Using Data to Drive Growth

From Market Research to Business Strategy

Much of my work involves analyzing interest rates, housing markets, credit markets, and broader financial markets, and then using that information to support investment decisions, business strategy, and business development efforts. Over time, I’ve built a workflow that allows me to collect, organize, and analyze large amounts of market data and turn it into research, models, and presentations that support investment analysis, strategic planning, and growth initiatives.

This work is used not only to understand market conditions, but also to communicate opportunities, support capital raising efforts, develop new relationships, and help guide strategic decisions. By combining market research, financial modeling, and clear communication, I aim to help organizations make better investment decisions, identify opportunities, and support long-term business growth.

Using Data for Business Development

Use In addition to investment analysis, I use market data and research to support business development and relationship building. This includes creating market reports, presentations, and data-driven insights that help communicate market opportunities, explain risk, and demonstrate where we see value in the market.

This research is often used in conversations with investors, lenders, and business partners, and helps support capital raising, new lending relationships, and strategic partnerships. Being able to clearly explain what is happening in the market, and support that view with data, has been an important part of developing new relationships and growing the business.

Data I Work With

The data I work with spans multiple asset classes and areas of the financial markets. This includes interest rates, mortgage markets, housing activity, credit markets, commodities, and equity markets, as well as financial securities and derivatives tied to these markets. I use this data to track market trends, monitor risk, and identify opportunities across both real estate and broader capital markets.

In addition to traditional asset classes, I am also interested in and spend time researching emerging and specialized markets such as emissions markets, weather derivatives, and event-based contracts. These types of markets are becoming increasingly relevant as financial markets continue to expand into new areas of risk transfer and hedging. Following a wide range of asset classes helps provide a broader macro view of the market and helps identify relationships between interest rates, commodities, economic activity, and financial markets, which is useful for both investment analysis and strategic decision-making.

Modeling & Analysis

I build financial models and market indicators that track changes in interest rates, mortgage spreads, housing supply, credit conditions, and broader capital market trends. These models are designed to help identify trends, monitor risk, and better understand how different parts of the financial markets interact, particularly as they relate to real estate and credit markets. The goal is to turn large amounts of market data into indicators and models that are easier to interpret and use in decision-making.

These models help support investment decisions, strategic planning, and business development efforts by providing a clearer picture of market conditions and potential risks or opportunities. They are also used to support research reports, presentations, and discussions with investors, partners, and clients by helping explain what is happening in the market and why it matters for investment strategy and business planning.

Automation

I use Python to automate many of the repetitive tasks involved in collecting data, updating datasets, generating charts, and producing research reports and presentations. Much of this work is integrated with Microsoft Excel, PowerPoint, and Word, where Excel is used for financial models and data organization, and Python helps update data, refresh models, and export charts that can be automatically inserted into presentations and reports. This creates a repeatable workflow where data can be pulled, processed, modeled, and turned into charts and written materials in a consistent and efficient way. The goal of this process is to keep information current and organized while reducing the amount of time spent on manual updates.

I also use these tools to support business development and communication efforts. Python can be used alongside Outlook and CRM systems to help organize contacts, track communication, prepare follow-up emails, and support outreach campaigns by incorporating market data, research, and presentations into client and investor communications. In many cases, significant efficiency can be achieved by connecting tools that companies already use — such as Excel, Word, PowerPoint, Outlook, and CRM systems — rather than relying on expensive third-party software. By automating repetitive tasks and connecting these systems, it is possible to create a more efficient workflow that supports analysis, reporting, strategic planning, and relationship management.