Home Builder Builds Profit Through New Insights

A successful home builder had two immediate concerns:

  • Profits were not growing at the same rate as home construction.

  • Cash flow forecasts were consistently missed.

Their data warehouse provided a lot of functional reports but little new information. Leadership needed to understand the operating and market drivers affecting performance.

Situation:

A successful home builder needed help to understand two immediate concerns:

  • Profits were not increasing at the same rate as home construction.

  • Cash flow forecasts were consistently missed.

Their data warehouse generated various functional production, financial, and marketing reports. Leadership needed help to identify the operating and market drivers affecting performance.

Insights:

The company used its data. The leadership team reviewed the profitability and construction status of every home each week. They knew what was “normal” and the cause of all “exceptions.” Each function depended on daily reports and actively sought new information. As a result, the leadership team scheduled a regular 60-minute meeting to prioritize requests submitted to IT.

No one used the data strategically to relate how results in one part of the company affected others. Trended information by home model, size, or location was non-existent. Leadership recognized the database of over 1,000 houses could offer insights to improve decisions, productivity, and profits. They set three objectives:

  • Analyze data across financial, construction management, sales, and buyer systems.

  • Analyze construction and profit trends.

  • Recommend strategies to improve profitability and cash flow.

Outcomes:

Within two months, Leadership reviewed a management summary and operating recommendations. The analysis directly linked market trends and process performance to financial results.

Profitability had three primary drivers:

  • Changing home buyer demographics

  • Greater than expected construction cost variability, even across houses of the same model, size, location.

  • Cost of change-orders late in construction

  • Cashflow forecast accuracy was driven by late option selections that delayed promised customer closing dates.

As part of the analysis, they also received recommended initiatives to implement a data management strategy and sales process improvement.