5 Key Concerns Big Data Can Answer In Real Estate

5 Key Concerns Big Data Can Answer In Real Estate

Real Estate Technology 

The final question that we are attempting to ask here is – How big data & analytics can help real estate buyers solve discoverability & transparency problems.

From allowing customers to

  • Discover new launches

  • Compare housing projects

  • Get information of neighbourhood

  • Calculate driving distances through Google maps

Real estate technology has come a long way in last 2 years. However the real estate industry has also become more complex and uncertain. Buyers are more probable than in recent past to be in a state of apprehension about the

  1. accuracy of a pricing scheme

  2. builder’s real intent to deliver on time

  3. how prices will move after purchase and

  4. overarching concern of what product will be finally delivered and by when

1. Missing touch with customers

What aspects of real estate are caught in a no man’s land? The problem that real estate professionals cite most frequently today is losing touch with their customers – a real struggle in a people-centred business – with existing technology not able to address the real concerns of a buyer.

Says a prominent real estate agent in Bangalore – “technology has made it easy to forget the importance of face-to-face meetings. But how does a buyer get answers to questions ranging from – what is the probability of the project getting delayed, what will be the quality of construction, how will the location shape up after 3 years when we actually move in? It is this gap that is causing a lot of information vacuum.”

Indian real estate is notorious for lack of information. The problem gets further acute due to complex and uncertain regulations – remember the issues concerning Okhla Bird Sanctuary, Allahabad High Court order to demolish 2 towers of Supertech Emerald Court in Noida & pending Supreme Court case on Crossing Republic Ghaziabad? – & poor democratization of information by city authorities and developers.

2. Which data to trust?

If you try to look for simple information like pricing of an apartment, you see different prices on different websites, brokers give different figures – so how does one know which one is accurate?

Says Kuldip Chawlla, Managing Director of Milestone Capital (a real estate focused private equity fund): “if you really see, has a real estate customer’s life become significantly better in last 10 years despite the advent of technology? The property search ecosystem provides comprehensive listing … but what about

  • transparency

  • data accuracy and

  • predictability

that are still acutely lacking in the industry?”

3. Important questions on customer’s mind

Can Big Data and Technology solve this?

So what are the key questions that should be answered for a customer to make a reliable and informed decision? Is there enough data in the ecosystem that can be accessed and analyzed to bring answers and solutions to these questions:

Is the property priced correctly?

Am I overpaying?

Will the property get delivered on time?

Will the quality of construction be good?

Will the location be livable by the time we move in?

Is this the right time to buy? Can I afford it?

The challenge is that there are no structured & single instance of information that can answer any of these questions.

Essentially if we attempt to answer a seemingly simple question on property pricing we find that there are several complexities that make price comparison & price discovery difficult.

4. Analytics & its use cases in real estate

Each project and many times each tower within a project has its unique pricing method, nomenclature, schemes, floor plan efficiency, non standard charges, freebies etc. that make scientific comparison next to impossible. Similarly to answer a “livability” question – one would literally get lost in maze of hazy, difficult-to-obtain information.

The good thing is that there is enough information available today from private and public data sources – albeit fragmented from multiple sources – that can be aggregated and standardized with analytics and big data approach to help potential customers make better decisions. E.g. big data based livability smart maps use data from public sources such as area & infrastructure master plans, census etc. and combine with private data sources such as real estate development, velocity and pattern of real estate sales, rate of habitation, commercial activities – and convert all these disparate datasets into analytics based maps showing how a specific location will shape up in 3 to 5 year timeframe.

Such analytics based tools also help to replace the existing technology “black boxes” – such as heat maps and price trends – that are mostly based on sparse linear data and lack accuracy and usefulness. Even on seemingly fuzzy issues such as timely delivery of project and quality of construction, big data tools mine information on approvals, velocity of sales, current project portfolio of the developer, credentials and customer reviews of project contractors, social media posts etc. – to give signals to potential customers. Such tools are also helping in democratizing information and taking performance accountability to a new level because consumers will be making decisions based on facts, data and past experience of other consumers.

Analytics is also beginning to have a profound impact on commercial leasing.

If you deep dive, often the questions that you want to get solved change and leave you with completely different answers that solve your pain point in unexpected way.

E.g. recently a media agency was trying to decide on a new office space. Instead of jumping into a real estate decision, it engaged in a process of collection of information about their business, challenges, people, customers etc. It turned out that one of their major challenges was recruiting and retaining talent.

Through rich information on all past and present employees – and using spatial mapping, real-time commute analysis, employee movements and preferences, the company was able to find a workplace that enabled it to attract and retain talent better.

5. Home loans – optimizing for customers and banks

Beyond the consumer and industry-facing aspects of big data, institutions such as banks can plug into big data resources to determine whether a home loan justifies affordability of a customer on basis of his credit profile and risk factors concerning the particular real estate. In many cases home loans turn delinquent due to customer’s inability to pay rent and EMI simultaneously for an extended period – caused by delayed project deliveries. Tapping into big data resources will help both customers and banks discover and choose the right financial solution.

Written By: Kaushik Guhathakurta, COO of With 12+ years of experience in some of the world’s leading management consulting firms & global private equity in Silicon Valley, Kaushik brings deep experience in business strategy, organization building, analytics and technology. Immediately prior to co-founding, Kaushik also advised leading real estate companies in India on Private Equity fund raising strategies which has given him deep insights into the working of the real estate industry in India. Kaushik is a rank-holder Chartered Accountant and MBA from XLRI Jamshedpur.

Accuracy and up to date information about the real estate industry make, a leading source for in-depth & reliable news on the realty sector. Known for providing the inside scoop on project feasibility and credibility, neighborhood demographics, sociability and market trends, aims at adding value to each and every property pursuit.