Case Study: How PropertyGuru is making use of data journalism

Like most Internet companies today, PropertyGuru processes terabytes of data daily, as users across Southeast Asia use its variety of platforms to keep themselves up to date on the real estate market.

Data is collected at every point of interaction to deliver help to property seekers in making more confident decisions.

At PropertyGuru, one of the key drivers for both new user acquisition as well as visitor engagement is content marketing, where rich, useful and proprietary content is used to differentiate itself from its competitors, and provide utility to its consumers.

While it may be tempting to be drawn by the promises of Big Data for content marketing, the immediate opportunity is with Small Data, according to PropertyGuru.

The company defined the term “data journalism” to describe its content built around data.

Data journalism “draws on the growing availability of data sets and data analysis tools to uncover and tell stories like the impact of vaccines on infectious diseases, the continuing problem of school segregation, or the differences in working hours across industries, often presenting the results through compelling visualizations or interactive applications," it noted in its white paper.

Data journalist David McCandless, as part of his work, emphasises that data journalism is bringing together sizeable bodies of disconnected facts and making sense of them through data visualization.

The company prefer ‘journalism,’ rather than ‘storytelling,’ to emphasise the timeliness, relevance and impact of the content to the reader, and place less emphasis on the narrative aspects of the content.

Data journalism, in its usage of the concept, hence, is distinctive from “big data”.

Big data refers to “datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyze”, often in the vernacular of zetabytes or brontobytes, rather than the commonly used denominations for storage, such as terabytes.

Big data also suggests a high velocity of data capture and / or processing, and involves the combination of unstructured and structured data sets. Big data has also given rise to a new form of technical specialisation, that of the data scientist.

In practice,  large variety of companies process datasets to provide predictive, actionable value for themselves or their clients. American retailer Macy’s, for instance, analyses over 73 million items for sale in the space of an hour to optimise item pricing while Thomson Reuters’ financial analysis tool Eikon uses social media monitoring to track buzz on stock prices and gauge sentiment.

Unfortunately, “big data” has become a marketing buzzword of the worst variety, used to cover such a wide degree of tools and products that it has become difficult to pin down. The term, in and of itself, is valuable.

For instance, at time of writing, Google’s suggested bid for the term “big data” was a pricey SG$15.91 cost-per-click.

The data journalism that PropertyGuru puts into practice however, looks at smaller scale data (or Small Data) to derive insights.

It involves the data that is already at its fingertips which has a tendency to be overlooked when it comes to content marketing purposes: market information, website traffic data, customer information and published, public data. The company then uses these data sources to create content that is engaging, ownable and allows it to build authority with its key audiences.

This is how PropertyGuru leveraged on data journalism, one of its content marketing strategies and an opportunity it believes is being under-utilised by many brands.

Case Study: Real Estate Data Content

PropertyGuru operates in the real estate space. One of the most exciting perspectives anyone in that space can bring, is a data-driven prediction of the prices and volumes in the market.

Some months ago, the company formulated a hypothesis around using its data sources, and market outcomes. It ran an experiment to test the hypothesis, and plotted PropertyGuru enquiry data (as a proxy of market demand), real estate agents listings (as a proxy of housing supply), against state published real estate transaction information.

It found that with a time lag of 3 months, the regression model proved highly significant with an adjusted R2 = 0.72, F(3,20) = 20.72, p < 0.000. In particular, it found two significant predictors of transactions activities - number of enquiries (β = 0.08, p < 0.000) and price (β = -4.82, p <0.001.

Figure 2 below illustrates the best-fit line plot between actual transactions, and the inquiries we received on PropertyGuru’s platforms.



With the ability to ‘predict’ real estate transactions, PropertyGuru was able to create ownable content pieces to forecast real estate market performance within the next time period. This is different from what real estate consultancies, who rely on past transaction information and insider knowledge sources, can provide. This information provides the company great cachet with a consumer audience.

Furthermore, it was able to use these data sources to create individual, customised monthly reports for another of its  key stakeholder groups – real estate agents.

For each of the 30,000 agent subscribers of PropertyGuru in Singapore, it crunched 200,000 data points per month, sort agents into cohort groups based on their transactions, property and location specialisations, and rank them within their cohorts.

Through this process, it was ble to provide a performance score for agents based on their listings, enquiries and advertisement views, and serve customized recommendations about how they can improve their score vis-à-vis the top performers within their cohort.

These insights are timely, relevant, and eminently useful to agents, as there are concrete, data-driven actions they can take to improve their outreach on PropertyGuru's platform, and hence, see better performance subsequently.


While Big Data holds great potential benefits for marketers and business in general, Small Data offers the most immediate untapped potential. Data journalism,  according to PropertyGuru, is a powerful strategy to create and amplify more impactful content.

Data journalism also allows the company to uncover exciting new insights and stories from an ingredient – data – that it already has available.

Marketers that adopt this practical perspective and are also able to work laterally within their own organizations by connecting the dots with internal stakeholders like their Chief Technical Officer (CTO) and Product Specialists, will be the winners in the increasingly cluttered content marketing space.