The Real Estate Industry and Big Data

Big data has finally been recognized by the real estate industry as one of the best ways to grow business and gain profit. Now more than ever, you can rely on big data and its avenues. The real estate industry has widely used traditional practices for marketing and learning about what buyers want. Now, they can use big data to help their business flourish while keeping cost-effectiveness in mind.

Here are some things to keep in mind when incorporating big data in the real estate business:

Thing big. Instead of setting limits, try looking at big data in a bigger way. Even small sets of data can be useful, but the amount of data that is out there is astounding. And more data means more possibilities when it comes to making business decisions and setting goals.

Don’t be biased. Instead of viewing different types of data as meaningless, look at them for their individual values. If you analyze them, they can be connected and give incredible insight. So, be selective with data without ruling anything out.

Collaborate and share. Data is now being sourced from conventional methods as well as newer ones like Facebook and Twitter. It’s also being shared throughout different types of industries and data teams are focusing on collaboration with their counterparts.

Be focused. Even before you begin to utilize your big data strategy, focus on your approach and your long-term goals for your company. This way, big data can help to answer the most important questions first by focusing on insights. It can also help you to prioritize your business goals.

Don’t rely solely on data. Remember that big data access and analysis do not override expertise and negotiation skills. While big data is extremely helpful in the long run for real estate companies, it certainly won’t solve all of your problems. Big data should be a complement to skills and knowledge in the industry.

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How to Store Your Data

Forward-thinking real estate companies have already started gathering and analyzing data. The problem with data is that it is mostly unorganized. And this poses many challenges for the data manager who needs an appropriate strategy to store and analyze data.

If you gather data, you will have huge amounts of both structured and unstructured data. How do you organize this data? This is a question that all data managers ask themselves.

One of the biggest data problems that real estate companies face is the large amounts of unorganized data that pile up. Data scientists are aware of the presence of unorganized data. They simply don't know what to do with it. Consequently, they ignore it. But after some time as the stockpile gets bigger and bigger, they will need to decide what to keep and what to throw away.

In some cases, industry regulations may ask real estate companies to keep data for specific periods of time. For example, builders and realtors will have to keep transaction details.

If you are a real estate company that has gathered a large amount of data, you should have a proper plan to sift through data repositories that need not necessarily have any value. You should also have proper strategies to de-duplicate data. You may have the same data in multiple locations. This is a mere wastage of valuable storage space.

Your instinctive drive is to throw away all old data; while at the same time, there is an inherent fear that you might require that data someday. How do organizations deal with the dilemma? The data may exist in internal legacy systems. It may also come from sources such as social media, web sources and machines. To make this information useful, the company will have to invest in long data capabilities.

You will perhaps have to reorganize your data systems over time. This involves sifting through your data repositories, and implementing standards that maintain accuracy. If your company lacks the expertise or resources to do this, you could consider outsourcing this process to data engineers who use cloud-based systems that unify this data into actionable information that will facilitate corporate analytics. And that makes sense because storing your data in the cloud is more economical than keeping it on your data center storage.

In addition, by moving your archival information to the cloud, you save yourself the trouble of having this information on the site. And when this data is finally required, you can retrieve it.

This strategy almost always works. But you may still have difficulty finding out which data you should keep and which data you should throw away. If you decide to keep all data, you will have to buy more storage space and pay more for it.

Data poses many challenges. Besides collecting it and analyzing it, you need to store it appropriately so that you can retrieve it when you need it. If you don't have an onsite system to store data, cloud-based solutions are your best bet. Besides storing your data, they analyze it and turn it into actionable insights.

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How Small and Medium Sized Real Estate Companies Can Benefit from Data Analytics

Data is being generated at an alarming pace. Every action that we perform today helps create data. Needless to say, in today's business world, data is a big force to reckon with.

Thanks to the availability of computers with large memory and storage, storing vast amounts of data is no longer a big deal. And hence small real estate companies have no excuses for ignoring the need to gather, store and manage data that pertains to their business. In fact, by using data analytics, a small builder or a realtor can gain advantage over their competition.

Here is why you need a data analytics strategy to retain your competitive advantage:

It helps you create a personalized experience for your customers

Customers are getting more and more demanding. These days, all customers want an experience which is tailor-made for them. Gone are the days when one size would fit all. But that is not surprising. Since you are working with customers of all kinds, it is not easy to create one business model or user experience that fits everyone.

On the flip side, if you have collected massive amounts of data, you can create an experience that will satisfy an entire category of customers having similar interests or needs. If you don't know anything about your customers this simply isn't possible.

Data analytics make your marketing more effective

Your marketing strategy should be designed to target specific groups of customers. If your marketing messages are personalized, the customer will get the impression that they are dealing with a trusted friend or associate. In other words, by leveraging data, you can create that personal connection with the customer.

By collecting as much information as possible about your customers, you will get to know them better. This will allow you to create personalized messages.

Data helps you clear your wrong assumptions

Many small real estate companies don't know what their customers want. They simply assume that they know everything but they don't.

By collecting and analyzing data about how you run your business, you can save yourself many troubles. If, on the other hand, your business strategy is built upon wrong assumptions, you will lose lots of money. Data analytics help prevent these problems.

It helps you identify your target markets

Who is your customer? Who are the people who buy your homes or hire your service? Until you know the answers to that question, you are simply guessing things. When you know who your customers are and what they want, you will be able to create an appropriate marketing strategy. This will also help you branch out into other markets.

Data analytics and quantification are great tools for your business. By making a small investment today, you can reap huge benefits later.

The resources that a small real estate company needs to implement data analytics are: data, the right tools and expertise. Data is now readily available. Thanks to open source technology, data analytics tools are also available. The only missing element is the expertise to analyze data.

However, if you are using a pre-packaged basic model, the expert support is needed only once.

By spending a few hundred dollars a month, a small business can host and maintain its data analytics model on a cloud-based server. The investment will result in productivity enhancements and typically pay for itself in a few months.

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Can Big Data Rescue People with Bad Debt Cycles?

Big data adoption is sweeping across the financial sector, gaining widespread acceptance among lending institutions as well. While some banks might call themselves pioneers of this movement, it is the medium and small scale lenders who are really driving this trend. The lending marketplace is abuzz with how big data is helping to decode more risk-appropriate lending profiles. Data analytics is being used to increase the quality of customer service and arrest cases of fraud.

Big Data Not Always Associated with Bigger Businesses

The adoption of big data doesn’t have a uniform pattern. Some of the bigger, more established financial institutes might be collecting more data from conventional points of customer interaction only. However, lending agencies handling smaller loans are more likely to analyze poor credit histories to ascertain the overall chances of recovery and the ability to repay another loan. Here, the idea is simple—take a calculated, manageable risk by offering smaller loans to credit-worthy consumers.

Big Data Can be Seemingly Insignificant Data

The indicators used to evaluate borrower profiles are breaking new ground with regularity. Even social media channels that carry employment information or email accounts with a regular history of making payments or receiving funds are being analyzed. The emphasis is on looking beyond the typical buying and payment history of a consumer. Instead, the focus is on the present and near-future ability of the borrower to repay a small loan. Having a stable job over the past few months can be a stronger argument against the non-payment of a small loan years ago. Similarly, small businesses paying taxes on time and interacting with customers on Facebook, Twitter, or LinkedIn are more likely to be approved for a loan.

Yes, demographic information and credit history are still important. However, these conventional parameters took a severe beating in the aftermath of the recession. People with otherwise good credit histories too were caught in this mess. Therefore, credit histories that show positive signs of complete recovery are worthy of being given another chance. When analyzed further with big data analytics, many such deserving borrowers can be identified.

Will big data overturn debt cycles that cripple households?

The answer lies in how you perceive the question:

Do you look at it as technology coming to the aid of people who are conventionally not credit-worthy? (unlikely)

Or,

Do you perceive it as traditional evaluation parameters being improved by using contemporary technologies? (practical and feasible)

The ideal way to look at this argument is accepting that big data provides a better way to identify consumers who can make monthly payments despite a somewhat-flawed credit history. Big data is not the magical cure that some families might expect it to be.

Similarly, big data cannot guarantee loan payments. It is essentially a relief to a market that is still recovering. It cannot alleviate poverty or the healthcare crisis. However, big data will ensure that a larger part of the credit-deserving population is well served. Much of the information collected as a part of the big data strategy is already in the public domain. If the reputation of a neighborhood or money earned during seasonal employment is used to determine credit reliability, is someone being harmed?

Why your Big Data Analysis System Should be Insightful

There is a lot of emphasis on data and data analysis these days. However, data analysis alone won't deliver the desired results; companies should be able to turn the insights acquired by analyzing data into valuable actions.

Using data analysis, a real estate agent should be able to identify potential customers who are likely to move homes in the immediate future. Proper analysis of data involves asking the right questions. This will help realtors detect trends and adjust their business processes.

If you are an agent you could perhaps ask:

Why am I making fewer sales? Or how can I know what my customers want? Remember that the goal of data analysis is to change compelling insights into tangible business actions.

Companies should build a data-first strategy that will help them gather valuable data using their new products. Data analysts attempt this type of exploration to discover critical business insights. They will then present these insights to stakeholders, who after interpreting the insights will use them in making tactical decisions. There is one problem, though. Thanks to its broad nature, open-ended explorations may uncover insights that have little value.

On the other hand, closed loop data explorations are more focused. For example, a real estate company might ask: Are there any real estate investors who bought more than 5 properties last month? This information allows the company to flag that investor as a very important customer eligible for special offers. However, even closed-loop data analysis carries the risk of producing insights of little value.

Why it is important to take quick decisions

One of the biggest challenges of doing business is making great decisions. While professionals in the IT sector are quite comfortable delivering reports, real estate brokers are yet to get into that habit. It is even more difficult to take an action based on available information. And if a company fails to take decisions in spite of having lots of insights, its value will begin to erode.

You may have invested in information management tools and generated all kinds of reports and dashboards, but all of these are useless if you cannot convert your observations into actions. In order to benefit from data analysis, companies should be able to quickly and efficiently transform insights into actions.

This wouldn't be possible without investing in streamlined data collection technologies and decision-making strategies.

The need to adopt a "data first"strategy

When you design or launch a new product, data collection and analysis is probably the last thing you do. However, it needs to be the first. To have a 'data first' strategy is to have the right tools in place to gather insights that will greatly improve user experience.

Partnerships

Many organizations do not have the technical expertise required to gather and analyze data. In this case, they should consider bringing in outside expertise through partnerships. And when you have the right partnerships, you will be able to reach customers with deals, offers, content, and ads across different channels.

Why is data so big?

Big data analysis is a powerful tool that will significantly improve user experience and increase profits. But it’s critical that businesses are aware of the process, have the right tools, and realize the need to move quickly and decisively once they have gathered insights.