5 ways Big Data is disrupting Singapore’s property market

Do you know that our property industry is facing a major disruption? And Big Data will be playing a major role in this disruption, similar to what is happening now in the finance, media, healthcare and manufacturing sectors?

What’s the big deal about Big Data?

In the age of IoT (Internet of Things), the transfer of data among connected devices is much more complex than humans can handle. Big Data is the capture and analysis of huge amount of structured or unstructured data to discover trends or patterns useful to organizations.

We generate new property data every day from different public or private sources – land sales, new projects, property listings, sales transactions, rental contracts, management fees, property taxes, housing loans, etc.

• How can we capture, integrate and analyze all these data to provide actionable insights in real-time?

• How can we offer transparent information to property buyers and investors to help them spot opportunities, reduce risks, and make better purchase decisions?

• How can we create powerful insights to stakeholders in the property industry to help them improve business practices, reduce costs and increase efficiency?

Turning Big Data into big benefits

There are five ways that Big Data can transform Singapore’s property market in the future.

1. User-defined Searches

Today there is still much room for improvement for property portals’ search engines to match buyers and sellers. Users are likely to give up halfway through their searches due to frustrations from irrelevant results and lack of insights.

• There is information on project background, photos and videos of the property. But are there timely and honest reviews or recommendations from current or past residents to help users make that buy decision?

• Property investors can find current market value and recent transactions in the past two years. But can the data help to spot market trends, highlight best buys or pinpoint current opportunities?

• There are filters for property type and flat size. But can the data show which floors, units or layouts are most rentable and profitable according to historical records of rental and sales transactions?

• Agents are paying or playing by the rules of the property portal to get to the top of the searches. But is there any relevance analysis ranking to show the most relevant search results according to specific needs of users?

• Each project has details on nearby schools and amenities. But is there any location analytics on peak-hour traffic, proximity to ERP, air quality, average rainfall, dengue history, etc.?

2. Highly-Transparent Market

Property report findings are questionable if there is no transparency on data collection and research methodologies. Similarly, it is meaningless to say a property is above or below valuation if we have no clue about the valuation method. It is like a developer who claims that the new project is priced to sell when there is no fixed pricing scheme and agents can offer discounts at their own discretion.

• Agents can check how much similar units in the same project or houses in the same neighborhood are asking for or recently transacted. Can endusers have real-time access to the same data to avoid unrealistic expectations of sellers and overpaying of buyers?

• Banks can use Big Data to prevent selling properties on mortgagee sale for less than what the market can absorb, so they can make the most profit by selling to a normal buyer but not a value investor. Can home sellers leverage similar analytics?

• There are analysis tools designed only for property appraisers, fund managers and analyst firms. Can they be available to retail investors too?

• New launch pricing and property valuations are now set by the property insiders, namely the developers, valuers and mortgage banks. Can endusers have influence in the market with transparent pricing set by affordability and supply-demand data?

3. Actionable Forecasts

The market is uninterested about an increase of 0.8 percent in sales volume or a drop of 0.5 percent in property prices in the last quarter because the data fail to provide actionable insights.

• With figures on unsold units and project pipelines, is there any supply-and-demand correlation to show the size of different property market sectors in a few years’ time? When is the next market boom or bust? How long will it last?

• After data mining of statistics provided by different government departments, can property investors spot trends from demographic analytics from population census, immigration statistics and employment data?

• Singapore government is trying to deploy Big Data for urban planning. Data collected from the tapping of EZ-Link cards can tell commuter patterns. Can we leverage the same data to tell future congestion areas from new projects, or generate a crowd heatmap in real-time?

• Crunching numbers from historical transactions, do we know what housing types buyers are looking for in different districts? Where are the high growth areas, next property hotspots and future ghost towns?

4. Sales Facilitation

Property is an imperfect market due to lack of information. Big Data can facilitate the buying process by increasing effectiveness between buyers and sellers. Predictive analytics can help to forecast home buying trends and predict profitability of upcoming projects.

• If there is data to show average years to upgrade for different property types, can agents predict when potential buyers are planning to buy and when owners are planning to sell?

• If buyer feedback can be collected and analysed in real-time, can they help sellers to adjust marketing and pricing strategies, or adapt the right negotiation tactics to speed up closing of deals?

• If latest information on references of developers and builders, past defect inspection reports, etc. are available online, will that increase the confidence of buyers to make the buy call?

• If latest management fee, owner credit report, right of owner to sell, ROI calculations, etc. can be obtained instantly, will that help investors to lower the risks and make better investment decisions?

5. Smart Buildings

Perhaps the biggest benefit Big Data can contribute to the future of Singapore’s real estate industry is the potential business values it can offer to smart buildings in a Smart Nation.

• Can developers analyze data to meet government regulations and improve projects’ time-to-market?

• Can management companies use data generated by sensors in smart buildings to set optimal lighting and temperature in order to maximize energy efficiency and lower operating cost?

• Can organizations track human traffic and occupancy to better utilize office space and lower operation cost?

• Can property managers arrange repairs in time or even fix problems before they arise?

Limitations and challenges of Big Data

Big Data is never a simple task of combining databanks from different sources, mash it up in the cloud and insights will be automatically generated. For the property industry to fully embrace Big Data, the stakeholders must be prepared to tackle the challenges arising from the adoption.

Common shortfalls of Big Data include but not limited to:

• Availability, timeliness and accuracy of data from different sources;

• Inadequate data (e.g. transactions) to show dependencies, especially in a quiet market; and

• Issues of data privacy, storage, protection and risks of hacking and leakage.Organizations have to be well prepared for the possible consequences and evaluate the costs and benefits before jumping into Big Data.