Leveraging Location Intelligence for Profitable Commercial Real Estate Investments
Technology Category
- Analytics & Modeling - Big Data Analytics
- Functional Applications - Enterprise Asset Management Systems (EAM)
Applicable Industries
- Buildings
- Retail
Applicable Functions
- Procurement
- Product Research & Development
Use Cases
- Asset Health Management (AHM)
- Time Sensitive Networking
Services
- Data Science Services
- Testing & Certification
About The Customer
Hodges Ward Elliott is a commercial real estate investment company that is at the forefront of leveraging data science in the real estate industry. Recognizing the potential of data science in understanding and predicting trends in real estate, the company has been proactive in adopting new technologies and methodologies to gain a competitive edge. The company believes that understanding the preferences of groups of people and how these preferences change over time is key to profiting in real estate. To this end, they have been using open data and the open source programming language R to analyze data and identify trends and patterns. They have also been using predictive modeling to predict the probability of a building selling in any given year in New York City.
The Challenge
Hodges Ward Elliott, a commercial real estate investment company, was looking to leverage data science to gain a competitive edge in the real estate market. The company recognized the potential of data science in real estate, an industry that has traditionally been slow to adopt such technologies. The challenge was to find a way to effectively use data science to understand the preferences of groups of people and how these preferences change over time. This understanding is crucial to predicting trends in real estate, as it can provide insights into where people want to live, shop, and work. However, obtaining and analyzing relevant data was a significant challenge due to the lack of readily available data in the real estate industry.
The Solution
The company turned to open data, a movement by governments around the world to make all of their government agency data available online. This provided a wealth of data that was previously unavailable for analysis, especially within urban markets. The company used the open source programming language R to analyze this data and identify trends and patterns. They used various data sources, including taxi traffic, city bikes, bank deposit data, new build permits, and more to understand people's preferences and how they change over time. They also used predictive modeling to predict the probability of a building selling in any given year in New York City. This model was based on NYC tax lot data and NYC sales data, and used random forest modeling for its speed and accuracy. The company also used spatial lag, a technique that summarizes data points around a point, to improve the predictive accuracy of their model.
Operational Impact
Quantitative Benefit
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