How Big Data Can Help the Real Estate Industry

For decades, real estate agents have gotten to know properties one by one, then presented their selling points to potential buyers. As the number of properties for sale increases and competition in the industry increases, big data can help real estate agents stay ahead of their competition and provide the best possible information to potential buyers. 

Let’s look at several ways that big data can help the real estate industry: 

Informed Real Estate Agents Stand Out in Online Marketing

While it used to be easy enough to stay on top of all the property listings in your area, it’s becoming more difficult for agents to compete with online listing sites that have access to large amounts of data. Informed real estate agents are using big data trends to gain unique insights into the areas that they serve. This allows them to stay ahead of the curve by using this information in their online marketing efforts.

As you build a social media presence, design a website, and market your real estate business, you’ll stand out in the crowd if you have cold, hard facts about the area you work in. This solid information sets you apart from other real estate agents in your area, showing potential buyers that you care about their needs and are willing to go the extra mile to answer their questions. 

Stay on Top of Foreclosure Information

Consumers are more interested in foreclosures now than ever before, as homes that have been foreclosed on are typically for sale for a much lower rate than the going rate in the neighborhood. Big data allows real estate agents to stay on top of trends in the industry, so that they are aware of which neighborhoods are struggling, as well as where and when foreclosures are happening. These informed agents are uniquely prepared to pick up foreclosures and add them to the list of homes that they market. 

Examine Health and Safety Issues

With big data, real estate agents can examine health and safety issues surrounding properties they are attempting to sell. By accessing public records for assessments and inspections in the area, real estate agents can gain information going back for multiple years. This information can be extremely important while trying to sell a property. Whether you’re selling residential or commercial properties, it’s crucial that you have all the information available before making a presentation to a potential buyer. 

 View Industry Wide Trends

Access to big data also allows real estate agents to compile information about an entire neighborhood, street, or school district. This information can be especially useful as you advertise homes in that area to potential buyers. Trends such as average income, school successes and limitations, arrest and drug abuse statistics, and more can all be very interesting to families moving into the area, and can also impact property values. As an agent, staying on top of these local industry-wide trends can be incredibly important. 

When you’re a real estate agent, finding the best possible information for your potential clients is essential. It can be tough to do this on a property by property basis. Instead, use big data and the internet to your advantage, so that you can gather the information you need quickly and effectively, then compile it in an attractive manner. 

Functions of Data Management

A data center is probably more important than many people think. It’s true that they serve as storage for network servers, but did you know that they are fundamental in providing power, cooling, and security measures?

Data centers also protect businesses against malicious attacks and natural disasters, as well maintaining access to data. And the cherry on top is the IT staff that makes sure everything in the data center is in place, both onsite offsite.

It’s safe to say that data centers are the foundation of information technology. But like anything else, data needs to be managed in order for successful access and protection to occur. The management of data involves four functions:

Storage provision

Simply put, data needs a storage resource. It also needs to be accessed when necessary. Storage provisioning of data makes this possible. Management of storage provision also involves data tiering, security, and performance management.

Data protection

The protection of data involves all forms of functions that are required to ensure that data remains readily available. Companies spend a great deal of money every year of protection of data.

Data replication

The replication of data impacts networking, network bandwidth management, capacity management, and security, and storage management. Data needs to be agile, and it has to stay that way. Data replication assures that data stays available if any unexpected disaster happens.

Recovery of data

The recovery of data involves many functions that are all involved with all of the data center’s pillars. If some equipment malfunctions or breaks, it absolutely cannot affect the operations of the business. Data recovery involves everything from how the data is stored to where it is stored.

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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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Big Data Strategy

Real estate companies are beginning to realize the importance of big data. However, many of them still do not have a well-thought out big data strategy. There is more to data analytics than discovering how many people visited your site yesterday. In fact, data has no value if it does not provide actionable insights. Builders and realtors need to experiment with volume and variety if they want their big data strategy to work.

Forward thinking organizations are quick to experiment with their products and services. This helps them discover what the customer wants. By making big data analytics an important aspect of your marketing strategy, you can accelerate that discovery.

To make your company more adaptable, you need to experiment with the different aspects of data:

Big data provides a wealth of insights

Big data is available in high volume. When you have access to insane amounts of data, you should be ready to experiment with them. Your experiments must focus on finding that project, which will do well in the existing market conditions.

If you are a builder, you will want to know what kind of projects will sell well in a particular locality.

Execute rapidly

Ask your team to come up with innovative ideas. Let these ideas eventually become your offerings. Here the key is making experiments at a fast pace. You should be able to change ideas into offerings in the shortest possible time. Market conditions can change anytime. You can't take years to launch a project you conceived today after experimenting with big data. You should minimize the time you need to bring that idea into the market.

If all of your offerings are based on one theme, you will land in serious trouble when that theme is off. That is why you need to experiment with ideas.

Think about the kind of services and products you can offer. Make sure that all of them are not of the same kind. You should also think about the potential market these products or services could serve. At any given time, you must have a mix of these products and services. So even if demand for one product decreases, you will still have something else to fall back on.

Don't forget your existing customers.

They are the biggest source of referrals for you. Retaining an existing client is easier than finding a new one, so keep in touch with them.  A real estate company tends to do better when it has a market-driven sales strategy. In this case, it understands a market very well and experiment with projects that tend to do well in that market.

Summary

Organizations that adopt big data tend to have a competitive advantage over others. An organization becomes adaptable only when it is ready to experiment and data analytics is the key to rapid experimentation. It doesn't really matter whether you are a big real estate company or an independent realtor who only serves one market, you can no longer ignore big data. At any given time, you must be experimenting with a large number of ideas. This helps you find that product or service which will ultimately sell well.

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Big Data and Its Impact on the Real Estate Industry

 

Big data can help sustainable development. Recent surveys have revealed that buyers are looking for homes with sustainable features. This presents both an opportunity and a challenge for the builder. Builders who incorporate energy efficiency features into their projects will have an edge over others who still rely on the construction standards of the previous century.

But how can a builder understand what sustainable features are popular with buyers? When they analyze big data they will know.

What is big data and why you should care?

Big data is a collection of large data sets that can transform the way buildings are built and managed. It may even affect government policy.

The biggest USP of big data is its speed. Data is being generated at an alarming pace. Every interaction we have with digital technology helps create big data. And to keep pace with the speed with which data is generated, organizations need to invest in competent analytics tools.

Big data provides reliable information in a matter of seconds. A real world analytics firm may take weeks or months to publish a report on sustainable features.

By simply analyzing the information your users leave behind as they navigate through your website, you can understand their priorities and requirements. You can, for example, use this information to identify the most important factors you should consider while choosing a locality to build its project. Data analysis has revealed that most buyers are looking to invest in a crime-free locality with lots of good schools. Buyers also consider commute time. When realtors know what their clients want, they can promote their listings accordingly.

Big data uses current technologies in innovative ways. Data helps investors assess the existing real estate market conditions. The real estate market in the US is recovering. Home prices and sales have gone up. Inventories and foreclosures are going down.

Data helps realtors understand what their customers want. Take for instance the case of a real estate firm that leases space to tenants. Since tenants in real estate are not an organized lot, it is not easy for realtors to make trends. Data helps fill that void. It helps the realtor target specific groups of customers.

Big data makes information accessible to just about everybody. This openness of information will ultimately force government to update archaic laws.

Data and its impact on sustainability

Hurricane Sandy brought issues such as resiliency and efficiency to the forefront. In order to improve a building's resiliency, you should be able to measure it.

A real estate data company recently analyzed data gathered from Local Law 84. This law requires buildings that measure 50,000 square feet or more to submit an energy benchmark report. People often believe that newer buildings are more energy efficient. However, the findings of this data analysis revealed that this was not always true.

Many masonry buildings built during the 1930s have comparatively low energy costs. As data becomes readily accessible, energy managers will be able to take measures necessary to reduce their energy bills. Homeowners can also use data to measure the ROI on their energy efficiency investments. This may even encourage others to invest in green technology.

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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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Why Builders and Realtors Need to Analyze Big Data

Big data has suddenly become big business. If you are a builder or a realtor, how can you benefit by gathering and analyzing big data? Will data help you sell more homes?

Yes. You might assume that only big organizations need to harness the power of big data. Far from it. Both small and large real estate companies can run their businesses better by analyzing customer data. However, a good understanding of how big data helps your business is essential to develop a suitable business strategy.

Data becomes valuable only when you analyze it. And proper data analysis helps you know your customer better.

Data analysis helps you gain an edge over your competitor

Several builders and agents still don't take the time or trouble to gather data about their target customers. By simply gathering and analyzing data, you can gain an edge over your competition.

By analyzing data, a real estate company can find out what kind of homes sell well in a particular locality. This knowledge will help the builder launch the right project in the right locality.

Data also helps builders and realtors find people who are more likely to buy or sell homes in the immediate future. This will help them create a marketing strategy that targets the right group of buyers.

Data gives you more comprehensive demographic information. It also shows you why certain category of homes sells well in certain localities.

Launch better marketing strategies

After analyzing big data about your customers and the real estate industry, you can use that information to expand your business. One advantage of using big data is that it helps you improve your marketing strategies.

If you run a website, by simply looking at who is visiting your site, you can create a marketing strategy tailor-made for them.

When you have access to data which shows you who your target customer is, you will have no difficulty launching a more personalized marketing strategy.

This kind of analytics is useful for several types of businesses. Data also helps you adapt your marketing strategies for future buyers.

Data helps you decide whether you should implement a marketing strategy or not

By simply analyzing the data gathered by your analytics software, you can decide whether or not you should implement a new marketing strategy. If you market your homes or services to people who are not interested in being a buyer or seller anytime soon, you are simply wasting your time and resources.

When you have real data about your customers and the real estate industry, you can find out whether you should expand your business to other markets.

Realtors sometimes make business decisions with no reason. However, when you work with data analytics, you can make sound business decisions with a reason. A well-thought out business strategy will only benefit you and your company.

How to gather data

It is not difficult for a real estate company to gather data. Most of them already run websites. By simply analyzing the key phrases that attract people to their website, a builder can find valuable information about their potential clients. Both free and paid data analytics software solutions are available.

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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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The Growing Importance of Data Analytics

It seems that retail is fast emerging as the biggest adopter of big data. Nearly every global retailer is exploring how big data analytics can help to decode consumer behaviors and penetrate segments that have traditionally remained outside their reach. However, this doesn’t mean that other sectors aren’t sensitized towards the growing importance of big data.

Retail Might be Leading but Real Estate is Driving the Big Data Wave Too

Healthcare is aggressively pursuing the path of digitization and big data plays an important role in this journey. Real estate institutions are increasingly acknowledging big data capabilities which give them a better chance to evaluate risk and add more value to customer services. Real estate firms are using big data to re-assess traditionally unserved consumer demographics and create more sustainable customer relationships.

Real estate has always been among the biggest creators of consumer data. Somehow, this fact wasn’t widely acknowledged until recently. Even when retailer activities were limited to merely creating summarized bills, real estate was creating lots of useful consumer data. This included details about household incomes, bill payment patterns, and credit history.

Data Analysis Driving Real Estate Decisions

Today, realtors understand that their consumer facing activities need to imitate the big data efficiencies that retailers have gained. This means capturing, storing, cleansing, indexing, and analyzing data for better outcomes. The role of traditional data centers has changed. Earlier, consumer interaction wasn’t as conclusive. Now, consumers review, recommend, and emote about nearly every product/service. This includes conversations about brand loyalty or buying preferences. These conversations provide valuable insight into how geographical and regional demographics affect property buying or refinancing decisions.

Contemporary data centers need to be more proactive to consistently stream the web for such meaningful data. From time spent per webpage to the number of clicks per banner ad, and daily webpage views, most types of browsing patterns can be quantified. Once expressed numerically, they can be further analyzed. The analysis yields actionable information—this kind of data helps managers to take decisions with more confidence.

Using big data analytics, real estate businesses can optimize their products, services, and even webpage layouts and promotional messages. The idea is to be in sync with the mindset of consumers. Some other benefits of an effective data analysis team include:

• Ease of conducting market segmentation studies
• Identification of points of losing consumer interest
• Evaluation of efficiency of different points of sale
• Evaluation of consumer engagement efficiency across different channels like physical campaigns, calls, emails, and mobile offers etc.
• Gaining future readiness, i.e. being intuitive about emerging market trends
• Gaining insight about internal processes and staff performance
• Developing better, more sensitive competitor pricing strategies
• Local and regional market analysis for revenue growth forecasts
• Establishing seasonal patterns in revenue hikes or dips
• Ease of conducting consumer sentiment studies
• Real time monitoring of established and emerging consumer behaviors

Please understand that big data becomes fully useful when a business can gain multi-channel analysis and large scale improvement capabilities. A true big data infrastructure should drive improvement of internal processes like supply chain, recruitment, and even administration. Every internal dynamic of a business, ranging from logistics to warehouse optimization, can be enhanced with big data analysis.

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How Experimentation Can Fast Track Big Data Capability

As more businesses explore the vast expanse of big data utilities, it is becoming clear that big data delivers more when managers are ready to be a bit creative. For a real estate business, big data can provide many standard utilities. Standard because soon most of the real estate organizations will gain similar big data advantages—this trend is already underway. So how does a real estate business make itself unique and more competitive?

This is where experimentation comes to the fore. Big data analysis can yield lots of actionable information provided you are ready to take some chances, allow room for creative experimentation, and use data sources that seem irrelevant at first sight. Here we discuss more about how experimentation can drive better ROI for your big data investment.

Big Data is Being Widely Embraced, but Who Will Benefit More?

We understand that big data is still new for most organizations. They are approaching big data with some apprehension along with lots of enthusiasm. It is unfair to expect such businesses to get creative with big data at the outset. This doesn’t make sense, particularly when digitization hasn’t been associated with the real estate industry in a larger perspective. We realize that most businesses in this niche are finding it difficult to let go of their legacy protocols, wanting to hold on to the traditional methods of working. However, the big data voyage has begun, and soon most businesses will be at ease working in a data-driven environment.

There is another aspect to this realization—some organizations have adaptability as a part of their core skills. On the contrary, some are deep rooted in hierarchies and don’t venture beyond the realm of expected outcomes. It seems that the more adaptable businesses have a better chance of extracting better ROI from their big data adoption. The more established businesses will take time as their bigger teams and numerous offices multiply the hierarchy, breeding lesser elasticity.

Big Data can Look Ungainly, But Don’t Fret - Placate Your Team

Big data involves high volumes, mammoth disparities, and real time performance. This means lots of data is collated from various resources, including data that is highly unstructured and processed in real time. During the initial few weeks, you might feel that your big data team is trying to do too much. However, don’t have doubts about this—the typical big data environment is very challenging and to others, it can seem very perplexing.

Data analysts work together, forming strategies on the go, hungry for processing more data. Once you have established a basic level of understanding with your big data team, try to establish an experimentation policy. The idea is to make the team feel that you support a certain degree of experimentation. The team should know that running correlations or comparisons that don’t amount to any conclusive information will not be ridiculed. You can ask the team to allocate certain hours or weekdays for running experimental data studies. This is vital to discover unexplored market service combinations and unearth new consumer zones.

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Big Data & Hadoop: What’s Cooking?

If you have been following big data, it is most likely you have come across repeated mention of Hadoop. Before we delve deeper into this discussion, let us take a step back in history and understand how data management has evolved and what led to the current scenario where Hadoop and big data are the favored technologies.

Understand Hadoop: The Early Days

Earlier, computing was limited in volumes and in terms of expectations from outcomes. Applications were created specifically for different data structures to ensure that a problem was comprehensively solved. Since computing volumes were humble, this approach didn’t present too many problems. Multiple data resources were uncommon, almost unheard of during this period. Using organizational databases or network data, larger applications functioned rather well. However, the relationship between data structure and application saw a major upheaval with the creation of relational databases.

Due to this, data access to SQL was standardized. More businesses started using spreadsheets. Together, these two developments created the way for more complex data management systems to emerge.

During the 90s, it became clear that there was more business value in combining data from different applications but this was challenging. Slowly, better and bigger data structures surfaced. However, this picture was soon challenged with the arrival of data-intensive business mammoths in the form of Yahoo and Google. Things changed and data didn’t have a similar, streamlined structure. These organizations were handling massive amounts of data on a daily basis, hoping to create a solution for faster access to retrieve and save data. This paved the way for the creation of Hadoop—a more dexterous, organized, and distributed file system that makes it easy to handle unstructured data.

As Hadoop Progressed, Big Data Arrived

Slowly, the advantages of Hadoop became known to businesses across the world. Managers realized that they could benefit a lot in terms of condensing their operational cycles if data access was made easier without compromising its security. The basics of free data management framework that Hadoop had initiated were then routed towards data-driven businesses that wanted data capabilities of bigger organizations.

The Hadoop-Big Data Connection—Hadoop is the platform that makes it easier for unstructured data to be fed into the big data ecosystem. Conventionally, real estate businesses were somewhat selective about retaining data. They feared massive amounts of data will not be accessible. Big data makes it easier to store everything that seems important. With big data, businesses can feel assured that their organizational and consumer data is secured and easily shareable when needed. With big data analysis, the information can be extracted in an orderly, indexed format and then assessed.

Did Google create Hadoop?

Yes, Google can be credited for bringing Hadoop to the world of IT. This happened with Google’s requirement to index enormous amounts of web data, collating, and categorizing it for its search results. Since the market at that time didn’t have a readymade platform that Google could use, it had to innovate. The platform was first called Nutch. Yahoo played a more integral role in helping Hadoop evolve for enterprise applications.

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Big Data Strategies in Real Estate

Predictive analysis and data mining are yet to become a big deal in real estate, but big data is slowly and surely penetrating this segment. Some analysts might question the relevancy of big data in a business where the consumer/homebuyer typically ventures into the market every 5 to 10 years. We argue that this is precisely the reason big data should be used in the real estate niche.

Why Remaining Connected with Customers is Necessary?

Property purchase decisions are heavily impacted by the goodwill a business can generate and sustain. Here, consumer preferences don’t undergo overnight changes like those seen among fast-moving consumer goods. Real estate industry has a special space for references where past clients are invaluable. Having happy, if not loyal, clients can translate into feeding other lines of business like refinancing. If you can maintain a connection with your past clients, many more serious queries are likely to flow from friends, family members, office colleagues, and even neighbors.

Some folks argue that typical retail businesses, like apparel, have more to benefit from big data rather than real estate. Let us take an example to argue our point here—a young family of four with working parents is expecting another child. Such information is often announced in the form of social media updates. A retail business can use this data to send emailers and SMS with links to newborn clothing range. Now, how can a real estate business use similar data?

When a baby is on the way, it is most likely that the mother has taken temporary leave of absence. There is going to be lesser money to pay the bills. With the newborn retail niche overburdened with branded products, the expenses are also likely to rise. Limited to a single income and facing more expenditure, the family might seriously consider refinancing to save money. With three children, they might reconsider moving to a bigger property.

A real estate business can make relevant interaction by emailing pictures of bigger accommodations with more study rooms and better schools. This strategy is a combination of tracking social media conversations and hyper-targeted marketing. Here, a single Facebook update can lay the basis for a new sale or at least create a very serious home-buying or refinancing query.

There is more to tracking consumers’ social media behavior. Online experts now offer targeted social media analysis. They identify audiences whose recent shopping behavior suggests a definite buying capacity. Home purchases might be offline, but online consumer behaviors can help real estate businesses identify more, promising consumers.

Big Data Strategies Deliver but Be Patient

The strategies we discussed above take time. Signing up with a social data analysis firm doesn’t mean that you will be overwhelmed with a glut of serious queries. This is a gradual process where hurrying into decisions can mean disaster. Secondly, keeping past customers engaged isn’t easy. This is particularly true for real estate where the consumer-connect is soon lost after the transaction. This is where data-sharing practices can solve the problem.

If you are ready to share data about the payment pattern or the mortgaging history of past clients, you can get valuable information in return. For instance, some of your past customers might be avid environmental enthusiasts. You can start a small blog or a newsletter about green building practices. Here, you reason to seek an association is logical since you are in the housing niche. Among consumers, such information is less likely to be spammed because it is relevant to them. The e-mailer/newsletter can carry a small, hyperlinked logo, directing the browser to your parent website.

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Why All Businesses Should Embrace Big Data

Some industry analysts have made less-than-enthusiastic forecasts about the widespread adoption of big data. According to them, big data will take time to be adopted by businesses—this can be up to five years.

Is this mere speculation?

There is some truth to this viewpoint. It seems that even among businesses using big data, the zone of maximum productivity remains elusive. This is because some companies have adopted big data without the necessary introspection. It should be understood that big data can deliver only when the resources managing it are well equipped. This means having the technical expertise for data collation, warehousing, sharing, and analyzing. Secondly, some businesses have acquired the expertise but lack the right insight necessary for driving business decisions using big data. Overall, it isn’t the ROI of using big data being questioned — the issue lies with handling it effectively.

The Transformation is Underway

You might think that big data hasn’t really taken off. However, the truth remains that it is much more than just another transformational technology. Despite the slightly-pessimistic perspective, it would be foolish to deny the growing interest in big data. Businesses of all sizes are realizing that big data is a definite, mainstream reality. It is just a matter of time when businesses of all sizes will incorporate the skills necessary to use big data. Big data is here to stay and this realization is underlined by the growing urgency to employ data-centric skills. Some businesses are aggressively hiring manpower that specializes in handling big data. Even better, some companies are looking for trustworthy data solution vendors who can empower them with big data capabilities at much lesser costs.

There is no doubt that big data yields better correlations that drive smarter business decisions. When combined with advanced analysis, big data can help you improve customer service and design better products/services. Data analysis can help you resolve problems that seemed exceptional and unsolvable.

Some Big Data Dynamics You Should Keep in Mind

If you have already begun the big data journey or are on the verge of starting it, please read the following:

Data Protocols

Ensure that you have effective data strategies in place. This includes protocols for collecting, saving, indexing, sharing, and analyzing data. Data should be accessible, but not at the cost of compromising data privacy and security. Whether it is the sales department or CEO’s desk, data protocols should be implemented across the organization.

Expertise to Use Big Data

One huge benefit with big data tools is that they can provide immediate results. This refers to simple data sets. Using this, managers can make decisions that affect the current, internal processes, customer service methods, and marketing strategies. Therefore, ensure that you have the required personnel in place to use big data. You should have data experts or managers who can put actionable data in motion.

Don’t be Apprehensive about being Data-centric

Once you start realizing the benefits of big data, it is likely that you will seek a data-centric approach to regular business processes too. This might upset a few employees and challenge authoritativeness of some managers. Accept this unpleasantness as a part of the transformation that will eventually subside.

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Have You Realized the Power of Big Data?

In the real estate niche, big data is still finding its way. Organizational ecosystems are still contemplating whether big data will deliver ROI and enable product innovation. Here, we present some aspects about adopting big data that might have escaped your attention:

The Big Data Acknowledgment

Some key elements about becoming data enabled now seem doable to most lending institutions. For instance, collecting relevant data means coordinating with regional or remotely-located business units. Lenders are prepared to collaborate with their partners and even customers to ensure that the maximum volume of usable data is collected.

Secondly, adopting big data presents a change in technological and management infrastructure which is necessary to derive ROI. The big data strategy is now better understood, helping lending businesses analyze their preparedness for this transformation. Many times, studying about the big data's journey of an equal sized, but differently-industry business can also help.

How can big data contribute towards your lending business?

Some people believe that big data is more applicable to the retail segment. However, its utility in the real estate market has been proven beyond doubt. Big data is extremely useful for segmenting or hyper-segmenting the market. This forms the basis for more penetrative customer targeting. By combining various data resources, data analysts can decode customer behavioral trends. These can be further grouped across various parameters. The finer nuances of real estate borrowing, such as the last-minute reasons to cancel a loan application, can be better realized through big data.

Sometimes, untapped financing or refinancing requirements can be deciphered by analyzing social media conversations or by analyzing recent credit transactions conducted online. This can help you create loan products, better tailored for a certain regional, age-based, or profession-related demographic. Analyzing customer risk profiles can help you explore the lending market. For instance, people with mid-range credit scores might have shown incredible consistency in bill payments along with reduced credit in recent months. Such consumers can be further assessed for smaller loans.

Is big data out to replace organizational management?

Not really. Big data can ease the decision making, making it more informed, always substantiated with confirmed numbers derived from data analysis. Big data helps managers see beyond their established market practices. Using data-driven business models, you might come across better alternatives to increase employee performance and reduce internal wastages. However, putting big data into practice involves human intervention. In fact, big data needs to be incorporated, run, and used with a reasonable amount of human management.

By streamlining your business practices, big data can reduce staffing and operational costs, but it cannot replace the need for management. Even if big data yields information about better ways to package loan products, it will take efficient management to drive new product presentation, marketing, and consumer education.

Can big data help you discover and create new business models?

Absolutely! This is among the most fascinating applications of big data. Big data has spawned across industries, from online retailers to banking giants. The reason for its success lies in the fact that our generation is information-driven. This information is invariably present in the digital form, i.e. data.

Upon analysis, it might turn out that newly-engaged couples are among the most aggressive loan seekers during the Christmas-New Year period. Sensing an opportunity, you can create customer engagement models that can aggressively tap into this newly engaged demographic. Similarly, data from past holidays inspired borrowing can help you forecast the kind of numbers feasible during the forthcoming holiday season. Consumer profiles that have been denied a mortgage by bigger financial institutions, despite reasonable credit scores, can be very useful. When combined with advanced analytics, it can help you identify borrowing households with steady incomes, greater propensity to pay bills on time, and lesser credit spending habits.

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