I work at the intersection of Singapore housing-market research, geospatial analysis, and technical systems. My focus is on turning complex market data into clearer explanations, better tools, and more defensible decisions.
My work is centered on Singapore's housing market: how prices move, how policy shapes behaviour, how location affects demand, and how better analytical systems can make the market easier to understand.
I am most interested in questions that sit between public commentary and technical execution. That includes market research, geospatial analysis, forecasting, reporting automation, and the data products that turn raw transactions, policy changes, and financing conditions into usable signals.
The direction of this site is deliberate. It is no longer a generic portfolio of unrelated technical work. It is becoming a tighter body of research, systems, and public-facing analysis built around real estate, market intelligence, and long-term research questions.
I look at housing through incentives, data quality, financing conditions, transport access, and the practical frictions that shape buyer and seller behaviour.
That usually means combining transaction data, macro context, maps, policy interpretation, and system design instead of relying on a single chart or a single market story.
I use SQL, Python, dashboards, APIs, and automation to build repeatable research and operating systems rather than one-off analysis.
The output can take different forms: a case study, a dashboard, a media explainer, a geospatial tool, an internal workflow, or a recurring reporting pipeline. The common thread is that the work should clarify a decision, not just display data.
I am about to begin a fully funded PhD in Real Estate at the National University of Singapore in August 2026. That next phase is a natural extension of the direction this site has been moving toward: deeper work on housing markets, stronger analytical rigor, and a longer-term research agenda around property systems, urban dynamics, and market behaviour.
In March 2026, I also started Autoflow, a Singapore-based AI and automation consultancy focused on helping lean teams remove manual work, improve execution speed, and scale without adding unnecessary headcount. The work is grounded in practical deployment: workflow automation, AI agents and copilots, systems integration, and reporting infrastructure designed around operational ROI rather than "AI for AI's sake."
That sits naturally beside my research interests. Both lines of work are ultimately about the same thing: taking fragmented, noisy systems and turning them into something clearer, more reliable, and more useful for decision making.
My recent professional work was shaped heavily by my time at Ohmyhome, where I worked across market research, geospatial analysis, automation, executive dashboards, compliance systems, digital marketing analytics, and decision-support for leadership. That period pushed me from broad analytics work into a much more specific focus on how housing markets and operational systems function in practice.
Earlier roles at Standard Chartered, Singtel Cyber Security, Jones Lang LaSalle, and the Singapore Police Force gave me a broader foundation across reporting, process improvement, governance, and business problem-solving. The through-line was always the same: reduce ambiguity, build better systems, and make decisions more defensible.
My background combines business, analytics, and management. I graduated from the University of Birmingham with First Class Honours in International Business and later completed a Master's degree in Analytics and Visualizations at the Singapore University of Social Sciences, where my research focused on using geospatial methods to improve real-estate price prediction in Singapore.
That combination still shapes how I work now. I care about technical depth, but I care just as much about whether the output is actually usable by operators, managers, journalists, policymakers, or the public.
This site is becoming a record of the work I want to keep compounding over the long term: housing-market research, analytical systems, media commentary, technical infrastructure, and eventually doctoral research. It is meant to show both the ideas and the machinery behind them.