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Data Science in Real Estate: Roles, Benefits and Applications

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11th Sep, 2023
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    Data Science in Real Estate: Roles, Benefits and Applications

    Artificial intelligence and data science are focused on disrupting the real estate industry. It has traditionally been slow for real estate to adopt developing technologies, but that has changed. Data science in real estate is becoming increasingly crucial to real estate companies as technology advances. From lead generation to efficiency and reporting, data disciplines are unlocking new levels of potential in real estate industries. With the help of Artificial Intelligence (AI), data science can benefit the real estate sector in many ways. You can go through the Data Science Bootcamp curriculum to learn how data science is used in real estate.  

    What is Data Science?

    Data science involves analyzing and interpreting data to make sense of it. In the present day, we produce an overwhelming amount of data every day, and the number is growing exponentially. Analyzing data allows us to discover patterns and trends that help us make better decisions and enhance products and experiences. Statisticians, mathematicians, computer scientists, data miners, and machine learners use data science for this analysis. 

    Keep reading this article to learn more about data science in real estate data analytic courses and why data science is important in real estate.

    Advantages of Data Science in Real Estate

    The real estate industry puts its best foot forward by utilizing multiple data science functions. Below are some of the advantages of data analytics for real estate. 

    1. Location

    Data science provides insight into the current market patterns and trends in commercial real estate, a more complex network than residential real estate. Considering rental income, traffic, investment return, occupancy, and ratings determines the viability of distinct locations. 

    2. Analyzing Customer Trends

    An in-depth understanding of market trends is crucial to investing in commercial or residential properties. Investors must understand the needs of their customers, and this is where the value of data science in real estate comes into play. Investors can make better investments if they know about factors such as primary rivals, demand for shopping malls, parents' need for new schools, nearby housing necessities, and affordability. 

    3. Taking a Close Look at Competitors

    It is also essential to keep an eye on the competitors’ position when monitoring a sector and its activities closely. Analyzing data allows you to examine the performance of your competitors. Taking a competitor's and customer's perspective into account helps clear up any confusion. Creating powerful strategies can be achieved through a careful analysis of the same. 

    4. Performance of Investments

    Unlike two peas in a pod, no two properties can be identical. There are unique variables to each property, which is also true for properties within a building. As time passes, these values also change. As a result, it becomes difficult to monitor property performance within the submarkets. Through data science, you can watch the changing price of assets over time. 

    5. Buying a Home is Simpler

    Besides benefiting the broker class and investors, data science for real estate facilitates home search, purchase, and rental inquiries. Several factors, including connectivity, neighborhood quality, and transportation options, can affect the price of a real estate property. Using data analysis, the buyer can decide where to buy based on the location. 

    6. Strategic Re-engineering of Marketing

    The purpose of data science is to collect and analyze information from multiple sources. This strategy allows agencies to understand consumer preferences and behavior better to market their facilities more creatively and compellingly. 3D renderings, visualizations, Google or Facebook ads, listings, and visual staging help attract the target audience. 

    Property listings are scrolled through online today by today's audience. It is, therefore, essential to ensure that the aesthetics of the posts relating to real estate are maintained while also paying attention to social media marketing. 

    Using data science tools, owners and users of real estate can benefit from vast volumes of digital and historical data: 

    • A faster workflow is achieved by implementing data science and AI. Identifying more appropriate and profitable properties helps both clients and real estate brokers. 
    • There is less risk of selecting the wrong project. 
    • Using sentiment and predictive analysis, filtered and customized projects give customers a competitive advantage. 
    • By predicting future price fluctuations accurately, Data Science helps prevent substantial risks in real estate. 
    • To offer personalized insurance coverage to customers, real estate companies can analyze data science insights on real-time regional prices and forecast price fluctuations. 
      Data Science benefits in real estate

    Applications of Data Science in Real Estate

    From lead generation to make informed decisions regarding a property using geographical information systems, data science can be applied to many areas of real estate. A Data Science course online will help you take a closer look at some of the data science applications in this sector.

    1. Property Value Estimation

    In the modern world, property valuations based on data science are game changers. Someone buying a property might use an automated valuation model, which looks like a simple website if they want to know the value of that property. The model will provide them with fair valuation results within seconds. The model can also predict future pricing and display historical data about the property. A real-time database is used in these property valuation systems. The resulting information will always be available to buyers and real estate agents. 

    In the United States, Zillow Estimate, ZOLO in Canada, and Zoopla in the UK are famous examples of property valuation. 

    2. Analyzing Locations Using GIS (Geographic Information Systems)

    Data science relies on geographic information systems for in-depth geospatial analysis. It gathers, surveys, and processes information by combining address data, postcode data, geographic coordinates, and other data types. 

    The map visualizations and insights provided by the system are derived from the data analysis. For example, by analyzing all schools and kindergartens in a particular area, real estate agents can determine whether to sell a house to a family with children in a specific area. 

    Using GIS, real estate companies can also identify the most suitable lands for building properties, retail shops, malls, and wetlands. Real estate investors can obtain information about it beforehand to determine whether it is worth investing in a property or not. 

    3. Mindful Investments and Price Index

    With the help of data science, investors can make informed decisions based on high-quality data and challenging insights. Investing in properties can be made more accurate with the use of data technology. This decision was based on historical information on property values, neighborhood characteristics, high-value amenities, etc., all of which are subject to change over time. Data science helps solve these problems by analyzing past data and predicting the future. 

    4. Progressive Forecasting

    The art of forecasting is indeed needed for all businesses worldwide. You can forecast the future of property markets using time series forecasting in data science. Now, data science models can combine data from hundreds, if not thousands, of sources to produce forecasts. Investments, developments, and financial returns will become more effective due to these advancements. 

    5. Examine Property Prices

    Property prices can be examined using various data science and AI analytics. This can be done with the help of historical and forecasted data. The result of the analysis gives visibility into the land and building valuation in the future. With Data Analytics, they can determine the value of a piece of land so they can purchase it at the lowest possible price. With managing development costs, historical data can be used to determine the number of raw materials needed for any building space to minimize wastage, resulting in an optimized cost. 

    In this way, it benefits not only the real estate brokers but also the buyers.

    Role of Data Science for Real Estate in the Future

    Data science and all its related fields will have a massive impact on the future of the real estate industry. We are just around the corner of some exciting changes as AI, machine learning, automation, and IoT (Internet of Things) become more advanced and widespread. The real estate industry will be able to suggest the perfect property to each customer based on complex behavioral data and their preferences and interests. 

    Through their smartphone, the customer can virtually walk around this property if they like it. Through an app or website, they will even be able to modify features about the property on the fly, such as the wall color and flooring type. 

    Benefits of the Real Estate Data Analytics Course 

    Doing a course in data analytics in commercial real estate can benefit you in the following ways.  

    1. Acquire Problem Solving Skills

    Problem-solving is at the heart of analytics. These problems are not just obstacles, they affect entire businesses and the staff and customers they serve. The ability to think analytically and approach problems correctly is always helpful in the professional world and everyday life. The ability to review different data types and draw conclusions is the most valuable skill an employee can possess. 

    2. Great Demand

    Currently, qualified data analytics professionals are in high demand. As a result, real estate data science jobs are increasing. We are just at the beginning of the professional data analytics shortage. As organizations seek ways to utilize big data, analysts with analytics experience are in high demand. From 2014 to 2017, Big Data jobs significantly increased by 106%. The increase in analytics implementations and the consequent need for analytics professionals may be responsible for this apparent surge. 

    According to a study by QuinStreet Inc., big data analytics is becoming more popular among U.S. businesses and is considered a high priority. Most organizations will implement this feature or action plan in the next two years. 

    3. Substantial Salary

    Professionals with data analytics skills earn more due to the strong demand for their services. Among data analysts, the annual median salary is $130,000, according to the Institute of Analytics Professionals of Australia's 2015 Skills and Salary Survey Report by cmo.com. Likewise, significant data analytics salary trends in the UK are positive and exponential.  

    4. Get Work in Multiple Industries

    Not only in real estate, but data analysts are also in high demand in all industries. Producing results through analyzing data is considered a best practice in all sectors worldwide. Sales, finance, data assurance, business intelligence, and marketing are all areas in which data analysts can use their skills. So, a real estate data science course can help you get jobs quickly and with excellent pay. 

    How will Data Analytics in the Future Be More Important?

    Listed below are some reasons why data analytics for real estate will be most important in the future. 

    • Globally, data generation is on the rise. 
    • There is a shift toward innovation and digital transformation in the world. 
    • Extensive large-scale adoption of data science and analytics tools among enterprises. 
    • There is a high demand for analytics professionals 
    • The use of big data analytics is widespread across several industries. 
    • There are plenty of opportunities to work with elite brands. 
    • There are a variety of job profiles to choose from. 
    • There is a shortage of the right talent and skill sets. 
    • The role of data analysts in decision-making is crucial. 
    • Data science in real estate salary is substantial.  

    Conclusion

    The role of data science in real estate is more important now than before. Modern technologies have transformed the real estate market. Data analytics, portfolio management, and property rental estimation are among the many uses for data science for real estate. Therefore, data science offers various future real estate opportunities. It is essential to understand how customers, groups, or organizations’ decisions about ideas, goods, and services are influenced, informed, and governed by how they select, purchase, use, and dispose of them. 

    We have learned how data science is used in real estate and plays a significant role in understanding consumer behavior and tailoring services accordingly. It should be noted. However, data science is still only a new field of exploration. Hence, it remains to be discovered what this science can do regarding its potential. Artificial intelligence, predictive analysis, and data science will forever change the real estate industry. It's easy to start a career as an expert in data science real estate with KnowledgeHut’s Data Science Bootcamp curriculum. 

    Frequently asked questions (FAQs)

    1How is data science used in real estate?

    By identifying and managing risks, forecasting customer behavior, and increasing engagement with clients, data science in real estate can boost employee performance and offer a more personalized experience for clients. 

    2How can I get real estate data science jobs? 

    Doing a real estate data science course or a data analytics course with KnowledgeHut can help you get a data science-related job in the real estate industry. 

    3How to become an expert in data science real estate?

    The following steps illustrate becoming an expert in data science real estate. 

    • Understand the fundamentals of both real estate and data analytics. 
    • Learn probability and statistics for doing predictive analysis 
    • Consider improving your programming skills, like R and Python 
    • Master exploratory data analysis skills 
    • Try to build real estate machine learning models 
    • Also, construct real estate-related unsupervised machine learning techniques. 
    Profile

    Ashish Gulati

    Data Science Expert

    Ashish is a techology consultant with 13+ years of experience and specializes in Data Science, the Python ecosystem and Django, DevOps and automation. He specializes in the design and delivery of key, impactful programs.

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