Web Scraping Real Estate

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The Complete Guide to Web Scraping Real Estate Data

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Use Cases
Summary
Web scraping is now an integral part of the real estate industry. Both agents and regular folks gain much from scrapers. Here’s how you do it:
Jul 14, 2021
9 min read
The property market is constantly expanding, and with it, real estate agents and businesses try to find new solutions to pinpoint what the future holds. While real estate, in general, doesn’t change drastically overnight, it’s affected by way too many factors for one person or even an organization to keep track, will the prices rise or will they go down? What neighborhoods are in high demand? Are there properties that just need a makeover to skyrocket their value? These are just a few of the questions real estate agents are frequently asking answer these questions, one needs loads of research data for comparison, and to manually gather such amounts of information would be like a wild goose chase. Here is where web scraping comes in handy, it collects and structures data as fast as you can say:As we all know by now, web scraping is the powerhouse of data extraction! So, if you want to know more about why anybody would want to scrape real estate data from the Internet and how to do it properly, let’s continue our journey together. We’ve prepared both a DIY solution and a step-by-step guide on how WebScrapingAPI can do you should scrape real estate dataScraping the web will ensure that the extracted information about real estate is precise, credible, and up to date. This way, one can predict if the real estate market will skyrocket any time soon or see in what price range their property will businesses, web data is valuable because it leads to better decisions, better pricing, and a more significant profit margin. However, the catch is that each bit of information needs to be as fresh as possible, making web scraping the obvious most commonly extracted types of real estate data are the following:Property typeSale priceLocationSizeAmenitiesMonthly rental priceParking spacesProperty agentThe information listed above can make or break a real estate agency. It makes a huge difference in communication, strategy, and efficiency, but the biggest advantage is how well agents get to know their properties and market. After that, it’s just a matter of finding the right ’s take a look at a few scenarios that illustrate the value of web scraping:Real estate agenciesDecision-making: Taking risks is part of the job, but that doesn’t mean you must do it blindly. Researching before buying or selling something is mandatory to work, and more info means better edicting the market: It is crucial to know when to buy and sell properties to get the best and most profitable outcome. Some types of properties soar in popularity while others lose their luster. Some areas flourish while others stagnate. Knowing what’s around the corner is the key to longevity as a gular folkWeb scraping isn’t all about helping businesses. Actually, part of what makes it so popular is how easy it is for a single person to use. Sure, you need some knowledge of computer science, but there are plenty of tutorials to help. Heck, this is one of them! Buying and selling: You need to accurately deduce the property’s value before buying or selling it. It would be a shame to sell your childhood home and see it a week later on a real estate website at double the price, wouldn’t it? Investing: If you like to invest in properties, either by buying at a small price to sell it later for profit or simply rent the property, it is highly recommended to know fast you’ll break even and what returns you should, that’s enough on use cases. Let’s look at some code! For starters, let’s assume we are searching for a new home in New York City. We want to buy a property with at least two bedrooms and, of course, a bathroom. So, we’ll start our search on Realtor, extract data from there and compare it to find the best are various ways one can extract content from web pages. This article will explain two methods: one in which we create our web scraper from scratch and one in which we use an already existing, let’s try to do it ourselves. The code will later prove helpful once we use a professional web scraping tool. I chose to write in Python because of how popular it is in web scraping. We have a general-purpose tutorial for extracting web data in Python that you should check out! Inspect the website codeThe data we need to extract can be found in the nested tags of the said webpage. Before we start scraping, we need to find it. To do this, simply right-click on the element and select “Inspect. ”A “Browser Inspector Box” window will pop up, like this:In this window, we will navigate to find the tags and classes under which our essential data can be found. It might seem a bit intimidating at first, but it only gets easier with experience! We can see that everything we need to extract is within the

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  • tag with the class ‘component_property-card’. If we go even deeper in the tag, we observe that the data referring to the number of beds and bathrooms are under the attribute ‘data-label’ with the values ‘pc-meta-beds’ and ‘pc-beta-baths’, respectively. Knowing this, we can proceed with writing our code! Prepare the workspaceAs mentioned before, we will use Python as our programming language, so you need to download and install can use whichever IDE you feel comfortable with, but I recommend using you’ve created a new project, make your work easier by using these libraries:Selenium: Used for web testing and automating browser autifulSoup: Used for parsing HTML and XML Used for data manipulation. The extracted data will be stored in a structured stalling them within the project is quite simple. Just use this command line in the project’s terminal: python -m pip install selenium beautifulsoup4 pandasWrite the codeLet’s start by importing the libraries we’ve installed earlier:from selenium import webdriver
    from bs4 import BeautifulSoup
    import pandas as pdTo extract the data from the website, we have to load it by configuring the webdriver to use the Chrome browser. To do this, we simply need to specify the path where the chromedriver is located. Don’t forget to add the name of the executable at the end – not just its location! driver = (‘your/path/here/chromedriver’)Besides the number of beds and bathrooms, we can also extract the address, price, and, why not, the size of the property? The more information we have, the easier it will be to decide on a new home. Declare the variables and set the URL of the to be scraped = []
    beds = []
    baths = []
    sizes = []
    addresses = []
    (”)We need to extract the data from the website, which is located in the nested tags as explained earlier. Find the tags with the previously mentioned attributes and store the data in the variables declared above. Remember that we only want to save properties with at least two beds and one bathroom! content = ge_source
    soup = BeautifulSoup(content, features=”)
    for element in ndAll(‘li’, attrs={‘class’: ‘component_property-card’}):
    price = (‘span’, attrs={‘data-label’: ‘pc-price’})
    bed = (‘li’, attrs={‘data-label’: ‘pc-meta-beds’})
    bath = (‘li’, attrs={‘data-label’: ‘pc-meta-baths’})
    size = (‘li’, attrs={‘data-label’: ‘pc-meta-sqft’})
    address = (‘div’, attrs={‘data-label’: ‘pc-address’})
    if bed and bath:
    nr_beds = (‘span’, attrs={‘data-label’: ‘meta-value’})
    nr_baths = (‘span’, attrs={‘data-label’: ‘meta-value’})
    if nr_beds and float() >= 2 and nr_baths and float() >= 1:
    ()
    if price and
    else:
    (‘No display data’)
    if size and
    if address and
    (‘No display data’)Great! We have all the information we need, but where should we store it? This is where the pandas library comes in handy and helps structure the data into a csv file for us to use in the = Frame({‘Address’: addresses, ‘Price’: prices, ‘Beds’: beds, ‘Baths’: baths, ‘Sizes’: sizes})
    _csv(”, index=False, encoding=’utf-8′)If we run the code, a file named ‘’ will be created, and in it, our precious data! We did it! We created our own web scraping tool! Now let’s jump right into it and see what steps we need to follow and which lines of code we need to modify to use a scraping a web scraping APIFor this scenario, we will use WebScrapingAPI, of a free WebScrapingAPI accountTo make use of WebScrapingAPI, you need to create an account. Don’t worry, the first 5000 API calls are free, and you don’t need to share any personal data, like credit card info. After you successfully create your account and validate your email, we can move to the next KeyTo use WebScrapingAPI, you will need to authenticate via the private API Key, which you can find on your account dashboard. Note that you mustn’t share this key with anyone, and if you suspect that it has been compromised, you can always reset the key by pressing the “Reset API Key” the codePerfect! Now that you have the API Key, let’s make the necessary won’t be using a webdriver anymore. Instead, the ‘requests’ library will send the request to WebScrapingAPI and retrieve the website’s HTML code as a requests
    import pandas as pdNext, we have to prepare a few parameters for the request: the url of the website we wish to extract data from (realtor) and our API = ”
    params = {
    “api_key”: “XXXXXXX”,
    “url”: “}
    response = quest(“GET”, url, params=params)Don’t forget to change which content beautifulsoup is parsing. Instead of the source from the chromedriver, we will use the response received from the ntent = response. textFrom this point on, you can use the same code from the previous scenario. The data will still be stored in a CVS file named ‘’All done! And that’s pretty much it; you can run the code. WebScrapingAPI will do the job, and you’ll get the necessary data to find the perfect home. But you might ask yourself: “What is the difference between using WebScrapingAPI and the scraper we built ourselves? ”. Well, allow me to vs. Pre-madeOne of the most significant advantages of using WebScrapingAPI is its proxies. The service has a huge rotating proxy pool that ensures its users’ anonymity while surfing the feature is also helpful when someone wishes to scrape a website en masse. Making multiple requests on a website in a short amount of time will surely block your IP, thinking it is a grief attempt or a bot with bad a rotating proxy pool will make the website think that multiple users are interacting with it, so you remain undetected and can scrape all day more obstacles can come your way when scraping the web, such as CAPTCHAs or browser fingerprinting. As you might expect, we built WebScrapingAPI to side-step all those hurdles and make data extractions as easy as possible for you. If you want to know more about this topic, check out our article on the most common problems web scrapers can all agree that scraping the web is an excellent solution for the real estate industry, but you can use it for other purposes as well. Here are just a few examples: monitoring your competition, comparing product prices, and training machine learning algorithms. I could go on, but that’s already a whole new subject. I won’t drag this article on forever, so I recommend you check out these seven use cases for web scraping eating a web scraping tool in your free time sounds pretty neat, but there are many things to consider, things that will burn a considerable amount of development time. Here you can find an in-depth discussion about DIY vs. pre-made web scraping we are talking about scraping a few web pages, building the tool yourself can be a fast solution. Still, a professional job needs a professional tool, ideally an API, WebScrapingAPI. Did I mention the free trial?
    Web Scraping in Real Estate: The Ultimate Tool for a Realtor

    Web Scraping in Real Estate: The Ultimate Tool for a Realtor

    There was a time when real estate dealings were discrete, paper-based operations done on a one to one basis. With the rise of the internet and every industry finding its way into it, real estate began to realize its true potential on the web. There is no denying the fact that the internet is the most useful tool at a seller’s a large number of potential buyers online, realtors find the internet an excellent source to advertise property listings, hereby automating the whole process. Statistics suggest that 40% of buyer’s inquiries stem from internet advertisements and nine out of ten people use the internet to search for property. Moreover, the same property can be enlisted on numerous sites to increase traffic and the corresponding chance of a implies endless opportunities for a realtor. But harnessing relevant data out of big data to a non-technical realtor is like looking for a needle in a haystack. The web has a staggering amount of information leading to a plethora of choices and comparisons can lead to significant confusion, making it difficult to fathom and make sense Scraping in real estate to the rescueWeb scraping is the process of sorting through overwhelming amounts of data, refine the user’s searches and provide a list of relevant information. In a realtor’s case, it is the go-to tool for organized property listings. Scraping the web provides parameters which the realtor can further study to determine sales and prospective buyers. Parameters extracted by web scraping are:SizeProperty typeLocationSale priceSizeAmenitiesMonthly rental priceParking spacesAgent contact
    This information is displayed in form of a spreadsheet, allowing the realtor to make comparisons of relevant parameters. 1. Property value trackingLet’s assume you decide to sell your property. Scraping the web for the value of similar properties can aid you in setting a good value on your own. This allows users searching for such properties to get fair deals, and on the other, you getting a profitable one. 2. Making the right investmentObtaining real estate data is hard, as result of which most investors make financial investments blindly. With web scraping, an investor can make decisions based on qualitative and relevant empirical data, rather than outdated or incomplete information. Aggregating property data from real estate listing websites is essential for investment analysis. 3. Rental YieldRental yield is the most important factor to be considered before investing in property. By scraping data from real estate websites, you can determine which properties have the best rental yield for any suburb. Moreover, scraping answers which property types (house, apartment, 1 bedroom, 2 bedrooms) are more preferred in a particular area and yield the best return on investment. 4. Track vacancy ratesA vacant investment property is risky. To minimize this risk, it is imperative to analyze property data and suburbs which have higher rental above parameters are the most relevant decoded by web scraping through numerous websites online. Having the above details at your fingertips improves a realtor’s efficacy at decision making, better communication and faster and profitable sales. The role of web scraping in retail is just getting started, its potential is however arching for a web scraper for your real estate needs? Contact us at Datahut, your big data experts. #BigData #bigdataanalytics #realestate #webscraping
    Using A MLS API And Real Estate API For Listings Data

    Using A MLS API And Real Estate API For Listings Data

    The real estate industry is in slow yet constant evolution. As the property market expands, agencies and independent realtors need to become fortune tellers to predict what the future holds. They need to keep track of even the most subtle changes in property value, neighborhood demand, and real estate trends to stay competitive and increase their chances of success.
    Table of Contents
    1. What Is Real Estate Scraping?
    2. Why Is Real Estate Scraping Valuable in Business?
    3. Use Cases of a Real Estate Scraper
    4. Why Use a Web Scraping API
    The data available is vast, and it’s often challenging to keep tabs on all the factors that affect the industry. Web scraping real estate is a powerful tool for monitoring all this information and keeping up with the times. It helps streamline data research and analysis so that agents can spend their energy on other important business activities.
    A real estate scraper helps collect and visualize all relevant data quickly and efficiently. If you want to learn more about how web scraping is affecting real estate, the value of web scraping in this industry, and the best approach to conduct real estate investor scraping effectively, read on.
    What Is Real Estate Scraping?
    As realtors and agencies work to grow their business and stay ahead of their competitors, they find themselves on the hunt for more effective and innovative tools. Real estate scraping offers an excellent alternative way to find prospective buyers and improve sales intelligence. It allows you to gather the information you need to fulfill your everyday business operations.
    Furthermore, web scraping real estate is a helpful method to navigate, collect, and organize large sets of data. It allows you to sort out common user inquiries online and provides you with relevant and actionable information. You can take advantage of the extracted data to set up more attractive listing descriptions and beat your competition. Moreover, this powerful technique can be adapted to your specific needs. You can customize your parameters for better, more accurate results.
    You might already be aware of how competitive it can get in the real estate market and the risks of investing in it. Web scraping will let you stay ahead of the curve to maximize gains while minimizing uncertainties. The right data collection strategy provides you with:
    Updated strategic data — It reveals business insights that will let you make informed decisions. Web scraping will give you access to hard-to-detect areas and detect data points you might otherwise miss.
    Listing proficiency — A real estate listings scraper will allow you to learn about rental income revenue and property value around you or in a specific area. You can compare and contrast this information to be better prepared to invest or sell.
    ‌Use real estate agent’s data — By scraping details from several online resources, you can harvest information that real estate agents have placed all over the web. Moreover, you can study agent profiles, directories, and competitor sites to learn more about your competitors and their business.
    Why Is Real Estate Scraping Valuable in Business?
    There are a vast number of reasons to choose scraping software to get real estate data. Whether you’re on the lookout for available property or you need to obtain information to make business decisions, web scraping will expedite things for you. You can scrape a trustworthy real estate site to gather information on property details, buyers, sellers, and agents in your area, and more. Web scraping can narrow down results among the massive amount of data available and make your life much easier.
    Instead of searching for the most credible information manually in the massive pool of data that is the web, a real estate web scraper will help you acquire
    current, precise, and reliable information from across the internet with little effort. Knowledge is power. It can help you increase your profit margins. Some of the information you might find relevant when web scraping real estate listings (and that can make or break a real estate deal) is:
    Type of property
    Average sale price
    Location
    Amenities
    Square footage
    Average rental price
    Property agent
    Neighborhood perks
    Long-term capital gain
    A sneak peek at this information can help you improve your communication strategy and better position your properties in the market. All you’ll need to worry about afterward is putting your business out there for the right clients to find you.
    Use Cases of a Real Estate Scraper
    The information obtained when web scraping for real estate has numerous case uses. These are some of the most relevant proven ways to use data in your favor in the real estate industry:
    1. Accurately deducing your property’s value
    Whether you’re selling your childhood home or one of the many properties in your real estate emporium, you need information on the current real estate pricing for your type of property in your area. Doing your research and analyzing what similar properties are worth could help you boost your profit margins and avoid underpricing your own. Market research is vital to getting the best deal every time.
    2. Real estate aggregators
    The real estate business relies heavily on staying up to date with all relevant information. Building an aggregator to gather real-time real estate information from numerous data sources will allow you to effortlessly make side-by-side comparisons. Having all the information you need neatly organized in the same place will help you expedite the whole process.
    3. Make better investment decisions
    When investing in real estate, you can’t simply rely on vague information that’s not the most accurate and current. That’s when you can benefit from scraping as a way to collect fresh real estate data to ease your way into real estate investing. Performing a timely analysis of your options will prevent you from trusting an educated guess when investing and stop you from leaving your decisions to chance.
    4. Maximize rental yield
    Rental yield is probably one of the most notable concepts in real estate. Scraping real estate websites will help you understand which properties have the best rental yield in your area. It will also offer clear data on the most common property types in a given neighborhood and which have the highest return on investment. This information will help you sustain a strategic advantage over your competitors.
    5. Forecasting market trends
    Investors, realtors, and brokerages can all benefit from making the right market projections. Real estate scraping is an excellent way to collect historical intelligence on past property value and other relevant factors to keep in mind. Analyzing this data is a reliable method that helps make more accurate predictions on how the market will evolve over time and adjust strategies accordingly.
    6. Monitoring competitors
    To stay competitive in the market, you must beat what others are doing. Data scraping will help you gather real-time information on your competitors’ efforts and learn more about their pricing and offers. This information is helpful when developing your own strategies and winning customers.
    7. Keeping tabs on vacancy rates.
    Data scraping will give you the information you need to assess both positive and negative rental stats. It’ll give you insights into the market cycle, rental growth, and investment opportunities that could improve your return on investment.
    Why Use a Web Scraping API
    A web scraping API can be an absolute lifesaver. It offers a better solution than relying on already existing real estate websites by allowing you to choose the information you actually need rather than wasting your time sorting messy listings.
    After reading of the many uses of web scraping for real estate and its benefits, you might feel tempted to build a scraper for real estate properties. While this might be a viable solution for some, it’s a time-consuming process that requires at least a little programming skill. If you’re a beginner and would rather spend your efforts on putting the results of your research to good use, you should consider purchasing a pre-built scraping bot. Creating your own from scratch may require you to:
    Learn a programming language
    Find the data you want to extract
    Learn data parsing fundamentals
    Inspect the website code
    Download and install libraries and other elements
    Write your own code
    Although creating your own scraping tool can potentially give you more customization options, it could also be a total headache if you don’t know what you’re doing. No two sites are the same, and finding different patterns and layouts could cause errors and other pitfalls that result in wasting valuable time while performing your research.
    What To Look for in a Web Scraping API for Real Estate
    Purchasing the right tool for scraping real estate data will save you lots of time in your data-gathering exercise. However, you must make sure the one you choose meets your data gathering needs and more. You’ll highly benefit from a scraping API that can:
    Manage and rotate your proxies
    Give you consistent and timely results
    Provide clear and organized outputs
    Allow browsing scalability
    Provide CAPTCHA-solving solutions
    Parse metadata for you
    Our Scraping Robot API makes an effective real estate scraper. If you want to test our modules and learn more about us, visit our site today. No signup or login required!
    The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

    Frequently Asked Questions about web scraping real estate

    What is Web scraping in real estate?

    Web scraping is the process of sorting through overwhelming amounts of data, refine the user’s searches and provide a list of relevant information. In a realtor’s case, it is the go-to tool for organized property listings.Feb 8, 2021

    Can I web scrape the MLS?

    You can scrape a trustworthy real estate site to gather information on property details, buyers, sellers, and agents in your area, and more. Web scraping can narrow down results among the massive amount of data available and make your life much easier.Oct 13, 2021

    Is it legal to web scrape Zillow?

    You may not use the Zillow Data to provide a service for other businesses. You must use commercially reasonable efforts to prevent the Zillow Data from being downloaded in bulk or otherwise scraped. … You may present data on no more than 20 individual properties at a time to any given user (e.g., per Web page).

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