The Ultimate Guide To Using Real Estate API To Navigate The Housing Market

In the last few years, low inventory, intense competition, and massive price increases have made it difficult for people to enter the housing market. In addition, because of the Federal Reserve’s aggressive monetary tightening, fixed mortgage rates also have spiked — now up to around 5%, as Forbes reports. As a result, most buyers can no longer afford homes in specific price ranges.

Given the difficulty of the present housing market, stakeholders in the industry are turning to technology for solutions to stay afloat. As a result, web scraping with real estate APIs is becoming increasingly popular. A good API can help you gather large amounts of accurate real-time data to help you navigate the market and stay ahead of competitors.

Why Has the Housing Market Been Difficult?

While high house prices and mortgage rates are a challenge, they merely reflect the real estate market’s issues. In the last two years, the world has undergone several changes that have negatively impacted real estate.

COVID-19 Pandemic

When the pandemic caused lockdowns worldwide, it strongly impacted the real estate market. As a result, it reduced general economic activity and changed how we think about spaces now.

One way this happened was the introduction of work-from-home and remote work. Companies that found no difference or an increase in productivity opted to downsize their offices and invest in virtual office space.

The other change is people becoming more aware of the number of people in a space. As a result, investors are wary of people insisting on lower densities in places they visit because that would result in investors needing to pay for larger spaces at higher prices to accommodate the same functions.

Affordability

The National Low Income Housing Coalition reports that America has a shortage of 7 million affordable rental homes for extremely low-income renters with household incomes at or below their area’s poverty guideline. However, even though most people can accept that America needs far more affordable housing, many are quick to add that it shouldn’t come up near where they live.

The lack of affordable homes also drives up rents and raises the price of multifamily investment properties, making it more challenging to enter the housing market.

Flow of people

People moving between and within countries is a crucial driver of the real estate market. However, with the current mobility challenges that the world faces, demand for residential, hospitality, and retail real estate has also reduced.

Use of space

The pandemic will likely permanently affect the design and use of real estate space. A new focus on the health of building occupants has developed, causing changes in aspects like building entry and indoor air quality. Urban planning will now have to consider this new focus along with new capacity expectations.

The desire for new technology

The rapid adoption of technology in our lives has changed people’s expectations of a property’s technology. Some features that people may have thought were nice to have are now becoming requirements, including:

• Tracking people in buildings
• Contactless doors and elevators
• Air and water quality monitoring
• Airflow and recirculation control
• Mandatory remote building services
• Health screenings

Infrastructure

The American Society of Civil Engineers grades the country’s current infrastructure as a C-minus. The organization’s website reports that a water main breaks every two minutes and 43% of the public roadways are in poor or mediocre condition. This makes underserved areas less and less livable, hurting real estate values and development in these areas.

Given these challenges affecting the real estate market, the best way to navigate the market is to get more data and make optimal decisions. That’s why more and more businesses are trying to implement data-driven decision-making strategies to increase profits, boost customer satisfaction, and minimize market risks. To do this, they need to gather vast amounts of real-time real estate data — which is why many turn to real estate web scraping.

What Is Real Estate Scraping?

Real estate scraping is a method of gathering vast amounts of data on the real estate market by using a tool to scour the web. To do this, you use automated software scripts called web crawlers or scrapers, which search the internet in a structured approach and download the real estate data they find.

In the constant struggle to grow the business and stay ahead of competitors, realtors and agencies need more innovative and effective tools. Real estate scraping is a powerful option that presents a new way to find prospective buyers, improve sales intelligence, and improve everyday business operations.

It also helps gather, sort, and navigate large data sets, which can be helpful in many situations. For instance, you could sort through the most common user inquiries and complaints online and identify actionable solutions. Or you could extrapolate the data you’ve gathered to optimize your listings and beat your competitors. Whatever specific needs you have, you can customize your scraping parameters to best and most accurately suit them.

Before the rise of real estate websites, real estate data was available only to a few key players who would hide this information from everyone but industry insiders. These reports were also expensive and only reflected a snapshot of the industry at the time of their release, so you would have to regularly buy the latest report to stay abreast of the industry.

Web scraping lets anyone collect massive amounts of real estate data independently, revolutionizing real estate business intelligence. Anyone can now combine scraping with business intelligence tools to analyze and transform data, helping them visualize and predict the market. This empowers more people to enter the market.

Since information from stakeholders in the real estate industry is primarily public online, scraping also blends commercial and residential data, giving the most transparent picture of factors like vacancy rates, demographics, and types of dwellings under construction.

Scraping Robot

A scraper like Scraping Robot is perfect for running a large number of scrapes at a low flat rate per scrape — which is much simpler than complicated subscription fees or varying models that you may find with other tools. And, since it frequently adds new modules, customers consistently enjoy new functionality that considers their requests to improve their experience.

With Scraping Robot, you won’t have to worry about all the headaches that come with scraping, like proxy management and rotation, server management, browser scalability, CAPTCHA solving, and looking out for new anti-scraping updates from target websites. There are no hidden fees, monthly costs, or complicated pricing tiers. In addition, they have a reliable support system and 24/7 customer assistance! It’s also regularly updated with solutions to new anti-scraping technologies. So you can focus on getting the valuable real estate data you need.

Who uses a real estate database?

If you’re reading this article, you’re likely one of the people who would benefit from a web scraper compiling a real estate database. If you’re unsure, a real estate web scraper is valuable for people who want to maximize their profit in the housing market. This includes real estate companies, independent real estate agents, people interested in finding a house, and real estate investors.

More broadly, it benefits people who want to:

• Learn detailed data on a specific property
• Get an in-depth analysis of the real estate industry
• Find ways to market their properties better

People with any of these interests would greatly benefit from being able to scrape real estate websites and compile large databases of current real estate data.

How to scrape real estate websites

The first step in real estate web scraping is to compile a list of sources you want to scrape. Consider the purpose of your search and put together your list accordingly. Some common sources of real estate data can include:

• Property listings websites to learn about property values and pricing
• National census data, to understand an area’s demographics
• Real estate and credit rating websites for market research
• Apartment industry organizations and advocacy groups for neighborhood and community research

Once you have your list, you need to scrape the websites on it. This entails gathering unstructured data from your online sources, putting it together, and transforming it into a structured, readable dataset to analyze later. Web scraping follows three steps:

1. First, you define the websites that the scraper needs to visit and the type of data it needs to extract.
2. The scraper sends a GET request to the website or app server carrying the data. Your request should include all your criteria for the data you want. The server will determine if your request is safe and legitimate. If it does, it’ll send back the data you requested in HTML or another raw web content format.
3. Then, you have to parse the code into a tree structure path so you can make sense of it.
4. Finally, you can search and organize your data in the way you see fit.

You could do this manually, but it would take an unrealistic amount of time to gather enough data to make useful conclusions. That’s why automated scrapers, which use multiple bots to make many requests in a short amount of time, are the better option.

This can slow down or otherwise harm the website, however — especially if it uses a low-powered server. This is why sites tend to ban IPs that send too many requests. If you try to scrape from your IP address, you can quickly find it difficult to visit any real estate websites.

That’s why it’s common to use a web-scraping proxy to enhance your business strategy. Proxies mask your IP address and let you send a request from a specific location, and both features help you significantly. For instance, you can use a proxy to

• Protect your source machine’s IP address from website blocklists
• Exceed websites’ request rate limits by distributing requests across multiple IP addresses
• Scrape websites that have different content depending on your location
• Bypass geo-locked content

Accessing a website from different locations is a handy feature for people with multiple properties in multiple different areas since it can help you better target your advertisements and marketing efforts in those areas. There are many types of proxies you could use for real estate scraping, depending on what best suits you. These include:

Residential proxies

The most popular proxies for web scraping, residential proxies increase efficiency because they use real IP addresses provided by internet service providers (ISPs). This makes servers think requests come from individual residences and not data centers. Most people access the internet through residential IP addresses. These proxies have the most authority, and websites don’t block them without reason. They don’t want to risk blocking actual users, although they will block residential IP addresses acting like bots.

Because residential proxies have to be obtained directly from end-users, ethical proxy providers have to take extra steps to ensure that they aren’t negatively affected when their IP address is used. Unfortunately, many proxy providers engage in unethical behavior when sourcing residential proxies, such as burying their terms of service in tiny text to confuse users. But not Rayobyte.

At Rayobyte, we set the industry standard for ethical residential proxy sourcing. We ensure our end users are fully informed and compensated for using their IP addresses. They can revoke their approval at any time and only use their IP addresses if they’re not using them and their devices are adequately charged or plugged in.

Our commitment to ethics doesn’t stop at how we acquire residential proxies. We also vet our customers. There’s no option for buying our residential proxies directly on our website. Potential buyers must demonstrate that their use case is legitimate before we sell them residential proxies. After purchasing our residential proxies, we continue to monitor their usage for any signs of illegal or unethical use.

Rayobyte’s residential proxies give you access to a large IP pool to help you scrape unperturbed. There are no limits on the number of concurrent connections you send, and each request gets a unique IP address. Our pool also helps you avoid CAPTCHAs and other anti-scraping technology that websites may employ.

Data center proxies

Data center proxies are the most common and affordable types of proxy. They’re plentiful and cheap. They will obscure your IP address, giving you anonymity, but websites are becoming increasingly aware of this type of proxy.

They’re also fast, so they can be an excellent option for gaming or other use cases that would suffer because of lag time. However, they’re not optimal for data collecting via web scraping. The main problem with using data center proxies for web scraping is that it’s easy to tell they’re data center proxies. Sites can easily detect when requests are coming from a data center and not an ISP, so many know if you’re using a proxy. As a result, some websites may restrict the data that data center IPs can access.

Most people don’t use data center proxies to access the internet. Some websites block all data center proxies, so your scraping project night never get off the ground. Other websites allow data center proxies, but if they detect bot-like behavior, they’ll ban the entire subnet instead of just one IP address. If you decide to use data center proxies for web scraping, you’ll want to pick a proxy provider that offers a lot of diversity and redundancy to avoid proxy bans.

ISP proxies

ISP proxies provide fast access to websites and get fewer bans than data center proxies. Why? Because they’re hosted in a data center but are issued by actual internet service providers or ISPs. So they have the authority of residential proxies, and you get the fast speeds of a data center proxy without raising red flags! In addition, Rayobyte puts no limits on bandwidth or threads, meaning more significant savings for you. Rayobyte currently offers ISP proxies from the US, UK, and Germany.

Dedicated proxies

These are private proxies that only one user can access, making them slightly faster than shared proxies. Exclusive access to the proxy IP address also means you can use it for longer since its activity will look more natural than proxies multiple people can use. Typically, people use these proxies to browse the web since it keeps their IP the same without leaking location data or identity. They’re also helpful for bypassing firewalls and geographic restrictions where other proxies may get caught.

However, the main benefit can also be a hindrance — since the same IP address repeatedly visits the same websites to scrape data, it’s easier for websites to trace dedicated proxies. If you get blocked, you also have less flexibility, and these proxies don’t scale easily if you need to expand your activities. If you need a single, stable IP address, they’re a great option — but if you need thousands, you may need something else.

Semi-dedicated proxies

Up to three users can share these proxies — making them more cost-effective than using dedicated proxies. These are a middle ground between private and shared proxies, providing you with a lower cost than dedicated proxies and more stability than shared proxies.

However, sharing an IP address — even with only a couple of other users — can impact reliability. If a different user gets flagged on a website, you might face their consequences — like getting blocked or slowed down — as well. Sharing bandwidth may also affect performance metrics.

How does an API work with proxies?

An API is an interactive interface that connects computer programs, making it easier for devices like phones and computers to communicate with servers that host information. They can help manage proxies by providing you with an interface that gives you simpler options than your proxy dashboard has.

These options group together multiple interactions with different programs that you would otherwise have to do individually. For instance, if you were crawling a large real estate database like a listings website, you would have to request all listings in a specific area, then ask it to filter the results based on factors like price and amenities. A proxy API knows how to collect this information for you and compile it in a readable format.

Many people use proxy APIs to add or exclude IPs, but you can get much more hands-on, even if you aren’t too comfortable with coding. For instance, you can access your proxy services and their settings, and you can even look at the traffic each proxy runs into.

A good proxy API will also let you request new proxies directly from your provider, helping you minimize downtime when one of your proxies gets blocked. It can also help you regularly rotate proxies so that you avoid burning through them. The API can send an API call to the proxy dashboard at regular intervals to make sure you’re not using one IP address for too much time.

How to choose the right real estate API

Now that you know what an API in real estate is, you may be wondering how to know which is best for you. There are many real estate APIs on the market with different offerings. When deciding on which one to use, remember that your main reason for using an API is to simplify using a proxy to scrape the web. With that in mind, pay attention to these factors to find the option that best suits you:

• Discoverability. This describes how easy it is to learn the API naturally. While important for anyone, this is an especially crucial feature if you don’t have much experience with real estate listings APIs.
• Consistency. A consistent API can streamline your development processes, lowering the risk of developing and deploying APIs
• Documentation. An API should record its work in an easily readable manner, so you or anyone else can understand what it has done.
• Accessibility. Your API should invite users to engage with it, rather than intimidating users with too many complicated features.
• Monitoring. If you outsource your API to a third party, you need to check that they regularly monitor its performance to prevent data outages.
• Support. A responsive customer support system can save you from significant data, time, and money losses in the event of an error.
• Data quality. Your API should deliver high-quality data, free of duplicate records and noise that can ruin results.
• Budget. While you do pay for quality, make sure your API is within your budget. To avoid running out of money, a cheap or free real estate API may be a better option than an expensive one that offers more useful features but exceeds your budget.

How To Use Real Estate Data

Success in the real estate market relies on having accurate data. Whether you’re trying to find an available property or need more information to make a decision, web scraping will speed up and improve the process. Instead of searching for credible information from the most trustworthy real estate site you can find, you can scrape massive amounts of current, precise, reliable data.

The common information you might collect on a listing through real estate scraping includes:

• Type of property
• Average sale price
• Square footage
• Location
• Property agent
• Amenities
• Average rental price
• Neighborhood perks
• Long-term capital gain

If you want to run a more advanced search, you can also gather property information like:

• Crime and safety in the area
• Foreclosure and auction listings
• Construction permits

You can then investigate this data to make actionable insights — a process also known as data mining. This involves methods like data preparation and cleaning, classification, clustering, tracking patterns, revealing anomalies, and building decision trees. Since there is so much processing, you would generally do this with specialized tools and algorithms, rather than tackling it yourself.

There are many ways to analyze real estate data, each with a different purpose. Here are some types of real estate data analytics that you might find useful:

Descriptive analytics

These are trends, describing what has happened over a specific period. For instance, you might look at how much a property’s rental income has increased in the past ten years. By looking at data from a wide range of sources, descriptive analytics tries to make useful insights from the past.

Diagnostic analytics

In this type, you examine historical data to deduce why an event took place. For instance, you can use diagnostic analytics to dive deeply into a rental property with a high turnover rate and uncover the reasons why people haven’t stayed there for long periods.

Predictive analytics

This type tries to forecast how the market will change. It applies observations from descriptive and diagnostic analytics to find clusters, exceptions, and tendencies that help predict the housing market.

Prescriptive analytics

This type of analysis tries to identify what you need to do to take advantage of an opportunity or avoid a problem in the future. For example, if you’re investing in a property to rent, you can use prescriptive analytics to figure out how many bedrooms and bathrooms the home you purchase should have.

Prescriptive analytics requires sophisticated technology and investment tools like algorithms, business rules, and machine learning, so it’s one of the hardest to implement and manage.

Whichever type of analysis you conduct, you can get a lot of information that helps you optimize ROI. Some ways in which the data mining and analysis process can help include:

• Analyzing general trends like overall real estate investment and individual incomes to identify market trends
• Researching real estate fluctuation periods helps you predict upcoming fluctuations and identify factors that affect it
• Understanding customers’ consumption habits, helping salespeople learn new client management tactics and techniques to retain old customers and drive new ones

Benefits of Real Estate Scraping

In a recent survey, the National Association of Realtors found that 41% of real estate searches start online and 95% use online tools at some point in their search process. With time, those numbers will continue to increase. The internet is a core of the real estate market and a large compendium of real estate data.

But manually accessing enough of this data to make actionable conclusions takes time and money. Real estate scraping helps you gather all of it in real time so you can make the most informed decisions possible. Here are some proven real estate data scraping use cases for buyers and sellers in the housing market:

Housing market research 

The National Association of Realtors reported that people who recently bought a house typically searched for eight weeks and looked at a median of eight homes. This is an arduous manual process, and people typically end up eliminating some good options out of fatigue or frustration. Using a real estate data API speeds up the process of searching for houses to help ensure you consider every good option.

You can also gather far more data in real-time, helping you get a better picture of the current market and recent trends. With this data, you can make much more informed buying and selling decisions.

Accurate appraisals

It’s vital to get an appraisal on an investment property before either buying or selling. You can use a real estate listings API to find accurate information about sales for similar properties in the area, making it easier and quicker for valuators to give you an accurate price estimation.

Predicting the market

Knowing the best time to sell properties is essential if you want to have the most profitable outcome. By collecting and analyzing data like market cycles, lowest values, peak prices, and purchase trends, you can identify properties that will gain value and ones that will stagnate. This helps you predict future trends and forecast your ROI and sales.

You can also use this information to find properties that are likely to skyrocket in value. By conducting diagnostic analytics on properties that gained value in the past, you can figure out conditions that will cause similar ones to gain value as well, such as the development of a new school.

Price optimization

By studying competitor data about similar properties, you can establish a pricing model that appropriately decreases a property’s price to find more customers and increases it to reflect a property’s value and quality at the right time.

You can also learn about the factors in a given area that affect the price, such as crime rate and the presence of nearby public transport, along with the degree to which they will affect a house’s price. With descriptive analytics, you can also find past trends that might be relevant. All this information can help you optimize your price and predict how the property’s value will change over time.

Maximizing rental yields

By analyzing the historical yields of different properties in an area, you can find the properties in the neighborhood with the highest rental yield to generate the best ROI. You can also look for trends among high-yield properties in different neighborhoods to identify common factors between them, which could help you find other properties that also have potential.

Increasing long-term sustainability

For many people, sustainability is an increasingly desirable quality in anything they buy, and they’re willing to pay a little more to get it. That increases the value of investments into climate-conscious features like rooftop solar panels, LED lighting, and smart-home technology.

These features don’t all hold the same value in every location, however, and choosing the wrong one could cost a significant amount. A real estate API can eliminate the guesswork by helping you identify the most successful and desirable sustainability features in a given area.

Analyzing homebuyer sentiment

Homebuyer reviews and rankings on listings websites can yield a lot of information, like buyers’ neighborhood requirements, the most valuable features of a home, the relationship between the realtors and the homeowner, and reasons why people move out of or to certain areas. You can use this data to guide and optimize your marketing efforts.

A broad view of homebuyer sentiment in the real estate market can also help you predict shifts in the market. For instance, if you notice that homebuyer sentiment is on a consistent downtrend, you know it’s not a good time to buy more real estate.

Lead generation

Real estate businesses need to constantly attract new leads because people rarely move in less than one or two years. You can scrape contact data from various sources for lead generation, including:

• Sellers of for-sale properties by owner websites
• Potential investors on rental property listing sites
• Potential realtor partners for co-brokerage opportunities in different areas
• Potential commercial real estate sellers on Google Maps
• Courthouses to find potentially motivated sellers
• Sellers with expired listings on listing portals

You could even configure the algorithm you use to compile this information so that it includes your monthly or quarterly revenue goals along with the number of leads you’d have to contact each day to reach the goals. You can further optimize this by having it account for current conversion rates according to the medium you use to contact these leads.

Advertisement targeting 

Using a proxy to scrape the web, you can target websites and pull data from specific geographic areas, helping you better understand price ranges and homebuyer expectations in different neighborhoods. With this knowledge, you can generate customized advertisements that best fit the homebuyers in a specific region.

Getting more information on the people living in an area also helps businesses target advertisements to engage with potential future customers before they’re ready to move so that the business is on the customer’s mind when they finally do begin searching for real estate.

Transferring data

You could set up a web scraper to scrape data on real estate listings from a partner agency, so you can list certain properties of theirs on your website. This could be a valuable option if you don’t have internal methods to exchange this data.

Monitoring vacancy rates 

By scraping data, you can analyze positive and negative rental cycles in the market, along with information like a potential investment’s length of vacancies and average vacancy rate over time. This will help you understand if the property is underperforming or outperforming similar ones and develop rent accurate estimates.

Limitations of Real Estate APIs

While the benefits of scraping are immense, it’s important to also keep in mind the potential hurdles you will face in real estate web scraping. These include:

• Sites that disallow crawling bots. Some sites prevent web scraping with various methods, including disallowing bots from crawling. To get around this, you will need a more sophisticated web scraper that can simulate a person browsing the website.
• Complicated web page structure. Since a scraper is a script, it relies on common website practices to accurately pull data. A complicated or changeable web page structure is more difficult to parse.
• Dynamic content. Websites use dynamic web content features like lazy loading images, infinite scrolling, and “show more info” buttons to improve their user experience. However, not all scrapers can navigate these functions.
• Login requirements. Some websites require login credentials to access protected information. Luckily, you can work around this. Once you log in, your browser automatically updates a cookie so the site knows that all requests from your device are coming from you. When you scrape websites that require a login, you just have to make sure you send the cookies with your requests.
• CAPTCHA checks. These are the questions that websites sometimes ask you to answer to prove that you’re a human. Many scrapers can’t get around these yet, so they may stop at this point until you type in the correct answer.
• Honeypot traps. These are content pieces, like links, that people can’t see on a website but a scraper can. When the scraper lands on one of these traps, the website knows you’re using a scraper and blocks its IP address.
• Constant maintenance. You will need to keep checking the websites that you’re sourcing information from to make sure they haven’t changed their HTML structure or content. If they do, you need to react quickly or your API for real estate won’t be able to read those sites.
• Diverse systems. Every system has its specific logic, which can lead to unique challenges when you’re trying to integrate an API with multiple platforms. As a result, you need to have at least a broad understanding of how the systems that you integrate with work, so you can troubleshoot and debug any problems that occur.

Final Words: Set Yourself Up for Success in Real Estate

Whether you’re a buyer, a seller, or just looking at the current housing market, using a real estate proxy to scrape the web can help you understand the current market conditions so you can discover and maximize opportunities that arise. With the present difficulties in traversing the real estate market, this tool can give you the edge you need to become and remain successful.

With a good real estate API and proxy, you can simplify your journey through the real estate market even further, avoiding the need to learn complex coding and freeing up more time to analyze the data you collect so you can make better decisions. Contact Rayobyte for more information on any of our services or if you have any custom requirements.

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.