This article brought to you by Lightbox
Most geospatial professionals will tell you that despite being Data Scientists, GIS Analysts, or any number of job titles, they still spend more time than they would like as Data Wranglers. Data gathering, cleaning, quality checking, and compatibility preparation is time–consuming and takes away from what we really want to get at – data exploration, analysis, and visualization.
Real estate data poses a particular challenge given the many scales, geographic units, attributes, and owners of the various datasets involved from building footprints to parcels and zoning. With critical information housed and maintained by local, city, county, and state sources, it can be challenging to coordinate geospatial real estate data in one city, much less to consistently do so across the entire country.
Connecting the real estate (data) dots
While data may be abundant, its data use remains limited if different sources do not align and cannot be used in meaningful, consistent ways. There is a significant need for this type of foundational data work – a need for data companies, which is how LightBox CEO Eric Frank describes their business at its core, as a data company. Through acquisition and organic investment, we’ve paired data and technology talent that has domain knowledge in our market verticals to ensure that we are addressing practical needs of the markets and connecting leading brands with an extensive customer base to exercise world-class business capabilities.
LightBox provides an interesting case study of the sheer volume of data wrangling and aggregation work to be done and the resulting data analysis potential from doing so. The company has compiled over 10,000 datasets that includes property, boundary, environmental, neighborhood, and zoning data across the US (with an increasing amount of data available for Canada as well). They tackle the standardization process for their users – navigating data gaps, orphans, and inconsistencies. Most importantly, they bring the array of data sources together into one vast, connected dataset they refer to as SmartFabric that covers the entire country. Using a unique and persistent digital ID to traverse datasets (referred to as the LID – LightBox Identifier), SmartFabric allows for data visualization and analysis across not just the aggregated public and private datasets, but also with third party data – such as economic and demographic variables – providing more context for real estate data.
Dataset utility hinges on maintenance. Without upkeep, data can quickly become historic. Data providers that communicate and deliver on a consistent maintenance strategy over time provide an important service to the geospatial data community by offering a reliably updated resource. LightBox follows a regular release schedule. Efforts to improve data are made whenever possible – such as attaching addresses to individual buildings, not just parcels. They also continue to add new data sources to increase the wealth of information as well as its accuracy. For example, in February 2023 they announced the SmartFabric integration of curated national zoning data.
The availability of a national-scale connected dataset of real estate allows users to focus directly on data exploration and applications, with no time lost on data preparation. But an additional, powerful benefit of a maintained, connected real estate dataset is its applicability to other fields and the potential for collaboration.
Collaborating to communicate climate risk
Understanding climate risk is no longer just the concern of high-level, broad scale decisionmakers. Individuals benefit from knowing risks and vulnerabilities on an immediate, local scale. Climate risk assessments of the built environment are crucial in assisting individual property owners for current maintenance and future planning.
Climate risk models are often developed, housed, and discussed in academia or other professional circles not easily accessible to everyday citizens. This particular knowledge gap is the motivation for the work of First Street Foundation, a non-profit research and technology group that aims to quantify and communicate environmental changes to the American public. They believe individuals should have access to the best climate data and models available so people can understand how they may be personally impacted both today and into the future. First Street Foundation’s team of researchers and data scientists have developed comprehensive risk models for flood, wildfire, extreme heat, and severe wind in the United States based on peer-reviewed science.
The crucial link in making climate risk models meaningful and useful to the public is tying such information to the individual property level. With their nationwide connected real estate data, LightBox collaborates with First Street Foundation to customize climate risk models to individual locations. Building details and structural characteristics are used to tailor climate risk information for specific properties. Property owners can easily and freely access detailed information on flood, wildfire, heat, and/or wind risks specific to their community, businesses, and homes. The partnership between LightBox and First Street Foundation delivers climate science and environmental risk assessment to a localized context that individuals can act upon, while also helping federal agencies evaluate climate risks to the U.S. economy.
The convenience and usefulness of a connected dataset saves professionals time from data wrangling and empowers them with greater volumes and accuracy of information. LightBox has taken the complicated, segmented real estate data landscape and created a unified resource of property information in the U.S. Just as other datasets inform their work, LightBox informs other datasets as seen in their collaboration with First Street Foundation. It is an example of a data partnership that connects and extends the work of two related, but different fields, real estate and climate risk assessment, to the benefit of all.