Introduction to Real Estate Data Analytics
The topics we will review include reasons to learn real estate data analytics today. prerequisites to get started in real estate analytics. Course Overview A topics we will cover the technical and soft skills you will gain from the Course Tools and software's used in the course and how to get started either with the course during the real estate tech community or resources Republic Learning Guides.
Ariel Herrera 0:00
In this module, we will cover an introduction to real estate analytics. The purpose of this module is to provide an overview of the course and what you will gain from it. The topics we will review include reasons to learn real estate data analytics today. prerequisites to get started in real estate analytics. Course Overview A topics we will cover the technical and soft skills you will gain from the Course Tools and software's used in the course and how to get started either with the course during the real estate tech community or resources Republic Learning Guides. My name is Ariel Herrera, and I will be your instructor for the course. I'm a data scientist based out in Tampa, Florida, with a bachelor's degree from Rutgers University and master's degree from NJIT, I have over seven years of experience in the data analytics space, largely in the financial services sector and more recently in SAS at the property management software company at folio. I'm the creator of analytics area, a platform that bridges the gap between real estate and technology. For fun, I enjoy traveling outdoor activities like the beach and watching the Yankees and Giants when I want you to succeed. No matter your experience level in real estate or data analytics, you have my support to get started. The way to be successful in this course, is first ask questions in the course you can ask questions after each module. Join discussions in the Facebook group. Here we talk about latest trends in real estate technology and share resources with one another. Third, review additional resources and course materials. I will share with you additional resources for each topic to help you digest the content for give yourself time to digest new knowledge learning a new topic can be exciting yet overwhelming. To stay focus. Allow yourself time to let new knowledge sink in. Now what if we are starting from ground zero? Is this the right course to take? Yes. As Steve Jobs said, Learn continually.
There's always one more thing to learn. I encourage you to seek learning as an opportunity rather than a challenge. Why learn real estate analytics. In this lesson, we're going to cover what is real estate and analytics, growth in the industry, growth and data availability, and careers you can have in a real estate data analytics space. The takeaways from this lesson include demonstrating an understanding of real estate data analytics, recognizing growth and the industry due to data availability, and identifying career opportunities. So what is real estate? As Investopedia puts it, real estate is defined as the land and any permanent structures like a home or improvements attached to the land, whether natural or manmade. From this chart, we can see there are different types of real estate that we already are familiar with. This includes residential homes, commercial buildings, like grocery stores or office buildings, and industrial buildings like an Amazon warehouse. It is also an asset that can lead to financial freedom, which is my goal with real estate. What is data. Data is information in a digital form that is stored on a computer or server and is used as a basis for analysis, reasoning, discussion or calculation. Data is everywhere. You may be watching this video on either YouTube or teachable. Each platform logs your actions including which videos you watch time spent per video, and what time you watched it. This data is collected and used to provide feedback for content creators and add value to your experience. The reason why we're talking about real estate today is because it is primed for disruptive technologies. Real estate has lagged behind other industries and adopting new technologies and providing accessible data some of these new technologies include virtual tours. During the pandemic real estate agents needed a quick way to adapt since homes can not be shown in person. This created a strong demand for virtual tours, drones. These are used by insurance companies like Allstate to assess damage of a property. This saves time and costs from sending a local agent, sensors and commercial building sensors are being implemented to the tech the number of people in a room and timeframes to better optimize cleaning times for staff 3d printed homes. This is the new technology that reduces time to construction and number of laborers for an on site job blockchain. Today if you were to purchase a home you need to go through a Title Agency to be sure the property has no liens and no one else holds ownership. But with Blockchain technology title searches can be done in minutes to add Clearly assess previous records fractional ownership. Here you can own a share real estate rather than the entire property. This can make it more accessible to own and share real estate. This surge and disruptive technology in the real estate industry can also be seen by increased funding to prop tech companies. Here we can see in this news headline, prop tech venture capital funding has hit 13 billion so this industry is growing. Not only is real estate technology growing, but there are massive trends occurring in the industry. This includes migration trends of individuals leaving northern states and moving to the south as well. housing affordability and availability is a large concern. Americans are coming to terms with our country becoming a renters nation. How will new real estate companies solve the relevant issues today, we are at a crossroads of real estate data analytics, we finally have the data to make observations and test new theories. We also have the backing of venture capitalists to promote these ideas through funding of startups. How can we take advantage of this early phase, there are several career opportunities that this course will prepare you for including business intelligence analysts,
so building visualizations, working with stakeholders to build dashboards, data analyst, data scientist, data engineering, and research analyst. These roles are needed across industries, and will be imperative in the rise of real estate data analytics. If you can niche yourself in the space, you can find much success before it becomes over saturated pre requisites. This lesson will briefly cover who should take this course and what previous experience is required. The goals here is to demonstrate a mindset of willingness to learn, because that will ultimately allow you to succeed in this course, before we go into prerequisites. Let's start with what this course won't do. I will not throw you in the deep end and expect you to figure it out on your own. I will give you the proper tools and guidance beforehand. Commonly in work scenarios we are forced to learn on the fly with pressure and important deliverables. While this can be effective for some it can be highly discouraging for others. This can result in gaps of knowledge to truly understand a new topic, and can create impostor syndrome. I do not expect you to already know how to program at all, even in Python, what real estate data even exist out there, or how to build a dashboard. Through incremental learning and open office hours you will feel guided through your journey in this course. So what do you need to get started in this course, first and most important willingness to learn. This is imperative for any tech related field, technology and data are constantly changing and evolving. You must be open minded to learn and be excited for many wins along the way. Second, basic understanding of spreadsheets like a CSV file, Excel, or Google Sheets, we will mostly work with tabular datasets with rows and columns within Python. Third, connectivity to the internet via a laptop, it is very difficult to code on a phone, so you must have a laptop or some sort of computer to work with fourth, basic searching skills on Google. Contrary to what you may think programmers do not memorize code. 90% of programming is determining what steps may need to be taken and searching for sample code online as to how to accomplish it Course Overview. In this lesson, I'll be covering the course syllabus, importance of housing, market and economic data to evaluate on market and skills you will learn the goals here to describe a natural progression to learn real estate analytics, showcase data sources to explore and explain the technical and soft skills that you will learn a course now for the course path. There are three main sections first section will cover the introductory course, which is this lesson setup where we will create free accounts for related applications and run our first piece of Python code. Next will be Python for beginners. This is an optional modules depending on your level of Python experience. I assume in this lesson you have zero programming experience. We will cover topics like data types, lists, loops, functions, and packages like pandas web scraping. This will include an overview of the Python package BeautifulSoup. Then in the milestone project, we will build a web scraping function that gets county population data from sites like Wikipedia pages the second section will cover public data sources, where to locate free data like Zillow, realtor, Redfin, and more sources, Introduction to API's, what API's are and how we use them to obtain economic housing and property data. Fret API, deep dive into gathering economic data like total population, median household income, demographics and more census API, deep dive to gather economic data at the zip code level. We will cap this section by creating simple visualizations to view market trends, for example, viewing unemployment over time within a given market. The third section, which is my absolute favorite will cover basic statistics such as mean median mode to view the distribution of our data and how to handle missing values, market data analysis, merging our datasets across state, county and zip code data, we will add features to monitor trends, Tableau visualization, here we will create an interactive dashboard to analyze our market. Bonus content includes a preview to future courses such as machine learning and real estate analytics, analyzing a real estate deal and automation techniques skills gained. In this lesson, we
will briefly review the technical and soft skills, you will learn the goals of describe technical skills that will be applied to real world datasets. We will identify opportunities for career growth and resume building, as well as explain analytical and communication skills that we will gain. For technical skills, there are two main skills that you will gain first Python, which is the most popular programming language in data analytics, we will learn within Python web scraping, which is a valuable skill set to work with public datasets. API's to query structured data, data wrangling to combine and clean datasets. reading documentation, which will give you the knowledge to search for new datasets and work with them on your own. The next major skill will be Tableau. We will learn how to load data and work with data sets within Tableau, and creating a dashboard that can be publicly shared and added to your portfolio of accomplished analytics projects for soft skills. We will learn problem solving. This will help to answer your questions like how do I know if a market is emerging or declining? How do I visualize these trends? How can I compare markets to one another? Through reasoning, we will use the tools and techniques to make decisions off of data presentation, we will create an interactive dashboard and be able to explain the purpose and meaning behind these visuals we create. On the right hand side is a screenshot on LinkedIn. By performing a quick search a real estate data analyst jobs on LinkedIn. You can see there are over 13,000 roles available across the United States, some of them in person and some of them fully remote. These are for top companies as well like CBRE, McKinsey, and U S See, you will be able to translate the skills learned from this course to real world roles for top companies tools used. In this lesson we will cover the tools and applications used in this course, the goal is to describe the reason for using each application including any pros and cons. Starting with the left hand side, we will be using Python and Google collab. Google collab is similar to Jupyter Notebooks. It is an environment for us to code and Python but all on the cloud. What that means is that it's free to use and it's owned by Google. And to we don't have to install Python on our machines at all. Second, we will use GitHub. This will allow us to save and have versions of our code, we can then build our own analytics portfolio and share it with recruiters or others in our network. With rapid API, we will be able to search different datasets that we could use for our own analysis. Tableau be used as our visualization tool within the public version. There are some limitations to using tools like Tableau, but overall the stunning visuals will allow us to translate market analysis and trends for more custom control and visualizations. We will use Plotly which is a library that can be accessed using Python getting started to get started. If you haven't already, sign up for the course with the link. Join the tech and real estate Facebook group which will allow you to get in addition They'll 20% off. Also check out related content that I have on sites like the medium. If you've any questions before joining the course, please feel free to reach out to me either directly through LinkedIn or at my email Ariel Herrera analytics erielle.com. I'm excited to help you through your journey of learning how to be an expert in real estate and data analytics. So join me in the next module where we get set up and then we will run your first piece of Python code want full access the introduction to real estate Data Analytics course. Then sign up for the course in the link below. You will learn Python programming all with real estate related examples. This includes web scraping, retrieving data from sources like Zillow, realtor, Redfin, Yahoo Finance, US census and more. If you haven't already, check out the introduction video to the course on YouTube to get a full understanding of what the course has to offer. Also, members of the free tech and real estate group on Facebook receive a 20% off the course seeing the next lesson.
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