Real estate data can help policy makers anticipate urban water needs

Researchers say real estate data can be used to predict residential water use. Photo by Free-Photos/Pixabay
Researchers say real estate data can be used to predict residential water use. Photo by Free-Photos/Pixabay

Nov. 18 (UPI) -- When making allocation decisions, water resource managers and other policy makers consider only population, economic growth and budgetary constraints, but these factors can't precisely predict changes in demand.

In a new study, published Wednesday in the journal Environmental Research Letters, scientists showed real estate data from online sites like Zillow can be used to anticipate shifts in residential water use.


If officials can better understand changing water use and demand patterns, they can make more informed decisions regarding infrastructure planning, drought management and sustainability efforts.

"Evolving development patterns can hold the key to our success in becoming more water-wise and building long-term water security," senior study author Newsha Ajami said in a news release.

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"Creating water-resilient cities under a changing climate is closely tied to how we can become more efficient in the way we use water as our population grows," said Ajami, director of urban water policy at Stanford's Water in the West program.


All over the world, cities and suburbs continue to grow, but not all growth is created equal, researchers say.

The types of building being constructed to accommodate urban and suburban growth, as well as the types of people moving into new buildings and neighborhoods, whether young families or retirees, dictate the ways water will be used as populations shift.

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Because sites like Zillow combine data from county and municipal agencies with information uploaded by local homeowners, they can be used as a rich source of information about the numbers and sizes of homes being built and occupied in a city's neighborhoods.

For the new study, researchers at Stanford combined Zillow data with demographic information from the U.S. Census Bureau to create a more detailed portrait of the mix of people and buildings in Redwood City, Calif., a fast-growing and economically diverse city.

When researchers used machine-learning algorithms to analyze the data, they were able to identify five community groupings, or clusters.

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Billing data from Redwood's public works department helped researchers identify both seasonal patterns and changes in water usage from 2007 to 2017 among the five clusters. Researchers were also able to identify conservation rates during California's historic drought from 2014 to 2017.


"With our methods incorporating Zillow data we were able to develop more accurate community groupings beyond simply clustering customers based on income and other socioeconomic qualities," said lead study author Kim Quesnel.

"This more granular view resulted in some unexpected findings and provided better insight into water-efficient communities," said Quesnel, a postdoctoral scholar at Stanford University's Woods Institute.

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The data showed the two lowest income clusters were characterized by larger numbers of people per household, but featured average levels of water usage.

Outdoor water usage was higher among the middle-income cluster, but the cluster's winter water use was below average, suggesting the use of more efficient indoor appliances.

Water usage patterns diverged among the two highest income clusters. One cluster featuring younger residents living in new homes on smaller lots had the lowest water usage rates in Redwood City. Conversely, the wealthiest residents, living in comparatively larger homes and larger lots, were the city's biggest water users.

All five clusters showed high rates of water conservation during California's mega drought, but the data showed the city's biggest water users were able to conserve the most.

The researchers suggest their data analysis can help policy makers make better decisions about where to build new pipes and water treatment facilities.


Better data can also help officials design more targeted conservation plans, they said.

"Emerging, accessible data sources are giving us a chance to develop a more informed understanding of water use patterns and behaviors," said Ajami. "If we rethink the way we build future cities and design infrastructure, we have the opportunity for more equitable and affordable access to water across various communities."

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