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. 

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.

Need lead product solutions? iLeads delivers results that fits your needs! We are a lead solutions provider with lead generation covering Insurance, Lending, Real Estate, Direct Marketing, as well as Lead and Data Analytics. Contact iLeads today!

 

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.

Need lead product solutions? iLeads delivers results that fits your needs! We are a lead solutions provider with lead generation covering Insurance, Lending, Real Estate, Direct Marketing, as well as Lead and Data Analytics. Contact iLeads today!

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.

Buy and work leads smarter, contact only the customers you want to engage, and enable your employees to be productive. Connect with your target audience with Live Connect today!

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.

Leads Direct provides quality business leads at 40-80% off retail prices so you can grow your business quickly and increase your ROI. Contact Leads Direct today!

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.

Buy and work leads smarter, contact only the customers you want to engage, and enable your employees to be productive. Connect with your target audience with Live Connect today!

 

Is Big Data Scaring Banks?

Borrowing money has always been very challenging for startup businesses. However, the trend is changing these days. Preference among startups to seek loans from smaller lending institutions and not banks is emerging as a nationwide trend.

Alternative Lending

Startups, including those that have just begun, are finding it easy to fund their dream projects. Lending is coming rather easily to this segment, including Subprime and Payday loans. The trend is also being referred to as Alternative Lending. This is essentially small business lending that has confused analysts with its unprecedented growth. It seems that this trend is largely fueled by a changing underwriting approach. Conventionally, underwriting has strictly been a standard-driven niche. Credit scores, like FICO scores, have had unchallenged acceptance across the lending sector. Now big data is challenging this typical landscape.

Why this shift?

We believe that this has something to do with the credit crisis that followed the 2008 slowdown. Banks have suffered a lot at the hands of Subprime mortgages. The pessimism continues with banks being apprehensive about anything that doesn’t guarantee repayment. On the other hand, smaller lending institutions have understood the potential of start-ups. They are ready to offer attractive interest rates. Secondly, smaller lending institutions are increasingly using analytical tools to retrieve actionable information from bigger volumes of data.

This big data shift is not as common in the banking sector. Based upon the big data indicators, new underwriting models have surfaced. Start-ups don’t mind their repayment potential being evaluated on the basis of their social media handles or email communication. These individual pieces of data form a part of a widespread big data structure that thrives on using data derived from unconventional resources. This approach is working for startups that are ready to share even more information, as long as the privacy of their business model is not compromised.

Startup Payday Loans Don’t Stretch for Decades

These loans have another advantage over traditional lending instruments like mortgages. An average mortgage might take 15-30 years to be realized. This is too long a period to derive decision driving data that can be used to curate more home loans. However, a bad loan emerging from the startup sector can be analyzed much sooner. Here, the average length of a loan is a few weeks or months. Credit factors that were under evaluated can be quickly identified and used to improve the current underwriting model.

Startup Payday Loan Underwriting isn’t So Demanding

The documentation and repeated screening by the underwriting team can test the consumers’ patience. However, payday loans aren’t as demanding. With little documentation and proofs to establish the borrower’s profile, a payday loan can be executed much quicker. Using analytics to improve this already simplified loan sanctioning process isn’t very difficult.

Payday Reputations are Not so Vulnerable

There is another risk associated with loans to not-so-worthy, newly launched businesses. In case of a default, the bank’s reputation is at risk. Even a few days of bad press can compromise the faith of customers in the bank. However, smaller lending institutions aren’t so sensitive about this issue.

Big Data Costs can be Challenging but Solutions are Emerging

It would be foolish to assume that creating a functional big data infrastructure is without any casualties. The traditional concept of learning as you proceed applies here as well. Using big data doesn’t guarantee the absolute removal of bad loans. Initially, a lender needs to test its big data parameters and their accuracy to observe patterns that can be used for future loan applications. The default rates at the start of the big data journey can be high.

Yes, a big data led underwriting model can be expensive, but another solution has surfaced recently. This is in the form of different business models that use elaborative data. Now lending institutions have the advantage of comparing their organizational structure or lending processes with similar business environments. They can choose a big data model that seems more appropriate for their business environment.

Yes! Data is Relevant Across the Mortgage Industry

Some folks might say that the operational basics of every business are essentially the same, but significant differences can exist. For instance, the manufacturing industry depends upon its performance to gauge its index of quality. Here, quality refers to the processes through which components are assembled and overall quality of these components. However, in the mortgage industry, data is evolving as the most important component. Here, products are the decisions based upon this data.

Understand the Issue: Data Perception among Bankers and Investors

It is generally believed that "Big Data" is beyond the reach of small to medium-level businesses. This notion is driven by the fact that corporations like Google and Facebook have pioneered the cause of being data-driven. Such brands look upon data as their most valuable resource. Many mortgage industry professionals look at cloud-based data and analytics with circumspection. They are not sure whether these data-driven technologies are useful for managing their day-to-day challenges. However, if looked closely, it becomes evident that these technologies are capable enough to find out meaningful processes and facilitate growth.

Is data the cure for all mortgage industry ills?

To be brutally honest, the answer is NO! Typical roadblocks towards using data efficiently are yet to be addressed. For instance, loss of data integrity shows no signs of abating. Many data mistakes might originate from the lender. This is where established dynamics of the industry come to the fore—sales being aggressive and often ignorant of quality or safety standards. To realize the true potential of data, the industry has to think beyond loan and risk performance. They will have to work towards ensuring accuracy of data incorporated into their database.

The Evolving Industry Shift towards Data

Since the economic crisis of 2008, the mortgage marketplace has become more introspective. The industry might not be using data robustly but it is exploring the utility of investing in big data. Investors have traditionally relied on performance of loan-term as the primary benchmark. Data analytics can provide insight beyond this aspect. Data can facilitate access to lesser-recognized factors. This can help industry regulators and investors in better evaluation of securitization or compliance. Data provides useful analytics that helps to achieve better accountability and well-informed decision-making.

The visible progression is towards creating a more comprehensive dataset. This will eventually make governance more effective. However, this is a colossal shift for an industry that has been primarily driven by financial assets. The changing perspective towards looking at data as an asset will take time. However, the change is underway and very palpable. There have been some slow-downs in the way.

 

Several regulations have been introduced that push towards adopting a more data-tolerant attitude. However, a lag is indicated since all organizations cannot jump on to the big data platform immediately. The big change lies in organizations becoming more open towards collaborating data. This is the first step towards creating uniform data that can be used seamlessly across the mortgage industry. This will help to create more effective home-finance regulations.