kahr, Michael C. Thomsett, 2006-02-10 A fresh, insightful look at how real estate professionals actually value properties and analyze markets. The focus on different product types as well as market segments are especially useful. --Barry Hersh, AICP, Associate Professor of Real Estate and Urban Planning, City University of New York This in-depth look at the core tools of real estate valuation will show you how to analyze the real estate market and assess the financial feasibility of a project. Many people go with their instincts or past experience when reviewing the financials and fail to utilize the useful data and analytical tools available in this field. Get the analytical data and tools you need to assess the financial feasibility of any project. Order your copy today. |
| data science and real estate: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science and real estate: Zillow Talk Spencer Rascoff, Stan Humphries, 2015-01-27 How do you spot an area poised for gentrification? Is spring or winter the best time to put your house on the market? Will a house on Swamp Road sell for less than one on Gingerbread Lane? The fact is that the rules of real estate have changed drastically over the past five years. To understand real estate in our fast-paced, technology-driven world, we need to toss out all of the outdated truisms and embrace today's brand new information. But how? Enter Zillow, the nation's #1 real estate website and mobile app. Thanks to its treasure trove of proprietary data and army of statisticians and data scientists, led by chief economist Stan Humphries, Zillow has been able to spot the trends and truths of today's housing market while acknowledging that a home is more than an economic asset. In Zillow Talk, Humphries and CEO Spencer Rascoff explain the science behind where and how we live now and reveal practical, data-driven insights about buying, selling, renting and financing real estate. Read this book to find out why: It's better to remodel your bathroom than your kitchen Putting the word cute in your listing could cost you thousands of dollars You shouldn't buy the worst house in the best neighborhood You should never list your house for $444,000 You shouldn't list your house for sale before March Madness or after the Masters Densely packed with entertaining anecdotes and invaluable how-to advice, Zillow Talk is poised to be the real estate almanac for the next generation. |
| data science and real estate: Data Science for Everyone Fatih AKAY, 2023-03-20 Data Science for Everyone: A Beginner's Guide to Big Data and Analytics is a comprehensive guide for anyone interested in exploring the field of data science. Written in a user-friendly style, this book is designed to be accessible to readers with no prior background in data science. The book covers the fundamentals of data science and analytics, including data collection, data analysis, and data visualization. It also provides an overview of the most commonly used tools and techniques for working with big data. The book begins with an introduction to data science and its applications, followed by an overview of the different types of data and the challenges of working with them. The subsequent chapters delve into the main topics of data science, such as data exploration, data cleaning, data modeling, and data visualization, providing step-by-step instructions and practical examples to help readers master each topic. Throughout the book, the authors emphasize the importance of data ethics and responsible data management. They also cover the basics of machine learning, artificial intelligence, and deep learning, and their applications in data science. By the end of this book, readers will have a solid understanding of the key concepts and techniques used in data science, and will be able to apply them to real-world problems. Whether you are a student, a professional, or simply someone interested in the field of data science, this book is an essential resource for learning about the power and potential of big data and analytics. |
| data science and real estate: Statistical Foundations of Data Science Jianqing Fan, Runze Li, Cun-Hui Zhang, Hui Zou, 2020-09-21 Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning. |
| data science and real estate: 97 Things Every Data Engineer Should Know Tobias Macey, 2021-06-11 Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail |
| data science and real estate: Creativity in Intelligent Technologies and Data Science Alla G. Kravets, Peter P. Groumpos, Maxim Shcherbakov, Marina Kultsova, 2019-08-29 This two-volume set constitutes the proceedings of the Third Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2019, held in Volgograd, Russia, in September 2019. The 67 full papers, 1 short paper and 3 keynote papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections in the two volumes. Part I: cyber-physical systems and Big Data-driven world. Part II: artificial intelligence and deep learning technologies for creative tasks; intelligent technologies in social engineering. |
| data science and real estate: Winning with Data Science Howard Steven Friedman, Akshay Swaminathan, 2024-01-30 Whether you are a newly minted MBA or a project manager at a Fortune 500 company, data science will play a major role in your career. Knowing how to communicate effectively with data scientists in order to obtain maximum value from their expertise is essential. This book is a compelling and comprehensive guide to data science, emphasizing its real-world business applications and focusing on how to collaborate productively with data science teams. Taking an engaging narrative approach, Winning with Data Science covers the fundamental concepts without getting bogged down in complex equations or programming languages. It provides clear explanations of key terms, tools, and techniques, illustrated through practical examples. The book follows the stories of Kamala and Steve, two professionals who need to collaborate with data science teams to achieve their business goals. Howard Steven Friedman and Akshay Swaminathan walk readers through each step of managing a data science project, from understanding the different roles on a data science team to identifying the right software. They equip readers with critical questions to ask data analysts, statisticians, data scientists, and other technical experts to avoid wasting time and money. Winning with Data Science is a must-read for anyone who works with data science teams or is interested in the practical side of the subject. |
| data science and real estate: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| data science and real estate: Fundamentals of Real Estate Development James A. Graaskamp, 1981 |
| data science and real estate: Economic Analysis of the Digital Economy Avi Goldfarb, Shane M. Greenstein, Catherine Tucker, 2015-05-08 There is a small and growing literature that explores the impact of digitization in a variety of contexts, but its economic consequences, surprisingly, remain poorly understood. This volume aims to set the agenda for research in the economics of digitization, with each chapter identifying a promising area of research. Economics of Digitization identifies urgent topics with research already underway that warrant further exploration from economists. In addition to the growing importance of digitization itself, digital technologies have some features that suggest that many well-studied economic models may not apply and, indeed, so many aspects of the digital economy throw normal economics in a loop. Economics of Digitization will be one of the first to focus on the economic implications of digitization and to bring together leading scholars in the economics of digitization to explore emerging research. |
| data science and real estate: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 A guide to the usefulness of data science covers such topics as algorithms, logistic regression, financial modeling, data visualization, and data engineering. |
| data science and real estate: Data Science Pallavi Vijay Chavan, Parikshit N Mahalle, Ramchandra Mangrulkar, Idongesit Williams, 2022-08-15 This book covers the topic of data science in a comprehensive manner and synthesizes both fundamental and advanced topics of a research area that has now reached its maturity. The book starts with the basic concepts of data science. It highlights the types of data and their use and importance, followed by a discussion on a wide range of applications of data science and widely used techniques in data science. Key Features • Provides an internationally respected collection of scientific research methods, technologies and applications in the area of data science. • Presents predictive outcomes by applying data science techniques to real-life applications. • Provides readers with the tools, techniques and cases required to excel with modern artificial intelligence methods. • Gives the reader a variety of intelligent applications that can be designed using data science and its allied fields. The book is aimed primarily at advanced undergraduates and graduates studying machine learning and data science. Researchers and professionals will also find this book useful. |
| data science and real estate: Data Science and Social Research II Paolo Mariani, Mariangela Zenga, 2020-11-25 The peer-reviewed contributions gathered in this book address methods, software and applications of statistics and data science in the social sciences. The data revolution in social science research has not only produced new business models, but has also provided policymakers with better decision-making support tools. In this volume, statisticians, computer scientists and experts on social research discuss the opportunities and challenges of the social data revolution in order to pave the way for addressing new research problems. The respective contributions focus on complex social systems and current methodological advances in extracting social knowledge from large data sets, as well as modern social research on human behavior and society using large data sets. Moreover, they analyze integrated systems designed to take advantage of new social data sources, and discuss quality-related issues. The papers were originally presented at the 2nd International Conference on Data Science and Social Research, held in Milan, Italy, on February 4-5, 2019. |
| data science and real estate: Machine Learning, Optimization, and Data Science Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca, 2020-01-03 This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications. |
| data science and real estate: Data Science Strategy For Dummies Ulrika Jägare, 2019-06-12 All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value. |
| data science and real estate: Machine Learning and Data Science Basics Cybellium Ltd, Your Essential Guide to Understanding Data-driven Technologies In a world inundated with data, the ability to harness its power through machine learning and data science is a vital skill. Machine Learning and Data Science Basics is your gateway to unraveling the complexities of these transformative technologies, offering a comprehensive introduction to the fundamental concepts that drive data-driven decision-making. About the Book: In an era where data has become the driving force behind innovation and growth, understanding the principles of machine learning and data science is no longer optional—it's essential. Machine Learning and Data Science Basics demystifies these disciplines, making them accessible to beginners while providing valuable insights for those looking to expand their knowledge. Key Features: Foundation Building: Start your journey by grasping the core concepts of data science, machine learning, and their intersection. Understand how data drives insights and empowers informed decisions. Data Exploration: Dive into data exploration techniques, learning how to clean, transform, and prepare data for analysis. Discover the crucial role data quality plays in obtaining accurate results. Machine Learning Essentials: Uncover the basics of machine learning algorithms, including supervised and unsupervised learning. Explore how algorithms learn patterns from data and make predictions or classifications. Feature Engineering: Learn the art of feature engineering—the process of selecting and transforming relevant data attributes to improve model performance and accuracy. Model Evaluation: Delve into model evaluation techniques to assess the performance of machine learning models. Understand metrics such as accuracy, precision, recall, and F1 score. Introduction to Data Science Tools: Familiarize yourself with essential data science tools and libraries, such as Python, NumPy, pandas, and scikit-learn. Gain hands-on experience with practical examples. Real-World Applications: Explore case studies showcasing how machine learning and data science are applied across industries. From recommendation systems to fraud detection, understand their impact on diverse domains. Why This Book Matters: In a landscape driven by data, proficiency in machine learning and data science is a competitive advantage. Machine Learning and Data Science Basics empowers individuals, students, and professionals to build a strong foundation in these fields, enabling them to contribute meaningfully to data-driven projects. Who Should Read This Book: Students and Beginners: Build a solid understanding of the principles underlying machine learning and data science. Professionals Seeking Knowledge: Enhance your expertise by familiarizing yourself with foundational concepts. Business Leaders: Grasp the potential of data-driven technologies to make informed strategic decisions. Embark on Your Data Journey: The era of data-driven decision-making is here to stay. Machine Learning and Data Science Basics equips you with the knowledge needed to embark on this exciting journey. Whether you're a novice eager to understand the basics or a professional looking to enhance your skill set, this book will guide you through the transformative landscape of machine learning and data science, setting the stage for continued learning and growth. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data science and real estate: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science and real estate: Introduction to Real Estate Development and Finance Richard M. Levy, 2019-11-05 This book provides readers with a basic understanding of the principles that underlie real estate development. A brief historical overview and an introduction to basic principles are followed by examples from practice. Case studies focus on how cities change and respond to the economic, technological, social, and political forces that shape urban development in North America. It is important to have a framework for understanding the risks and rewards in real estate investing. In measuring return, consideration must be given to both investment appreciation and the cash flow generated over the life of a project. In addition, metrics are presented that can be useful in assessing the financial feasibility of a real estate development proposal. This book also provides an overview of the forces of supply and demand that gauge the potential market for a new project. In determining the size of “residual demand”, estimates for population growth, family formation, and new development are important. All development projects fall under the auspices of one or several jurisdictions. Though every jurisdiction has different rules and procedures, basic knowledge of the planning process is critical to the success of all development projects regardless of location. Furthermore, all projects have a legal component. Basic issues of land ownership, property rights, property transfer, and land registration are reviewed, all of which need to be considered when a property is sold or purchased. This book also provides a primary on the design and construction process. In constructing a building, a team of experts is first required to design the architectural, structural, and heating, ventilation, and air conditioning (HVAC) systems for a building. An overview is provided of each building system: wood, concrete, and steel. Critical to a successful real estate development, project management principles for the processes of design, bidding, and construction are explored, with close attention given to budgeting, scheduling, and resource management. Essential reading for anyone involved in the development of our built environment, this is a must-read introduction for students and professionals in architecture, urban planning, engineering or real estate seeking an approachable and broad view of real estate development and finance. |
| data science and real estate: Handbook of Research on Advances in Data Analytics and Complex Communication Networks P. Venkata Krishna, 2021 This edited book discusses data analytics and complex communication networks and recommends new methodologies, system architectures, and other solutions to prevail over the current limitations faced by the field-- |
| data science and real estate: Advanced Methods in Statistics, Data Science and Related Applications Matilde Bini, |
| data science and real estate: Simple Data Science (R) Narayana Nemani, 2022-10-26 The book Simple Data Science (R) covers R language, graphing, and machine learning. It is beginner-friendly, precise, and complete. The book explains data science concepts in a simple language, followed by implementing them in R language. It is one of the fastest ways to learn data science. The hands-on projects provide a detailed step-by-step guide for implementing machine learning solutions. Topics covers - * Data science introduction * Basic statistics * Data visualization * Machine Learning (linear regression, logistic regression, random forests, and other machine learning algorithms) * Hands-on projects |
| data science and real estate: Data Scientist Diploma (master's level) - City of London College of Economics - 6 months - 100% online / self-paced City of London College of Economics, Overview This diploma course covers all aspects you need to know to become a successful Data Scientist. Content - Getting Started with Data Science - Data Analytic Thinking - Business Problems and Data Science Solutions - Introduction to Predictive Modeling: From Correlation to Supervised Segmentation - Fitting a Model to Data - Overfitting and Its Avoidance - Similarity, Neighbors, and Clusters Decision Analytic Thinking I: What Is a Good Model? - Visualizing Model Performance - Evidence and Probabilities - Representing and Mining Text - Decision Analytic Thinking II: Toward Analytical Engineering - Other Data Science Tasks and Techniques - Data Science and Business Strategy - Machine Learning: Learning from Data with Your Machine. - And much more Duration 6 months Assessment The assessment will take place on the basis of one assignment at the end of the course. Tell us when you feel ready to take the exam and we’ll send you the assignment questions. Study material The study material will be provided in separate files by email / download link. |
| data science and real estate: Machine Learning Pocket Reference Matt Harrison, 2019-08-27 With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines |
| data science and real estate: PropTech and Real Estate Innovations Olayiwola Oladiran, Louisa Dickins, 2024-08-22 This textbook serves as a guide to real estate students and educators on the various property innovations and digital technologies that continue to shape the property industry. The advancement of PropTech in the last few decades has led to significant changes in real estate systems, operations, and practice, and this new textbook provides insight on the past, present, and future of PropTech innovations that have spread across the value chain of real estate through planning, development, management, finance, investment, operations, and transactions. The textbook approaches this subject from the real estate components, asset classes, and submarkets and links them to the associated innovations and digital technologies. It concludes by reviewing the role of education, innovation, skill development, and professionalism as major elements of the future of real estate operations and practice. This book’s unique contributions are in putting the “property” element at the forefront and then illustrating how technology can enhance the various areas of real estate; the focus on how the different innovations and technologies can enhance the economic, environmental, social, and physical efficiency of real estate; and its coverage of some non‐technological innovations like flexible working and more practical areas of real estate innovation such as skills, employability, creativity, and education. It contains 21 case studies and 29 case summaries, which can serve as practice exercises for students. This book will be useful to students in helping them build a knowledge base and understanding of innovation and digital technologies in the industry. Real estate educators can use the textbook as a guide to incorporate real estate innovation and digital technologies into their current teaching and also to develop their real estate curricula through PropTech‐related modules and courses where necessary. It will also be valuable to real estate researchers in search of the theoretical and conceptual linkages, as well as industry practitioners who seek insight into the current and future potential of digital technologies and their applications to real estate operations and practice. |
| data science and real estate: Emerging Trends, Techniques, and Applications in Geospatial Data Science Gaur, Loveleen, Garg, P.K., 2023-04-24 With the emergence of smart technology and automated systems in today’s world, big data is being incorporated into many applications. Trends in data can be detected and objects can be tracked based on the real-time data that is utilized in everyday life. These connected sensor devices and objects will provide a large amount of data that is to be analyzed quickly, as it can accelerate the transformation of smart technology. The accuracy of prediction of artificial intelligence (AI) systems is drastically increasing by using machine learning and other probability and statistical approaches. Big data and geospatial data help to solve complex issues and play a vital role in future applications. Emerging Trends, Techniques, and Applications in Geospatial Data Science provides an overview of the basic concepts of data science, related tools and technologies, and algorithms for managing the relevant challenges in real-time application domains. The book covers a detailed description for readers with practical ideas using AI, the internet of things (IoT), and machine learning to deal with the analysis, modeling, and predictions from big data. Covering topics such as field spectra, high-resolution sensing imagery, and spatiotemporal data engineering, this premier reference source is an excellent resource for data scientists, computer and IT professionals, managers, mathematicians and statisticians, health professionals, technology developers, students and educators of higher education, librarians, researchers, and academicians. |
| data science and real estate: Data Science Ivo D. Dinov, Milen Velchev Velev, 2021-12-06 The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences. Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces. By lifting the concept of time from a positive real number to a 2D complex time (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D spacetime to a higher dimension where longitudinal data (e.g., time-series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many open mathematical problems that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated. Spacekime analytics provide mechanisms to effectively handle, process, and interpret large, heterogeneous, and continuously-tracked digital information from multiple sources. The authors propose computational methods, probability model-based techniques, and analytical strategies to estimate, approximate, or simulate the complex time phases (kime directions). This allows transforming time-varying data, such as time-series observations, into higher-dimensional manifolds representing complex-valued and kime-indexed surfaces (kime-surfaces). The book includes many illustrations of model-based and model-free spacekime analytic techniques applied to economic forecasting, identification of functional brain activation, and high-dimensional cohort phenotyping. Specific case-study examples include unsupervised clustering using the Michigan Consumer Sentiment Index (MCSI), model-based inference using functional magnetic resonance imaging (fMRI) data, and model-free inference using the UK Biobank data archive. The material includes mathematical, inferential, computational, and philosophical topics such as Heisenberg uncertainty principle and alternative approaches to large sample theory, where a few spacetime observations can be amplified by a series of derived, estimated, or simulated kime-phases. The authors extend Newton-Leibniz calculus of integration and differentiation to the spacekime manifold and discuss possible solutions to some of the problems of time. The coverage also includes 5D spacekime formulations of classical 4D spacetime mathematical equations describing natural laws of physics, as well as, statistical articulation of spacekime analytics in a Bayesian inference framework. The steady increase of the volume and complexity of observed and recorded digital information drives the urgent need to develop novel data analytical strategies. Spacekime analytics represents one new data-analytic approach, which provides a mechanism to understand compound phenomena that are observed as multiplex longitudinal processes and computationally tracked by proxy measures. This book may be of interest to academic scholars, graduate students, postdoctoral fellows, artificial intelligence and machine learning engineers, biostatisticians, econometricians, and data analysts. Some of the material may also resonate with philosophers, futurists, astrophysicists, space industry technicians, biomedical researchers, health practitioners, and the general public. |
| data science and real estate: Advanced Studies in Classification and Data Science Tadashi Imaizumi, Akinori Okada, Sadaaki Miyamoto, Fumitake Sakaori, Yoshiro Yamamoto, Maurizio Vichi, 2020-09-25 This edited volume focuses on the latest developments in classification and data science and covers a wide range of topics in the context of data analysis and related areas, e.g. the analysis of complex data, analysis of qualitative data, methods for high-dimensional data, dimensionality reduction, data visualization, multivariate statistical methods, and various applications to real data in the social sciences, medical sciences, and other disciplines. In addition to sharing theoretical and methodological findings, the book shows how to apply the proposed methods to a variety of problems — e.g. in consumer behavior, decision-making, marketing data and social network structures. Both methodological aspects and applications to a wide range of areas such as economics, behavioral science, marketing science, management science and the social sciences are covered. The book is chiefly intended for researchers and practitioners who are interested in the latest developments and practical applications in these fields, as well as applied statisticians and data analysts. Its combination of methodological advances with a wide range of real-world applications gathered from several fields makes it of unique value in helping readers solve their research problems. |
| data science and real estate: The Data Science Design Manual Steven S. Skiena, 2017-07-01 This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com) |
| data science and real estate: Big Data Science in Finance Irene Aldridge, Marco Avellaneda, 2021-01-27 Explains the mathematics, theory, and methods of Big Data as applied to finance and investing Data science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data. Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book: Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samples Explains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) Covers vital topics in the field in a clear, straightforward manner Compares, contrasts, and discusses Big Data and Small Data Includes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slides Big Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners. |
| data science and real estate: The Data Science Workshop Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare, 2020-01-29 Cut through the noise and get real results with a step-by-step approach to data science Key Features Ideal for the data science beginner who is getting started for the first time A data science tutorial with step-by-step exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book DescriptionYou already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.What you will learn Find out the key differences between supervised and unsupervised learning Manipulate and analyze data using scikit-learn and pandas libraries Learn about different algorithms such as regression, classification, and clustering Discover advanced techniques to improve model ensembling and accuracy Speed up the process of creating new features with automated feature tool Simplify machine learning using open source Python packages Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life. |
WHY IS DATA IMPORTANT FOR REAL ESTATE? - Real Estate …
In this brief, we outline what data can be for real estate development in a broader sense. We ask the following: what real estate data is out there? Where is data science headed in helping us …
Better data, better decisions - KPMG
There were 188 respondents that included various real estate leaders and technology professionals. The data strategy study focused on gaining an understanding of data …
The Role of Data Analytics in Commercial Real Estate Siting
To gain a sense of how CRE firms are using advanced data analytics, the NAIOP Research Foundation commissioned this report to examine applications in site selection, design and …
Getting ahead of the market: How big data is transforming …
big data is transforming real estate Many real estate firms have long made decisions based on a combination of intuition and traditional, retrospective data. Today, a host of new variables …
and Data Science Real Estate through Big Data
Real Estate through Big Data and Data Science Jasjeet Thind Sr. Director, Data Science & Engineering
Data science drives performance in real estate private equity
Tom Parker, Institutional Real Estate, Inc.’s executive vice president and publisher, recently spoke with John Carrafiell, co-founder and co-CEO, and Chris Liedtke, chief data scientist and …
Data Science Strategies for Real Estate Development
a. What is a data science framework for real estate development? b. How can the current landscape of data science support affordable housing multifamily development? With these …
Data and Algorithms: Reviewing the Role of Machine Learning …
Abstract - This study investigates the impact of Machine Learning (ML) on the real estate sector, analyzing its role in enhancing market prediction accuracy, valuation precision, operational …
MASSACHUSETTS INSTITUTE OF TECHNOLOGY DATA …
Learn from the expert insights of leading real estate data scientists, economists and machine learning practitioners as you explore the different aspects of property performance. These …
Real Estate Data Marketplace - Springer
Real estate financing, construction, and management are being revolutionised by the data science and infrastructure tech-nologies of Artificial Intelligence (AI), Internet of Things (IoT), Big Data, …
Data Analytics for Real Estate - Actionable Strategies
Data analytics was viewed as a strategic program with enduring value. The long term model included data beyond the operational realm. This included a number of domains outside of the …
BIG DATA ANALYTICS FOR REAL ESTATE ASSET …
The present thesis has the aim of exposing and debating applications of big data analytics on the real estate sector, more specifically through the point of view of asset management, as a way …
House Price Prediction Using Exploratory Data Analysis and …
Jan 1, 2022 · To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net …
SMU Data Science Review
Due to high barriers to conduct housing market research, many home sellers opt to go to the market with asymmetric information or invest large sums of money into hiring a professional.
PropTech: Turning real estate into a data-driven market?
In this paper, we investigate whether PropTech is actually turning real esate into a data-driven market. The quantitative ndings from an analy-sis of more than 7,000 PropTech rms reveal …
Core skills for development,de- sign and planning are shifting …
Data Science and Machine Learning for Real Estate Lecture 1 19 data attributes Data classification is a fundamen-tal skill. Learning how to classi-fy data or to work with categorical …
DATA MINING ANALYSIS AND PREDICTIONS OF REAL ESTATE …
With the availability of publicly recorded real estate sales transactions from the King County Assessor’s Office, we built the models to predict home prices using the Decision Trees and …
Leveraging data science in real estate
dependencies, make it challenging to reap the full benefits of data science. This paper examines both the added value and challenges of data science in real estate research, the conditions for …
Embracing the world of data for Real Estate: Fit for the future
To achieve this, Real Estate companies will need to invest in modernising their IT and data infrastructure, from new data management tools and information portals to artificial …
WHY IS DATA IMPORTANT FOR REAL ESTATE? - Real …
In this brief, we outline what data can be for real estate development in a broader sense. We ask the following: what real estate data is out there? Where is data science headed in helping us to …
SMU Data Science Review
In this article, machine learning techniques are used to predict potential long-tail considerations for investors, builders, and/or consumers interested in future real-estate market. Over the course of …
Better data, better decisions - KPMG
There were 188 respondents that included various real estate leaders and technology professionals. The data strategy study focused on gaining an understanding of data management trends in the …
The Role of Data Analytics in Commercial Real Estate Siting
To gain a sense of how CRE firms are using advanced data analytics, the NAIOP Research Foundation commissioned this report to examine applications in site selection, design and …
Getting ahead of the market: How big data is transforming real …
big data is transforming real estate Many real estate firms have long made decisions based on a combination of intuition and traditional, retrospective data. Today, a host of new variables make …
and Data Science Real Estate through Big Data
Real Estate through Big Data and Data Science Jasjeet Thind Sr. Director, Data Science & Engineering
Data science drives performance in real estate private equity
Tom Parker, Institutional Real Estate, Inc.’s executive vice president and publisher, recently spoke with John Carrafiell, co-founder and co-CEO, and Chris Liedtke, chief data scientist and …
Data Science Strategies for Real Estate Development
a. What is a data science framework for real estate development? b. How can the current landscape of data science support affordable housing multifamily development? With these two questions …
Data and Algorithms: Reviewing the Role of Machine Learning …
Abstract - This study investigates the impact of Machine Learning (ML) on the real estate sector, analyzing its role in enhancing market prediction accuracy, valuation precision, operational …
MASSACHUSETTS INSTITUTE OF TECHNOLOGY DATA …
Learn from the expert insights of leading real estate data scientists, economists and machine learning practitioners as you explore the different aspects of property performance. These …
Real Estate Data Marketplace - Springer
Real estate financing, construction, and management are being revolutionised by the data science and infrastructure tech-nologies of Artificial Intelligence (AI), Internet of Things (IoT), Big Data, …
Data Analytics for Real Estate - Actionable Strategies
Data analytics was viewed as a strategic program with enduring value. The long term model included data beyond the operational realm. This included a number of domains outside of the …
BIG DATA ANALYTICS FOR REAL ESTATE ASSET …
The present thesis has the aim of exposing and debating applications of big data analytics on the real estate sector, more specifically through the point of view of asset management, as a way to …
House Price Prediction Using Exploratory Data Analysis and …
Jan 1, 2022 · To forecast house prices effectively, this study investigates the exploratory data analysis based on linear regression, ridge regression, Lasso regression, and Elastic Net …
SMU Data Science Review
Due to high barriers to conduct housing market research, many home sellers opt to go to the market with asymmetric information or invest large sums of money into hiring a professional.
PropTech: Turning real estate into a data-driven market?
In this paper, we investigate whether PropTech is actually turning real esate into a data-driven market. The quantitative ndings from an analy-sis of more than 7,000 PropTech rms reveal that …
Core skills for development,de- sign and planning are shifting …
Data Science and Machine Learning for Real Estate Lecture 1 19 data attributes Data classification is a fundamen-tal skill. Learning how to classi-fy data or to work with categorical and numerical …
DATA MINING ANALYSIS AND PREDICTIONS OF REAL …
With the availability of publicly recorded real estate sales transactions from the King County Assessor’s Office, we built the models to predict home prices using the Decision Trees and …
Leveraging data science in real estate
dependencies, make it challenging to reap the full benefits of data science. This paper examines both the added value and challenges of data science in real estate research, the conditions for …
Embracing the world of data for Real Estate: Fit for the …
To achieve this, Real Estate companies will need to invest in modernising their IT and data infrastructure, from new data management tools and information portals to artificial intelligence, …
